diff --git a/.env b/.env
new file mode 100644
index 0000000000000000000000000000000000000000..b361ad2d1e3bd52d040ea1434f2cccf1ce861f44
--- /dev/null
+++ b/.env
@@ -0,0 +1,19 @@
+# To overwrite these variables, create a .env.local file
+
+# The path to the directory where the data will be downloaded on machine
+LILAC_DATA_PATH=./data
+
+# Set to 1 for duckdb to use views instead of materialized tables (lower memory usage, but slower).
+DUCKDB_USE_VIEWS=0
+
+# Variables that can be set in .env.local
+#
+# Get key from https://www.cohere.ai/api-keys
+# COHERE_API_KEY=
+
+# GCS_REGION=
+# GCS_ACCESS_KEY=
+# GCS_SECRET_KEY=
+
+# Get key from https://platform.openai.com/account/api-keys
+# OPENAI_API_KEY=
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..228e638276b0b207581efebd161f874be9ca4309
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,24 @@
+# NOTE: When we upgrade to 3.11 we can use a slimmer docker image which comes with gcc.
+FROM python:3.9-bullseye
+
+# Allow statements and log messages to immediately appear in the Knative logs
+ENV PYTHONUNBUFFERED True
+
+# Set the working directory in the container.
+WORKDIR /code
+
+COPY .env .
+COPY LICENSE .
+
+# Copy static files.
+COPY /web/blueprint/build ./web/blueprint/build
+
+# Copy python files.
+COPY /src ./src/
+
+# Install the dependencies. This requires exporting requirements.txt from poetry first, which
+# happens from ./build_docker.sh.
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["uvicorn", "src.server:app", "--host", "0.0.0.0", "--port", "5432"]
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..ee2d284c1bc2a474a74d00cd52ee7492f2a42c57
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
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+ "Work" shall mean the work of authorship, whether in Source or
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+ "Derivative Works" shall mean any work, whether in Source or Object
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+ "Contribution" shall mean any work of authorship, including
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+ "Contributor" shall mean Licensor and any individual or Legal Entity
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+ 2. Grant of Copyright License. Subject to the terms and conditions of
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+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
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+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
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+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
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+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
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+ Copyright 2023 Lilac AI Inc.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b920effd15da9e64c8e677eeaad6609bdbe57892
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,130 @@
+aiohttp==3.8.4 ; python_version >= "3.9" and python_version < "3.10"
+aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.10"
+anyio==3.7.0 ; python_version >= "3.9" and python_version < "3.10"
+async-timeout==4.0.2 ; python_version >= "3.9" and python_version < "3.10"
+attrs==23.1.0 ; python_version >= "3.9" and python_version < "3.10"
+blis==0.7.9 ; python_version >= "3.9" and python_version < "3.10"
+cachetools==5.3.1 ; python_version >= "3.9" and python_version < "3.10"
+catalogue==2.0.8 ; python_version >= "3.9" and python_version < "3.10"
+certifi==2023.5.7 ; python_version >= "3.9" and python_version < "3.10"
+charset-normalizer==3.1.0 ; python_version >= "3.9" and python_version < "3.10"
+click==8.1.3 ; python_version >= "3.9" and python_version < "3.10"
+cloudpickle==2.2.1 ; python_version >= "3.9" and python_version < "3.10"
+cohere==3.10.0 ; python_version >= "3.9" and python_version < "3.10"
+colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.10" and (platform_system == "Windows" or sys_platform == "win32")
+confection==0.0.4 ; python_version >= "3.9" and python_version < "3.10"
+cymem==2.0.7 ; python_version >= "3.9" and python_version < "3.10"
+cytoolz==0.12.1 ; python_version >= "3.9" and python_version < "3.10"
+dask==2023.5.1 ; python_version >= "3.9" and python_version < "3.10"
+datasets==2.12.0 ; python_version >= "3.9" and python_version < "3.10"
+decorator==5.1.1 ; python_version >= "3.9" and python_version < "3.10"
+dill==0.3.6 ; python_version >= "3.9" and python_version < "3.10"
+distributed==2023.5.1 ; python_version >= "3.9" and python_version < "3.10"
+duckdb==0.8.0 ; python_version >= "3.9" and python_version < "3.10"
+email-reply-parser==0.5.12 ; python_version >= "3.9" and python_version < "3.10"
+exceptiongroup==1.1.1 ; python_version >= "3.9" and python_version < "3.10"
+fastapi==0.95.2 ; python_version >= "3.9" and python_version < "3.10"
+filelock==3.12.0 ; python_version >= "3.9" and python_version < "3.10"
+floret==0.10.3 ; python_version >= "3.9" and python_version < "3.10"
+frozenlist==1.3.3 ; python_version >= "3.9" and python_version < "3.10"
+fsspec==2023.5.0 ; python_version >= "3.9" and python_version < "3.10"
+fsspec[http]==2023.5.0 ; python_version >= "3.9" and python_version < "3.10"
+gcsfs==2023.5.0 ; python_version >= "3.9" and python_version < "3.10"
+google-api-core==2.11.0 ; python_version >= "3.9" and python_version < "3.10"
+google-api-python-client==2.88.0 ; python_version >= "3.9" and python_version < "3.10"
+google-auth-httplib2==0.1.0 ; python_version >= "3.9" and python_version < "3.10"
+google-auth-oauthlib==1.0.0 ; python_version >= "3.9" and python_version < "3.10"
+google-auth==2.19.1 ; python_version >= "3.9" and python_version < "3.10"
+google-cloud-core==2.3.2 ; python_version >= "3.9" and python_version < "3.10"
+google-cloud-storage==2.9.0 ; python_version >= "3.9" and python_version < "3.10"
+google-crc32c==1.5.0 ; python_version >= "3.9" and python_version < "3.10"
+google-resumable-media==2.5.0 ; python_version >= "3.9" and python_version < "3.10"
+googleapis-common-protos==1.59.0 ; python_version >= "3.9" and python_version < "3.10"
+h11==0.14.0 ; python_version >= "3.9" and python_version < "3.10"
+httplib2==0.22.0 ; python_version >= "3.9" and python_version < "3.10"
+httptools==0.5.0 ; python_version >= "3.9" and python_version < "3.10"
+huggingface-hub==0.15.1 ; python_version >= "3.9" and python_version < "3.10"
+idna==3.4 ; python_version >= "3.9" and python_version < "3.10"
+importlib-metadata==6.6.0 ; python_version >= "3.9" and python_version < "3.10"
+jellyfish==0.11.2 ; python_version >= "3.9" and python_version < "3.10"
+jinja2==3.1.2 ; python_version >= "3.9" and python_version < "3.10"
+joblib==1.2.0 ; python_version >= "3.9" and python_version < "3.10"
+langcodes==3.3.0 ; python_version >= "3.9" and python_version < "3.10"
+locket==1.0.0 ; python_version >= "3.9" and python_version < "3.10"
+markupsafe==2.1.3 ; python_version >= "3.9" and python_version < "3.10"
+mpmath==1.3.0 ; python_version >= "3.9" and python_version < "3.10"
+msgpack==1.0.5 ; python_version >= "3.9" and python_version < "3.10"
+multidict==6.0.4 ; python_version >= "3.9" and python_version < "3.10"
+multiprocess==0.70.14 ; python_version >= "3.9" and python_version < "3.10"
+murmurhash==1.0.9 ; python_version >= "3.9" and python_version < "3.10"
+networkx==3.1 ; python_version >= "3.9" and python_version < "3.10"
+nltk==3.8.1 ; python_version >= "3.9" and python_version < "3.10"
+numpy==1.24.3 ; python_version >= "3.9" and python_version < "3.10"
+oauthlib==3.2.2 ; python_version >= "3.9" and python_version < "3.10"
+openai-function-call==0.0.5 ; python_version >= "3.9" and python_version < "3.10"
+openai==0.27.8 ; python_version >= "3.9" and python_version < "3.10"
+orjson==3.9.0 ; python_version >= "3.9" and python_version < "3.10"
+packaging==23.1 ; python_version >= "3.9" and python_version < "3.10"
+pandas==2.0.2 ; python_version >= "3.9" and python_version < "3.10"
+partd==1.4.0 ; python_version >= "3.9" and python_version < "3.10"
+pathy==0.10.1 ; python_version >= "3.9" and python_version < "3.10"
+pillow==9.5.0 ; python_version >= "3.9" and python_version < "3.10"
+preshed==3.0.8 ; python_version >= "3.9" and python_version < "3.10"
+protobuf==4.23.2 ; python_version >= "3.9" and python_version < "3.10"
+psutil==5.9.5 ; python_version >= "3.9" and python_version < "3.10"
+pyarrow==9.0.0 ; python_version >= "3.9" and python_version < "3.10"
+pyasn1-modules==0.3.0 ; python_version >= "3.9" and python_version < "3.10"
+pyasn1==0.5.0 ; python_version >= "3.9" and python_version < "3.10"
+pydantic==1.10.9 ; python_version >= "3.9" and python_version < "3.10"
+pyparsing==3.0.9 ; python_version >= "3.9" and python_version < "3.10"
+pyphen==0.14.0 ; python_version >= "3.9" and python_version < "3.10"
+python-dateutil==2.8.2 ; python_version >= "3.9" and python_version < "3.10"
+python-dotenv==1.0.0 ; python_version >= "3.9" and python_version < "3.10"
+pytz==2023.3 ; python_version >= "3.9" and python_version < "3.10"
+pyyaml==6.0 ; python_version >= "3.9" and python_version < "3.10"
+regex==2023.6.3 ; python_version >= "3.9" and python_version < "3.10"
+requests-oauthlib==1.3.1 ; python_version >= "3.9" and python_version < "3.10"
+requests==2.31.0 ; python_version >= "3.9" and python_version < "3.10"
+responses==0.18.0 ; python_version >= "3.9" and python_version < "3.10"
+rsa==4.9 ; python_version >= "3.9" and python_version < "3.10"
+scikit-learn==1.2.2 ; python_version >= "3.9" and python_version < "3.10"
+scipy==1.10.1 ; python_version >= "3.9" and python_version < "3.10"
+sentence-transformers==2.2.2 ; python_version >= "3.9" and python_version < "3.10"
+sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.10"
+setuptools==65.7.0 ; python_version >= "3.9" and python_version < "3.10"
+six==1.16.0 ; python_version >= "3.9" and python_version < "3.10"
+smart-open==6.3.0 ; python_version >= "3.9" and python_version < "3.10"
+sniffio==1.3.0 ; python_version >= "3.9" and python_version < "3.10"
+sortedcontainers==2.4.0 ; python_version >= "3.9" and python_version < "3.10"
+spacy-legacy==3.0.12 ; python_version >= "3.9" and python_version < "3.10"
+spacy-loggers==1.0.4 ; python_version >= "3.9" and python_version < "3.10"
+spacy==3.5.3 ; python_version >= "3.9" and python_version < "3.10"
+srsly==2.4.6 ; python_version >= "3.9" and python_version < "3.10"
+starlette==0.27.0 ; python_version >= "3.9" and python_version < "3.10"
+sympy==1.12 ; python_version >= "3.9" and python_version < "3.10"
+tblib==1.7.0 ; python_version >= "3.9" and python_version < "3.10"
+textacy==0.13.0 ; python_version >= "3.9" and python_version < "3.10"
+thinc==8.1.10 ; python_version >= "3.9" and python_version < "3.10"
+threadpoolctl==3.1.0 ; python_version >= "3.9" and python_version < "3.10"
+tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "3.10"
+toolz==0.12.0 ; python_version >= "3.9" and python_version < "3.10"
+torch==2.0.1 ; python_version >= "3.9" and python_version < "3.10"
+torchvision==0.15.2 ; python_version >= "3.9" and python_version < "3.10"
+tornado==6.3.2 ; python_version >= "3.9" and python_version < "3.10"
+tqdm==4.65.0 ; python_version >= "3.9" and python_version < "3.10"
+transformers==4.29.2 ; python_version >= "3.9" and python_version < "3.10"
+typer==0.7.0 ; python_version >= "3.9" and python_version < "3.10"
+types-psutil==5.9.5.13 ; python_version >= "3.9" and python_version < "3.10"
+typing-extensions==4.6.3 ; python_version >= "3.9" and python_version < "3.10"
+tzdata==2023.3 ; python_version >= "3.9" and python_version < "3.10"
+uritemplate==4.1.1 ; python_version >= "3.9" and python_version < "3.10"
+urllib3==1.26.16 ; python_version >= "3.9" and python_version < "3.10"
+uvicorn[standard]==0.20.0 ; python_version >= "3.9" and python_version < "3.10"
+uvloop==0.17.0 ; (sys_platform != "win32" and sys_platform != "cygwin") and platform_python_implementation != "PyPy" and python_version >= "3.9" and python_version < "3.10"
+wasabi==1.1.1 ; python_version >= "3.9" and python_version < "3.10"
+watchfiles==0.19.0 ; python_version >= "3.9" and python_version < "3.10"
+websockets==11.0.3 ; python_version >= "3.9" and python_version < "3.10"
+xxhash==3.2.0 ; python_version >= "3.9" and python_version < "3.10"
+yarl==1.9.2 ; python_version >= "3.9" and python_version < "3.10"
+zict==3.0.0 ; python_version >= "3.9" and python_version < "3.10"
+zipp==3.15.0 ; python_version >= "3.9" and python_version < "3.10"
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/__pycache__/__init__.cpython-39.pyc b/src/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1ee897073846deb777e052e9b9c1025af995af89
Binary files /dev/null and b/src/__pycache__/__init__.cpython-39.pyc differ
diff --git a/src/__pycache__/config.cpython-39.pyc b/src/__pycache__/config.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1062458a8c83833cb0fb83f7f3b1d6ec5c5ed473
Binary files /dev/null and b/src/__pycache__/config.cpython-39.pyc differ
diff --git a/src/__pycache__/conftest.cpython-39-pytest-7.3.1.pyc b/src/__pycache__/conftest.cpython-39-pytest-7.3.1.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..41d6597837cbcaf4a8427380a924e762a2293173
Binary files /dev/null and b/src/__pycache__/conftest.cpython-39-pytest-7.3.1.pyc differ
diff --git a/src/__pycache__/constants.cpython-39.pyc b/src/__pycache__/constants.cpython-39.pyc
new file mode 100644
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+++ b/src/concepts/concept.py
@@ -0,0 +1,271 @@
+"""Defines the concept and the concept models."""
+import random
+from typing import Iterable, Literal, Optional, Union
+
+import numpy as np
+from pydantic import BaseModel, validator
+from sklearn.exceptions import NotFittedError
+from sklearn.linear_model import LogisticRegression
+
+from ..db_manager import get_dataset
+from ..embeddings.embedding import get_embed_fn
+from ..schema import Path, RichData, SignalInputType, normalize_path
+from ..signals.signal import TextEmbeddingSignal, get_signal_cls
+from ..utils import DebugTimer
+
+LOCAL_CONCEPT_NAMESPACE = 'local'
+
+# Number of randomly sampled negative examples to use for training. This is used to obtain a more
+# balanced model that works with a specific dataset.
+DEFAULT_NUM_NEG_EXAMPLES = 300
+
+
+class ConceptColumnInfo(BaseModel):
+ """Information about a dataset associated with a concept."""
+ # Namespace of the dataset.
+ namespace: str
+ # Name of the dataset.
+ name: str
+ # Path holding the text to use for negative examples.
+ path: Path
+
+ num_negative_examples = DEFAULT_NUM_NEG_EXAMPLES
+
+
+class ExampleOrigin(BaseModel):
+ """The origin of an example."""
+ # The namespace that holds the dataset.
+ dataset_namespace: str
+
+ # The name of the dataset.
+ dataset_name: str
+
+ # The id of row in the dataset that the example was added from.
+ dataset_row_id: str
+
+
+DraftId = Union[Literal['main'], str]
+DRAFT_MAIN = 'main'
+
+
+class ExampleIn(BaseModel):
+ """An example in a concept without the id (used for adding new examples)."""
+ label: bool
+ text: Optional[str]
+ img: Optional[bytes]
+ origin: Optional[ExampleOrigin]
+ # The name of the draft to put the example in. If None, puts it in the main draft.
+ draft: Optional[DraftId] = DRAFT_MAIN
+
+ @validator('text')
+ def parse_text(cls, text: str) -> str:
+ """Fixes surrogate errors in text: https://github.com/ijl/orjson/blob/master/README.md#str ."""
+ return text.encode('utf-8', 'replace').decode('utf-8')
+
+
+class Example(ExampleIn):
+ """A single example in a concept used for training a concept model."""
+ id: str
+
+
+class Concept(BaseModel):
+ """A concept is a collection of examples."""
+ # The namespace of the concept.
+ namespace: str = LOCAL_CONCEPT_NAMESPACE
+ # The name of the concept.
+ concept_name: str
+ # The type of the data format that this concept represents.
+ type: SignalInputType
+ data: dict[str, Example]
+ version: int = 0
+
+ def drafts(self) -> list[DraftId]:
+ """Gets all the drafts for the concept."""
+ drafts: set[DraftId] = set([DRAFT_MAIN]) # Always return the main draft.
+ for example in self.data.values():
+ if example.draft:
+ drafts.add(example.draft)
+ return list(sorted(drafts))
+
+
+class LogisticEmbeddingModel(BaseModel):
+ """A model that uses logistic regression with embeddings."""
+
+ class Config:
+ arbitrary_types_allowed = True
+ underscore_attrs_are_private = True
+
+ version: int = -1
+
+ # The following fields are excluded from JSON serialization, but still pickleable.
+ # See `notebooks/Toxicity.ipynb` for an example of training a concept model.
+ _model: LogisticRegression = LogisticRegression(
+ class_weight=None, C=30, tol=1e-5, warm_start=True, max_iter=1_000, n_jobs=-1)
+
+ def score_embeddings(self, embeddings: np.ndarray) -> np.ndarray:
+ """Get the scores for the provided embeddings."""
+ try:
+ return self._model.predict_proba(embeddings)[:, 1]
+ except NotFittedError:
+ return np.random.rand(len(embeddings))
+
+ def fit(self, embeddings: np.ndarray, labels: list[bool], sample_weights: list[float]) -> None:
+ """Fit the model to the provided embeddings and labels."""
+ if len(set(labels)) < 2:
+ return
+ if len(labels) != len(embeddings):
+ raise ValueError(
+ f'Length of embeddings ({len(embeddings)}) must match length of labels ({len(labels)})')
+ if len(sample_weights) != len(labels):
+ raise ValueError(
+ f'Length of sample_weights ({len(sample_weights)}) must match length of labels '
+ f'({len(labels)})')
+ self._model.fit(embeddings, labels, sample_weights)
+
+
+def draft_examples(concept: Concept, draft: DraftId) -> dict[str, Example]:
+ """Get the examples in the provided draft by overriding the main draft."""
+ draft_examples: dict[str, dict[str, Example]] = {}
+ for id, example in concept.data.items():
+ draft_examples.setdefault(example.draft or DRAFT_MAIN, {})[example.id] = example
+
+ if draft == DRAFT_MAIN:
+ return draft_examples.get(DRAFT_MAIN, {})
+
+ if draft not in draft_examples:
+ raise ValueError(
+ f'Draft {draft} not found in concept. Found drafts: {list(draft_examples.keys())}')
+
+ # Map the text of the draft to its id so we can dedup with main.
+ draft_text_ids = {example.text: id for id, example in draft_examples[draft].items()}
+
+ # Write each of examples from main to the draft examples only if the text does not appear in the
+ # draft.
+ for id, example in draft_examples[DRAFT_MAIN].items():
+ if example.text not in draft_text_ids:
+ draft_examples[draft][id] = example
+
+ return draft_examples[draft]
+
+
+class ConceptModel(BaseModel):
+ """A concept model. Stores all concept model drafts and manages syncing."""
+ # The concept that this model is for.
+ namespace: str
+ concept_name: str
+
+ # The name of the embedding for this model.
+ embedding_name: str
+ version: int = -1
+
+ # The following fields are excluded from JSON serialization, but still pickleable.
+ # Maps a concept id to the embeddings.
+ _embeddings: dict[str, np.ndarray] = {}
+ _logistic_models: dict[DraftId, LogisticEmbeddingModel] = {}
+ _negative_vectors: Optional[np.ndarray] = None
+
+ class Config:
+ arbitrary_types_allowed = True
+ underscore_attrs_are_private = True
+
+ def calibrate_on_dataset(self, column_info: ConceptColumnInfo) -> None:
+ """Calibrate the model on the embeddings in the provided vector store."""
+ db = get_dataset(column_info.namespace, column_info.name)
+ vector_store = db.get_vector_store(normalize_path(column_info.path))
+ keys = vector_store.keys()
+ num_samples = min(column_info.num_negative_examples, len(keys))
+ sample_keys = random.sample(keys, num_samples)
+ self._negative_vectors = vector_store.get(sample_keys)
+
+ def score_embeddings(self, draft: DraftId, embeddings: np.ndarray) -> np.ndarray:
+ """Get the scores for the provided embeddings."""
+ return self._get_logistic_model(draft).score_embeddings(embeddings)
+
+ def score(self, draft: DraftId, examples: Iterable[RichData]) -> list[float]:
+ """Get the scores for the provided examples."""
+ embedding_signal = get_signal_cls(self.embedding_name)()
+ if not isinstance(embedding_signal, TextEmbeddingSignal):
+ raise ValueError(f'Only text embedding signals are currently supported for concepts. '
+ f'"{self.embedding_name}" is a {type(embedding_signal)}.')
+
+ embed_fn = get_embed_fn(self.embedding_name)
+ embeddings = np.array(embed_fn(examples))
+ return self._get_logistic_model(draft).score_embeddings(embeddings).tolist()
+
+ def coef(self, draft: DraftId) -> np.ndarray:
+ """Get the coefficients of the underlying ML model."""
+ return self._get_logistic_model(draft)._model.coef_.reshape(-1)
+
+ def _get_logistic_model(self, draft: DraftId) -> LogisticEmbeddingModel:
+ """Get the logistic model for the provided draft."""
+ if draft not in self._logistic_models:
+ self._logistic_models[draft] = LogisticEmbeddingModel(
+ namespace=self.namespace,
+ concept_name=self.concept_name,
+ embedding_name=self.embedding_name,
+ version=-1)
+ return self._logistic_models[draft]
+
+ def sync(self, concept: Concept) -> bool:
+ """Update the model with the latest labeled concept data."""
+ if concept.version == self.version:
+ # The model is up to date.
+ return False
+
+ concept_path = (f'{self.namespace}/{self.concept_name}/'
+ f'{self.embedding_name}')
+ with DebugTimer(f'Computing embeddings for "{concept_path}"'):
+ self._compute_embeddings(concept)
+
+ # Fit each of the drafts, sort by draft name for deterministic behavior.
+ for draft in concept.drafts():
+ examples = draft_examples(concept, draft)
+ embeddings = np.array([self._embeddings[id] for id in examples.keys()])
+ labels = [example.label for example in examples.values()]
+ num_pos_labels = len([x for x in labels if x])
+ num_neg_labels = len([x for x in labels if not x])
+ sample_weights = [(1.0 / num_pos_labels if x else 1.0 / num_neg_labels) for x in labels]
+ if self._negative_vectors is not None:
+ num_implicit_labels = len(self._negative_vectors)
+ embeddings = np.concatenate([self._negative_vectors, embeddings])
+ labels = [False] * num_implicit_labels + labels
+ sample_weights = [1.0 / num_implicit_labels] * num_implicit_labels + sample_weights
+
+ model = self._get_logistic_model(draft)
+ with DebugTimer(f'Fitting model for "{concept_path}"'):
+ model.fit(embeddings, labels, sample_weights)
+
+ # Synchronize the model version with the concept version.
+ model.version = concept.version
+
+ # Synchronize the model version with the concept version.
+ self.version = concept.version
+
+ return True
+
+ def _compute_embeddings(self, concept: Concept) -> None:
+ embedding_signal = get_signal_cls(self.embedding_name)()
+ if not isinstance(embedding_signal, TextEmbeddingSignal):
+ raise ValueError(f'Only text embedding signals are currently supported for concepts. '
+ f'"{self.embedding_name}" is a {type(embedding_signal)}.')
+
+ embed_fn = get_embed_fn(self.embedding_name)
+ concept_embeddings: dict[str, np.ndarray] = {}
+
+ # Compute the embeddings for the examples with cache miss.
+ texts_of_missing_embeddings: dict[str, str] = {}
+ for id, example in concept.data.items():
+ if id in self._embeddings:
+ # Cache hit.
+ concept_embeddings[id] = self._embeddings[id]
+ else:
+ # Cache miss.
+ # TODO(smilkov): Support images.
+ texts_of_missing_embeddings[id] = example.text or ''
+
+ missing_ids = texts_of_missing_embeddings.keys()
+ missing_embeddings = embed_fn(list(texts_of_missing_embeddings.values()))
+
+ for id, embedding in zip(missing_ids, missing_embeddings):
+ concept_embeddings[id] = embedding
+ self._embeddings = concept_embeddings
diff --git a/src/concepts/concept_test.py b/src/concepts/concept_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..57b21944ffe7e28eb15f9bced2d3b6b17254bdc6
--- /dev/null
+++ b/src/concepts/concept_test.py
@@ -0,0 +1,84 @@
+"""Tests for concept."""
+
+from ..schema import SignalInputType
+from .concept import DRAFT_MAIN, Concept, Example, draft_examples
+
+
+def test_draft_examples_main() -> None:
+ concept = Concept(
+ namespace='test_namespace',
+ concept_name='test_name',
+ type=SignalInputType.TEXT,
+ data={
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ },
+ version=0)
+
+ assert draft_examples(concept, DRAFT_MAIN) == {
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ }
+
+
+def test_draft_examples_simple_draft() -> None:
+ concept = Concept(
+ namespace='test_namespace',
+ concept_name='test_name',
+ type=SignalInputType.TEXT,
+ data={
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ '2': Example(id='2', label=True, text='hello draft 1', draft='draft1'),
+ '3': Example(id='3', label=False, text='world draft 1', draft='draft1'),
+ '4': Example(id='4', label=True, text='hello draft 2', draft='draft2'),
+ '5': Example(id='5', label=False, text='world draft 2', draft='draft2'),
+ },
+ version=0)
+
+ assert draft_examples(concept, DRAFT_MAIN) == {
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ }
+
+ assert draft_examples(concept, 'draft1') == {
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ '2': Example(id='2', label=True, text='hello draft 1', draft='draft1'),
+ '3': Example(id='3', label=False, text='world draft 1', draft='draft1'),
+ }
+
+ assert draft_examples(concept, 'draft2') == {
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ '4': Example(id='4', label=True, text='hello draft 2', draft='draft2'),
+ '5': Example(id='5', label=False, text='world draft 2', draft='draft2'),
+ }
+
+
+def test_draft_examples_draft_dedupe() -> None:
+ concept = Concept(
+ namespace='test_namespace',
+ concept_name='test_name',
+ type=SignalInputType.TEXT,
+ data={
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ # Duplicate text.
+ '2': Example(id='2', label=False, text='hello', draft='draft'),
+ '3': Example(id='3', label=False, text='world draft', draft='draft'),
+ },
+ version=0)
+
+ assert draft_examples(concept, DRAFT_MAIN) == {
+ '0': Example(id='0', label=True, text='hello'),
+ '1': Example(id='1', label=False, text='world'),
+ }
+
+ assert draft_examples(concept, 'draft') == {
+ # 0 is deduplicated with 2.
+ '1': Example(id='1', label=False, text='world'),
+ # 2 overrides 0's label.
+ '2': Example(id='2', label=False, text='hello', draft='draft'),
+ '3': Example(id='3', label=False, text='world draft', draft='draft'),
+ }
diff --git a/src/concepts/db_concept.py b/src/concepts/db_concept.py
new file mode 100644
index 0000000000000000000000000000000000000000..876804f02890e17dee4ef9826ffa181c69146a6d
--- /dev/null
+++ b/src/concepts/db_concept.py
@@ -0,0 +1,391 @@
+"""The concept database."""
+
+import abc
+import glob
+import os
+import pickle
+import shutil
+
+# NOTE: We have to import the module for uuid so it can be mocked.
+import uuid
+from typing import List, Optional, Union, cast
+
+from pydantic import BaseModel
+from typing_extensions import override
+
+from ..config import data_path
+from ..schema import PATH_WILDCARD, SignalInputType, normalize_path
+from ..signals.signal import get_signal_cls
+from ..utils import DATASETS_DIR_NAME, delete_file, file_exists, get_dataset_output_dir, open_file
+from .concept import (
+ DRAFT_MAIN,
+ Concept,
+ ConceptColumnInfo,
+ ConceptModel,
+ DraftId,
+ Example,
+ ExampleIn,
+)
+
+DATASET_CONCEPTS_DIR = '.concepts'
+CONCEPT_JSON_FILENAME = 'concept.json'
+
+
+class ConceptInfo(BaseModel):
+ """Information about a concept."""
+ namespace: str
+ name: str
+ type: SignalInputType
+ drafts: list[DraftId]
+
+
+class ConceptUpdate(BaseModel):
+ """An update to a concept."""
+ # List of examples to be inserted.
+ insert: Optional[list[ExampleIn]] = []
+
+ # List of examples to be updated.
+ update: Optional[list[Example]] = []
+
+ # The ids of the examples to be removed.
+ remove: Optional[list[str]] = []
+
+
+class ConceptDB(abc.ABC):
+ """Interface for the concept database."""
+
+ @abc.abstractmethod
+ def list(self) -> list[ConceptInfo]:
+ """List all the concepts."""
+ pass
+
+ @abc.abstractmethod
+ def get(self, namespace: str, name: str) -> Optional[Concept]:
+ """Return a concept or None if there isn't one."""
+ pass
+
+ @abc.abstractmethod
+ def create(self, namespace: str, name: str, type: SignalInputType) -> Concept:
+ """Create a concept."""
+ pass
+
+ @abc.abstractmethod
+ def edit(self, namespace: str, name: str, change: ConceptUpdate) -> Concept:
+ """Edit a concept. If the concept doesn't exist, throw an error."""
+ pass
+
+ @abc.abstractmethod
+ def remove(self, namespace: str, name: str) -> None:
+ """Remove a concept."""
+ pass
+
+ @abc.abstractmethod
+ def merge_draft(self, namespace: str, name: str, draft: DraftId) -> Concept:
+ """Merge a draft concept.."""
+ pass
+
+
+class ConceptModelDB(abc.ABC):
+ """Interface for the concept model database."""
+
+ _concept_db: ConceptDB
+
+ def __init__(self, concept_db: ConceptDB) -> None:
+ self._concept_db = concept_db
+
+ @abc.abstractmethod
+ def create(self,
+ namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ column_info: Optional[ConceptColumnInfo] = None) -> ConceptModel:
+ """Create the concept model."""
+ pass
+
+ @abc.abstractmethod
+ def get(self,
+ namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ column_info: Optional[ConceptColumnInfo] = None) -> Optional[ConceptModel]:
+ """Get the model associated with the provided concept the embedding.
+
+ Returns None if the model does not exist.
+ """
+ pass
+
+ @abc.abstractmethod
+ def _save(self, model: ConceptModel, column_info: Optional[ConceptColumnInfo]) -> None:
+ """Save the concept model."""
+ pass
+
+ def in_sync(self, model: ConceptModel) -> bool:
+ """Return True if the model is up to date with the concept."""
+ concept = self._concept_db.get(model.namespace, model.concept_name)
+ if not concept:
+ raise ValueError(f'Concept "{model.namespace}/{model.concept_name}" does not exist.')
+ return concept.version == model.version
+
+ def sync(self, model: ConceptModel, column_info: Optional[ConceptColumnInfo]) -> bool:
+ """Sync the concept model. Returns true if the model was updated."""
+ concept = self._concept_db.get(model.namespace, model.concept_name)
+ if not concept:
+ raise ValueError(f'Concept "{model.namespace}/{model.concept_name}" does not exist.')
+ model_updated = model.sync(concept)
+ self._save(model, column_info)
+ return model_updated
+
+ @abc.abstractmethod
+ def remove(self,
+ namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ column_info: Optional[ConceptColumnInfo] = None) -> None:
+ """Remove the model of a concept."""
+ pass
+
+ @abc.abstractmethod
+ def remove_all(self, namespace: str, concept_name: str) -> None:
+ """Remove all the models associated with a concept."""
+ pass
+
+
+class DiskConceptModelDB(ConceptModelDB):
+ """Interface for the concept model database."""
+
+ @override
+ def create(self,
+ namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ column_info: Optional[ConceptColumnInfo] = None) -> ConceptModel:
+ if self.get(namespace, concept_name, embedding_name, column_info):
+ raise ValueError('Concept model already exists.')
+
+ return ConceptModel(
+ namespace=namespace,
+ concept_name=concept_name,
+ embedding_name=embedding_name,
+ version=-1,
+ column_info=column_info)
+
+ @override
+ def get(self,
+ namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ column_info: Optional[ConceptColumnInfo] = None) -> Optional[ConceptModel]:
+ # Make sure the concept exists.
+ concept = self._concept_db.get(namespace, concept_name)
+ if not concept:
+ raise ValueError(f'Concept "{namespace}/{concept_name}" does not exist.')
+
+ # Make sure that the embedding signal exists.
+ if not get_signal_cls(embedding_name):
+ raise ValueError(f'Embedding signal "{embedding_name}" not found in the registry.')
+
+ concept_model_path = _concept_model_path(namespace, concept_name, embedding_name, column_info)
+ if not file_exists(concept_model_path):
+ return None
+
+ with open_file(concept_model_path, 'rb') as f:
+ return pickle.load(f)
+
+ def _save(self, model: ConceptModel, column_info: Optional[ConceptColumnInfo]) -> None:
+ """Save the concept model."""
+ concept_model_path = _concept_model_path(model.namespace, model.concept_name,
+ model.embedding_name, column_info)
+ with open_file(concept_model_path, 'wb') as f:
+ pickle.dump(model, f)
+
+ @override
+ def remove(self,
+ namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ column_info: Optional[ConceptColumnInfo] = None) -> None:
+ concept_model_path = _concept_model_path(namespace, concept_name, embedding_name, column_info)
+
+ if not file_exists(concept_model_path):
+ raise ValueError(f'Concept model {namespace}/{concept_name}/{embedding_name} does not exist.')
+
+ delete_file(concept_model_path)
+
+ @override
+ def remove_all(self, namespace: str, concept_name: str) -> None:
+ datasets_path = os.path.join(data_path(), DATASETS_DIR_NAME)
+ # Skip if 'datasets' doesn't exist.
+ if not os.path.isdir(datasets_path):
+ return
+
+ dirs = glob.iglob(
+ os.path.join(datasets_path, '**', DATASET_CONCEPTS_DIR, namespace, concept_name),
+ recursive=True)
+ for dir in dirs:
+ shutil.rmtree(dir, ignore_errors=True)
+
+
+def _concept_output_dir(namespace: str, name: str) -> str:
+ """Return the output directory for a given concept."""
+ return os.path.join(data_path(), 'concept', namespace, name)
+
+
+def _concept_json_path(namespace: str, name: str) -> str:
+ return os.path.join(_concept_output_dir(namespace, name), CONCEPT_JSON_FILENAME)
+
+
+def _concept_model_path(namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ column_info: Optional[ConceptColumnInfo] = None) -> str:
+ if not column_info:
+ return os.path.join(_concept_output_dir(namespace, concept_name), f'{embedding_name}.pkl')
+
+ dataset_dir = get_dataset_output_dir(data_path(), column_info.namespace, column_info.name)
+ path_tuple = normalize_path(column_info.path)
+ path_without_wildcards = (p for p in path_tuple if p != PATH_WILDCARD)
+ path_dir = os.path.join(dataset_dir, *path_without_wildcards)
+ return os.path.join(path_dir, DATASET_CONCEPTS_DIR, namespace, concept_name,
+ f'{embedding_name}.pkl')
+
+
+class DiskConceptDB(ConceptDB):
+ """A concept database."""
+
+ @override
+ def list(self) -> list[ConceptInfo]:
+ # Read the concepts and return a ConceptInfo containing the namespace and name.
+ concept_infos = []
+ for root, _, files in os.walk(data_path()):
+ for file in files:
+ if file == CONCEPT_JSON_FILENAME:
+ namespace, name = root.split('/')[-2:]
+ concept = cast(Concept, self.get(namespace, name))
+
+ concept_infos.append(
+ ConceptInfo(
+ namespace=namespace,
+ name=name,
+ # TODO(nsthorat): Generalize this to images.
+ type=SignalInputType.TEXT,
+ drafts=concept.drafts()))
+
+ return concept_infos
+
+ @override
+ def get(self, namespace: str, name: str) -> Optional[Concept]:
+ concept_json_path = _concept_json_path(namespace, name)
+
+ if not file_exists(concept_json_path):
+ return None
+
+ with open_file(concept_json_path) as f:
+ return Concept.parse_raw(f.read())
+
+ @override
+ def create(self, namespace: str, name: str, type: SignalInputType) -> Concept:
+ """Create a concept."""
+ concept_json_path = _concept_json_path(namespace, name)
+ if file_exists(concept_json_path):
+ raise ValueError(f'Concept with namespace "{namespace}" and name "{name}" already exists.')
+
+ concept = Concept(namespace=namespace, concept_name=name, type=type, data={}, version=0)
+ self._save(concept)
+ return concept
+
+ def _validate_examples(self, examples: List[Union[ExampleIn, Example]],
+ type: SignalInputType) -> None:
+ for example in examples:
+ inferred_type = 'text' if example.text else 'img'
+ if inferred_type != type:
+ raise ValueError(f'Example type "{inferred_type}" does not match concept type "{type}".')
+
+ @override
+ def edit(self, namespace: str, name: str, change: ConceptUpdate) -> Concept:
+ concept_json_path = _concept_json_path(namespace, name)
+
+ if not file_exists(concept_json_path):
+ raise ValueError(f'Concept with namespace "{namespace}" and name "{name}" does not exist. '
+ 'Please call create() first.')
+
+ inserted_points = change.insert or []
+ updated_points = change.update or []
+ removed_points = change.remove or []
+
+ concept = cast(Concept, self.get(namespace, name))
+
+ self._validate_examples([*inserted_points, *updated_points], concept.type)
+
+ for remove_example in removed_points:
+ if remove_example not in concept.data:
+ raise ValueError(f'Example with id "{remove_example}" does not exist.')
+ concept.data.pop(remove_example)
+
+ for example in inserted_points:
+ id = uuid.uuid4().hex
+ concept.data[id] = Example(id=id, **example.dict())
+
+ for example in updated_points:
+ if example.id not in concept.data:
+ raise ValueError(f'Example with id "{example.id}" does not exist.')
+
+ # Remove the old example and make a new one with a new id to keep it functional.
+ concept.data.pop(example.id)
+ concept.data[example.id] = example.copy()
+
+ concept.version += 1
+
+ self._save(concept)
+
+ return concept
+
+ def _save(self, concept: Concept) -> None:
+ concept_json_path = _concept_json_path(concept.namespace, concept.concept_name)
+
+ with open_file(concept_json_path, 'w') as f:
+ f.write(concept.json(exclude_none=True, indent=2, exclude_defaults=True))
+
+ @override
+ def remove(self, namespace: str, name: str) -> None:
+ concept_dir = _concept_output_dir(namespace, name)
+
+ if not file_exists(concept_dir):
+ raise ValueError(f'Concept with namespace "{namespace}" and name "{name}" does not exist.')
+
+ shutil.rmtree(concept_dir, ignore_errors=True)
+
+ @override
+ def merge_draft(self, namespace: str, name: str, draft: DraftId) -> Concept:
+ """Merge a draft concept.."""
+ concept = self.get(namespace, name)
+ if not concept:
+ raise ValueError(f'Concept with namespace "{namespace}" and name "{name}" does not exist.')
+
+ if draft == DRAFT_MAIN:
+ return concept
+
+ # Map the text of examples in main so we can remove them if they are duplicates.
+ main_text_ids: dict[Optional[str], str] = {
+ example.text: id for id, example in concept.data.items() if example.draft == DRAFT_MAIN
+ }
+
+ draft_examples: dict[str, Example] = {
+ id: example for id, example in concept.data.items() if example.draft == draft
+ }
+ for example in draft_examples.values():
+ example.draft = DRAFT_MAIN
+ # Remove duplicates in main.
+ main_text_id = main_text_ids.get(example.text)
+ if main_text_id:
+ del concept.data[main_text_id]
+
+ concept.version += 1
+
+ self._save(concept)
+
+ return concept
+
+
+# A singleton concept database.
+DISK_CONCEPT_DB = DiskConceptDB()
+DISK_CONCEPT_MODEL_DB = DiskConceptModelDB(DISK_CONCEPT_DB)
diff --git a/src/concepts/db_concept_test.py b/src/concepts/db_concept_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1e0e2e5ac6ab54a007d6f42db338afd66be171e
--- /dev/null
+++ b/src/concepts/db_concept_test.py
@@ -0,0 +1,600 @@
+"""Tests for the the database concept."""
+
+from pathlib import Path
+from typing import Generator, Iterable, Type, cast
+
+import numpy as np
+import pytest
+from pytest_mock import MockerFixture
+from typing_extensions import override
+
+from ..config import CONFIG
+from ..data.dataset_duckdb import DatasetDuckDB
+from ..data.dataset_utils import lilac_embedding
+from ..db_manager import set_default_dataset_cls
+from ..schema import Item, RichData, SignalInputType
+from ..signals.signal import TextEmbeddingSignal, clear_signal_registry, register_signal
+from .concept import (
+ DRAFT_MAIN,
+ Concept,
+ ConceptModel,
+ DraftId,
+ Example,
+ ExampleIn,
+ LogisticEmbeddingModel,
+)
+from .db_concept import (
+ ConceptDB,
+ ConceptInfo,
+ ConceptModelDB,
+ ConceptUpdate,
+ DiskConceptDB,
+ DiskConceptModelDB,
+)
+
+ALL_CONCEPT_DBS = [DiskConceptDB]
+ALL_CONCEPT_MODEL_DBS = [DiskConceptModelDB]
+
+
+@pytest.fixture(autouse=True)
+def set_data_path(tmp_path: Path, mocker: MockerFixture) -> None:
+ mocker.patch.dict(CONFIG, {'LILAC_DATA_PATH': str(tmp_path)})
+
+
+EMBEDDING_MAP: dict[str, list[float]] = {
+ 'not in concept': [1.0, 0.0, 0.0],
+ 'in concept': [0.9, 0.1, 0.0],
+ 'a new data point': [0.1, 0.2, 0.3],
+ 'a true draft point': [0.4, 0.5, 0.6],
+ 'a false draft point': [0.7, 0.8, 0.9],
+}
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Embed the examples, use a hashmap to the vector for simplicity."""
+ for example in data:
+ if example not in EMBEDDING_MAP:
+ raise ValueError(f'Example "{str(example)}" not in embedding map')
+ yield [lilac_embedding(0, len(example), np.array(EMBEDDING_MAP[cast(str, example)]))]
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Generator:
+ set_default_dataset_cls(DatasetDuckDB)
+ register_signal(TestEmbedding)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+@pytest.mark.parametrize('db_cls', ALL_CONCEPT_DBS)
+class ConceptDBSuite:
+
+ def test_create_concept(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ db.create(namespace='test', name='test_concept', type=SignalInputType.TEXT)
+
+ assert db.list() == [
+ ConceptInfo(
+ namespace='test', name='test_concept', type=SignalInputType.TEXT, drafts=[DRAFT_MAIN])
+ ]
+
+ # Make sure list with drafts relects the drafts.
+ train_data = [
+ ExampleIn(label=False, text='not in concept', draft='test_draft'),
+ ExampleIn(label=True, text='in concept', draft='test_draft')
+ ]
+ db.edit('test', 'test_concept', ConceptUpdate(insert=train_data))
+
+ assert db.list() == [
+ ConceptInfo(
+ namespace='test',
+ name='test_concept',
+ type=SignalInputType.TEXT,
+ drafts=[DRAFT_MAIN, 'test_draft'])
+ ]
+
+ def test_add_example(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ concept = db.get(namespace, concept_name)
+
+ assert concept is not None
+
+ keys = list(concept.data.keys())
+ assert concept == Concept(
+ namespace=namespace,
+ concept_name=concept_name,
+ type=SignalInputType.TEXT,
+ data={
+ keys[0]: Example(id=keys[0], label=False, text='not in concept'),
+ keys[1]: Example(id=keys[1], label=True, text='in concept')
+ },
+ version=1)
+
+ # Add a draft labels.
+ db.edit(
+ namespace, concept_name,
+ ConceptUpdate(insert=[
+ ExampleIn(label=False, text='really not in concept', draft='test_draft'),
+ ExampleIn(label=True, text='really in concept', draft='test_draft')
+ ]))
+
+ concept = db.get(namespace, concept_name)
+ assert concept is not None
+
+ keys = list(concept.data.keys())
+ assert concept == Concept(
+ namespace=namespace,
+ concept_name=concept_name,
+ type=SignalInputType.TEXT,
+ data={
+ keys[0]: Example(id=keys[0], label=False, text='not in concept'),
+ keys[1]: Example(id=keys[1], label=True, text='in concept'),
+ keys[2]: Example(id=keys[2], label=False, text='really not in concept', draft='test_draft'),
+ keys[3]: Example(id=keys[3], label=True, text='really in concept', draft='test_draft'),
+ },
+ version=2)
+
+ def test_update_concept(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept'),
+ ExampleIn(label=False, text='really not in concept', draft='test_draft'),
+ ExampleIn(label=True, text='really in concept', draft='test_draft')
+ ]
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ concept = db.get(namespace, concept_name)
+ assert concept is not None
+
+ keys = list(concept.data.keys())
+ # Edit the first example.
+ db.edit(
+ namespace, concept_name,
+ ConceptUpdate(update=[Example(id=keys[0], label=False, text='not in concept, updated')]))
+ concept = db.get(namespace, concept_name)
+
+ assert concept == Concept(
+ namespace=namespace,
+ concept_name=concept_name,
+ type=SignalInputType.TEXT,
+ data={
+ # The first example should be updated alone.
+ keys[0]: Example(id=keys[0], label=False, text='not in concept, updated'),
+ keys[1]: Example(id=keys[1], label=True, text='in concept'),
+ # Drafts are untouched.
+ keys[2]: Example(id=keys[2], label=False, text='really not in concept', draft='test_draft'),
+ keys[3]: Example(id=keys[3], label=True, text='really in concept', draft='test_draft'),
+ },
+ version=2)
+
+ # Edit the second example on the draft.
+ db.edit(
+ namespace, concept_name,
+ ConceptUpdate(update=[
+ Example(id=keys[3], label=True, text='really in concept, updated', draft='test_draft')
+ ]))
+ concept = db.get(namespace, concept_name)
+
+ assert concept == Concept(
+ namespace=namespace,
+ concept_name=concept_name,
+ type=SignalInputType.TEXT,
+ data={
+ # Main remains the same.
+ keys[0]: Example(id=keys[0], label=False, text='not in concept, updated'),
+ keys[1]: Example(id=keys[1], label=True, text='in concept'),
+ keys[2]: Example(id=keys[2], label=False, text='really not in concept', draft='test_draft'),
+ keys[3]: Example(
+ id=keys[3], label=True, text='really in concept, updated', draft='test_draft'),
+ },
+ version=3)
+
+ def test_remove_concept(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+ concept = db.get(namespace, concept_name)
+
+ db.remove(namespace, concept_name)
+
+ concept = db.get(namespace, concept_name)
+
+ assert concept is None
+
+ def test_remove_concept_examples(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+ concept = db.get(namespace, concept_name)
+ assert concept is not None
+
+ keys = list(concept.data.keys())
+
+ db.edit(namespace, concept_name, ConceptUpdate(remove=[keys[0]]))
+ concept = db.get(namespace, concept_name)
+
+ assert concept == Concept(
+ namespace=namespace,
+ concept_name=concept_name,
+ type=SignalInputType.TEXT,
+ data={
+ # key_0 was removed.
+ keys[1]: Example(id=keys[1], label=True, text='in concept')
+ },
+ version=2)
+
+ def test_remove_concept_examples_draft(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept'),
+ ExampleIn(label=False, text='really not in concept', draft='test_draft'),
+ ExampleIn(label=True, text='really in concept', draft='test_draft')
+ ]
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+ concept = db.get(namespace, concept_name)
+ assert concept is not None
+
+ keys = list(concept.data.keys())
+
+ db.edit(namespace, concept_name, ConceptUpdate(remove=[keys[2]]))
+ concept = db.get(namespace, concept_name)
+
+ assert concept == Concept(
+ namespace=namespace,
+ concept_name=concept_name,
+ type=SignalInputType.TEXT,
+ data={
+ keys[0]: Example(id=keys[0], label=False, text='not in concept'),
+ keys[1]: Example(id=keys[1], label=True, text='in concept'),
+ # The first draft example is removed.
+ keys[3]: Example(id=keys[3], label=True, text='really in concept', draft='test_draft'),
+ },
+ version=2)
+
+ def test_remove_invalid_id(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept'),
+ ExampleIn(label=False, text='really not in concept', draft='test_draft'),
+ ExampleIn(label=True, text='really in concept', draft='test_draft')
+ ]
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ with pytest.raises(ValueError, match='Example with id "invalid_id" does not exist'):
+ db.edit(namespace, concept_name, ConceptUpdate(remove=['invalid_id']))
+
+ def test_edit_before_creation(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+
+ with pytest.raises(
+ ValueError, match='Concept with namespace "test" and name "test_concept" does not exist'):
+ db.edit(namespace, concept_name,
+ ConceptUpdate(insert=[
+ ExampleIn(label=False, text='not in concept'),
+ ]))
+
+ def test_edit_invalid_id(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ with pytest.raises(ValueError, match='Example with id "invalid_id" does not exist'):
+ db.edit(namespace, concept_name,
+ ConceptUpdate(update=[Example(id='invalid_id', label=False, text='not in concept')]))
+
+ def test_merge_draft(self, db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=True, text='hello'),
+ ExampleIn(label=False, text='world'),
+ ExampleIn(label=True, text='hello draft 1', draft='draft1'),
+ ExampleIn(label=False, text='world draft 1', draft='draft1'),
+ # Duplicate of main.
+ ExampleIn(label=False, text='hello', draft='draft2'),
+ ExampleIn(label=True, text='world draft 2', draft='draft2'),
+ ]
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ db.merge_draft(namespace, concept_name, 'draft1')
+
+ concept = db.get(namespace, concept_name)
+ assert concept is not None
+ keys = list(concept.data.keys())
+
+ assert concept.dict() == Concept(
+ namespace='test',
+ concept_name='test_concept',
+ type=SignalInputType.TEXT,
+ data={
+ keys[0]: Example(id=keys[0], label=True, text='hello'),
+ keys[1]: Example(id=keys[1], label=False, text='world'),
+ # Draft examples are merged.
+ keys[2]: Example(id=keys[2], label=True, text='hello draft 1'),
+ keys[3]: Example(id=keys[3], label=False, text='world draft 1'),
+ # Draft 2 is untouched.
+ keys[4]: Example(id=keys[4], label=False, text='hello', draft='draft2'),
+ keys[5]: Example(id=keys[5], label=True, text='world draft 2', draft='draft2'),
+ },
+ version=2).dict()
+
+ db.merge_draft(namespace, concept_name, 'draft2')
+
+ concept = db.get(namespace, concept_name)
+ assert concept is not None
+
+ assert concept == Concept(
+ namespace='test',
+ concept_name='test_concept',
+ type=SignalInputType.TEXT,
+ data={
+ # The first example is a duplicate of the label from the draft, so it is removed.
+ keys[1]: Example(id=keys[1], label=False, text='world'),
+ # Draft examples are merged.
+ keys[2]: Example(id=keys[2], label=True, text='hello draft 1'),
+ keys[3]: Example(id=keys[3], label=False, text='world draft 1'),
+ # Draft examples are merged.
+ keys[4]: Example(id=keys[4], label=False, text='hello'),
+ keys[5]: Example(id=keys[5], label=True, text='world draft 2'),
+ },
+ version=3)
+
+
+def _make_test_concept_model(
+ concept_db: ConceptDB,
+ logistic_models: dict[DraftId, LogisticEmbeddingModel] = {}) -> ConceptModel:
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+ model = ConceptModel(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ model._logistic_models = logistic_models
+ return model
+
+
+class TestLogisticModel(LogisticEmbeddingModel):
+
+ @override
+ def score_embeddings(self, embeddings: np.ndarray) -> np.ndarray:
+ """Get the scores for the provided embeddings."""
+ return np.array([.1])
+
+ @override
+ def fit(self, embeddings: np.ndarray, labels: list[bool], sample_weights: list[float]) -> None:
+ pass
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+@pytest.mark.parametrize('model_db_cls', ALL_CONCEPT_MODEL_DBS)
+class ConceptModelDBSuite:
+
+ def test_save_and_get_model(self, concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB]) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ model = _make_test_concept_model(concept_db)
+ model_db.sync(model, column_info=None)
+ retrieved_model = model_db.get(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ if not retrieved_model:
+ retrieved_model = model_db.create(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ assert retrieved_model == model
+
+ def test_sync_model(self, concept_db_cls: Type[ConceptDB], model_db_cls: Type[ConceptModelDB],
+ mocker: MockerFixture) -> None:
+
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ logistic_model = TestLogisticModel(embedding_name='test_embedding')
+ score_embeddings_mock = mocker.spy(TestLogisticModel, 'score_embeddings')
+ fit_mock = mocker.spy(TestLogisticModel, 'fit')
+
+ model = _make_test_concept_model(concept_db, logistic_models={DRAFT_MAIN: logistic_model})
+
+ assert model_db.in_sync(model) is False
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 0
+
+ model_db.sync(model, column_info=None)
+
+ assert model_db.in_sync(model) is True
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 1
+
+ def test_out_of_sync_model(self, concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB], mocker: MockerFixture) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ score_embeddings_mock = mocker.spy(TestLogisticModel, 'score_embeddings')
+ fit_mock = mocker.spy(TestLogisticModel, 'fit')
+ logistic_model = TestLogisticModel(embedding_name='test_embedding')
+ model = _make_test_concept_model(concept_db, logistic_models={DRAFT_MAIN: logistic_model})
+ model_db.sync(model, column_info=None)
+ assert model_db.in_sync(model) is True
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 1
+
+ (called_model, called_embeddings, called_labels,
+ called_weights) = fit_mock.call_args_list[-1].args
+ assert called_model == logistic_model
+ np.testing.assert_array_equal(
+ called_embeddings, np.array([EMBEDDING_MAP['not in concept'], EMBEDDING_MAP['in concept']]))
+ assert called_labels == [False, True]
+ assert called_weights == [1.0, 1.0]
+
+ # Edit the concept.
+ concept_db.edit('test', 'test_concept',
+ ConceptUpdate(insert=[ExampleIn(label=False, text='a new data point')]))
+
+ # Make sure the model is out of sync.
+ assert model_db.in_sync(model) is False
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 1
+
+ model_db.sync(model, column_info=None)
+ assert model_db.in_sync(model) is True
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 2
+ # Fit is called again with new points on main only.
+ (called_model, called_embeddings, called_labels,
+ called_weights) = fit_mock.call_args_list[-1].args
+ assert called_model == logistic_model
+ np.testing.assert_array_equal(
+ called_embeddings,
+ np.array([
+ EMBEDDING_MAP['not in concept'], EMBEDDING_MAP['in concept'],
+ EMBEDDING_MAP['a new data point']
+ ]))
+ assert called_labels == [False, True, False]
+ assert called_weights == pytest.approx([1 / 2, 1.0, 1 / 2])
+
+ def test_out_of_sync_draft_model(self, concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB],
+ mocker: MockerFixture) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ score_embeddings_mock = mocker.spy(TestLogisticModel, 'score_embeddings')
+ fit_mock = mocker.spy(TestLogisticModel, 'fit')
+ logistic_model = TestLogisticModel(embedding_name='test_embedding')
+ draft_model = TestLogisticModel(embedding_name='test_embedding')
+ model = _make_test_concept_model(
+ concept_db, logistic_models={
+ DRAFT_MAIN: logistic_model,
+ 'test_draft': draft_model
+ })
+ model_db.sync(model, column_info=None)
+ assert model_db.in_sync(model) is True
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 1
+
+ # Make sure drafts cause the model to be out of sync.
+ concept_db.edit(
+ 'test',
+ 'test_concept',
+ ConceptUpdate(insert=[
+ ExampleIn(label=True, text='a true draft point', draft='test_draft'),
+ ExampleIn(label=False, text='a false draft point', draft='test_draft'),
+ # This point exists in main, but we switched the label.
+ ExampleIn(label=False, text='in concept', draft='test_draft'),
+ ]))
+
+ # Make sure the model is out of sync.
+ assert model_db.in_sync(model) is False
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 1
+
+ model_db.sync(model, column_info=None)
+ assert model_db.in_sync(model) is True
+ assert score_embeddings_mock.call_count == 0
+ assert fit_mock.call_count == 3 # Fit is called on both the draft, and main.
+
+ # Fit is called again with the same points.
+ ((called_model, called_embeddings, called_labels, called_weights),
+ (called_draft_model, called_draft_embeddings, called_draft_labels, called_draft_weights)) = (
+ c.args for c in fit_mock.call_args_list[-2:])
+
+ # The draft model is called with the data from main, and the data from draft.
+ assert called_draft_model == draft_model
+ np.testing.assert_array_equal(
+ called_draft_embeddings,
+ np.array([
+ EMBEDDING_MAP['a true draft point'], EMBEDDING_MAP['a false draft point'],
+ EMBEDDING_MAP['in concept'], EMBEDDING_MAP['not in concept']
+ ]))
+ assert called_draft_labels == [
+ True,
+ False,
+ # This was overriden by the draft.
+ False,
+ False
+ ]
+ assert called_draft_weights == pytest.approx([1.0, 1 / 3, 1 / 3, 1 / 3])
+
+ # The main model was fit without the data from the draft.
+ assert called_model == draft_model
+ np.testing.assert_array_equal(
+ called_embeddings, np.array([EMBEDDING_MAP['not in concept'], EMBEDDING_MAP['in concept']]))
+ assert called_labels == [False, True]
+ assert called_weights == pytest.approx([1.0, 1.0])
+
+ def test_embedding_not_found_in_map(self, concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB]) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ model = _make_test_concept_model(concept_db)
+ model_db.sync(model, column_info=None)
+
+ # Edit the concept.
+ concept_db.edit('test', 'test_concept',
+ ConceptUpdate(insert=[ExampleIn(label=False, text='unknown text')]))
+
+ # Make sure the model is out of sync.
+ assert model_db.in_sync(model) is False
+
+ with pytest.raises(ValueError, match='Example "unknown text" not in embedding map'):
+ model_db.sync(model, column_info=None)
+ model_db.sync(model, column_info=None)
diff --git a/src/config.py b/src/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbd9b37b508dd0a6b7c30ffc276b806247c1b3a5
--- /dev/null
+++ b/src/config.py
@@ -0,0 +1,18 @@
+"""Load environment variables from .env file."""
+import os
+from typing import Optional
+
+from dotenv import dotenv_values
+
+CONFIG: dict[str, Optional[str]] = {
+ **dotenv_values('.env'), # load shared variables
+ **dotenv_values('.env.local'), # load locally set variables
+ **os.environ, # override loaded values with environment variables
+}
+
+
+def data_path() -> str:
+ """Return the base path for data."""
+ if CONFIG['LILAC_DATA_PATH']:
+ return CONFIG['LILAC_DATA_PATH']
+ return './data'
diff --git a/src/conftest.py b/src/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdd110757295ab2f0227dfc93a40edc68f324776
--- /dev/null
+++ b/src/conftest.py
@@ -0,0 +1,28 @@
+"""Fixtures for dataset tests."""
+import pathlib
+from typing import Generator, Optional, Type
+
+import pytest
+from pytest_mock import MockerFixture
+
+from .config import CONFIG
+from .data.dataset import Dataset
+from .data.dataset_duckdb import DatasetDuckDB
+from .data.dataset_test_utils import make_dataset
+from .db_manager import set_default_dataset_cls
+from .schema import Item, Schema
+
+
+@pytest.fixture(scope='function', params=[DatasetDuckDB])
+def make_test_data(tmp_path: pathlib.Path, mocker: MockerFixture,
+ request: pytest.FixtureRequest) -> Generator:
+ """A pytest fixture for creating temporary test datasets."""
+ mocker.patch.dict(CONFIG, {'LILAC_DATA_PATH': str(tmp_path)})
+ dataset_cls: Type[Dataset] = request.param
+ set_default_dataset_cls(dataset_cls)
+
+ def _make_test_data(items: list[Item], schema: Optional[Schema] = None) -> Dataset:
+ return make_dataset(dataset_cls, tmp_path, items, schema)
+
+ # Return the factory for datasets that test methods can use.
+ yield _make_test_data
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+++ b/src/data/dataset.py
@@ -0,0 +1,407 @@
+"""The interface for the database."""
+import abc
+import datetime
+import enum
+from typing import Any, Iterator, Literal, Optional, Sequence, Union
+
+import pandas as pd
+from pydantic import BaseModel
+from pydantic import Field as PydanticField
+from pydantic import StrictBool, StrictBytes, StrictFloat, StrictInt, StrictStr, validator
+
+from ..embeddings.vector_store import VectorStore
+from ..schema import VALUE_KEY, Bin, Path, PathTuple, Schema, normalize_path
+from ..signals.signal import Signal, resolve_signal
+from ..tasks import TaskStepId
+
+# Threshold for rejecting certain queries (e.g. group by) for columns with large cardinality.
+TOO_MANY_DISTINCT = 200_000
+
+
+class SelectRowsResult:
+ """The result of a select rows query."""
+
+ def __init__(self, df: pd.DataFrame, total_num_rows: int) -> None:
+ """Initialize the result."""
+ self._df = df
+ self.total_num_rows = total_num_rows
+
+ def __iter__(self) -> Iterator:
+ return (row.to_dict() for _, row in self._df.iterrows())
+
+ def df(self) -> pd.DataFrame:
+ """Convert the result to a pandas DataFrame."""
+ return self._df
+
+
+class StatsResult(BaseModel):
+ """The result of a stats() query."""
+ path: PathTuple
+ # The number of leaf values.
+ total_count: int
+ # The approximate number of distinct leaf values.
+ approx_count_distinct: int
+
+ # Defined for ordinal features.
+ min_val: Optional[Union[float, datetime.date, datetime.datetime]]
+ max_val: Optional[Union[float, datetime.date, datetime.datetime]]
+
+ # Defined for text features.
+ avg_text_length: Optional[float]
+
+
+class MediaResult(BaseModel):
+ """The result of a media() query."""
+ data: bytes
+
+
+class BinaryOp(str, enum.Enum):
+ """The comparison operator between a column and a feature value."""
+ EQUALS = 'equals'
+ NOT_EQUAL = 'not_equal'
+ GREATER = 'greater'
+ GREATER_EQUAL = 'greater_equal'
+ LESS = 'less'
+ LESS_EQUAL = 'less_equal'
+
+
+SearchType = Union[Literal['keyword'], Literal['semantic'], Literal['concept']]
+
+
+class UnaryOp(str, enum.Enum):
+ """A unary operator on a feature."""
+ EXISTS = 'exists'
+
+
+class ListOp(str, enum.Enum):
+ """A list operator on a feature."""
+ IN = 'in'
+
+
+class SortOrder(str, enum.Enum):
+ """The sort order for a database query."""
+ DESC = 'DESC'
+ ASC = 'ASC'
+
+
+class GroupsSortBy(str, enum.Enum):
+ """The sort for groups queries.
+
+ Either "count" which sorts by the count of feature value, or "value" which sorts by the
+ feature value itself.
+ """
+ COUNT = 'count'
+ VALUE = 'value'
+
+
+class SortResult(BaseModel):
+ """The information about what is sorted after combining searches and explicit sorts."""
+ # The column that was sorted.
+ path: PathTuple
+ # The sort order.
+ order: SortOrder
+ # The alias of the column if it was aliased.
+ alias: Optional[str]
+ # The search index if the sort is by a search.
+ search_index: Optional[int]
+
+
+class SearchResultInfo(BaseModel):
+ """The resulting sort order returned by the select rows schema."""
+ # The input path to the search.
+ search_path: PathTuple
+ # The resulting column that was searched.
+ result_path: PathTuple
+ # The alias of the UDF.
+ alias: Optional[str]
+
+
+class SelectRowsSchemaUDF(BaseModel):
+ """The UDF for a select rows schema query."""
+ path: PathTuple
+ alias: Optional[str]
+
+
+class SelectRowsSchemaResult(BaseModel):
+ """The result of a select rows schema query."""
+ data_schema: Schema
+ udfs: list[SelectRowsSchemaUDF] = []
+ search_results: list[SearchResultInfo] = []
+ sorts: Optional[list[SortResult]]
+
+
+class Column(BaseModel):
+ """A column in the dataset."""
+ path: PathTuple
+ alias: Optional[str] # This is the renamed column during querying and response.
+
+ # Defined when the feature is another column.
+ signal_udf: Optional[Signal] = None
+
+ class Config:
+ smart_union = True
+
+ def __init__(self,
+ path: Path,
+ alias: Optional[str] = None,
+ signal_udf: Optional[Signal] = None,
+ **kwargs: Any):
+ """Initialize a column. We override __init__ to allow positional arguments for brevity."""
+ super().__init__(path=normalize_path(path), alias=alias, signal_udf=signal_udf, **kwargs)
+
+ @validator('signal_udf', pre=True)
+ def parse_signal_udf(cls, signal_udf: Optional[dict]) -> Optional[Signal]:
+ """Parse a signal to its specific subclass instance."""
+ if not signal_udf:
+ return None
+ return resolve_signal(signal_udf)
+
+
+ColumnId = Union[Path, Column]
+
+
+class DatasetManifest(BaseModel):
+ """The manifest for a dataset."""
+ namespace: str
+ dataset_name: str
+ data_schema: Schema
+ # Number of items in the dataset.
+ num_items: int
+
+
+def column_from_identifier(column: ColumnId) -> Column:
+ """Create a column from a column identifier."""
+ if isinstance(column, Column):
+ return column.copy()
+ return Column(path=column)
+
+
+FeatureValue = Union[StrictInt, StrictFloat, StrictBool, StrictStr, StrictBytes]
+FeatureListValue = list[StrictStr]
+BinaryFilterTuple = tuple[Path, BinaryOp, FeatureValue]
+ListFilterTuple = tuple[Path, ListOp, FeatureListValue]
+UnaryFilterTuple = tuple[Path, UnaryOp]
+
+FilterOp = Union[BinaryOp, UnaryOp, ListOp]
+
+
+class SelectGroupsResult(BaseModel):
+ """The result of a select groups query."""
+ too_many_distinct: bool
+ counts: list[tuple[Optional[FeatureValue], int]]
+ bins: Optional[list[Bin]] = None
+
+
+class Filter(BaseModel):
+ """A filter on a column."""
+ path: PathTuple
+ op: FilterOp
+ value: Optional[Union[FeatureValue, FeatureListValue]] = None
+
+
+FilterLike = Union[Filter, BinaryFilterTuple, UnaryFilterTuple, ListFilterTuple]
+
+SearchValue = StrictStr
+
+
+class KeywordQuery(BaseModel):
+ """A keyword search query on a column."""
+ type: Literal['keyword']
+ search: SearchValue
+
+
+class SemanticQuery(BaseModel):
+ """A semantic search on a column."""
+ type: Literal['semantic']
+ search: SearchValue
+ embedding: str
+
+
+class ConceptQuery(BaseModel):
+ """A concept search query on a column."""
+ type: Literal['concept']
+ concept_namespace: str
+ concept_name: str
+ embedding: str
+
+
+class Search(BaseModel):
+ """A search on a column."""
+ path: Path
+ query: Union[KeywordQuery, SemanticQuery, ConceptQuery] = PydanticField(discriminator='type')
+
+
+class Dataset(abc.ABC):
+ """The database implementation to query a dataset."""
+
+ def __init__(self, namespace: str, dataset_name: str):
+ """Initialize a dataset.
+
+ Args:
+ namespace: The dataset namespace.
+ dataset_name: The dataset name.
+ """
+ self.namespace = namespace
+ self.dataset_name = dataset_name
+
+ @abc.abstractmethod
+ def delete(self) -> None:
+ """Deletes the dataset."""
+ pass
+
+ @abc.abstractmethod
+ def manifest(self) -> DatasetManifest:
+ """Return the manifest for the dataset."""
+ pass
+
+ @abc.abstractmethod
+ def get_vector_store(self, path: PathTuple) -> VectorStore:
+ # TODO: Instead of this, allow selecting vectors via select_rows.
+ """Get the vector store for a column."""
+ pass
+
+ @abc.abstractmethod
+ def compute_signal(self,
+ signal: Signal,
+ leaf_path: Path,
+ task_step_id: Optional[TaskStepId] = None) -> None:
+ """Compute a signal for a column.
+
+ Args:
+ signal: The signal to compute over the given columns.
+ leaf_path: The leaf path to compute the signal on.
+ task_step_id: The TaskManager `task_step_id` for this process run. This is used to update the
+ progress of the task.
+ """
+ pass
+
+ @abc.abstractmethod
+ def delete_signal(self, signal_path: Path) -> None:
+ """Delete a computed signal from the dataset.
+
+ Args:
+ signal_path: The path holding the computed data of the signal.
+ """
+ pass
+
+ @abc.abstractmethod
+ def select_groups(
+ self,
+ leaf_path: Path,
+ filters: Optional[Sequence[FilterLike]] = None,
+ sort_by: Optional[GroupsSortBy] = None,
+ sort_order: Optional[SortOrder] = SortOrder.DESC,
+ limit: Optional[int] = None,
+ bins: Optional[Union[Sequence[Bin], Sequence[float]]] = None) -> SelectGroupsResult:
+ """Select grouped columns to power a histogram.
+
+ Args:
+ leaf_path: The leaf path to group by. The path can be a dot-seperated string path, or a tuple
+ of fields.
+ filters: The filters to apply to the query.
+ sort_by: What to sort by, either "count" or "value".
+ sort_order: The sort order.
+ limit: The maximum number of rows to return.
+ bins: The bins to use when bucketizing a float column.
+
+ Returns
+ A `SelectGroupsResult` iterator where each row is a group.
+ """
+ raise NotImplementedError
+
+ @abc.abstractmethod
+ def select_rows(self,
+ columns: Optional[Sequence[ColumnId]] = None,
+ searches: Optional[Sequence[Search]] = None,
+ filters: Optional[Sequence[FilterLike]] = None,
+ sort_by: Optional[Sequence[Path]] = None,
+ sort_order: Optional[SortOrder] = SortOrder.DESC,
+ limit: Optional[int] = 100,
+ offset: Optional[int] = 0,
+ task_step_id: Optional[TaskStepId] = None,
+ resolve_span: bool = False,
+ combine_columns: bool = False) -> SelectRowsResult:
+ """Select grouped columns to power a histogram.
+
+ Args:
+ columns: The columns to select. A column is an instance of `Column` which can either
+ define a path to a feature, or a column with an applied Transform, e.g. a Concept. If none,
+ it selects all columns.
+ searches: The searches to apply to the query.
+ filters: The filters to apply to the query.
+ sort_by: An ordered list of what to sort by. When defined, this is a list of aliases of column
+ names defined by the "alias" field in Column. If no alias is provided for a column, an
+ automatic alias is generated by combining each path element with a "."
+ For example: e.g. ('person', 'name') => person.name. For columns that are transform columns,
+ an alias must be provided explicitly. When sorting by a (nested) list of values, the sort
+ takes the minumum value when `sort_order` is `ASC`, and the maximum value when `sort_order`
+ is `DESC`.
+ sort_order: The sort order.
+ limit: The maximum number of rows to return.
+ offset: The offset to start returning rows from.
+ task_step_id: The TaskManager `task_step_id` for this process run. This is used to update the
+ progress.
+ resolve_span: Whether to resolve the span of the row.
+ combine_columns: Whether to combine columns into a single object. The object will be pruned
+ to only include sub-fields that correspond to the requested columns.
+
+ Returns
+ A SelectRowsResult iterator with rows of `Item`s.
+ """
+ pass
+
+ @abc.abstractmethod
+ def select_rows_schema(self,
+ columns: Optional[Sequence[ColumnId]] = None,
+ sort_by: Optional[Sequence[Path]] = None,
+ sort_order: Optional[SortOrder] = SortOrder.DESC,
+ searches: Optional[Sequence[Search]] = None,
+ combine_columns: bool = False) -> SelectRowsSchemaResult:
+ """Returns the schema of the result of `select_rows` above with the same arguments."""
+ pass
+
+ @abc.abstractmethod
+ def stats(self, leaf_path: Path) -> StatsResult:
+ """Compute stats for a leaf path.
+
+ Args:
+ leaf_path: The leaf path to compute stats for.
+
+ Returns
+ A StatsResult.
+ """
+ pass
+
+ @abc.abstractmethod
+ def media(self, item_id: str, leaf_path: Path) -> MediaResult:
+ """Return the media for a leaf path.
+
+ Args:
+ item_id: The item id to get media for.
+ leaf_path: The leaf path for the media.
+
+ Returns
+ A MediaResult.
+ """
+ pass
+
+
+def make_parquet_id(signal: Signal,
+ source_path: PathTuple,
+ is_computed_signal: Optional[bool] = False) -> str:
+ """Return a unique identifier for this parquet table."""
+ # Don't use the VALUE_KEY as part of the parquet id to reduce the size of paths.
+ path = source_path[:-1] if source_path[-1] == VALUE_KEY else source_path
+ column_alias = '.'.join(map(str, path))
+ if column_alias.endswith('.*'):
+ # Remove the trailing .* from the column name.
+ column_alias = column_alias[:-2]
+
+ return f'{signal.key(is_computed_signal=is_computed_signal)}({column_alias})'
+
+
+def val(path: Path) -> PathTuple:
+ """Returns the value at a path."""
+ if path[-1] == VALUE_KEY:
+ raise ValueError(f'Path "{path}" already is a value path.')
+ return (*normalize_path(path), VALUE_KEY)
diff --git a/src/data/dataset_compute_signal_chain_test.py b/src/data/dataset_compute_signal_chain_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..0607d65ab887e8f14af13e4b542323dd3978b8ad
--- /dev/null
+++ b/src/data/dataset_compute_signal_chain_test.py
@@ -0,0 +1,255 @@
+"""Tests for dataset.compute_signal() when signals are chained."""
+
+import re
+from typing import Iterable, List, Optional, cast
+
+import numpy as np
+import pytest
+from pytest_mock import MockerFixture
+from typing_extensions import override
+
+from ..embeddings.vector_store import VectorStore
+from ..schema import UUID_COLUMN, Field, Item, RichData, VectorKey, field, schema
+from ..signals.signal import (
+ TextEmbeddingModelSignal,
+ TextEmbeddingSignal,
+ TextSignal,
+ TextSplitterSignal,
+ clear_signal_registry,
+ register_signal,
+)
+from .dataset import DatasetManifest
+from .dataset_test_utils import (
+ TEST_DATASET_NAME,
+ TEST_NAMESPACE,
+ TestDataMaker,
+ enriched_embedding_span,
+ enriched_embedding_span_field,
+ enriched_item,
+)
+from .dataset_utils import lilac_embedding, lilac_span
+
+SIMPLE_ITEMS: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'str': 'a',
+ 'int': 1,
+ 'bool': False,
+ 'float': 3.0
+}, {
+ UUID_COLUMN: '2',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 2.0
+}, {
+ UUID_COLUMN: '3',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 1.0
+}]
+
+EMBEDDINGS: list[tuple[str, list[float]]] = [('hello.', [1.0, 0.0, 0.0]),
+ ('hello2.', [1.0, 1.0, 0.0]),
+ ('hello world.', [1.0, 1.0, 1.0]),
+ ('hello world2.', [2.0, 1.0, 1.0])]
+
+STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for text, embedding in EMBEDDINGS}
+
+
+class TestSplitter(TextSplitterSignal):
+ """Split documents into sentence by splitting on period."""
+ name = 'test_splitter'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ for text in data:
+ if not isinstance(text, str):
+ raise ValueError(f'Expected text to be a string, got {type(text)} instead.')
+ sentences = [f'{sentence.strip()}.' for sentence in text.split('.') if sentence]
+ yield [
+ lilac_span(text.index(sentence),
+ text.index(sentence) + len(sentence)) for sentence in sentences
+ ]
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[cast(str, example)]))]
+
+
+class TestEmbeddingSumSignal(TextEmbeddingModelSignal):
+ """Sums the embeddings to return a single floating point value."""
+ name = 'test_embedding_sum'
+
+ @override
+ def fields(self) -> Field:
+ return field('float32')
+
+ @override
+ def vector_compute(self, keys: Iterable[VectorKey], vector_store: VectorStore) -> Iterable[Item]:
+ # The signal just sums the values of the embedding.
+ embedding_sums = vector_store.get(keys).sum(axis=1)
+ for embedding_sum in embedding_sums.tolist():
+ yield embedding_sum
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(TestSplitter)
+ register_signal(TestEmbedding)
+ register_signal(TestEmbeddingSumSignal)
+ register_signal(NamedEntity)
+ # Unit test runs.
+ yield
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_manual_embedding_signal(make_test_data: TestDataMaker, mocker: MockerFixture) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ embed_mock = mocker.spy(TestEmbedding, 'compute')
+
+ embedding_signal = TestEmbedding()
+ dataset.compute_signal(embedding_signal, 'text')
+ embedding_sum_signal = TestEmbeddingSumSignal(embedding=TestEmbedding.name)
+ dataset.compute_signal(embedding_sum_signal, 'text')
+
+ # Make sure the embedding signal is not called twice.
+ assert embed_mock.call_count == 1
+
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'test_embedding': field(
+ signal=embedding_signal.dict(),
+ fields=[
+ enriched_embedding_span_field(
+ {'test_embedding_sum': field('float32', embedding_sum_signal.dict())})
+ ])
+ }),
+ }),
+ num_items=2)
+
+ result = dataset.select_rows()
+ expected_result = [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item(
+ 'hello.', {'test_embedding': [enriched_embedding_span(0, 6, {'test_embedding_sum': 1.0})]})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item(
+ 'hello2.', {'test_embedding': [enriched_embedding_span(0, 7, {'test_embedding_sum': 2.0})]})
+ }]
+ assert list(result) == expected_result
+
+
+def test_auto_embedding_signal(make_test_data: TestDataMaker, mocker: MockerFixture) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ embed_mock = mocker.spy(TestEmbedding, 'compute')
+
+ # The embedding is automatically computed from the TestEmbeddingSumSignal.
+ embedding_sum_signal = TestEmbeddingSumSignal(embedding=TestEmbedding.name)
+ dataset.compute_signal(embedding_sum_signal, 'text')
+
+ # Make sure the embedding signal is not called twice.
+ assert embed_mock.call_count == 1
+
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'test_embedding': field(
+ signal=embedding_sum_signal._embedding_signal.dict(),
+ fields=[
+ enriched_embedding_span_field(
+ {'test_embedding_sum': field('float32', embedding_sum_signal.dict())})
+ ])
+ }),
+ }),
+ num_items=2)
+
+ result = dataset.select_rows()
+ expected_result = [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item(
+ 'hello.', {'test_embedding': [enriched_embedding_span(0, 6, {'test_embedding_sum': 1.0})]})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item(
+ 'hello2.', {'test_embedding': [enriched_embedding_span(0, 7, {'test_embedding_sum': 2.0})]})
+ }]
+ assert list(result) == expected_result
+
+
+ENTITY_REGEX = r'[A-Za-z]+@[A-Za-z]+'
+
+
+class NamedEntity(TextSignal):
+ """Find special entities."""
+ name = 'entity'
+
+ @override
+ def fields(self) -> Field:
+ return field(fields=['string_span'])
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[List[Item]]]:
+ for text in data:
+ if not isinstance(text, str):
+ yield None
+ continue
+ yield [lilac_span(m.start(0), m.end(0)) for m in re.finditer(ENTITY_REGEX, text)]
+
+
+def test_entity_on_split_signal(make_test_data: TestDataMaker) -> None:
+ text = 'Hello nik@test. Here are some other entities like pii@gmail and all@lilac.'
+ dataset = make_test_data([{UUID_COLUMN: '1', 'text': text}])
+ entity = NamedEntity()
+ dataset.compute_signal(TestSplitter(), 'text')
+ dataset.compute_signal(entity, ('text', 'test_splitter', '*'))
+
+ result = dataset.select_rows(['text'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item(
+ text, {
+ 'test_splitter': [
+ lilac_span(0, 15, {'entity': [lilac_span(6, 14)]}),
+ lilac_span(16, 74, {'entity': [
+ lilac_span(50, 59),
+ lilac_span(64, 73),
+ ]}),
+ ]
+ })
+ }]
diff --git a/src/data/dataset_compute_signal_test.py b/src/data/dataset_compute_signal_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..8831c17d4bbf4275a1d8934366770e312837b44c
--- /dev/null
+++ b/src/data/dataset_compute_signal_test.py
@@ -0,0 +1,669 @@
+"""Tests for dataset.compute_signal()."""
+
+from typing import Iterable, Optional, Union, cast
+
+import numpy as np
+import pytest
+from typing_extensions import override
+
+from ..concepts.concept import ExampleIn
+from ..concepts.db_concept import ConceptUpdate, DiskConceptDB
+from ..schema import UUID_COLUMN, VALUE_KEY, Field, Item, RichData, SignalInputType, field, schema
+from ..signals.concept_scorer import ConceptScoreSignal
+from ..signals.signal import (
+ TextEmbeddingSignal,
+ TextSignal,
+ TextSplitterSignal,
+ clear_signal_registry,
+ register_signal,
+)
+from .dataset import Column, DatasetManifest, GroupsSortBy, SortOrder, val
+from .dataset_test_utils import (
+ TEST_DATASET_NAME,
+ TEST_NAMESPACE,
+ TestDataMaker,
+ enriched_embedding_span_field,
+ enriched_item,
+)
+from .dataset_utils import lilac_embedding, lilac_span
+
+SIMPLE_ITEMS: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'str': 'a',
+ 'int': 1,
+ 'bool': False,
+ 'float': 3.0
+}, {
+ UUID_COLUMN: '2',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 2.0
+}, {
+ UUID_COLUMN: '3',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 1.0
+}]
+
+
+class TestInvalidSignal(TextSignal):
+ name = 'test_invalid_signal'
+
+ @override
+ def fields(self) -> Field:
+ return field('int32')
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ # Return an invalid output that doesn't match the input length.
+ return []
+
+
+class TestSparseSignal(TextSignal):
+ name = 'test_sparse_signal'
+
+ @override
+ def fields(self) -> Field:
+ return field('int32')
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text in data:
+ if text == 'hello':
+ # Skip this input.
+ yield None
+ else:
+ yield len(text)
+
+
+class TestSparseRichSignal(TextSignal):
+ """Find personally identifiable information (emails, phone numbers, etc)."""
+ name = 'test_sparse_rich_signal'
+
+ @override
+ def fields(self) -> Field:
+ return field(fields={'emails': ['string']})
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text in data:
+ if text == 'hello':
+ # Skip this input.
+ yield None
+ else:
+ yield {'emails': ['test1@hello.com', 'test2@hello.com']}
+
+
+class TestParamSignal(TextSignal):
+ name = 'param_signal'
+ param: str
+
+ def fields(self) -> Field:
+ return field('string')
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ yield f'{str(text_content)}_{self.param}'
+
+
+class TestSignal(TextSignal):
+ name = 'test_signal'
+
+ @override
+ def fields(self) -> Field:
+ return field(fields={'len': 'int32', 'flen': 'float32'})
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ return [{'len': len(text_content), 'flen': float(len(text_content))} for text_content in data]
+
+
+class TestSplitSignal(TextSplitterSignal):
+ """Split documents into sentence by splitting on period, generating entities."""
+ name = 'test_split'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ for text in data:
+ if not isinstance(text, str):
+ raise ValueError(f'Expected text to be a string, got {type(text)} instead.')
+ sentences = [f'{sentence.strip()}.' for sentence in text.split('.') if sentence]
+ yield [
+ lilac_span(text.index(sentence),
+ text.index(sentence) + len(sentence)) for sentence in sentences
+ ]
+
+
+EMBEDDINGS: list[tuple[str, Union[list[float], list[list[float]]]]] = [
+ ('hello.', [1.0, 0.0, 0.0]),
+ # This embedding has an outer dimension of 1.
+ ('hello2.', [[1.0, 1.0, 0.0]]),
+ ('hello3.', [[0, 0, 1.]])
+]
+
+STR_EMBEDDINGS: dict[str, Union[list[float], list[list[float]]]] = {
+ text: embedding for text, embedding in EMBEDDINGS
+}
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ example = cast(str, example)
+ yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[example]))]
+
+
+class ComputedKeySignal(TextSignal):
+ name = 'computed_key'
+
+ @override
+ def fields(self) -> Field:
+ return field('int64')
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text in data:
+ yield 1
+
+ def key(self, is_computed_signal: Optional[bool] = False) -> str:
+ return f'key_{is_computed_signal}'
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(TestSparseSignal)
+ register_signal(TestSparseRichSignal)
+ register_signal(TestParamSignal)
+ register_signal(TestSignal)
+ register_signal(TestSplitSignal)
+ register_signal(TestEmbedding)
+ register_signal(ComputedKeySignal)
+ register_signal(ConceptScoreSignal)
+
+ # Unit test runs.
+ yield
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_signal_output_validation(make_test_data: TestDataMaker) -> None:
+ signal = TestInvalidSignal()
+
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world',
+ }])
+
+ with pytest.raises(
+ ValueError, match='The signal generated 0 values but the input data had 2 values.'):
+ dataset.compute_signal(signal, 'text')
+
+
+def test_sparse_signal(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world',
+ }])
+
+ dataset.compute_signal(TestSparseSignal(), 'text')
+
+ result = dataset.select_rows(['text'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {'test_sparse_signal': None})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('hello world', {'test_sparse_signal': 11})
+ }]
+
+
+def test_sparse_rich_signal(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world',
+ }])
+
+ dataset.compute_signal(TestSparseRichSignal(), 'text')
+
+ result = dataset.select_rows(['text'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {'test_sparse_rich_signal': None})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item(
+ 'hello world',
+ {'test_sparse_rich_signal': {
+ 'emails': ['test1@hello.com', 'test2@hello.com']
+ }})
+ }]
+
+
+def test_source_joined_with_signal(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(SIMPLE_ITEMS)
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'str': 'string',
+ 'int': 'int32',
+ 'bool': 'boolean',
+ 'float': 'float32',
+ }),
+ num_items=3)
+
+ test_signal = TestSignal()
+ dataset.compute_signal(test_signal, 'str')
+
+ # Check the enriched dataset manifest has 'text' enriched.
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'str': field(
+ 'string',
+ fields={
+ 'test_signal': field(
+ signal=test_signal.dict(), fields={
+ 'len': 'int32',
+ 'flen': 'float32'
+ }),
+ }),
+ 'int': 'int32',
+ 'bool': 'boolean',
+ 'float': 'float32',
+ }),
+ num_items=3)
+
+ result = dataset.select_rows(['str'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'str': enriched_item('a', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ }, {
+ UUID_COLUMN: '2',
+ 'str': enriched_item('b', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ }, {
+ UUID_COLUMN: '3',
+ 'str': enriched_item('b', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ }]
+
+ # Select a specific signal leaf test_signal.flen with val('str').
+ result = dataset.select_rows([val('str'), ('str', 'test_signal', 'flen')])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ f'str.{VALUE_KEY}': 'a',
+ 'str.test_signal.flen': 1.0
+ }, {
+ UUID_COLUMN: '2',
+ f'str.{VALUE_KEY}': 'b',
+ 'str.test_signal.flen': 1.0
+ }, {
+ UUID_COLUMN: '3',
+ f'str.{VALUE_KEY}': 'b',
+ 'str.test_signal.flen': 1.0
+ }]
+
+ # Select a specific signal leaf test_signal.flen and the whole 'str' subtree.
+ result = dataset.select_rows(['str', ('str', 'test_signal', 'flen')])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'str': enriched_item('a', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'str.test_signal.flen': 1.0
+ }, {
+ UUID_COLUMN: '2',
+ 'str': enriched_item('b', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'str.test_signal.flen': 1.0
+ }, {
+ UUID_COLUMN: '3',
+ 'str': enriched_item('b', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'str.test_signal.flen': 1.0
+ }]
+
+ # Select multiple signal leafs with aliasing.
+ result = dataset.select_rows([
+ val('str'),
+ Column(('str', 'test_signal', 'flen'), alias='flen'),
+ Column(('str', 'test_signal', 'len'), alias='len')
+ ])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ f'str.{VALUE_KEY}': 'a',
+ 'flen': 1.0,
+ 'len': 1
+ }, {
+ UUID_COLUMN: '2',
+ f'str.{VALUE_KEY}': 'b',
+ 'flen': 1.0,
+ 'len': 1
+ }, {
+ UUID_COLUMN: '3',
+ f'str.{VALUE_KEY}': 'b',
+ 'flen': 1.0,
+ 'len': 1
+ }]
+
+
+def test_parameterized_signal(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody'
+ }])
+ test_signal_a = TestParamSignal(param='a')
+ test_signal_b = TestParamSignal(param='b')
+ dataset.compute_signal(test_signal_a, 'text')
+ dataset.compute_signal(test_signal_b, 'text')
+
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'param_signal(param=a)': field('string', test_signal_a.dict()),
+ 'param_signal(param=b)': field('string', test_signal_b.dict()),
+ }),
+ }),
+ num_items=2)
+
+ result = dataset.select_rows(['text'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {
+ 'param_signal(param=a)': 'hello_a',
+ 'param_signal(param=b)': 'hello_b',
+ })
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('everybody', {
+ 'param_signal(param=a)': 'everybody_a',
+ 'param_signal(param=b)': 'everybody_b',
+ })
+ }]
+
+
+def test_split_signal(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': '[1, 1] first sentence. [1, 1] second sentence.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'b2 [2, 1] first sentence. [2, 1] second sentence.',
+ }])
+
+ signal = TestSplitSignal()
+ dataset.compute_signal(signal, 'text')
+
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string', fields={'test_split': field(signal=signal.dict(), fields=[field('string_span')])})
+ }),
+ num_items=2)
+
+ result = dataset.select_rows(['text'])
+ expected_result = [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('[1, 1] first sentence. [1, 1] second sentence.',
+ {'test_split': [lilac_span(0, 22), lilac_span(23, 46)]})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('b2 [2, 1] first sentence. [2, 1] second sentence.',
+ {'test_split': [
+ lilac_span(0, 25),
+ lilac_span(26, 49),
+ ]})
+ }]
+ assert list(result) == expected_result
+
+
+def test_signal_on_repeated_field(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': ['hello', 'everybody'],
+ }, {
+ UUID_COLUMN: '2',
+ 'text': ['hello2', 'everybody2'],
+ }])
+ test_signal = TestSignal()
+ # Run the signal on the repeated field.
+ dataset.compute_signal(test_signal, ('text', '*'))
+
+ # Check the enriched dataset manifest has 'text' enriched.
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(fields=[
+ field(
+ 'string',
+ fields={
+ 'test_signal': field(
+ signal=test_signal.dict(), fields={
+ 'len': 'int32',
+ 'flen': 'float32'
+ })
+ })
+ ])
+ }),
+ num_items=2)
+
+ result = dataset.select_rows([('text', '*')])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text.*': [
+ enriched_item('hello', {'test_signal': {
+ 'len': 5,
+ 'flen': 5.0
+ }}),
+ enriched_item('everybody', {'test_signal': {
+ 'len': 9,
+ 'flen': 9.0
+ }})
+ ]
+ }, {
+ UUID_COLUMN: '2',
+ 'text.*': [
+ enriched_item('hello2', {'test_signal': {
+ 'len': 6,
+ 'flen': 6.0
+ }}),
+ enriched_item('everybody2', {'test_signal': {
+ 'len': 10,
+ 'flen': 10.0
+ }})
+ ]
+ }]
+
+
+def test_text_splitter(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': '[1, 1] first sentence. [1, 1] second sentence.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'b2 [2, 1] first sentence. [2, 1] second sentence.',
+ }])
+
+ dataset.compute_signal(TestSplitSignal(), 'text')
+
+ result = dataset.select_rows(['text'])
+ expected_result = [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('[1, 1] first sentence. [1, 1] second sentence.',
+ {'test_split': [
+ lilac_span(0, 22),
+ lilac_span(23, 46),
+ ]}),
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('b2 [2, 1] first sentence. [2, 1] second sentence.',
+ {'test_split': [
+ lilac_span(0, 25),
+ lilac_span(26, 49),
+ ]}),
+ }]
+ assert list(result) == expected_result
+
+
+def test_embedding_signal(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ embedding_signal = TestEmbedding()
+ dataset.compute_signal(embedding_signal, 'text')
+
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'test_embedding': field(
+ signal=embedding_signal.dict(), fields=[enriched_embedding_span_field()])
+ }),
+ }),
+ num_items=2)
+
+ result = dataset.select_rows()
+
+ # Embeddings are replaced with "None".
+ expected_result = [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello.', {'test_embedding': [lilac_embedding(0, 6, None)]})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('hello2.', {'test_embedding': [lilac_embedding(0, 7, None)]})
+ }]
+ assert list(result) == expected_result
+
+
+def test_is_computed_signal_key(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ signal = ComputedKeySignal()
+ dataset.compute_signal(signal, 'text')
+
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field('string', fields={'key_True': field('int64', signal=signal.dict())}),
+ }),
+ num_items=2)
+
+ result = dataset.select_rows()
+
+ # Embeddings are replaced with "None".
+ expected_result = [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello.', {'key_True': 1})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('hello2.', {'key_True': 1})
+ }]
+ assert list(result) == expected_result
+
+
+def test_concept_signal_with_select_groups(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'hello3.',
+ }])
+
+ embedding_signal = TestEmbedding()
+ dataset.compute_signal(embedding_signal, 'text')
+
+ concept_db = DiskConceptDB()
+ concept_db.create(namespace='test_namespace', name='test_concept', type=SignalInputType.TEXT)
+ concept_db.edit(
+ 'test_namespace', 'test_concept',
+ ConceptUpdate(insert=[
+ ExampleIn(label=False, text='hello.'),
+ ExampleIn(label=True, text='hello2.'),
+ ExampleIn(label=False, text='hello3.')
+ ]))
+
+ concept_signal = ConceptScoreSignal(
+ namespace='test_namespace', concept_name='test_concept', embedding='test_embedding')
+
+ dataset.compute_signal(concept_signal, 'text')
+
+ concept_key = concept_signal.key(is_computed_signal=True)
+ result = dataset.select_groups(f'text.test_embedding.*.embedding.{concept_key}')
+ assert result.counts == [('Not in concept', 2), ('In concept', 1)]
+
+ result = dataset.select_groups(
+ f'text.test_embedding.*.embedding.{concept_key}',
+ sort_by=GroupsSortBy.COUNT,
+ sort_order=SortOrder.ASC)
+ assert result.counts == [('In concept', 1), ('Not in concept', 2)]
diff --git a/src/data/dataset_duckdb.py b/src/data/dataset_duckdb.py
new file mode 100644
index 0000000000000000000000000000000000000000..3672b12d7b0cedb6407048c7d342ff23f3609ac5
--- /dev/null
+++ b/src/data/dataset_duckdb.py
@@ -0,0 +1,1666 @@
+"""The DuckDB implementation of the dataset database."""
+import functools
+import glob
+import math
+import os
+import re
+import shutil
+import threading
+from typing import Any, Iterable, Optional, Sequence, Type, Union, cast
+
+import duckdb
+import numpy as np
+import pandas as pd
+from pandas.api.types import is_object_dtype
+from pydantic import BaseModel, validator
+from typing_extensions import override
+
+from ..concepts.concept import ConceptColumnInfo
+from ..config import CONFIG, data_path
+from ..embeddings.vector_store import VectorStore
+from ..embeddings.vector_store_numpy import NumpyVectorStore
+from ..schema import (
+ MANIFEST_FILENAME,
+ PATH_WILDCARD,
+ TEXT_SPAN_END_FEATURE,
+ TEXT_SPAN_START_FEATURE,
+ UUID_COLUMN,
+ VALUE_KEY,
+ Bin,
+ DataType,
+ Field,
+ Item,
+ Path,
+ PathTuple,
+ RichData,
+ Schema,
+ SignalInputType,
+ SourceManifest,
+ VectorKey,
+ column_paths_match,
+ is_float,
+ is_integer,
+ is_ordinal,
+ normalize_path,
+ signal_compute_type_supports_dtype,
+)
+from ..signals.concept_labels import ConceptLabelsSignal
+from ..signals.concept_scorer import ConceptScoreSignal
+from ..signals.semantic_similarity import SemanticSimilaritySignal
+from ..signals.signal import (
+ EMBEDDING_KEY,
+ Signal,
+ TextEmbeddingModelSignal,
+ TextEmbeddingSignal,
+ resolve_signal,
+)
+from ..signals.substring_search import SubstringSignal
+from ..tasks import TaskStepId, TaskStepInfo, progress, set_worker_steps
+from ..utils import DebugTimer, get_dataset_output_dir, log, open_file
+from . import dataset
+from .dataset import (
+ BinaryOp,
+ Column,
+ ColumnId,
+ Dataset,
+ DatasetManifest,
+ FeatureListValue,
+ FeatureValue,
+ Filter,
+ FilterLike,
+ GroupsSortBy,
+ ListOp,
+ MediaResult,
+ Search,
+ SearchResultInfo,
+ SelectGroupsResult,
+ SelectRowsResult,
+ SelectRowsSchemaResult,
+ SelectRowsSchemaUDF,
+ SortOrder,
+ SortResult,
+ StatsResult,
+ UnaryOp,
+ column_from_identifier,
+ make_parquet_id,
+)
+from .dataset_utils import (
+ count_primitives,
+ create_signal_schema,
+ flatten,
+ flatten_keys,
+ merge_schemas,
+ read_embedding_index,
+ replace_embeddings_with_none,
+ schema_contains_path,
+ unflatten,
+ wrap_in_dicts,
+ write_item_embeddings_to_disk,
+ write_items_to_parquet,
+)
+
+DEBUG = CONFIG['DEBUG'] == 'true' if 'DEBUG' in CONFIG else False
+UUID_INDEX_FILENAME = 'uuids.npy'
+
+SIGNAL_MANIFEST_FILENAME = 'signal_manifest.json'
+SOURCE_VIEW_NAME = 'source'
+
+# Sample size for approximating the distinct count of a column.
+SAMPLE_SIZE_DISTINCT_COUNT = 100_000
+NUM_AUTO_BINS = 15
+
+BINARY_OP_TO_SQL: dict[BinaryOp, str] = {
+ BinaryOp.EQUALS: '=',
+ BinaryOp.NOT_EQUAL: '!=',
+ BinaryOp.GREATER: '>',
+ BinaryOp.GREATER_EQUAL: '>=',
+ BinaryOp.LESS: '<',
+ BinaryOp.LESS_EQUAL: '<='
+}
+
+
+class DuckDBSearchUDF(BaseModel):
+ """The transformation of searches to column UDFs."""
+ udf: Column
+ search_path: PathTuple
+ output_path: PathTuple
+ sort: Optional[tuple[PathTuple, SortOrder]]
+
+
+class DuckDBSearchUDFs(BaseModel):
+ """The transformation of searches to column UDFs with sorts."""
+ udfs: list[Column]
+ output_paths: list[PathTuple]
+ sorts: list[tuple[PathTuple, SortOrder]]
+
+
+class DatasetDuckDB(Dataset):
+ """The DuckDB implementation of the dataset database."""
+
+ def __init__(self,
+ namespace: str,
+ dataset_name: str,
+ vector_store_cls: Type[VectorStore] = NumpyVectorStore):
+ super().__init__(namespace, dataset_name)
+
+ self.dataset_path = get_dataset_output_dir(data_path(), namespace, dataset_name)
+
+ # TODO: Infer the manifest from the parquet files so this is lighter weight.
+ self._source_manifest = read_source_manifest(self.dataset_path)
+ self._signal_manifests: list[SignalManifest] = []
+ self.con = duckdb.connect(database=':memory:')
+
+ # Maps a column path and embedding to the vector store. This is lazily generated as needed.
+ self._col_vector_stores: dict[PathTuple, VectorStore] = {}
+ self.vector_store_cls = vector_store_cls
+ self._manifest_lock = threading.Lock()
+
+ @override
+ def delete(self) -> None:
+ """Deletes the dataset."""
+ self.con.close()
+ shutil.rmtree(self.dataset_path, ignore_errors=True)
+
+ def _create_view(self, view_name: str, files: list[str]) -> None:
+ self.con.execute(f"""
+ CREATE OR REPLACE VIEW {_escape_col_name(view_name)} AS (SELECT * FROM read_parquet({files}));
+ """)
+
+ # NOTE: This is cached, but when the latest mtime of any file in the dataset directory changes
+ # the results are invalidated.
+ @functools.cache
+ def _recompute_joint_table(self, latest_mtime_micro_sec: int) -> DatasetManifest:
+ del latest_mtime_micro_sec # This is used as the cache key.
+ merged_schema = self._source_manifest.data_schema.copy(deep=True)
+ self._signal_manifests = []
+ # Make a joined view of all the column groups.
+ self._create_view(SOURCE_VIEW_NAME,
+ [os.path.join(self.dataset_path, f) for f in self._source_manifest.files])
+
+ # Add the signal column groups.
+ for root, _, files in os.walk(self.dataset_path):
+ for file in files:
+ if not file.endswith(SIGNAL_MANIFEST_FILENAME):
+ continue
+
+ with open_file(os.path.join(root, file)) as f:
+ signal_manifest = SignalManifest.parse_raw(f.read())
+ self._signal_manifests.append(signal_manifest)
+ signal_files = [os.path.join(root, f) for f in signal_manifest.files]
+ self._create_view(signal_manifest.parquet_id, signal_files)
+
+ merged_schema = merge_schemas([self._source_manifest.data_schema] +
+ [m.data_schema for m in self._signal_manifests])
+
+ # The logic below generates the following example query:
+ # CREATE OR REPLACE VIEW t AS (
+ # SELECT
+ # source.*,
+ # "parquet_id1"."root_column" AS "parquet_id1",
+ # "parquet_id2"."root_column" AS "parquet_id2"
+ # FROM source JOIN "parquet_id1" USING (uuid,) JOIN "parquet_id2" USING (uuid,)
+ # );
+ # NOTE: "root_column" for each signal is defined as the top-level column.
+ select_sql = ', '.join([f'{SOURCE_VIEW_NAME}.*'] + [(
+ f'{_escape_col_name(manifest.parquet_id)}.{_escape_col_name(_root_column(manifest))} '
+ f'AS {_escape_col_name(manifest.parquet_id)}') for manifest in self._signal_manifests])
+ join_sql = ' '.join([SOURCE_VIEW_NAME] + [
+ f'join {_escape_col_name(manifest.parquet_id)} using ({UUID_COLUMN},)'
+ for manifest in self._signal_manifests
+ ])
+ view_or_table = 'TABLE'
+ use_views = CONFIG.get('DUCKDB_USE_VIEWS', 0) or 0
+ if int(use_views):
+ view_or_table = 'VIEW'
+ sql_cmd = f"""CREATE OR REPLACE {view_or_table} t AS (SELECT {select_sql} FROM {join_sql})"""
+ self.con.execute(sql_cmd)
+
+ # Get the total size of the table.
+ size_query = 'SELECT COUNT() as count FROM t'
+ size_query_result = cast(Any, self._query(size_query)[0])
+ num_items = cast(int, size_query_result[0])
+
+ return DatasetManifest(
+ namespace=self.namespace,
+ dataset_name=self.dataset_name,
+ data_schema=merged_schema,
+ num_items=num_items)
+
+ @override
+ def manifest(self) -> DatasetManifest:
+ # Use the latest modification time of all files under the dataset path as the cache key for
+ # re-computing the manifest and the joined view.
+ with self._manifest_lock:
+ all_dataset_files = glob.iglob(os.path.join(self.dataset_path, '**'), recursive=True)
+ latest_mtime = max(map(os.path.getmtime, all_dataset_files))
+ latest_mtime_micro_sec = int(latest_mtime * 1e6)
+ return self._recompute_joint_table(latest_mtime_micro_sec)
+
+ def count(self, filters: Optional[list[FilterLike]] = None) -> int:
+ """Count the number of rows."""
+ raise NotImplementedError('count is not yet implemented for DuckDB.')
+
+ @override
+ def get_vector_store(self, path: PathTuple) -> VectorStore:
+ # Refresh the manifest to make sure we have the latest signal manifests.
+ self.manifest()
+
+ if path not in self._col_vector_stores:
+ manifest = next(
+ m for m in self._signal_manifests if schema_contains_path(m.data_schema, path))
+ if not manifest:
+ raise ValueError(f'No embedding found for path {path}.')
+ if not manifest.embedding_filename_prefix:
+ raise ValueError(f'Signal manifest for path {path} is not an embedding. '
+ f'Got signal manifest: {manifest}')
+
+ signal_name = cast(str, manifest.signal.signal_name)
+ filepath_prefix = os.path.join(self.dataset_path, _signal_dir(manifest.enriched_path),
+ signal_name, manifest.embedding_filename_prefix)
+ keys, embeddings = read_embedding_index(filepath_prefix)
+ # Get all the embeddings and pass it to the vector store.
+ vector_store = self.vector_store_cls()
+ vector_store.add(keys, embeddings)
+ # Cache the vector store.
+ self._col_vector_stores[path] = vector_store
+
+ return self._col_vector_stores[path]
+
+ def _prepare_signal(
+ self,
+ signal: Signal,
+ source_path: PathTuple,
+ manifest: DatasetManifest,
+ compute_dependencies: Optional[bool] = False,
+ task_step_id: Optional[TaskStepId] = None) -> tuple[PathTuple, Optional[TaskStepId]]:
+ """Run all the signals depedencies required to run this signal.
+
+ Args:
+ signal: The signal to prepare.
+ source_path: The source path the signal is running over.
+ compute_dependencies: If True, signals will get computed for the whole column. If False,
+ throw if the required inputs are not computed yet.
+ task_step_id: The TaskStepId used to run the signal.
+
+ Returns
+ The final path the signal will be run over and the new step id for the final signal.
+ """
+ is_value_path = False
+ if source_path[-1] == VALUE_KEY:
+ is_value_path = True
+ source_path = source_path[:-1]
+
+ new_path = source_path
+
+ signals_to_compute: list[tuple[PathTuple, Signal]] = []
+ if isinstance(signal, TextEmbeddingModelSignal):
+ embedding_signal = signal.get_embedding_signal()
+ new_path = (*new_path, embedding_signal.key(), PATH_WILDCARD, EMBEDDING_KEY)
+ if new_path not in manifest.data_schema.leafs:
+ if not compute_dependencies:
+ raise ValueError(f'Embedding signal "{embedding_signal.key()}" is not computed over '
+ f'{source_path}. Please run `dataset.compute_signal` over '
+ f'{source_path} first.')
+ signals_to_compute.append((new_path, embedding_signal))
+
+ new_steps = len(signals_to_compute)
+ # Setup the task steps so the task progress indicator knows the number of steps before they are
+ # computed.
+ if task_step_id:
+ (task_id, step_id) = task_step_id
+ if new_steps:
+ # Make a step for the parent.
+ set_worker_steps(task_id, [TaskStepInfo()] * (new_steps + 1))
+
+ for i, (new_path, signal) in enumerate(signals_to_compute):
+ if new_path not in manifest.data_schema.leafs:
+ self.compute_signal(
+ signal, source_path, task_step_id=(task_id, i) if task_step_id else None)
+
+ if is_value_path:
+ new_path = (*new_path, VALUE_KEY)
+
+ return (new_path, (task_id, step_id + new_steps) if task_step_id else None)
+
+ @override
+ def compute_signal(self,
+ signal: Signal,
+ leaf_path: Path,
+ task_step_id: Optional[TaskStepId] = None) -> None:
+ source_path = normalize_path(leaf_path)
+ manifest = self.manifest()
+
+ # Prepare the dependencies of this signal.
+ signal_source_path, task_step_id = self._prepare_signal(
+ signal, source_path, manifest, compute_dependencies=True, task_step_id=task_step_id)
+
+ # The manifest may have changed after computing the dependencies.
+ manifest = self.manifest()
+
+ if isinstance(signal, ConceptScoreSignal):
+ # Set dataset information on the signal.
+ signal.set_column_info(
+ ConceptColumnInfo(namespace=self.namespace, name=self.dataset_name, path=source_path))
+
+ signal_col = Column(path=source_path, alias='value', signal_udf=signal)
+ select_rows_result = self.select_rows([signal_col],
+ task_step_id=task_step_id,
+ resolve_span=True)
+ df = select_rows_result.df()
+ values = df['value']
+
+ source_path = signal_source_path
+ signal_col.path = source_path
+
+ enriched_path = _col_destination_path(signal_col, is_computed_signal=True)
+ spec = _split_path_into_subpaths_of_lists(enriched_path)
+ output_dir = os.path.join(self.dataset_path, _signal_dir(enriched_path))
+ signal_schema = create_signal_schema(signal, source_path, manifest.data_schema)
+ enriched_signal_items = cast(Iterable[Item], wrap_in_dicts(values, spec))
+ for uuid, item in zip(df[UUID_COLUMN], enriched_signal_items):
+ item[UUID_COLUMN] = uuid
+
+ is_embedding = isinstance(signal, TextEmbeddingSignal)
+ embedding_filename_prefix = None
+ if is_embedding:
+ embedding_filename_prefix = os.path.basename(
+ write_item_embeddings_to_disk(
+ keys=df[UUID_COLUMN],
+ embeddings=values,
+ output_dir=output_dir,
+ shard_index=0,
+ num_shards=1))
+
+ # Replace the embeddings with None so they are not serialized in the parquet file.
+ enriched_signal_items = (replace_embeddings_with_none(item) for item in enriched_signal_items)
+
+ enriched_signal_items = list(enriched_signal_items)
+ parquet_filename, _ = write_items_to_parquet(
+ items=enriched_signal_items,
+ output_dir=output_dir,
+ schema=signal_schema,
+ filename_prefix='data',
+ shard_index=0,
+ num_shards=1)
+
+ signal_manifest = SignalManifest(
+ files=[parquet_filename],
+ data_schema=signal_schema,
+ signal=signal,
+ enriched_path=source_path,
+ parquet_id=make_parquet_id(signal, source_path, is_computed_signal=True),
+ embedding_filename_prefix=embedding_filename_prefix)
+ signal_manifest_filepath = os.path.join(output_dir, SIGNAL_MANIFEST_FILENAME)
+ with open_file(signal_manifest_filepath, 'w') as f:
+ f.write(signal_manifest.json(exclude_none=True, indent=2))
+ log(f'Wrote signal manifest to {signal_manifest_filepath}')
+
+ @override
+ def delete_signal(self, signal_path: Path) -> None:
+ signal_path = normalize_path(signal_path)
+ manifest = self.manifest()
+ if not manifest.data_schema.has_field(signal_path):
+ raise ValueError(f'Unknown signal path: {signal_path}')
+
+ output_dir = os.path.join(self.dataset_path, _signal_dir(signal_path))
+ shutil.rmtree(output_dir, ignore_errors=True)
+
+ def _validate_filters(self, filters: Sequence[Filter], col_aliases: dict[str, PathTuple],
+ manifest: DatasetManifest) -> None:
+ for filter in filters:
+ if filter.path[0] in col_aliases:
+ # This is a filter on a column alias, which is always allowed.
+ continue
+
+ current_field = Field(fields=manifest.data_schema.fields)
+ for path_part in filter.path:
+ if path_part == VALUE_KEY:
+ if not current_field.dtype:
+ raise ValueError(f'Unable to filter on path {filter.path}. The field has no value.')
+ continue
+ if current_field.fields:
+ if path_part not in current_field.fields:
+ raise ValueError(f'Unable to filter on path {filter.path}. '
+ f'Path part "{path_part}" not found in the dataset.')
+ current_field = current_field.fields[str(path_part)]
+ continue
+ elif current_field.repeated_field:
+ current_field = current_field.repeated_field
+ continue
+ else:
+ raise ValueError(f'Unable to filter on path {filter.path}. '
+ f'Path part "{path_part}" is not defined on a primitive value.')
+ if not current_field.dtype:
+ raise ValueError(f'Unable to filter on path {filter.path}. The field has no value.')
+
+ def _validate_udfs(self, udf_cols: Sequence[Column], source_schema: Schema) -> None:
+ for col in udf_cols:
+ path = col.path
+
+ # Signal transforms must operate on a leaf field.
+ leaf = source_schema.leafs.get(path)
+ if not leaf or not leaf.dtype:
+ raise ValueError(f'Leaf "{path}" not found in dataset. '
+ 'Signal transforms must operate on a leaf field.')
+
+ # Signal transforms must have the same dtype as the leaf field.
+ signal = cast(Signal, col.signal_udf)
+ compute_type = signal.compute_type
+ if not signal_compute_type_supports_dtype(compute_type, leaf.dtype):
+ raise ValueError(f'Leaf "{path}" has dtype "{leaf.dtype}" which is not supported '
+ f'by "{signal.key()}" with signal input type "{compute_type}".')
+
+ def _validate_selection(self, columns: Sequence[Column], select_schema: Schema) -> None:
+ # Validate all the columns and make sure they exist in the `select_schema`.
+ for column in columns:
+ current_field = Field(fields=select_schema.fields)
+ path = column.path
+ for path_part in path:
+ if path_part == VALUE_KEY:
+ if not current_field.dtype:
+ raise ValueError(f'Unable to select path {path}. The field that has no value.')
+ continue
+ if current_field.fields:
+ if path_part not in current_field.fields:
+ raise ValueError(f'Unable to select path {path}. '
+ f'Path part "{path_part}" not found in the dataset.')
+ current_field = current_field.fields[path_part]
+ continue
+ elif current_field.repeated_field:
+ if path_part.isdigit():
+ raise ValueError(f'Unable to select path {path}. Selecting a specific index of '
+ 'a repeated field is currently not supported.')
+ if path_part != PATH_WILDCARD:
+ raise ValueError(f'Unable to select path {path}. '
+ f'Path part "{path_part}" should be a wildcard.')
+ current_field = current_field.repeated_field
+ elif not current_field.dtype:
+ raise ValueError(f'Unable to select path {path}. '
+ f'Path part "{path_part}" is not defined on a primitive value.')
+
+ def _validate_columns(self, columns: Sequence[Column], source_schema: Schema,
+ select_schema: Schema) -> None:
+ udf_cols = [col for col in columns if col.signal_udf]
+ self._validate_udfs(udf_cols, source_schema)
+ self._validate_selection(columns, select_schema)
+
+ def _validate_sort_path(self, path: PathTuple, schema: Schema) -> None:
+ current_field = Field(fields=schema.fields)
+ for path_part in path:
+ if path_part == VALUE_KEY:
+ if not current_field.dtype:
+ raise ValueError(f'Unable to sort by path {path}. The field that has no value.')
+ continue
+ if current_field.fields:
+ if path_part not in current_field.fields:
+ raise ValueError(f'Unable to sort by path {path}. '
+ f'Path part "{path_part}" not found in the dataset.')
+ current_field = current_field.fields[path_part]
+ continue
+ elif current_field.repeated_field:
+ if path_part.isdigit():
+ raise ValueError(f'Unable to sort by path {path}. Selecting a specific index of '
+ 'a repeated field is currently not supported.')
+ if path_part != PATH_WILDCARD:
+ raise ValueError(f'Unable to sort by path {path}. '
+ f'Path part "{path_part}" should be a wildcard.')
+ current_field = current_field.repeated_field
+ elif not current_field.dtype:
+ raise ValueError(f'Unable to sort by path {path}. '
+ f'Path part "{path_part}" is not defined on a primitive value.')
+ if not current_field.dtype:
+ raise ValueError(f'Unable to sort by path {path}. The field has no value.')
+
+ @override
+ def stats(self, leaf_path: Path) -> StatsResult:
+ if not leaf_path:
+ raise ValueError('leaf_path must be provided')
+ path = normalize_path(leaf_path)
+ manifest = self.manifest()
+ leaf = manifest.data_schema.leafs.get(path)
+ if not leaf or not leaf.dtype:
+ raise ValueError(f'Leaf "{path}" not found in dataset')
+
+ value_path = _make_value_path(path)
+ duckdb_path = self._leaf_path_to_duckdb_path(value_path, manifest.data_schema)
+ inner_select = _select_sql(
+ duckdb_path, flatten=True, unnest=True, span_from=self._get_span_from(path, manifest))
+
+ # Compute approximate count by sampling the data to avoid OOM.
+ sample_size = SAMPLE_SIZE_DISTINCT_COUNT
+ avg_length_query = ''
+ if leaf.dtype == DataType.STRING:
+ avg_length_query = ', avg(length(val)) as avgTextLength'
+
+ if leaf.dtype == DataType.BOOLEAN:
+ approx_count_distinct = 2
+ else:
+ approx_count_query = f"""
+ SELECT approx_count_distinct(val) as approxCountDistinct {avg_length_query}
+ FROM (SELECT {inner_select} AS val FROM t LIMIT {sample_size});
+ """
+ row = self._query(approx_count_query)[0]
+ approx_count_distinct = row[0]
+
+ total_count_query = f'SELECT count(val) FROM (SELECT {inner_select} as val FROM t)'
+ total_count = self._query(total_count_query)[0][0]
+
+ if leaf.dtype != DataType.BOOLEAN:
+ # Adjust the counts for the sample size.
+ factor = max(1, total_count / sample_size)
+ approx_count_distinct = round(approx_count_distinct * factor)
+
+ result = StatsResult(
+ path=path, total_count=total_count, approx_count_distinct=approx_count_distinct)
+
+ if leaf.dtype == DataType.STRING:
+ result.avg_text_length = row[1]
+
+ # Compute min/max values for ordinal leafs, without sampling the data.
+ if is_ordinal(leaf.dtype):
+ min_max_query = f"""
+ SELECT MIN(val) AS minVal, MAX(val) AS maxVal
+ FROM (SELECT {inner_select} as val FROM t);
+ """
+ row = self._query(min_max_query)[0]
+ result.min_val, result.max_val = row
+
+ return result
+
+ @override
+ def select_groups(
+ self,
+ leaf_path: Path,
+ filters: Optional[Sequence[FilterLike]] = None,
+ sort_by: Optional[GroupsSortBy] = GroupsSortBy.COUNT,
+ sort_order: Optional[SortOrder] = SortOrder.DESC,
+ limit: Optional[int] = None,
+ bins: Optional[Union[Sequence[Bin], Sequence[float]]] = None) -> SelectGroupsResult:
+ if not leaf_path:
+ raise ValueError('leaf_path must be provided')
+ path = normalize_path(leaf_path)
+ manifest = self.manifest()
+ leaf = manifest.data_schema.leafs.get(path)
+ if not leaf or not leaf.dtype:
+ raise ValueError(f'Leaf "{path}" not found in dataset')
+
+ inner_val = 'inner_val'
+ outer_select = inner_val
+ # Normalize the bins to be `list[Bin]`.
+ named_bins = _normalize_bins(bins or leaf.bins)
+ stats = self.stats(leaf_path)
+
+ if is_float(leaf.dtype) or is_integer(leaf.dtype):
+ if named_bins is None:
+ # Auto-bin.
+ named_bins = _auto_bins(stats, NUM_AUTO_BINS)
+
+ sql_bounds = []
+ for label, start, end in named_bins:
+ if start is None:
+ start = cast(float, "'-Infinity'")
+ if end is None:
+ end = cast(float, "'Infinity'")
+ sql_bounds.append(f"('{label}', {start}, {end})")
+
+ bin_index_col = 'col0'
+ bin_min_col = 'col1'
+ bin_max_col = 'col2'
+ # We cast the field to `double` so bining works for both `float` and `int` fields.
+ outer_select = f"""(
+ SELECT {bin_index_col} FROM (
+ VALUES {', '.join(sql_bounds)}
+ ) WHERE {inner_val}::DOUBLE >= {bin_min_col} AND {inner_val}::DOUBLE < {bin_max_col}
+ )"""
+ else:
+ if stats.approx_count_distinct >= dataset.TOO_MANY_DISTINCT:
+ return SelectGroupsResult(too_many_distinct=True, counts=[], bins=named_bins)
+
+ count_column = 'count'
+ value_column = 'value'
+
+ limit_query = f'LIMIT {limit}' if limit else ''
+ duckdb_path = self._leaf_path_to_duckdb_path(path, manifest.data_schema)
+ inner_select = _select_sql(duckdb_path, flatten=True, unnest=True)
+
+ filters, _ = self._normalize_filters(filters, col_aliases={}, udf_aliases={}, manifest=manifest)
+ filter_queries = self._create_where(manifest, filters, searches=[])
+ where_query = ''
+ if filter_queries:
+ where_query = f"WHERE {' AND '.join(filter_queries)}"
+
+ query = f"""
+ SELECT {outer_select} AS {value_column}, COUNT() AS {count_column}
+ FROM (SELECT {inner_select} AS {inner_val} FROM t {where_query})
+ GROUP BY {value_column}
+ ORDER BY {sort_by} {sort_order}
+ {limit_query}
+ """
+ df = self._query_df(query)
+ counts = list(df.itertuples(index=False, name=None))
+ return SelectGroupsResult(too_many_distinct=False, counts=counts, bins=named_bins)
+
+ def _topk_udf_to_sort_by(
+ self,
+ udf_columns: list[Column],
+ sort_by: list[PathTuple],
+ limit: Optional[int],
+ sort_order: Optional[SortOrder],
+ ) -> Optional[Column]:
+ if (sort_order != SortOrder.DESC) or (not limit) or (not sort_by):
+ return None
+ if len(sort_by) < 1:
+ return None
+ primary_sort_by = sort_by[0]
+ udf_cols_to_sort_by = [
+ col for col in udf_columns
+ if col.alias == primary_sort_by[0] or _col_destination_path(col) == primary_sort_by
+ ]
+ if not udf_cols_to_sort_by:
+ return None
+ udf_col = udf_cols_to_sort_by[0]
+ if udf_col.signal_udf and (udf_col.signal_udf.compute_type
+ not in [SignalInputType.TEXT_EMBEDDING]):
+ return None
+ return udf_col
+
+ def _normalize_columns(self, columns: Optional[Sequence[ColumnId]],
+ schema: Schema) -> list[Column]:
+ """Normalizes the columns to a list of `Column` objects."""
+ cols = [column_from_identifier(col) for col in columns or []]
+ star_in_cols = any(col.path == ('*',) for col in cols)
+ if not cols or star_in_cols:
+ # Select all columns.
+ cols.extend([Column(name) for name in schema.fields.keys()])
+ if star_in_cols:
+ cols = [col for col in cols if col.path != ('*',)]
+ return cols
+
+ def _merge_sorts(self, search_udfs: list[DuckDBSearchUDF], sort_by: Optional[Sequence[Path]],
+ sort_order: Optional[SortOrder]) -> list[SortResult]:
+ if sort_by and not sort_order:
+ raise ValueError('`sort_order` is required when `sort_by` is specified.')
+
+ # True when the user has explicitly sorted by the alias of a search UDF (e.g. in ASC order).
+ is_explicit_search_sort = False
+ for sort_by_path in sort_by or []:
+ for search_udf in search_udfs:
+ if column_paths_match(sort_by_path, search_udf.output_path):
+ is_explicit_search_sort = True
+ break
+
+ sort_results: list[SortResult] = []
+ if sort_by and not is_explicit_search_sort:
+ # If the user has explicitly set a sort by, and it's not a search UDF alias, override.
+ sort_results = [
+ SortResult(path=normalize_path(sort_by), order=sort_order) for sort_by in sort_by if sort_by
+ ]
+ else:
+ search_udfs_with_sort = [search_udf for search_udf in search_udfs if search_udf.sort]
+ if search_udfs_with_sort:
+ # Override the sort by by the last search sort order when the user hasn't provided an
+ # explicit sort order.
+ last_search_udf = search_udfs_with_sort[-1]
+ if sort_order is None:
+ _, sort_order = cast(tuple[PathTuple, SortOrder], last_search_udf.sort)
+ sort_results = [
+ SortResult(
+ path=last_search_udf.output_path,
+ order=sort_order,
+ search_index=len(search_udfs_with_sort) - 1)
+ ]
+
+ return sort_results
+
+ @override
+ def select_rows(self,
+ columns: Optional[Sequence[ColumnId]] = None,
+ searches: Optional[Sequence[Search]] = None,
+ filters: Optional[Sequence[FilterLike]] = None,
+ sort_by: Optional[Sequence[Path]] = None,
+ sort_order: Optional[SortOrder] = SortOrder.DESC,
+ limit: Optional[int] = None,
+ offset: Optional[int] = 0,
+ task_step_id: Optional[TaskStepId] = None,
+ resolve_span: bool = False,
+ combine_columns: bool = False) -> SelectRowsResult:
+ manifest = self.manifest()
+ cols = self._normalize_columns(columns, manifest.data_schema)
+
+ # Always return the UUID column.
+ col_paths = [col.path for col in cols]
+ if (UUID_COLUMN,) not in col_paths:
+ cols.append(column_from_identifier(UUID_COLUMN))
+
+ # Prepare UDF columns. Throw an error if they are not computed. Update the paths of the UDFs so
+ # they match the paths of the columns defined by splits and embeddings.
+ for col in cols:
+ if col.signal_udf:
+ # Do not auto-compute dependencies, throw an error if they are not computed.
+ col.path, _ = self._prepare_signal(
+ col.signal_udf, col.path, manifest, compute_dependencies=False)
+
+ schema = manifest.data_schema
+
+ if combine_columns:
+ schema = self.select_rows_schema(
+ columns, sort_by, sort_order, searches, combine_columns=True).data_schema
+
+ self._validate_columns(cols, manifest.data_schema, schema)
+ self._normalize_searches(searches, manifest)
+ search_udfs = self._search_udfs(searches, manifest)
+ cols.extend([search_udf.udf for search_udf in search_udfs])
+ udf_columns = [col for col in cols if col.signal_udf]
+
+ # Set dataset information on any concept signals.
+ for udf_col in udf_columns:
+ if isinstance(udf_col.signal_udf, ConceptScoreSignal):
+ # Set dataset information on the signal.
+ udf_col.signal_udf.set_column_info(
+ ConceptColumnInfo(namespace=self.namespace, name=self.dataset_name, path=udf_col.path))
+
+ # Decide on the exact sorting order.
+ sort_results = self._merge_sorts(search_udfs, sort_by, sort_order)
+ sort_by = cast(list[PathTuple],
+ [(sort.alias,) if sort.alias else sort.path for sort in sort_results])
+ # Choose the first sort order as we only support a single sort order for now.
+ sort_order = sort_results[0].order if sort_results else None
+
+ col_aliases: dict[str, PathTuple] = {col.alias: col.path for col in cols if col.alias}
+ udf_aliases: dict[str, PathTuple] = {
+ col.alias: col.path for col in cols if col.signal_udf and col.alias
+ }
+ path_to_udf_col_name: dict[PathTuple, str] = {}
+ for col in cols:
+ if col.signal_udf:
+ alias = col.alias or _unique_alias(col)
+ dest_path = _col_destination_path(col)
+ path_to_udf_col_name[dest_path] = alias
+
+ # Filtering and searching.
+ where_query = ''
+ filters, udf_filters = self._normalize_filters(filters, col_aliases, udf_aliases, manifest)
+ filter_queries = self._create_where(manifest, filters, searches)
+ if filter_queries:
+ where_query = f"WHERE {' AND '.join(filter_queries)}"
+
+ total_num_rows = manifest.num_items
+ con = self.con.cursor()
+
+ topk_udf_col = self._topk_udf_to_sort_by(udf_columns, sort_by, limit, sort_order)
+ if topk_udf_col:
+ key_prefixes: Optional[list[VectorKey]] = None
+ if where_query:
+ # If there are filters, we need to send UUIDs to the topk query.
+ df = con.execute(f'SELECT {UUID_COLUMN} FROM t {where_query}').df()
+ total_num_rows = len(df)
+ key_prefixes = df[UUID_COLUMN]
+
+ signal = cast(Signal, topk_udf_col.signal_udf)
+ # The input is an embedding.
+ vector_store = self.get_vector_store(topk_udf_col.path)
+ k = (limit or 0) + (offset or 0)
+ topk = signal.vector_compute_topk(k, vector_store, key_prefixes)
+ topk_uuids = list(dict.fromkeys([cast(str, key[0]) for key, _ in topk]))
+
+ # Ignore all the other filters and filter DuckDB results only by the topk UUIDs.
+ uuid_filter = Filter(path=(UUID_COLUMN,), op=ListOp.IN, value=topk_uuids)
+ filter_query = self._create_where(manifest, [uuid_filter])[0]
+ where_query = f'WHERE {filter_query}'
+
+ # Map a final column name to a list of temporary namespaced column names that need to be merged.
+ columns_to_merge: dict[str, dict[str, Column]] = {}
+ temp_column_to_offset_column: dict[str, tuple[str, Field]] = {}
+ select_queries: list[str] = []
+
+ for column in cols:
+ path = column.path
+ # If the signal is vector-based, we don't need to select the actual data, just the uuids
+ # plus an arbitrarily nested array of `None`s`.
+ empty = bool(column.signal_udf and schema.get_field(path).dtype == DataType.EMBEDDING)
+
+ select_sqls: list[str] = []
+ final_col_name = column.alias or _unique_alias(column)
+ if final_col_name not in columns_to_merge:
+ columns_to_merge[final_col_name] = {}
+
+ duckdb_paths = self._column_to_duckdb_paths(column, schema)
+ span_from = self._get_span_from(path, manifest) if resolve_span or column.signal_udf else None
+
+ for parquet_id, duckdb_path in duckdb_paths:
+ sql = _select_sql(
+ duckdb_path, flatten=False, unnest=False, empty=empty, span_from=span_from)
+ temp_column_name = (
+ final_col_name if len(duckdb_paths) == 1 else f'{final_col_name}/{parquet_id}')
+ select_sqls.append(f'{sql} AS {_escape_string_literal(temp_column_name)}')
+ columns_to_merge[final_col_name][temp_column_name] = column
+
+ if column.signal_udf and span_from and _schema_has_spans(column.signal_udf.fields()):
+ sql = _select_sql(duckdb_path, flatten=False, unnest=False, empty=empty, span_from=None)
+ temp_offset_column_name = f'{temp_column_name}/offset'
+ temp_offset_column_name = temp_offset_column_name.replace("'", "\\'")
+ select_sqls.append(f'{sql} AS {_escape_string_literal(temp_offset_column_name)}')
+ temp_column_to_offset_column[temp_column_name] = (temp_offset_column_name,
+ column.signal_udf.fields())
+
+ # `select_sqls` can be empty if this column points to a path that will be created by a UDF.
+ if select_sqls:
+ select_queries.append(', '.join(select_sqls))
+
+ sort_sql_before_udf: list[str] = []
+ sort_sql_after_udf: list[str] = []
+
+ for path in sort_by:
+ # We only allow sorting by nodes with a value.
+ first_subpath = str(path[0])
+ rest_of_path = path[1:]
+ signal_alias = '.'.join(map(str, path))
+
+ udf_path = _path_to_udf_duckdb_path(path, path_to_udf_col_name)
+ if not udf_path:
+ # Re-route the path if it starts with an alias by pointing it to the actual path.
+ if first_subpath in col_aliases:
+ path = (*col_aliases[first_subpath], *rest_of_path)
+ self._validate_sort_path(path, schema)
+ path = self._leaf_path_to_duckdb_path(path, schema)
+ else:
+ path = udf_path
+
+ sort_sql = _select_sql(path, flatten=True, unnest=False)
+ has_repeated_field = any(subpath == PATH_WILDCARD for subpath in path)
+ if has_repeated_field:
+ sort_sql = (f'list_min({sort_sql})'
+ if sort_order == SortOrder.ASC else f'list_max({sort_sql})')
+
+ # Separate sort columns into two groups: those that need to be sorted before and after UDFs.
+ if udf_path:
+ sort_sql_after_udf.append(sort_sql)
+ else:
+ sort_sql_before_udf.append(sort_sql)
+
+ order_query = ''
+ if sort_sql_before_udf:
+ order_query = (f'ORDER BY {", ".join(sort_sql_before_udf)} '
+ f'{cast(SortOrder, sort_order).value}')
+
+ limit_query = ''
+ if limit:
+ if topk_udf_col:
+ limit_query = f'LIMIT {limit + (offset or 0)}'
+ elif sort_sql_after_udf:
+ limit_query = ''
+ else:
+ limit_query = f'LIMIT {limit} OFFSET {offset or 0}'
+
+ if not topk_udf_col and where_query:
+ total_num_rows = cast(tuple,
+ con.execute(f'SELECT COUNT(*) FROM t {where_query}').fetchone())[0]
+
+ # Fetch the data from DuckDB.
+ df = con.execute(f"""
+ SELECT {', '.join(select_queries)} FROM t
+ {where_query}
+ {order_query}
+ {limit_query}
+ """).df()
+ df = _replace_nan_with_none(df)
+
+ # Run UDFs on the transformed columns.
+ for udf_col in udf_columns:
+ signal = cast(Signal, udf_col.signal_udf)
+ signal_alias = udf_col.alias or _unique_alias(udf_col)
+ temp_signal_cols = columns_to_merge[signal_alias]
+ if len(temp_signal_cols) != 1:
+ raise ValueError(
+ f'Unable to compute signal {signal.name}. Signal UDFs only operate on leafs, but got '
+ f'{len(temp_signal_cols)} underlying columns that contain data related to {udf_col.path}.'
+ )
+ signal_column = list(temp_signal_cols.keys())[0]
+ input = df[signal_column]
+
+ with DebugTimer(f'Computing signal "{signal}"'):
+ signal.setup()
+
+ if signal.compute_type in [SignalInputType.TEXT_EMBEDDING]:
+ # The input is an embedding.
+ vector_store = self.get_vector_store(udf_col.path)
+ flat_keys = flatten_keys(df[UUID_COLUMN], input)
+ signal_out = signal.vector_compute(flat_keys, vector_store)
+ # Add progress.
+ if task_step_id is not None:
+ signal_out = progress(
+ signal_out,
+ task_step_id=task_step_id,
+ estimated_len=len(flat_keys),
+ step_description=f'Computing {signal.key()}...')
+ df[signal_column] = unflatten(signal_out, input)
+ else:
+ num_rich_data = count_primitives(input)
+ flat_input = cast(Iterable[RichData], flatten(input))
+ signal_out = signal.compute(flat_input)
+ # Add progress.
+ if task_step_id is not None:
+ signal_out = progress(
+ signal_out,
+ task_step_id=task_step_id,
+ estimated_len=num_rich_data,
+ step_description=f'Computing {signal.key()}...')
+ signal_out = list(signal_out)
+
+ if signal_column in temp_column_to_offset_column:
+ offset_column_name, field = temp_column_to_offset_column[signal_column]
+ nested_spans: Iterable[Item] = df[offset_column_name]
+ flat_spans = list(flatten(nested_spans))
+ for span, item in zip(flat_spans, signal_out):
+ _offset_any_span(cast(int, span[VALUE_KEY][TEXT_SPAN_START_FEATURE]), item, field)
+
+ if len(signal_out) != num_rich_data:
+ raise ValueError(
+ f'The signal generated {len(signal_out)} values but the input data had '
+ f"{num_rich_data} values. This means the signal either didn't generate a "
+ '"None" for a sparse output, or generated too many items.')
+
+ df[signal_column] = unflatten(signal_out, input)
+
+ signal.teardown()
+
+ if udf_filters or sort_sql_after_udf:
+ # Re-upload the udf outputs to duckdb so we can filter/sort on them.
+ rel = con.from_df(df)
+
+ if udf_filters:
+ udf_filter_queries = self._create_where(manifest, udf_filters)
+ if udf_filter_queries:
+ rel = rel.filter(' AND '.join(udf_filter_queries))
+ total_num_rows = cast(tuple, rel.count('*').fetchone())[0]
+
+ if sort_sql_after_udf:
+ if not sort_order:
+ raise ValueError('`sort_order` is required when `sort_by` is specified.')
+ rel = rel.order(f'{", ".join(sort_sql_after_udf)} {sort_order.value}')
+
+ if limit:
+ rel = rel.limit(limit, offset or 0)
+
+ df = _replace_nan_with_none(rel.df())
+
+ if combine_columns:
+ all_columns: dict[str, Column] = {}
+ for col_dict in columns_to_merge.values():
+ all_columns.update(col_dict)
+ columns_to_merge = {'*': all_columns}
+
+ for offset_column, _ in temp_column_to_offset_column.values():
+ del df[offset_column]
+
+ for final_col_name, temp_columns in columns_to_merge.items():
+ for temp_col_name, column in temp_columns.items():
+ if combine_columns:
+ dest_path = _col_destination_path(column)
+ spec = _split_path_into_subpaths_of_lists(dest_path)
+ df[temp_col_name] = wrap_in_dicts(df[temp_col_name], spec)
+
+ # If the temp col name is the same as the final name, we can skip merging. This happens when
+ # we select a source leaf column.
+ if temp_col_name == final_col_name:
+ continue
+
+ if final_col_name not in df:
+ df[final_col_name] = df[temp_col_name]
+ else:
+ df[final_col_name] = merge_series(df[final_col_name], df[temp_col_name])
+ del df[temp_col_name]
+
+ con.close()
+
+ if combine_columns:
+ # Since we aliased every column to `*`, the object with have only '*' as the key. We need to
+ # elevate the all the columns under '*'.
+ df = pd.DataFrame.from_records(df['*'])
+
+ return SelectRowsResult(df, total_num_rows)
+
+ @override
+ def select_rows_schema(self,
+ columns: Optional[Sequence[ColumnId]] = None,
+ sort_by: Optional[Sequence[Path]] = None,
+ sort_order: Optional[SortOrder] = None,
+ searches: Optional[Sequence[Search]] = None,
+ combine_columns: bool = False) -> SelectRowsSchemaResult:
+ """Returns the schema of the result of `select_rows` above with the same arguments."""
+ if not combine_columns:
+ raise NotImplementedError(
+ 'select_rows_schema with combine_columns=False is not yet supported.')
+ manifest = self.manifest()
+ cols = self._normalize_columns(columns, manifest.data_schema)
+
+ # Always return the UUID column.
+ col_paths = [col.path for col in cols]
+ if (UUID_COLUMN,) not in col_paths:
+ cols.append(column_from_identifier(UUID_COLUMN))
+
+ # Prepare UDF columns. Throw an error if they are not computed. Update the paths of the UDFs so
+ # they match the paths of the columns defined by splits and embeddings.
+ for col in cols:
+ if col.signal_udf:
+ # Do not auto-compute dependencies, throw an error if they are not computed.
+ col.path, _ = self._prepare_signal(
+ col.signal_udf, col.path, manifest, compute_dependencies=False)
+
+ self._normalize_searches(searches, manifest)
+ search_udfs = self._search_udfs(searches, manifest)
+ cols.extend([search_udf.udf for search_udf in search_udfs])
+
+ udfs: list[SelectRowsSchemaUDF] = []
+ col_schemas: list[Schema] = []
+ for col in cols:
+ dest_path = _col_destination_path(col)
+ if col.signal_udf:
+ udfs.append(SelectRowsSchemaUDF(path=dest_path, alias=col.alias))
+ field = col.signal_udf.fields()
+ field.signal = col.signal_udf.dict()
+ elif manifest.data_schema.has_field(dest_path):
+ field = manifest.data_schema.get_field(dest_path)
+ else:
+ # This column might refer to an output of a udf. We postpone validation to later.
+ continue
+ col_schemas.append(_make_schema_from_path(dest_path, field))
+
+ sort_results = self._merge_sorts(search_udfs, sort_by, sort_order)
+
+ search_results = [
+ SearchResultInfo(search_path=search_udf.search_path, result_path=search_udf.output_path)
+ for search_udf in search_udfs
+ ]
+
+ new_schema = merge_schemas(col_schemas)
+
+ # Now that we have the new schema, we can validate all the column selections.
+ self._validate_columns(cols, manifest.data_schema, new_schema)
+
+ return SelectRowsSchemaResult(
+ data_schema=new_schema, udfs=udfs, search_results=search_results, sorts=sort_results or None)
+
+ @override
+ def media(self, item_id: str, leaf_path: Path) -> MediaResult:
+ raise NotImplementedError('Media is not yet supported for the DuckDB implementation.')
+
+ def _get_span_from(self, path: PathTuple, manifest: DatasetManifest) -> Optional[PathTuple]:
+ leafs = manifest.data_schema.leafs
+ # Remove the value key so we can check the dtype from leafs.
+ span_path = path[:-1] if path[-1] == VALUE_KEY else path
+ is_span = (span_path in leafs and leafs[span_path].dtype == DataType.STRING_SPAN)
+ return _derived_from_path(path, manifest.data_schema) if is_span else None
+
+ def _leaf_path_to_duckdb_path(self, leaf_path: PathTuple, schema: Schema) -> PathTuple:
+ leaf_path = _make_value_path(leaf_path)
+ ((_, duckdb_path),) = self._column_to_duckdb_paths(Column(leaf_path), schema)
+ return duckdb_path
+
+ def _column_to_duckdb_paths(self, column: Column, schema: Schema) -> list[tuple[str, PathTuple]]:
+ path = column.path
+ parquet_manifests: list[Union[SourceManifest, SignalManifest]] = [
+ self._source_manifest, *self._signal_manifests
+ ]
+ duckdb_paths: list[tuple[str, PathTuple]] = []
+
+ select_a_leaf_value = column.signal_udf is not None
+ if path[-1] == VALUE_KEY:
+ select_a_leaf_value = True
+ path = path[:-1]
+
+ for m in parquet_manifests:
+ # Skip this parquet file if it doesn't contain the path.
+ if not schema_contains_path(m.data_schema, path):
+ continue
+
+ # Skip this parquet file if the path doesn't have a dtype.
+ if select_a_leaf_value and not m.data_schema.get_field(path).dtype:
+ continue
+
+ if isinstance(m, SignalManifest) and path == (UUID_COLUMN,):
+ # Do not select UUID from the signal because it's already in the source.
+ continue
+
+ duckdb_path = path
+ parquet_id = 'source'
+
+ if isinstance(m, SignalManifest):
+ duckdb_path = (m.parquet_id, *path[1:])
+ parquet_id = m.parquet_id
+
+ duckdb_paths.append((parquet_id, duckdb_path))
+
+ if not duckdb_paths:
+ # This path is probably a result of a udf. Make sure the result schema contains it.
+ if not schema.has_field(path):
+ raise ValueError(f'Invalid path "{path}": No manifest contains path. Valid paths: '
+ f'{list(schema.leafs.keys())}')
+
+ return duckdb_paths
+
+ def _normalize_filters(self, filter_likes: Optional[Sequence[FilterLike]],
+ col_aliases: dict[str, PathTuple], udf_aliases: dict[str, PathTuple],
+ manifest: DatasetManifest) -> tuple[list[Filter], list[Filter]]:
+ """Normalize `FilterLike` to `Filter` and split into filters on source and filters on UDFs."""
+ filter_likes = filter_likes or []
+ filters: list[Filter] = []
+ udf_filters: list[Filter] = []
+
+ for filter in filter_likes:
+ # Normalize `FilterLike` to `Filter`.
+ if not isinstance(filter, Filter):
+ if len(filter) == 3:
+ path, op, value = filter # type: ignore
+ elif len(filter) == 2:
+ path, op = filter # type: ignore
+ value = None
+ else:
+ raise ValueError(f'Invalid filter: {filter}. Must be a tuple with 2 or 3 elements.')
+ filter = Filter(path=normalize_path(path), op=op, value=value)
+
+ if str(filter.path[0]) in udf_aliases:
+ udf_filters.append(filter)
+ else:
+ filters.append(filter)
+
+ self._validate_filters(filters, col_aliases, manifest)
+ return filters, udf_filters
+
+ def _normalize_searches(self, searches: Optional[Sequence[Search]],
+ manifest: DatasetManifest) -> None:
+ """Validate searches."""
+ if not searches:
+ return
+
+ for search in searches:
+ search.path = normalize_path(search.path)
+ field = manifest.data_schema.get_field(search.path)
+ if field.dtype != DataType.STRING:
+ raise ValueError(f'Invalid search path: {search.path}. '
+ f'Must be a string field, got dtype {field.dtype}')
+
+ def _search_udfs(self, searches: Optional[Sequence[Search]],
+ manifest: DatasetManifest) -> list[DuckDBSearchUDF]:
+ searches = searches or []
+ """Create a UDF for each search for finding the location of the text with spans."""
+ search_udfs: list[DuckDBSearchUDF] = []
+ for search in searches:
+ if search.query.type == 'keyword':
+ udf = Column(path=search.path, signal_udf=SubstringSignal(query=search.query.search))
+ search_udfs.append(
+ DuckDBSearchUDF(
+ udf=udf,
+ search_path=search.path,
+ output_path=(*_col_destination_path(udf), PATH_WILDCARD)))
+ elif search.query.type == 'semantic' or search.query.type == 'concept':
+ embedding = search.query.embedding
+ if not embedding:
+ raise ValueError(f'Please provide an embedding for semantic search. Got search: {search}')
+
+ embedding_path = (*search.path, embedding, PATH_WILDCARD, EMBEDDING_KEY)
+ try:
+ manifest.data_schema.get_field(embedding_path)
+ except Exception as e:
+ raise ValueError(
+ f'Embedding {embedding} has not been computed. '
+ f'Please compute the embedding index before issuing a {search.query.type} query.'
+ ) from e
+
+ search_signal: Signal
+ if search.query.type == 'semantic':
+ search_signal = SemanticSimilaritySignal(
+ query=search.query.search, embedding=search.query.embedding)
+ elif search.query.type == 'concept':
+ search_signal = ConceptScoreSignal(
+ namespace=search.query.concept_namespace,
+ concept_name=search.query.concept_name,
+ embedding=search.query.embedding)
+
+ # Add the label UDF.
+ concept_labels_signal = ConceptLabelsSignal(
+ namespace=search.query.concept_namespace, concept_name=search.query.concept_name)
+ concept_labels_udf = Column(path=search.path, signal_udf=concept_labels_signal)
+ search_udfs.append(
+ DuckDBSearchUDF(
+ udf=concept_labels_udf,
+ search_path=search.path,
+ output_path=_col_destination_path(concept_labels_udf),
+ sort=None))
+
+ udf = Column(path=embedding_path, signal_udf=search_signal)
+
+ output_path = _col_destination_path(udf)
+ search_udfs.append(
+ DuckDBSearchUDF(
+ udf=udf,
+ search_path=search.path,
+ output_path=_col_destination_path(udf),
+ sort=(output_path, SortOrder.DESC)))
+ else:
+ raise ValueError(f'Unknown search operator {search.query.type}.')
+
+ return search_udfs
+
+ def _create_where(self,
+ manifest: DatasetManifest,
+ filters: list[Filter],
+ searches: Optional[Sequence[Search]] = []) -> list[str]:
+ if not filters and not searches:
+ return []
+ searches = searches or []
+ sql_filter_queries: list[str] = []
+
+ # Add search where queries.
+ for search in searches:
+ duckdb_path = self._leaf_path_to_duckdb_path(
+ normalize_path(search.path), manifest.data_schema)
+ select_str = _select_sql(duckdb_path, flatten=False, unnest=False)
+ if search.query.type == 'keyword':
+ sql_op = 'ILIKE'
+ query_val = _escape_like_value(search.query.search)
+ elif search.query.type == 'semantic' or search.query.type == 'concept':
+ # Semantic search and concepts don't yet filter.
+ continue
+ else:
+ raise ValueError(f'Unknown search operator {search.query.type}.')
+
+ filter_query = f'{select_str} {sql_op} {query_val}'
+
+ sql_filter_queries.append(filter_query)
+
+ # Add filter where queries.
+ binary_ops = set(BinaryOp)
+ unary_ops = set(UnaryOp)
+ list_ops = set(ListOp)
+ for filter in filters:
+ duckdb_path = self._leaf_path_to_duckdb_path(filter.path, manifest.data_schema)
+ select_str = _select_sql(duckdb_path, flatten=True, unnest=False)
+ is_array = any(subpath == PATH_WILDCARD for subpath in filter.path)
+
+ if filter.op in binary_ops:
+ sql_op = BINARY_OP_TO_SQL[cast(BinaryOp, filter.op)]
+ filter_val = cast(FeatureValue, filter.value)
+ if isinstance(filter_val, str):
+ filter_val = f"'{filter_val}'"
+ elif isinstance(filter_val, bytes):
+ filter_val = _bytes_to_blob_literal(filter_val)
+ else:
+ filter_val = str(filter_val)
+ filter_query = (f'len(list_filter({select_str}, x -> x {sql_op} {filter_val})) > 0'
+ if is_array else f'{select_str} {sql_op} {filter_val}')
+ elif filter.op in unary_ops:
+ if filter.op == UnaryOp.EXISTS:
+ filter_query = f'len({select_str}) > 0' if is_array else f'{select_str} IS NOT NULL'
+ else:
+ raise ValueError(f'Unary op: {filter.op} is not yet supported')
+ elif filter.op in list_ops:
+ if filter.op == ListOp.IN:
+ filter_list_val = cast(FeatureListValue, filter.value)
+ if not isinstance(filter_list_val, list):
+ raise ValueError('filter with array value can only use the IN comparison')
+ wrapped_filter_val = [f"'{part}'" for part in filter_list_val]
+ filter_val = f'({", ".join(wrapped_filter_val)})'
+ filter_query = f'{select_str} IN {filter_val}'
+ else:
+ raise ValueError(f'List op: {filter.op} is not yet supported')
+ else:
+ raise ValueError(f'Invalid filter op: {filter.op}')
+ sql_filter_queries.append(filter_query)
+ return sql_filter_queries
+
+ def _execute(self, query: str) -> duckdb.DuckDBPyConnection:
+ """Execute a query in duckdb."""
+ # FastAPI is multi-threaded so we have to create a thread-specific connection cursor to allow
+ # these queries to be thread-safe.
+ local_con = self.con.cursor()
+ if not DEBUG:
+ return local_con.execute(query)
+
+ # Debug mode.
+ log('Executing:')
+ log(query)
+ with DebugTimer('Query'):
+ return local_con.execute(query)
+
+ def _query(self, query: str) -> list[tuple]:
+ result = self._execute(query)
+ rows = result.fetchall()
+ result.close()
+ return rows
+
+ def _query_df(self, query: str) -> pd.DataFrame:
+ """Execute a query that returns a dataframe."""
+ result = self._execute(query)
+ df = _replace_nan_with_none(result.df())
+ result.close()
+ return df
+
+ def _path_to_col(self, path: Path, quote_each_part: bool = True) -> str:
+ """Convert a path to a column name."""
+ if isinstance(path, str):
+ path = (path,)
+ return '.'.join([
+ f'{_escape_col_name(path_comp)}' if quote_each_part else str(path_comp) for path_comp in path
+ ])
+
+
+def _escape_string_literal(string: str) -> str:
+ string = string.replace("'", "''")
+ return f"'{string}'"
+
+
+def _escape_col_name(col_name: str) -> str:
+ col_name = col_name.replace('"', '""')
+ return f'"{col_name}"'
+
+
+def _escape_like_value(value: str) -> str:
+ value = value.replace('%', '\\%').replace('_', '\\_')
+ return f"'%{value}%' ESCAPE '\\'"
+
+
+def _inner_select(sub_paths: list[PathTuple],
+ inner_var: Optional[str] = None,
+ empty: bool = False,
+ span_from: Optional[PathTuple] = None) -> str:
+ """Recursively generate the inner select statement for a list of sub paths."""
+ current_sub_path = sub_paths[0]
+ lambda_var = inner_var + 'x' if inner_var else 'x'
+ if not inner_var:
+ lambda_var = 'x'
+ inner_var = _escape_col_name(current_sub_path[0])
+ current_sub_path = current_sub_path[1:]
+ # Select the path inside structs. E.g. x['a']['b']['c'] given current_sub_path = [a, b, c].
+ path_key = inner_var + ''.join([f"['{p}']" for p in current_sub_path])
+ if len(sub_paths) == 1:
+ if span_from:
+ derived_col = _select_sql(span_from, flatten=False, unnest=False)
+ path_key = (f'{derived_col}[{path_key}.{VALUE_KEY}.{TEXT_SPAN_START_FEATURE}+1:'
+ f'{path_key}.{VALUE_KEY}.{TEXT_SPAN_END_FEATURE}]')
+ return 'NULL' if empty else path_key
+ return (f'list_transform({path_key}, {lambda_var} -> '
+ f'{_inner_select(sub_paths[1:], lambda_var, empty, span_from)})')
+
+
+def _split_path_into_subpaths_of_lists(leaf_path: PathTuple) -> list[PathTuple]:
+ """Split a path into a subpath of lists.
+
+ E.g. [a, b, c, *, d, *, *] gets splits [[a, b, c], [d], [], []].
+ """
+ sub_paths: list[PathTuple] = []
+ offset = 0
+ while offset <= len(leaf_path):
+ new_offset = leaf_path.index(PATH_WILDCARD,
+ offset) if PATH_WILDCARD in leaf_path[offset:] else len(leaf_path)
+ sub_path = leaf_path[offset:new_offset]
+ sub_paths.append(sub_path)
+ offset = new_offset + 1
+ return sub_paths
+
+
+def _select_sql(path: PathTuple,
+ flatten: bool,
+ unnest: bool,
+ empty: bool = False,
+ span_from: Optional[PathTuple] = None) -> str:
+ """Create a select column for a path.
+
+ Args:
+ path: A path to a feature. E.g. ['a', 'b', 'c'].
+ flatten: Whether to flatten the result.
+ unnest: Whether to unnest the result.
+ empty: Whether to return an empty list (used for embedding signals that don't need the data).
+ span_from: The path this span is derived from. If specified, the span will be resolved
+ to a substring of the original string.
+ """
+ sub_paths = _split_path_into_subpaths_of_lists(path)
+ selection = _inner_select(sub_paths, None, empty, span_from)
+ # We only flatten when the result of a nested list to avoid segfault.
+ is_result_nested_list = len(sub_paths) >= 3 # E.g. subPaths = [[a, b, c], *, *].
+ if flatten and is_result_nested_list:
+ selection = f'flatten({selection})'
+ # We only unnest when the result is a list. // E.g. subPaths = [[a, b, c], *].
+ is_result_a_list = len(sub_paths) >= 2
+ if unnest and is_result_a_list:
+ selection = f'unnest({selection})'
+ return selection
+
+
+def read_source_manifest(dataset_path: str) -> SourceManifest:
+ """Read the manifest file."""
+ with open_file(os.path.join(dataset_path, MANIFEST_FILENAME), 'r') as f:
+ return SourceManifest.parse_raw(f.read())
+
+
+def _signal_dir(enriched_path: PathTuple) -> str:
+ """Get the filename prefix for a signal parquet file."""
+ path_without_wildcards = (p for p in enriched_path if p != PATH_WILDCARD)
+ return os.path.join(*path_without_wildcards)
+
+
+def split_column_name(column: str, split_name: str) -> str:
+ """Get the name of a split column."""
+ return f'{column}.{split_name}'
+
+
+def split_parquet_prefix(column_name: str, splitter_name: str) -> str:
+ """Get the filename prefix for a split parquet file."""
+ return f'{column_name}.{splitter_name}'
+
+
+def _bytes_to_blob_literal(bytes: bytes) -> str:
+ """Convert bytes to a blob literal."""
+ escaped_hex = re.sub(r'(.{2})', r'\\x\1', bytes.hex())
+ return f"'{escaped_hex}'::BLOB"
+
+
+class SignalManifest(BaseModel):
+ """The manifest that describes a signal computation including schema and parquet files."""
+ # List of a parquet filepaths storing the data. The paths are relative to the manifest.
+ files: list[str]
+
+ # An identifier for this parquet table. Will be used as the view name in SQL.
+ parquet_id: str
+
+ data_schema: Schema
+ signal: Signal
+
+ # The column path that this signal is derived from.
+ enriched_path: PathTuple
+
+ # The filename prefix for the embedding. Present when the signal is an embedding.
+ embedding_filename_prefix: Optional[str]
+
+ @validator('signal', pre=True)
+ def parse_signal(cls, signal: dict) -> Signal:
+ """Parse a signal to its specific subclass instance."""
+ return resolve_signal(signal)
+
+
+def _merge_cells(dest_cell: Item, source_cell: Item) -> Item:
+ if source_cell is None or isinstance(source_cell, float) and math.isnan(source_cell):
+ # Nothing to merge here (missing value).
+ return dest_cell
+ if isinstance(dest_cell, dict):
+ if isinstance(source_cell, list):
+ raise ValueError(f'Failed to merge cells. Destination is a dict ({dest_cell!r}), '
+ f'but source is a list ({source_cell!r}).')
+ if isinstance(source_cell, dict):
+ res = {**dest_cell}
+ for key, value in source_cell.items():
+ res[key] = (value if key not in dest_cell else _merge_cells(dest_cell[key], value))
+ return res
+ else:
+ return {VALUE_KEY: source_cell, **dest_cell}
+ elif isinstance(dest_cell, list):
+ if not isinstance(source_cell, list):
+ raise ValueError('Failed to merge cells. Destination is a list, but source is not.')
+ return [
+ _merge_cells(dest_subcell, source_subcell)
+ for dest_subcell, source_subcell in zip(dest_cell, source_cell)
+ ]
+ else:
+ # The destination is a primitive.
+ if isinstance(source_cell, list):
+ raise ValueError(f'Failed to merge cells. Destination is a primitive ({dest_cell!r}), '
+ f'but source is a list ({source_cell!r}).')
+ if isinstance(source_cell, dict):
+ return {VALUE_KEY: dest_cell, **source_cell}
+ else:
+ # Primitives can be merged together if they are equal. This can happen if a user selects a
+ # column that is the child of another.
+ # NOTE: This can be removed if we fix https://github.com/lilacai/lilac/issues/166.
+ if source_cell != dest_cell:
+ raise ValueError(f'Cannot merge source "{source_cell!r}" into destination "{dest_cell!r}"')
+ return dest_cell
+
+
+def merge_series(destination: pd.Series, source: pd.Series) -> list[Item]:
+ """Merge two series of values recursively."""
+ return _merge_cells(destination.tolist(), source.tolist())
+
+
+def _unique_alias(column: Column) -> str:
+ """Get a unique alias for a selection column."""
+ if column.signal_udf:
+ return make_parquet_id(column.signal_udf, column.path)
+ return '.'.join(map(str, column.path))
+
+
+def _path_contains(parent_path: PathTuple, child_path: PathTuple) -> bool:
+ """Check if a path contains another path."""
+ if len(parent_path) > len(child_path):
+ return False
+ return all(parent_path[i] == child_path[i] for i in range(len(parent_path)))
+
+
+def _path_to_udf_duckdb_path(path: PathTuple,
+ path_to_udf_col_name: dict[PathTuple, str]) -> Optional[PathTuple]:
+ first_subpath, *rest_of_path = path
+ for parent_path, udf_col_name in path_to_udf_col_name.items():
+ # If the user selected udf(document.*.text) as "udf" and wanted to sort by "udf.len", we need to
+ # sort by "udf.*.len" where the "*" came from the fact that the udf was applied to a list of
+ # "text" fields.
+ wildcards = [x for x in parent_path if x == PATH_WILDCARD]
+ if _path_contains(parent_path, path):
+ return (udf_col_name, *wildcards, *path[len(parent_path):])
+ elif first_subpath == udf_col_name:
+ return (udf_col_name, *wildcards, *rest_of_path)
+
+ return None
+
+
+def _col_destination_path(column: Column, is_computed_signal: Optional[bool] = False) -> PathTuple:
+ """Get the destination path where the output of this selection column will be stored."""
+ source_path = column.path
+
+ if not column.signal_udf:
+ return source_path
+
+ signal_key = column.signal_udf.key(is_computed_signal=is_computed_signal)
+ # If we are enriching a value we should store the signal data in the value's parent.
+ if source_path[-1] == VALUE_KEY:
+ dest_path = (*source_path[:-1], signal_key)
+ else:
+ dest_path = (*source_path, signal_key)
+
+ return dest_path
+
+
+def _root_column(manifest: SignalManifest) -> str:
+ """Returns the root column of a signal manifest."""
+ field_keys = manifest.data_schema.fields.keys()
+ if len(field_keys) != 2:
+ raise ValueError('Expected exactly two fields in signal manifest, '
+ f'the row UUID and root this signal is enriching. Got {field_keys}.')
+ return next(filter(lambda field: field != UUID_COLUMN, manifest.data_schema.fields.keys()))
+
+
+def _derived_from_path(path: PathTuple, schema: Schema) -> PathTuple:
+ # Find the closest parent of `path` that is a signal root.
+ for i in reversed(range(len(path))):
+ sub_path = path[:i]
+ if schema.get_field(sub_path).signal is not None:
+ # Skip the signal name at the end to get the source path that was enriched.
+ return sub_path[:-1]
+ raise ValueError('Cannot find the source path for the enriched path: {path}')
+
+
+def _make_value_path(path: PathTuple) -> PathTuple:
+ """Returns the path to the value field of the given path."""
+ if path[-1] != VALUE_KEY and path[0] != UUID_COLUMN:
+ return (*path, VALUE_KEY)
+ return path
+
+
+def _make_schema_from_path(path: PathTuple, field: Field) -> Schema:
+ """Returns a schema that contains only the given path."""
+ for sub_path in reversed(path):
+ if sub_path == PATH_WILDCARD:
+ field = Field(repeated_field=field)
+ else:
+ field = Field(fields={sub_path: field})
+ return Schema(fields=field.fields)
+
+
+def _replace_nan_with_none(df: pd.DataFrame) -> pd.DataFrame:
+ """DuckDB returns np.nan for missing field in string column, replace with None for correctness."""
+ # TODO(https://github.com/duckdb/duckdb/issues/4066): Remove this once duckdb fixes upstream.
+ for col in df.columns:
+ if is_object_dtype(df[col]):
+ df[col].replace(np.nan, None, inplace=True)
+ return df
+
+
+def _offset_any_span(offset: int, item: Item, schema: Field) -> None:
+ """Offsets any spans inplace by the given parent offset."""
+ if schema.dtype == DataType.STRING_SPAN:
+ item = cast(dict, item)
+ item[VALUE_KEY][TEXT_SPAN_START_FEATURE] += offset
+ item[VALUE_KEY][TEXT_SPAN_END_FEATURE] += offset
+ if schema.fields:
+ item = cast(dict, item)
+ for key, sub_schema in schema.fields.items():
+ _offset_any_span(offset, item[key], sub_schema)
+ if schema.repeated_field:
+ item = cast(list, item)
+ for sub_item in item:
+ _offset_any_span(offset, sub_item, schema.repeated_field)
+
+
+def _schema_has_spans(field: Field) -> bool:
+ if field.dtype and field.dtype == DataType.STRING_SPAN:
+ return True
+ if field.fields:
+ children_have_spans = any(_schema_has_spans(sub_field) for sub_field in field.fields.values())
+ if children_have_spans:
+ return True
+ if field.repeated_field:
+ return _schema_has_spans(field.repeated_field)
+ return False
+
+
+def _normalize_bins(bins: Optional[Union[Sequence[Bin], Sequence[float]]]) -> Optional[list[Bin]]:
+ if bins is None:
+ return None
+ if not isinstance(bins[0], (float, int)):
+ return cast(list[Bin], bins)
+ named_bins: list[Bin] = []
+ for i in range(len(bins) + 1):
+ start = cast(float, bins[i - 1]) if i > 0 else None
+ end = cast(float, bins[i]) if i < len(bins) else None
+ named_bins.append((str(i), start, end))
+ return named_bins
+
+
+def _auto_bins(stats: StatsResult, num_bins: int) -> list[Bin]:
+ min_val = cast(float, stats.min_val)
+ max_val = cast(float, stats.max_val)
+ bin_width = (max_val - min_val) / num_bins
+ bins: list[Bin] = []
+ for i in range(num_bins):
+ start = None if i == 0 else min_val + i * bin_width
+ end = None if i == num_bins - 1 else min_val + (i + 1) * bin_width
+ bins.append((str(i), start, end))
+ return bins
diff --git a/src/data/dataset_select_groups_test.py b/src/data/dataset_select_groups_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..97aa4f4829bd144b901293a6e8e8b6ef9915f4a1
--- /dev/null
+++ b/src/data/dataset_select_groups_test.py
@@ -0,0 +1,313 @@
+"""Tests for dataset.select_groups()."""
+
+import re
+
+import pytest
+from pytest_mock import MockerFixture
+
+from ..schema import UUID_COLUMN, Item, field, schema
+from . import dataset as dataset_module
+from .dataset import BinaryOp
+from .dataset_test_utils import TestDataMaker
+
+
+def test_flat_data(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [
+ {
+ 'name': 'Name1',
+ 'age': 34,
+ 'active': False
+ },
+ {
+ 'name': 'Name2',
+ 'age': 45,
+ 'active': True
+ },
+ {
+ 'age': 17,
+ 'active': True
+ }, # Missing "name".
+ {
+ 'name': 'Name3',
+ 'active': True
+ }, # Missing "age".
+ {
+ 'name': 'Name4',
+ 'age': 55
+ } # Missing "active".
+ ]
+ dataset = make_test_data(items)
+
+ result = dataset.select_groups(leaf_path='name')
+ assert result.counts == [('Name1', 1), ('Name2', 1), (None, 1), ('Name3', 1), ('Name4', 1)]
+
+ result = dataset.select_groups(leaf_path='age', bins=[20, 50, 60])
+ assert result.counts == [('1', 2), ('0', 1), (None, 1), ('2', 1)]
+
+ result = dataset.select_groups(leaf_path='active')
+ assert result.counts == [
+ (True, 3),
+ (False, 1),
+ (None, 1), # Missing "active".
+ ]
+
+
+def test_result_counts(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [
+ {
+ 'active': False
+ },
+ {
+ 'active': True
+ },
+ {
+ 'active': True
+ },
+ {
+ 'active': True
+ },
+ {} # Missing "active".
+ ]
+ dataset = make_test_data(items, schema=schema({UUID_COLUMN: 'string', 'active': 'boolean'}))
+
+ result = dataset.select_groups(leaf_path='active')
+ assert result.counts == [(True, 3), (False, 1), (None, 1)]
+
+
+def test_list_of_structs(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ 'list_of_structs': [{
+ 'name': 'a'
+ }, {
+ 'name': 'b'
+ }]
+ }, {
+ 'list_of_structs': [{
+ 'name': 'c'
+ }, {
+ 'name': 'a'
+ }, {
+ 'name': 'd'
+ }]
+ }, {
+ 'list_of_structs': [{
+ 'name': 'd'
+ }]
+ }]
+ dataset = make_test_data(items)
+
+ result = dataset.select_groups(leaf_path='list_of_structs.*.name')
+ assert result.counts == [('a', 2), ('d', 2), ('b', 1), ('c', 1)]
+
+
+def test_nested_lists(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ 'nested_list': [[{
+ 'name': 'a'
+ }], [{
+ 'name': 'b'
+ }]]
+ }, {
+ 'nested_list': [[{
+ 'name': 'c'
+ }, {
+ 'name': 'a'
+ }], [{
+ 'name': 'd'
+ }]]
+ }, {
+ 'nested_list': [[{
+ 'name': 'd'
+ }]]
+ }]
+ dataset = make_test_data(items)
+
+ result = dataset.select_groups(leaf_path='nested_list.*.*.name')
+ assert result.counts == [('a', 2), ('d', 2), ('b', 1), ('c', 1)]
+
+
+def test_nested_struct(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [
+ {
+ 'nested_struct': {
+ 'struct': {
+ 'name': 'c'
+ }
+ }
+ },
+ {
+ 'nested_struct': {
+ 'struct': {
+ 'name': 'b'
+ }
+ }
+ },
+ {
+ 'nested_struct': {
+ 'struct': {
+ 'name': 'a'
+ }
+ }
+ },
+ ]
+ dataset = make_test_data(items)
+
+ result = dataset.select_groups(leaf_path='nested_struct.struct.name')
+ assert result.counts == [('c', 1), ('b', 1), ('a', 1)]
+
+
+def test_named_bins(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ 'age': 34,
+ }, {
+ 'age': 45,
+ }, {
+ 'age': 17,
+ }, {
+ 'age': 80
+ }, {
+ 'age': 55
+ }]
+ dataset = make_test_data(items)
+
+ result = dataset.select_groups(
+ leaf_path='age',
+ bins=[
+ ('young', None, 20),
+ ('adult', 20, 50),
+ ('middle-aged', 50, 65),
+ ('senior', 65, None),
+ ])
+ assert result.counts == [('adult', 2), ('young', 1), ('senior', 1), ('middle-aged', 1)]
+
+
+def test_schema_with_bins(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ 'age': 34,
+ }, {
+ 'age': 45,
+ }, {
+ 'age': 17,
+ }, {
+ 'age': 80
+ }, {
+ 'age': 55
+ }]
+ data_schema = schema({
+ UUID_COLUMN: 'string',
+ 'age': field(
+ 'int32',
+ bins=[
+ ('young', None, 20),
+ ('adult', 20, 50),
+ ('middle-aged', 50, 65),
+ ('senior', 65, None),
+ ])
+ })
+ dataset = make_test_data(items, data_schema)
+
+ result = dataset.select_groups(leaf_path='age')
+ assert result.counts == [('adult', 2), ('young', 1), ('senior', 1), ('middle-aged', 1)]
+
+
+def test_filters(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [
+ {
+ 'name': 'Name1',
+ 'age': 34,
+ 'active': False
+ },
+ {
+ 'name': 'Name2',
+ 'age': 45,
+ 'active': True
+ },
+ {
+ 'age': 17,
+ 'active': True
+ }, # Missing "name".
+ {
+ 'name': 'Name3',
+ 'active': True
+ }, # Missing "age".
+ {
+ 'name': 'Name4',
+ 'age': 55
+ } # Missing "active".
+ ]
+ dataset = make_test_data(items)
+
+ # active = True.
+ result = dataset.select_groups(leaf_path='name', filters=[('active', BinaryOp.EQUALS, True)])
+ assert result.counts == [('Name2', 1), (None, 1), ('Name3', 1)]
+
+ # age < 35.
+ result = dataset.select_groups(leaf_path='name', filters=[('age', BinaryOp.LESS, 35)])
+ assert result.counts == [('Name1', 1), (None, 1)]
+
+ # age < 35 and active = True.
+ result = dataset.select_groups(
+ leaf_path='name', filters=[('age', BinaryOp.LESS, 35), ('active', BinaryOp.EQUALS, True)])
+ assert result.counts == [(None, 1)]
+
+
+def test_invalid_leaf(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [
+ {
+ 'nested_struct': {
+ 'struct': {
+ 'name': 'c'
+ }
+ }
+ },
+ {
+ 'nested_struct': {
+ 'struct': {
+ 'name': 'b'
+ }
+ }
+ },
+ {
+ 'nested_struct': {
+ 'struct': {
+ 'name': 'a'
+ }
+ }
+ },
+ ]
+ dataset = make_test_data(items)
+
+ with pytest.raises(
+ ValueError, match=re.escape("Leaf \"('nested_struct',)\" not found in dataset")):
+ dataset.select_groups(leaf_path='nested_struct')
+
+ with pytest.raises(
+ ValueError, match=re.escape("Leaf \"('nested_struct', 'struct')\" not found in dataset")):
+ dataset.select_groups(leaf_path='nested_struct.struct')
+
+ with pytest.raises(
+ ValueError,
+ match=re.escape("Leaf \"('nested_struct', 'struct', 'wrong_name')\" not found in dataset")):
+ dataset.select_groups(leaf_path='nested_struct.struct.wrong_name')
+
+
+def test_too_many_distinct(make_test_data: TestDataMaker, mocker: MockerFixture) -> None:
+ too_many_distinct = 5
+ mocker.patch(f'{dataset_module.__name__}.TOO_MANY_DISTINCT', too_many_distinct)
+
+ items: list[Item] = [{'feature': str(i)} for i in range(too_many_distinct + 10)]
+ dataset = make_test_data(items)
+
+ res = dataset.select_groups('feature')
+ assert res.too_many_distinct is True
+ assert res.counts == []
+
+
+def test_auto_bins_for_float(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{'feature': float(i)} for i in range(5)]
+ dataset = make_test_data(items)
+
+ res = dataset.select_groups('feature')
+ assert res.counts == [('0', 1), ('3', 1), ('7', 1), ('11', 1), ('14', 1)]
+ assert res.too_many_distinct is False
+ assert res.bins
diff --git a/src/data/dataset_select_rows_filter_test.py b/src/data/dataset_select_rows_filter_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..86bad45b87beb9e05ad38cb61abcfd90cc32f6a9
--- /dev/null
+++ b/src/data/dataset_select_rows_filter_test.py
@@ -0,0 +1,112 @@
+"""Tests for dataset.select_rows(filters=[...])."""
+
+import pytest
+
+from ..schema import UUID_COLUMN, Item, schema
+from .dataset import BinaryFilterTuple, BinaryOp, ListFilterTuple, ListOp, UnaryOp
+from .dataset_test_utils import TestDataMaker
+
+TEST_DATA: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'str': 'a',
+ 'int': 1,
+ 'bool': False,
+ 'float': 3.0
+}, {
+ UUID_COLUMN: '2',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 2.0
+}, {
+ UUID_COLUMN: '3',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 1.0
+}]
+
+
+def test_filter_by_ids(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ id_filter: BinaryFilterTuple = (UUID_COLUMN, BinaryOp.EQUALS, '1')
+ result = dataset.select_rows(filters=[id_filter])
+
+ assert list(result) == [{UUID_COLUMN: '1', 'str': 'a', 'int': 1, 'bool': False, 'float': 3.0}]
+
+ id_filter = (UUID_COLUMN, BinaryOp.EQUALS, '2')
+ result = dataset.select_rows(filters=[id_filter])
+
+ assert list(result) == [{UUID_COLUMN: '2', 'str': 'b', 'int': 2, 'bool': True, 'float': 2.0}]
+
+ id_filter = (UUID_COLUMN, BinaryOp.EQUALS, b'f')
+ result = dataset.select_rows(filters=[id_filter])
+
+ assert list(result) == []
+
+
+def test_filter_by_list_of_ids(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ id_filter: ListFilterTuple = (UUID_COLUMN, ListOp.IN, ['1', '2'])
+ result = dataset.select_rows(filters=[id_filter])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'str': 'a',
+ 'int': 1,
+ 'bool': False,
+ 'float': 3.0
+ }, {
+ UUID_COLUMN: '2',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 2.0
+ }]
+
+
+def test_filter_by_exists(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'name': 'A',
+ 'info': {
+ 'lang': 'en'
+ },
+ 'ages': []
+ }, {
+ UUID_COLUMN: '2',
+ 'info': {
+ 'lang': 'fr'
+ },
+ }, {
+ UUID_COLUMN: '3',
+ 'name': 'C',
+ 'ages': [[1, 2], [3, 4]]
+ }]
+ dataset = make_test_data(
+ items,
+ schema=schema({
+ UUID_COLUMN: 'string',
+ 'name': 'string',
+ 'info': {
+ 'lang': 'string'
+ },
+ 'ages': [['int32']]
+ }))
+
+ exists_filter = ('name', UnaryOp.EXISTS)
+ result = dataset.select_rows(['name'], filters=[exists_filter])
+ assert list(result) == [{UUID_COLUMN: '1', 'name': 'A'}, {UUID_COLUMN: '3', 'name': 'C'}]
+
+ exists_filter = ('info.lang', UnaryOp.EXISTS)
+ result = dataset.select_rows(['name'], filters=[exists_filter])
+ assert list(result) == [{UUID_COLUMN: '1', 'name': 'A'}, {UUID_COLUMN: '2', 'name': None}]
+
+ exists_filter = ('ages.*.*', UnaryOp.EXISTS)
+ result = dataset.select_rows(['name'], filters=[exists_filter])
+ assert list(result) == [{UUID_COLUMN: '3', 'name': 'C'}]
+
+ with pytest.raises(ValueError, match='Unable to filter on path'):
+ dataset.select_rows(['name'], filters=[('info', UnaryOp.EXISTS)])
diff --git a/src/data/dataset_select_rows_schema_test.py b/src/data/dataset_select_rows_schema_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a0b3055a9fc66cd264db217a8f25fb9174a81db
--- /dev/null
+++ b/src/data/dataset_select_rows_schema_test.py
@@ -0,0 +1,551 @@
+"""Tests for `db.select_rows_schema()`."""
+
+from typing import Iterable, Optional, cast
+
+import numpy as np
+import pytest
+from typing_extensions import override
+
+from ..embeddings.vector_store import VectorStore
+from ..schema import PATH_WILDCARD, UUID_COLUMN, Field, Item, RichData, VectorKey, field, schema
+from ..signals.concept_labels import ConceptLabelsSignal
+from ..signals.concept_scorer import ConceptScoreSignal
+from ..signals.semantic_similarity import SemanticSimilaritySignal
+from ..signals.signal import (
+ EMBEDDING_KEY,
+ TextEmbeddingModelSignal,
+ TextEmbeddingSignal,
+ TextSignal,
+ TextSplitterSignal,
+ clear_signal_registry,
+ register_signal,
+)
+from ..signals.substring_search import SubstringSignal
+from .dataset import (
+ Column,
+ ConceptQuery,
+ KeywordQuery,
+ Search,
+ SearchResultInfo,
+ SelectRowsSchemaResult,
+ SelectRowsSchemaUDF,
+ SemanticQuery,
+ SortOrder,
+ SortResult,
+)
+from .dataset_test_utils import (
+ TEST_DATASET_NAME,
+ TEST_NAMESPACE,
+ TestDataMaker,
+ enriched_embedding_span_field,
+)
+from .dataset_utils import lilac_embedding, lilac_span
+
+TEST_DATA: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'erased': False,
+ 'people': [{
+ 'name': 'A',
+ 'zipcode': 0,
+ 'locations': [{
+ 'city': 'city1',
+ 'state': 'state1'
+ }, {
+ 'city': 'city2',
+ 'state': 'state2'
+ }]
+ }]
+}, {
+ UUID_COLUMN: '2',
+ 'erased': True,
+ 'people': [{
+ 'name': 'B',
+ 'zipcode': 1,
+ 'locations': [{
+ 'city': 'city3',
+ 'state': 'state3'
+ }, {
+ 'city': 'city4'
+ }, {
+ 'city': 'city5'
+ }]
+ }, {
+ 'name': 'C',
+ 'zipcode': 2,
+ 'locations': [{
+ 'city': 'city1',
+ 'state': 'state1'
+ }]
+ }]
+}]
+
+
+class TestSplitter(TextSplitterSignal):
+ """Split documents into sentence by splitting on period."""
+ name = 'test_splitter'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ for text in data:
+ if not isinstance(text, str):
+ raise ValueError(f'Expected text to be a string, got {type(text)} instead.')
+ sentences = [f'{sentence.strip()}.' for sentence in text.split('.') if sentence]
+ yield [
+ lilac_span(text.index(sentence),
+ text.index(sentence) + len(sentence)) for sentence in sentences
+ ]
+
+
+EMBEDDINGS: list[tuple[str, list[float]]] = [('hello.', [1.0, 0.0, 0.0]),
+ ('hello2.', [1.0, 1.0, 0.0]),
+ ('hello world.', [1.0, 1.0, 1.0]),
+ ('hello world2.', [2.0, 1.0, 1.0])]
+
+STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for text, embedding in EMBEDDINGS}
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[cast(str, example)]))]
+
+
+class TestEmbeddingSumSignal(TextEmbeddingModelSignal):
+ """Sums the embeddings to return a single floating point value."""
+ name = 'test_embedding_sum'
+
+ @override
+ def fields(self) -> Field:
+ return field('float32')
+
+ @override
+ def vector_compute(self, keys: Iterable[VectorKey], vector_store: VectorStore) -> Iterable[Item]:
+ # The signal just sums the values of the embedding.
+ embedding_sums = vector_store.get(keys).sum(axis=1)
+ for embedding_sum in embedding_sums.tolist():
+ yield embedding_sum
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(LengthSignal)
+ register_signal(AddSpaceSignal)
+ register_signal(TestSplitter)
+ register_signal(TestEmbedding)
+ register_signal(TestEmbeddingSumSignal)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+class LengthSignal(TextSignal):
+ name = 'length_signal'
+
+ def fields(self) -> Field:
+ return field('int32')
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ yield len(text_content)
+
+
+class AddSpaceSignal(TextSignal):
+ name = 'add_space_signal'
+
+ def fields(self) -> Field:
+ return field('string')
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ yield cast(str, text_content) + ' '
+
+
+def test_simple_schema(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+ result = dataset.select_rows_schema(combine_columns=True)
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'erased': 'boolean',
+ 'people': [{
+ 'name': 'string',
+ 'zipcode': 'int32',
+ 'locations': [{
+ 'city': 'string',
+ 'state': 'string'
+ }]
+ }]
+ }))
+
+
+def test_subselection_with_combine_cols(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ result = dataset.select_rows_schema([('people', '*', 'zipcode'),
+ ('people', '*', 'locations', '*', 'city')],
+ combine_columns=True)
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'people': [{
+ 'zipcode': 'int32',
+ 'locations': [{
+ 'city': 'string'
+ }]
+ }]
+ }))
+
+ result = dataset.select_rows_schema([('people', '*', 'name'), ('people', '*', 'locations')],
+ combine_columns=True)
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'people': [{
+ 'name': 'string',
+ 'locations': [{
+ 'city': 'string',
+ 'state': 'string'
+ }]
+ }]
+ }))
+
+ result = dataset.select_rows_schema([('people', '*')], combine_columns=True)
+ assert result == SelectRowsSchemaResult(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'people': [{
+ 'name': 'string',
+ 'zipcode': 'int32',
+ 'locations': [{
+ 'city': 'string',
+ 'state': 'string'
+ }]
+ }]
+ }))
+
+
+def test_udf_with_combine_cols(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ length_signal = LengthSignal()
+ result = dataset.select_rows_schema([('people', '*', 'locations', '*', 'city'),
+ Column(('people', '*', 'name'), signal_udf=length_signal)],
+ combine_columns=True)
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'people': [{
+ 'name': {
+ 'length_signal': field('int32', length_signal.dict())
+ },
+ 'locations': [{
+ 'city': 'string'
+ }]
+ }],
+ }),
+ udfs=[
+ SelectRowsSchemaUDF(path=('people', '*', 'name', length_signal.key())),
+ ],
+ )
+
+
+def test_embedding_udf_with_combine_cols(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ add_space_signal = AddSpaceSignal()
+ path = ('people', '*', 'name')
+ dataset.compute_signal(add_space_signal, path)
+ result = dataset.select_rows_schema([path, Column(path, signal_udf=add_space_signal)],
+ combine_columns=True)
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'people': [{
+ 'name': field(
+ 'string', fields={'add_space_signal': field('string', signal=add_space_signal.dict())})
+ }],
+ }),
+ udfs=[
+ SelectRowsSchemaUDF(path=(*path, add_space_signal.key())),
+ ],
+ )
+
+
+def test_udf_chained_with_combine_cols(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello. hello2.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world. hello world2.',
+ }])
+
+ test_splitter = TestSplitter()
+ dataset.compute_signal(test_splitter, ('text'))
+ add_space_signal = AddSpaceSignal()
+ result = dataset.select_rows_schema(
+ [('text'), Column(('text'), signal_udf=add_space_signal)], combine_columns=True)
+
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'add_space_signal': field('string', add_space_signal.dict()),
+ 'test_splitter': field(signal=test_splitter.dict(), fields=['string_span'])
+ })
+ }),
+ udfs=[
+ SelectRowsSchemaUDF(path=('text', add_space_signal.key())),
+ ],
+ )
+
+
+def test_udf_embedding_chained_with_combine_cols(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello. hello2.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world. hello world2.',
+ }])
+
+ test_splitter = TestSplitter()
+ dataset.compute_signal(test_splitter, 'text')
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text', 'test_splitter', '*'))
+
+ embedding_sum_signal = TestEmbeddingSumSignal(embedding='test_embedding')
+ udf_col = Column(('text', 'test_splitter', '*'), signal_udf=embedding_sum_signal)
+ result = dataset.select_rows_schema([('text'), udf_col], combine_columns=True)
+
+ expected_schema = schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'test_splitter': field(
+ signal=test_splitter.dict(),
+ fields=[
+ field(
+ 'string_span',
+ fields={
+ 'test_embedding': field(
+ signal=test_embedding.dict(),
+ fields=[
+ enriched_embedding_span_field(
+ {'test_embedding_sum': field('float32', embedding_sum_signal.dict())})
+ ])
+ })
+ ])
+ })
+ })
+ output_path = ('text', 'test_splitter', '*', 'test_embedding', '*', 'embedding',
+ 'test_embedding_sum')
+ assert result == SelectRowsSchemaResult(
+ data_schema=expected_schema,
+ udfs=[SelectRowsSchemaUDF(path=output_path)],
+ )
+
+ # Alias the udf.
+ udf_col.alias = 'udf1'
+ result = dataset.select_rows_schema([('text'), udf_col], combine_columns=True)
+ assert result == SelectRowsSchemaResult(
+ data_schema=expected_schema,
+ udfs=[SelectRowsSchemaUDF(path=output_path, alias='udf1')],
+ )
+
+
+def test_search_keyword_schema(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world',
+ 'text2': 'hello world2',
+ }])
+ query_world = 'world'
+ query_hello = 'hello'
+
+ result = dataset.select_rows_schema(
+ searches=[
+ Search(path='text', query=KeywordQuery(type='keyword', search=query_world)),
+ Search(path='text2', query=KeywordQuery(type='keyword', search=query_hello)),
+ ],
+ combine_columns=True)
+
+ expected_world_signal = SubstringSignal(query=query_world)
+ expected_hello_signal = SubstringSignal(query=query_hello)
+
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ expected_world_signal.key(): field(
+ signal=expected_world_signal.dict(), fields=['string_span'])
+ }),
+ 'text2': field(
+ 'string',
+ fields={
+ expected_hello_signal.key(): field(
+ signal=expected_hello_signal.dict(), fields=['string_span'])
+ })
+ }),
+ search_results=[
+ SearchResultInfo(
+ search_path=('text',),
+ result_path=('text', expected_world_signal.key(), PATH_WILDCARD),
+ ),
+ SearchResultInfo(
+ search_path=('text2',),
+ result_path=('text2', expected_hello_signal.key(), PATH_WILDCARD),
+ )
+ ],
+ udfs=[
+ SelectRowsSchemaUDF(path=('text', expected_world_signal.key())),
+ SelectRowsSchemaUDF(path=('text2', expected_hello_signal.key())),
+ ],
+ )
+
+
+def test_search_semantic_schema(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world.',
+ }])
+ query_world = 'world'
+
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text'))
+
+ result = dataset.select_rows_schema(
+ searches=[
+ Search(
+ path='text',
+ query=SemanticQuery(type='semantic', search=query_world, embedding='test_embedding')),
+ ],
+ combine_columns=True)
+
+ test_embedding = TestEmbedding()
+ expected_world_signal = SemanticSimilaritySignal(query=query_world, embedding='test_embedding')
+
+ similarity_score_path = ('text', 'test_embedding', PATH_WILDCARD, EMBEDDING_KEY,
+ expected_world_signal.key())
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'test_embedding': field(
+ signal=test_embedding.dict(),
+ fields=[
+ enriched_embedding_span_field(
+ {expected_world_signal.key(): field('float32', expected_world_signal.dict())})
+ ])
+ })
+ }),
+ udfs=[SelectRowsSchemaUDF(path=similarity_score_path)],
+ search_results=[SearchResultInfo(search_path=('text',), result_path=similarity_score_path)],
+ sorts=[SortResult(path=similarity_score_path, order=SortOrder.DESC, search_index=0)])
+
+
+def test_search_concept_schema(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world.',
+ }])
+
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text'))
+
+ result = dataset.select_rows_schema(
+ searches=[
+ Search(
+ path='text',
+ query=ConceptQuery(
+ type='concept',
+ concept_namespace='test_namespace',
+ concept_name='test_concept',
+ embedding='test_embedding')),
+ ],
+ combine_columns=True)
+
+ test_embedding = TestEmbedding()
+ expected_world_signal = ConceptScoreSignal(
+ namespace='test_namespace', concept_name='test_concept', embedding='test_embedding')
+ expected_labels_signal = ConceptLabelsSignal(
+ namespace='test_namespace', concept_name='test_concept')
+
+ concept_score_path = ('text', 'test_embedding', PATH_WILDCARD, EMBEDDING_KEY,
+ expected_world_signal.key())
+ concept_labels_path = ('text', expected_labels_signal.key())
+ assert result == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'text': field(
+ 'string',
+ fields={
+ 'test_embedding': field(
+ signal=test_embedding.dict(),
+ fields=[
+ enriched_embedding_span_field({
+ expected_world_signal.key(): field(
+ 'float32',
+ expected_world_signal.dict(),
+ bins=[('Not in concept', None, 0.5), ('In concept', 0.5, None)])
+ })
+ ]),
+ 'test_namespace/test_concept/labels': field(
+ fields=[field('string_span', fields={
+ 'label': 'boolean',
+ 'draft': 'string'
+ })],
+ signal=expected_labels_signal.dict())
+ })
+ }),
+ udfs=[
+ SelectRowsSchemaUDF(path=concept_labels_path),
+ SelectRowsSchemaUDF(path=concept_score_path)
+ ],
+ search_results=[
+ SearchResultInfo(search_path=('text',), result_path=concept_labels_path),
+ SearchResultInfo(search_path=('text',), result_path=concept_score_path)
+ ],
+ sorts=[SortResult(path=concept_score_path, order=SortOrder.DESC, search_index=0)])
+
+
+def test_search_sort_override(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world.',
+ }])
+ query_world = 'world'
+
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text'))
+
+ result = dataset.select_rows_schema(
+ searches=[
+ Search(
+ path='text',
+ query=SemanticQuery(type='semantic', search=query_world, embedding='test_embedding')),
+ ],
+ # Explicit sort by overrides the semantic search.
+ sort_by=[('text',)],
+ sort_order=SortOrder.DESC,
+ combine_columns=True)
+
+ assert result.sorts == [SortResult(path=('text',), order=SortOrder.DESC)]
diff --git a/src/data/dataset_select_rows_search_test.py b/src/data/dataset_select_rows_search_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9ec36641b22b8fd9e2d1f46ba036b9f94ac9eff
--- /dev/null
+++ b/src/data/dataset_select_rows_search_test.py
@@ -0,0 +1,401 @@
+"""Tests for dataset.select_rows(searches=[...])."""
+
+from typing import Iterable, cast
+
+import numpy as np
+import pytest
+from pytest import approx
+from pytest_mock import MockerFixture
+from sklearn.preprocessing import normalize
+from typing_extensions import override
+
+from ..concepts.concept import ExampleIn, LogisticEmbeddingModel
+from ..concepts.db_concept import ConceptUpdate, DiskConceptDB
+from ..db_manager import set_default_dataset_cls
+from ..schema import UUID_COLUMN, Item, RichData, SignalInputType
+from ..signals.concept_scorer import ConceptScoreSignal
+from ..signals.semantic_similarity import SemanticSimilaritySignal
+from ..signals.signal import TextEmbeddingSignal, clear_signal_registry, register_signal
+from ..signals.substring_search import SubstringSignal
+from .dataset import ConceptQuery, KeywordQuery, ListOp, Search, SemanticQuery, SortOrder
+from .dataset_duckdb import DatasetDuckDB
+from .dataset_test_utils import TestDataMaker, enriched_embedding_span, enriched_item
+from .dataset_utils import lilac_embedding, lilac_span
+
+TEST_DATA: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'text': 'hello world',
+ 'text2': 'again hello world',
+}, {
+ UUID_COLUMN: '2',
+ 'text': 'looking for world in text',
+ 'text2': 'again looking for world in text',
+}, {
+ UUID_COLUMN: '3',
+ 'text': 'unrelated text',
+ 'text2': 'again unrelated text'
+}]
+
+EMBEDDINGS: list[tuple[str, list[float]]] = [
+ ('hello.', [1.0, 0.0, 0.0]),
+ ('hello2.', [1.0, 1.0, 0.0]),
+ ('hello world.', [1.0, 1.0, 1.0]),
+ ('hello world2.', [2.0, 1.0, 1.0]),
+ ('random negative 1', [0, 0, 0.3]),
+ ('random negative 2', [0, 0, 0.4]),
+ ('random negative 3', [0, 0.1, 0.5]),
+ ('random negative 4', [0.1, 0, 0.4]),
+]
+
+STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for text, embedding in EMBEDDINGS}
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ set_default_dataset_cls(DatasetDuckDB)
+ register_signal(TestEmbedding)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_search_keyword(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ query = 'world'
+ result = dataset.select_rows(
+ searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
+ combine_columns=True)
+
+ expected_signal_udf = SubstringSignal(query=query)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello world', {expected_signal_udf.key(): [lilac_span(6, 11)]}),
+ 'text2': 'again hello world'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('looking for world in text',
+ {expected_signal_udf.key(): [lilac_span(12, 17)]}),
+ 'text2': 'again looking for world in text',
+ }]
+
+
+def test_search_keyword_special_chars(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'This is 100%',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'This has _underscore_',
+ }])
+
+ query = '100%'
+ result = dataset.select_rows(
+ searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
+ combine_columns=True)
+
+ expected_signal_udf = SubstringSignal(query=query)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('This is 100%', {expected_signal_udf.key(): [lilac_span(8, 12)]}),
+ }]
+
+ query = '_underscore_'
+ result = dataset.select_rows(
+ searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
+ combine_columns=True)
+
+ expected_signal_udf = SubstringSignal(query=query)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'text': enriched_item('This has _underscore_',
+ {expected_signal_udf.key(): [lilac_span(9, 21)]}),
+ }]
+
+
+def test_search_keyword_multiple(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ query_world = 'world'
+ query_looking_world = 'looking for world'
+ expected_world_udf = SubstringSignal(query=query_world)
+ expected_again_looking_udf = SubstringSignal(query=query_looking_world)
+
+ result = dataset.select_rows(
+ searches=[
+ Search(path='text', query=KeywordQuery(type='keyword', search=query_world)),
+ Search(path='text2', query=KeywordQuery(type='keyword', search=query_looking_world)),
+ ],
+ combine_columns=True)
+
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'text': enriched_item('looking for world in text', {
+ expected_world_udf.key(): [lilac_span(12, 17)],
+ }),
+ 'text2': enriched_item('again looking for world in text',
+ {expected_again_looking_udf.key(): [lilac_span(6, 23)]})
+ }]
+
+
+def test_search_keyword_with_filters(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(TEST_DATA)
+
+ query = 'world'
+ result = dataset.select_rows(
+ filters=[(UUID_COLUMN, ListOp.IN, ['1', '3'])],
+ searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
+ combine_columns=True)
+
+ expected_signal_udf = SubstringSignal(query=query)
+ assert list(result) == [
+ {
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello world', {expected_signal_udf.key(): [lilac_span(6, 11)]}),
+ 'text2': 'again hello world'
+ },
+ # The second row doesn't match the UUID filter.
+ ]
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ embedding = np.array(STR_EMBEDDINGS[cast(str, example)])
+ embedding = normalize([embedding])[0]
+ yield [lilac_embedding(0, len(example), embedding)]
+
+
+def test_semantic_search(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world2.',
+ }])
+
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text'))
+
+ query = 'hello2.'
+ result = dataset.select_rows(
+ searches=[
+ Search(
+ path='text', query=SemanticQuery(type='semantic', search=query, embedding='test_embedding'))
+ ],
+ combine_columns=True)
+ expected_signal_udf = SemanticSimilaritySignal(query=query, embedding='test_embedding')
+ assert list(result) == [
+ # Results are sorted by score desc.
+ {
+ UUID_COLUMN: '2',
+ 'text': enriched_item(
+ 'hello world2.', {
+ test_embedding.key():
+ [enriched_embedding_span(0, 13, {expected_signal_udf.key(): approx(0.916, 1e-3)})]
+ })
+ },
+ {
+ UUID_COLUMN: '1',
+ 'text': enriched_item(
+ 'hello world.', {
+ test_embedding.key():
+ [enriched_embedding_span(0, 12, {expected_signal_udf.key(): approx(0.885, 1e-3)})]
+ })
+ },
+ ]
+
+
+def test_concept_search(make_test_data: TestDataMaker, mocker: MockerFixture) -> None:
+ concept_model_mock = mocker.spy(LogisticEmbeddingModel, 'fit')
+
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world2.',
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'random negative 1',
+ }, {
+ UUID_COLUMN: '4',
+ 'text': 'random negative 2',
+ }, {
+ UUID_COLUMN: '5',
+ 'text': 'random negative 3',
+ }, {
+ UUID_COLUMN: '6',
+ 'text': 'random negative 4',
+ }])
+
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text'))
+
+ concept_db = DiskConceptDB()
+ concept_db.create(namespace='test_namespace', name='test_concept', type=SignalInputType.TEXT)
+ concept_db.edit(
+ 'test_namespace', 'test_concept',
+ ConceptUpdate(insert=[
+ ExampleIn(label=False, text='hello world.'),
+ ExampleIn(label=True, text='hello world2.')
+ ]))
+
+ result = dataset.select_rows(
+ searches=[
+ Search(
+ path='text',
+ query=ConceptQuery(
+ type='concept',
+ concept_namespace='test_namespace',
+ concept_name='test_concept',
+ embedding='test_embedding'))
+ ],
+ filters=[(UUID_COLUMN, ListOp.IN, ['1', '2'])],
+ combine_columns=True)
+ expected_signal_udf = ConceptScoreSignal(
+ namespace='test_namespace', concept_name='test_concept', embedding='test_embedding')
+
+ assert list(result) == [
+ # Results are sorted by score desc.
+ {
+ UUID_COLUMN: '2',
+ 'text': enriched_item(
+ 'hello world2.', {
+ test_embedding.key():
+ [enriched_embedding_span(0, 13, {expected_signal_udf.key(): approx(0.75, abs=0.25)})],
+ 'test_namespace/test_concept/labels': [lilac_span(0, 13, {'label': True})]
+ })
+ },
+ {
+ UUID_COLUMN: '1',
+ 'text': enriched_item(
+ 'hello world.', {
+ test_embedding.key():
+ [enriched_embedding_span(0, 12, {expected_signal_udf.key(): approx(0.25, abs=0.25)})],
+ 'test_namespace/test_concept/labels': [lilac_span(0, 12, {'label': False})]
+ })
+ },
+ ]
+
+ # Make sure fit was called with negative examples.
+ (_, embeddings, labels, _) = concept_model_mock.call_args_list[-1].args
+ assert embeddings.shape == (8, 3)
+ assert labels == [
+ # Negative implicit labels.
+ False,
+ False,
+ False,
+ False,
+ False,
+ False,
+ # Explicit labels.
+ False,
+ True
+ ]
+
+
+def test_sort_override_search(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world.',
+ 'value': 10
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world2.',
+ 'value': 20
+ }])
+
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text'))
+
+ query = 'hello2.'
+ search = Search(
+ path='text', query=SemanticQuery(type='semantic', search=query, embedding='test_embedding'))
+
+ expected_signal_udf = SemanticSimilaritySignal(query=query, embedding='test_embedding')
+ expected_item_1 = {
+ UUID_COLUMN: '1',
+ 'text': enriched_item(
+ 'hello world.', {
+ test_embedding.key():
+ [enriched_embedding_span(0, 12, {expected_signal_udf.key(): approx(0.885, 1e-3)})]
+ }),
+ 'value': 10
+ }
+ expected_item_2 = {
+ UUID_COLUMN: '2',
+ 'text': enriched_item(
+ 'hello world2.', {
+ test_embedding.key():
+ [enriched_embedding_span(0, 13, {expected_signal_udf.key(): approx(0.916, 1e-3)})]
+ }),
+ 'value': 20
+ }
+
+ sort_order = SortOrder.ASC
+ result = dataset.select_rows(
+ searches=[search], sort_by=[('value',)], sort_order=sort_order, combine_columns=True)
+ assert list(result) == [
+ # Results are sorted by score ascending.
+ expected_item_1,
+ expected_item_2
+ ]
+
+ sort_order = SortOrder.DESC
+ result = dataset.select_rows(
+ searches=[search], sort_by=[('text',)], sort_order=sort_order, combine_columns=True)
+ assert list(result) == [
+ # Results are sorted by score descending.
+ expected_item_2,
+ expected_item_1
+ ]
+
+
+def test_search_keyword_and_semantic(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello world.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello world2.',
+ }])
+
+ test_embedding = TestEmbedding()
+ dataset.compute_signal(test_embedding, ('text'))
+
+ query = 'hello2.'
+ keyword_query = 'rld2'
+ result = dataset.select_rows(
+ searches=[
+ Search(
+ path='text', query=SemanticQuery(type='semantic', search=query,
+ embedding='test_embedding')),
+ Search(path='text', query=KeywordQuery(type='keyword', search=keyword_query))
+ ],
+ combine_columns=True)
+ expected_semantic_signal = SemanticSimilaritySignal(query=query, embedding='test_embedding')
+ expected_keyword_signal = SubstringSignal(query=keyword_query)
+ assert list(result) == [
+ # Results are sorted by score desc.
+ {
+ UUID_COLUMN: '2',
+ 'text': enriched_item(
+ 'hello world2.', {
+ test_embedding.key():
+ [enriched_embedding_span(0, 13, {expected_semantic_signal.key(): approx(0.916, 1e-3)})],
+ expected_keyword_signal.key(): [lilac_span(8, 12)],
+ })
+ },
+ # UUID '1' is not returned because it does not match the keyword query.
+ ]
diff --git a/src/data/dataset_select_rows_sort_test.py b/src/data/dataset_select_rows_sort_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bd0a91f5bd75f9f049e80af56ef78b7711942f4
--- /dev/null
+++ b/src/data/dataset_select_rows_sort_test.py
@@ -0,0 +1,904 @@
+"""Tests for dataset.select_rows(sort_by=...)."""
+
+from typing import Iterable, Optional, Sequence, cast
+
+import numpy as np
+import pytest
+from typing_extensions import override
+
+from ..embeddings.vector_store import VectorStore
+from ..schema import UUID_COLUMN, Field, Item, RichData, VectorKey, field
+from ..signals.signal import (
+ TextEmbeddingModelSignal,
+ TextEmbeddingSignal,
+ TextSignal,
+ clear_signal_registry,
+ register_signal,
+)
+from .dataset import BinaryOp, Column, SortOrder
+from .dataset_test_utils import TestDataMaker, enriched_item
+from .dataset_utils import lilac_embedding
+
+
+class TestSignal(TextSignal):
+ name = 'test_signal'
+
+ def fields(self) -> Field:
+ return field(fields={'len': 'int32', 'is_all_cap': 'boolean'})
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ yield {'len': len(text_content), 'is_all_cap': text_content.isupper()}
+
+
+class TestPrimitiveSignal(TextSignal):
+ name = 'primitive_signal'
+
+ def fields(self) -> Field:
+ return field('int32')
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ yield len(text_content) + 1
+
+
+class NestedArraySignal(TextSignal):
+ name = 'nested_array'
+
+ def fields(self) -> Field:
+ return field(fields=[['int32']])
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ yield [[len(text_content) + 1], [len(text_content)]]
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(TestSignal)
+ register_signal(TestPrimitiveSignal)
+ register_signal(NestedArraySignal)
+ register_signal(TopKEmbedding)
+ # Unit test runs.
+ yield
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_sort_by_source_no_alias_no_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'erased': True,
+ 'score': 4.1,
+ 'document': {
+ 'num_pages': 4,
+ 'header': {
+ 'title': 'c'
+ }
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'erased': False,
+ 'score': 3.5,
+ 'document': {
+ 'num_pages': 5,
+ 'header': {
+ 'title': 'b'
+ }
+ },
+ }, {
+ UUID_COLUMN: '3',
+ 'erased': True,
+ 'score': 3.7,
+ 'document': {
+ 'num_pages': 3,
+ 'header': {
+ 'title': 'a'
+ }
+ },
+ }])
+
+ # Sort by bool.
+ result = dataset.select_rows(columns=[UUID_COLUMN], sort_by=['erased'], sort_order=SortOrder.ASC)
+ assert list(result) == [{UUID_COLUMN: '2'}, {UUID_COLUMN: '1'}, {UUID_COLUMN: '3'}]
+ result = dataset.select_rows(columns=[UUID_COLUMN], sort_by=['erased'], sort_order=SortOrder.DESC)
+ assert list(result) == [{UUID_COLUMN: '1'}, {UUID_COLUMN: '3'}, {UUID_COLUMN: '2'}]
+
+ # Sort by float.
+ result = dataset.select_rows(columns=[UUID_COLUMN], sort_by=['score'], sort_order=SortOrder.ASC)
+ assert list(result) == [{UUID_COLUMN: '2'}, {UUID_COLUMN: '3'}, {UUID_COLUMN: '1'}]
+ result = dataset.select_rows(columns=[UUID_COLUMN], sort_by=['score'], sort_order=SortOrder.DESC)
+ assert list(result) == [{UUID_COLUMN: '1'}, {UUID_COLUMN: '3'}, {UUID_COLUMN: '2'}]
+
+ # Sort by nested int.
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['document.num_pages'], sort_order=SortOrder.ASC)
+ assert list(result) == [{UUID_COLUMN: '3'}, {UUID_COLUMN: '1'}, {UUID_COLUMN: '2'}]
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['document.num_pages'], sort_order=SortOrder.DESC)
+ assert list(result) == [{UUID_COLUMN: '2'}, {UUID_COLUMN: '1'}, {UUID_COLUMN: '3'}]
+
+ # Sort by double nested string.
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['document.header.title'], sort_order=SortOrder.ASC)
+ assert list(result) == [{UUID_COLUMN: '3'}, {UUID_COLUMN: '2'}, {UUID_COLUMN: '1'}]
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['document.header.title'], sort_order=SortOrder.DESC)
+ assert list(result) == [{UUID_COLUMN: '1'}, {UUID_COLUMN: '2'}, {UUID_COLUMN: '3'}]
+
+
+def test_sort_by_signal_no_alias_no_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'HEY'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone'
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI'
+ }])
+
+ dataset.compute_signal(TestSignal(), 'text')
+
+ # Sort by `signal.len`.
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['text.test_signal.len'], sort_order=SortOrder.ASC)
+ assert list(result) == [{UUID_COLUMN: '3'}, {UUID_COLUMN: '1'}, {UUID_COLUMN: '2'}]
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['text.test_signal.len'], sort_order=SortOrder.DESC)
+ assert list(result) == [{UUID_COLUMN: '2'}, {UUID_COLUMN: '1'}, {UUID_COLUMN: '3'}]
+
+ # Sort by `signal.is_all_cap`.
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['text.test_signal.is_all_cap'], sort_order=SortOrder.ASC)
+ assert list(result) == [{UUID_COLUMN: '2'}, {UUID_COLUMN: '1'}, {UUID_COLUMN: '3'}]
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN], sort_by=['text.test_signal.is_all_cap'], sort_order=SortOrder.DESC)
+ assert list(result) == [{UUID_COLUMN: '1'}, {UUID_COLUMN: '3'}, {UUID_COLUMN: '2'}]
+
+
+def test_sort_by_signal_alias_no_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'HEY'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone'
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI'
+ }])
+
+ dataset.compute_signal(TestSignal(), 'text')
+
+ # Sort by `signal.len`.
+ signal_alias = Column('text.test_signal', alias='signal')
+ result = dataset.select_rows(
+ columns=[signal_alias], sort_by=['signal.len'], sort_order=SortOrder.ASC)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'signal': {
+ 'len': 2,
+ 'is_all_cap': True
+ }
+ }, {
+ UUID_COLUMN: '1',
+ 'signal': {
+ 'len': 3,
+ 'is_all_cap': True
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'signal': {
+ 'len': 8,
+ 'is_all_cap': False
+ }
+ }]
+ result = dataset.select_rows(
+ columns=[signal_alias], sort_by=['signal.len'], sort_order=SortOrder.DESC)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'signal': {
+ 'len': 8,
+ 'is_all_cap': False
+ }
+ }, {
+ UUID_COLUMN: '1',
+ 'signal': {
+ 'len': 3,
+ 'is_all_cap': True
+ }
+ }, {
+ UUID_COLUMN: '3',
+ 'signal': {
+ 'len': 2,
+ 'is_all_cap': True
+ }
+ }]
+
+
+def test_sort_by_enriched_alias_no_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'HEY'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone'
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI'
+ }])
+
+ dataset.compute_signal(TestSignal(), 'text')
+
+ # Sort by `document.test_signal.is_all_cap` where 'document' is an alias to 'text'.
+ text_alias = Column('text', alias='document')
+ result = dataset.select_rows(
+ columns=[text_alias], sort_by=['document.test_signal.is_all_cap'], sort_order=SortOrder.ASC)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'document': enriched_item('everyone', {'test_signal': {
+ 'len': 8,
+ 'is_all_cap': False
+ }})
+ }, {
+ UUID_COLUMN: '1',
+ 'document': enriched_item('HEY', {'test_signal': {
+ 'len': 3,
+ 'is_all_cap': True
+ }})
+ }, {
+ UUID_COLUMN: '3',
+ 'document': enriched_item('HI', {'test_signal': {
+ 'len': 2,
+ 'is_all_cap': True
+ }})
+ }]
+
+ result = dataset.select_rows(
+ columns=[text_alias], sort_by=['document.test_signal.is_all_cap'], sort_order=SortOrder.DESC)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'document': enriched_item('HEY', {'test_signal': {
+ 'len': 3,
+ 'is_all_cap': True
+ }})
+ }, {
+ UUID_COLUMN: '3',
+ 'document': enriched_item('HI', {'test_signal': {
+ 'len': 2,
+ 'is_all_cap': True
+ }})
+ }, {
+ UUID_COLUMN: '2',
+ 'document': enriched_item('everyone', {'test_signal': {
+ 'len': 8,
+ 'is_all_cap': False
+ }})
+ }]
+
+
+def test_sort_by_udf_alias_no_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'HEY'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone'
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI'
+ }])
+
+ # Equivalent to: SELECT `TestSignal(text) AS udf`.
+ text_udf = Column('text', signal_udf=TestSignal(), alias='udf')
+ # Sort by `udf.len`, where `udf` is an alias to `TestSignal(text)`.
+ result = dataset.select_rows(['*', text_udf], sort_by=['udf.len'], sort_order=SortOrder.ASC)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'text': 'HI',
+ 'udf': {
+ 'len': 2,
+ 'is_all_cap': True
+ }
+ }, {
+ UUID_COLUMN: '1',
+ 'text': 'HEY',
+ 'udf': {
+ 'len': 3,
+ 'is_all_cap': True
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone',
+ 'udf': {
+ 'len': 8,
+ 'is_all_cap': False
+ }
+ }]
+
+
+def test_sort_by_udf_no_alias_no_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'HEY'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone'
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI'
+ }])
+
+ text_udf = Column('text', signal_udf=TestSignal())
+ # Sort by `text.test_signal.len`, produced by executing the udf `TestSignal(text)`.
+ result = dataset.select_rows(['*', text_udf],
+ sort_by=[('text', 'test_signal', 'len')],
+ sort_order=SortOrder.ASC,
+ combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'text': enriched_item('HI', {'test_signal': {
+ 'len': 2,
+ 'is_all_cap': True
+ }}),
+ }, {
+ UUID_COLUMN: '1',
+ 'text': enriched_item('HEY', {'test_signal': {
+ 'len': 3,
+ 'is_all_cap': True
+ }}),
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('everyone', {'test_signal': {
+ 'len': 8,
+ 'is_all_cap': False
+ }}),
+ }]
+
+ # Sort descending.
+ result = dataset.select_rows(['*', text_udf],
+ sort_by=[('text', 'test_signal', 'len')],
+ sort_order=SortOrder.DESC,
+ combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'text': enriched_item('everyone', {'test_signal': {
+ 'len': 8,
+ 'is_all_cap': False
+ }}),
+ }, {
+ UUID_COLUMN: '1',
+ 'text': enriched_item('HEY', {'test_signal': {
+ 'len': 3,
+ 'is_all_cap': True
+ }}),
+ }, {
+ UUID_COLUMN: '3',
+ 'text': enriched_item('HI', {'test_signal': {
+ 'len': 2,
+ 'is_all_cap': True
+ }}),
+ }]
+
+
+def test_sort_by_primitive_udf_alias_no_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'HEY'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone'
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI'
+ }])
+
+ # Equivalent to: SELECT `TestPrimitiveSignal(text) AS udf`.
+ text_udf = Column('text', signal_udf=TestPrimitiveSignal(), alias='udf')
+ # Sort by the primitive value returned by the udf.
+ result = dataset.select_rows(['*', text_udf], sort_by=['udf'], sort_order=SortOrder.ASC)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'text': 'HI',
+ 'udf': 3
+ }, {
+ UUID_COLUMN: '1',
+ 'text': 'HEY',
+ 'udf': 4
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone',
+ 'udf': 9
+ }]
+
+
+def test_sort_by_source_non_leaf_errors(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'vals': [7, 1]
+ }, {
+ UUID_COLUMN: '2',
+ 'vals': [3, 4]
+ }, {
+ UUID_COLUMN: '3',
+ 'vals': [9, 0]
+ }])
+
+ # Sort by repeated.
+ with pytest.raises(ValueError, match='Unable to sort by path'):
+ dataset.select_rows(columns=[UUID_COLUMN], sort_by=['vals'], sort_order=SortOrder.ASC)
+
+
+def test_sort_by_source_no_alias_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'vals': [[{
+ 'score': 7
+ }, {
+ 'score': 1
+ }], [{
+ 'score': 1
+ }, {
+ 'score': 7
+ }]]
+ }, {
+ UUID_COLUMN: '2',
+ 'vals': [[{
+ 'score': 3
+ }, {
+ 'score': 4
+ }]]
+ }, {
+ UUID_COLUMN: '3',
+ 'vals': [[{
+ 'score': 9
+ }, {
+ 'score': 0
+ }]]
+ }])
+
+ # Sort by repeated 'vals'.
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN, 'vals'], sort_by=['vals.*.*.score'], sort_order=SortOrder.ASC)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'vals': [[{
+ 'score': 9
+ }, {
+ 'score': 0
+ }]]
+ }, {
+ UUID_COLUMN: '1',
+ 'vals': [[{
+ 'score': 7
+ }, {
+ 'score': 1
+ }], [{
+ 'score': 1
+ }, {
+ 'score': 7
+ }]]
+ }, {
+ UUID_COLUMN: '2',
+ 'vals': [[{
+ 'score': 3
+ }, {
+ 'score': 4
+ }]]
+ }]
+
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN, 'vals'], sort_by=['vals.*.*.score'], sort_order=SortOrder.DESC)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'vals': [[{
+ 'score': 9
+ }, {
+ 'score': 0
+ }]]
+ }, {
+ UUID_COLUMN: '1',
+ 'vals': [[{
+ 'score': 7
+ }, {
+ 'score': 1
+ }], [{
+ 'score': 1
+ }, {
+ 'score': 7
+ }]]
+ }, {
+ UUID_COLUMN: '2',
+ 'vals': [[{
+ 'score': 3
+ }, {
+ 'score': 4
+ }]]
+ }]
+
+
+def test_sort_by_source_alias_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'vals': [[7, 1], [1, 7]]
+ }, {
+ UUID_COLUMN: '2',
+ 'vals': [[3], [11]]
+ }, {
+ UUID_COLUMN: '3',
+ 'vals': [[9, 0]]
+ }])
+
+ # Sort by repeated 'vals'.
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN, Column('vals', alias='scores')],
+ sort_by=['scores.*.*'],
+ sort_order=SortOrder.ASC)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'scores': [[9, 0]]
+ }, {
+ UUID_COLUMN: '1',
+ 'scores': [[7, 1], [1, 7]]
+ }, {
+ UUID_COLUMN: '2',
+ 'scores': [[3], [11]]
+ }]
+
+ result = dataset.select_rows(
+ columns=[UUID_COLUMN, Column('vals', alias='scores')],
+ sort_by=['scores.*.*'],
+ sort_order=SortOrder.DESC)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'scores': [[3], [11]]
+ }, {
+ UUID_COLUMN: '3',
+ 'scores': [[9, 0]]
+ }, {
+ UUID_COLUMN: '1',
+ 'scores': [[7, 1], [1, 7]]
+ }]
+
+
+def test_sort_by_udf_alias_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'HEY'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone'
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI'
+ }])
+
+ # Equivalent to: SELECT `NestedArraySignal(text) AS udf`.
+ text_udf = Column('text', signal_udf=NestedArraySignal(), alias='udf')
+ # Sort by `udf.*.*`, where `udf` is an alias to `NestedArraySignal(text)`.
+ result = dataset.select_rows(['*', text_udf], sort_by=['udf.*.*'], sort_order=SortOrder.ASC)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'text': 'HI',
+ 'udf': [[3], [2]]
+ }, {
+ UUID_COLUMN: '1',
+ 'text': 'HEY',
+ 'udf': [[4], [3]]
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everyone',
+ 'udf': [[9], [8]]
+ }]
+ result = dataset.select_rows(['*', text_udf], sort_by=['udf.*.*'], sort_order=SortOrder.DESC)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'text': 'everyone',
+ 'udf': [[9], [8]]
+ }, {
+ UUID_COLUMN: '1',
+ 'text': 'HEY',
+ 'udf': [[4], [3]]
+ }, {
+ UUID_COLUMN: '3',
+ 'text': 'HI',
+ 'udf': [[3], [2]]
+ }]
+
+
+def test_sort_by_complex_signal_udf_alias_called_on_repeated(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'texts': [{
+ 'text': 'eardrop'
+ }, {
+ 'text': 'I'
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'texts': [{
+ 'text': 'hey'
+ }, {
+ 'text': 'CARS'
+ }]
+ }, {
+ UUID_COLUMN: '3',
+ 'texts': [{
+ 'text': 'everyone'
+ }, {
+ 'text': ''
+ }]
+ }])
+
+ # Equivalent to: SELECT `TestSignal(texts.*.text) AS udf`.
+ texts_udf = Column('texts.*.text', signal_udf=TestSignal(), alias='udf')
+ # Sort by `udf.len`, where `udf` is an alias to `TestSignal(texts.*.text)`.
+ result = dataset.select_rows(['*', texts_udf],
+ sort_by=['udf.len'],
+ sort_order=SortOrder.ASC,
+ combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'texts': [{
+ 'text': enriched_item('everyone', {'test_signal': {
+ 'len': 8,
+ 'is_all_cap': False
+ }})
+ }, {
+ 'text': enriched_item('', {'test_signal': {
+ 'len': 0,
+ 'is_all_cap': False
+ }})
+ }]
+ }, {
+ UUID_COLUMN: '1',
+ 'texts': [{
+ 'text': enriched_item('eardrop', {'test_signal': {
+ 'len': 7,
+ 'is_all_cap': False
+ }})
+ }, {
+ 'text': enriched_item('I', {'test_signal': {
+ 'len': 1,
+ 'is_all_cap': True
+ }})
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'texts': [{
+ 'text': enriched_item('hey', {'test_signal': {
+ 'len': 3,
+ 'is_all_cap': False
+ }})
+ }, {
+ 'text': enriched_item('CARS', {'test_signal': {
+ 'len': 4,
+ 'is_all_cap': True
+ }})
+ }]
+ }]
+
+
+def test_sort_by_primitive_signal_udf_alias_called_on_repeated(
+ make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'texts': [{
+ 'text': 'eardrop'
+ }, {
+ 'text': 'I'
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'texts': [{
+ 'text': 'hey'
+ }, {
+ 'text': 'CARS'
+ }]
+ }, {
+ UUID_COLUMN: '3',
+ 'texts': [{
+ 'text': 'everyone'
+ }, {
+ 'text': ''
+ }]
+ }])
+
+ # Equivalent to: SELECT `TestPrimitiveSignal(texts.*.text) AS udf`.
+ texts_udf = Column('texts.*.text', signal_udf=TestPrimitiveSignal(), alias='udf')
+ # Sort by `udf`, where `udf` is an alias to `TestPrimitiveSignal(texts.*.text)`.
+ result = dataset.select_rows(['*', texts_udf],
+ sort_by=['udf'],
+ sort_order=SortOrder.ASC,
+ combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'texts': [{
+ 'text': enriched_item('everyone', {'primitive_signal': 9})
+ }, {
+ 'text': enriched_item('', {'primitive_signal': 1})
+ }]
+ }, {
+ UUID_COLUMN: '1',
+ 'texts': [{
+ 'text': enriched_item('eardrop', {'primitive_signal': 8})
+ }, {
+ 'text': enriched_item('I', {'primitive_signal': 2})
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'texts': [{
+ 'text': enriched_item('hey', {'primitive_signal': 4})
+ }, {
+ 'text': enriched_item('CARS', {'primitive_signal': 5})
+ }]
+ }]
+ result = dataset.select_rows(['*', texts_udf],
+ sort_by=['udf'],
+ sort_order=SortOrder.DESC,
+ combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'texts': [{
+ 'text': enriched_item('everyone', {'primitive_signal': 9})
+ }, {
+ 'text': enriched_item('', {'primitive_signal': 1})
+ }]
+ }, {
+ UUID_COLUMN: '1',
+ 'texts': [{
+ 'text': enriched_item('eardrop', {'primitive_signal': 8})
+ }, {
+ 'text': enriched_item('I', {'primitive_signal': 2})
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'texts': [{
+ 'text': enriched_item('hey', {'primitive_signal': 4})
+ }, {
+ 'text': enriched_item('CARS', {'primitive_signal': 5})
+ }]
+ }]
+
+
+class TopKEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'topk_embedding'
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ example = cast(str, example)
+ emb_spans: list[Item] = []
+ for i, score in enumerate(example.split('_')):
+ start, end = i * 2, i * 2 + 1
+ vector = np.array([int(score)])
+ emb_spans.append(lilac_embedding(start, end, vector))
+ yield emb_spans
+
+
+class TopKSignal(TextEmbeddingModelSignal):
+ """Compute scores along a given concept for documents."""
+ name = 'topk_signal'
+
+ _query = np.array([1])
+
+ def fields(self) -> Field:
+ return field('float32')
+
+ @override
+ def vector_compute(self, keys: Iterable[VectorKey],
+ vector_store: VectorStore) -> Iterable[Optional[Item]]:
+ text_embeddings = vector_store.get(keys)
+ dot_products = text_embeddings.dot(self._query).reshape(-1)
+ return dot_products.tolist()
+
+ @override
+ def vector_compute_topk(
+ self,
+ topk: int,
+ vector_store: VectorStore,
+ keys: Optional[Iterable[VectorKey]] = None) -> Sequence[tuple[VectorKey, Optional[Item]]]:
+ return vector_store.topk(self._query, topk, keys)
+
+
+def test_sort_by_topk_embedding_udf(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'scores': '8_1',
+ }, {
+ UUID_COLUMN: '2',
+ 'scores': '3_5'
+ }, {
+ UUID_COLUMN: '3',
+ 'scores': '9_7'
+ }])
+
+ dataset.compute_signal(TopKEmbedding(), 'scores')
+
+ # Equivalent to: SELECT `TopKSignal(scores, embedding='...') AS udf`.
+ text_udf = Column('scores', signal_udf=TopKSignal(embedding='topk_embedding'), alias='udf')
+ # Sort by `udf`, where `udf` is an alias to `TopKSignal(scores, embedding='...')`.
+ result = dataset.select_rows(['*', text_udf], sort_by=['udf'], sort_order=SortOrder.DESC, limit=3)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'scores': enriched_item(
+ '9_7', {'topk_embedding': [lilac_embedding(0, 1, None),
+ lilac_embedding(2, 3, None)]}),
+ 'udf': [9.0, 7.0]
+ }, {
+ UUID_COLUMN: '1',
+ 'scores': enriched_item(
+ '8_1', {'topk_embedding': [lilac_embedding(0, 1, None),
+ lilac_embedding(2, 3, None)]}),
+ 'udf': [8.0, 1.0]
+ }]
+
+ # Same but set limit to 4.
+ result = dataset.select_rows(['*', text_udf], sort_by=['udf'], sort_order=SortOrder.DESC, limit=4)
+ assert list(result) == [{
+ UUID_COLUMN: '3',
+ 'scores': enriched_item(
+ '9_7', {'topk_embedding': [lilac_embedding(0, 1, None),
+ lilac_embedding(2, 3, None)]}),
+ 'udf': [9.0, 7.0]
+ }, {
+ UUID_COLUMN: '1',
+ 'scores': enriched_item(
+ '8_1', {'topk_embedding': [lilac_embedding(0, 1, None),
+ lilac_embedding(2, 3, None)]}),
+ 'udf': [8.0, 1.0]
+ }, {
+ UUID_COLUMN: '2',
+ 'scores': enriched_item(
+ '3_5', {'topk_embedding': [lilac_embedding(0, 1, None),
+ lilac_embedding(2, 3, None)]}),
+ 'udf': [3.0, 5.0]
+ }]
+
+
+def test_sort_by_topk_udf_with_filter(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'scores': '8_1',
+ 'active': True
+ }, {
+ UUID_COLUMN: '2',
+ 'scores': '3_5',
+ 'active': True
+ }, {
+ UUID_COLUMN: '3',
+ 'scores': '9_7',
+ 'active': False
+ }])
+
+ dataset.compute_signal(TopKEmbedding(), 'scores')
+
+ # Equivalent to: SELECT `TopKSignal(scores, embedding='...') AS udf`.
+ text_udf = Column('scores', signal_udf=TopKSignal(embedding='topk_embedding'), alias='udf')
+ # Sort by `udf`, where `udf` is an alias to `TopKSignal(scores, embedding='...')`.
+ result = dataset.select_rows(['*', text_udf],
+ sort_by=['udf'],
+ filters=[('active', BinaryOp.EQUALS, True)],
+ sort_order=SortOrder.DESC,
+ limit=2)
+ # We make sure that '3' is not in the result, because it is not active, even though it has the
+ # highest topk score.
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'active': True,
+ 'scores': enriched_item(
+ '8_1', {'topk_embedding': [lilac_embedding(0, 1, None),
+ lilac_embedding(2, 3, None)]}),
+ 'udf': [8.0, 1.0]
+ }, {
+ UUID_COLUMN: '2',
+ 'active': True,
+ 'scores': enriched_item(
+ '3_5', {'topk_embedding': [lilac_embedding(0, 1, None),
+ lilac_embedding(2, 3, None)]}),
+ 'udf': [3.0, 5.0]
+ }]
diff --git a/src/data/dataset_select_rows_udf_test.py b/src/data/dataset_select_rows_udf_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..99ac2ce8ce5d40c4af68f6482c2b1aee71d36e39
--- /dev/null
+++ b/src/data/dataset_select_rows_udf_test.py
@@ -0,0 +1,404 @@
+"""Tests for dataset.select_rows(udf_col)."""
+
+from typing import Iterable, Optional, cast
+
+import numpy as np
+import pytest
+from typing_extensions import override
+
+from ..embeddings.vector_store import VectorStore
+from ..schema import UUID_COLUMN, VALUE_KEY, Field, Item, RichData, VectorKey, field
+from ..signals.signal import (
+ TextEmbeddingModelSignal,
+ TextEmbeddingSignal,
+ TextSignal,
+ TextSplitterSignal,
+ clear_signal_registry,
+ register_signal,
+)
+from .dataset import BinaryFilterTuple, BinaryOp, Column, val
+from .dataset_test_utils import TestDataMaker, enriched_item
+from .dataset_utils import lilac_embedding, lilac_span
+
+EMBEDDINGS: list[tuple[str, list[float]]] = [('hello.', [1.0, 0.0, 0.0]),
+ ('hello2.', [1.0, 1.0, 0.0]),
+ ('hello world.', [1.0, 1.0, 1.0]),
+ ('hello world2.', [2.0, 1.0, 1.0])]
+
+STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for text, embedding in EMBEDDINGS}
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[cast(str, example)]))]
+
+
+class LengthSignal(TextSignal):
+ name = 'length_signal'
+
+ _call_count: int = 0
+
+ def fields(self) -> Field:
+ return field('int32')
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ self._call_count += 1
+ yield len(text_content)
+
+
+class TestSignal(TextSignal):
+ name = 'test_signal'
+
+ @override
+ def fields(self) -> Field:
+ return field(fields={'len': 'int32', 'flen': 'float32'})
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ return [{'len': len(text_content), 'flen': float(len(text_content))} for text_content in data]
+
+
+class TestEmbeddingSumSignal(TextEmbeddingModelSignal):
+ """Sums the embeddings to return a single floating point value."""
+ name = 'test_embedding_sum'
+
+ @override
+ def fields(self) -> Field:
+ return field('float32')
+
+ @override
+ def vector_compute(self, keys: Iterable[VectorKey], vector_store: VectorStore) -> Iterable[Item]:
+ # The signal just sums the values of the embedding.
+ embedding_sums = vector_store.get(keys).sum(axis=1)
+ for embedding_sum in embedding_sums.tolist():
+ yield embedding_sum
+
+
+class ComputedKeySignal(TextSignal):
+ name = 'computed_key'
+
+ @override
+ def fields(self) -> Field:
+ return field('int64')
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text in data:
+ yield 1
+
+ def key(self, is_computed_signal: Optional[bool] = False) -> str:
+ return f'key_{is_computed_signal}'
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(LengthSignal)
+ register_signal(TestSplitter)
+ register_signal(TestEmbedding)
+ register_signal(TestSignal)
+ register_signal(TestEmbeddingSumSignal)
+ register_signal(ComputedKeySignal)
+
+ # Unit test runs.
+ yield
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_udf(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody'
+ }])
+
+ signal_col = Column('text', signal_udf=TestSignal())
+ result = dataset.select_rows(['text', signal_col])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ 'test_signal(text)': {
+ 'len': 5,
+ 'flen': 5.0
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody',
+ 'test_signal(text)': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ }]
+
+
+def test_udf_with_filters(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody'
+ }])
+
+ signal_col = Column('text', signal_udf=TestSignal())
+ # Filter by source feature.
+ filters: list[BinaryFilterTuple] = [('text', BinaryOp.EQUALS, 'everybody')]
+ result = dataset.select_rows(['text', signal_col], filters=filters)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'text': 'everybody',
+ 'test_signal(text)': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ }]
+
+
+def test_udf_with_uuid_filter(make_test_data: TestDataMaker) -> None:
+
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody'
+ }])
+
+ # Filter by a specific UUID.
+ filters: list[BinaryFilterTuple] = [(UUID_COLUMN, BinaryOp.EQUALS, '1')]
+ udf_col = Column('text', signal_udf=LengthSignal())
+ result = dataset.select_rows(['text', udf_col], filters=filters)
+ assert list(result) == [{UUID_COLUMN: '1', 'text': 'hello', 'length_signal(text)': 5}]
+ assert cast(LengthSignal, udf_col.signal_udf)._call_count == 1
+
+ filters = [(UUID_COLUMN, BinaryOp.EQUALS, '2')]
+ result = dataset.select_rows(['text', udf_col], filters=filters)
+ assert list(result) == [{UUID_COLUMN: '2', 'text': 'everybody', 'length_signal(text)': 9}]
+ assert cast(LengthSignal, udf_col.signal_udf)._call_count == 1 + 1
+
+ # No filters.
+ result = dataset.select_rows(['text', udf_col])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ 'length_signal(text)': 5
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody',
+ 'length_signal(text)': 9
+ }]
+ assert cast(LengthSignal, udf_col.signal_udf)._call_count == 2 + 2
+
+
+def test_udf_with_uuid_filter_repeated(make_test_data: TestDataMaker) -> None:
+
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': ['hello', 'hi']
+ }, {
+ UUID_COLUMN: '2',
+ 'text': ['everybody', 'bye', 'test']
+ }])
+
+ # Filter by a specific UUID.
+ filters: list[BinaryFilterTuple] = [(UUID_COLUMN, BinaryOp.EQUALS, '1')]
+ udf_col = Column(('text', '*'), signal_udf=LengthSignal())
+ result = dataset.select_rows(['text', udf_col], filters=filters)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': ['hello', 'hi'],
+ 'length_signal(text)': [5, 2]
+ }]
+ assert cast(LengthSignal, udf_col.signal_udf)._call_count == 2
+
+ # Filter by a specific UUID.
+ filters = [(UUID_COLUMN, BinaryOp.EQUALS, '2')]
+ result = dataset.select_rows(['text', udf_col], filters=filters)
+ assert list(result) == [{
+ UUID_COLUMN: '2',
+ 'text': ['everybody', 'bye', 'test'],
+ 'length_signal(text)': [9, 3, 4]
+ }]
+ assert cast(LengthSignal, udf_col.signal_udf)._call_count == 2 + 3
+
+
+def test_udf_deeply_nested(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': [['hello'], ['hi', 'bye']]
+ }, {
+ UUID_COLUMN: '2',
+ 'text': [['everybody', 'bye'], ['test']]
+ }])
+
+ udf_col = Column(('text', '*', '*'), signal_udf=LengthSignal())
+ result = dataset.select_rows([udf_col])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'length_signal(text.*)': [[5], [2, 3]]
+ }, {
+ UUID_COLUMN: '2',
+ 'length_signal(text.*)': [[9, 3], [4]]
+ }]
+ assert cast(LengthSignal, udf_col.signal_udf)._call_count == 6
+
+
+def test_udf_with_embedding(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ dataset.compute_signal(TestEmbedding(), 'text')
+
+ signal_col = Column('text', signal_udf=TestEmbeddingSumSignal(embedding='test_embedding'))
+ result = dataset.select_rows([val('text'), signal_col])
+
+ expected_result: list[Item] = [{
+ UUID_COLUMN: '1',
+ f'text.{VALUE_KEY}': 'hello.',
+ 'test_embedding_sum(text.test_embedding.*.embedding)': [1.0]
+ }, {
+ UUID_COLUMN: '2',
+ f'text.{VALUE_KEY}': 'hello2.',
+ 'test_embedding_sum(text.test_embedding.*.embedding)': [2.0]
+ }]
+ assert list(result) == expected_result
+
+ # Select rows with alias.
+ signal_col = Column(
+ 'text', signal_udf=TestEmbeddingSumSignal(embedding='test_embedding'), alias='emb_sum')
+ result = dataset.select_rows([val('text'), signal_col])
+ expected_result = [{
+ UUID_COLUMN: '1',
+ f'text.{VALUE_KEY}': 'hello.',
+ 'emb_sum': [1.0]
+ }, {
+ UUID_COLUMN: '2',
+ f'text.{VALUE_KEY}': 'hello2.',
+ 'emb_sum': [2.0]
+ }]
+ assert list(result) == expected_result
+
+
+def test_udf_with_nested_embedding(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': ['hello.', 'hello world.'],
+ }, {
+ UUID_COLUMN: '2',
+ 'text': ['hello world2.', 'hello2.'],
+ }])
+
+ dataset.compute_signal(TestEmbedding(), ('text', '*'))
+
+ signal_col = Column(('text', '*'), signal_udf=TestEmbeddingSumSignal(embedding='test_embedding'))
+ result = dataset.select_rows([val(('text', '*')), signal_col])
+ expected_result = [{
+ UUID_COLUMN: '1',
+ f'text.*.{VALUE_KEY}': ['hello.', 'hello world.'],
+ 'test_embedding_sum(text.*.test_embedding.*.embedding)': [[1.0], [3.0]]
+ }, {
+ UUID_COLUMN: '2',
+ f'text.*.{VALUE_KEY}': ['hello world2.', 'hello2.'],
+ 'test_embedding_sum(text.*.test_embedding.*.embedding)': [[4.0], [2.0]]
+ }]
+ assert list(result) == expected_result
+
+
+def test_udf_throws_without_precomputing(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ # Embedding is not precomputed, yet we ask for the embedding.
+
+ signal_col = Column('text', signal_udf=TestEmbeddingSumSignal(embedding='test_embedding'))
+
+ with pytest.raises(ValueError, match='Embedding signal "test_embedding" is not computed'):
+ dataset.select_rows([val('text'), signal_col])
+
+
+class TestSplitter(TextSplitterSignal):
+ """Split documents into sentence by splitting on period."""
+ name = 'test_splitter'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ for text in data:
+ if not isinstance(text, str):
+ raise ValueError(f'Expected text to be a string, got {type(text)} instead.')
+ result: list[Item] = []
+ for sentence in text.split('.'):
+ start = text.index(sentence)
+ end = start + len(sentence)
+ result.append(lilac_span(start, end))
+ yield result
+
+
+def test_udf_after_precomputed_split(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'sentence 1. sentence 2 is longer',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'sentence 1 is longer. sent2 is short',
+ }])
+ dataset.compute_signal(TestSplitter(), 'text')
+ udf = Column('text', signal_udf=LengthSignal())
+ result = dataset.select_rows(['*', udf], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('sentence 1. sentence 2 is longer', {
+ 'length_signal': 32,
+ 'test_splitter': [lilac_span(0, 10), lilac_span(11, 32)]
+ })
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('sentence 1 is longer. sent2 is short', {
+ 'length_signal': 36,
+ 'test_splitter': [lilac_span(0, 20), lilac_span(21, 36)]
+ })
+ }]
+
+
+def test_is_computed_signal_key(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ signal_col = Column('text', signal_udf=ComputedKeySignal())
+ # Filter by source feature.
+ filters: list[BinaryFilterTuple] = [('text', BinaryOp.EQUALS, 'everybody')]
+ result = dataset.select_rows(['text', signal_col])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ 'key_False(text)': 1
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ 'key_False(text)': 1
+ }]
diff --git a/src/data/dataset_stats_test.py b/src/data/dataset_stats_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..37acaa8b39cf3441809844056814d013d3058021
--- /dev/null
+++ b/src/data/dataset_stats_test.py
@@ -0,0 +1,122 @@
+"""Tests for dataset.stats()."""
+
+from typing import Any, cast
+
+import pytest
+from pytest_mock import MockerFixture
+
+from ..schema import UUID_COLUMN, Item, schema
+from . import dataset_duckdb
+from .dataset import StatsResult
+from .dataset_test_utils import TestDataMaker
+
+SIMPLE_ITEMS: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'str': 'a',
+ 'int': 1,
+ 'bool': False,
+ 'float': 3.0
+}, {
+ UUID_COLUMN: '2',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 2.0
+}, {
+ UUID_COLUMN: '3',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 1.0
+}]
+
+
+def test_simple_stats(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(SIMPLE_ITEMS)
+
+ result = dataset.stats(leaf_path='str')
+ assert result == StatsResult(
+ path=('str',), total_count=3, approx_count_distinct=2, avg_text_length=1)
+
+ result = dataset.stats(leaf_path='float')
+ assert result == StatsResult(
+ path=('float',), total_count=3, approx_count_distinct=3, min_val=1.0, max_val=3.0)
+
+ result = dataset.stats(leaf_path='bool')
+ assert result == StatsResult(path=('bool',), total_count=3, approx_count_distinct=2)
+
+ result = dataset.stats(leaf_path='int')
+ assert result == StatsResult(
+ path=('int',), total_count=3, approx_count_distinct=2, min_val=1, max_val=2)
+
+
+def test_nested_stats(make_test_data: TestDataMaker) -> None:
+ nested_items: list[Item] = [
+ {
+ 'name': 'Name1',
+ 'addresses': [{
+ 'zips': [5, 8]
+ }]
+ },
+ {
+ 'name': 'Name2',
+ 'addresses': [{
+ 'zips': [3]
+ }, {
+ 'zips': [11, 8]
+ }]
+ },
+ {
+ 'name': 'Name2',
+ 'addresses': []
+ }, # No addresses.
+ {
+ 'name': 'Name2',
+ 'addresses': [{
+ 'zips': []
+ }]
+ } # No zips in the first address.
+ ]
+ nested_schema = schema({
+ UUID_COLUMN: 'string',
+ 'name': 'string',
+ 'addresses': [{
+ 'zips': ['int32']
+ }]
+ })
+ dataset = make_test_data(nested_items, schema=nested_schema)
+
+ result = dataset.stats(leaf_path='name')
+ assert result == StatsResult(
+ path=('name',), total_count=4, approx_count_distinct=2, avg_text_length=5)
+
+ result = dataset.stats(leaf_path='addresses.*.zips.*')
+ assert result == StatsResult(
+ path=('addresses', '*', 'zips', '*'),
+ total_count=5,
+ approx_count_distinct=4,
+ min_val=3,
+ max_val=11)
+
+
+def test_stats_approximation(make_test_data: TestDataMaker, mocker: MockerFixture) -> None:
+ sample_size = 5
+ mocker.patch(f'{dataset_duckdb.__name__}.SAMPLE_SIZE_DISTINCT_COUNT', sample_size)
+
+ nested_items: list[Item] = [{'feature': str(i)} for i in range(sample_size * 10)]
+ nested_schema = schema({UUID_COLUMN: 'string', 'feature': 'string'})
+ dataset = make_test_data(nested_items, schema=nested_schema)
+
+ result = dataset.stats(leaf_path='feature')
+ assert result == StatsResult(
+ path=('feature',), total_count=50, approx_count_distinct=50, avg_text_length=1)
+
+
+def test_error_handling(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(SIMPLE_ITEMS)
+
+ with pytest.raises(ValueError, match='leaf_path must be provided'):
+ dataset.stats(cast(Any, None))
+
+ with pytest.raises(ValueError, match='Leaf "\\(\'unknown\',\\)" not found in dataset'):
+ dataset.stats(leaf_path='unknown')
diff --git a/src/data/dataset_test.py b/src/data/dataset_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..84053606137b9d84c85288cc9973a3b1abbe9113
--- /dev/null
+++ b/src/data/dataset_test.py
@@ -0,0 +1,860 @@
+"""Implementation-agnostic tests of the Dataset DB API."""
+
+from typing import Iterable, Optional, cast
+
+import numpy as np
+import pytest
+from typing_extensions import override
+
+from ..schema import UUID_COLUMN, VALUE_KEY, Field, Item, RichData, field, schema
+from ..signals.signal import TextEmbeddingSignal, TextSignal, clear_signal_registry, register_signal
+from .dataset import Column, DatasetManifest, val
+from .dataset_test_utils import TEST_DATASET_NAME, TEST_NAMESPACE, TestDataMaker, enriched_item
+from .dataset_utils import lilac_embedding
+
+SIMPLE_ITEMS: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'str': 'a',
+ 'int': 1,
+ 'bool': False,
+ 'float': 3.0
+}, {
+ UUID_COLUMN: '2',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 2.0
+}, {
+ UUID_COLUMN: '3',
+ 'str': 'b',
+ 'int': 2,
+ 'bool': True,
+ 'float': 1.0
+}]
+
+EMBEDDINGS: list[tuple[str, list[float]]] = [('hello.', [1.0, 0.0, 0.0]),
+ ('hello2.', [1.0, 1.0, 0.0]),
+ ('hello world.', [1.0, 1.0, 1.0]),
+ ('hello world2.', [2.0, 1.0, 1.0])]
+
+STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for text, embedding in EMBEDDINGS}
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[cast(str, example)]))]
+
+
+class LengthSignal(TextSignal):
+ name = 'length_signal'
+
+ _call_count: int = 0
+
+ def fields(self) -> Field:
+ return field('int32')
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ self._call_count += 1
+ yield len(text_content)
+
+
+class TestSignal(TextSignal):
+ name = 'test_signal'
+
+ @override
+ def fields(self) -> Field:
+ return field(fields={'len': 'int32', 'flen': 'float32'})
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ return [{'len': len(text_content), 'flen': float(len(text_content))} for text_content in data]
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(TestSignal)
+ register_signal(LengthSignal)
+ register_signal(SignalWithQuoteInIt)
+ register_signal(SignalWithDoubleQuoteInIt)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_select_all_columns(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(SIMPLE_ITEMS)
+
+ result = dataset.select_rows()
+ assert list(result) == SIMPLE_ITEMS
+
+
+def test_select_subcols_with_dot_seperator(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'people': [{
+ 'name': 'A',
+ 'address': {
+ 'zip': 1
+ }
+ }, {
+ 'name': 'B',
+ 'address': {
+ 'zip': 2
+ }
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'people': [{
+ 'name': 'C',
+ 'address': {
+ 'zip': 3
+ }
+ }]
+ }]
+ dataset = make_test_data(items)
+
+ result = dataset.select_rows(['people.*.name', 'people.*.address.zip'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'people.*.name': ['A', 'B'],
+ 'people.*.address.zip': [1, 2]
+ }, {
+ UUID_COLUMN: '2',
+ 'people.*.name': ['C'],
+ 'people.*.address.zip': [3]
+ }]
+
+ result = dataset.select_rows(['people.*.address.zip'], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'people': [{
+ 'address': {
+ 'zip': 1
+ }
+ }, {
+ 'address': {
+ 'zip': 2
+ }
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'people': [{
+ 'address': {
+ 'zip': 3
+ }
+ }]
+ }]
+
+ result = dataset.select_rows(['people'])
+ assert list(result) == items
+
+
+def test_select_subcols_with_escaped_dot(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'people.new': [{
+ 'name': 'A'
+ }, {
+ 'name': 'B'
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'people.new': [{
+ 'name': 'C'
+ }]
+ }]
+ dataset = make_test_data(items)
+
+ result = dataset.select_rows(['"people.new".*.name'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'people.new.*.name': ['A', 'B'],
+ }, {
+ UUID_COLUMN: '2',
+ 'people.new.*.name': ['C'],
+ }]
+
+ # Escape name even though it does not need to be.
+ result = dataset.select_rows(['"people.new".*."name"'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'people.new.*.name': ['A', 'B'],
+ }, {
+ UUID_COLUMN: '2',
+ 'people.new.*.name': ['C'],
+ }]
+
+
+def test_select_star(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'name': 'A',
+ 'info': {
+ 'age': 40
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'name': 'B',
+ 'info': {
+ 'age': 42
+ }
+ }]
+ dataset = make_test_data(items)
+
+ # Select *.
+ result = dataset.select_rows(['*'])
+ assert list(result) == items
+
+ # Select (*,).
+ result = dataset.select_rows([('*',)])
+ assert list(result) == items
+
+ # Select *, plus a redundant `info` column.
+ result = dataset.select_rows(['*', 'info'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'name': 'A',
+ 'info': {
+ 'age': 40
+ },
+ 'info_2': {
+ 'age': 40
+ },
+ }, {
+ UUID_COLUMN: '2',
+ 'name': 'B',
+ 'info': {
+ 'age': 42
+ },
+ 'info_2': {
+ 'age': 42
+ },
+ }]
+
+ # Select * plus an inner `info.age` column.
+ result = dataset.select_rows(['*', ('info', 'age')])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'name': 'A',
+ 'info': {
+ 'age': 40
+ },
+ 'info.age': 40
+ }, {
+ UUID_COLUMN: '2',
+ 'name': 'B',
+ 'info': {
+ 'age': 42
+ },
+ 'info.age': 42
+ }]
+
+
+def test_select_star_with_combine_cols(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'name': 'A',
+ 'info': {
+ 'age': 40
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'name': 'B',
+ 'info': {
+ 'age': 42
+ }
+ }]
+ dataset = make_test_data(items)
+
+ # Select *.
+ result = dataset.select_rows(['*'], combine_columns=True)
+ assert list(result) == items
+
+ # Select *, plus a redundant `info` column.
+ result = dataset.select_rows(['*', 'info'], combine_columns=True)
+ assert list(result) == items
+
+ # Select * plus an inner `info.age` column.
+ result = dataset.select_rows(['*', ('info', 'age')], combine_columns=True)
+ assert list(result) == items
+
+ # Select *, plus redundant `name`, plus a udf.
+ udf = Column('name', signal_udf=TestSignal())
+ result = dataset.select_rows(['*', 'name', udf], combine_columns=True)
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'name': enriched_item('A', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'info': {
+ 'age': 40
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'name': enriched_item('B', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'info': {
+ 'age': 42
+ }
+ }]
+
+
+def test_select_ids(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(SIMPLE_ITEMS)
+
+ result = dataset.select_rows([UUID_COLUMN])
+
+ assert list(result) == [{UUID_COLUMN: '1'}, {UUID_COLUMN: '2'}, {UUID_COLUMN: '3'}]
+
+
+def test_select_ids_with_limit_and_offset(make_test_data: TestDataMaker) -> None:
+ items: list[Item] = [{UUID_COLUMN: str(i)} for i in range(10, 20)]
+ dataset = make_test_data(items)
+
+ result = dataset.select_rows([UUID_COLUMN], offset=1, limit=3)
+ assert list(result) == [{UUID_COLUMN: '11'}, {UUID_COLUMN: '12'}, {UUID_COLUMN: '13'}]
+
+ result = dataset.select_rows([UUID_COLUMN], offset=7, limit=2)
+ assert list(result) == [{UUID_COLUMN: '17'}, {UUID_COLUMN: '18'}]
+
+ result = dataset.select_rows([UUID_COLUMN], offset=9, limit=200)
+ assert list(result) == [{UUID_COLUMN: '19'}]
+
+ result = dataset.select_rows([UUID_COLUMN], offset=10, limit=200)
+ assert list(result) == []
+
+
+def test_columns(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(SIMPLE_ITEMS)
+
+ result = dataset.select_rows(['str', 'float'])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'str': 'a',
+ 'float': 3.0
+ }, {
+ UUID_COLUMN: '2',
+ 'str': 'b',
+ 'float': 2.0
+ }, {
+ UUID_COLUMN: '3',
+ 'str': 'b',
+ 'float': 1.0
+ }]
+
+
+def test_merge_values(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello'
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody'
+ }])
+ test_signal = TestSignal()
+ dataset.compute_signal(test_signal, 'text')
+ length_signal = LengthSignal()
+ dataset.compute_signal(length_signal, 'text')
+
+ result = dataset.select_rows(['text'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {
+ 'length_signal': 5,
+ 'test_signal': {
+ 'len': 5,
+ 'flen': 5.0
+ }
+ })
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('everybody', {
+ 'length_signal': 9,
+ 'test_signal': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ }),
+ }]
+
+ # Test subselection.
+ result = dataset.select_rows(
+ [val('text'), ('text', 'test_signal', 'flen'), ('text', 'test_signal', 'len')])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ f'text.{VALUE_KEY}': 'hello',
+ 'text.test_signal.flen': 5.0,
+ 'text.test_signal.len': 5
+ }, {
+ UUID_COLUMN: '2',
+ f'text.{VALUE_KEY}': 'everybody',
+ 'text.test_signal.flen': 9.0,
+ 'text.test_signal.len': 9
+ }]
+
+ # Test subselection with combine_columns=True.
+ result = dataset.select_rows(
+ ['text', ('text', 'test_signal', 'flen'), ('text', 'test_signal', 'len')], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {
+ 'length_signal': 5,
+ 'test_signal': {
+ 'len': 5,
+ 'flen': 5.0
+ }
+ })
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('everybody', {
+ 'length_signal': 9,
+ 'test_signal': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ }),
+ }]
+
+ # Test subselection with aliasing.
+ result = dataset.select_rows(
+ columns=[val('text'), Column(('text', 'test_signal', 'len'), alias='metadata')])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ f'text.{VALUE_KEY}': 'hello',
+ 'metadata': 5
+ }, {
+ UUID_COLUMN: '2',
+ f'text.{VALUE_KEY}': 'everybody',
+ 'metadata': 9
+ }]
+
+ result = dataset.select_rows(columns=[Column(('text'), alias='text_enrichment')])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text_enrichment': enriched_item('hello', {
+ 'length_signal': 5,
+ 'test_signal': {
+ 'len': 5,
+ 'flen': 5.0
+ }
+ })
+ }, {
+ UUID_COLUMN: '2',
+ 'text_enrichment': enriched_item('everybody', {
+ 'length_signal': 9,
+ 'test_signal': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ })
+ }]
+
+
+def test_merge_array_values(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'texts': ['hello', 'everybody']
+ }, {
+ UUID_COLUMN: '2',
+ 'texts': ['a', 'bc', 'def']
+ }])
+
+ test_signal = TestSignal()
+ dataset.compute_signal(test_signal, ('texts', '*'))
+ length_signal = LengthSignal()
+ dataset.compute_signal(length_signal, ('texts', '*'))
+
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'texts': [
+ field(
+ 'string',
+ fields={
+ 'length_signal': field('int32', length_signal.dict()),
+ 'test_signal': field(
+ signal=test_signal.dict(), fields={
+ 'len': 'int32',
+ 'flen': 'float32'
+ })
+ })
+ ],
+ }),
+ num_items=2)
+
+ result = dataset.select_rows(['texts'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'texts': [
+ enriched_item('hello', {
+ 'length_signal': 5,
+ 'test_signal': {
+ 'len': 5,
+ 'flen': 5.0
+ }
+ }),
+ enriched_item('everybody', {
+ 'length_signal': 9,
+ 'test_signal': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ })
+ ],
+ }, {
+ UUID_COLUMN: '2',
+ 'texts': [
+ enriched_item('a', {
+ 'length_signal': 1,
+ 'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }
+ }),
+ enriched_item('bc', {
+ 'length_signal': 2,
+ 'test_signal': {
+ 'len': 2,
+ 'flen': 2.0
+ }
+ }),
+ enriched_item('def', {
+ 'length_signal': 3,
+ 'test_signal': {
+ 'len': 3,
+ 'flen': 3.0
+ }
+ })
+ ],
+ }]
+
+ # Test subselection.
+ result = dataset.select_rows(
+ [val(('texts', '*')), ('texts', '*', 'length_signal'), ('texts', '*', 'test_signal', 'flen')])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ f'texts.*.{VALUE_KEY}': ['hello', 'everybody'],
+ 'texts.*.test_signal.flen': [5.0, 9.0],
+ 'texts.*.length_signal': [5, 9]
+ }, {
+ UUID_COLUMN: '2',
+ f'texts.*.{VALUE_KEY}': ['a', 'bc', 'def'],
+ 'texts.*.test_signal.flen': [1.0, 2.0, 3.0],
+ 'texts.*.length_signal': [1, 2, 3]
+ }]
+
+
+def test_combining_columns(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ 'extra': {
+ 'text': {
+ 'length_signal': 5,
+ 'test_signal': {
+ 'len': 5,
+ 'flen': 5.0
+ }
+ }
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody',
+ 'extra': {
+ 'text': {
+ 'length_signal': 9,
+ 'test_signal': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ }
+ }
+ }])
+
+ # Sub-select text and test_signal.
+ result = dataset.select_rows(['text', ('extra', 'text', 'test_signal')], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ 'extra': {
+ 'text': {
+ 'test_signal': {
+ 'len': 5,
+ 'flen': 5.0
+ }
+ }
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody',
+ 'extra': {
+ 'text': {
+ 'test_signal': {
+ 'len': 9,
+ 'flen': 9.0
+ }
+ }
+ }
+ }]
+
+ # Sub-select text and length_signal.
+ result = dataset.select_rows(['text', ('extra', 'text', 'length_signal')], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ 'extra': {
+ 'text': {
+ 'length_signal': 5
+ }
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'everybody',
+ 'extra': {
+ 'text': {
+ 'length_signal': 9
+ }
+ }
+ }]
+
+ # Sub-select length_signal only.
+ result = dataset.select_rows([('extra', 'text', 'length_signal')], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'extra': {
+ 'text': {
+ 'length_signal': 5
+ }
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'extra': {
+ 'text': {
+ 'length_signal': 9
+ }
+ }
+ }]
+
+ # Aliases are ignored when combing columns.
+ len_col = Column(('extra', 'text', 'length_signal'), alias='hello')
+ result = dataset.select_rows([len_col], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'extra': {
+ 'text': {
+ 'length_signal': 5
+ }
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'extra': {
+ 'text': {
+ 'length_signal': 9
+ }
+ }
+ }]
+
+ # Works with UDFs and aliases are ignored.
+ udf_col = Column('text', alias='ignored', signal_udf=LengthSignal())
+ result = dataset.select_rows(['text', udf_col], combine_columns=True)
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {'length_signal': 5})
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('everybody', {'length_signal': 9})
+ }]
+
+
+def test_source_joined_with_named_signal(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data(SIMPLE_ITEMS)
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'str': 'string',
+ 'int': 'int32',
+ 'bool': 'boolean',
+ 'float': 'float32',
+ }),
+ num_items=3)
+
+ test_signal = TestSignal()
+ dataset.compute_signal(test_signal, 'str')
+
+ # Check the enriched dataset manifest has 'text' enriched.
+ assert dataset.manifest() == DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'str': field(
+ 'string',
+ fields={
+ 'test_signal': field(
+ signal=test_signal.dict(), fields={
+ 'len': 'int32',
+ 'flen': 'float32'
+ })
+ }),
+ 'int': 'int32',
+ 'bool': 'boolean',
+ 'float': 'float32',
+ }),
+ num_items=3)
+
+ # Select both columns, without val() on str.
+ result = dataset.select_rows(['str', Column(('str', 'test_signal'), alias='test_signal_on_str')])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'str': enriched_item('a', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'test_signal_on_str': {
+ 'len': 1,
+ 'flen': 1.0
+ }
+ }, {
+ UUID_COLUMN: '2',
+ 'str': enriched_item('b', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'test_signal_on_str': {
+ 'len': 1,
+ 'flen': 1.0
+ }
+ }, {
+ UUID_COLUMN: '3',
+ 'str': enriched_item('b', {'test_signal': {
+ 'len': 1,
+ 'flen': 1.0
+ }}),
+ 'test_signal_on_str': {
+ 'len': 1,
+ 'flen': 1.0
+ }
+ }]
+
+ # Select both columns, with val() on str.
+ result = dataset.select_rows(
+ [val('str'), Column(('str', 'test_signal'), alias='test_signal_on_str')])
+
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ f'str.{VALUE_KEY}': 'a',
+ 'test_signal_on_str': {
+ 'len': 1,
+ 'flen': 1.0
+ }
+ }, {
+ UUID_COLUMN: '2',
+ f'str.{VALUE_KEY}': 'b',
+ 'test_signal_on_str': {
+ 'len': 1,
+ 'flen': 1.0
+ }
+ }, {
+ UUID_COLUMN: '3',
+ f'str.{VALUE_KEY}': 'b',
+ 'test_signal_on_str': {
+ 'len': 1,
+ 'flen': 1.0
+ }
+ }]
+
+
+def test_invalid_column_paths(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {'test_signal': {
+ 'len': 5
+ }}),
+ 'text2': [
+ enriched_item('hello', {'test_signal': {
+ 'len': 5
+ }}),
+ enriched_item('hi', {'test_signal': {
+ 'len': 2
+ }})
+ ],
+ }])
+
+ with pytest.raises(ValueError, match='Path part "invalid" not found in the dataset'):
+ dataset.select_rows([('text', 'test_signal', 'invalid')])
+
+ with pytest.raises(ValueError, match='Selecting a specific index of a repeated field'):
+ dataset.select_rows([('text2', '4', 'test_signal')])
+
+
+def test_signal_with_quote(make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'world',
+ }])
+ dataset.compute_signal(SignalWithQuoteInIt(), 'text')
+ dataset.compute_signal(SignalWithDoubleQuoteInIt(), 'text')
+ result = dataset.select_rows(['text'])
+ assert list(result) == [{
+ UUID_COLUMN: '1',
+ 'text': enriched_item('hello', {
+ "test'signal": True,
+ 'test"signal': True
+ })
+ }, {
+ UUID_COLUMN: '2',
+ 'text': enriched_item('world', {
+ "test'signal": True,
+ 'test"signal': True
+ }),
+ }]
+
+
+class SignalWithQuoteInIt(TextSignal):
+ name = "test'signal"
+
+ @override
+ def fields(self) -> Field:
+ return field('boolean')
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for d in data:
+ yield True
+
+
+class SignalWithDoubleQuoteInIt(TextSignal):
+ name = 'test"signal'
+
+ @override
+ def fields(self) -> Field:
+ return field('boolean')
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for d in data:
+ yield True
diff --git a/src/data/dataset_test_utils.py b/src/data/dataset_test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e55f1c6ae1a04b72c2da2905bf7888ecffc6f03
--- /dev/null
+++ b/src/data/dataset_test_utils.py
@@ -0,0 +1,116 @@
+"""Tests utils of for dataset_test."""
+import os
+import pathlib
+from typing import Optional, Type, cast
+
+from typing_extensions import Protocol
+
+from ..schema import (
+ MANIFEST_FILENAME,
+ PARQUET_FILENAME_PREFIX,
+ VALUE_KEY,
+ DataType,
+ Field,
+ Item,
+ Schema,
+ SourceManifest,
+ field,
+)
+from ..signals.signal import EMBEDDING_KEY
+from ..utils import get_dataset_output_dir, open_file
+from .dataset import Dataset
+from .dataset_utils import is_primitive, lilac_span, write_items_to_parquet
+
+TEST_NAMESPACE = 'test_namespace'
+TEST_DATASET_NAME = 'test_dataset'
+
+
+def _infer_dtype(value: Item) -> DataType:
+ if isinstance(value, str):
+ return DataType.STRING
+ elif isinstance(value, bool):
+ return DataType.BOOLEAN
+ elif isinstance(value, bytes):
+ return DataType.BINARY
+ elif isinstance(value, float):
+ return DataType.FLOAT32
+ elif isinstance(value, int):
+ return DataType.INT32
+ else:
+ raise ValueError(f'Cannot infer dtype of primitive value: {value}')
+
+
+def _infer_field(item: Item) -> Field:
+ """Infer the schema from the items."""
+ if isinstance(item, dict):
+ fields: dict[str, Field] = {}
+ for k, v in item.items():
+ fields[k] = _infer_field(cast(Item, v))
+ dtype = None
+ if VALUE_KEY in fields:
+ dtype = fields[VALUE_KEY].dtype
+ del fields[VALUE_KEY]
+ return Field(fields=fields, dtype=dtype)
+ elif is_primitive(item):
+ return Field(dtype=_infer_dtype(item))
+ elif isinstance(item, list):
+ return Field(repeated_field=_infer_field(item[0]))
+ else:
+ raise ValueError(f'Cannot infer schema of item: {item}')
+
+
+def _infer_schema(items: list[Item]) -> Schema:
+ """Infer the schema from the items."""
+ schema = Schema(fields={})
+ for item in items:
+ field = _infer_field(item)
+ if not field.fields:
+ raise ValueError(f'Invalid schema of item. Expected an object, but got: {item}')
+ schema.fields = {**schema.fields, **field.fields}
+ return schema
+
+
+class TestDataMaker(Protocol):
+ """A function that creates a test dataset."""
+
+ def __call__(self, items: list[Item], schema: Optional[Schema] = None) -> Dataset:
+ """Create a test dataset."""
+ ...
+
+
+def make_dataset(dataset_cls: Type[Dataset],
+ tmp_path: pathlib.Path,
+ items: list[Item],
+ schema: Optional[Schema] = None) -> Dataset:
+ """Create a test dataset."""
+ schema = schema or _infer_schema(items)
+ _write_items(tmp_path, TEST_DATASET_NAME, items, schema)
+ return dataset_cls(TEST_NAMESPACE, TEST_DATASET_NAME)
+
+
+def _write_items(tmpdir: pathlib.Path, dataset_name: str, items: list[Item],
+ schema: Schema) -> None:
+ """Write the items JSON to the dataset format: manifest.json and parquet files."""
+ source_dir = get_dataset_output_dir(str(tmpdir), TEST_NAMESPACE, dataset_name)
+ os.makedirs(source_dir)
+
+ simple_parquet_files, _ = write_items_to_parquet(
+ items, source_dir, schema, filename_prefix=PARQUET_FILENAME_PREFIX, shard_index=0, num_shards=1)
+ manifest = SourceManifest(files=[simple_parquet_files], data_schema=schema)
+ with open_file(os.path.join(source_dir, MANIFEST_FILENAME), 'w') as f:
+ f.write(manifest.json(indent=2, exclude_none=True))
+
+
+def enriched_item(value: Optional[Item] = None, metadata: dict[str, Item] = {}) -> Item:
+ """Wrap a value in a dict with the value key."""
+ return {VALUE_KEY: value, **metadata}
+
+
+def enriched_embedding_span(start: int, end: int, metadata: dict[str, Item] = {}) -> Item:
+ """Makes an item that represents an embedding span that was enriched with metadata."""
+ return lilac_span(start, end, {EMBEDDING_KEY: {VALUE_KEY: None, **metadata}})
+
+
+def enriched_embedding_span_field(metadata: Optional[object] = {}) -> Field:
+ """Makes a field that represents an embedding span that was enriched with metadata."""
+ return field('string_span', fields={EMBEDDING_KEY: field('embedding', fields=metadata)})
diff --git a/src/data/dataset_utils.py b/src/data/dataset_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..195a00cb4b3ec279da366eecc340d9971b115f81
--- /dev/null
+++ b/src/data/dataset_utils.py
@@ -0,0 +1,343 @@
+"""Utilities for working with datasets."""
+
+import math
+import os
+import pickle
+import pprint
+import secrets
+from collections.abc import Iterable
+from typing import Any, Callable, Generator, Iterator, Optional, Sequence, TypeVar, Union, cast
+
+import numpy as np
+import pyarrow as pa
+
+from ..parquet_writer import ParquetWriter
+from ..schema import (
+ PATH_WILDCARD,
+ TEXT_SPAN_END_FEATURE,
+ TEXT_SPAN_START_FEATURE,
+ UUID_COLUMN,
+ VALUE_KEY,
+ Field,
+ Item,
+ PathTuple,
+ Schema,
+ VectorKey,
+ field,
+ schema,
+ schema_to_arrow_schema,
+)
+from ..signals.signal import EMBEDDING_KEY, Signal
+from ..utils import file_exists, log, open_file
+
+_KEYS_SUFFIX = '.keys.pkl'
+_EMBEDDINGS_SUFFIX = '.npy'
+
+
+def is_primitive(obj: object) -> bool:
+ """Returns True if the object is a primitive."""
+ if isinstance(obj, (str, bytes, np.ndarray, int, float)):
+ return True
+ if isinstance(obj, Iterable):
+ return False
+ return True
+
+
+def _replace_embeddings_with_none(input: Union[Item, Item]) -> Union[Item, Item]:
+ if isinstance(input, np.ndarray):
+ return None
+ if isinstance(input, dict):
+ return {k: _replace_embeddings_with_none(v) for k, v in input.items()}
+ if isinstance(input, list):
+ return [_replace_embeddings_with_none(v) for v in input]
+
+ return input
+
+
+def replace_embeddings_with_none(input: Union[Item, Item]) -> Item:
+ """Replaces all embeddings with None."""
+ return cast(Item, _replace_embeddings_with_none(input))
+
+
+def lilac_span(start: int, end: int, metadata: dict[str, Any] = {}) -> Item:
+ """Creates a lilac span item, representing a pointer to a slice of text."""
+ return {VALUE_KEY: {TEXT_SPAN_START_FEATURE: start, TEXT_SPAN_END_FEATURE: end}, **metadata}
+
+
+def lilac_embedding(start: int, end: int, embedding: Optional[np.ndarray]) -> Item:
+ """Creates a lilac embedding item, representing a vector with a pointer to a slice of text."""
+ return lilac_span(start, end, {EMBEDDING_KEY: embedding})
+
+
+Tflatten = TypeVar('Tflatten', object, np.ndarray)
+
+
+def _flatten(input: Union[Iterable, object], is_primitive_predicate: Callable[[object],
+ bool]) -> Generator:
+ """Flattens a nested iterable."""
+ if is_primitive_predicate(input):
+ yield input
+ elif isinstance(input, dict):
+ yield input
+ elif is_primitive(input):
+ yield input
+ else:
+ for elem in cast(Iterable, input):
+ yield from _flatten(elem, is_primitive_predicate)
+
+
+def flatten(input: Union[Iterable, Tflatten],
+ is_primitive_predicate: Callable[[object], bool] = is_primitive) -> Iterable[Tflatten]:
+ """Flattens a nested iterable.
+
+ Primitives and dictionaries are not flattened. The user can also provide a predicate to determine
+ what is a primitive.
+ """
+ return _flatten(input, is_primitive_predicate)
+
+
+def count_primitives(input: Iterable) -> int:
+ """Iterate through each element of the input, flattening each one, computing a count.
+
+ Sum the final set of counts. This is the important iterable not to exhaust.
+ """
+ return sum((len(list(flatten(i))) for i in input))
+
+
+def _wrap_value_in_dict(input: Union[object, dict], props: PathTuple) -> Union[object, dict]:
+ # If the signal produced no value, or nan, we should return None so the parquet value is sparse.
+ if isinstance(input, float) and math.isnan(input):
+ input = None
+ for prop in reversed(props):
+ input = {prop: input}
+ return input
+
+
+def _unflatten(flat_input: Iterator[list[object]],
+ original_input: Union[Iterable, object]) -> Union[list, dict]:
+ """Unflattens a flattened iterable according to the original iterable's structure."""
+ if is_primitive(original_input):
+ return next(flat_input)
+ else:
+ values: Iterable
+ if isinstance(original_input, dict):
+ values = original_input.values()
+ else:
+ values = cast(Iterable, original_input)
+ return [_unflatten(flat_input, orig_elem) for orig_elem in values]
+
+
+def unflatten(flat_input: Iterable, original_input: Union[Iterable, object]) -> list:
+ """Unflattens a flattened iterable according to the original iterable's structure."""
+ return cast(list, _unflatten(iter(flat_input), original_input))
+
+
+def _wrap_in_dicts(input: Union[object, Iterable[object]],
+ spec: list[PathTuple]) -> Union[object, Iterable[object]]:
+ """Wraps an object or iterable in a dict according to the spec."""
+ props = spec[0] if spec else tuple()
+ if len(spec) == 1:
+ return _wrap_value_in_dict(input, props)
+ if input is None or isinstance(input, float) and math.isnan(input):
+ # Return empty dict for missing inputs.
+ return {}
+ res = [_wrap_in_dicts(elem, spec[1:]) for elem in cast(Iterable, input)]
+ return _wrap_value_in_dict(res, props)
+
+
+def wrap_in_dicts(input: Iterable[object], spec: list[PathTuple]) -> Iterable[object]:
+ """Wraps an object or iterable in a dict according to the spec."""
+ return [_wrap_in_dicts(elem, spec) for elem in input]
+
+
+def _merge_field_into(schema: Field, destination: Field) -> None:
+ if isinstance(schema, Field):
+ destination.signal = destination.signal or schema.signal
+ destination.dtype = destination.dtype or schema.dtype
+ if schema.fields:
+ destination.fields = destination.fields or {}
+ for field_name, subfield in schema.fields.items():
+ if field_name not in destination.fields:
+ destination.fields[field_name] = subfield.copy(deep=True)
+ else:
+ _merge_field_into(subfield, destination.fields[field_name])
+ elif schema.repeated_field:
+ if not destination.repeated_field:
+ raise ValueError('Failed to merge schemas. Origin schema is repeated, but destination is not')
+ _merge_field_into(schema.repeated_field, destination.repeated_field)
+ else:
+ if destination.dtype != schema.dtype:
+ raise ValueError(f'Failed to merge schemas. Origin schema has dtype {schema.dtype}, '
+ f'but destination has dtype {destination.dtype}')
+
+
+def merge_schemas(schemas: Sequence[Union[Schema, Field]]) -> Schema:
+ """Merge a list of schemas."""
+ merged_schema = Schema(fields={})
+ for s in schemas:
+ _merge_field_into(cast(Field, s), cast(Field, merged_schema))
+ return merged_schema
+
+
+def schema_contains_path(schema: Schema, path: PathTuple) -> bool:
+ """Check if a schema contains a path."""
+ current_field = cast(Field, schema)
+ for path_part in path:
+ # When we reach a value key, the schema should have a dtype defined on it. If not, it is derived
+ # and this schema does not contain the value.
+ if path_part == VALUE_KEY:
+ return current_field.dtype is not None
+
+ if path_part == PATH_WILDCARD:
+ if current_field.repeated_field is None:
+ return False
+ current_field = current_field.repeated_field
+ else:
+ if current_field.fields is None or path_part not in current_field.fields:
+ return False
+ current_field = current_field.fields[str(path_part)]
+ return True
+
+
+def create_signal_schema(signal: Signal, source_path: PathTuple, current_schema: Schema) -> Schema:
+ """Create a schema describing the enriched fields added an enrichment."""
+ leafs = current_schema.leafs
+ # Validate that the enrich fields are actually a valid leaf path.
+ if source_path not in leafs:
+ raise ValueError(f'"{source_path}" is not a valid leaf path. Leaf paths: {leafs.keys()}')
+
+ signal_schema = signal.fields()
+ signal_schema.signal = signal.dict()
+
+ enriched_schema = field(fields={signal.key(is_computed_signal=True): signal_schema})
+
+ for path_part in reversed(source_path):
+ if path_part == PATH_WILDCARD:
+ enriched_schema = Field(repeated_field=enriched_schema)
+ else:
+ enriched_schema = Field(fields={path_part: enriched_schema})
+
+ if not enriched_schema.fields:
+ raise ValueError('This should not happen since enriched_schema always has fields (see above)')
+
+ return schema({UUID_COLUMN: 'string', **cast(dict, enriched_schema.fields)})
+
+
+def write_item_embeddings_to_disk(keys: Iterable[str], embeddings: Iterable[object],
+ output_dir: str, shard_index: int, num_shards: int) -> str:
+ """Write a set of embeddings to disk."""
+ output_path_prefix = embedding_index_filename_prefix(output_dir, shard_index, num_shards)
+
+ # Restrict the keys to only those that are embeddings.
+ def embedding_predicate(input: Any) -> bool:
+ return isinstance(input, np.ndarray)
+
+ flat_keys = flatten_keys(keys, embeddings, is_primitive_predicate=embedding_predicate)
+ embedding_vectors: list[np.ndarray] = []
+ for lilac_embedding in flatten(embeddings, is_primitive_predicate=embedding_predicate):
+ # We use squeeze here because embedding functions can return outer dimensions of 1.
+ embedding_vector = lilac_embedding[EMBEDDING_KEY].reshape(-1)
+ if embedding_vector.ndim != 1:
+ raise ValueError(f'Expected embeddings to be 1-dimensional, got {embedding_vector.ndim} '
+ f'with shape {embedding_vector.shape}.')
+ embedding_vectors.append(embedding_vector)
+
+ flat_embeddings = np.array(embedding_vectors)
+
+ # Write the embedding index and the ordered UUID column to disk so they can be joined later.
+
+ with open_file(output_path_prefix + _EMBEDDINGS_SUFFIX, 'wb') as f:
+ np.save(f, flat_embeddings, allow_pickle=False)
+ with open_file(output_path_prefix + _KEYS_SUFFIX, 'wb') as f:
+ pickle.dump(flat_keys, f)
+
+ return output_path_prefix
+
+
+def read_embedding_index(filepath_prefix: str) -> tuple[list[VectorKey], np.ndarray]:
+ """Reads the embedding index for a column from disk."""
+ if not file_exists(filepath_prefix + _EMBEDDINGS_SUFFIX):
+ raise ValueError(F'Embedding index does not exist at path {filepath_prefix}. '
+ 'Please run dataset.compute_signal() on the embedding signal first.')
+
+ # Read the embedding index from disk.
+ embeddings = np.load(filepath_prefix + _EMBEDDINGS_SUFFIX, allow_pickle=False)
+ with open_file(filepath_prefix + _KEYS_SUFFIX, 'rb') as f:
+ index_keys: list[VectorKey] = pickle.load(f)
+ return index_keys, embeddings
+
+
+def write_items_to_parquet(items: Iterable[Item], output_dir: str, schema: Schema,
+ filename_prefix: str, shard_index: int,
+ num_shards: int) -> tuple[str, int]:
+ """Write a set of items to a parquet file, in columnar format."""
+ arrow_schema = schema_to_arrow_schema(schema)
+ out_filename = parquet_filename(filename_prefix, shard_index, num_shards)
+ filepath = os.path.join(output_dir, out_filename)
+ f = open_file(filepath, mode='wb')
+ writer = ParquetWriter(schema)
+ writer.open(f)
+ num_items = 0
+ for item in items:
+ # Add a UUID column.
+ if UUID_COLUMN not in item:
+ item[UUID_COLUMN] = secrets.token_urlsafe(nbytes=12) # 16 base64 characters.
+ if os.getenv('DEBUG'):
+ _validate(item, arrow_schema)
+ writer.write(item)
+ num_items += 1
+ writer.close()
+ f.close()
+ return out_filename, num_items
+
+
+def _validate(item: Item, schema: pa.Schema) -> None:
+ # Try to parse the item using the inferred schema.
+ try:
+ pa.RecordBatch.from_pylist([item], schema=schema)
+ except pa.ArrowTypeError:
+ log('Failed to parse arrow item using the arrow schema.')
+ log('Item:')
+ log(pprint.pformat(item, indent=2))
+ log('Arrow schema:')
+ log(schema)
+ raise # Re-raise the same exception, same stacktrace.
+
+
+def parquet_filename(prefix: str, shard_index: int, num_shards: int) -> str:
+ """Return the filename for a parquet file."""
+ return f'{prefix}-{shard_index:05d}-of-{num_shards:05d}.parquet'
+
+
+def _flatten_keys(uuid: str, nested_input: Iterable, location: list[int],
+ is_primitive_predicate: Callable[[object], bool]) -> list[VectorKey]:
+ if is_primitive_predicate(nested_input):
+ return [(uuid, *location)]
+ elif is_primitive(nested_input):
+ return []
+ else:
+ result: list[VectorKey] = []
+ if isinstance(nested_input, dict):
+ for value in nested_input.values():
+ result.extend(_flatten_keys(uuid, value, location, is_primitive_predicate))
+ else:
+ for i, input in enumerate(nested_input):
+ result.extend(_flatten_keys(uuid, input, [*location, i], is_primitive_predicate))
+ return result
+
+
+def flatten_keys(
+ uuids: Iterable[str],
+ nested_input: Iterable,
+ is_primitive_predicate: Callable[[object], bool] = is_primitive) -> list[VectorKey]:
+ """Flatten the uuid keys of a nested input."""
+ result: list[VectorKey] = []
+ for uuid, input in zip(uuids, nested_input):
+ result.extend(_flatten_keys(uuid, input, [], is_primitive_predicate))
+ return result
+
+
+def embedding_index_filename_prefix(output_dir: str, shard_index: int, num_shards: int) -> str:
+ """Return the filename prefix for the embedding index."""
+ npy_filename = f'embeddings-{shard_index:05d}-of-{num_shards:05d}'
+ return os.path.join(output_dir, npy_filename)
diff --git a/src/data/dataset_utils_test.py b/src/data/dataset_utils_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3c477f52481245b5acab359548bffec17b49806
--- /dev/null
+++ b/src/data/dataset_utils_test.py
@@ -0,0 +1,114 @@
+"""Tests for dataset utils."""
+from ..schema import PathTuple
+from .dataset_utils import count_primitives, flatten, unflatten, wrap_in_dicts
+
+
+def test_flatten() -> None:
+ a = [[1, 2], [[3]], [4, 5, 5]]
+ result = list(flatten(a))
+ assert result == [1, 2, 3, 4, 5, 5]
+
+
+def test_flatten_primitive() -> None:
+ result = list(flatten('hello'))
+ assert result == ['hello']
+
+
+def test_unflatten() -> None:
+ a = [[1, 2], [[3]], [4, 5, 5]]
+ flat_a = list(flatten(a))
+ result = unflatten(flat_a, a)
+ assert result == [[1, 2], [[3]], [4, 5, 5]]
+
+
+def test_count_nested() -> None:
+ a = [[1, 2], [[3]], [4, 5, 6]]
+ assert 6 == count_primitives(a)
+
+
+def test_wrap_in_dicts_with_spec_of_one_repeated() -> None:
+ a = [[1, 2], [3], [4, 5, 5]]
+ spec: list[PathTuple] = [('a', 'b', 'c'), ('d',)] # Corresponds to a.b.c.*.d.
+ result = wrap_in_dicts(a, spec)
+ assert result == [{
+ 'a': {
+ 'b': {
+ 'c': [{
+ 'd': 1
+ }, {
+ 'd': 2
+ }]
+ }
+ }
+ }, {
+ 'a': {
+ 'b': {
+ 'c': [{
+ 'd': 3
+ }]
+ }
+ }
+ }, {
+ 'a': {
+ 'b': {
+ 'c': [{
+ 'd': 4
+ }, {
+ 'd': 5
+ }, {
+ 'd': 5
+ }]
+ }
+ }
+ }]
+
+
+def test_wrap_in_dicts_with_spec_of_double_repeated() -> None:
+ a = [[[1, 2], [3, 4, 5]], [[6]], [[7], [8], [9, 10]]]
+ spec: list[PathTuple] = [('a', 'b'), tuple(), ('c',)] # Corresponds to a.b.*.*.c.
+ result = wrap_in_dicts(a, spec)
+ assert result == [{
+ 'a': {
+ 'b': [[{
+ 'c': 1
+ }, {
+ 'c': 2
+ }], [{
+ 'c': 3
+ }, {
+ 'c': 4
+ }, {
+ 'c': 5
+ }]]
+ }
+ }, {
+ 'a': {
+ 'b': [[{
+ 'c': 6
+ }]]
+ }
+ }, {
+ 'a': {
+ 'b': [[{
+ 'c': 7
+ }], [{
+ 'c': 8
+ }], [{
+ 'c': 9
+ }, {
+ 'c': 10
+ }]]
+ }
+ }]
+
+
+def test_unflatten_primitive() -> None:
+ original = 'hello'
+ result = unflatten(['hello'], original)
+ assert result == 'hello'
+
+
+def test_unflatten_primitive_list() -> None:
+ original = ['hello', 'world']
+ result = unflatten(['hello', 'world'], original)
+ assert result == ['hello', 'world']
diff --git a/src/data/duckdb_utils.py b/src/data/duckdb_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecf1bb8838d23636466a30314af711f2eabb0913
--- /dev/null
+++ b/src/data/duckdb_utils.py
@@ -0,0 +1,19 @@
+"""Utils for duckdb."""
+import duckdb
+
+from ..config import CONFIG
+
+
+def duckdb_gcs_setup(con: duckdb.DuckDBPyConnection) -> str:
+ """Setup DuckDB for GCS."""
+ con.install_extension('httpfs')
+ con.load_extension('httpfs')
+
+ if 'GCS_REGION' in CONFIG:
+ return f"""
+ SET s3_region='{CONFIG['GCS_REGION']}';
+ SET s3_access_key_id='{CONFIG['GCS_ACCESS_KEY']}';
+ SET s3_secret_access_key='{CONFIG['GCS_SECRET_KEY']}';
+ SET s3_endpoint='storage.googleapis.com';
+ """
+ return ''
diff --git a/src/data/sources/__init__.py b/src/data/sources/__init__.py
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diff --git a/src/data/sources/__pycache__/tfds_source.cpython-39.pyc b/src/data/sources/__pycache__/tfds_source.cpython-39.pyc
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diff --git a/src/data/sources/csv_source.py b/src/data/sources/csv_source.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b0ef9658a76bee3db9f61533de737a166dc8e52
--- /dev/null
+++ b/src/data/sources/csv_source.py
@@ -0,0 +1,73 @@
+"""CSV source."""
+from typing import Iterable, Optional
+
+import duckdb
+import pandas as pd
+from pydantic import Field
+from typing_extensions import override
+
+from ...schema import Item
+from ...utils import download_http_files
+from ..duckdb_utils import duckdb_gcs_setup
+from .source import Source, SourceSchema, schema_from_df
+
+LINE_NUMBER_COLUMN = '__line_number__'
+
+
+class CSVDataset(Source):
+ """CSV data loader
+
+ CSV files can live locally as a filepath, or point to an external URL.
+ """ # noqa: D415, D400
+ name = 'csv'
+
+ filepaths: list[str] = Field(description='A list of filepaths to CSV files.')
+ delim: Optional[str] = Field(default=',', description='The CSV file delimiter to use.')
+ header: Optional[bool] = Field(default=True, description='Whether the CSV file has a header row.')
+ names: Optional[list[str]] = Field(
+ default=None, description='Provide header names if the file does not contain a header.')
+
+ _source_schema: SourceSchema
+ _df: pd.DataFrame
+
+ @override
+ def setup(self) -> None:
+ # Download CSV files to /tmp if they are via HTTP to speed up duckdb.
+ filepaths = download_http_files(self.filepaths)
+
+ con = duckdb.connect(database=':memory:')
+
+ # DuckDB expects s3 protocol: https://duckdb.org/docs/guides/import/s3_import.html.
+ s3_filepaths = [path.replace('gs://', 's3://') for path in filepaths]
+
+ # NOTE: We use duckdb here to increase parallelism for multiple files.
+ # NOTE: We turn off the parallel reader because of https://github.com/lilacai/lilac/issues/373.
+ self._df = con.execute(f"""
+ {duckdb_gcs_setup(con)}
+ SELECT * FROM read_csv_auto(
+ {s3_filepaths},
+ SAMPLE_SIZE=500000,
+ HEADER={self.header},
+ {f'NAMES={self.names},' if self.names else ''}
+ DELIM='{self.delim or ','}',
+ IGNORE_ERRORS=true,
+ PARALLEL=false
+ )
+ """).df()
+
+ # Create the source schema in prepare to share it between process and source_schema.
+ self._source_schema = schema_from_df(self._df, LINE_NUMBER_COLUMN)
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ """Return the source schema."""
+ return self._source_schema
+
+ @override
+ def process(self) -> Iterable[Item]:
+ """Process the source upload request."""
+ cols = self._df.columns.tolist()
+ yield from ({
+ LINE_NUMBER_COLUMN: idx,
+ **dict(zip(cols, item_vals)),
+ } for idx, *item_vals in self._df.itertuples())
diff --git a/src/data/sources/csv_source_test.py b/src/data/sources/csv_source_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..3287cf8c2bdb95aad5c86c1203beffbb4200dac8
--- /dev/null
+++ b/src/data/sources/csv_source_test.py
@@ -0,0 +1,42 @@
+"""Tests for the CSV source."""
+import csv
+import os
+import pathlib
+
+from ...schema import schema
+from .csv_source import LINE_NUMBER_COLUMN, CSVDataset
+from .source import SourceSchema
+
+
+def test_csv(tmp_path: pathlib.Path) -> None:
+ csv_rows = [{'x': 1, 'y': 'ten'}, {'x': 2, 'y': 'twenty'}]
+
+ filename = 'test-dataset.csv'
+ filepath = os.path.join(tmp_path, filename)
+ with open(filepath, 'w') as f:
+ writer = csv.DictWriter(f, fieldnames=list(csv_rows[0].keys()))
+ writer.writeheader()
+ writer.writerows(csv_rows)
+
+ source = CSVDataset(filepaths=[filepath])
+ source.setup()
+
+ source_schema = source.source_schema()
+ assert source_schema == SourceSchema(
+ fields=schema({
+ LINE_NUMBER_COLUMN: 'int64',
+ 'x': 'int64',
+ 'y': 'string'
+ }).fields, num_items=2)
+
+ items = list(source.process())
+
+ assert items == [{
+ LINE_NUMBER_COLUMN: 0,
+ 'x': 1,
+ 'y': 'ten'
+ }, {
+ LINE_NUMBER_COLUMN: 1,
+ 'x': 2,
+ 'y': 'twenty'
+ }]
diff --git a/src/data/sources/default_sources.py b/src/data/sources/default_sources.py
new file mode 100644
index 0000000000000000000000000000000000000000..924a5975e84f2b8c9185d45bdadb6744547d1229
--- /dev/null
+++ b/src/data/sources/default_sources.py
@@ -0,0 +1,18 @@
+"""Registers all available default sources."""
+from .csv_source import CSVDataset
+from .gmail_source import Gmail
+from .huggingface_source import HuggingFaceDataset
+from .json_source import JSONDataset
+from .pandas_source import PandasDataset
+from .reddit_hf_source import RedditDataset
+from .source_registry import register_source
+
+
+def register_default_sources() -> None:
+ """Register all the default sources."""
+ register_source(CSVDataset)
+ register_source(HuggingFaceDataset)
+ register_source(RedditDataset)
+ register_source(JSONDataset)
+ register_source(PandasDataset)
+ register_source(Gmail)
diff --git a/src/data/sources/gmail_source.py b/src/data/sources/gmail_source.py
new file mode 100644
index 0000000000000000000000000000000000000000..731cd0e2e6aaaad1871f9771b466172d9d537a88
--- /dev/null
+++ b/src/data/sources/gmail_source.py
@@ -0,0 +1,220 @@
+"""Gmail source."""
+import base64
+import dataclasses
+import os.path
+import random
+import re
+from datetime import datetime
+from time import sleep
+from typing import Any, Iterable, Optional
+
+from email_reply_parser import EmailReplyParser
+from google.auth.transport.requests import Request
+from google.oauth2.credentials import Credentials
+from google_auth_oauthlib.flow import InstalledAppFlow
+from googleapiclient.discovery import build
+from googleapiclient.errors import HttpError
+from pydantic import Field as PydanticField
+from typing_extensions import override
+
+from ...config import data_path
+from ...schema import Item, field
+from ...utils import log
+from .source import Source, SourceSchema
+
+# If modifying these scopes, delete the token json file.
+_SCOPES = ['https://www.googleapis.com/auth/gmail.readonly']
+_GMAIL_CONFIG_DIR = os.path.join(data_path(), '.gmail')
+_TOKEN_FILENAME = 'token.json'
+_CREDS_FILENAME = 'credentials.json'
+_NUM_RETRIES = 10
+_MAX_NUM_THREADS = 30_000
+
+_UNWRAP_PATTERN = re.compile(r'(\S)\n(\S)')
+HTTP_PATTERN = re.compile(r'https?://[^\s]+')
+
+
+class Gmail(Source):
+ """Connects to your Gmail and loads the text of your emails.
+
+ **One time setup**
+
+ Download the OAuth credentials file from the
+ [Google Cloud Console](https://console.cloud.google.com/apis/credentials) and save it to the
+ correct location. See
+ [guide](https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application)
+ for details.
+ """
+
+ name = 'gmail'
+
+ credentials_file = PydanticField(
+ description='Path to the OAuth credentials file.',
+ default=os.path.join(_GMAIL_CONFIG_DIR, _CREDS_FILENAME))
+
+ _creds: Optional[Credentials] = None
+
+ class Config:
+ # Language is required even though it has a default value.
+ schema_extra = {'required': ['credentials_file']}
+
+ @override
+ def setup(self) -> None:
+ # The token file stores the user's access and refresh tokens, and is created automatically when
+ # the authorization flow completes for the first time.
+ token_filepath = os.path.join(_GMAIL_CONFIG_DIR, _TOKEN_FILENAME)
+ if os.path.exists(token_filepath):
+ self._creds = Credentials.from_authorized_user_file(token_filepath, _SCOPES)
+ # If there are no (valid) credentials available, let the user log in.
+ if not self._creds or not self._creds.valid:
+ if self._creds and self._creds.expired and self._creds.refresh_token:
+ self._creds.refresh(Request())
+ else:
+ if not os.path.exists(self.credentials_file):
+ raise ValueError(
+ f'Could not find the OAuth credentials file at "{self.credentials_file}". Make sure to '
+ 'download it from the Google Cloud Console and save it to the correct location.')
+ flow = InstalledAppFlow.from_client_secrets_file(self.credentials_file, _SCOPES)
+ self._creds = flow.run_local_server()
+
+ os.makedirs(os.path.dirname(token_filepath), exist_ok=True)
+ # Save the token for the next run.
+ with open(token_filepath, 'w') as token:
+ token.write(self._creds.to_json())
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ return SourceSchema(
+ fields={
+ 'body': field('string'),
+ 'snippet': field('string'),
+ 'dates': field(fields=['string']),
+ 'subject': field('string'),
+ })
+
+ @override
+ def process(self) -> Iterable[Item]:
+ # Call the Gmail API
+ service = build('gmail', 'v1', credentials=self._creds)
+
+ # threads.list API
+ threads_resource = service.users().threads()
+
+ thread_batch: list[Item] = []
+ retry_batch: set[str] = set()
+ num_retries = 0
+ num_threads_fetched = 0
+
+ def _thread_fetched(request_id: str, response: Any, exception: Optional[HttpError]) -> None:
+ if exception is not None:
+ retry_batch.add(request_id)
+ return
+
+ replies: list[str] = []
+ dates: list[str] = []
+ snippets: list[str] = []
+ subject: Optional[str] = None
+
+ for msg in response['messages']:
+ epoch_sec = int(msg['internalDate']) / 1000.
+ date = datetime.fromtimestamp(epoch_sec).strftime('%Y-%m-%d %H:%M:%S')
+ dates.append(date)
+ if 'snippet' in msg:
+ snippets.append(msg['snippet'])
+ email_info = _parse_payload(msg['payload'])
+ subject = subject or email_info.subject
+ parsed_parts: list[str] = []
+ for body in email_info.parts:
+ if not body:
+ continue
+ text = base64.urlsafe_b64decode(body).decode('utf-8')
+ text = EmailReplyParser.parse_reply(text)
+ # Unwrap text.
+ text = _UNWRAP_PATTERN.sub('\\1 \\2', text)
+ # Remove URLs.
+ text = HTTP_PATTERN.sub('', text)
+
+ if text:
+ parsed_parts.append(text)
+ if email_info.sender and parsed_parts:
+ parsed_parts = [
+ f'--------------------{email_info.sender}--------------------', *parsed_parts
+ ]
+ if parsed_parts:
+ replies.append('\n'.join(parsed_parts))
+
+ if replies:
+ thread_batch.append({
+ 'body': '\n\n'.join(replies),
+ 'snippet': '\n'.join(snippets) if snippets else None,
+ 'dates': dates,
+ 'subject': subject,
+ })
+ if request_id in retry_batch:
+ retry_batch.remove(request_id)
+
+ # First request.
+ thread_list_req = threads_resource.list(userId='me', includeSpamTrash=False) or None
+ thread_list = thread_list_req.execute(num_retries=_NUM_RETRIES) if thread_list_req else None
+
+ while (num_threads_fetched < _MAX_NUM_THREADS and thread_list and thread_list_req):
+ batch = service.new_batch_http_request(callback=_thread_fetched)
+
+ for gmail_thread in thread_list['threads']:
+ thread_id = gmail_thread['id']
+ if not retry_batch or (thread_id in retry_batch):
+ batch.add(
+ service.users().threads().get(userId='me', id=thread_id, format='full'),
+ request_id=thread_id)
+
+ batch.execute()
+ num_threads_fetched += len(thread_batch)
+ yield from thread_batch
+ thread_batch = []
+
+ if retry_batch:
+ log(f'Failed to fetch {len(retry_batch)} threads. Retrying...')
+ timeout = 2**(num_retries - 1) + random.uniform(0, 1)
+ sleep(timeout)
+ num_retries += 1
+ else:
+ retry_batch = set()
+ num_retries = 0
+ # Fetch next page.
+ thread_list_req = threads_resource.list_next(thread_list_req, thread_list)
+ thread_list = thread_list_req.execute(num_retries=_NUM_RETRIES) if thread_list_req else None
+
+
+@dataclasses.dataclass
+class EmailInfo:
+ """Stores parsed information about an email."""
+ sender: Optional[str] = None
+ subject: Optional[str] = None
+ parts: list[bytes] = dataclasses.field(default_factory=list)
+
+
+def _get_header(payload: Any, name: str) -> Optional[str]:
+ if 'headers' not in payload:
+ return None
+ values = [h['value'] for h in payload['headers'] if h['name'].lower().strip() == name]
+ return values[0] if values else None
+
+
+def _parse_payload(payload: Any) -> EmailInfo:
+ sender = _get_header(payload, 'from')
+ subject = _get_header(payload, 'subject')
+ parts: list[bytes] = []
+
+ # Process the message body.
+ if 'mimeType' in payload and 'text/plain' in payload['mimeType']:
+ if 'body' in payload and 'data' in payload['body']:
+ parts.append(payload['body']['data'].encode('ascii'))
+
+ # Process the message parts.
+ for part in payload.get('parts', []):
+ email_info = _parse_payload(part)
+ sender = sender or email_info.sender
+ subject = subject or email_info.subject
+ parts.extend(email_info.parts)
+
+ return EmailInfo(sender, subject, parts)
diff --git a/src/data/sources/huggingface_source.py b/src/data/sources/huggingface_source.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3b449ed84a5fdfcee420c1d4ce613c21196561b
--- /dev/null
+++ b/src/data/sources/huggingface_source.py
@@ -0,0 +1,160 @@
+"""Huggingface source."""
+import multiprocessing
+from typing import Iterable, Optional, Union
+
+import numpy as np
+from datasets import ClassLabel, DatasetDict, Sequence, Value, load_dataset, load_from_disk
+from pydantic import BaseModel
+from pydantic import Field as PydanticField
+from typing_extensions import override
+
+from ...schema import DataType, Field, Item, arrow_dtype_to_dtype
+from .source import Source, SourceSchema
+
+HF_SPLIT_COLUMN = '__hfsplit__'
+
+# Used when the dataset is saved locally.
+DEFAULT_LOCAL_SPLIT_NAME = 'default'
+
+
+class SchemaInfo(BaseModel):
+ """Information about the processed huggingface schema."""
+ fields: dict[str, Field] = {}
+ class_labels: dict[str, list[str]]
+ num_items: int
+
+
+def _infer_field(feature_value: Union[Value, dict]) -> Field:
+ """Infer the field type from the feature value."""
+ if isinstance(feature_value, dict):
+ return Field(fields={name: _infer_field(value) for name, value in feature_value.items()})
+ elif isinstance(feature_value, Value):
+ return Field(dtype=arrow_dtype_to_dtype(feature_value.pa_type))
+ elif isinstance(feature_value, Sequence):
+ # Huggingface Sequences can contain a dictionary of feature values, e.g.
+ # Sequence(feature={'x': Value(dtype='int32'), 'y': Value(dtype='float32')}}
+ # These are converted to {'x': [...]} and {'y': [...]}
+ if isinstance(feature_value.feature, dict):
+ return Field(
+ fields={
+ name: Field(repeated_field=_infer_field(value))
+ for name, value in feature_value.feature.items()
+ })
+ else:
+ return Field(repeated_field=_infer_field(feature_value.feature))
+ elif isinstance(feature_value, list):
+ if len(feature_value) > 1:
+ raise ValueError('Field arrays with multiple values are not supported.')
+ return Field(repeated_field=_infer_field(feature_value[0]))
+ elif isinstance(feature_value, ClassLabel):
+ raise ValueError('Nested ClassLabel is not supported.')
+
+ else:
+ raise ValueError(f'Feature is not a `Value`, `Sequence`, or `dict`: {feature_value}')
+
+
+def hf_schema_to_schema(hf_dataset_dict: DatasetDict, split: Optional[str]) -> SchemaInfo:
+ """Convert the HuggingFace schema to our schema."""
+ if split:
+ split_datasets = [hf_dataset_dict[split]]
+ else:
+ split_datasets = [hf_dataset_dict[split] for split in hf_dataset_dict.keys()]
+
+ fields: dict[str, Field] = {}
+ class_labels: dict[str, list[str]] = {}
+ num_items = 0
+
+ for split_dataset in split_datasets:
+ num_items += len(split_dataset)
+ features = split_dataset.features
+ for feature_name, feature_value in features.items():
+ if feature_name in fields:
+ continue
+
+ if isinstance(feature_value, ClassLabel):
+ # Class labels act as strings and we map the integer to a string before writing.
+ fields[feature_name] = Field(dtype=DataType.STRING)
+ class_labels[feature_name] = feature_value.names
+ else:
+ fields[feature_name] = _infer_field(feature_value)
+
+ # Add the split column to the schema.
+ fields[HF_SPLIT_COLUMN] = Field(dtype=DataType.STRING)
+
+ return SchemaInfo(fields=fields, class_labels=class_labels, num_items=num_items)
+
+
+class HuggingFaceDataset(Source):
+ """HuggingFace data loader
+
+ For a list of datasets see: [huggingface.co/datasets](https://huggingface.co/datasets).
+
+ For documentation on dataset loading see:
+ [huggingface.co/docs/datasets/index](https://huggingface.co/docs/datasets/index)
+ """ # noqa: D415, D400
+ name = 'huggingface'
+
+ dataset_name: str = PydanticField(
+ required=True,
+ description='Either in the format `user/dataset` or `dataset`.',
+ )
+ config_name: Optional[str] = PydanticField(
+ title='Dataset config name', description='Some datasets require this.', default=None)
+ split: Optional[str] = PydanticField(
+ title='Dataset split', description='Loads all splits by default.', default=None)
+ revision: Optional[str] = PydanticField(title='Dataset revision', default=None)
+ load_from_disk: Optional[bool] = PydanticField(
+ description='Load from local disk instead of the hub.', default=False)
+
+ _dataset_dict: DatasetDict
+ _schema_info: SchemaInfo
+
+ @override
+ def setup(self) -> None:
+ if self.load_from_disk:
+ # Load from disk.
+ hf_dataset_dict = {DEFAULT_LOCAL_SPLIT_NAME: load_from_disk(self.dataset_name)}
+ else:
+ hf_dataset_dict = load_dataset(
+ self.dataset_name, self.config_name, num_proc=multiprocessing.cpu_count())
+ self._dataset_dict = hf_dataset_dict
+ self._schema_info = hf_schema_to_schema(self._dataset_dict, self.split)
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ return SourceSchema(fields=self._schema_info.fields, num_items=self._schema_info.num_items)
+
+ @override
+ def process(self) -> Iterable[Item]:
+ if self.split:
+ split_names = [self.split]
+ else:
+ split_names = list(self._dataset_dict.keys())
+
+ for split_name in split_names:
+ split_dataset = self._dataset_dict[split_name]
+
+ for example in split_dataset:
+ # Replace the class labels with strings.
+ for feature_name in self._schema_info.class_labels.keys():
+ if feature_name in example:
+ example[feature_name] = self._schema_info.class_labels[feature_name][
+ example[feature_name]]
+
+ # Inject the split name.
+ example[HF_SPLIT_COLUMN] = split_name
+
+ # Huggingface Sequences are represented as np.arrays. Convert them to lists.
+ example = _np_array_to_list_deep(example)
+
+ yield example
+
+
+def _np_array_to_list_deep(item: Item) -> Item:
+ """Convert all numpy arrays to lists."""
+ for key, value in item.items():
+ if isinstance(value, np.ndarray):
+ item[key] = value.tolist()
+ elif isinstance(value, dict):
+ item[key] = _np_array_to_list_deep(value)
+ return item
diff --git a/src/data/sources/huggingface_source_test.py b/src/data/sources/huggingface_source_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4e5c6f77b88527b6d88cad669d7c09d09391daa
--- /dev/null
+++ b/src/data/sources/huggingface_source_test.py
@@ -0,0 +1,170 @@
+"""Tests for the pandas source."""
+import os
+import pathlib
+
+# mypy: disable-error-code="attr-defined"
+from datasets import Dataset, Features, Sequence, Value
+
+from ...schema import schema
+from .huggingface_source import HF_SPLIT_COLUMN, HuggingFaceDataset
+from .source import SourceSchema
+
+
+def test_hf(tmp_path: pathlib.Path) -> None:
+ dataset = Dataset.from_list([{'x': 1, 'y': 'ten'}, {'x': 2, 'y': 'twenty'}])
+
+ dataset_name = os.path.join(tmp_path, 'hf-test-dataset')
+ dataset.save_to_disk(dataset_name)
+
+ source = HuggingFaceDataset(dataset_name=dataset_name, load_from_disk=True)
+
+ items = source.process()
+ source.setup()
+
+ source_schema = source.source_schema()
+ assert source_schema == SourceSchema(
+ fields=schema({
+ HF_SPLIT_COLUMN: 'string',
+ 'x': 'int64',
+ 'y': 'string'
+ }).fields, num_items=2)
+
+ items = list(source.process())
+
+ assert items == [{
+ HF_SPLIT_COLUMN: 'default',
+ 'x': 1,
+ 'y': 'ten'
+ }, {
+ HF_SPLIT_COLUMN: 'default',
+ 'x': 2,
+ 'y': 'twenty'
+ }]
+
+
+def test_hf_sequence(tmp_path: pathlib.Path) -> None:
+ dataset = Dataset.from_list([{
+ 'scalar': 1,
+ 'seq': [1, 0],
+ 'seq_dict': {
+ 'x': [1, 2, 3],
+ 'y': ['four', 'five', 'six']
+ }
+ }, {
+ 'scalar': 2,
+ 'seq': [2, 0],
+ 'seq_dict': {
+ 'x': [10, 20, 30],
+ 'y': ['forty', 'fifty', 'sixty']
+ }
+ }],
+ features=Features({
+ 'scalar': Value(dtype='int64'),
+ 'seq': Sequence(feature=Value(dtype='int64')),
+ 'seq_dict': Sequence(feature={
+ 'x': Value(dtype='int64'),
+ 'y': Value(dtype='string')
+ })
+ }))
+
+ dataset_name = os.path.join(tmp_path, 'hf-test-dataset')
+ dataset.save_to_disk(dataset_name)
+
+ source = HuggingFaceDataset(dataset_name=dataset_name, load_from_disk=True)
+
+ items = source.process()
+ source.setup()
+
+ source_schema = source.source_schema()
+ assert source_schema == SourceSchema(
+ fields=schema({
+ HF_SPLIT_COLUMN: 'string',
+ 'scalar': 'int64',
+ 'seq': ['int64'],
+ 'seq_dict': {
+ 'x': ['int64'],
+ 'y': ['string'],
+ },
+ }).fields,
+ num_items=2)
+
+ items = list(source.process())
+
+ assert items == [{
+ HF_SPLIT_COLUMN: 'default',
+ 'scalar': 1,
+ 'seq': [1, 0],
+ 'seq_dict': {
+ 'x': [1, 2, 3],
+ 'y': ['four', 'five', 'six']
+ }
+ }, {
+ HF_SPLIT_COLUMN: 'default',
+ 'scalar': 2,
+ 'seq': [2, 0],
+ 'seq_dict': {
+ 'x': [10, 20, 30],
+ 'y': ['forty', 'fifty', 'sixty']
+ }
+ }]
+
+
+def test_hf_list(tmp_path: pathlib.Path) -> None:
+ dataset = Dataset.from_list([{
+ 'scalar': 1,
+ 'list': [{
+ 'x': 1,
+ 'y': 'two'
+ }]
+ }, {
+ 'scalar': 2,
+ 'list': [{
+ 'x': 3,
+ 'y': 'four'
+ }]
+ }],
+ features=Features({
+ 'scalar': Value(dtype='int64'),
+ 'list': [{
+ 'x': Value(dtype='int64'),
+ 'y': Value(dtype='string')
+ }]
+ }))
+
+ dataset_name = os.path.join(tmp_path, 'hf-test-dataset')
+ dataset.save_to_disk(dataset_name)
+
+ source = HuggingFaceDataset(dataset_name=dataset_name, load_from_disk=True)
+
+ items = source.process()
+ source.setup()
+
+ source_schema = source.source_schema()
+ assert source_schema == SourceSchema(
+ fields=schema({
+ HF_SPLIT_COLUMN: 'string',
+ 'scalar': 'int64',
+ 'list': [{
+ 'x': 'int64',
+ 'y': 'string',
+ }],
+ }).fields,
+ num_items=2)
+
+ items = list(source.process())
+
+ assert items == [{
+ HF_SPLIT_COLUMN: 'default',
+ 'scalar': 1,
+ 'list': [{
+ 'x': 1,
+ 'y': 'two'
+ }]
+ }, {
+ HF_SPLIT_COLUMN: 'default',
+ 'scalar': 2,
+ 'list': [{
+ 'x': 3,
+ 'y': 'four'
+ }]
+ }]
diff --git a/src/data/sources/json_source.py b/src/data/sources/json_source.py
new file mode 100644
index 0000000000000000000000000000000000000000..444e35a1c86d59143c1b874f5b8c764206cee14f
--- /dev/null
+++ b/src/data/sources/json_source.py
@@ -0,0 +1,65 @@
+"""CSV source."""
+from typing import Iterable
+
+import duckdb
+import pandas as pd
+from pydantic import Field as PydanticField
+from typing_extensions import override
+
+from ...schema import Item
+from ...utils import download_http_files
+from ..duckdb_utils import duckdb_gcs_setup
+from .source import Source, SourceSchema, schema_from_df
+
+ROW_ID_COLUMN = '__row_id__'
+
+
+class JSONDataset(Source):
+ """JSON data loader
+
+ Supports both JSON and JSONL.
+
+ JSON files can live locally as a filepath, or point to an external URL.
+ """ # noqa: D415, D400
+ name = 'json'
+
+ filepaths: list[str] = PydanticField(description='A list of filepaths to JSON files.')
+
+ _source_schema: SourceSchema
+ _df: pd.DataFrame
+
+ @override
+ def setup(self) -> None:
+ # Download JSON files to local cache if they are via HTTP to speed up duckdb.
+ filepaths = download_http_files(self.filepaths)
+
+ con = duckdb.connect(database=':memory:')
+
+ # DuckDB expects s3 protocol: https://duckdb.org/docs/guides/import/s3_import.html.
+ s3_filepaths = [path.replace('gs://', 's3://') for path in filepaths]
+
+ # NOTE: We use duckdb here to increase parallelism for multiple files.
+ self._df = con.execute(f"""
+ {duckdb_gcs_setup(con)}
+ SELECT * FROM read_json_auto(
+ {s3_filepaths},
+ IGNORE_ERRORS=true
+ )
+ """).df()
+
+ # Create the source schema in prepare to share it between process and source_schema.
+ self._source_schema = schema_from_df(self._df, ROW_ID_COLUMN)
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ """Return the source schema."""
+ return self._source_schema
+
+ @override
+ def process(self) -> Iterable[Item]:
+ """Process the source upload request."""
+ cols = self._df.columns.tolist()
+ yield from ({
+ ROW_ID_COLUMN: idx,
+ **dict(zip(cols, item_vals)),
+ } for idx, *item_vals in self._df.itertuples())
diff --git a/src/data/sources/json_source_test.py b/src/data/sources/json_source_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..b26b2733770e52119fbc5e17b7d53e6d15dc8dc3
--- /dev/null
+++ b/src/data/sources/json_source_test.py
@@ -0,0 +1,74 @@
+"""Tests for the JSON source."""
+import json
+import os
+import pathlib
+
+from ...schema import schema
+from .json_source import ROW_ID_COLUMN, JSONDataset
+from .source import SourceSchema
+
+
+def test_simple_json(tmp_path: pathlib.Path) -> None:
+ json_records = [{'x': 1, 'y': 'ten'}, {'x': 2, 'y': 'twenty'}]
+
+ filename = 'test-dataset.jsonl'
+ filepath = os.path.join(tmp_path, filename)
+ with open(filepath, 'w') as f:
+ f.write(json.dumps(json_records))
+
+ source = JSONDataset(filepaths=[filepath])
+ source.setup()
+
+ source_schema = source.source_schema()
+ assert source_schema == SourceSchema(
+ fields=schema({
+ ROW_ID_COLUMN: 'int64',
+ 'x': 'int64',
+ 'y': 'string'
+ }).fields, num_items=2)
+
+ items = list(source.process())
+
+ assert items == [{
+ ROW_ID_COLUMN: 0,
+ 'x': 1,
+ 'y': 'ten'
+ }, {
+ ROW_ID_COLUMN: 1,
+ 'x': 2,
+ 'y': 'twenty'
+ }]
+
+
+def test_simple_jsonl(tmp_path: pathlib.Path) -> None:
+ json_records = [{'x': 1, 'y': 'ten'}, {'x': 2, 'y': 'twenty'}]
+ json_lines = [json.dumps(record) + '\n' for record in json_records]
+
+ filename = 'test-dataset.jsonl'
+ filepath = os.path.join(tmp_path, filename)
+ with open(filepath, 'w') as f:
+ f.writelines(json_lines)
+
+ source = JSONDataset(dataset_name='test_dataset', filepaths=[filepath])
+ source.setup()
+
+ source_schema = source.source_schema()
+
+ assert source_schema == SourceSchema(
+ fields=schema({
+ ROW_ID_COLUMN: 'int64',
+ 'x': 'int64',
+ 'y': 'string'
+ }).fields, num_items=2)
+
+ items = list(source.process())
+
+ assert items == [{
+ ROW_ID_COLUMN: 0,
+ 'x': 1,
+ 'y': 'ten'
+ }, {
+ ROW_ID_COLUMN: 1,
+ 'x': 2,
+ 'y': 'twenty'
+ }]
diff --git a/src/data/sources/pandas_source.py b/src/data/sources/pandas_source.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4368bbf3b5e03805cdb7e9e599fda68316e02a9
--- /dev/null
+++ b/src/data/sources/pandas_source.py
@@ -0,0 +1,44 @@
+"""Pandas source."""
+from typing import Any, Iterable
+
+import pandas as pd
+from typing_extensions import override
+
+from ...schema import Item
+from .source import Source, SourceSchema, schema_from_df
+
+PANDAS_INDEX_COLUMN = '__pd_index__'
+
+
+class PandasDataset(Source):
+ """Pandas source."""
+ name = 'pandas'
+
+ _df: pd.DataFrame
+ _source_schema: SourceSchema
+
+ class Config:
+ underscore_attrs_are_private = True
+
+ def __init__(self, df: pd.DataFrame, **kwargs: Any):
+ super().__init__(**kwargs)
+ self._df = df
+
+ @override
+ def setup(self) -> None:
+ # Create the source schema in prepare to share it between process and source_schema.
+ self._source_schema = schema_from_df(self._df, PANDAS_INDEX_COLUMN)
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ """Return the source schema."""
+ return self._source_schema
+
+ @override
+ def process(self) -> Iterable[Item]:
+ """Process the source upload request."""
+ cols = self._df.columns.tolist()
+ yield from ({
+ PANDAS_INDEX_COLUMN: idx,
+ **dict(zip(cols, item_vals)),
+ } for idx, *item_vals in self._df.itertuples())
diff --git a/src/data/sources/pandas_source_test.py b/src/data/sources/pandas_source_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..72d241a993e53c6c81e39986a051962eb4892be5
--- /dev/null
+++ b/src/data/sources/pandas_source_test.py
@@ -0,0 +1,91 @@
+"""Tests for the pandas source."""
+
+import pandas as pd
+
+from ...schema import schema
+from .pandas_source import PANDAS_INDEX_COLUMN, PandasDataset
+from .source import SourceSchema
+
+
+def test_simple_dataframe() -> None:
+ df = pd.DataFrame.from_records([{
+ 'name': 'a',
+ 'age': 1
+ }, {
+ 'name': 'b',
+ 'age': 2
+ }, {
+ 'name': 'c',
+ 'age': 3
+ }])
+
+ source = PandasDataset(df)
+ source.setup()
+
+ source_schema = source.source_schema()
+ assert source_schema == SourceSchema(
+ fields=schema({
+ PANDAS_INDEX_COLUMN: 'int64',
+ 'name': 'string',
+ 'age': 'int64'
+ }).fields,
+ num_items=3)
+
+ items = list(source.process())
+
+ assert items == [{
+ PANDAS_INDEX_COLUMN: 0,
+ 'name': 'a',
+ 'age': 1
+ }, {
+ PANDAS_INDEX_COLUMN: 1,
+ 'name': 'b',
+ 'age': 2
+ }, {
+ PANDAS_INDEX_COLUMN: 2,
+ 'name': 'c',
+ 'age': 3
+ }]
+
+
+def test_simple_dataframe_with_index() -> None:
+ df = pd.DataFrame.from_records([{
+ 'name': 'a',
+ 'age': 1
+ }, {
+ 'name': 'b',
+ 'age': 2
+ }, {
+ 'name': 'c',
+ 'age': 3
+ }],
+ index=['id1', 'id2', 'id3'])
+
+ source = PandasDataset(df)
+ source.setup()
+
+ source_schema = source.source_schema()
+ assert source_schema == SourceSchema(
+ fields=schema({
+ PANDAS_INDEX_COLUMN: 'string',
+ 'name': 'string',
+ 'age': 'int64'
+ }).fields,
+ num_items=3)
+
+ items = list(source.process())
+
+ # The PANDAS_INDEX_COLUMN aligns with the pandas index.
+ assert items == [{
+ PANDAS_INDEX_COLUMN: 'id1',
+ 'name': 'a',
+ 'age': 1
+ }, {
+ PANDAS_INDEX_COLUMN: 'id2',
+ 'name': 'b',
+ 'age': 2
+ }, {
+ PANDAS_INDEX_COLUMN: 'id3',
+ 'name': 'c',
+ 'age': 3
+ }]
diff --git a/src/data/sources/reddit_hf_source.py b/src/data/sources/reddit_hf_source.py
new file mode 100644
index 0000000000000000000000000000000000000000..7480eb528b8b6e42d56e446f8f6ade95079c3a14
--- /dev/null
+++ b/src/data/sources/reddit_hf_source.py
@@ -0,0 +1,53 @@
+"""Loads reddit data from Huggingface."""
+from typing import Iterable, Optional
+
+from pydantic import Field as PydanticField
+from typing_extensions import override
+
+from ...schema import Item
+from .huggingface_source import HuggingFaceDataset
+from .source import Source, SourceSchema
+
+HF_REDDIT_DATASET_NAME = 'reddit'
+HF_SUBREDDIT_COL = 'subreddit'
+
+
+class RedditDataset(Source):
+ """Reddit data loader, using Huggingface.
+
+ Loads data from [huggingface.co/datasets/reddit](https://huggingface.co/datasets/reddit).
+ """ # noqa: D415, D400
+ name = 'reddit'
+
+ subreddits: Optional[list[str]] = PydanticField(
+ required=False,
+ description='If defined, only loads the subset of reddit data in these subreddit.',
+ )
+
+ _hf_dataset: HuggingFaceDataset
+
+ @override
+ def setup(self) -> None:
+ self._hf_dataset = HuggingFaceDataset(dataset_name=HF_REDDIT_DATASET_NAME)
+ self._hf_dataset.setup()
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ return self._hf_dataset.source_schema()
+
+ @override
+ def process(self) -> Iterable[Item]:
+ items = self._hf_dataset.process()
+
+ if not self.subreddits:
+ return items
+
+ lower_subreddits = [subreddit.lower() for subreddit in self.subreddits]
+
+ for item in items:
+ item_subreddit = item[HF_SUBREDDIT_COL]
+ if item_subreddit.lower() not in lower_subreddits:
+ # Yield None so that the progress bar is accurate.
+ yield None
+ continue
+ yield item
diff --git a/src/data/sources/source.py b/src/data/sources/source.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2f7df406fb1431189f1365ff7cea6b2534fa7f4
--- /dev/null
+++ b/src/data/sources/source.py
@@ -0,0 +1,107 @@
+"""Interface for implementing a source."""
+
+import abc
+from typing import ClassVar, Iterable, Optional
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from pydantic import BaseModel, validator
+
+from ...schema import (
+ Field,
+ ImageInfo,
+ Item,
+ Schema,
+ arrow_dtype_to_dtype,
+ arrow_schema_to_schema,
+ field,
+)
+
+
+class SourceSchema(BaseModel):
+ """The schema of a source."""
+ fields: dict[str, Field]
+ num_items: Optional[int]
+
+
+class SourceProcessResult(BaseModel):
+ """The result after processing all the shards of a source dataset."""
+ filepaths: list[str]
+ data_schema: Schema
+ images: Optional[list[ImageInfo]]
+ num_items: int
+
+
+class Source(abc.ABC, BaseModel):
+ """Interface for sources to implement. A source processes a set of shards and writes files."""
+ # ClassVars do not get serialized with pydantic.
+ name: ClassVar[str]
+
+ # The source_name will get populated in init automatically from the class name so it gets
+ # serialized and the source author doesn't have to define both the static property and the field.
+ source_name: Optional[str]
+
+ class Config:
+ underscore_attrs_are_private = True
+
+ @validator('source_name', always=True)
+ def validate_source_name(cls, source_name: str) -> str:
+ """Return the static name when the source_name name hasn't yet been set."""
+ # When it's already been set from JSON, just return it.
+ if source_name:
+ return source_name
+
+ if 'name' not in cls.__dict__:
+ raise ValueError('Source attribute "name" must be defined.')
+
+ return cls.name
+
+ @abc.abstractmethod
+ def source_schema(self) -> SourceSchema:
+ """Return the source schema for this source.
+
+ Returns
+ A SourceSchema with
+ fields: mapping top-level columns to fields that describes the schema of the source.
+ num_items: the number of items in the source, used for progress.
+
+ """
+ pass
+
+ def setup(self) -> None:
+ """Prepare the source for processing.
+
+ This allows the source to do setup outside the constructor, but before its processed. This
+ avoids potentially expensive computation the pydantic model is deserialized.
+ """
+ pass
+
+ def teardown(self) -> None:
+ """Tears down the source after processing."""
+ pass
+
+ @abc.abstractmethod
+ def process(self) -> Iterable[Item]:
+ """Process the source upload request.
+
+ Args:
+ task_step_id: The TaskManager `task_step_id` for this process run. This is used to update the
+ progress of the task.
+ """
+ pass
+
+
+def schema_from_df(df: pd.DataFrame, index_colname: str) -> SourceSchema:
+ """Create a source schema from a dataframe."""
+ index_np_dtype = df.index.dtype
+ # String index dtypes are stored as objects.
+ if index_np_dtype == np.dtype(object):
+ index_np_dtype = np.dtype(str)
+ index_dtype = arrow_dtype_to_dtype(pa.from_numpy_dtype(index_np_dtype))
+
+ schema = arrow_schema_to_schema(pa.Schema.from_pandas(df, preserve_index=False))
+ return SourceSchema(
+ fields={
+ **schema.fields, index_colname: field(dtype=index_dtype)
+ }, num_items=len(df))
diff --git a/src/data/sources/source_registry.py b/src/data/sources/source_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d4b1a292aaf7c9bd6e498b7ed422d52c2730087
--- /dev/null
+++ b/src/data/sources/source_registry.py
@@ -0,0 +1,47 @@
+"""Source registry for the dataset sources."""
+
+from typing import Type, Union
+
+from .source import Source
+
+SOURCE_REGISTRY: dict[str, Type[Source]] = {}
+
+
+def register_source(source_cls: Type[Source]) -> None:
+ """Register a source configuration globally."""
+ if source_cls.name in SOURCE_REGISTRY:
+ raise ValueError(f'Source "{source_cls.name}" has already been registered!')
+
+ SOURCE_REGISTRY[source_cls.name] = source_cls
+
+
+def get_source_cls(source_name: str) -> Type[Source]:
+ """Return a registered source given the name in the registry."""
+ if source_name not in SOURCE_REGISTRY:
+ raise ValueError(f'Source "{source_name}" not found in the registry')
+
+ return SOURCE_REGISTRY[source_name]
+
+
+def registered_sources() -> dict[str, Type[Source]]:
+ """Return all registered sources."""
+ return SOURCE_REGISTRY
+
+
+def resolve_source(source: Union[dict, Source]) -> Source:
+ """Resolve a generic source base class to a specific source class."""
+ if isinstance(source, Source):
+ # The source is already parsed.
+ return source
+
+ source_name = source.get('source_name')
+ if not source_name:
+ raise ValueError('"source_name" needs to be defined in the json dict.')
+
+ source_cls = get_source_cls(source_name)
+ return source_cls(**source)
+
+
+def clear_source_registry() -> None:
+ """Clear the source registry."""
+ SOURCE_REGISTRY.clear()
diff --git a/src/data/sources/source_registry_test.py b/src/data/sources/source_registry_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d910bc0c2bae139cda69eff25d54e01ace2bf59
--- /dev/null
+++ b/src/data/sources/source_registry_test.py
@@ -0,0 +1,55 @@
+"""A source to compute semantic search for a document."""
+from typing import Iterable, cast
+
+import pytest
+from typing_extensions import override
+
+from ...schema import Item
+from .source import Source, SourceSchema
+from .source_registry import clear_source_registry, get_source_cls, register_source, resolve_source
+
+
+class TestSource(Source):
+ """A test source."""
+ name = 'test_source'
+
+ @override
+ def setup(self) -> None:
+ pass
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ """Return the source schema."""
+ return cast(SourceSchema, None)
+
+ @override
+ def process(self) -> Iterable[Item]:
+ yield None
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_source(TestSource)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_source_registry()
+
+
+def test_get_source_cls() -> None:
+ """Test getting a source."""
+ assert TestSource == get_source_cls('test_source')
+
+
+def test_resolve_source() -> None:
+ """Test resolving a source."""
+ test_source = TestSource()
+
+ # sources pass through.
+ assert resolve_source(test_source) == test_source
+
+ # Dicts resolve to the base class.
+ assert resolve_source(test_source.dict()) == test_source
diff --git a/src/data_loader.py b/src/data_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..f122e659c9572f3831ff29cb2391346cab004690
--- /dev/null
+++ b/src/data_loader.py
@@ -0,0 +1,150 @@
+"""A data loader standalone binary. This should only be run as a script to load a dataset.
+
+To run the source loader as a binary directly:
+
+poetry run python -m src.data_loader \
+ --dataset_name=movies_dataset \
+ --output_dir=./data/ \
+ --config_path=./datasets/the_movies_dataset.json
+"""
+import json
+import math
+import os
+import pathlib
+import uuid
+from typing import Iterable, Optional, Union, cast
+
+import click
+import pandas as pd
+from distributed import Client
+
+from .data.dataset_utils import write_items_to_parquet
+from .data.sources.default_sources import register_default_sources
+from .data.sources.source import Source
+from .data.sources.source_registry import resolve_source
+from .schema import (
+ MANIFEST_FILENAME,
+ PARQUET_FILENAME_PREFIX,
+ UUID_COLUMN,
+ DataType,
+ Field,
+ Item,
+ Schema,
+ SourceManifest,
+ field,
+)
+from .tasks import TaskStepId, progress
+from .utils import get_dataset_output_dir, log, open_file
+
+
+def process_source(base_dir: Union[str, pathlib.Path],
+ namespace: str,
+ dataset_name: str,
+ source: Source,
+ task_step_id: Optional[TaskStepId] = None) -> tuple[str, int]:
+ """Process a source."""
+ output_dir = get_dataset_output_dir(base_dir, namespace, dataset_name)
+
+ source.setup()
+ source_schema = source.source_schema()
+ items = source.process()
+
+ # Add UUIDs and fix NaN in string columns.
+ items = normalize_items(items, source_schema.fields)
+
+ # Add progress.
+ if task_step_id is not None:
+ items = progress(
+ items,
+ task_step_id=task_step_id,
+ estimated_len=source_schema.num_items,
+ step_description=f'Reading from source {source.name}...')
+
+ # Filter out the `None`s after progress.
+ items = cast(Iterable[Item], (item for item in items if item is not None))
+
+ data_schema = Schema(fields={**source_schema.fields, UUID_COLUMN: field('string')})
+ filepath, num_items = write_items_to_parquet(
+ items=items,
+ output_dir=output_dir,
+ schema=data_schema,
+ filename_prefix=PARQUET_FILENAME_PREFIX,
+ shard_index=0,
+ num_shards=1)
+
+ filenames = [os.path.basename(filepath)]
+ manifest = SourceManifest(files=filenames, data_schema=data_schema, images=None)
+ with open_file(os.path.join(output_dir, MANIFEST_FILENAME), 'w') as f:
+ f.write(manifest.json(indent=2, exclude_none=True))
+ log(f'Manifest for dataset "{dataset_name}" written to {output_dir}')
+
+ return output_dir, num_items
+
+
+def normalize_items(items: Iterable[Item], fields: dict[str, Field]) -> Item:
+ """Sanitize items by removing NaNs and NaTs."""
+ string_fields = set(
+ [field_name for field_name, field in fields.items() if field.dtype == DataType.STRING])
+ timestamp_fields = set(
+ [field_name for field_name, field in fields.items() if field.dtype == DataType.TIMESTAMP])
+ for item in items:
+ if item is None:
+ yield item
+ continue
+
+ # Add row uuid if it doesn't exist.
+ if UUID_COLUMN not in item:
+ item[UUID_COLUMN] = uuid.uuid4().hex
+
+ # Fix NaN string fields.
+ for name in string_fields:
+ item_value = item.get(name)
+ if item_value and not isinstance(item_value, str):
+ if math.isnan(item_value):
+ item[name] = None
+ else:
+ item[name] = str(item_value)
+
+ # Fix NaT (not a time) timestamp fields.
+ for name in timestamp_fields:
+ item_value = item.get(name)
+ if item_value and pd.isnull(item_value):
+ item[name] = None
+
+ yield item
+
+
+@click.command()
+@click.option(
+ '--output_dir',
+ required=True,
+ type=str,
+ help='The output directory to write the parquet files to.')
+@click.option(
+ '--config_path',
+ required=True,
+ type=str,
+ help='The path to a json file describing the source configuration.')
+@click.option(
+ '--dataset_name', required=True, type=str, help='The dataset name, used for binary mode only.')
+@click.option(
+ '--namespace',
+ required=False,
+ default='local',
+ type=str,
+ help='The namespace to use. Defaults to "local".')
+def main(output_dir: str, config_path: str, dataset_name: str, namespace: str) -> None:
+ """Run the source loader as a binary."""
+ register_default_sources()
+
+ with open_file(config_path) as f:
+ # Parse the json file in a dict.
+ source_dict = json.load(f)
+ source = resolve_source(source_dict)
+
+ client = Client()
+ client.submit(process_source, output_dir, namespace, dataset_name, source).result()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/src/data_loader_test.py b/src/data_loader_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c476fa62f15c37723dc993d6dff02d277884e458
--- /dev/null
+++ b/src/data_loader_test.py
@@ -0,0 +1,74 @@
+"""Tests for data_loader.py."""
+
+import os
+import pathlib
+import uuid
+from typing import Iterable
+
+from pytest_mock import MockerFixture
+from typing_extensions import override
+
+from .data.dataset_duckdb import read_source_manifest
+from .data.dataset_utils import parquet_filename
+from .data.sources.source import Source, SourceSchema
+from .data_loader import process_source
+from .schema import PARQUET_FILENAME_PREFIX, UUID_COLUMN, Item, SourceManifest, schema
+from .test_utils import fake_uuid, read_items
+from .utils import DATASETS_DIR_NAME
+
+
+class TestSource(Source):
+ """A test source."""
+ name = 'test_source'
+
+ @override
+ def setup(self) -> None:
+ pass
+
+ @override
+ def source_schema(self) -> SourceSchema:
+ """Return the source schema."""
+ return SourceSchema(fields=schema({'x': 'int64', 'y': 'string'}).fields, num_items=2)
+
+ @override
+ def process(self) -> Iterable[Item]:
+ return [{'x': 1, 'y': 'ten'}, {'x': 2, 'y': 'twenty'}]
+
+
+def test_data_loader(tmp_path: pathlib.Path, mocker: MockerFixture) -> None:
+ mock_uuid = mocker.patch.object(uuid, 'uuid4', autospec=True)
+ mock_uuid.side_effect = [fake_uuid(b'1'), fake_uuid(b'2')]
+
+ source = TestSource()
+ setup_mock = mocker.spy(TestSource, 'setup')
+
+ output_dir, num_items = process_source(tmp_path, 'test_namespace', 'test_dataset', source)
+
+ assert setup_mock.call_count == 1
+
+ assert output_dir == os.path.join(tmp_path, DATASETS_DIR_NAME, 'test_namespace', 'test_dataset')
+ assert num_items == 2
+
+ source_manifest = read_source_manifest(output_dir)
+
+ assert source_manifest == SourceManifest(
+ files=[parquet_filename(PARQUET_FILENAME_PREFIX, 0, 1)],
+ data_schema=schema({
+ # UUID_COLUMN is generated by the data loader.
+ UUID_COLUMN: 'string',
+ 'x': 'int64',
+ 'y': 'string'
+ }),
+ )
+
+ items = read_items(output_dir, source_manifest.files, source_manifest.data_schema)
+
+ assert items == [{
+ UUID_COLUMN: fake_uuid(b'1').hex,
+ 'x': 1,
+ 'y': 'ten'
+ }, {
+ UUID_COLUMN: fake_uuid(b'2').hex,
+ 'x': 2,
+ 'y': 'twenty'
+ }]
diff --git a/src/db_manager.py b/src/db_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc8f2bda84d5a6cf48b7bd2bb778a3487914d1da
--- /dev/null
+++ b/src/db_manager.py
@@ -0,0 +1,42 @@
+"""Manages mapping the dataset name to the database instance."""
+import os
+import threading
+from typing import Type
+
+from .data.dataset import Dataset
+
+_DEFAULT_DATASET_CLS: Type[Dataset]
+
+_CACHED_DATASETS: dict[str, Dataset] = {}
+
+_db_lock = threading.Lock()
+
+
+def get_dataset(namespace: str, dataset_name: str) -> Dataset:
+ """Get the dataset instance."""
+ if not _DEFAULT_DATASET_CLS:
+ raise ValueError('Default dataset class not set.')
+ cache_key = f'{namespace}/{dataset_name}'
+ # https://docs.pytest.org/en/latest/example/simple.html#pytest-current-test-environment-variable
+ inside_test = 'PYTEST_CURRENT_TEST' in os.environ
+ with _db_lock:
+ if cache_key not in _CACHED_DATASETS or inside_test:
+ _CACHED_DATASETS[cache_key] = _DEFAULT_DATASET_CLS(
+ namespace=namespace, dataset_name=dataset_name)
+ return _CACHED_DATASETS[cache_key]
+
+
+def remove_dataset_from_cache(namespace: str, dataset_name: str) -> None:
+ """Remove the dataset from the db manager cache."""
+ cache_key = f'{namespace}/{dataset_name}'
+ with _db_lock:
+ if cache_key in _CACHED_DATASETS:
+ del _CACHED_DATASETS[cache_key]
+
+
+# TODO(nsthorat): Make this a registry once we have multiple dataset implementations. This breaks a
+# circular dependency.
+def set_default_dataset_cls(dataset_cls: Type[Dataset]) -> None:
+ """Set the default dataset class."""
+ global _DEFAULT_DATASET_CLS
+ _DEFAULT_DATASET_CLS = dataset_cls
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diff --git a/src/embeddings/cohere.py b/src/embeddings/cohere.py
new file mode 100644
index 0000000000000000000000000000000000000000..49756abbda188e56d53615ffbc61b87f08ef256f
--- /dev/null
+++ b/src/embeddings/cohere.py
@@ -0,0 +1,51 @@
+"""Cohere embeddings."""
+import functools
+from typing import Iterable, cast
+
+import cohere
+import numpy as np
+from typing_extensions import override
+
+from ..config import CONFIG
+from ..schema import Item, RichData
+from ..signals.signal import TextEmbeddingSignal
+from ..signals.splitters.chunk_splitter import split_text
+from .embedding import compute_split_embeddings
+
+NUM_PARALLEL_REQUESTS = 10
+COHERE_BATCH_SIZE = 96
+
+
+@functools.cache
+def _cohere() -> cohere.Client:
+ api_key = CONFIG['COHERE_API_KEY']
+ if not api_key:
+ raise ValueError('`COHERE_API_KEY` environment variable not set.')
+ return cohere.Client(api_key, max_retries=10)
+
+
+class Cohere(TextEmbeddingSignal):
+ """Computes embeddings using Cohere's embedding API.
+
+
**Important**: This will send data to an external server!
+
+
To use this signal, you must get a Cohere API key from
+ [cohere.com/embed](https://cohere.com/embed) and add it to your .env.local.
+
+
For details on pricing, see: https://cohere.com/pricing.
+ """
+
+ name = 'cohere'
+ display_name = 'Cohere Embeddings'
+
+ @override
+ def compute(self, docs: Iterable[RichData]) -> Iterable[Item]:
+ """Compute embeddings for the given documents."""
+
+ def embed_fn(texts: list[str]) -> list[np.ndarray]:
+ return _cohere().embed(texts, truncate='END').embeddings
+
+ docs = cast(Iterable[str], docs)
+ split_fn = split_text if self._split else None
+ yield from compute_split_embeddings(
+ docs, COHERE_BATCH_SIZE, embed_fn, split_fn, num_parallel_requests=NUM_PARALLEL_REQUESTS)
diff --git a/src/embeddings/embedding.py b/src/embeddings/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffb64ae899f0416c2931ffa443010e227af5dc18
--- /dev/null
+++ b/src/embeddings/embedding.py
@@ -0,0 +1,99 @@
+"""Embedding registry."""
+from concurrent.futures import ThreadPoolExecutor
+from typing import Callable, Generator, Iterable, Optional, Union, cast
+
+import numpy as np
+from pydantic import StrictStr
+from sklearn.preprocessing import normalize
+
+from ..data.dataset_utils import lilac_embedding
+from ..schema import Item, RichData
+from ..signals.signal import EMBEDDING_KEY, TextEmbeddingSignal, get_signal_by_type
+from ..signals.splitters.chunk_splitter import TextChunk
+from ..utils import chunks
+
+EmbeddingId = Union[StrictStr, TextEmbeddingSignal]
+
+EmbedFn = Callable[[Iterable[RichData]], np.ndarray]
+
+
+def get_embed_fn(embedding_name: str) -> EmbedFn:
+ """Return a function that returns the embedding matrix for the given embedding signal."""
+ embedding_cls = get_signal_by_type(embedding_name, TextEmbeddingSignal)
+ embedding = embedding_cls(split=False)
+ embedding.setup()
+
+ def _embed_fn(data: Iterable[RichData]) -> np.ndarray:
+ items = embedding.compute(data)
+
+ embedding_vectors: list[np.ndarray] = []
+ for item in items:
+ if not item:
+ raise ValueError('Embedding signal returned None.')
+ if len(item) != 1:
+ raise ValueError(
+ f'Embedding signal returned {len(item)} items, but expected 1 since split was False')
+ embedding_vector = item[0][EMBEDDING_KEY]
+ if not isinstance(embedding_vector, np.ndarray):
+ raise ValueError(
+ f'Embedding signal returned {type(embedding_vector)} which is not an ndarray.')
+ # We use squeeze here because embedding functions can return outer dimensions of 1.
+ embedding_vector = embedding_vector.reshape(-1)
+ if embedding_vector.ndim != 1:
+ raise ValueError(f'Expected embeddings to be 1-dimensional, got {embedding_vector.ndim} '
+ f'with shape {embedding_vector.shape}.')
+ embedding_vectors.append(embedding_vector)
+ return np.array(embedding_vectors)
+
+ return _embed_fn
+
+
+def compute_split_embeddings(docs: Iterable[str],
+ batch_size: int,
+ embed_fn: Callable[[list[str]], list[np.ndarray]],
+ split_fn: Optional[Callable[[str], list[TextChunk]]] = None,
+ num_parallel_requests: int = 1) -> Generator[Item, None, None]:
+ """Compute text embeddings in batches of chunks, using the provided splitter and embedding fn."""
+ pool = ThreadPoolExecutor()
+
+ def _splitter(doc: str) -> list[TextChunk]:
+ if doc is None:
+ return []
+ if split_fn:
+ return split_fn(doc)
+ else:
+ # Return a single chunk that spans the entire document.
+ return [(doc, (0, len(doc)))]
+
+ def _flat_split_batch_docs(docs: Iterable[str]) -> Generator[tuple[int, TextChunk], None, None]:
+ """Split a batch of documents into chunks and yield them."""
+ for i, doc in enumerate(docs):
+ chunks = _splitter(doc) or [cast(TextChunk, ('', (0, 0)))]
+ for chunk in chunks:
+ yield (i, chunk)
+
+ doc_chunks = _flat_split_batch_docs(docs)
+ items_to_yield: list[Item] = []
+ current_index = 0
+
+ mega_batch_size = batch_size * num_parallel_requests
+
+ for batch in chunks(doc_chunks, mega_batch_size):
+ texts = [text for _, (text, _) in batch]
+ embeddings: list[np.ndarray] = []
+ for x in list(pool.map(lambda x: embed_fn(x), chunks(texts, batch_size))):
+ embeddings.extend(x)
+ matrix = normalize(np.array(embeddings)).astype(np.float16)
+ # np.split returns a shallow copy of each embedding so we don't increase the mem footprint.
+ embeddings_batch = cast(list[np.ndarray], np.split(matrix, matrix.shape[0]))
+ for (index, (_, (start, end))), embedding in zip(batch, embeddings_batch):
+ if index == current_index:
+ items_to_yield.append(lilac_embedding(start, end, embedding))
+ else:
+ yield items_to_yield
+ items_to_yield = [lilac_embedding(start, end, embedding)]
+ current_index = index
+
+ # Yield the last batch.
+ if items_to_yield:
+ yield items_to_yield
diff --git a/src/embeddings/embedding_test.py b/src/embeddings/embedding_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..eae244d7421dfad40c9e4d665f2d1f35799dac83
--- /dev/null
+++ b/src/embeddings/embedding_test.py
@@ -0,0 +1,87 @@
+"""Tests for embedding.py."""
+
+import numpy as np
+
+from ..data.dataset_utils import lilac_embedding
+from ..signals.splitters.chunk_splitter import TextChunk
+from .embedding import compute_split_embeddings
+
+
+def char_splitter(text: str) -> list[TextChunk]:
+ return [(letter, (i, i + 1)) for i, letter in enumerate(text)]
+
+
+def test_split_and_combine_text_embeddings_batch_across_two_docs() -> None:
+ docs = ['This is', '123']
+ batch_size = 3
+
+ embed_fn_inputs: list[list[str]] = []
+
+ def embed_fn(batch: list[str]) -> list[np.ndarray]:
+ embed_fn_inputs.append(batch)
+ return [np.ones(1) for _ in batch]
+
+ result = list(compute_split_embeddings(docs, batch_size, embed_fn, char_splitter))
+
+ # Each input to embed_fn is a batch of at most 3 letters.
+ assert embed_fn_inputs == [
+ ['T', 'h', 'i'],
+ ['s', ' ', 'i'],
+ ['s', '1', '2'],
+ ['3'],
+ ]
+
+ assert result == [
+ [
+ lilac_embedding(0, 1, np.array(1)), # T
+ lilac_embedding(1, 2, np.array(1)), # h
+ lilac_embedding(2, 3, np.array(1)), # i
+ lilac_embedding(3, 4, np.array(1)), # s
+ lilac_embedding(4, 5, np.array(1)), # ' '
+ lilac_embedding(5, 6, np.array(1)), # i
+ lilac_embedding(6, 7, np.array(1)), # s
+ ],
+ [
+ lilac_embedding(0, 1, np.array(1)), # 1
+ lilac_embedding(1, 2, np.array(1)), # 2
+ lilac_embedding(2, 3, np.array(1)), # 3
+ ],
+ ]
+
+
+def test_split_and_combine_text_embeddings_no_docs() -> None:
+ docs: list[str] = []
+ batch_size = 3
+
+ embed_fn_inputs: list[list[str]] = []
+
+ def embed_fn(batch: list[str]) -> list[np.ndarray]:
+ embed_fn_inputs.append(batch)
+ return [np.ones(1) for _ in batch]
+
+ result = list(compute_split_embeddings(docs, batch_size, embed_fn, char_splitter))
+ assert embed_fn_inputs == []
+ assert result == []
+
+
+def test_split_and_combine_text_embeddings_empty_docs() -> None:
+ docs: list[str] = ['', '', '123']
+ batch_size = 3
+
+ embed_fn_inputs: list[list[str]] = []
+
+ def embed_fn(batch: list[str]) -> list[np.ndarray]:
+ embed_fn_inputs.append(batch)
+ return [np.ones(1) for _ in batch]
+
+ result = list(compute_split_embeddings(docs, batch_size, embed_fn, char_splitter))
+ assert embed_fn_inputs == [['', '', '1'], ['2', '3']]
+ assert result == [
+ [lilac_embedding(0, 0, np.array(1))], # ''
+ [lilac_embedding(0, 0, np.array(1))], # ''
+ [
+ lilac_embedding(0, 1, np.array(1)), # 1
+ lilac_embedding(1, 2, np.array(1)), # 2
+ lilac_embedding(2, 3, np.array(1)), # 3
+ ],
+ ]
diff --git a/src/embeddings/sbert.py b/src/embeddings/sbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbe6043084bb5790fd99e9e09b9738ec7d7571a6
--- /dev/null
+++ b/src/embeddings/sbert.py
@@ -0,0 +1,60 @@
+"""Sentence-BERT embeddings. Open-source models, designed to run on device."""
+import functools
+from typing import Iterable, Optional, cast
+
+import torch
+from sentence_transformers import SentenceTransformer
+from typing_extensions import override
+
+from ..schema import Item, RichData
+from ..signals.signal import TextEmbeddingSignal
+from ..signals.splitters.chunk_splitter import split_text
+from ..utils import log
+from .embedding import compute_split_embeddings
+
+# The `all-mpnet-base-v2` model provides the best quality, while `all-MiniLM-L6-v2`` is 5 times
+# faster and still offers good quality. See https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models/
+MINI_LM_MODEL = 'all-MiniLM-L6-v2'
+
+SBERT_DEFAULT_BATCH_SIZE = 64
+# Maps a tuple of model name and device to the optimal batch size, found empirically.
+SBERT_OPTIMAL_BATCH_SIZE: dict[tuple[str, str], int] = {
+ (MINI_LM_MODEL, 'mps'): 256,
+}
+
+MODEL_NAME = MINI_LM_MODEL
+
+
+@functools.cache
+def _sbert() -> tuple[Optional[str], SentenceTransformer]:
+ preferred_device: Optional[str] = None
+ if torch.backends.mps.is_available():
+ preferred_device = 'mps'
+ elif not torch.backends.mps.is_built():
+ log('MPS not available because the current PyTorch install was not built with MPS enabled.')
+ return preferred_device, SentenceTransformer(MODEL_NAME, device=preferred_device)
+
+
+def _optimal_batch_size(preferred_device: Optional[str]) -> int:
+ model_device = (MODEL_NAME, str(preferred_device))
+ if model_device in SBERT_OPTIMAL_BATCH_SIZE:
+ return SBERT_OPTIMAL_BATCH_SIZE[model_device]
+ return SBERT_DEFAULT_BATCH_SIZE
+
+
+class SBERT(TextEmbeddingSignal):
+ """Computes embeddings using Sentence-BERT library."""
+
+ name = 'sbert'
+ display_name = 'SBERT Embeddings'
+
+ @override
+ def compute(self, docs: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ preferred_device, model = _sbert()
+ batch_size = _optimal_batch_size(preferred_device)
+
+ embed_fn = model.encode
+ split_fn = split_text if self._split else None
+ docs = cast(Iterable[str], docs)
+ yield from compute_split_embeddings(docs, batch_size, embed_fn=embed_fn, split_fn=split_fn)
diff --git a/src/embeddings/vector_store.py b/src/embeddings/vector_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9648ee46e441b839cb09e19fb11be5e061ebe76
--- /dev/null
+++ b/src/embeddings/vector_store.py
@@ -0,0 +1,57 @@
+"""Interface for storing vectors."""
+
+import abc
+from typing import Iterable, Optional
+
+import numpy as np
+
+from ..schema import VectorKey
+
+
+class VectorStore(abc.ABC):
+ """Interface for storing and retrieving vectors."""
+
+ @abc.abstractmethod
+ def keys(self) -> list[VectorKey]:
+ """Return the keys in the store."""
+ pass
+
+ @abc.abstractmethod
+ def add(self, keys: list[VectorKey], embeddings: np.ndarray) -> None:
+ """Add or edit the given keyed embeddings to the store.
+
+ If the keys already exist they will be overwritten, acting as an "upsert".
+
+ Args:
+ keys: The keys to add the embeddings for.
+ embeddings: The embeddings to add. This should be a 2D matrix with the same length as keys.
+ """
+ pass
+
+ @abc.abstractmethod
+ def get(self, keys: Iterable[VectorKey]) -> np.ndarray:
+ """Return the embeddings for given keys.
+
+ Args:
+ keys: The keys to return the embeddings for. If None, return all embeddings.
+
+ Returns
+ The embeddings for the given keys.
+ """
+ pass
+
+ def topk(self,
+ query: np.ndarray,
+ k: int,
+ key_prefixes: Optional[Iterable[VectorKey]] = None) -> list[tuple[VectorKey, float]]:
+ """Return the top k most similar vectors.
+
+ Args:
+ query: The query vector.
+ k: The number of results to return.
+ key_prefixes: Optional key prefixes to restrict the search to.
+
+ Returns
+ A list of (key, score) tuples.
+ """
+ raise NotImplementedError
diff --git a/src/embeddings/vector_store_numpy.py b/src/embeddings/vector_store_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..78683b2674745da8d3ea3e18777c43d8cec4af84
--- /dev/null
+++ b/src/embeddings/vector_store_numpy.py
@@ -0,0 +1,81 @@
+"""NumpyVectorStore class for storing vectors in numpy arrays."""
+
+from typing import Iterable, Optional, cast
+
+import numpy as np
+import pandas as pd
+from typing_extensions import override
+
+from ..schema import VectorKey
+from .vector_store import VectorStore
+
+
+class NumpyVectorStore(VectorStore):
+ """Stores vectors as in-memory np arrays."""
+ _embeddings: np.ndarray
+ _keys: list[VectorKey]
+ # Maps a `VectorKey` to a row index in `_embeddings`.
+ _lookup: pd.Series
+
+ @override
+ def keys(self) -> list[VectorKey]:
+ return self._keys
+
+ @override
+ def add(self, keys: list[VectorKey], embeddings: np.ndarray) -> None:
+ if hasattr(self, '_embeddings') or hasattr(self, '_keys'):
+ raise ValueError('Embeddings already exist in this store. Upsert is not yet supported.')
+
+ if len(keys) != embeddings.shape[0]:
+ raise ValueError(
+ f'Length of keys ({len(keys)}) does not match number of embeddings {embeddings.shape[0]}.')
+
+ self._keys = keys
+ # Cast to float32 since dot product with float32 is 40-50x faster than float16 and 2.5x faster
+ # than float64.
+ self._embeddings = embeddings.astype(np.float32)
+
+ index = pd.MultiIndex.from_tuples(keys)
+ row_indices = np.arange(len(self._embeddings), dtype=np.uint32)
+ self._lookup = pd.Series(row_indices, index=index)
+
+ @override
+ def get(self, keys: Iterable[VectorKey]) -> np.ndarray:
+ """Return the embeddings for given keys.
+
+ Args:
+ keys: The keys to return the embeddings for.
+
+ Returns
+ The embeddings for the given keys.
+ """
+ return self._embeddings.take(self._lookup.loc[keys], axis=0)
+
+ @override
+ def topk(self,
+ query: np.ndarray,
+ k: int,
+ key_prefixes: Optional[Iterable[VectorKey]] = None) -> list[tuple[VectorKey, float]]:
+ if key_prefixes is not None:
+ # Cast tuples of length 1 to the element itself to avoid a pandas bug.
+ key_prefixes = cast(
+ list[VectorKey],
+ [k[0] if isinstance(k, tuple) and len(k) == 1 else k for k in key_prefixes])
+ # This uses the hierarchical index (MutliIndex) to do a prefix lookup.
+ row_indices = self._lookup.loc[key_prefixes]
+ keys, embeddings = list(row_indices.index), self._embeddings.take(row_indices, axis=0)
+ else:
+ keys, embeddings = self._keys, self._embeddings
+
+ query = query.astype(embeddings.dtype)
+ similarities: np.ndarray = np.dot(embeddings, query).reshape(-1)
+ k = min(k, len(similarities))
+
+ # We do a partition + sort only top K to save time: O(n + klogk) instead of O(nlogn).
+ indices = np.argpartition(similarities, -k)[-k:]
+ # Indices sorted by value from largest to smallest.
+ indices = indices[np.argsort(similarities[indices])][::-1]
+
+ topk_similarities = similarities[indices]
+ topk_keys = [keys[idx] for idx in indices]
+ return list(zip(topk_keys, topk_similarities))
diff --git a/src/embeddings/vector_store_test.py b/src/embeddings/vector_store_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..1652b04802232408325e240f428e11ee5bf64551
--- /dev/null
+++ b/src/embeddings/vector_store_test.py
@@ -0,0 +1,69 @@
+"""Tests the vector store interface."""
+
+from typing import Type
+
+import numpy as np
+import pytest
+
+from .vector_store import VectorStore
+from .vector_store_numpy import NumpyVectorStore
+
+ALL_STORES = [NumpyVectorStore]
+
+
+@pytest.mark.parametrize('store_cls', ALL_STORES)
+class VectorStoreSuite:
+
+ def test_get_all(self, store_cls: Type[VectorStore]) -> None:
+ store = store_cls()
+
+ store.add([('a',), ('b',), ('c',)], np.array([[1, 2], [3, 4], [5, 6]]))
+
+ np.testing.assert_array_equal(
+ store.get([('a',), ('b',), ('c',)]), np.array([[1, 2], [3, 4], [5, 6]]))
+
+ def test_get_subset(self, store_cls: Type[VectorStore]) -> None:
+ store = store_cls()
+
+ store.add([('a',), ('b',), ('c',)], np.array([[1, 2], [3, 4], [5, 6]]))
+
+ np.testing.assert_array_equal(store.get([('b',), ('c',)]), np.array([[3, 4], [5, 6]]))
+
+ def test_topk(self, store_cls: Type[VectorStore]) -> None:
+ store = store_cls()
+ embedding = np.array([[0.45, 0.89], [0.6, 0.8], [0.64, 0.77]])
+ query = np.array([0.89, 0.45])
+ topk = 3
+ store.add([('a',), ('b',), ('c',)], embedding)
+ result = store.topk(query, topk)
+ assert [key for key, _ in result] == [('c',), ('b',), ('a',)]
+ assert [score for _, score in result] == pytest.approx([0.9161, 0.894, 0.801], 1e-3)
+
+ def test_topk_with_restricted_keys(self, store_cls: Type[VectorStore]) -> None:
+ store = store_cls()
+ embedding = np.array([[0.45, 0.89], [0.6, 0.8], [0.64, 0.77]])
+ query = np.array([0.89, 0.45])
+ topk = 3
+ store.add([('a',), ('b',), ('c',)], embedding)
+ result = store.topk(query, topk, key_prefixes=[('b',), ('a',)])
+ assert [key for key, _ in result] == [('b',), ('a',)]
+ assert [score for _, score in result] == pytest.approx([0.894, 0.801], 1e-3)
+
+ result = store.topk(query, topk, key_prefixes=[('a',), ('b',)])
+ assert [key for key, _ in result] == [('b',), ('a',)]
+ assert [score for _, score in result] == pytest.approx([0.894, 0.801], 1e-3)
+
+ result = store.topk(query, topk, key_prefixes=[('a',), ('c',)])
+ assert [key for key, _ in result] == [('c',), ('a',)]
+ assert [score for _, score in result] == pytest.approx([0.9161, 0.801], 1e-3)
+
+ def test_topk_with_key_prefixes(self, store_cls: Type[VectorStore]) -> None:
+ store = store_cls()
+ embedding = np.array([[8], [9], [3], [10]])
+ store.add([('a', 0), ('a', 1), ('b', 0), ('c', 0)], embedding)
+ query = np.array([1])
+ result = store.topk(query, k=2, key_prefixes=[('b',), ('c',)])
+ assert result == [(('c', 0), 10.0), (('b', 0), 3.0)]
+
+ result = store.topk(query, k=10, key_prefixes=[('b',), ('a',)])
+ assert result == [(('a', 1), 9.0), (('a', 0), 8.0), (('b', 0), 3.0)]
diff --git a/src/integration_tests/__pycache__/db_integration_test.cpython-39-pytest-7.2.0.pyc b/src/integration_tests/__pycache__/db_integration_test.cpython-39-pytest-7.2.0.pyc
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diff --git a/src/integration_tests/__pycache__/db_integration_test.cpython-39-pytest-7.2.2.pyc b/src/integration_tests/__pycache__/db_integration_test.cpython-39-pytest-7.2.2.pyc
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diff --git a/src/make_openapi.py b/src/make_openapi.py
new file mode 100644
index 0000000000000000000000000000000000000000..1530df69122e5dfca05344f89276b40c823d84b7
--- /dev/null
+++ b/src/make_openapi.py
@@ -0,0 +1,29 @@
+"""Writes the openapi.json file to the specified output.
+
+This is meant to run as a standalone script. It lives in src/ so that we can import the FastAPI app.
+"""
+import json
+
+import click
+from fastapi.openapi.utils import get_openapi
+
+from .server import app
+
+
+@click.command()
+@click.option(
+ '--output', required=True, type=str, help='The output filepath for the opepnapi.json file.')
+def main(output: str) -> None:
+ """Create the openapi.json file for the API to generate TypeScript stubs."""
+ with open(output, 'w') as f:
+ json.dump(
+ get_openapi(
+ title=app.title,
+ version=app.version,
+ openapi_version=app.openapi_version,
+ description=app.description,
+ routes=app.routes), f)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/src/parquet_writer.py b/src/parquet_writer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3e32b170d6998cb631909835a26859e979cbe79
--- /dev/null
+++ b/src/parquet_writer.py
@@ -0,0 +1,70 @@
+"""A Parquet file writer that wraps the pyarrow writer."""
+from typing import IO, Optional
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+from .schema import Item, Schema, schema_to_arrow_schema
+
+
+class ParquetWriter:
+ """A writer to parquet."""
+
+ def __init__(self,
+ schema: Schema,
+ codec: str = 'snappy',
+ row_group_buffer_size: int = 128 * 1024 * 1024,
+ record_batch_size: int = 10_000):
+ self._schema = schema_to_arrow_schema(schema)
+ self._codec = codec
+ self._row_group_buffer_size = row_group_buffer_size
+ self._buffer: list[list[Optional[Item]]] = [[] for _ in range(len(self._schema.names))]
+ self._buffer_size = record_batch_size
+ self._record_batches: list[pa.RecordBatch] = []
+ self._record_batches_byte_size = 0
+ self.writer: pq.ParquetWriter = None
+
+ def open(self, file_handle: IO) -> None:
+ """Open the destination file for writing."""
+ self.writer = pq.ParquetWriter(file_handle, self._schema, compression=self._codec)
+
+ def write(self, record: Item) -> None:
+ """Write the record to the destination file."""
+ if len(self._buffer[0]) >= self._buffer_size:
+ self._flush_buffer()
+
+ if self._record_batches_byte_size >= self._row_group_buffer_size:
+ self._write_batches()
+
+ # reorder the data in columnar format.
+ for i, n in enumerate(self._schema.names):
+ self._buffer[i].append(record.get(n))
+
+ def close(self) -> None:
+ """Flushes the write buffer and closes the destination file."""
+ if len(self._buffer[0]) > 0:
+ self._flush_buffer()
+ if self._record_batches_byte_size > 0:
+ self._write_batches()
+
+ self.writer.close()
+
+ def _write_batches(self) -> None:
+ table = pa.Table.from_batches(self._record_batches, schema=self._schema)
+ self._record_batches = []
+ self._record_batches_byte_size = 0
+ self.writer.write_table(table)
+
+ def _flush_buffer(self) -> None:
+ arrays: list[pa.array] = [[] for _ in range(len(self._schema.names))]
+ for x, y in enumerate(self._buffer):
+ arrays[x] = pa.array(y, type=self._schema.types[x])
+ self._buffer[x] = []
+ rb = pa.RecordBatch.from_arrays(arrays, schema=self._schema)
+ self._record_batches.append(rb)
+ size = 0
+ for x in arrays:
+ for b in x.buffers(): # type: ignore
+ if b is not None:
+ size = size + b.size
+ self._record_batches_byte_size = self._record_batches_byte_size + size
diff --git a/src/router_concept.py b/src/router_concept.py
new file mode 100644
index 0000000000000000000000000000000000000000..85b17afe958a9a76847c2a3ce55983335e6e55b8
--- /dev/null
+++ b/src/router_concept.py
@@ -0,0 +1,169 @@
+"""Router for the concept database."""
+
+from typing import Optional
+
+import openai
+from fastapi import APIRouter, HTTPException
+from openai_function_call import OpenAISchema
+from pydantic import BaseModel, Field
+
+from .concepts.concept import DRAFT_MAIN, Concept, ConceptModel, DraftId, draft_examples
+from .concepts.db_concept import DISK_CONCEPT_DB, DISK_CONCEPT_MODEL_DB, ConceptInfo, ConceptUpdate
+from .config import CONFIG
+from .router_utils import RouteErrorHandler
+from .schema import SignalInputType
+
+router = APIRouter(route_class=RouteErrorHandler)
+
+
+@router.get('/', response_model_exclude_none=True)
+def get_concepts() -> list[ConceptInfo]:
+ """List the concepts."""
+ return DISK_CONCEPT_DB.list()
+
+
+@router.get('/{namespace}/{concept_name}', response_model_exclude_none=True)
+def get_concept(namespace: str,
+ concept_name: str,
+ draft: Optional[DraftId] = DRAFT_MAIN) -> Concept:
+ """Get a concept from a database."""
+ concept = DISK_CONCEPT_DB.get(namespace, concept_name)
+ if not concept:
+ raise HTTPException(
+ status_code=404, detail=f'Concept "{namespace}/{concept_name}" was not found')
+
+ # Only return the examples from the draft.
+ concept.data = draft_examples(concept, draft or DRAFT_MAIN)
+
+ return concept
+
+
+class CreateConceptOptions(BaseModel):
+ """Options for creating a concept."""
+ # Namespace of the concept.
+ namespace: str
+ # Name of the concept.
+ name: str
+ # Input type (modality) of the concept.
+ type: SignalInputType
+
+
+@router.post('/create', response_model_exclude_none=True)
+def create_concept(options: CreateConceptOptions) -> Concept:
+ """Edit a concept in the database."""
+ return DISK_CONCEPT_DB.create(options.namespace, options.name, options.type)
+
+
+@router.post('/{namespace}/{concept_name}', response_model_exclude_none=True)
+def edit_concept(namespace: str, concept_name: str, change: ConceptUpdate) -> Concept:
+ """Edit a concept in the database."""
+ return DISK_CONCEPT_DB.edit(namespace, concept_name, change)
+
+
+@router.delete('/{namespace}/{concept_name}')
+def delete_concept(namespace: str, concept_name: str) -> None:
+ """Deletes the concept from the database."""
+ DISK_CONCEPT_DB.remove(namespace, concept_name)
+ # Delete concept models from all datasets that are using this concept.
+ DISK_CONCEPT_MODEL_DB.remove_all(namespace, concept_name)
+
+
+class MergeConceptDraftOptions(BaseModel):
+ """Merge a draft into main."""
+ draft: DraftId
+
+
+@router.post('/{namespace}/{concept_name}/merge_draft', response_model_exclude_none=True)
+def merge_concept_draft(namespace: str, concept_name: str,
+ options: MergeConceptDraftOptions) -> Concept:
+ """Merge a draft in the concept into main."""
+ return DISK_CONCEPT_DB.merge_draft(namespace, concept_name, options.draft)
+
+
+class ScoreExample(BaseModel):
+ """Example to score along a specific concept."""
+ text: Optional[str]
+ img: Optional[bytes]
+
+
+class ScoreBody(BaseModel):
+ """Request body for the score endpoint."""
+ examples: list[ScoreExample]
+ draft: str = DRAFT_MAIN
+
+
+class ScoreResponse(BaseModel):
+ """Response body for the score endpoint."""
+ scores: list[float]
+ model_synced: bool
+
+
+class ConceptModelResponse(BaseModel):
+ """Response body for the get_concept_model endpoint."""
+ model: ConceptModel
+ model_synced: bool
+
+
+@router.get('/{namespace}/{concept_name}/{embedding_name}')
+def get_concept_model(namespace: str,
+ concept_name: str,
+ embedding_name: str,
+ sync_model: bool = False) -> ConceptModelResponse:
+ """Get a concept model from a database."""
+ concept = DISK_CONCEPT_DB.get(namespace, concept_name)
+ if not concept:
+ raise HTTPException(
+ status_code=404, detail=f'Concept "{namespace}/{concept_name}" was not found')
+
+ model = DISK_CONCEPT_MODEL_DB.get(namespace, concept_name, embedding_name)
+ if not model:
+ model = DISK_CONCEPT_MODEL_DB.create(namespace, concept_name, embedding_name)
+
+ if sync_model:
+ model_synced = DISK_CONCEPT_MODEL_DB.sync(model, column_info=None)
+ else:
+ model_synced = DISK_CONCEPT_MODEL_DB.in_sync(model)
+ return ConceptModelResponse(model=model, model_synced=model_synced)
+
+
+@router.post('/{namespace}/{concept_name}/{embedding_name}/score', response_model_exclude_none=True)
+def score(namespace: str, concept_name: str, embedding_name: str, body: ScoreBody) -> ScoreResponse:
+ """Score examples along the specified concept."""
+ concept = DISK_CONCEPT_DB.get(namespace, concept_name)
+ if not concept:
+ raise HTTPException(
+ status_code=404, detail=f'Concept "{namespace}/{concept_name}" was not found')
+ model = DISK_CONCEPT_MODEL_DB.get(namespace, concept_name, embedding_name)
+ if model is None:
+ model = DISK_CONCEPT_MODEL_DB.create(namespace, concept_name, embedding_name)
+ models_updated = DISK_CONCEPT_MODEL_DB.sync(model, column_info=None)
+ # TODO(smilkov): Support images.
+ texts = [example.text or '' for example in body.examples]
+ return ScoreResponse(scores=model.score(body.draft, texts), model_synced=models_updated)
+
+
+class Examples(OpenAISchema):
+ """Generated text examples."""
+ examples: list[str] = Field(..., description='List of generated examples')
+
+
+@router.get('/generate_examples')
+def generate_examples(description: str) -> list[str]:
+ """Generate positive examples for a given concept using an LLM model."""
+ openai.api_key = CONFIG['OPENAI_API_KEY']
+ completion = openai.ChatCompletion.create(
+ model='gpt-3.5-turbo-0613',
+ functions=[Examples.openai_schema],
+ messages=[
+ {
+ 'role': 'system',
+ 'content': 'You must call the `Examples` function with the generated sentences.',
+ },
+ {
+ 'role': 'user',
+ 'content': f'Give me 5 diverse examples of sentences that demonstrate "{description}"',
+ },
+ ],
+ )
+ result = Examples.from_response(completion)
+ return result.examples
diff --git a/src/router_data_loader.py b/src/router_data_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee223ea3288fef09bdf56b1f97c5b2e43d4310a1
--- /dev/null
+++ b/src/router_data_loader.py
@@ -0,0 +1,79 @@
+"""The source loader runner which loads data into parquet files for the app.
+
+To run the source loader as a binary directly:
+
+poetry run python -m src.datasets.loader \
+ --dataset_name=$DATASET \
+ --output_dir=./data/ \
+ --config_path=./datasets/the_movies_dataset.json
+"""
+from typing import Any
+
+from fastapi import APIRouter, Request
+from pydantic import BaseModel
+
+from .config import data_path
+from .data.sources.default_sources import register_default_sources
+from .data.sources.source_registry import get_source_cls, registered_sources
+from .data_loader import process_source
+from .router_utils import RouteErrorHandler
+from .tasks import TaskId, task_manager
+
+REQUEST_TIMEOUT_SEC = 30 * 60 # 30 mins.
+
+register_default_sources()
+
+router = APIRouter(route_class=RouteErrorHandler)
+
+
+class ProcessSourceRequest(BaseModel):
+ """The interface to the /process_source endpoint."""
+ username: str
+ dataset_name: str
+
+
+class SourcesList(BaseModel):
+ """The interface to the /process_source endpoint."""
+ sources: list[str]
+
+
+@router.get('/')
+def get_sources() -> SourcesList:
+ """Get the list of available sources."""
+ sources = registered_sources()
+ return SourcesList(sources=list(sources.keys()))
+
+
+@router.get('/{source_name}')
+def get_source_schema(source_name: str) -> dict[str, Any]:
+ """Get the fields for a source."""
+ source_cls = get_source_cls(source_name)
+ return source_cls.schema()
+
+
+class LoadDatasetOptions(BaseModel):
+ """Options for loading a dataset."""
+ namespace: str
+ dataset_name: str
+ config: dict[str, Any]
+
+
+class LoadDatasetResponse(BaseModel):
+ """Response of the load dataset endpoint."""
+ task_id: TaskId
+
+
+@router.post('/{source_name}/load')
+async def load(source_name: str, options: LoadDatasetOptions,
+ request: Request) -> LoadDatasetResponse:
+ """Load a dataset."""
+ source_cls = get_source_cls(source_name)
+ source = source_cls(**options.config)
+
+ task_id = task_manager().task_id(
+ name=f'[{options.namespace}/{options.dataset_name}] Load dataset',
+ description=f'Loader: {source.name}. \n Config: {source}')
+ task_manager().execute(task_id, process_source, data_path(), options.namespace,
+ options.dataset_name, source, (task_id, 0))
+
+ return LoadDatasetResponse(task_id=task_id)
diff --git a/src/router_dataset.py b/src/router_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..24d44486189d954fe27843daf3c4ebf39d074a51
--- /dev/null
+++ b/src/router_dataset.py
@@ -0,0 +1,311 @@
+"""Router for the dataset database."""
+import os
+from typing import Optional, Sequence, Union, cast
+from urllib.parse import unquote
+
+from fastapi import APIRouter, Response
+from fastapi.responses import ORJSONResponse
+from pydantic import BaseModel, validator
+
+from .config import data_path
+from .data.dataset import BinaryOp
+from .data.dataset import Column as DBColumn
+from .data.dataset import DatasetManifest, FeatureListValue, FeatureValue
+from .data.dataset import Filter as PyFilter
+from .data.dataset import (
+ GroupsSortBy,
+ ListOp,
+ Search,
+ SelectGroupsResult,
+ SelectRowsSchemaResult,
+ SortOrder,
+ StatsResult,
+ UnaryOp,
+)
+from .data.dataset_duckdb import DatasetDuckDB
+from .db_manager import get_dataset, remove_dataset_from_cache, set_default_dataset_cls
+from .router_utils import RouteErrorHandler
+from .schema import Bin, Path, normalize_path
+from .signals.concept_scorer import ConceptScoreSignal
+from .signals.default_signals import register_default_signals
+from .signals.signal import (
+ Signal,
+ TextEmbeddingModelSignal,
+ TextEmbeddingSignal,
+ TextSignal,
+ resolve_signal,
+)
+from .tasks import TaskId, task_manager
+from .utils import DATASETS_DIR_NAME
+
+router = APIRouter(route_class=RouteErrorHandler)
+
+register_default_signals()
+set_default_dataset_cls(DatasetDuckDB)
+
+
+class DatasetInfo(BaseModel):
+ """Information about a dataset."""
+ namespace: str
+ dataset_name: str
+ description: Optional[str]
+
+
+@router.get('/', response_model_exclude_none=True)
+def get_datasets() -> list[DatasetInfo]:
+ """List the datasets."""
+ datasets_path = os.path.join(data_path(), DATASETS_DIR_NAME)
+ # Skip if 'datasets' doesn't exist.
+ if not os.path.isdir(datasets_path):
+ return []
+
+ dataset_infos: list[DatasetInfo] = []
+ for namespace in os.listdir(datasets_path):
+ dataset_dir = os.path.join(datasets_path, namespace)
+ # Skip if namespace is not a directory.
+ if not os.path.isdir(dataset_dir):
+ continue
+ if namespace.startswith('.'):
+ continue
+
+ for dataset_name in os.listdir(dataset_dir):
+ # Skip if dataset_name is not a directory.
+ dataset_path = os.path.join(dataset_dir, dataset_name)
+ if not os.path.isdir(dataset_path):
+ continue
+ if dataset_name.startswith('.'):
+ continue
+
+ dataset_infos.append(DatasetInfo(namespace=namespace, dataset_name=dataset_name))
+
+ return dataset_infos
+
+
+class WebManifest(BaseModel):
+ """Information about a dataset."""
+ dataset_manifest: DatasetManifest
+
+
+@router.get('/{namespace}/{dataset_name}')
+def get_manifest(namespace: str, dataset_name: str) -> WebManifest:
+ """Get the web manifest for the dataset."""
+ dataset = get_dataset(namespace, dataset_name)
+ res = WebManifest(dataset_manifest=dataset.manifest())
+ # Avoids the error that Signal abstract class is not serializable.
+ return cast(WebManifest, ORJSONResponse(res.dict(exclude_none=True)))
+
+
+class ComputeSignalOptions(BaseModel):
+ """The request for the compute signal endpoint."""
+ signal: Signal
+
+ # The leaf path to compute the signal on.
+ leaf_path: Path
+
+ @validator('signal', pre=True)
+ def parse_signal(cls, signal: dict) -> Signal:
+ """Parse a signal to its specific subclass instance."""
+ return resolve_signal(signal)
+
+
+@router.delete('/{namespace}/{dataset_name}')
+def delete_dataset(namespace: str, dataset_name: str) -> None:
+ """Delete the dataset."""
+ dataset = get_dataset(namespace, dataset_name)
+ dataset.delete()
+ remove_dataset_from_cache(namespace, dataset_name)
+
+
+class ComputeSignalResponse(BaseModel):
+ """Response of the compute signal column endpoint."""
+ task_id: TaskId
+
+
+@router.post('/{namespace}/{dataset_name}/compute_signal')
+def compute_signal(namespace: str, dataset_name: str,
+ options: ComputeSignalOptions) -> ComputeSignalResponse:
+ """Compute a signal for a dataset."""
+
+ def _task_compute_signal(namespace: str, dataset_name: str, options_dict: dict,
+ task_id: TaskId) -> None:
+ # NOTE: We manually call .dict() to avoid the dask serializer, which doesn't call the underlying
+ # pydantic serializer.
+ options = ComputeSignalOptions(**options_dict)
+ dataset = get_dataset(namespace, dataset_name)
+ dataset.compute_signal(options.signal, options.leaf_path, task_step_id=(task_id, 0))
+
+ path_str = '.'.join(map(str, options.leaf_path))
+ task_id = task_manager().task_id(
+ name=f'[{namespace}/{dataset_name}] Compute signal "{options.signal.name}" on "{path_str}"',
+ description=f'Config: {options.signal}')
+ task_manager().execute(task_id, _task_compute_signal, namespace, dataset_name, options.dict(),
+ task_id)
+
+ return ComputeSignalResponse(task_id=task_id)
+
+
+class DeleteSignalOptions(BaseModel):
+ """The request for the delete signal endpoint."""
+ # The signal path holding the data from the signal.
+ signal_path: Path
+
+
+class DeleteSignalResponse(BaseModel):
+ """Response of the compute signal column endpoint."""
+ completed: bool
+
+
+@router.delete('/{namespace}/{dataset_name}/delete_signal')
+def delete_signal(namespace: str, dataset_name: str,
+ options: DeleteSignalOptions) -> DeleteSignalResponse:
+ """Delete a signal from a dataset."""
+ dataset = get_dataset(namespace, dataset_name)
+ dataset.delete_signal(options.signal_path)
+ return DeleteSignalResponse(completed=True)
+
+
+class GetStatsOptions(BaseModel):
+ """The request for the get stats endpoint."""
+ leaf_path: Path
+
+
+@router.post('/{namespace}/{dataset_name}/stats')
+def get_stats(namespace: str, dataset_name: str, options: GetStatsOptions) -> StatsResult:
+ """Get the stats for the dataset."""
+ dataset = get_dataset(namespace, dataset_name)
+ return dataset.stats(options.leaf_path)
+
+
+class BinaryFilter(BaseModel):
+ """A filter on a column."""
+ path: Path
+ op: BinaryOp
+ value: FeatureValue
+
+
+class UnaryFilter(BaseModel):
+ """A filter on a column."""
+ path: Path
+ op: UnaryOp
+ value: None = None
+
+
+class ListFilter(BaseModel):
+ """A filter on a column."""
+ path: Path
+ op: ListOp
+ value: FeatureListValue
+
+
+Filter = Union[BinaryFilter, UnaryFilter, ListFilter]
+
+AllSignalTypes = Union[ConceptScoreSignal, TextEmbeddingModelSignal, TextEmbeddingSignal,
+ TextSignal, Signal]
+
+
+# We override the `Column` class so we can add explicitly all signal types for better OpenAPI spec.
+class Column(DBColumn):
+ """A column in the dataset."""
+ signal_udf: Optional[AllSignalTypes] = None
+
+
+class SelectRowsOptions(BaseModel):
+ """The request for the select rows endpoint."""
+ columns: Optional[Sequence[Union[Path, Column]]]
+ searches: Optional[Sequence[Search]]
+ filters: Optional[Sequence[Filter]]
+ sort_by: Optional[Sequence[Path]]
+ sort_order: Optional[SortOrder] = SortOrder.DESC
+ limit: Optional[int]
+ offset: Optional[int]
+ combine_columns: Optional[bool]
+
+
+class SelectRowsSchemaOptions(BaseModel):
+ """The request for the select rows schema endpoint."""
+ columns: Optional[Sequence[Union[Path, Column]]]
+ searches: Optional[Sequence[Search]]
+ sort_by: Optional[Sequence[Path]]
+ sort_order: Optional[SortOrder] = SortOrder.DESC
+ combine_columns: Optional[bool]
+
+
+class SelectRowsResponse(BaseModel):
+ """The response for the select rows endpoint."""
+ rows: list[dict]
+ total_num_rows: int
+
+
+@router.get('/{namespace}/{dataset_name}/select_rows_download', response_model=None)
+def select_rows_download(namespace: str, dataset_name: str, url_safe_options: str) -> list[dict]:
+ """Select rows from the dataset database and downloads them."""
+ options = SelectRowsOptions.parse_raw(unquote(url_safe_options))
+ return select_rows(namespace, dataset_name, options).rows
+
+
+@router.post('/{namespace}/{dataset_name}/select_rows', response_model_exclude_none=True)
+def select_rows(namespace: str, dataset_name: str,
+ options: SelectRowsOptions) -> SelectRowsResponse:
+ """Select rows from the dataset database."""
+ dataset = get_dataset(namespace, dataset_name)
+
+ sanitized_filters = [
+ PyFilter(path=normalize_path(f.path), op=f.op, value=f.value) for f in (options.filters or [])
+ ]
+
+ res = dataset.select_rows(
+ columns=options.columns,
+ searches=options.searches or [],
+ filters=sanitized_filters,
+ sort_by=options.sort_by,
+ sort_order=options.sort_order,
+ limit=options.limit,
+ offset=options.offset,
+ combine_columns=options.combine_columns or False)
+
+ return SelectRowsResponse(rows=list(res), total_num_rows=res.total_num_rows)
+
+
+@router.post('/{namespace}/{dataset_name}/select_rows_schema', response_model_exclude_none=True)
+def select_rows_schema(namespace: str, dataset_name: str,
+ options: SelectRowsSchemaOptions) -> SelectRowsSchemaResult:
+ """Select rows from the dataset database."""
+ dataset = get_dataset(namespace, dataset_name)
+ return dataset.select_rows_schema(
+ columns=options.columns,
+ searches=options.searches or [],
+ sort_by=options.sort_by,
+ sort_order=options.sort_order,
+ combine_columns=options.combine_columns or False)
+
+
+class SelectGroupsOptions(BaseModel):
+ """The request for the select groups endpoint."""
+ leaf_path: Path
+ filters: Optional[Sequence[Filter]]
+ sort_by: Optional[GroupsSortBy] = GroupsSortBy.COUNT
+ sort_order: Optional[SortOrder] = SortOrder.DESC
+ limit: Optional[int] = 100
+ bins: Optional[list[Bin]]
+
+
+@router.post('/{namespace}/{dataset_name}/select_groups')
+def select_groups(namespace: str, dataset_name: str,
+ options: SelectGroupsOptions) -> SelectGroupsResult:
+ """Select groups from the dataset database."""
+ dataset = get_dataset(namespace, dataset_name)
+ sanitized_filters = [
+ PyFilter(path=normalize_path(f.path), op=f.op, value=f.value) for f in (options.filters or [])
+ ]
+ return dataset.select_groups(options.leaf_path, sanitized_filters, options.sort_by,
+ options.sort_order, options.limit, options.bins)
+
+
+@router.get('/{namespace}/{dataset_name}/media')
+def get_media(namespace: str, dataset_name: str, item_id: str, leaf_path: str) -> Response:
+ """Get the media for the dataset."""
+ dataset = get_dataset(namespace, dataset_name)
+ path = tuple(leaf_path.split('.'))
+ result = dataset.media(item_id, path)
+ # Return the response via HTTP.
+ return Response(content=result.data)
diff --git a/src/router_signal.py b/src/router_signal.py
new file mode 100644
index 0000000000000000000000000000000000000000..982a67b18bc132b917c8ea74c8c0d7b9cf4c9811
--- /dev/null
+++ b/src/router_signal.py
@@ -0,0 +1,50 @@
+"""Router for the signal registry."""
+
+import math
+from typing import Any
+
+from fastapi import APIRouter
+from pydantic import BaseModel
+
+from .router_utils import RouteErrorHandler
+from .schema import SignalInputType
+from .signals.signal import SIGNAL_REGISTRY, TextEmbeddingSignal
+
+router = APIRouter(route_class=RouteErrorHandler)
+
+EMBEDDING_SORT_PRIORITIES = ['sbert']
+
+
+class SignalInfo(BaseModel):
+ """Information about a signal."""
+ name: str
+ input_type: SignalInputType
+ json_schema: dict[str, Any]
+
+
+@router.get('/', response_model_exclude_none=True)
+def get_signals() -> list[SignalInfo]:
+ """List the signals."""
+ return [
+ SignalInfo(name=s.name, input_type=s.input_type, json_schema=s.schema())
+ for s in SIGNAL_REGISTRY.values()
+ if not issubclass(s, TextEmbeddingSignal)
+ ]
+
+
+@router.get('/embeddings', response_model_exclude_none=True)
+def get_embeddings() -> list[SignalInfo]:
+ """List the embeddings."""
+ embedding_infos = [
+ SignalInfo(name=s.name, input_type=s.input_type, json_schema=s.schema())
+ for s in SIGNAL_REGISTRY.values()
+ if issubclass(s, TextEmbeddingSignal)
+ ]
+
+ # Sort the embedding infos by priority.
+ embedding_infos = sorted(
+ embedding_infos,
+ key=lambda s: EMBEDDING_SORT_PRIORITIES.index(s.name)
+ if s.name in EMBEDDING_SORT_PRIORITIES else math.inf)
+
+ return embedding_infos
diff --git a/src/router_tasks.py b/src/router_tasks.py
new file mode 100644
index 0000000000000000000000000000000000000000..1eb86b8862cefeb6f52492131309af13fc957f63
--- /dev/null
+++ b/src/router_tasks.py
@@ -0,0 +1,14 @@
+"""Router for tasks."""
+
+from fastapi import APIRouter
+
+from .router_utils import RouteErrorHandler
+from .tasks import TaskManifest, task_manager
+
+router = APIRouter(route_class=RouteErrorHandler)
+
+
+@router.get('/')
+async def get_task_manifest() -> TaskManifest:
+ """Get the tasks, both completed and pending."""
+ return await task_manager().manifest()
diff --git a/src/router_utils.py b/src/router_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4b7328524575ba9997fc3cd9dfbd24d1f8e736d
--- /dev/null
+++ b/src/router_utils.py
@@ -0,0 +1,31 @@
+"""Utils for routers."""
+
+import traceback
+from typing import Callable
+
+from fastapi import HTTPException, Request, Response
+from fastapi.routing import APIRoute
+
+
+class RouteErrorHandler(APIRoute):
+ """Custom APIRoute that handles application errors and exceptions."""
+
+ def get_route_handler(self) -> Callable:
+ """Get the route handler."""
+ original_route_handler = super().get_route_handler()
+
+ async def custom_route_handler(request: Request) -> Response:
+ try:
+ return await original_route_handler(request)
+ except Exception as ex:
+ if isinstance(ex, HTTPException):
+ raise ex
+
+ print('Route error:', request.url)
+ print(ex)
+ print(traceback.format_exc())
+
+ # wrap error into pretty 500 exception
+ raise HTTPException(status_code=500, detail=traceback.format_exc()) from ex
+
+ return custom_route_handler
diff --git a/src/schema.py b/src/schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec1c627bc22686d496cfbb3956d2d1afeccfa5db
--- /dev/null
+++ b/src/schema.py
@@ -0,0 +1,559 @@
+"""Item: an individual entry in the dataset."""
+
+import csv
+import io
+from collections import deque
+from datetime import datetime
+from enum import Enum
+from typing import Any, Optional, Union, cast
+
+import pyarrow as pa
+from pydantic import BaseModel, StrictInt, StrictStr, validator
+
+MANIFEST_FILENAME = 'manifest.json'
+PARQUET_FILENAME_PREFIX = 'data'
+
+# We choose `__rowid__` inspired by the standard `rowid` pseudocolumn in DBs:
+# https://docs.oracle.com/cd/B19306_01/server.102/b14200/pseudocolumns008.htm
+UUID_COLUMN = '__rowid__'
+PATH_WILDCARD = '*'
+VALUE_KEY = '__value__'
+SIGNAL_METADATA_KEY = '__metadata__'
+TEXT_SPAN_START_FEATURE = 'start'
+TEXT_SPAN_END_FEATURE = 'end'
+
+# Python doesn't work with recursive types. These types provide some notion of type-safety.
+Scalar = Union[bool, datetime, int, float, str, bytes]
+Item = Any
+
+# Contains a string field name, a wildcard for repeateds, or a specific integer index for repeateds.
+# This path represents a path to a particular column.
+# Examples:
+# ['article', 'field'] represents {'article': {'field': VALUES}}
+# ['article', '*', 'field'] represents {'article': [{'field': VALUES}, {'field': VALUES}]}
+# ['article', '0', 'field'] represents {'article': {'field': VALUES}}
+PathTuple = tuple[StrictStr, ...]
+Path = Union[PathTuple, StrictStr]
+
+PathKeyedItem = tuple[Path, Item]
+
+# These fields are for for python only and not written to a schema.
+RichData = Union[str, bytes]
+VectorKey = tuple[Union[StrictStr, StrictInt], ...]
+
+
+class DataType(str, Enum):
+ """Enum holding the dtype for a field."""
+ STRING = 'string'
+ # Contains {start, end} offset integers with a reference_column.
+ STRING_SPAN = 'string_span'
+ BOOLEAN = 'boolean'
+
+ # Ints.
+ INT8 = 'int8'
+ INT16 = 'int16'
+ INT32 = 'int32'
+ INT64 = 'int64'
+ UINT8 = 'uint8'
+ UINT16 = 'uint16'
+ UINT32 = 'uint32'
+ UINT64 = 'uint64'
+
+ # Floats.
+ FLOAT16 = 'float16'
+ FLOAT32 = 'float32'
+ FLOAT64 = 'float64'
+
+ ### Time ###
+ # Time of day (no time zone).
+ TIME = 'time'
+ # Calendar date (year, month, day), no time zone.
+ DATE = 'date'
+ # An "Instant" stored as number of microseconds (µs) since 1970-01-01 00:00:00+00 (UTC time zone).
+ TIMESTAMP = 'timestamp'
+ # Time span, stored as microseconds.
+ INTERVAL = 'interval'
+
+ BINARY = 'binary'
+
+ EMBEDDING = 'embedding'
+
+ def __repr__(self) -> str:
+ return self.value
+
+
+class SignalInputType(str, Enum):
+ """Enum holding the signal input type."""
+ TEXT = 'text'
+ TEXT_EMBEDDING = 'text_embedding'
+ IMAGE = 'image'
+
+ def __repr__(self) -> str:
+ return self.value
+
+
+SIGNAL_COMPUTE_TYPE_TO_VALID_DTYPES: dict[SignalInputType, list[DataType]] = {
+ SignalInputType.TEXT: [DataType.STRING, DataType.STRING_SPAN],
+ SignalInputType.TEXT_EMBEDDING: [DataType.EMBEDDING],
+ SignalInputType.IMAGE: [DataType.BINARY],
+}
+
+
+def signal_compute_type_supports_dtype(input_type: SignalInputType, dtype: DataType) -> bool:
+ """Returns True if the signal compute type supports the dtype."""
+ return dtype in SIGNAL_COMPUTE_TYPE_TO_VALID_DTYPES[input_type]
+
+
+Bin = tuple[str, Optional[Union[float, int]], Optional[Union[float, int]]]
+
+
+class Field(BaseModel):
+ """Holds information for a field in the schema."""
+ repeated_field: Optional['Field']
+ fields: Optional[dict[str, 'Field']]
+ dtype: Optional[DataType]
+ # Defined as the serialized signal when this field is the root result of a signal.
+ signal: Optional[dict[str, Any]]
+ # Maps a named bin to a tuple of (start, end) values.
+ bins: Optional[list[Bin]]
+
+ @validator('fields')
+ def either_fields_or_repeated_field_is_defined(
+ cls, fields: Optional[dict[str, 'Field']], values: dict[str,
+ Any]) -> Optional[dict[str, 'Field']]:
+ """Error if both `fields` and `repeated_fields` are defined."""
+ if not fields:
+ return fields
+ if values.get('repeated_field'):
+ raise ValueError('Both "fields" and "repeated_field" should not be defined')
+ if VALUE_KEY in fields:
+ raise ValueError(f'{VALUE_KEY} is a reserved field name.')
+ return fields
+
+ @validator('dtype', always=True)
+ def infer_default_dtype(cls, dtype: Optional[DataType], values: dict[str,
+ Any]) -> Optional[DataType]:
+ """Infers the default value for dtype if not explicitly provided."""
+ if dtype and values.get('repeated_field'):
+ raise ValueError('dtype and repeated_field cannot both be defined.')
+ if not values.get('repeated_field') and not values.get('fields') and not dtype:
+ raise ValueError('One of "fields", "repeated_field", or "dtype" should be defined')
+ return dtype
+
+ @validator('bins')
+ def validate_bins(cls, bins: list[Bin]) -> list[Bin]:
+ """Validate the bins."""
+ if len(bins) < 2:
+ raise ValueError('Please specify at least two bins.')
+ _, first_start, _ = bins[0]
+ if first_start is not None:
+ raise ValueError('The first bin should have a `None` start value.')
+ _, _, last_end = bins[-1]
+ if last_end is not None:
+ raise ValueError('The last bin should have a `None` end value.')
+ for i, (_, start, _) in enumerate(bins):
+ if i == 0:
+ continue
+ prev_bin = bins[i - 1]
+ _, _, prev_end = prev_bin
+ if start != prev_end:
+ raise ValueError(
+ f'Bin {i} start ({start}) should be equal to the previous bin end {prev_end}.')
+ return bins
+
+ def __str__(self) -> str:
+ return _str_field(self, indent=0)
+
+ def __repr__(self) -> str:
+ return f' {self.__class__.__name__}::{self.json(exclude_none=True, indent=2)}'
+
+
+class Schema(BaseModel):
+ """Database schema."""
+ fields: dict[str, Field]
+ # Cached leafs.
+ _leafs: Optional[dict[PathTuple, Field]] = None
+
+ class Config:
+ arbitrary_types_allowed = True
+ underscore_attrs_are_private = True
+
+ @property
+ def leafs(self) -> dict[PathTuple, Field]:
+ """Return all the leaf fields in the schema. A leaf is defined as a node that contains a value.
+
+ NOTE: Leafs may contain children. Leafs can be found as any node that has a dtype defined.
+ """
+ if self._leafs:
+ return self._leafs
+ result: dict[PathTuple, Field] = {}
+ q: deque[tuple[PathTuple, Field]] = deque([((), Field(fields=self.fields))])
+ while q:
+ path, field = q.popleft()
+ if field.dtype:
+ # Nodes with dtypes act as leafs. They also may have children.
+ result[path] = field
+ if field.fields:
+ for name, child_field in field.fields.items():
+ child_path = (*path, name)
+ q.append((child_path, child_field))
+ elif field.repeated_field:
+ child_path = (*path, PATH_WILDCARD)
+ q.append((child_path, field.repeated_field))
+
+ self._leafs = result
+ return result
+
+ def has_field(self, path: PathTuple) -> bool:
+ """Returns if the field is found at the given path."""
+ field = cast(Field, self)
+ for path_part in path:
+ if field.fields:
+ field = cast(Field, field.fields.get(path_part))
+ if not field:
+ return False
+ elif field.repeated_field:
+ if path_part != PATH_WILDCARD:
+ return False
+ field = field.repeated_field
+ else:
+ return False
+ return True
+
+ def get_field(self, path: PathTuple) -> Field:
+ """Returns the field at the given path."""
+ field = cast(Field, self)
+ for name in path:
+ if field.fields:
+ field = field.fields[str(name)]
+ elif field.repeated_field:
+ if name != PATH_WILDCARD:
+ raise ValueError(f'Invalid path {path}')
+ field = field.repeated_field
+ else:
+ raise ValueError(f'Invalid path {path}')
+ return field
+
+ def __str__(self) -> str:
+ return _str_fields(self.fields, indent=0)
+
+ def __repr__(self) -> str:
+ return self.json(exclude_none=True, indent=2)
+
+
+def schema(schema_like: object) -> Schema:
+ """Parse a schema-like object to a Schema object."""
+ field = _parse_field_like(schema_like)
+ return Schema(fields=field.fields)
+
+
+def field(
+ dtype: Optional[Union[DataType, str]] = None,
+ signal: Optional[dict] = None,
+ fields: Optional[object] = None,
+ bins: Optional[list[Bin]] = None,
+) -> Field:
+ """Parse a field-like object to a Field object."""
+ field = _parse_field_like(fields or {}, dtype)
+ if signal:
+ field.signal = signal
+ if dtype:
+ if isinstance(dtype, str):
+ dtype = DataType(dtype)
+ field.dtype = dtype
+ if bins:
+ field.bins = bins
+ return field
+
+
+def _parse_field_like(field_like: object, dtype: Optional[Union[DataType, str]] = None) -> Field:
+ if isinstance(field_like, Field):
+ return field_like
+ elif isinstance(field_like, dict):
+ fields: dict[str, Field] = {}
+ for k, v in field_like.items():
+ fields[k] = _parse_field_like(v)
+ if isinstance(dtype, str):
+ dtype = DataType(dtype)
+ return Field(fields=fields or None, dtype=dtype)
+ elif isinstance(field_like, str):
+ return Field(dtype=DataType(field_like))
+ elif isinstance(field_like, list):
+ return Field(repeated_field=_parse_field_like(field_like[0], dtype=dtype))
+ else:
+ raise ValueError(f'Cannot parse field like: {field_like}')
+
+
+def child_item_from_column_path(item: Item, path: Path) -> Item:
+ """Return the last (child) item from a column path."""
+ child_item_value = item
+ for path_part in path:
+ if path_part == PATH_WILDCARD:
+ raise ValueError(
+ 'child_item_from_column_path cannot be called with a path that contains a repeated '
+ f'wildcard: "{path}"')
+ # path_part can either be an integer or a string for a dictionary, both of which we can
+ # directly index with.
+ child_path = int(path_part) if path_part.isdigit() else path_part
+ child_item_value = child_item_value[child_path]
+ return child_item_value
+
+
+def column_paths_match(path_match: Path, specific_path: Path) -> bool:
+ """Test whether two column paths match.
+
+ Args:
+ path_match: A column path that contains wildcards, and sub-paths. This path will be used for
+ testing the second specific path.
+ specific_path: A column path that specifically identifies an field.
+
+ Returns
+ Whether specific_path matches the path_match. This will only match when the
+ paths are equal length. If a user wants to enrich everything with an array, they must use the
+ path wildcard '*' in their patch match.
+ """
+ if isinstance(path_match, str):
+ path_match = (path_match,)
+ if isinstance(specific_path, str):
+ specific_path = (specific_path,)
+
+ if len(path_match) != len(specific_path):
+ return False
+
+ for path_match_p, specific_path_p in zip(path_match, specific_path):
+ if path_match_p == PATH_WILDCARD:
+ continue
+
+ if path_match_p != specific_path_p:
+ return False
+
+ return True
+
+
+def normalize_path(path: Path) -> PathTuple:
+ """Normalizes a dot seperated path, but ignores dots inside quotes, like regular SQL.
+
+ Examples
+ - 'a.b.c' will be parsed as ('a', 'b', 'c').
+ - '"a.b".c' will be parsed as ('a.b', 'c').
+ - '"a".b.c' will be parsed as ('a', 'b', 'c').
+ """
+ if isinstance(path, str):
+ return tuple(next(csv.reader(io.StringIO(path), delimiter='.')))
+ return path
+
+
+class ImageInfo(BaseModel):
+ """Info about an individual image."""
+ path: Path
+
+
+class SourceManifest(BaseModel):
+ """The manifest that describes the dataset run, including schema and parquet files."""
+ # List of a parquet filepaths storing the data. The paths can be relative to `manifest.json`.
+ files: list[str]
+ # The data schema.
+ data_schema: Schema
+
+ # Image information for the dataset.
+ images: Optional[list[ImageInfo]]
+
+
+def _str_fields(fields: dict[str, Field], indent: int) -> str:
+ prefix = ' ' * indent
+ out: list[str] = []
+ for name, field in fields.items():
+ out.append(f'{prefix}{name}:{_str_field(field, indent=indent + 2)}')
+ return '\n'.join(out)
+
+
+def _str_field(field: Field, indent: int) -> str:
+ if field.fields:
+ prefix = '\n' if indent > 0 else ''
+ return f'{prefix}{_str_fields(field.fields, indent)}'
+ if field.repeated_field:
+ return f' list({_str_field(field.repeated_field, indent)})'
+ return f' {cast(DataType, field.dtype)}'
+
+
+def dtype_to_arrow_schema(dtype: DataType) -> Union[pa.Schema, pa.DataType]:
+ """Convert the dtype to an arrow dtype."""
+ if dtype == DataType.STRING:
+ return pa.string()
+ elif dtype == DataType.BOOLEAN:
+ return pa.bool_()
+ elif dtype == DataType.FLOAT16:
+ return pa.float16()
+ elif dtype == DataType.FLOAT32:
+ return pa.float32()
+ elif dtype == DataType.FLOAT64:
+ return pa.float64()
+ elif dtype == DataType.INT8:
+ return pa.int8()
+ elif dtype == DataType.INT16:
+ return pa.int16()
+ elif dtype == DataType.INT32:
+ return pa.int32()
+ elif dtype == DataType.INT64:
+ return pa.int64()
+ elif dtype == DataType.UINT8:
+ return pa.uint8()
+ elif dtype == DataType.UINT16:
+ return pa.uint16()
+ elif dtype == DataType.UINT32:
+ return pa.uint32()
+ elif dtype == DataType.UINT64:
+ return pa.uint64()
+ elif dtype == DataType.BINARY:
+ return pa.binary()
+ elif dtype == DataType.TIME:
+ return pa.time64()
+ elif dtype == DataType.DATE:
+ return pa.date64()
+ elif dtype == DataType.TIMESTAMP:
+ return pa.timestamp('us')
+ elif dtype == DataType.INTERVAL:
+ return pa.duration('us')
+ elif dtype == DataType.EMBEDDING:
+ # We reserve an empty column for embeddings in parquet files so they can be queried.
+ # The values are *not* filled out. If parquet and duckdb support embeddings in the future, we
+ # can set this dtype to the relevant pyarrow type.
+ return pa.null()
+ elif dtype == DataType.STRING_SPAN:
+ return pa.struct({
+ VALUE_KEY: pa.struct({
+ TEXT_SPAN_START_FEATURE: pa.int32(),
+ TEXT_SPAN_END_FEATURE: pa.int32()
+ })
+ })
+ else:
+ raise ValueError(f'Can not convert dtype "{dtype}" to arrow dtype')
+
+
+def schema_to_arrow_schema(schema: Union[Schema, Field]) -> pa.Schema:
+ """Convert our schema to arrow schema."""
+ arrow_schema = cast(pa.Schema, _schema_to_arrow_schema_impl(schema))
+ arrow_fields = {field.name: field.type for field in arrow_schema}
+ return pa.schema(arrow_fields)
+
+
+def _schema_to_arrow_schema_impl(schema: Union[Schema, Field]) -> Union[pa.Schema, pa.DataType]:
+ """Convert a schema to an apache arrow schema."""
+ if schema.fields:
+ arrow_fields: dict[str, Union[pa.Schema, pa.DataType]] = {}
+ for name, field in schema.fields.items():
+ if name == UUID_COLUMN:
+ arrow_schema = dtype_to_arrow_schema(cast(DataType, field.dtype))
+ else:
+ arrow_schema = _schema_to_arrow_schema_impl(field)
+ arrow_fields[name] = arrow_schema
+
+ if isinstance(schema, Schema):
+ # Top-level schemas do not have __value__ fields.
+ return pa.schema(arrow_fields)
+ else:
+ # When nodes have both dtype and children, we add __value__ alongside the fields.
+ if schema.dtype:
+ value_schema = dtype_to_arrow_schema(schema.dtype)
+ if schema.dtype == DataType.STRING_SPAN:
+ value_schema = value_schema[VALUE_KEY].type
+ arrow_fields[VALUE_KEY] = value_schema
+
+ return pa.struct(arrow_fields)
+
+ field = cast(Field, schema)
+ if field.repeated_field:
+ return pa.list_(_schema_to_arrow_schema_impl(field.repeated_field))
+
+ return dtype_to_arrow_schema(cast(DataType, field.dtype))
+
+
+def arrow_dtype_to_dtype(arrow_dtype: pa.DataType) -> DataType:
+ """Convert arrow dtype to our dtype."""
+ # Ints.
+ if arrow_dtype == pa.int8():
+ return DataType.INT8
+ elif arrow_dtype == pa.int16():
+ return DataType.INT16
+ elif arrow_dtype == pa.int32():
+ return DataType.INT32
+ elif arrow_dtype == pa.int64():
+ return DataType.INT64
+ elif arrow_dtype == pa.uint8():
+ return DataType.UINT8
+ elif arrow_dtype == pa.uint16():
+ return DataType.UINT16
+ elif arrow_dtype == pa.uint32():
+ return DataType.UINT32
+ elif arrow_dtype == pa.uint64():
+ return DataType.UINT64
+ # Floats.
+ elif arrow_dtype == pa.float16():
+ return DataType.FLOAT16
+ elif arrow_dtype == pa.float32():
+ return DataType.FLOAT32
+ elif arrow_dtype == pa.float64():
+ return DataType.FLOAT64
+ # Time.
+ elif pa.types.is_time(arrow_dtype):
+ return DataType.TIME
+ elif pa.types.is_date(arrow_dtype):
+ return DataType.DATE
+ elif pa.types.is_timestamp(arrow_dtype):
+ return DataType.TIMESTAMP
+ elif pa.types.is_duration(arrow_dtype):
+ return DataType.INTERVAL
+ # Others.
+ elif arrow_dtype == pa.string():
+ return DataType.STRING
+ elif pa.types.is_binary(arrow_dtype) or pa.types.is_fixed_size_binary(arrow_dtype):
+ return DataType.BINARY
+ elif pa.types.is_boolean(arrow_dtype):
+ return DataType.BOOLEAN
+ else:
+ raise ValueError(f'Can not convert arrow dtype "{arrow_dtype}" to our dtype')
+
+
+def arrow_schema_to_schema(schema: pa.Schema) -> Schema:
+ """Convert arrow schema to our schema."""
+ # TODO(nsthorat): Change this implementation to allow more complicated reading of arrow schemas
+ # into our schema by inferring values when {__value__: value} is present in the pyarrow schema.
+ # This isn't necessary today as this util is only needed by sources which do not have data in the
+ # lilac format.
+ return cast(Schema, _arrow_schema_to_schema_impl(schema))
+
+
+def _arrow_schema_to_schema_impl(schema: Union[pa.Schema, pa.DataType]) -> Union[Schema, Field]:
+ """Convert an apache arrow schema to our schema."""
+ if isinstance(schema, (pa.Schema, pa.StructType)):
+ fields: dict[str, Field] = {
+ field.name: cast(Field, _arrow_schema_to_schema_impl(field.type)) for field in schema
+ }
+ return Schema(fields=fields) if isinstance(schema, pa.Schema) else Field(fields=fields)
+ elif isinstance(schema, pa.ListType):
+ return Field(repeated_field=cast(Field, _arrow_schema_to_schema_impl(schema.value_field.type)))
+ else:
+ return Field(dtype=arrow_dtype_to_dtype(schema))
+
+
+def is_float(dtype: DataType) -> bool:
+ """Check if a dtype is a float dtype."""
+ return dtype in [DataType.FLOAT16, DataType.FLOAT32, DataType.FLOAT64]
+
+
+def is_integer(dtype: DataType) -> bool:
+ """Check if a dtype is an integer dtype."""
+ return dtype in [
+ DataType.INT8, DataType.INT16, DataType.INT32, DataType.INT64, DataType.UINT8, DataType.UINT16,
+ DataType.UINT32, DataType.UINT64
+ ]
+
+
+def is_temporal(dtype: DataType) -> bool:
+ """Check if a dtype is a temporal dtype."""
+ return dtype in [DataType.TIME, DataType.DATE, DataType.TIMESTAMP, DataType.INTERVAL]
+
+
+def is_ordinal(dtype: DataType) -> bool:
+ """Check if a dtype is an ordinal dtype."""
+ return is_float(dtype) or is_integer(dtype) or is_temporal(dtype)
diff --git a/src/schema_test.py b/src/schema_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..26a936b410de3e96a6ef0118b424d937284eca30
--- /dev/null
+++ b/src/schema_test.py
@@ -0,0 +1,262 @@
+"""Tests for item.py."""
+
+import pyarrow as pa
+import pytest
+
+from .schema import (
+ PATH_WILDCARD,
+ TEXT_SPAN_END_FEATURE,
+ TEXT_SPAN_START_FEATURE,
+ VALUE_KEY,
+ DataType,
+ Field,
+ Item,
+ arrow_schema_to_schema,
+ child_item_from_column_path,
+ column_paths_match,
+ field,
+ schema,
+ schema_to_arrow_schema,
+)
+
+NESTED_TEST_SCHEMA = schema({
+ 'person': {
+ 'name': 'string',
+ 'last_name': 'string_span',
+ # Contains a double nested array of primitives.
+ 'data': [['float32']],
+ # Contains a value and children.
+ 'description': field(
+ 'string',
+ fields={
+ 'toxicity': 'float32',
+ 'sentences': [field('string_span', fields={'len': 'int32'})]
+ })
+ },
+ 'addresses': [{
+ 'city': 'string',
+ 'zipcode': 'int16',
+ 'current': 'boolean',
+ 'locations': [{
+ 'latitude': 'float16',
+ 'longitude': 'float64'
+ }]
+ }],
+ 'blob': 'binary'
+})
+NESTED_TEST_ITEM: Item = {
+ 'person': {
+ 'name': 'Test Name',
+ 'last_name': (5, 9)
+ },
+ 'addresses': [{
+ 'city': 'a',
+ 'zipcode': 1,
+ 'current': False,
+ 'locations': [{
+ 'latitude': 1.5,
+ 'longitude': 3.8
+ }, {
+ 'latitude': 2.9,
+ 'longitude': 15.3
+ }],
+ }, {
+ 'city': 'b',
+ 'zipcode': 2,
+ 'current': True,
+ 'locations': [{
+ 'latitude': 11.2,
+ 'longitude': 20.1
+ }, {
+ 'latitude': 30.1,
+ 'longitude': 40.2
+ }],
+ }]
+}
+
+
+def test_field_ctor_validation() -> None:
+ with pytest.raises(
+ ValueError, match='One of "fields", "repeated_field", or "dtype" should be defined'):
+ Field()
+
+ with pytest.raises(ValueError, match='Both "fields" and "repeated_field" should not be defined'):
+ Field(
+ fields={'name': Field(dtype=DataType.STRING)},
+ repeated_field=Field(dtype=DataType.INT32),
+ )
+
+ with pytest.raises(ValueError, match=f'{VALUE_KEY} is a reserved field name'):
+ Field(fields={VALUE_KEY: Field(dtype=DataType.STRING)},)
+
+
+def test_schema_leafs() -> None:
+ expected = {
+ ('addresses', PATH_WILDCARD, 'city'): Field(dtype=DataType.STRING),
+ ('addresses', PATH_WILDCARD, 'current'): Field(dtype=DataType.BOOLEAN),
+ ('addresses', PATH_WILDCARD, 'locations', PATH_WILDCARD, 'latitude'):
+ Field(dtype=DataType.FLOAT16),
+ ('addresses', PATH_WILDCARD, 'locations', PATH_WILDCARD, 'longitude'):
+ Field(dtype=DataType.FLOAT64),
+ ('addresses', PATH_WILDCARD, 'zipcode'): Field(dtype=DataType.INT16),
+ ('blob',): Field(dtype=DataType.BINARY),
+ ('person', 'name'): Field(dtype=DataType.STRING),
+ ('person', 'last_name'): Field(dtype=DataType.STRING_SPAN),
+ ('person', 'data', PATH_WILDCARD, PATH_WILDCARD): Field(dtype=DataType.FLOAT32),
+ ('person', 'description'): Field(
+ dtype=DataType.STRING,
+ fields={
+ 'toxicity': Field(dtype=DataType.FLOAT32),
+ 'sentences': Field(
+ repeated_field=Field(
+ dtype=DataType.STRING_SPAN, fields={'len': Field(dtype=DataType.INT32)}))
+ }),
+ ('person', 'description', 'toxicity'): Field(dtype=DataType.FLOAT32),
+ ('person', 'description', 'sentences', PATH_WILDCARD): Field(
+ fields={'len': Field(dtype=DataType.INT32)}, dtype=DataType.STRING_SPAN),
+ ('person', 'description', 'sentences', PATH_WILDCARD, 'len'): Field(dtype=DataType.INT32),
+ }
+ assert NESTED_TEST_SCHEMA.leafs == expected
+
+
+def test_schema_to_arrow_schema() -> None:
+ arrow_schema = schema_to_arrow_schema(NESTED_TEST_SCHEMA)
+
+ assert arrow_schema == pa.schema({
+ 'person': pa.struct({
+ 'name': pa.string(),
+ # The dtype for STRING_SPAN is implemented as a struct with a {start, end}.
+ 'last_name': pa.struct({
+ VALUE_KEY: pa.struct({
+ TEXT_SPAN_START_FEATURE: pa.int32(),
+ TEXT_SPAN_END_FEATURE: pa.int32(),
+ })
+ }),
+ 'data': pa.list_(pa.list_(pa.float32())),
+ 'description': pa.struct({
+ 'toxicity': pa.float32(),
+ 'sentences': pa.list_(
+ pa.struct({
+ 'len': pa.int32(),
+ VALUE_KEY: pa.struct({
+ TEXT_SPAN_START_FEATURE: pa.int32(),
+ TEXT_SPAN_END_FEATURE: pa.int32(),
+ })
+ })),
+ VALUE_KEY: pa.string(),
+ })
+ }),
+ 'addresses': pa.list_(
+ pa.struct({
+ 'city': pa.string(),
+ 'zipcode': pa.int16(),
+ 'current': pa.bool_(),
+ 'locations': pa.list_(pa.struct({
+ 'latitude': pa.float16(),
+ 'longitude': pa.float64()
+ })),
+ })),
+ 'blob': pa.binary(),
+ })
+
+
+def test_arrow_schema_to_schema() -> None:
+ arrow_schema = pa.schema({
+ 'person': pa.struct({
+ 'name': pa.string(),
+ 'data': pa.list_(pa.list_(pa.float32()))
+ }),
+ 'addresses': pa.list_(
+ pa.struct({
+ 'city': pa.string(),
+ 'zipcode': pa.int16(),
+ 'current': pa.bool_(),
+ 'locations': pa.list_(pa.struct({
+ 'latitude': pa.float16(),
+ 'longitude': pa.float64()
+ })),
+ })),
+ 'blob': pa.binary(),
+ })
+ expected_schema = schema({
+ 'person': {
+ 'name': 'string',
+ 'data': [['float32']]
+ },
+ 'addresses': [{
+ 'city': 'string',
+ 'zipcode': 'int16',
+ 'current': 'boolean',
+ 'locations': [{
+ 'latitude': 'float16',
+ 'longitude': 'float64',
+ }]
+ }],
+ 'blob': 'binary',
+ })
+ assert arrow_schema_to_schema(arrow_schema) == expected_schema
+
+
+def test_simple_schema_str() -> None:
+ assert str(schema({'person': 'string'})) == 'person: string'
+
+
+def test_child_item_from_column_path() -> None:
+ assert child_item_from_column_path(NESTED_TEST_ITEM,
+ ('addresses', '0', 'locations', '0', 'longitude')) == 3.8
+ assert child_item_from_column_path(NESTED_TEST_ITEM, ('addresses', '1', 'city')) == 'b'
+
+
+def test_child_item_from_column_path_raises_wildcard() -> None:
+ with pytest.raises(
+ ValueError, match='cannot be called with a path that contains a repeated wildcard'):
+ child_item_from_column_path(NESTED_TEST_ITEM, ('addresses', PATH_WILDCARD, 'city'))
+
+
+def test_column_paths_match() -> None:
+ assert column_paths_match(path_match=('person', 'name'), specific_path=('person', 'name')) is True
+ assert column_paths_match(
+ path_match=('person', 'name'), specific_path=('person', 'not_name')) is False
+
+ # Wildcards work for structs.
+ assert column_paths_match(
+ path_match=(PATH_WILDCARD, 'name'), specific_path=('person', 'name')) is True
+ assert column_paths_match(
+ path_match=(PATH_WILDCARD, 'name'), specific_path=('person', 'not_name')) is False
+
+ # Wildcards work for repeateds.
+ assert column_paths_match(
+ path_match=('person', PATH_WILDCARD, 'name'), specific_path=('person', '0', 'name')) is True
+ assert column_paths_match(
+ path_match=('person', PATH_WILDCARD, 'name'),
+ specific_path=('person', '0', 'not_name')) is False
+
+ # Sub-path matches always return False.
+ assert column_paths_match(path_match=(PATH_WILDCARD,), specific_path=('person', 'name')) is False
+ assert column_paths_match(
+ path_match=(
+ 'person',
+ PATH_WILDCARD,
+ ), specific_path=('person', '0', 'name')) is False
+
+
+def test_nested_schema_str() -> None:
+
+ assert str(NESTED_TEST_SCHEMA) == """\
+person:
+ name: string
+ last_name: string_span
+ data: list( list( float32))
+ description:
+ toxicity: float32
+ sentences: list(
+ len: int32)
+addresses: list(
+ city: string
+ zipcode: int16
+ current: boolean
+ locations: list(
+ latitude: float16
+ longitude: float64))
+blob: binary\
+"""
diff --git a/src/server.py b/src/server.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a7f841de64985a00ccca175770ec92453ec0e5b
--- /dev/null
+++ b/src/server.py
@@ -0,0 +1,70 @@
+"""Serves the Lilac server."""
+
+import logging
+import os
+from typing import Any
+
+from fastapi import APIRouter, FastAPI
+from fastapi.responses import ORJSONResponse
+from fastapi.routing import APIRoute
+from fastapi.staticfiles import StaticFiles
+
+from . import router_concept, router_data_loader, router_dataset, router_signal, router_tasks
+from .router_utils import RouteErrorHandler
+from .tasks import task_manager
+
+DIST_PATH = os.path.abspath(os.path.join('web', 'blueprint', 'build'))
+
+tags_metadata: list[dict[str, Any]] = [{
+ 'name': 'datasets',
+ 'description': 'API for querying a dataset.',
+}, {
+ 'name': 'concepts',
+ 'description': 'API for managing concepts.',
+}, {
+ 'name': 'data_loaders',
+ 'description': 'API for loading data.',
+}, {
+ 'name': 'signals',
+ 'description': 'API for managing signals.',
+}]
+
+
+def custom_generate_unique_id(route: APIRoute) -> str:
+ """Generate the name for the API endpoint."""
+ return route.name
+
+
+app = FastAPI(
+ default_response_class=ORJSONResponse,
+ generate_unique_id_function=custom_generate_unique_id,
+ openapi_tags=tags_metadata)
+
+v1_router = APIRouter(route_class=RouteErrorHandler)
+v1_router.include_router(router_dataset.router, prefix='/datasets', tags=['datasets'])
+v1_router.include_router(router_concept.router, prefix='/concepts', tags=['concepts'])
+v1_router.include_router(router_data_loader.router, prefix='/data_loaders', tags=['data_loaders'])
+v1_router.include_router(router_signal.router, prefix='/signals', tags=['signals'])
+v1_router.include_router(router_tasks.router, prefix='/tasks', tags=['tasks'])
+
+app.include_router(v1_router, prefix='/api/v1')
+
+# Serve static files in production mode.
+app.mount('/', StaticFiles(directory=os.path.join(DIST_PATH), html=True, check_dir=False))
+
+
+@app.on_event('shutdown')
+async def shutdown_event() -> None:
+ """Kill the task manager when FastAPI shuts down."""
+ await task_manager().stop()
+
+
+class GetTasksFilter(logging.Filter):
+ """Task filter for /tasks."""
+
+ def filter(self, record: logging.LogRecord) -> bool:
+ """Filters out /api/v1/tasks/ from the logs."""
+ return record.getMessage().find('/api/v1/tasks/') == -1
+
+
+logging.getLogger('uvicorn.access').addFilter(GetTasksFilter())
diff --git a/src/server_concept_test.py b/src/server_concept_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a6e74b4a1d826c7d1766765627415ccf5047dc4
--- /dev/null
+++ b/src/server_concept_test.py
@@ -0,0 +1,412 @@
+"""Test the public REST API for concepts."""
+import uuid
+from pathlib import Path
+from typing import Iterable, cast
+
+import numpy as np
+import pytest
+from fastapi.testclient import TestClient
+from pydantic import parse_obj_as
+from pytest_mock import MockerFixture
+from typing_extensions import override
+
+from .concepts.concept import (
+ DRAFT_MAIN,
+ Concept,
+ ConceptModel,
+ Example,
+ ExampleIn,
+ ExampleOrigin,
+ LogisticEmbeddingModel,
+)
+from .concepts.db_concept import ConceptInfo, ConceptUpdate
+from .config import CONFIG
+from .data.dataset_utils import lilac_embedding
+from .router_concept import (
+ ConceptModelResponse,
+ CreateConceptOptions,
+ MergeConceptDraftOptions,
+ ScoreBody,
+ ScoreExample,
+ ScoreResponse,
+)
+from .schema import Item, RichData, SignalInputType
+from .server import app
+from .signals.signal import TextEmbeddingSignal, clear_signal_registry, register_signal
+from .test_utils import fake_uuid
+
+client = TestClient(app)
+
+EMBEDDINGS: list[tuple[str, list[float]]] = [('hello', [1.0, 0.0, 0.0]), ('hello2', [1.0, 1.0,
+ 0.0]),
+ ('hello world', [1.0, 1.0, 1.0]),
+ ('hello world2', [2.0, 1.0, 1.0])]
+
+STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for text, embedding in EMBEDDINGS}
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Call the embedding function."""
+ for example in data:
+ yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[cast(str, example)]))]
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(TestEmbedding)
+ # Unit test runs.
+ yield
+ # Teardown.
+ clear_signal_registry()
+
+
+@pytest.fixture(scope='function', autouse=True)
+def setup_data_dir(tmp_path: Path, mocker: MockerFixture) -> None:
+ mocker.patch.dict(CONFIG, {'LILAC_DATA_PATH': str(tmp_path)})
+
+
+def test_concept_create() -> None:
+ url = '/api/v1/concepts/'
+ response = client.get(url)
+
+ assert response.status_code == 200
+ assert response.json() == []
+
+ # Create a concept.
+ url = '/api/v1/concepts/create'
+ create_concept = CreateConceptOptions(
+ namespace='concept_namespace', name='concept', type=SignalInputType.TEXT)
+ response = client.post(url, json=create_concept.dict())
+ assert response.status_code == 200
+ assert response.json() == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type=SignalInputType.TEXT,
+ data={},
+ version=0).dict()
+
+ # Make sure list shows us the new concept.
+ url = '/api/v1/concepts/'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert parse_obj_as(list[ConceptInfo], response.json()) == [
+ ConceptInfo(
+ namespace='concept_namespace', name='concept', type=SignalInputType.TEXT, drafts=[DRAFT_MAIN])
+ ]
+
+
+def test_concept_edits(mocker: MockerFixture) -> None:
+ mock_uuid = mocker.patch.object(uuid, 'uuid4', autospec=True)
+
+ # Create the concept.
+ response = client.post(
+ '/api/v1/concepts/create',
+ json=CreateConceptOptions(
+ namespace='concept_namespace', name='concept', type=SignalInputType.TEXT).dict())
+
+ # Make sure we can add an example.
+ mock_uuid.return_value = fake_uuid(b'1')
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(insert=[
+ ExampleIn(
+ label=True,
+ text='hello',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d1'))
+ ])
+ response = client.post(url, json=concept_update.dict())
+ assert response.status_code == 200
+ assert Concept.parse_obj(response.json()) == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type='text',
+ data={
+ fake_uuid(b'1').hex: Example(
+ id=fake_uuid(b'1').hex,
+ label=True,
+ text='hello',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d1'))
+ },
+ version=1)
+
+ url = '/api/v1/concepts/'
+ response = client.get(url)
+
+ assert response.status_code == 200
+ assert parse_obj_as(list[ConceptInfo], response.json()) == [
+ ConceptInfo(
+ namespace='concept_namespace', name='concept', type=SignalInputType.TEXT, drafts=[DRAFT_MAIN])
+ ]
+
+ # Add another example.
+ mock_uuid.return_value = fake_uuid(b'2')
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(insert=[
+ ExampleIn(
+ label=True,
+ text='hello2',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d2'))
+ ])
+ response = client.post(url, json=concept_update.dict())
+ assert response.status_code == 200
+ assert Concept.parse_obj(response.json()) == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type='text',
+ data={
+ fake_uuid(b'1').hex: Example(
+ id=fake_uuid(b'1').hex,
+ label=True,
+ text='hello',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d1')),
+ fake_uuid(b'2').hex: Example(
+ id=fake_uuid(b'2').hex,
+ label=True,
+ text='hello2',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d2'))
+ },
+ version=2)
+
+ # Edit both examples.
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(update=[
+ # Switch the label.
+ Example(id=fake_uuid(b'1').hex, label=False, text='hello'),
+ # Switch the text.
+ Example(id=fake_uuid(b'2').hex, label=True, text='hello world'),
+ ])
+ response = client.post(url, json=concept_update.dict())
+ assert response.status_code == 200
+ assert Concept.parse_obj(response.json()) == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type='text',
+ data={
+ fake_uuid(b'1').hex: Example(id=fake_uuid(b'1').hex, label=False, text='hello'),
+ fake_uuid(b'2').hex: Example(id=fake_uuid(b'2').hex, label=True, text='hello world')
+ },
+ version=3)
+
+ # Delete the first example.
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(remove=[fake_uuid(b'1').hex])
+ response = client.post(url, json=concept_update.dict())
+ assert response.status_code == 200
+ assert Concept.parse_obj(response.json()) == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type='text',
+ data={fake_uuid(b'2').hex: Example(id=fake_uuid(b'2').hex, label=True, text='hello world')},
+ version=4)
+
+ # The concept still exists.
+ url = '/api/v1/concepts/'
+ response = client.get(url)
+
+ assert response.status_code == 200
+ assert parse_obj_as(list[ConceptInfo], response.json()) == [
+ ConceptInfo(
+ namespace='concept_namespace', name='concept', type=SignalInputType.TEXT, drafts=[DRAFT_MAIN])
+ ]
+
+
+def test_concept_drafts(mocker: MockerFixture) -> None:
+ mock_uuid = mocker.patch.object(uuid, 'uuid4', autospec=True)
+
+ # Create the concept.
+ response = client.post(
+ '/api/v1/concepts/create',
+ json=CreateConceptOptions(
+ namespace='concept_namespace', name='concept', type=SignalInputType.TEXT).dict())
+
+ # Add examples, some drafts.
+ mock_uuid.side_effect = [fake_uuid(b'1'), fake_uuid(b'2'), fake_uuid(b'3'), fake_uuid(b'4')]
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(insert=[
+ ExampleIn(label=True, text='in concept'),
+ ExampleIn(label=False, text='out of concept'),
+ ExampleIn(label=False, text='in concept', draft='test_draft'),
+ ExampleIn(label=False, text='out of concept draft', draft='test_draft')
+ ])
+ response = client.post(url, json=concept_update.dict())
+ assert response.status_code == 200
+
+ # Make sure list shows us the drafts
+ url = '/api/v1/concepts/'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert parse_obj_as(list[ConceptInfo], response.json()) == [
+ ConceptInfo(
+ namespace='concept_namespace',
+ name='concept',
+ type=SignalInputType.TEXT,
+ drafts=[DRAFT_MAIN, 'test_draft'])
+ ]
+
+ # Make sure when we request main, we only get data in main.
+ url = '/api/v1/concepts/concept_namespace/concept'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert Concept.parse_obj(response.json()) == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type='text',
+ data={
+ # Only main are returned.
+ fake_uuid(b'1').hex: Example(id=fake_uuid(b'1').hex, label=True, text='in concept'),
+ fake_uuid(b'2').hex: Example(id=fake_uuid(b'2').hex, label=False, text='out of concept')
+ },
+ version=1)
+
+ # Make sure when we request the draft, we get the draft data deduped with main.
+ url = '/api/v1/concepts/concept_namespace/concept?draft=test_draft'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert Concept.parse_obj(response.json()) == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type='text',
+ data={
+ # b'1' is deduped with b'3'.
+ fake_uuid(b'2').hex: Example(id=fake_uuid(b'2').hex, label=False, text='out of concept'),
+ # ID 3 is a duplicate of main's 1.
+ fake_uuid(b'3').hex: Example(
+ id=fake_uuid(b'3').hex, label=False, text='in concept', draft='test_draft'),
+ fake_uuid(b'4').hex: Example(
+ id=fake_uuid(b'4').hex, label=False, text='out of concept draft', draft='test_draft')
+ },
+ version=1)
+
+ # Merge the draft.
+ response = client.post(
+ '/api/v1/concepts/concept_namespace/concept/merge_draft',
+ json=MergeConceptDraftOptions(draft='test_draft').dict())
+ assert response.status_code == 200
+
+ # Make sure we get the merged drafts.
+ url = '/api/v1/concepts/concept_namespace/concept'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert Concept.parse_obj(response.json()).dict() == Concept(
+ namespace='concept_namespace',
+ concept_name='concept',
+ type='text',
+ data={
+ # b'1' is deduped with b'3'.
+ fake_uuid(b'2').hex: Example(id=fake_uuid(b'2').hex, label=False, text='out of concept'),
+ # ID 3 is a duplicate of main's 1.
+ fake_uuid(b'3').hex: Example(id=fake_uuid(b'3').hex, label=False, text='in concept'),
+ fake_uuid(b'4').hex: Example(
+ id=fake_uuid(b'4').hex, label=False, text='out of concept draft')
+ },
+ version=2).dict()
+
+
+def test_concept_model_sync(mocker: MockerFixture) -> None:
+ mock_uuid = mocker.patch.object(uuid, 'uuid4', autospec=True)
+
+ # Create the concept.
+ response = client.post(
+ '/api/v1/concepts/create',
+ json=CreateConceptOptions(
+ namespace='concept_namespace', name='concept', type=SignalInputType.TEXT).dict())
+
+ # Add two examples.
+ mock_uuid.side_effect = [fake_uuid(b'1'), fake_uuid(b'2')]
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(insert=[
+ ExampleIn(
+ label=True,
+ text='hello',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d1')),
+ ExampleIn(
+ label=False,
+ text='hello world',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d2'))
+ ])
+ response = client.post(url, json=concept_update.dict())
+ assert response.status_code == 200
+
+ # Get the concept model.
+ url = '/api/v1/concepts/concept_namespace/concept/test_embedding?sync_model=False'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert ConceptModelResponse.parse_obj(response.json()) == ConceptModelResponse(
+ model=ConceptModel(
+ namespace='concept_namespace',
+ concept_name='concept',
+ embedding_name='test_embedding',
+ version=-1),
+ # The model shouldn't yet be synced because we set sync_model=False.
+ model_synced=False)
+
+ # Sync the concept model.
+ url = '/api/v1/concepts/concept_namespace/concept/test_embedding?sync_model=True'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert ConceptModelResponse.parse_obj(response.json()) == ConceptModelResponse(
+ model=ConceptModel(
+ namespace='concept_namespace',
+ concept_name='concept',
+ embedding_name='test_embedding',
+ version=1),
+ # The model should be synced because we set sync_model=True.
+ model_synced=True)
+
+ # Score an example.
+ mock_score_emb = mocker.patch.object(LogisticEmbeddingModel, 'score_embeddings', autospec=True)
+ mock_score_emb.return_value = np.array([0.9, 1.0])
+ url = '/api/v1/concepts/concept_namespace/concept/test_embedding/score'
+ score_body = ScoreBody(examples=[ScoreExample(text='hello world'), ScoreExample(text='hello')])
+ response = client.post(url, json=score_body.dict())
+ assert response.status_code == 200
+ assert ScoreResponse.parse_obj(response.json()) == ScoreResponse(
+ scores=[0.9, 1.0],
+ # The model should already be synced.
+ model_synced=False)
+
+
+def test_concept_edits_error_before_create(mocker: MockerFixture) -> None:
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(insert=[
+ ExampleIn(
+ label=True,
+ text='hello',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d1'))
+ ])
+ response = client.post(url, json=concept_update.dict())
+ assert response.is_error is True
+ assert response.status_code == 500
+
+
+def test_concept_edits_wrong_type(mocker: MockerFixture) -> None:
+ # Create the concept.
+ response = client.post(
+ '/api/v1/concepts/create',
+ json=CreateConceptOptions(
+ namespace='concept_namespace', name='concept', type=SignalInputType.IMAGE).dict())
+
+ url = '/api/v1/concepts/concept_namespace/concept'
+ concept_update = ConceptUpdate(insert=[
+ ExampleIn(
+ label=True,
+ text='hello',
+ origin=ExampleOrigin(
+ dataset_namespace='dataset_namespace', dataset_name='dataset', dataset_row_id='d1'))
+ ])
+ response = client.post(url, json=concept_update.dict())
+ assert response.is_error is True
+ assert response.status_code == 500
diff --git a/src/server_test.py b/src/server_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..092dbfc68c8f4ad65a1221f7f0b2dfe9c7481e3c
--- /dev/null
+++ b/src/server_test.py
@@ -0,0 +1,286 @@
+"""Test our public REST API."""
+from typing import Iterable, Optional, Type
+
+import pytest
+from fastapi.testclient import TestClient
+from pytest_mock import MockerFixture
+
+from .config import CONFIG
+from .data.dataset import (
+ Column,
+ Dataset,
+ DatasetManifest,
+ SelectRowsSchemaResult,
+ SelectRowsSchemaUDF,
+)
+from .data.dataset_duckdb import DatasetDuckDB
+from .data.dataset_test_utils import TEST_DATASET_NAME, TEST_NAMESPACE, enriched_item, make_dataset
+from .router_dataset import (
+ SelectRowsOptions,
+ SelectRowsResponse,
+ SelectRowsSchemaOptions,
+ WebManifest,
+)
+from .schema import UUID_COLUMN, Field, Item, RichData, field, schema
+from .server import app
+from .signals.signal import TextSignal, clear_signal_registry, register_signal
+
+client = TestClient(app)
+
+DATASET_CLASSES = [DatasetDuckDB]
+
+TEST_DATA: list[Item] = [{
+ UUID_COLUMN: '1',
+ 'erased': False,
+ 'people': [{
+ 'name': 'A',
+ 'zipcode': 0,
+ 'locations': [{
+ 'city': 'city1',
+ 'state': 'state1'
+ }, {
+ 'city': 'city2',
+ 'state': 'state2'
+ }]
+ }]
+}, {
+ UUID_COLUMN: '2',
+ 'erased': True,
+ 'people': [{
+ 'name': 'B',
+ 'zipcode': 1,
+ 'locations': [{
+ 'city': 'city3',
+ 'state': 'state3'
+ }, {
+ 'city': 'city4'
+ }, {
+ 'city': 'city5'
+ }]
+ }, {
+ 'name': 'C',
+ 'zipcode': 2,
+ 'locations': [{
+ 'city': 'city1',
+ 'state': 'state1'
+ }]
+ }]
+}, {
+ UUID_COLUMN: '3',
+ 'erased': True,
+}]
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(LengthSignal)
+ # Unit test runs.
+ yield
+ # Teardown.
+ clear_signal_registry()
+
+
+@pytest.fixture(scope='module', autouse=True, params=DATASET_CLASSES)
+def test_data(tmp_path_factory: pytest.TempPathFactory, module_mocker: MockerFixture,
+ request: pytest.FixtureRequest) -> None:
+ tmp_path = tmp_path_factory.mktemp('data')
+ module_mocker.patch.dict(CONFIG, {'LILAC_DATA_PATH': str(tmp_path)})
+ dataset_cls: Type[Dataset] = request.param
+ make_dataset(dataset_cls, tmp_path, TEST_DATA)
+
+
+def test_get_manifest() -> None:
+ url = f'/api/v1/datasets/{TEST_NAMESPACE}/{TEST_DATASET_NAME}'
+ response = client.get(url)
+ assert response.status_code == 200
+ assert WebManifest.parse_obj(response.json()) == WebManifest(
+ dataset_manifest=DatasetManifest(
+ namespace=TEST_NAMESPACE,
+ dataset_name=TEST_DATASET_NAME,
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'erased': 'boolean',
+ 'people': [{
+ 'name': 'string',
+ 'zipcode': 'int32',
+ 'locations': [{
+ 'city': 'string',
+ 'state': 'string'
+ }]
+ }]
+ }),
+ num_items=3))
+
+
+def test_select_rows_no_options() -> None:
+ url = f'/api/v1/datasets/{TEST_NAMESPACE}/{TEST_DATASET_NAME}/select_rows'
+ options = SelectRowsOptions()
+ response = client.post(url, json=options.dict())
+ assert response.status_code == 200
+ assert SelectRowsResponse.parse_obj(response.json()) == SelectRowsResponse(
+ rows=TEST_DATA, total_num_rows=3)
+
+
+def test_select_rows_with_cols_and_limit() -> None:
+ url = f'/api/v1/datasets/{TEST_NAMESPACE}/{TEST_DATASET_NAME}/select_rows'
+ options = SelectRowsOptions(
+ columns=[('people', '*', 'zipcode'), ('people', '*', 'locations', '*', 'city')],
+ limit=1,
+ offset=1)
+ response = client.post(url, json=options.dict())
+ assert response.status_code == 200
+ assert SelectRowsResponse.parse_obj(response.json()) == SelectRowsResponse(
+ rows=[{
+ UUID_COLUMN: '2',
+ 'people.*.zipcode': [1, 2],
+ 'people.*.locations.*.city': [['city3', 'city4', 'city5'], ['city1']]
+ }],
+ total_num_rows=3)
+
+
+def test_select_rows_with_cols_and_combine() -> None:
+ url = f'/api/v1/datasets/{TEST_NAMESPACE}/{TEST_DATASET_NAME}/select_rows'
+ options = SelectRowsOptions(
+ columns=[('people', '*', 'zipcode'), ('people', '*', 'locations', '*', 'city')],
+ combine_columns=True)
+ response = client.post(url, json=options.dict())
+ assert response.status_code == 200
+ assert SelectRowsResponse.parse_obj(response.json()) == SelectRowsResponse(
+ rows=[{
+ UUID_COLUMN: '1',
+ 'people': [{
+ 'zipcode': 0,
+ 'locations': [{
+ 'city': 'city1',
+ }, {
+ 'city': 'city2',
+ }]
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'people': [{
+ 'zipcode': 1,
+ 'locations': [{
+ 'city': 'city3',
+ }, {
+ 'city': 'city4'
+ }, {
+ 'city': 'city5'
+ }]
+ }, {
+ 'zipcode': 2,
+ 'locations': [{
+ 'city': 'city1'
+ }]
+ }]
+ }, {
+ UUID_COLUMN: '3',
+ 'people': None
+ }],
+ total_num_rows=3)
+
+
+class LengthSignal(TextSignal):
+ name = 'length_signal'
+
+ def fields(self) -> Field:
+ return field('int32')
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text_content in data:
+ yield len(text_content) if text_content is not None else None
+
+
+def test_select_rows_star_plus_udf() -> None:
+ url = f'/api/v1/datasets/{TEST_NAMESPACE}/{TEST_DATASET_NAME}/select_rows'
+ udf = Column('people.*.name', alias='len', signal_udf=LengthSignal())
+ options = SelectRowsOptions(columns=['*', udf], combine_columns=True)
+ response = client.post(url, json=options.dict())
+ assert response.status_code == 200
+ assert SelectRowsResponse.parse_obj(response.json()) == SelectRowsResponse(
+ rows=[{
+ UUID_COLUMN: '1',
+ 'erased': False,
+ 'people': [{
+ 'name': enriched_item('A', {'length_signal': 1}),
+ 'zipcode': 0,
+ 'locations': [{
+ 'city': 'city1',
+ 'state': 'state1'
+ }, {
+ 'city': 'city2',
+ 'state': 'state2'
+ }]
+ }]
+ }, {
+ UUID_COLUMN: '2',
+ 'erased': True,
+ 'people': [{
+ 'name': enriched_item('B', {'length_signal': 1}),
+ 'zipcode': 1,
+ 'locations': [{
+ 'city': 'city3',
+ 'state': 'state3'
+ }, {
+ 'city': 'city4'
+ }, {
+ 'city': 'city5'
+ }]
+ }, {
+ 'name': enriched_item('C', {'length_signal': 1}),
+ 'zipcode': 2,
+ 'locations': [{
+ 'city': 'city1',
+ 'state': 'state1'
+ }]
+ }]
+ }, {
+ UUID_COLUMN: '3',
+ 'erased': True,
+ }],
+ total_num_rows=3)
+
+
+def test_select_rows_schema_star_plus_udf() -> None:
+ url = f'/api/v1/datasets/{TEST_NAMESPACE}/{TEST_DATASET_NAME}/select_rows_schema'
+ signal = LengthSignal()
+ udf = Column('people.*.name', alias='len', signal_udf=signal)
+ options = SelectRowsSchemaOptions(columns=['*', udf], combine_columns=True)
+ response = client.post(url, json=options.dict())
+ assert response.status_code == 200
+ assert SelectRowsSchemaResult.parse_obj(response.json()) == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'erased': 'boolean',
+ 'people': [{
+ 'name': field(
+ 'string', fields={'length_signal': field('int32', signal.dict(exclude_none=True))}),
+ 'zipcode': 'int32',
+ 'locations': [{
+ 'city': 'string',
+ 'state': 'string'
+ }]
+ }]
+ }),
+ udfs=[SelectRowsSchemaUDF(path=('people', '*', 'name', 'length_signal'), alias='len')])
+
+
+def test_select_rows_schema_no_cols() -> None:
+ url = f'/api/v1/datasets/{TEST_NAMESPACE}/{TEST_DATASET_NAME}/select_rows_schema'
+ options = SelectRowsSchemaOptions(combine_columns=True)
+ response = client.post(url, json=options.dict())
+ assert response.status_code == 200
+ assert SelectRowsSchemaResult.parse_obj(response.json()) == SelectRowsSchemaResult(
+ data_schema=schema({
+ UUID_COLUMN: 'string',
+ 'erased': 'boolean',
+ 'people': [{
+ 'name': 'string',
+ 'zipcode': 'int32',
+ 'locations': [{
+ 'city': 'string',
+ 'state': 'string'
+ }]
+ }]
+ }))
diff --git a/src/signals/__init__.py b/src/signals/__init__.py
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diff --git a/src/signals/concept_labels.py b/src/signals/concept_labels.py
new file mode 100644
index 0000000000000000000000000000000000000000..774b0514173b88621a082da0cbbac46abfbef0c6
--- /dev/null
+++ b/src/signals/concept_labels.py
@@ -0,0 +1,79 @@
+"""A signal to compute span offsets of already labeled concept text."""
+from typing import Iterable, Optional
+
+from typing_extensions import override
+
+from ..concepts.concept import DRAFT_MAIN, draft_examples
+from ..concepts.db_concept import DISK_CONCEPT_DB, ConceptDB
+from ..data.dataset_utils import lilac_span
+from ..schema import Field, Item, RichData, field
+from .signal import TextSignal
+
+
+class ConceptLabelsSignal(TextSignal):
+ """Computes spans where text is labeled for the concept, either positive or negative."""
+ name = 'concept_labels'
+ display_name = 'Concept Labels'
+
+ namespace: str
+ concept_name: str
+
+ # The draft version of the concept to use. If not provided, the latest version is used.
+ draft: str = DRAFT_MAIN
+
+ _concept_db: ConceptDB = DISK_CONCEPT_DB
+
+ @override
+ def fields(self) -> Field:
+ return field(fields=[field('string_span', fields={'label': 'boolean', 'draft': 'string'})])
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ concept = self._concept_db.get(self.namespace, self.concept_name)
+ if not concept:
+ raise ValueError(f'Concept "{self.namespace}/{self.concept_name}" does not exist.')
+
+ examples = draft_examples(concept, draft=self.draft)
+ for text in data:
+ if not text:
+ yield None
+ continue
+
+ if not isinstance(text, str):
+ raise ValueError(f'{str(text)} is a {type(text)}, which is not a string.')
+
+ label_spans: list[Item] = []
+ for example in examples.values():
+ if not example.text:
+ continue
+
+ offset = 0
+ while offset < len(text):
+ offset = text.find(example.text, offset)
+ if offset == -1:
+ break
+ label_spans.append(
+ lilac_span(
+ offset, offset + len(example.text), {
+ 'label': example.label,
+ **({
+ 'draft': example.draft
+ } if example.draft != DRAFT_MAIN else {})
+ }))
+ offset += len(example.text)
+
+ if label_spans:
+ yield label_spans
+ else:
+ yield None
+
+ @override
+ def key(self, is_computed_signal: Optional[bool] = False) -> str:
+ version = ''
+ if is_computed_signal:
+ concept = self._concept_db.get(self.namespace, self.concept_name)
+ if not concept:
+ raise ValueError(f'Concept "{self.namespace}/{self.concept_name}" does not exist.')
+ version = f'/v{concept.version}'
+
+ return f'{self.namespace}/{self.concept_name}/labels{version}'
diff --git a/src/signals/concept_labels_test.py b/src/signals/concept_labels_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..64212f6dd447bafc8f6e8fb40447fb966616e46e
--- /dev/null
+++ b/src/signals/concept_labels_test.py
@@ -0,0 +1,129 @@
+"""Test for the concept label signal."""
+
+import pathlib
+from typing import Generator, Type
+
+import pytest
+from pytest_mock import MockerFixture
+
+from ..concepts.concept import ExampleIn
+from ..concepts.db_concept import ConceptDB, ConceptUpdate, DiskConceptDB, DiskConceptModelDB
+from ..config import CONFIG
+from ..data.dataset_duckdb import DatasetDuckDB
+from ..data.dataset_utils import lilac_span
+from ..db_manager import set_default_dataset_cls
+from ..schema import SignalInputType
+from .concept_labels import ConceptLabelsSignal
+from .signal import clear_signal_registry
+
+ALL_CONCEPT_DBS = [DiskConceptDB]
+ALL_CONCEPT_MODEL_DBS = [DiskConceptModelDB]
+
+
+@pytest.fixture(autouse=True)
+def set_data_path(tmp_path: pathlib.Path, mocker: MockerFixture) -> None:
+ mocker.patch.dict(CONFIG, {'LILAC_DATA_PATH': str(tmp_path)})
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Generator:
+ # Setup.
+ set_default_dataset_cls(DatasetDuckDB)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_concept_does_not_exist() -> None:
+ signal = ConceptLabelsSignal(namespace='test', concept_name='concept_doesnt_exist')
+ with pytest.raises(ValueError, match='Concept "test/concept_doesnt_exist" does not exist'):
+ list(signal.compute(['a new data point', 'not in concept']))
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+def test_concept_labels(concept_db_cls: Type[ConceptDB]) -> None:
+ concept_db = concept_db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='no in concept'),
+ ExampleIn(label=True, text='yes in concept'),
+ # This should never show since we request the main draft.
+ ExampleIn(label=False, text='this is unrelated', draft='test_draft')
+ ]
+ concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ signal = ConceptLabelsSignal(namespace='test', concept_name='test_concept')
+ results = list(
+ signal.compute([
+ 'this is no in concept', 'this is yes in concept',
+ 'this is no in concept. filler. this is yes in concept.', 'this is unrelated'
+ ]))
+
+ assert results == [
+ [lilac_span(8, 8 + len('no in concept'), {'label': False})],
+ [lilac_span(8, 8 + len('yes in concept'), {'label': True})],
+ [
+ lilac_span(8, 8 + len('no in concept'), {'label': False}),
+ lilac_span(39, 39 + len('yes in concept'), {'label': True})
+ ],
+ # This example is in the draft, which was not requested.
+ None
+ ]
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+def test_concept_labels_draft(concept_db_cls: Type[ConceptDB]) -> None:
+ concept_db = concept_db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ concept_update = ConceptUpdate(insert=[
+ ExampleIn(label=True, text='in concept'),
+ ExampleIn(label=False, text='out of concept'),
+ ExampleIn(label=True, text='in draft', draft='test_draft'),
+ ExampleIn(label=False, text='out draft', draft='test_draft')
+ ])
+
+ concept_db.edit(namespace, concept_name, concept_update)
+
+ signal = ConceptLabelsSignal(namespace='test', concept_name='test_concept', draft='test_draft')
+ results = list(signal.compute(['this is in concept', 'this is in draft', 'this is out draft']))
+
+ assert results == [[lilac_span(8, 8 + len('in concept'), {'label': True})],
+ [lilac_span(8, 8 + len('in draft'), {
+ 'label': True,
+ 'draft': 'test_draft'
+ })],
+ [lilac_span(8, 8 + len('out draft'), {
+ 'label': False,
+ 'draft': 'test_draft'
+ })]]
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+def test_concept_labels_key(concept_db_cls: Type[ConceptDB]) -> None:
+ concept_db = concept_db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ signal = ConceptLabelsSignal(namespace='test', concept_name='test_concept')
+ assert signal.key() == 'test/test_concept/labels'
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+def test_concept_labels_compute_signal_key(concept_db_cls: Type[ConceptDB]) -> None:
+ concept_db = concept_db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ signal = ConceptLabelsSignal(namespace='test', concept_name='test_concept')
+ assert signal.key(is_computed_signal=True) == 'test/test_concept/labels/v0'
diff --git a/src/signals/concept_scorer.py b/src/signals/concept_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e4d698f1f896bd08b515d463fcc03b12d391ce6
--- /dev/null
+++ b/src/signals/concept_scorer.py
@@ -0,0 +1,83 @@
+"""A signal to compute a score along a concept."""
+from typing import Iterable, Optional
+
+import numpy as np
+from typing_extensions import override
+
+from ..concepts.concept import DEFAULT_NUM_NEG_EXAMPLES, DRAFT_MAIN, ConceptColumnInfo, ConceptModel
+from ..concepts.db_concept import DISK_CONCEPT_MODEL_DB, ConceptModelDB
+from ..embeddings.vector_store import VectorStore
+from ..schema import Field, Item, RichData, VectorKey, field
+from .signal import TextEmbeddingModelSignal
+
+
+class ConceptScoreSignal(TextEmbeddingModelSignal):
+ """Compute scores along a given concept for documents."""
+ name = 'concept_score'
+ display_name = 'Concept'
+
+ namespace: str
+ concept_name: str
+
+ # The draft version of the concept to use. If not provided, the latest version is used.
+ draft: str = DRAFT_MAIN
+
+ # Number of randomly chosen negative examples to use when training the concept. This is used to
+ # obtain a better suited model for the concrete dataset.
+ num_negative_examples = DEFAULT_NUM_NEG_EXAMPLES
+
+ _column_info: Optional[ConceptColumnInfo] = None
+ _concept_model_db: ConceptModelDB = DISK_CONCEPT_MODEL_DB
+
+ @override
+ def fields(self) -> Field:
+ return field(
+ 'float32',
+ bins=[('Not in concept', None, 0.5), ('In concept', 0.5, None)],
+ )
+
+ def set_column_info(self, column_info: ConceptColumnInfo) -> None:
+ """Set the dataset info for this signal."""
+ self._column_info = column_info
+ self._column_info.num_negative_examples = self.num_negative_examples
+
+ def _get_concept_model(self) -> ConceptModel:
+ model = self._concept_model_db.get(self.namespace, self.concept_name, self.embedding,
+ self._column_info)
+ if not model:
+ model = self._concept_model_db.create(self.namespace, self.concept_name, self.embedding,
+ self._column_info)
+ if self._column_info:
+ model.calibrate_on_dataset(self._column_info)
+ self._concept_model_db.sync(model, self._column_info)
+ return model
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ concept_model = self._get_concept_model()
+ return concept_model.score(self.draft, data)
+
+ @override
+ def vector_compute(self, keys: Iterable[VectorKey],
+ vector_store: VectorStore) -> Iterable[Optional[Item]]:
+ concept_model = self._get_concept_model()
+ embeddings = vector_store.get(keys)
+ return concept_model.score_embeddings(self.draft, embeddings).tolist()
+
+ @override
+ def vector_compute_topk(
+ self,
+ topk: int,
+ vector_store: VectorStore,
+ keys: Optional[Iterable[VectorKey]] = None) -> list[tuple[VectorKey, Optional[Item]]]:
+ concept_model = self._get_concept_model()
+ query: np.ndarray = concept_model.coef(self.draft)
+ topk_keys = [key for key, _ in vector_store.topk(query, topk, keys)]
+ return list(zip(topk_keys, self.vector_compute(topk_keys, vector_store)))
+
+ @override
+ def key(self, is_computed_signal: Optional[bool] = False) -> str:
+ # NOTE: The embedding is a value so already exists in the path structure. This means we do not
+ # need to provide the name as part of the key, which still guarantees uniqueness.
+ version = f'/v{self._get_concept_model().version}' if is_computed_signal else ''
+ return f'{self.namespace}/{self.concept_name}{version}'
diff --git a/src/signals/concept_scorer_test.py b/src/signals/concept_scorer_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..e000782280633b506691ab22fc326f31cb6f8ee1
--- /dev/null
+++ b/src/signals/concept_scorer_test.py
@@ -0,0 +1,311 @@
+"""Test for the concept scorer."""
+
+import pathlib
+from typing import Generator, Iterable, Type, cast
+
+import numpy as np
+import pytest
+from pytest_mock import MockerFixture
+from typing_extensions import override
+
+from ..concepts.concept import ConceptColumnInfo, ConceptModel, ExampleIn
+from ..concepts.db_concept import (
+ ConceptDB,
+ ConceptModelDB,
+ ConceptUpdate,
+ DiskConceptDB,
+ DiskConceptModelDB,
+)
+from ..config import CONFIG
+from ..data.dataset_duckdb import DatasetDuckDB
+from ..data.dataset_test_utils import TestDataMaker
+from ..data.dataset_utils import lilac_embedding
+from ..db_manager import set_default_dataset_cls
+from ..embeddings.vector_store_numpy import NumpyVectorStore
+from ..schema import UUID_COLUMN, Item, RichData, SignalInputType
+from .concept_scorer import ConceptScoreSignal
+from .signal import TextEmbeddingSignal, clear_signal_registry, register_signal
+
+ALL_CONCEPT_DBS = [DiskConceptDB]
+ALL_CONCEPT_MODEL_DBS = [DiskConceptModelDB]
+
+
+@pytest.fixture(autouse=True)
+def set_data_path(tmp_path: pathlib.Path, mocker: MockerFixture) -> None:
+ mocker.patch.dict(CONFIG, {'LILAC_DATA_PATH': str(tmp_path)})
+
+
+EMBEDDING_MAP: dict[str, list[float]] = {
+ 'not in concept': [0.1, 0.9, 0.0],
+ 'in concept': [0.9, 0.1, 0.0],
+ 'a new data point': [0.1, 0.2, 0.3],
+ 'hello.': [0.1, 0.2, 0.3],
+ 'hello2.': [0.1, 0.2, 0.3],
+}
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Embed the examples, use a hashmap to the vector for simplicity."""
+ for example in data:
+ if example not in EMBEDDING_MAP:
+ raise ValueError(f'Example "{str(example)}" not in embedding map')
+ yield [lilac_embedding(0, len(example), np.array(EMBEDDING_MAP[cast(str, example)]))]
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Generator:
+ # Setup.
+ set_default_dataset_cls(DatasetDuckDB)
+ register_signal(TestEmbedding)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+@pytest.mark.parametrize('db_cls', ALL_CONCEPT_DBS)
+def test_embedding_does_not_exist(db_cls: Type[ConceptDB]) -> None:
+ db = db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ with pytest.raises(ValueError, match='Signal "unknown_embedding" not found in the registry'):
+ ConceptScoreSignal(namespace='test', concept_name='test_concept', embedding='unknown_embedding')
+
+
+def test_concept_does_not_exist() -> None:
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding='test_embedding')
+ with pytest.raises(ValueError, match='Concept "test/test_concept" does not exist'):
+ signal.compute(['a new data point', 'not in concept'])
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+@pytest.mark.parametrize('model_db_cls', ALL_CONCEPT_MODEL_DBS)
+def test_concept_model_score(concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB]) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding='test_embedding')
+
+ # Explicitly sync the model with the concept.
+ model = ConceptModel(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ model_db.sync(model, column_info=None)
+
+ scores = cast(list[float], list(signal.compute(['a new data point', 'not in concept'])))
+ assert scores[0] > 0 and scores[0] < 1
+ assert scores[1] < 0.5
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+@pytest.mark.parametrize('model_db_cls', ALL_CONCEPT_MODEL_DBS)
+def test_concept_model_with_dataset_score(concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB],
+ make_test_data: TestDataMaker) -> None:
+ dataset = make_test_data([{
+ UUID_COLUMN: '1',
+ 'text': 'hello.',
+ }, {
+ UUID_COLUMN: '2',
+ 'text': 'hello2.',
+ }])
+
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ column_info = ConceptColumnInfo(
+ namespace=dataset.namespace, name=dataset.dataset_name, path='text')
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding='test_embedding')
+ signal.set_column_info(column_info)
+
+ # Explicitly sync the model with the concept.
+ model = ConceptModel(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ model_db.sync(model, column_info)
+
+ scores = cast(list[float],
+ list(signal.compute(['a new data point', 'in concept', 'not in concept'])))
+ assert scores[0] > 0 and scores[0] < 1 # 'a new data point' may or may not be in the concept.
+ assert scores[1] > 0.5 # 'in concept' is in the concept.
+ assert scores[2] < 0.5 # 'not in concept' is not in the concept.
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+@pytest.mark.parametrize('model_db_cls', ALL_CONCEPT_MODEL_DBS)
+def test_concept_model_vector_score(concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB]) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding='test_embedding')
+
+ # Explicitly sync the model with the concept.
+ model = ConceptModel(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ model_db.sync(model, column_info=None)
+
+ vector_store = NumpyVectorStore()
+ embeddings = np.array([
+ EMBEDDING_MAP['in concept'], EMBEDDING_MAP['not in concept'], EMBEDDING_MAP['a new data point']
+ ])
+ vector_store.add([('1',), ('2',), ('3',)], embeddings)
+
+ scores = cast(list[float], list(signal.vector_compute([('1',), ('2',), ('3',)], vector_store)))
+ assert scores[0] > 0.5 # '1' is in the concept.
+ assert scores[1] < 0.5 # '2' is not in the concept.
+ assert scores[2] > 0 and scores[2] < 1 # '3' may or may not be in the concept.
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+@pytest.mark.parametrize('model_db_cls', ALL_CONCEPT_MODEL_DBS)
+def test_concept_model_topk_score(concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB]) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept')
+ ]
+ concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding='test_embedding')
+
+ # Explicitly sync the model with the concept.
+ model = ConceptModel(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ model_db.sync(model, column_info=None)
+ vector_store = NumpyVectorStore()
+ vector_store.add([('1',), ('2',), ('3',)],
+ np.array([[0.1, 0.2, 0.3], [0.1, 0.87, 0.0], [1.0, 0.0, 0.0]]))
+
+ # Compute topk without id restriction.
+ topk_result = signal.vector_compute_topk(3, vector_store)
+ expected_result = [('3',), ('1',), ('2',)]
+ for (id, _), expected_id in zip(topk_result, expected_result):
+ assert id == expected_id
+
+ # Compute top 1.
+ topk_result = signal.vector_compute_topk(1, vector_store)
+ expected_result = [('3',)]
+ for (id, _), expected_id in zip(topk_result, expected_result):
+ assert id == expected_id
+
+ # Compute topk with id restriction.
+ topk_result = signal.vector_compute_topk(3, vector_store, keys=[('1',), ('2',)])
+ expected_result = [('1',), ('2',)]
+ for (id, _), expected_id in zip(topk_result, expected_result):
+ assert id == expected_id
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+@pytest.mark.parametrize('model_db_cls', ALL_CONCEPT_MODEL_DBS)
+def test_concept_model_draft(concept_db_cls: Type[ConceptDB],
+ model_db_cls: Type[ConceptModelDB]) -> None:
+ concept_db = concept_db_cls()
+ model_db = model_db_cls(concept_db)
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ train_data = [
+ ExampleIn(label=False, text='not in concept'),
+ ExampleIn(label=True, text='in concept'),
+ ExampleIn(label=False, text='a new data point', draft='test_draft'),
+ ]
+ concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
+
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding='test_embedding')
+ draft_signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding='test_embedding', draft='test_draft')
+
+ # Explicitly sync the model with the concept.
+ model = ConceptModel(
+ namespace='test', concept_name='test_concept', embedding_name='test_embedding')
+ model_db.sync(model, column_info=None)
+
+ vector_store = NumpyVectorStore()
+ vector_store.add([('1',), ('2',), ('3',)],
+ np.array([[1.0, 0.0, 0.0], [0.9, 0.1, 0.0], [0.1, 0.9, 0.0]]))
+
+ scores = cast(list[float], list(signal.vector_compute([('1',), ('2',), ('3',)], vector_store)))
+ assert scores[0] > 0.5
+ assert scores[1] > 0.5
+ assert scores[2] < 0.5
+
+ # Make sure the draft signal works. It has different values than the original signal.
+ vector_store = NumpyVectorStore()
+ vector_store.add([('1',), ('2',), ('3',)],
+ np.array([[1.0, 0.0, 0.0], [0.9, 0.1, 0.0], [0.1, 0.2, 0.3]]))
+
+ draft_scores = draft_signal.vector_compute([('1',), ('2',), ('3',)], vector_store)
+ assert draft_scores != scores
+
+
+def test_concept_score_key() -> None:
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding=TestEmbedding.name)
+ assert signal.key() == 'test/test_concept'
+
+
+@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
+def test_concept_score_compute_signal_key(concept_db_cls: Type[ConceptDB]) -> None:
+ concept_db = concept_db_cls()
+ namespace = 'test'
+ concept_name = 'test_concept'
+ concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
+
+ signal = ConceptScoreSignal(
+ namespace='test', concept_name='test_concept', embedding=TestEmbedding.name)
+ assert signal.key(is_computed_signal=True) == 'test/test_concept/v0'
diff --git a/src/signals/default_signals.py b/src/signals/default_signals.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3acd05194ad01d48d13f2f1f542aaf4d5d473e8
--- /dev/null
+++ b/src/signals/default_signals.py
@@ -0,0 +1,21 @@
+"""Registers all available default signals."""
+from ..embeddings.cohere import Cohere
+from ..embeddings.sbert import SBERT
+from .concept_scorer import ConceptScoreSignal
+from .pii import PIISignal
+from .signal import register_signal
+from .text_statistics import TextStatisticsSignal
+
+
+def register_default_signals() -> None:
+ """Register all the default signals."""
+ # Concepts.
+ register_signal(ConceptScoreSignal)
+
+ # Text.
+ register_signal(PIISignal)
+ register_signal(TextStatisticsSignal)
+
+ # Embeddings.
+ register_signal(Cohere)
+ register_signal(SBERT)
diff --git a/src/signals/pii.py b/src/signals/pii.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3eebdf85c08f399586f8378f85c864e36fce30f
--- /dev/null
+++ b/src/signals/pii.py
@@ -0,0 +1,40 @@
+"""Compute text statistics for a document."""
+import re
+from typing import Iterable, Optional
+
+from typing_extensions import override
+
+from ..data.dataset_utils import lilac_span
+from ..schema import Field, Item, RichData, SignalInputType, field
+from .signal import TextSignal
+
+EMAILS_KEY = 'emails'
+NUM_EMAILS_KEY = 'num_emails'
+
+# This regex is a fully RFC 5322 regex for email addresses.
+# https://uibakery.io/regex-library/email-regex-python
+EMAIL_REGEX = re.compile(
+ "(?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*|\"(?:[\\x01-\\x08\\x0b\\x0c\\x0e-\\x1f\\x21\\x23-\\x5b\\x5d-\\x7f]|\\\\[\\x01-\\x09\\x0b\\x0c\\x0e-\\x7f])*\")@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\\[(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?|[a-z0-9-]*[a-z0-9]:(?:[\\x01-\\x08\\x0b\\x0c\\x0e-\\x1f\\x21-\\x5a\\x53-\\x7f]|\\\\[\\x01-\\x09\\x0b\\x0c\\x0e-\\x7f])+)\\])",
+ re.IGNORECASE)
+
+
+class PIISignal(TextSignal):
+ """Find personally identifiable information (emails, phone numbers, etc)."""
+ name = 'pii'
+ display_name = 'Personal Information (PII)'
+
+ input_type = SignalInputType.TEXT
+ compute_type = SignalInputType.TEXT
+
+ @override
+ def fields(self) -> Field:
+ return field(fields={EMAILS_KEY: ['string_span'], NUM_EMAILS_KEY: 'int32'})
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text in data:
+ if not isinstance(text, str):
+ yield None
+ continue
+ emails = [lilac_span(m.start(0), m.end(0)) for m in EMAIL_REGEX.finditer(text)]
+ yield {EMAILS_KEY: emails, NUM_EMAILS_KEY: len(emails)}
diff --git a/src/signals/pii_test.py b/src/signals/pii_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ae080c3d30663b81d3f20d742482f8518e6976f
--- /dev/null
+++ b/src/signals/pii_test.py
@@ -0,0 +1,32 @@
+"""Test the PII signal."""
+
+from ..schema import field
+from .pii import EMAILS_KEY, NUM_EMAILS_KEY, PIISignal
+from .splitters.text_splitter_test_utils import text_to_expected_spans
+
+
+def test_pii_fields() -> None:
+ signal = PIISignal()
+ assert signal.fields() == field(fields={EMAILS_KEY: ['string_span'], NUM_EMAILS_KEY: 'int32'})
+
+
+def test_pii_compute() -> None:
+ signal = PIISignal()
+
+ text = 'This is an email nik@test.com. pii@gmail.com are where emails are read.'
+ emails = list(signal.compute([text]))
+
+ expected_spans = text_to_expected_spans(text, ['nik@test.com', 'pii@gmail.com'])
+
+ assert emails == [{EMAILS_KEY: expected_spans, NUM_EMAILS_KEY: 2}]
+
+
+def test_pii_case_insensitive() -> None:
+ signal = PIISignal()
+
+ text = 'These are some emails: NIK@Test.com. pII@gmAIL.COM are where emails are read.'
+ emails = list(signal.compute([text]))
+
+ expected_spans = text_to_expected_spans(text, ['NIK@Test.com', 'pII@gmAIL.COM'])
+
+ assert emails == [{EMAILS_KEY: expected_spans, NUM_EMAILS_KEY: 2}]
diff --git a/src/signals/semantic_similarity.py b/src/signals/semantic_similarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..b22253b27e43e04805793857fdbfb8625f139e8e
--- /dev/null
+++ b/src/signals/semantic_similarity.py
@@ -0,0 +1,75 @@
+"""A signal to compute semantic search for a document."""
+from typing import Any, Iterable, Optional, Union
+
+import numpy as np
+from scipy.interpolate import interp1d
+from typing_extensions import override
+
+from ..embeddings.embedding import EmbedFn, get_embed_fn
+from ..embeddings.vector_store import VectorStore
+from ..schema import Field, Item, RichData, VectorKey, field
+from .signal import TextEmbeddingModelSignal
+
+
+class SemanticSimilaritySignal(TextEmbeddingModelSignal):
+ """Compute semantic similarity for a query and a document.
+
+
This is done by embedding the query with the same embedding as the document and computing a
+ a similarity score between them.
+ """
+ name = 'semantic_similarity'
+ display_name = 'Semantic Similarity'
+
+ query: str
+
+ _embed_fn: EmbedFn
+ # Dot products are in the range [-1, 1]. We want to map this to [0, 1] for the similarity score
+ # with a slight bias towards 1 since dot product of <0.2 is not really relevant.
+ _interpolate_fn = interp1d([-1, 0.2, 1], [0, 0.5, 1])
+ _search_text_embedding: Optional[np.ndarray] = None
+
+ def __init__(self, query: Union[str, bytes], embedding: str, **kwargs: Any):
+ if isinstance(query, bytes):
+ raise ValueError('Image queries are not yet supported for SemanticSimilarity.')
+
+ super().__init__(query=query, embedding=embedding, **kwargs)
+
+ # TODO(nsthorat): The embedding cls might have arguments. This needs to be resolved.
+ self._embed_fn = get_embed_fn(embedding)
+
+ @override
+ def fields(self) -> Field:
+ return field('float32')
+
+ def _get_search_embedding(self) -> np.ndarray:
+ """Return the embedding for the search text."""
+ if self._search_text_embedding is None:
+ self._search_text_embedding = self._embed_fn([self.query])[0].reshape(-1)
+
+ return self._search_text_embedding
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ text_embeddings = self._embed_fn(data)
+ similarities = text_embeddings.dot(self._get_search_embedding()).reshape(-1)
+ return similarities.tolist()
+
+ @override
+ def vector_compute(self, keys: Iterable[VectorKey],
+ vector_store: VectorStore) -> Iterable[Optional[Item]]:
+ text_embeddings = vector_store.get(keys)
+ similarities = text_embeddings.dot(self._get_search_embedding()).reshape(-1)
+ # Clip the similarities since float precision can cause these to be barely outside the range and
+ # throw an exception with interp1d.
+ similarities = np.clip(similarities, -1., 1.)
+ return self._interpolate_fn(similarities).tolist()
+
+ @override
+ def vector_compute_topk(
+ self,
+ topk: int,
+ vector_store: VectorStore,
+ keys: Optional[Iterable[VectorKey]] = None) -> list[tuple[VectorKey, Optional[Item]]]:
+ query = self._get_search_embedding()
+ topk_keys = [key for key, _ in vector_store.topk(query, topk, keys)]
+ return list(zip(topk_keys, self.vector_compute(topk_keys, vector_store)))
diff --git a/src/signals/semantic_similarity_test.py b/src/signals/semantic_similarity_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..abfd4085825749a44ae6d2d1bca7556682f656c0
--- /dev/null
+++ b/src/signals/semantic_similarity_test.py
@@ -0,0 +1,95 @@
+"""Test the semantic search signal."""
+
+from typing import Iterable, cast
+
+import numpy as np
+import pytest
+from pytest import approx
+from pytest_mock import MockerFixture
+from typing_extensions import override
+
+from ..data.dataset_utils import lilac_embedding
+from ..embeddings.vector_store import VectorStore
+from ..schema import Item, RichData, VectorKey
+from .semantic_similarity import SemanticSimilaritySignal
+from .signal import TextEmbeddingSignal, clear_signal_registry, register_signal
+
+EMBEDDINGS: dict[VectorKey, list[float]] = {
+ ('1',): [1.0, 0.0, 0.0],
+ ('2',): [0.9, 0.1, 0.0],
+ ('3',): [0.0, 0.0, 1.0]
+}
+
+STR_EMBEDDINGS: dict[str, list[float]] = {
+ 'hello': [1.0, 0.0, 0.0],
+ 'hello world': [0.9, 0.1, 0.0],
+ 'far': [0.0, 0.0, 1.0]
+}
+
+
+class TestVectorStore(VectorStore):
+ """A test vector store with fixed embeddings."""
+
+ @override
+ def keys(self) -> list[VectorKey]:
+ return []
+
+ @override
+ def add(self, keys: list[VectorKey], embeddings: np.ndarray) -> None:
+ # We fix the vectors for the test vector store.
+ pass
+
+ @override
+ def get(self, keys: Iterable[VectorKey]) -> np.ndarray:
+ keys = keys or []
+ return np.array([EMBEDDINGS[row_id] for row_id in keys])
+
+
+class TestEmbedding(TextEmbeddingSignal):
+ """A test embed function."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
+ """Embed the examples, use a hashmap to the vector for simplicity."""
+ for example in data:
+ yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[cast(str, example)]))]
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(TestEmbedding)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_semantic_similarity_compute_keys(mocker: MockerFixture) -> None:
+ vector_store = TestVectorStore()
+
+ embed_mock = mocker.spy(TestEmbedding, 'compute')
+
+ signal = SemanticSimilaritySignal(query='hello', embedding=TestEmbedding.name)
+ scores = list(signal.vector_compute([('1',), ('2',), ('3',)], vector_store))
+
+ # Embeddings should be called only 1 time for the search.
+ assert embed_mock.call_count == 1
+
+ assert scores == [1.0, approx(0.938, 1e-3), approx(0.417, 1e-3)]
+
+
+def test_semantic_similarity_compute_data(mocker: MockerFixture) -> None:
+ embed_mock = mocker.spy(TestEmbedding, 'compute')
+
+ signal = SemanticSimilaritySignal(query='hello', embedding=TestEmbedding.name)
+ # Compute over the text.
+ scores = list(signal.compute(STR_EMBEDDINGS.keys()))
+
+ # Embeddings should be called only 2 times, once for the search, once for the query itself.
+ assert embed_mock.call_count == 2
+
+ assert scores == [1.0, 0.9, 0.0]
diff --git a/src/signals/signal.py b/src/signals/signal.py
new file mode 100644
index 0000000000000000000000000000000000000000..87bfae6e06811a83726d728490f4160639817c71
--- /dev/null
+++ b/src/signals/signal.py
@@ -0,0 +1,315 @@
+"""Interface for implementing a signal."""
+
+import abc
+from typing import Any, ClassVar, Iterable, Optional, Sequence, Type, TypeVar, Union
+
+from pydantic import BaseModel, Extra, validator
+from pydantic.fields import ModelField
+from typing_extensions import override
+
+from ..embeddings.vector_store import VectorStore
+from ..schema import Field, Item, RichData, SignalInputType, VectorKey, field
+
+EMBEDDING_KEY = 'embedding'
+
+
+class Signal(abc.ABC, BaseModel):
+ """Interface for signals to implement. A signal can score documents and a dataset column."""
+ # ClassVars do not get serialized with pydantic.
+ name: ClassVar[str]
+ # The display name is just used for rendering in the UI.
+ display_name: ClassVar[Optional[str]]
+
+ signal_type: ClassVar[Type['Signal']]
+ # The input type is used to populate the UI for signals that require other signals. For example,
+ # if a signal is an TextEmbeddingModelSignal, it computes over embeddings, but it's input type
+ # will be text.
+ input_type: ClassVar[SignalInputType]
+ # The compute type defines what should be passed to compute().
+ compute_type: ClassVar[SignalInputType]
+
+ # The signal_name will get populated in init automatically from the class name so it gets
+ # serialized and the signal author doesn't have to define both the static property and the field.
+ signal_name: Optional[str]
+
+ class Config:
+ underscore_attrs_are_private = True
+ extra = Extra.forbid
+
+ @staticmethod
+ def schema_extra(schema: dict[str, Any], signal: Type['Signal']) -> None:
+ """Add the title to the schema from the display name and name.
+
+ Pydantic defaults this to the class name.
+ """
+ if hasattr(signal, 'display_name'):
+ schema['title'] = signal.display_name
+ if hasattr(signal, 'name'):
+ schema['properties']['signal_name']['enum'] = [signal.name]
+
+ @validator('signal_name', pre=True, always=True)
+ def validate_signal_name(cls, signal_name: str) -> str:
+ """Return the static name when the signal name hasn't yet been set."""
+ # When it's already been set from JSON, just return it.
+ if signal_name:
+ return signal_name
+
+ if 'name' not in cls.__dict__:
+ raise ValueError('Signal attribute "name" must be defined.')
+
+ return cls.name
+
+ @abc.abstractmethod
+ def fields(self) -> Field:
+ """Return the fields schema for this signal.
+
+ Returns
+ A Field object that describes the schema of the signal.
+ """
+ pass
+
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ """Compute the signal for an iterable of documents or images.
+
+ Args:
+ data: An iterable of rich data to compute the signal over.
+
+ Returns
+ An iterable of items. Sparse signals should return "None" for skipped inputs.
+ """
+ raise NotImplementedError
+
+ def vector_compute(self, keys: Iterable[VectorKey],
+ vector_store: VectorStore) -> Iterable[Optional[Item]]:
+ """Compute the signal for an iterable of keys that point to documents or images.
+
+ Args:
+ keys: An iterable of value ids (at row-level or lower) to lookup precomputed embeddings.
+ vector_store: The vector store to lookup pre-computed embeddings.
+
+ Returns
+ An iterable of items. Sparse signals should return "None" for skipped inputs.
+ """
+ raise NotImplementedError
+
+ def vector_compute_topk(
+ self,
+ topk: int,
+ vector_store: VectorStore,
+ keys: Optional[Iterable[VectorKey]] = None) -> Sequence[tuple[VectorKey, Optional[Item]]]:
+ """Return signal results only for the top k documents or images.
+
+ Signals decide how to rank each document/image in the dataset, usually by a similarity score
+ obtained via the vector store.
+
+ Args:
+ topk: The number of items to return, ranked by the signal.
+ vector_store: The vector store to lookup pre-computed embeddings.
+ keys: Optional iterable of row ids to restrict the search to.
+
+ Returns
+ A list of (key, signal_output) tuples containing the "topk" items. Sparse signals should
+ return "None" for skipped inputs.
+ """
+ raise NotImplementedError
+
+ def key(self, is_computed_signal: Optional[bool] = False) -> str:
+ """Get the key for a signal.
+
+ This is used to make sure signals with multiple arguments do not collide.
+
+ NOTE: Overriding this method is sensitive. If you override it, make sure that it is globally
+ unique. It will be used as the dictionary key for enriched values.
+
+ Args:
+ is_computed_signal: True when the signal is computed over the column and written to
+ disk. False when the signal is used as a preview UDF.
+ """
+ args_dict = self.dict(exclude_unset=True, exclude_defaults=True)
+ # If a user explicitly defines a signal name for whatever reason, remove it as it's redundant.
+ if 'signal_name' in args_dict:
+ del args_dict['signal_name']
+
+ return self.name + _args_key_from_dict(args_dict)
+
+ def setup(self) -> None:
+ """Setup the signal."""
+ pass
+
+ def teardown(self) -> None:
+ """Tears down the signal."""
+ pass
+
+
+def _args_key_from_dict(args_dict: dict[str, Any]) -> str:
+ args = None
+ args_list: list[str] = []
+ for k, v in args_dict.items():
+ if v:
+ args_list.append(f'{k}={v}')
+
+ args = ','.join(args_list)
+ return '' if not args_list else f'({args})'
+
+
+class SignalTypeEnum(str):
+ """A class that represents a string enum for a signal type.
+
+ This allows us to populate the JSON schema enum field in the UI.
+ """
+ signal_type: ClassVar[Type[Signal]]
+
+ @classmethod
+ def __modify_schema__(cls, field_schema: dict[str, Any], field: Optional[ModelField]) -> None:
+ if field:
+ field_schema['enum'] = [x.name for x in get_signals_by_type(cls.signal_type)]
+ return None
+
+
+class TextSplitterSignal(Signal):
+ """An interface for signals that compute over text."""
+ input_type = SignalInputType.TEXT
+ compute_type = SignalInputType.TEXT
+
+ @override
+ def fields(self) -> Field:
+ return field(fields=['string_span'])
+
+
+class TextSplitterEnum(SignalTypeEnum):
+ """A string enum that represents a text splitter signal."""
+ signal_type = TextSplitterSignal
+
+
+# Signal base classes, used for inferring the dependency chain required for computing a signal.
+class TextSignal(Signal):
+ """An interface for signals that compute over text."""
+ input_type = SignalInputType.TEXT
+ compute_type = SignalInputType.TEXT
+
+ @override
+ def key(self, is_computed_signal: Optional[bool] = False) -> str:
+ args_dict = self.dict(exclude_unset=True, exclude_defaults=True)
+ if 'signal_name' in args_dict:
+ del args_dict['signal_name']
+ return self.name + _args_key_from_dict(args_dict)
+
+
+class TextEmbeddingSignal(TextSignal):
+ """An interface for signals that compute embeddings for text."""
+ input_type = SignalInputType.TEXT
+ compute_type = SignalInputType.TEXT
+
+ _split = True
+
+ def __init__(self, split: bool = True, **kwargs: Any):
+ super().__init__(**kwargs)
+ self._split = split
+
+ @override
+ def fields(self) -> Field:
+ """NOTE: Override this method at your own risk if you want to add extra metadata.
+
+ Embeddings should not come with extra metadata.
+ """
+ return field(fields=[field('string_span', fields={EMBEDDING_KEY: 'embedding'})])
+
+
+class TextEmbeddingEnum(SignalTypeEnum):
+ """A string enum that represents a text embedding signal."""
+ signal_type = TextEmbeddingSignal
+
+
+class TextEmbeddingModelSignal(TextSignal):
+ """An interface for signals that take embeddings and produce items."""
+ input_type = SignalInputType.TEXT
+ # compute() takes embeddings, while it operates over text fields by transitively computing splits
+ # and embeddings.
+ compute_type = SignalInputType.TEXT_EMBEDDING
+
+ embedding: TextEmbeddingEnum
+ _embedding_signal: TextEmbeddingSignal
+
+ def __init__(self, **kwargs: Any):
+ super().__init__(**kwargs)
+
+ # Validate the embedding signal is registered and the correct type.
+ # TODO(nsthorat): Allow arguments passed to the embedding signal.
+ self._embedding_signal = get_signal_by_type(self.embedding, TextEmbeddingSignal)()
+
+ def get_embedding_signal(self) -> TextEmbeddingSignal:
+ """Return the embedding signal."""
+ return self._embedding_signal
+
+ @override
+ def key(self, is_computed_signal: Optional[bool] = False) -> str:
+ # NOTE: The embedding and split already exists in the path structure. This means we do not
+ # need to provide the signal names as part of the key, which still guarantees uniqueness.
+
+ args_dict = self.dict(exclude_unset=True)
+ if 'signal_name' in args_dict:
+ del args_dict['signal_name']
+ del args_dict['embedding']
+ return self.name + _args_key_from_dict(args_dict)
+
+
+Tsignal = TypeVar('Tsignal', bound=Signal)
+
+
+def get_signal_by_type(signal_name: str, signal_type: Type[Tsignal]) -> Type[Tsignal]:
+ """Return a signal class by name and signal type."""
+ if signal_name not in SIGNAL_REGISTRY:
+ raise ValueError(f'Signal "{signal_name}" not found in the registry')
+
+ signal_cls = SIGNAL_REGISTRY[signal_name]
+ if not issubclass(signal_cls, signal_type):
+ raise ValueError(f'"{signal_name}" is a `{signal_cls.__name__}`, '
+ f'which is not a subclass of `{signal_type.__name__}`.')
+ return signal_cls
+
+
+def get_signals_by_type(signal_type: Type[Tsignal]) -> list[Type[Tsignal]]:
+ """Return all signals that match a signal type."""
+ signal_clses: list[Type[Tsignal]] = []
+ for signal_cls in SIGNAL_REGISTRY.values():
+ if issubclass(signal_cls, signal_type):
+ signal_clses.append(signal_cls)
+ return signal_clses
+
+
+SIGNAL_REGISTRY: dict[str, Type[Signal]] = {}
+
+
+def register_signal(signal_cls: Type[Signal]) -> None:
+ """Register a signal in the global registry."""
+ if signal_cls.name in SIGNAL_REGISTRY:
+ raise ValueError(f'Signal "{signal_cls.name}" has already been registered!')
+
+ SIGNAL_REGISTRY[signal_cls.name] = signal_cls
+
+
+def get_signal_cls(signal_name: str) -> Type[Signal]:
+ """Return a registered signal given the name in the registry."""
+ if signal_name not in SIGNAL_REGISTRY:
+ raise ValueError(f'Signal "{signal_name}" not found in the registry')
+
+ return SIGNAL_REGISTRY[signal_name]
+
+
+def resolve_signal(signal: Union[dict, Signal]) -> Signal:
+ """Resolve a generic signal base class to a specific signal class."""
+ if isinstance(signal, Signal):
+ # The signal config is already parsed.
+ return signal
+
+ signal_name = signal.get('signal_name')
+ if not signal_name:
+ raise ValueError('"signal_name" needs to be defined in the json dict.')
+
+ signal_cls = get_signal_cls(signal_name)
+ return signal_cls(**signal)
+
+
+def clear_signal_registry() -> None:
+ """Clear the signal registry."""
+ SIGNAL_REGISTRY.clear()
diff --git a/src/signals/signal_test.py b/src/signals/signal_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d9db14c3c00e4a0e93c7d897dfee4d9d1a935a
--- /dev/null
+++ b/src/signals/signal_test.py
@@ -0,0 +1,151 @@
+"""Test signal base class."""
+from typing import Iterable, Optional
+
+import pytest
+from typing_extensions import override
+
+from ..embeddings.vector_store import VectorStore
+from ..schema import Field, Item, RichData, SignalInputType, VectorKey, field
+from .signal import (
+ Signal,
+ TextEmbeddingModelSignal,
+ TextEmbeddingSignal,
+ TextSplitterSignal,
+ clear_signal_registry,
+ get_signal_by_type,
+ get_signal_cls,
+ get_signals_by_type,
+ register_signal,
+ resolve_signal,
+)
+
+
+class TestSignal(Signal):
+ """A test signal."""
+
+ # Pydantic fields
+ name = 'test_signal'
+ input_type = SignalInputType.TEXT
+
+ query: str
+
+ @override
+ def fields(self) -> Field:
+ return field('float32')
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ del data
+ return []
+
+
+class TestTextSplitter(TextSplitterSignal):
+ """A test text splitter."""
+ name = 'test_splitter'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ del data
+ return []
+
+
+class TestTextEmbedding(TextEmbeddingSignal):
+ """A test text embedding."""
+ name = 'test_embedding'
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ del data
+ return []
+
+
+class TestTextEmbeddingModelSignal(TextEmbeddingModelSignal):
+ """A test text embedding model."""
+ name = 'test_embedding_model'
+
+ @override
+ def fields(self) -> Field:
+ return field('float32')
+
+ @override
+ def vector_compute(self, keys: Iterable[VectorKey], vector_store: VectorStore) -> Iterable[Item]:
+ # The signal just sums the values of the embedding.
+ del keys, vector_store
+ return []
+
+
+@pytest.fixture(scope='module', autouse=True)
+def setup_teardown() -> Iterable[None]:
+ # Setup.
+ register_signal(TestSignal)
+ register_signal(TestTextSplitter)
+ register_signal(TestTextEmbedding)
+ register_signal(TestTextEmbeddingModelSignal)
+
+ # Unit test runs.
+ yield
+
+ # Teardown.
+ clear_signal_registry()
+
+
+def test_signal_serialization() -> None:
+ signal = TestSignal(query='test')
+
+ # The class variables should not be included.
+ assert signal.dict() == {'signal_name': 'test_signal', 'query': 'test'}
+
+
+def test_get_signal_cls() -> None:
+ """Test getting a signal."""
+ assert TestSignal == get_signal_cls('test_signal')
+
+
+def test_resolve_signal() -> None:
+ """Test resolving a signal."""
+ test_signal = TestSignal(query='hello')
+
+ # Signals pass through.
+ assert resolve_signal(test_signal) == test_signal
+
+ # Dicts resolve to the base class.
+ assert resolve_signal(test_signal.dict()) == test_signal
+
+
+def test_get_signal_by_type() -> None:
+ assert get_signal_by_type(TestTextSplitter.name, TextSplitterSignal) == TestTextSplitter
+ assert get_signal_by_type(TestTextEmbedding.name, TextEmbeddingSignal) == TestTextEmbedding
+
+
+def test_get_signal_by_type_validation() -> None:
+ with pytest.raises(ValueError, match='Signal "invalid_signal" not found in the registry'):
+ get_signal_by_type('invalid_signal', TextSplitterSignal)
+
+ with pytest.raises(
+ ValueError, match=f'"{TestTextSplitter.name}" is a `{TestTextSplitter.__name__}`'):
+ get_signal_by_type(TestTextSplitter.name, TextEmbeddingSignal)
+
+
+def test_get_signals_by_type() -> None:
+ assert get_signals_by_type(TextSplitterSignal) == [TestTextSplitter]
+ assert get_signals_by_type(TextEmbeddingSignal) == [TestTextEmbedding]
+
+
+def test_signal_type_enum() -> None:
+ model_signal = TestTextEmbeddingModelSignal(embedding='test_embedding')
+ schema_properties = model_signal.schema()['properties']
+ assert schema_properties['embedding']['enum'] == [TestTextEmbedding.name]
+
+
+class TestSignalNoDisplayName(Signal):
+ name = 'signal_no_name'
+
+
+class TestSignalDisplayName(Signal):
+ name = 'signal_display_name'
+ display_name = 'test display name'
+
+
+def test_signal_title_schema() -> None:
+ assert TestSignalNoDisplayName.schema()['title'] == TestSignalNoDisplayName.__name__
+ assert TestSignalDisplayName.schema()['title'] == 'test display name'
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diff --git a/src/signals/splitters/chunk_splitter.py b/src/signals/splitters/chunk_splitter.py
new file mode 100644
index 0000000000000000000000000000000000000000..12753b6585e3332f548e2ed5f6608781838b7b79
--- /dev/null
+++ b/src/signals/splitters/chunk_splitter.py
@@ -0,0 +1,198 @@
+"""Implementation of splitting text that looks at characters.
+
+Recursively tries to split by different characters to find one that works.
+
+The implementation below is forked from the LangChain project with the MIT license below.
+See `RecursiveCharacterTextSplitter` in
+https://github.com/hwchase17/langchain/blob/master/langchain/text_splitter.py
+"""
+
+# The MIT License
+
+# Copyright (c) Harrison Chase
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+from typing import Any, Callable, Iterable, Optional
+
+from pydantic import validator
+from typing_extensions import override
+
+from ...data.dataset_utils import lilac_span
+from ...schema import Item, RichData
+from ...utils import log
+from ..signal import TextSplitterSignal
+
+TextChunk = tuple[str, tuple[int, int]]
+
+DEFAULT_SEPARATORS = ['\n\n', '\n', ' ', '']
+CHUNK_SIZE = 400
+CHUNK_OVERLAP = 50
+
+
+class ChunkSplitter(TextSplitterSignal):
+ """Recursively split documents by different characters to find one that works."""
+
+ name = 'chunk'
+ display_name = 'Chunk Splitter'
+
+ chunk_size: int = CHUNK_SIZE
+ chunk_overlap: int = CHUNK_OVERLAP
+ separators: list[str] = DEFAULT_SEPARATORS
+
+ _length_function: Callable[[str], int] = len
+
+ @validator('chunk_overlap')
+ def check_overlap_smaller_than_chunk(cls, chunk_overlap: int, values: dict[str, Any]) -> int:
+ """Check that the chunk overlap is smaller than the chunk size."""
+ chunk_size: int = values['chunk_size']
+ if chunk_overlap > chunk_size:
+ raise ValueError(f'Got a larger chunk overlap ({chunk_overlap}) than chunk size '
+ f'({chunk_size}), should be smaller.')
+ return chunk_overlap
+
+ @validator('separators')
+ def check_separators_are_strings(cls, separators: list[str]) -> list[str]:
+ """Check that the separators are strings."""
+ separators = list(separators) or DEFAULT_SEPARATORS
+ for sep in separators:
+ if not isinstance(sep, str):
+ raise ValueError(f'Got separator {sep} that is not a string.')
+ return separators
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text in data:
+ if not isinstance(text, str):
+ yield None
+ continue
+
+ chunks = split_text(text, self.chunk_size, self.chunk_overlap, self.separators,
+ self._length_function)
+ if not chunks:
+ yield None
+ continue
+
+ yield [lilac_span(start, end) for _, (start, end) in chunks]
+
+
+def _sep_split(text: str, separator: str) -> list[TextChunk]:
+ if separator == '':
+ # We need to split by char.
+ return [(letter, (i, i + 1)) for i, letter in enumerate(text)]
+
+ offset = 0
+ chunks: list[TextChunk] = []
+ end_index = text.find(separator, offset)
+
+ while end_index >= 0:
+ chunks.append((text[offset:end_index], (offset, end_index)))
+ offset = end_index + len(separator)
+ end_index = text.find(separator, offset)
+
+ # Append the last chunk.
+ chunks.append((text[offset:], (offset, len(text))))
+
+ return chunks
+
+
+def split_text(text: str,
+ chunk_size: int = CHUNK_SIZE,
+ chunk_overlap: int = CHUNK_OVERLAP,
+ separators: list[str] = DEFAULT_SEPARATORS,
+ length_function: Callable[[str], int] = len) -> list[TextChunk]:
+ """Split incoming text and return chunks."""
+
+ def _merge_splits(splits: Iterable[TextChunk], separator: str) -> list[TextChunk]:
+ # We now want to combine these smaller pieces into medium size
+ # chunks to send to the LLM.
+ separator_len = length_function(separator)
+
+ docs: list[TextChunk] = []
+ current_doc: list[TextChunk] = []
+ total = 0
+ for chunk in splits:
+ text_chunk, _ = chunk
+ _len = length_function(text_chunk)
+ if (total + _len + (separator_len if len(current_doc) > 0 else 0) > chunk_size):
+ if total > chunk_size:
+ log(f'Created a chunk of size {total}, '
+ f'which is longer than the specified {chunk_size}')
+ if len(current_doc) > 0:
+ doc = _join_chunks(current_doc, separator)
+ if doc is not None:
+ docs.append(doc)
+ # Keep on popping if:
+ # - we have a larger chunk than in the chunk overlap
+ # - or if we still have any chunks and the length is long
+ while total > chunk_overlap or (
+ total + _len +
+ (separator_len if len(current_doc) > 0 else 0) > chunk_size and total > 0):
+ total -= length_function(current_doc[0][0]) + (
+ separator_len if len(current_doc) > 1 else 0)
+ current_doc = current_doc[1:]
+ current_doc.append(chunk)
+ total += _len + (separator_len if len(current_doc) > 1 else 0)
+ doc = _join_chunks(current_doc, separator)
+ if doc is not None:
+ docs.append(doc)
+ return docs
+
+ final_chunks: list[TextChunk] = []
+ # Get appropriate separator to use
+ separator = separators[-1]
+ for _s in separators:
+ if _s == '':
+ separator = _s
+ break
+ if _s in text:
+ separator = _s
+ break
+ # Now that we have the separator, split the text.
+ splits = _sep_split(text, separator)
+ # Now go merging things, recursively splitting longer texts.
+ good_splits: list[TextChunk] = []
+ for chunk in splits:
+ text_chunk, (start, _) = chunk
+ if length_function(text_chunk) < chunk_size:
+ good_splits.append(chunk)
+ else:
+ if good_splits:
+ merged_text = _merge_splits(good_splits, separator)
+ final_chunks.extend(merged_text)
+ good_splits = []
+ other_chunks = split_text(text_chunk, chunk_size, chunk_overlap, separators, length_function)
+ # Adjust the offsets of the other chunks.
+ other_chunks = [(t, (s + start, e + start)) for t, (s, e) in other_chunks]
+ final_chunks.extend(other_chunks)
+ if good_splits:
+ merged_text = _merge_splits(good_splits, separator)
+ final_chunks.extend(merged_text)
+ return final_chunks
+
+
+def _join_chunks(chunks: list[TextChunk], separator: str) -> Optional[TextChunk]:
+ text = separator.join([text for text, _ in chunks])
+ text = text.strip()
+ if text == '':
+ return None
+
+ _, (first_span_start, _) = chunks[0]
+ _, (_, last_span_end) = chunks[-1]
+ return (text, (first_span_start, last_span_end))
diff --git a/src/signals/splitters/chunk_splitter_test.py b/src/signals/splitters/chunk_splitter_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce414814c0114d6754633c2f83ad898d82e8d024
--- /dev/null
+++ b/src/signals/splitters/chunk_splitter_test.py
@@ -0,0 +1,43 @@
+"""Tests the chunk splitter."""
+
+from .chunk_splitter import ChunkSplitter
+from .text_splitter_test_utils import spans_to_text, text_to_expected_spans
+
+
+def test_paragraphs_no_overlap() -> None:
+ signal = ChunkSplitter(chunk_size=12, chunk_overlap=0)
+ text = 'Hello.\n\nThis will get split.\n\nThe sentence\n\nA.\n\nB.\n\nC.'
+ split_items = list(signal.compute([text]))
+
+ # "This will get split" should split in 2 chunks, and "A.\n\nB.\n\nC." should be 1 chunk.
+ expected_spans = text_to_expected_spans(
+ text, ['Hello.', 'This will', 'get split.', 'The sentence', 'A.\n\nB.\n\nC.'])
+ assert split_items == [expected_spans]
+
+
+def test_single_world_is_too_long_no_overlap() -> None:
+ signal = ChunkSplitter(chunk_size=6, chunk_overlap=0)
+ text = 'ThisIsASingleWordThatIsTooLong'
+ split_items = list(signal.compute([text]))
+
+ expected_spans = text_to_expected_spans(text, ['ThisIs', 'ASingl', 'eWordT', 'hatIsT', 'ooLong'])
+ assert split_items == [expected_spans]
+
+
+def test_newlines_with_overlap() -> None:
+ signal = ChunkSplitter(chunk_size=12, chunk_overlap=5)
+ text = 'Hello.\n\nWorld.\n\nThis will get split.'
+ spans = list(signal.compute([text]))[0]
+
+ expected_chunks = ['Hello.', 'World.', 'This will', 'will get', 'get split.']
+ assert spans_to_text(text, spans) == expected_chunks
+
+
+def test_serialization() -> None:
+ signal = ChunkSplitter(chunk_size=12, chunk_overlap=5)
+ assert signal.dict() == {
+ 'signal_name': 'chunk',
+ 'chunk_size': 12,
+ 'chunk_overlap': 5,
+ 'separators': ['\n\n', '\n', ' ', '']
+ }
diff --git a/src/signals/splitters/text_splitter_spacy.py b/src/signals/splitters/text_splitter_spacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..59b7e345e49fcfce23101c0359caf750025072ea
--- /dev/null
+++ b/src/signals/splitters/text_splitter_spacy.py
@@ -0,0 +1,43 @@
+"""Text splitters using spaCy."""
+from typing import Any, Iterable, Optional
+
+import spacy
+from spacy import Language
+from typing_extensions import override
+
+from ...data.dataset_utils import lilac_span
+from ...schema import Item, RichData
+from ...signals.signal import TextSplitterSignal
+
+
+class SentenceSplitterSpacy(TextSplitterSignal):
+ """Splits documents into sentences using the SpaCy sentence tokenizer."""
+ name = 'sentences'
+ display_name = 'Sentence Splitter'
+
+ language: str = 'en'
+
+ _tokenizer: Language
+
+ def __init__(self, **kwargs: Any):
+ super().__init__(**kwargs)
+ self._tokenizer = spacy.blank(self.language)
+ self._tokenizer.add_pipe('sentencizer')
+ # Increase the number of characters of the tokenizer as we're not using a parser or NER.
+ self._tokenizer.max_length = 10_000_000
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ text_data = (row if isinstance(row, str) else '' for row in data)
+
+ for doc in self._tokenizer.pipe(text_data):
+ sentences = doc.sents
+ result = [lilac_span(token.start_char, token.end_char) for token in sentences]
+ if result:
+ yield result
+ else:
+ yield None
+
+ class Config:
+ # Language is required even though it has a default value.
+ schema_extra = {'required': ['language']}
diff --git a/src/signals/splitters/text_splitter_spacy_test.py b/src/signals/splitters/text_splitter_spacy_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce3681613fcb09b50914b0b12fca07ec709edb5a
--- /dev/null
+++ b/src/signals/splitters/text_splitter_spacy_test.py
@@ -0,0 +1,38 @@
+"""Tests the spacy sentence splitter."""
+
+from typing import cast
+
+from .text_splitter_spacy import SentenceSplitterSpacy
+from .text_splitter_test_utils import text_to_expected_spans
+
+
+def test_splitter_spacy() -> None:
+ signal = SentenceSplitterSpacy()
+ text = 'Hello. This is a test. Final sentence.'
+
+ # Compute over the text.
+ split_items = list(signal.compute([text]))
+
+ expected_spans = text_to_expected_spans(text, ['Hello.', 'This is a test.', 'Final sentence.'])
+ assert split_items == [expected_spans]
+
+
+def test_spacy_key() -> None:
+ signal = SentenceSplitterSpacy()
+ assert signal.key() == 'sentences'
+
+
+def test_spacy_non_en_key() -> None:
+ signal = SentenceSplitterSpacy(language='es')
+ assert signal.key() == 'sentences(language=es)'
+
+
+def test_splitter_spacy_float() -> None:
+ signal = SentenceSplitterSpacy()
+ text = 1.2
+
+ # Compute over the input, make sure it doesn't crash when we pass a non-string value which can
+ # happen accidentally in user data.
+ split_items = list(signal.compute([cast(str, text)]))
+
+ assert split_items == [None]
diff --git a/src/signals/splitters/text_splitter_test_utils.py b/src/signals/splitters/text_splitter_test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5bf42fd48d8444c91b868f6ca4d135366876e48
--- /dev/null
+++ b/src/signals/splitters/text_splitter_test_utils.py
@@ -0,0 +1,29 @@
+"""Utilities for testing text splitters."""
+
+from typing import Optional
+
+from ...data.dataset_utils import lilac_span
+from ...schema import TEXT_SPAN_END_FEATURE, TEXT_SPAN_START_FEATURE, VALUE_KEY, Item
+
+
+def spans_to_text(text: str, spans: Optional[list[Item]]) -> list[str]:
+ """Convert text and a list of spans to a list of strings."""
+ if not spans:
+ return []
+ return [
+ text[span[VALUE_KEY][TEXT_SPAN_START_FEATURE]:span[VALUE_KEY][TEXT_SPAN_END_FEATURE]]
+ for span in spans
+ ]
+
+
+def text_to_expected_spans(text: str, splits: list[str]) -> list[Item]:
+ """Convert text and a list of splits to a list of expected spans."""
+ start_offset = 0
+ expected_spans: list[Item] = []
+ for split in splits:
+ start = text.find(split, start_offset)
+ end = start + len(split)
+ expected_spans.append(lilac_span(start=start, end=end))
+ start_offset = end
+
+ return expected_spans
diff --git a/src/signals/splitters/text_splitter_test_utils_test.py b/src/signals/splitters/text_splitter_test_utils_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b3959ae459ac0c9a939264f2031899f2d7e3a0e
--- /dev/null
+++ b/src/signals/splitters/text_splitter_test_utils_test.py
@@ -0,0 +1,13 @@
+"""Test the text splitter utils."""
+
+from ...data.dataset_utils import lilac_span
+from .text_splitter_test_utils import text_to_expected_spans
+
+
+def test_text_to_expected_spans() -> None:
+ """Tests the sentences_to_expected_spans function."""
+ text = 'Hello. Hello. Final sentence.'
+ sentences = ['Hello.', 'Hello.', 'Final sentence.']
+ assert text_to_expected_spans(
+ text, sentences) == [lilac_span(0, 6), lilac_span(7, 13),
+ lilac_span(14, 29)]
diff --git a/src/signals/substring_search.py b/src/signals/substring_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bc4a9cd38cb03d59b245277c9a51ef301412b7e
--- /dev/null
+++ b/src/signals/substring_search.py
@@ -0,0 +1,37 @@
+"""A signal to search for a substring in a document."""
+import re
+from typing import Any, Iterable, Optional
+
+from typing_extensions import override
+
+from ..data.dataset_utils import lilac_span
+from ..schema import Field, Item, RichData, SignalInputType, field
+from .signal import Signal
+
+
+class SubstringSignal(Signal):
+ """Find a substring in a document."""
+ name = 'substring_search'
+ display_name = 'Substring Search'
+ input_type = SignalInputType.TEXT
+ compute_type = SignalInputType.TEXT
+
+ query: str
+
+ _regex: re.Pattern[str]
+
+ def __init__(self, **kwargs: Any):
+ super().__init__(**kwargs)
+ self._regex = re.compile(self.query, re.IGNORECASE)
+
+ @override
+ def fields(self) -> Field:
+ return field(fields=['string_span'])
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for text in data:
+ if not isinstance(text, str):
+ yield None
+ continue
+ yield [lilac_span(m.start(), m.end()) for m in self._regex.finditer(text)]
diff --git a/src/signals/substring_search_test.py b/src/signals/substring_search_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..cedf61438ad0bfd38c4c5aeb1974bd28d19516a2
--- /dev/null
+++ b/src/signals/substring_search_test.py
@@ -0,0 +1,28 @@
+"""Test the Substring Search signal."""
+
+import pytest
+from pydantic import ValidationError
+
+from ..schema import field
+from .splitters.text_splitter_test_utils import text_to_expected_spans
+from .substring_search import SubstringSignal
+
+
+def test_substring_fields() -> None:
+ signal = SubstringSignal(query='test')
+ assert signal.fields() == field(fields=['string_span'])
+
+
+def test_query_is_required() -> None:
+ with pytest.raises(ValidationError):
+ SubstringSignal()
+
+
+def test_compute() -> None:
+ signal = SubstringSignal(query='test')
+
+ text = 'The word TEST shows up 3 times, teST and test'
+ spans = list(signal.compute([text]))
+
+ expected_spans = text_to_expected_spans(text, ['TEST', 'teST', 'test'])
+ assert [expected_spans] == spans
diff --git a/src/signals/text_statistics.py b/src/signals/text_statistics.py
new file mode 100644
index 0000000000000000000000000000000000000000..e55e621a596f641da34431282468b2687b44ff85
--- /dev/null
+++ b/src/signals/text_statistics.py
@@ -0,0 +1,68 @@
+"""Compute text statistics for a document."""
+from typing import Iterable, Optional, cast
+
+import spacy
+import textacy
+from spacy import Language
+from spacy.tokens import Doc
+from textacy import text_stats
+from typing_extensions import override
+
+from ..schema import Field, Item, RichData, field
+from ..utils import chunks
+from .signal import TextSignal
+
+SPACY_LANG_MODEL = 'en_core_web_sm'
+SPACY_BATCH_SIZE = 128
+
+NUM_CHARS = 'num_characters'
+READABILITY = 'readability'
+TYPE_TOKEN_RATIO = 'type_token_ratio'
+
+
+class TextStatisticsSignal(TextSignal):
+ """Compute text statistics for a document such as readability scores, type-token-ratio, etc.."""
+ name = 'text_statistics'
+ display_name = 'Text Statistics'
+
+ _lang: Optional[Language] = None
+
+ @override
+ def fields(self) -> Field:
+ return field(fields={
+ NUM_CHARS: 'int32',
+ READABILITY: 'float32',
+ TYPE_TOKEN_RATIO: 'float32',
+ })
+
+ @override
+ def setup(self) -> None:
+ if not spacy.util.is_package(SPACY_LANG_MODEL):
+ spacy.cli.download(SPACY_LANG_MODEL)
+ self._lang = spacy.load(
+ SPACY_LANG_MODEL,
+ disable=[
+ 'parser', 'tagger', 'ner', 'lemmatizer', 'textcat', 'custom', 'tok2vec', 'attribute_ruler'
+ ])
+
+ @override
+ def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
+ for batch in chunks(data, SPACY_BATCH_SIZE):
+ # Replace None with empty strings to avoid spacy errors.
+ batch = [x or '' for x in batch]
+ # See https://textacy.readthedocs.io/en/0.11.0/api_reference/text_stats.html for a list of
+ # available statistics.
+ corpus = textacy.Corpus(lang=self._lang, data=batch)
+ for doc in cast(Iterable[Doc], corpus):
+ if not len(doc):
+ yield None
+ continue
+ readability = text_stats.readability.automated_readability_index(doc)
+ ttr = text_stats.diversity.ttr(doc)
+ num_chars = text_stats.basics.n_chars(doc)
+
+ yield {
+ NUM_CHARS: num_chars,
+ READABILITY: readability,
+ TYPE_TOKEN_RATIO: ttr,
+ }
diff --git a/src/signals/text_statistics_test.py b/src/signals/text_statistics_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb2b6978b2bea6977cf68256444a7458a7791b50
--- /dev/null
+++ b/src/signals/text_statistics_test.py
@@ -0,0 +1,53 @@
+"""Test the semantic search signal."""
+
+from typing import cast
+
+from pytest import approx
+
+from ..schema import DataType, Field
+from .text_statistics import NUM_CHARS, READABILITY, TYPE_TOKEN_RATIO, TextStatisticsSignal
+
+
+def test_text_statistics_fields() -> None:
+ signal = TextStatisticsSignal()
+ signal.setup()
+ assert signal.fields() == Field(
+ fields={
+ NUM_CHARS: Field(dtype=DataType.INT32),
+ READABILITY: Field(dtype=DataType.FLOAT32),
+ TYPE_TOKEN_RATIO: Field(dtype=DataType.FLOAT32)
+ })
+
+
+def test_text_statistics_compute() -> None:
+ signal = TextStatisticsSignal()
+ signal.setup()
+
+ scores = signal.compute(['hello', 'hello world'])
+
+ assert list(scores) == [{
+ NUM_CHARS: 5,
+ READABILITY: approx(2.62),
+ TYPE_TOKEN_RATIO: 1.0
+ }, {
+ NUM_CHARS: 10,
+ READABILITY: approx(3.12),
+ TYPE_TOKEN_RATIO: 1.0
+ }]
+
+
+def test_text_statistics_missing_value() -> None:
+ signal = TextStatisticsSignal()
+ signal.setup()
+
+ scores = signal.compute(['hello', cast(str, None), 'everybody'])
+
+ assert list(scores) == [{
+ NUM_CHARS: 5,
+ READABILITY: approx(2.62),
+ TYPE_TOKEN_RATIO: 1.0
+ }, None, {
+ NUM_CHARS: 9,
+ READABILITY: approx(21.46),
+ TYPE_TOKEN_RATIO: 1.0
+ }]
diff --git a/src/tasks.py b/src/tasks.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ffffc821a45c16b90af673f0283be3fbfe60a54
--- /dev/null
+++ b/src/tasks.py
@@ -0,0 +1,296 @@
+"""Manage FastAPI background tasks."""
+
+import asyncio
+import functools
+import time
+import traceback
+import uuid
+from datetime import datetime, timedelta
+from enum import Enum
+from typing import Any, Awaitable, Callable, Iterable, Optional, TypeVar, Union
+
+import dask
+import psutil
+from dask.distributed import Client, Variable
+from distributed import Future, get_client, get_worker
+from pydantic import BaseModel, parse_obj_as
+from tqdm import tqdm
+
+from .utils import log, pretty_timedelta
+
+TaskId = str
+# ID for the step of a task.
+TaskStepId = tuple[str, int]
+Task = Union[Callable[..., Any], Callable[..., Awaitable[Any]]]
+
+
+class TaskStatus(str, Enum):
+ """Enum holding a tasks status."""
+ PENDING = 'pending'
+ COMPLETED = 'completed'
+ ERROR = 'error'
+
+
+class TaskStepInfo(BaseModel):
+ """Information about a step of the task.."""
+ progress: Optional[float]
+ description: Optional[str]
+ details: Optional[str]
+
+
+class TaskInfo(BaseModel):
+ """Metadata about a task."""
+ name: str
+ status: TaskStatus
+ progress: Optional[float]
+ message: Optional[str]
+ details: Optional[str]
+ # The current step's progress.
+ step_progress: Optional[float]
+
+ # A task may have multiple progress indicators, e.g. for chained signals that compute 3 signals.
+ steps: Optional[list[TaskStepInfo]]
+ description: Optional[str]
+ start_timestamp: str
+ end_timestamp: Optional[str]
+ error: Optional[str]
+
+
+class TaskManifest(BaseModel):
+ """Information for tasks that are running or completed."""
+ tasks: dict[str, TaskInfo]
+ progress: Optional[float]
+
+
+STEPS_LOG_KEY = 'steps'
+
+
+class TaskManager:
+ """Manage FastAPI background tasks."""
+ _tasks: dict[str, TaskInfo] = {}
+
+ def __init__(self, dask_client: Optional[Client] = None) -> None:
+ """By default, use a dask multi-processing client.
+
+ A user can pass in a dask client to use a different executor.
+ """
+ # Set dasks workers to be non-daemonic so they can spawn child processes if they need to. This
+ # is particularly useful for signals that use libraries with multiprocessing support.
+ dask.config.set({'distributed.worker.daemon': False})
+
+ total_memory_gb = psutil.virtual_memory().total / (1024**3)
+ self._dask_client = dask_client or Client(
+ asynchronous=True, memory_limit=f'{total_memory_gb} GB')
+
+ async def _update_tasks(self) -> None:
+ for task_id, task in self._tasks.items():
+ if task.status == TaskStatus.COMPLETED:
+ continue
+
+ step_events = self._dask_client.get_events(_progress_event_topic(task_id))
+ # This allows us to work with both sync and async clients.
+ if not isinstance(step_events, tuple):
+ step_events = await step_events
+
+ if step_events:
+ _, log_message = step_events[-1]
+ steps = parse_obj_as(list[TaskStepInfo], log_message[STEPS_LOG_KEY])
+ task.steps = steps
+ if steps:
+ cur_step = 0
+ for i, step in enumerate(reversed(steps)):
+ if step.progress is not None:
+ cur_step = len(steps) - i - 1
+ break
+ task.details = steps[cur_step].details
+ task.step_progress = steps[cur_step].progress
+ task.progress = (sum([step.progress or 0.0 for step in steps])) / len(steps)
+ # Don't show an indefinite jump if there are multiple steps.
+ if cur_step > 0 and task.step_progress is None:
+ task.step_progress = 0.0
+
+ task.message = f'Step {cur_step+1}/{len(steps)}'
+ if steps[cur_step].description:
+ task.message += f': {steps[cur_step].description}'
+ else:
+ task.progress = None
+
+ async def manifest(self) -> TaskManifest:
+ """Get all tasks."""
+ await self._update_tasks()
+ tasks_with_progress = [
+ task.progress
+ for task in self._tasks.values()
+ if task.progress and task.status != TaskStatus.COMPLETED
+ ]
+ return TaskManifest(
+ tasks=self._tasks,
+ progress=sum(tasks_with_progress) / len(tasks_with_progress) if tasks_with_progress else None)
+
+ def task_id(self, name: str, description: Optional[str] = None) -> TaskId:
+ """Create a unique ID for a task."""
+ task_id = uuid.uuid4().hex
+ self._tasks[task_id] = TaskInfo(
+ name=name,
+ status=TaskStatus.PENDING,
+ progress=None,
+ description=description,
+ start_timestamp=datetime.now().isoformat())
+ return task_id
+
+ def _set_task_completed(self, task_id: TaskId, task_future: Future) -> None:
+ end_timestamp = datetime.now().isoformat()
+ self._tasks[task_id].end_timestamp = end_timestamp
+
+ elapsed = datetime.fromisoformat(end_timestamp) - datetime.fromisoformat(
+ self._tasks[task_id].start_timestamp)
+ elapsed_formatted = pretty_timedelta(elapsed)
+
+ if task_future.status == 'error':
+ self._tasks[task_id].status = TaskStatus.ERROR
+ tb = traceback.format_tb(task_future.traceback())
+ e = task_future.exception()
+ self._tasks[task_id].error = f'{e}: \n{tb}'
+ raise e
+ else:
+ # This runs in dask callback thread, so we have to make a new event loop.
+ loop = asyncio.new_event_loop()
+ loop.run_until_complete(self._update_tasks())
+ for step in self._tasks[task_id].steps or []:
+ step.progress = 1.0
+
+ self._tasks[task_id].status = TaskStatus.COMPLETED
+ self._tasks[task_id].progress = 1.0
+ self._tasks[task_id].message = f'Completed in {elapsed_formatted}'
+
+ log(f'Task completed "{task_id}": "{self._tasks[task_id].name}" in '
+ f'{elapsed_formatted}.')
+
+ def execute(self, task_id: str, task: Task, *args: Any) -> None:
+ """Create a unique ID for a task."""
+ log(f'Scheduling task "{task_id}": "{self._tasks[task_id].name}".')
+
+ task_info = self._tasks[task_id]
+ task_future = self._dask_client.submit(
+ functools.partial(_execute_task, task, task_info, task_id), *args, key=task_id)
+ task_future.add_done_callback(
+ lambda task_future: self._set_task_completed(task_id, task_future))
+
+ async def stop(self) -> None:
+ """Stop the task manager and close the dask client."""
+ await self._dask_client.close()
+
+
+@functools.cache
+def task_manager() -> TaskManager:
+ """The global singleton for the task manager."""
+ return TaskManager()
+
+
+def _execute_task(task: Task, task_info: TaskInfo, task_id: str, *args: Any) -> None:
+ get_worker().state.tasks[task_id].annotations['task_info'] = task_info
+ task(*args)
+
+
+def _progress_event_topic(task_id: TaskId) -> Variable:
+ return f'{task_id}_progress'
+
+
+TProgress = TypeVar('TProgress')
+
+
+def progress(it: Iterable[TProgress],
+ task_step_id: Optional[TaskStepId],
+ estimated_len: Optional[int],
+ step_description: Optional[str] = None,
+ emit_every_frac: float = .01) -> Iterable[TProgress]:
+ """An iterable wrapper that emits progress and yields the original iterable."""
+ if not task_step_id:
+ yield from it
+ return
+
+ task_id, step_id = task_step_id
+ steps = get_worker_steps(task_id)
+ if not steps:
+ steps = [TaskStepInfo(description=step_description, progress=0.0)]
+ elif len(steps) <= step_id:
+ # If the step given exceeds the length of the last step, add a new step.
+ steps.append(TaskStepInfo(description=step_description, progress=0.0))
+ else:
+ steps[step_id].description = step_description
+ steps[step_id].progress = 0.0
+ set_worker_steps(task_id, steps)
+
+ estimated_len = max(1, estimated_len) if estimated_len else None
+
+ emit_every = max(1, int(estimated_len * emit_every_frac)) if estimated_len else None
+
+ task_info: TaskInfo = get_worker().state.tasks[task_id].annotations['task_info']
+
+ it_idx = 0
+ start_time = time.time()
+ with tqdm(it, desc=task_info.name, total=estimated_len) as tq:
+ for t in tq:
+ if estimated_len and emit_every and it_idx % emit_every == 0:
+ it_per_sec = tq.format_dict['rate'] or 0.0
+ set_worker_task_progress(
+ task_step_id=task_step_id,
+ it_idx=it_idx,
+ elapsed_sec=tq.format_dict['elapsed'] or 0.0,
+ it_per_sec=it_per_sec or 0.0,
+ estimated_total_sec=((estimated_len) / it_per_sec if it_per_sec else 0),
+ estimated_len=estimated_len)
+ yield t
+ it_idx += 1
+
+ total_time = time.time() - start_time
+ set_worker_task_progress(
+ task_step_id=task_step_id,
+ it_idx=estimated_len if estimated_len else it_idx,
+ elapsed_sec=total_time,
+ it_per_sec=(estimated_len or it_idx) / total_time,
+ estimated_total_sec=total_time,
+ estimated_len=estimated_len or it_idx)
+
+
+def set_worker_steps(task_id: TaskId, steps: list[TaskStepInfo]) -> None:
+ """Sets up worker steps. Use to provide task step descriptions before they compute."""
+ get_worker().log_event(
+ _progress_event_topic(task_id), {STEPS_LOG_KEY: [step.dict() for step in steps]})
+
+
+def get_worker_steps(task_id: TaskId) -> list[TaskStepInfo]:
+ """Gets the last worker steps."""
+ events = get_client().get_events(_progress_event_topic(task_id))
+ if not events or not events[-1]:
+ return []
+
+ (_, last_event) = events[-1]
+ last_info = last_event.get(STEPS_LOG_KEY)
+ return [TaskStepInfo(**step_info) for step_info in last_info]
+
+
+def set_worker_task_progress(task_step_id: TaskStepId, it_idx: int, elapsed_sec: float,
+ it_per_sec: float, estimated_total_sec: float,
+ estimated_len: int) -> None:
+ """Updates a task step with a progress between 0 and 1.
+
+ This method does not exist on the TaskManager as it is meant to be a standalone method used by
+ workers running tasks on separate processes so does not have access to task manager state.
+ """
+ progress = float(it_idx) / estimated_len
+ task_id, step_id = task_step_id
+ steps = get_worker_steps(task_id)
+ if len(steps) <= step_id:
+ raise ValueError(f'No step with idx {step_id} exists. Got steps: {steps}')
+ steps[step_id].progress = progress
+
+ # 1748/1748 [elapsed 00:16<00:00, 106.30 ex/s]
+ elapsed = f'{pretty_timedelta(timedelta(seconds=elapsed_sec))}'
+ if it_idx != estimated_len:
+ # Only show estimated when in progress.
+ elapsed = f'{elapsed} < {pretty_timedelta(timedelta(seconds=estimated_total_sec))}'
+ steps[step_id].details = (f'{it_idx:,}/{estimated_len:,} '
+ f'[{elapsed}, {it_per_sec:,.2f} ex/s]')
+
+ set_worker_steps(task_id, steps)
diff --git a/src/test_utils.py b/src/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..504119bc0db95d1fa032e42972658244ff787cca
--- /dev/null
+++ b/src/test_utils.py
@@ -0,0 +1,26 @@
+"""Utilities for unit tests."""
+
+import os
+import pathlib
+import uuid
+from typing import Union
+
+import pyarrow.parquet as pq
+
+from .schema import Item, Schema, schema_to_arrow_schema
+
+
+def read_items(data_dir: Union[str, pathlib.Path], filepaths: list[str],
+ schema: Schema) -> list[Item]:
+ """Read the source items from a dataset output directory."""
+ items: list[Item] = []
+ for filepath in filepaths:
+ items.extend(
+ pq.read_table(os.path.join(data_dir, filepath),
+ schema=schema_to_arrow_schema(schema)).to_pylist())
+ return items
+
+
+def fake_uuid(id: bytes) -> uuid.UUID:
+ """Create a test UUID."""
+ return uuid.UUID((id * 16).hex())
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb99ceaba72e27a7eb728e7e7778c8759fdcba2f
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,274 @@
+"""Utils for the python server."""
+import asyncio
+import functools
+import itertools
+import logging
+import os
+import pathlib
+import re
+import shutil
+import threading
+import time
+import uuid
+from asyncio import AbstractEventLoop
+from concurrent.futures import Executor, ThreadPoolExecutor
+from datetime import timedelta
+from functools import partial, wraps
+from typing import IO, Any, Awaitable, Callable, Iterable, Optional, TypeVar, Union
+
+import requests
+from google.cloud.storage import Blob, Client
+from pydantic import BaseModel
+
+from .config import data_path
+from .schema import Path
+
+GCS_PROTOCOL = 'gs://'
+GCS_REGEX = re.compile(f'{GCS_PROTOCOL}(.*?)/(.*)')
+GCS_COPY_CHUNK_SIZE = 1_000
+IMAGES_DIR_NAME = 'images'
+DATASETS_DIR_NAME = 'datasets'
+
+
+@functools.cache
+def _get_storage_client(thread_id: Optional[int] = None) -> Client:
+ # The storage client is not thread safe so we use a thread_id to make sure each thread gets a
+ # separate storage client.
+ del thread_id
+ return Client()
+
+
+def _parse_gcs_path(filepath: str) -> tuple[str, str]:
+ # match a regular expression to extract the bucket and filename
+ if matches := GCS_REGEX.match(filepath):
+ bucket_name, object_name = matches.groups()
+ return bucket_name, object_name
+ raise ValueError(f'Failed to parse GCS path: {filepath}')
+
+
+def _get_gcs_blob(filepath: str) -> Blob:
+ bucket_name, object_name = _parse_gcs_path(filepath)
+ storage_client = _get_storage_client(threading.get_ident())
+ bucket = storage_client.bucket(bucket_name)
+ return bucket.blob(object_name)
+
+
+def open_file(filepath: str, mode: str = 'r') -> IO:
+ """Open a file handle. It works with both GCS and local paths."""
+ if filepath.startswith(GCS_PROTOCOL):
+ blob = _get_gcs_blob(filepath)
+ return blob.open(mode)
+
+ write_mode = 'w' in mode
+ binary_mode = 'b' in mode
+
+ if write_mode:
+ base_path = os.path.dirname(filepath)
+ os.makedirs(base_path, exist_ok=True)
+
+ encoding = None if binary_mode else 'utf-8'
+ return open(filepath, mode=mode, encoding=encoding)
+
+
+def download_http_files(filepaths: list[str]) -> list[str]:
+ """Download files from HTTP(s) URLs."""
+ out_filepaths: list[str] = []
+ for filepath in filepaths:
+ if filepath.startswith(('http://', 'https://')):
+ tmp_filename = uuid.uuid4().hex
+ tmp_filepath = f'/tmp/{data_path()}/local_cache/{tmp_filename}'
+ log(f'Downloading from url {filepath} to {tmp_filepath}')
+ dl = requests.get(filepath, timeout=10000, allow_redirects=True)
+ with open_file(tmp_filepath, 'wb') as f:
+ f.write(dl.content)
+ filepath = tmp_filepath
+
+ out_filepaths.append(filepath)
+
+ return out_filepaths
+
+
+def makedirs(dir_path: str) -> None:
+ """Recursively makes the directories. It works with both GCS and local paths."""
+ if dir_path.startswith(GCS_PROTOCOL):
+ return
+ os.makedirs(dir_path, exist_ok=True)
+
+
+def get_datasets_dir(base_dir: Union[str, pathlib.Path]) -> str:
+ """Return the output directory that holds all datasets."""
+ return os.path.join(base_dir, DATASETS_DIR_NAME)
+
+
+def get_dataset_output_dir(base_dir: Union[str, pathlib.Path], namespace: str,
+ dataset_name: str) -> str:
+ """Return the output directory for a dataset."""
+ return os.path.join(get_datasets_dir(base_dir), namespace, dataset_name)
+
+
+class CopyRequest(BaseModel):
+ """A request to copy a file from source to destination path. Used to copy media files to GCS."""
+ from_path: str
+ to_path: str
+
+
+def copy_batch(copy_requests: list[CopyRequest]) -> None:
+ """Copy a single item from a CopyRequest."""
+ storage_client = _get_storage_client(threading.get_ident())
+ with storage_client.batch():
+ for copy_request in copy_requests:
+ from_gcs = False
+ if GCS_REGEX.match(copy_request.from_path):
+ from_gcs = True
+ to_gcs = False
+ if GCS_REGEX.match(copy_request.to_path):
+ to_gcs = True
+
+ makedirs(os.path.dirname(copy_request.to_path))
+
+ # When both source and destination are local, use the shutil copy.
+ if not from_gcs and not to_gcs:
+ shutil.copyfile(copy_request.from_path, copy_request.to_path)
+ continue
+
+ if from_gcs:
+ from_bucket_name, from_object_name = _parse_gcs_path(copy_request.from_path)
+ from_bucket = storage_client.bucket(from_bucket_name)
+ from_gcs_blob = from_bucket.blob(from_object_name)
+
+ if to_gcs:
+ to_bucket_name, to_object_name = _parse_gcs_path(copy_request.to_path)
+ to_bucket = storage_client.bucket(to_bucket_name)
+
+ if from_gcs and to_gcs:
+ from_bucket.copy_blob(from_gcs_blob, from_bucket, to_object_name)
+ elif from_gcs and not to_gcs:
+ from_gcs_blob.download_to_filename(copy_request.to_path)
+ elif not from_gcs and to_gcs:
+ to_gcs_blob = to_bucket.blob(to_object_name)
+ to_gcs_blob.upload_from_filename(copy_request.from_path)
+
+
+def copy_files(copy_requests: Iterable[CopyRequest], input_gcs: bool, output_gcs: bool) -> None:
+ """Copy media files from an input gcs path to an output gcs path."""
+ start_time = time.time()
+
+ chunk_size = 1
+ if output_gcs and input_gcs:
+ # When downloading or uploading locally, batching greatly slows down the parallelism as GCS
+ # batching with storage.batch() has no effect.
+ # When copying files locally, storage.batch() has no effect and it's better to run each copy in
+ # separate thread.
+ chunk_size = GCS_COPY_CHUNK_SIZE
+
+ batched_copy_requests = chunks(copy_requests, chunk_size)
+ with ThreadPoolExecutor() as executor:
+ executor.map(copy_batch, batched_copy_requests)
+
+ log(f'Copy took {time.time() - start_time} seconds.')
+
+
+def delete_file(filepath: str) -> None:
+ """Delete a file. It works for both GCS and local paths."""
+ if filepath.startswith(GCS_PROTOCOL):
+ blob = _get_gcs_blob(filepath)
+ blob.delete()
+ return
+
+ os.remove(filepath)
+
+
+def file_exists(filepath: str) -> bool:
+ """Return true if the file exists. It works with both GCS and local paths."""
+ if filepath.startswith(GCS_PROTOCOL):
+ return _get_gcs_blob(filepath).exists()
+ return os.path.exists(filepath)
+
+
+def get_image_path(output_dir: str, path: Path, row_id: bytes) -> str:
+ """Return the GCS file path to an image associated with a specific row."""
+ path_subdir = '_'.join([str(p) for p in path])
+ filename = row_id.hex()
+ return os.path.join(output_dir, IMAGES_DIR_NAME, path_subdir, filename)
+
+
+Tout = TypeVar('Tout')
+
+
+def async_wrap(func: Callable[..., Tout],
+ loop: Optional[AbstractEventLoop] = None,
+ executor: Optional[Executor] = None) -> Callable[..., Awaitable[Tout]]:
+ """Wrap a sync function into an async function."""
+
+ @wraps(func)
+ async def run(*args: Any, **kwargs: Any) -> Any:
+ current_loop = loop or asyncio.get_running_loop()
+ pfunc: Callable = partial(func, *args, **kwargs)
+ return await current_loop.run_in_executor(executor, pfunc)
+
+ return run
+
+
+Tchunk = TypeVar('Tchunk')
+
+
+def chunks(iterable: Iterable[Tchunk], size: int) -> Iterable[list[Tchunk]]:
+ """Split a list of items into equal-sized chunks. The last chunk might be smaller."""
+ it = iter(iterable)
+ chunk = list(itertools.islice(it, size))
+ while chunk:
+ yield chunk
+ chunk = list(itertools.islice(it, size))
+
+
+# TODO(nsthorat): Centralize environment flags.
+DISABLE_LOGS = os.getenv('DISABLE_LOGS', False)
+
+
+def log(log_str: str) -> None:
+ """Print and logs a message so it shows up in the logs on cloud."""
+ if DISABLE_LOGS:
+ return
+
+ print(log_str)
+ logging.info(log_str)
+
+
+class DebugTimer:
+ """A context manager that prints the time elapsed in a block of code.
+
+ ```py
+ with DebugTimer('dot product'):
+ np.dot(np.random.randn(1000), np.random.randn(1000))
+ ```
+
+ $ dot product took 0.001s.
+ """
+
+ def __init__(self, name: str) -> None:
+ self.name = name
+
+ def __enter__(self) -> 'DebugTimer':
+ """Start a timer."""
+ self.start = time.perf_counter()
+ return self
+
+ def __exit__(self, *args: list[Any]) -> None:
+ """Stop the timer and print the elapsed time."""
+ log(f'{self.name} took {(time.perf_counter() - self.start):.3f}s.')
+
+
+def pretty_timedelta(delta: timedelta) -> str:
+ """Pretty-prints a `timedelta`."""
+ seconds = delta.total_seconds()
+ days, seconds = divmod(seconds, 86400)
+ hours, seconds = divmod(seconds, 3600)
+ minutes, seconds = divmod(seconds, 60)
+ if days > 0:
+ return '%dd%dh%dm%ds' % (days, hours, minutes, seconds)
+ elif hours > 0:
+ return '%dh%dm%ds' % (hours, minutes, seconds)
+ elif minutes > 0:
+ return '%dm%ds' % (minutes, seconds)
+ else:
+ return '%ds' % (seconds,)
diff --git a/web/blueprint/build/_app/immutable/assets/0.5f010142.css b/web/blueprint/build/_app/immutable/assets/0.5f010142.css
new file mode 100644
index 0000000000000000000000000000000000000000..db182b96bf77e9023cdba7bbad333cbb53ae5202
--- /dev/null
+++ b/web/blueprint/build/_app/immutable/assets/0.5f010142.css
@@ -0,0 +1 @@
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#defbe6;--cds-notification-background-info: #edf5ff;--cds-notification-background-warning: #fdf6dd;--cds-notification-action-hover: #ffffff}:root[theme=g10]{--cds-interactive-01: #0f62fe;--cds-interactive-02: #393939;--cds-interactive-03: #0f62fe;--cds-interactive-04: #0f62fe;--cds-ui-background: #f4f4f4;--cds-ui-01: #ffffff;--cds-ui-02: #f4f4f4;--cds-ui-03: #e0e0e0;--cds-ui-04: #8d8d8d;--cds-ui-05: #161616;--cds-text-01: #161616;--cds-text-02: #525252;--cds-text-03: #a8a8a8;--cds-text-04: #ffffff;--cds-text-05: #6f6f6f;--cds-text-error: #da1e28;--cds-icon-01: #161616;--cds-icon-02: #525252;--cds-icon-03: #ffffff;--cds-link-01: #0f62fe;--cds-link-02: #0043ce;--cds-inverse-link: #78a9ff;--cds-field-01: #ffffff;--cds-field-02: #f4f4f4;--cds-inverse-01: #ffffff;--cds-inverse-02: #393939;--cds-support-01: #da1e28;--cds-support-02: #198038;--cds-support-03: #f1c21b;--cds-support-04: #0043ce;--cds-inverse-support-01: #fa4d56;--cds-inverse-support-02: #42be65;--cds-inverse-support-03: #f1c21b;--cds-inverse-support-04: #4589ff;--cds-overlay-01: rgba(22, 22, 22, .5);--cds-danger-01: #da1e28;--cds-danger-02: #da1e28;--cds-focus: #0f62fe;--cds-inverse-focus-ui: #ffffff;--cds-hover-primary: #0353e9;--cds-active-primary: #002d9c;--cds-hover-primary-text: #0043ce;--cds-hover-secondary: #4c4c4c;--cds-active-secondary: #6f6f6f;--cds-hover-tertiary: #0353e9;--cds-active-tertiary: #002d9c;--cds-hover-ui: #e5e5e5;--cds-hover-light-ui: #e5e5e5;--cds-hover-selected-ui: #cacaca;--cds-active-ui: #c6c6c6;--cds-active-light-ui: #c6c6c6;--cds-selected-ui: #e0e0e0;--cds-selected-light-ui: #e0e0e0;--cds-inverse-hover-ui: #4c4c4c;--cds-hover-danger: #b81921;--cds-active-danger: #750e13;--cds-hover-row: #e5e5e5;--cds-visited-link: #8a3ffc;--cds-disabled-01: #ffffff;--cds-disabled-02: #c6c6c6;--cds-disabled-03: #8d8d8d;--cds-highlight: #d0e2ff;--cds-decorative-01: #e0e0e0;--cds-button-separator: #e0e0e0;--cds-skeleton-01: #e5e5e5;--cds-skeleton-02: #c6c6c6;--cds-background: #f4f4f4;--cds-layer: #ffffff;--cds-layer-accent: #e0e0e0;--cds-layer-accent-hover: #d1d1d1;--cds-layer-accent-active: #a8a8a8;--cds-field: #ffffff;--cds-background-inverse: #393939;--cds-background-brand: #0f62fe;--cds-interactive: #0f62fe;--cds-border-subtle: #e0e0e0;--cds-border-strong: #8d8d8d;--cds-border-inverse: #161616;--cds-border-interactive: #0f62fe;--cds-text-primary: #161616;--cds-text-secondary: #525252;--cds-text-placeholder: #a8a8a8;--cds-text-helper: #6f6f6f;--cds-text-on-color: #ffffff;--cds-text-inverse: #ffffff;--cds-link-primary: #0f62fe;--cds-link-secondary: #0043ce;--cds-link-visited: #8a3ffc;--cds-link-inverse: #78a9ff;--cds-icon-primary: #161616;--cds-icon-secondary: #525252;--cds-icon-on-color: #ffffff;--cds-icon-inverse: #ffffff;--cds-support-error: #da1e28;--cds-support-success: #198038;--cds-support-warning: #f1c21b;--cds-support-info: #0043ce;--cds-support-error-inverse: #fa4d56;--cds-support-success-inverse: #42be65;--cds-support-warning-inverse: #f1c21b;--cds-support-info-inverse: #4589ff;--cds-overlay: rgba(22, 22, 22, .5);--cds-toggle-off: #8d8d8d;--cds-shadow: rgba(0, 0, 0, .3);--cds-button-primary: #0f62fe;--cds-button-secondary: #393939;--cds-button-tertiary: #0f62fe;--cds-button-danger-primary: #da1e28;--cds-button-danger-secondary: #da1e28;--cds-background-active: #c6c6c6;--cds-layer-active: #c6c6c6;--cds-button-danger-active: #750e13;--cds-button-primary-active: #002d9c;--cds-button-secondary-active: #6f6f6f;--cds-button-tertiary-active: #002d9c;--cds-focus-inset: #ffffff;--cds-focus-inverse: #ffffff;--cds-background-hover: #e5e5e5;--cds-layer-hover: #e5e5e5;--cds-field-hover: #e5e5e5;--cds-background-inverse-hover: #4c4c4c;--cds-link-primary-hover: #0043ce;--cds-button-danger-hover: #b81921;--cds-button-primary-hover: #0353e9;--cds-button-secondary-hover: #4c4c4c;--cds-button-tertiary-hover: #0353e9;--cds-background-selected: #e0e0e0;--cds-background-selected-hover: #cacaca;--cds-layer-selected: #e0e0e0;--cds-layer-selected-hover: #cacaca;--cds-layer-selected-inverse: #161616;--cds-border-subtle-selected: #c6c6c6;--cds-border-disabled: #ffffff;--cds-text-disabled: #c6c6c6;--cds-button-disabled: #c6c6c6;--cds-icon-disabled: #c6c6c6;--cds-text-on-color-disabled: #8d8d8d;--cds-icon-on-color-disabled: #8d8d8d;--cds-layer-selected-disabled: #8d8d8d;--cds-skeleton-background: #e5e5e5;--cds-skeleton-element: #c6c6c6;--cds-brand-01: #0f62fe;--cds-brand-02: #393939;--cds-brand-03: #0f62fe;--cds-active-01: #c6c6c6;--cds-hover-field: #e5e5e5;--cds-danger: #da1e28;--cds-caption-01-font-size: .75rem;--cds-caption-01-font-weight: 400;--cds-caption-01-line-height: 1.33333;--cds-caption-01-letter-spacing: .32px;--cds-caption-02-font-size: .875rem;--cds-caption-02-font-weight: 400;--cds-caption-02-line-height: 1.28572;--cds-caption-02-letter-spacing: .32px;--cds-label-01-font-size: .75rem;--cds-label-01-font-weight: 400;--cds-label-01-line-height: 1.33333;--cds-label-01-letter-spacing: .32px;--cds-label-02-font-size: .875rem;--cds-label-02-font-weight: 400;--cds-label-02-line-height: 1.28572;--cds-label-02-letter-spacing: .16px;--cds-helper-text-01-font-size: .75rem;--cds-helper-text-01-line-height: 1.33333;--cds-helper-text-01-letter-spacing: .32px;--cds-helper-text-02-font-size: .875rem;--cds-helper-text-02-line-height: 1.28572;--cds-helper-text-02-letter-spacing: .16px;--cds-body-short-01-font-size: .875rem;--cds-body-short-01-font-weight: 400;--cds-body-short-01-line-height: 1.28572;--cds-body-short-01-letter-spacing: .16px;--cds-body-long-01-font-size: .875rem;--cds-body-long-01-font-weight: 400;--cds-body-long-01-line-height: 1.42857;--cds-body-long-01-letter-spacing: .16px;--cds-body-short-02-font-size: 1rem;--cds-body-short-02-font-weight: 400;--cds-body-short-02-line-height: 1.375;--cds-body-short-02-letter-spacing: 0;--cds-body-long-02-font-size: 1rem;--cds-body-long-02-font-weight: 400;--cds-body-long-02-line-height: 1.5;--cds-body-long-02-letter-spacing: 0;--cds-code-01-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-01-font-size: .75rem;--cds-code-01-font-weight: 400;--cds-code-01-line-height: 1.33333;--cds-code-01-letter-spacing: .32px;--cds-code-02-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-02-font-size: .875rem;--cds-code-02-font-weight: 400;--cds-code-02-line-height: 1.42857;--cds-code-02-letter-spacing: .32px;--cds-heading-01-font-size: .875rem;--cds-heading-01-font-weight: 600;--cds-heading-01-line-height: 1.42857;--cds-heading-01-letter-spacing: .16px;--cds-productive-heading-01-font-size: .875rem;--cds-productive-heading-01-font-weight: 600;--cds-productive-heading-01-line-height: 1.28572;--cds-productive-heading-01-letter-spacing: .16px;--cds-heading-02-font-size: 1rem;--cds-heading-02-font-weight: 600;--cds-heading-02-line-height: 1.5;--cds-heading-02-letter-spacing: 0;--cds-productive-heading-02-font-size: 1rem;--cds-productive-heading-02-font-weight: 600;--cds-productive-heading-02-line-height: 1.375;--cds-productive-heading-02-letter-spacing: 0;--cds-productive-heading-03-font-size: 1.25rem;--cds-productive-heading-03-font-weight: 400;--cds-productive-heading-03-line-height: 1.4;--cds-productive-heading-03-letter-spacing: 0;--cds-productive-heading-04-font-size: 1.75rem;--cds-productive-heading-04-font-weight: 400;--cds-productive-heading-04-line-height: 1.28572;--cds-productive-heading-04-letter-spacing: 0;--cds-productive-heading-05-font-size: 2rem;--cds-productive-heading-05-font-weight: 400;--cds-productive-heading-05-line-height: 1.25;--cds-productive-heading-05-letter-spacing: 0;--cds-productive-heading-06-font-size: 2.625rem;--cds-productive-heading-06-font-weight: 300;--cds-productive-heading-06-line-height: 1.199;--cds-productive-heading-06-letter-spacing: 0;--cds-productive-heading-07-font-size: 3.375rem;--cds-productive-heading-07-font-weight: 300;--cds-productive-heading-07-line-height: 1.199;--cds-productive-heading-07-letter-spacing: 0;--cds-expressive-heading-01-font-size: .875rem;--cds-expressive-heading-01-font-weight: 600;--cds-expressive-heading-01-line-height: 1.25;--cds-expressive-heading-01-letter-spacing: .16px;--cds-expressive-heading-02-font-size: 1rem;--cds-expressive-heading-02-font-weight: 600;--cds-expressive-heading-02-line-height: 1.5;--cds-expressive-heading-02-letter-spacing: 0;--cds-expressive-heading-03-font-size: 1.25rem;--cds-expressive-heading-03-font-weight: 400;--cds-expressive-heading-03-line-height: 1.4;--cds-expressive-heading-03-letter-spacing: 0;--cds-expressive-heading-04-font-size: 1.75rem;--cds-expressive-heading-04-font-weight: 400;--cds-expressive-heading-04-line-height: 1.28572;--cds-expressive-heading-04-letter-spacing: 0;--cds-expressive-heading-05-font-size: 2rem;--cds-expressive-heading-05-font-weight: 400;--cds-expressive-heading-05-line-height: 1.25;--cds-expressive-heading-05-letter-spacing: 0;--cds-expressive-heading-06-font-size: 2rem;--cds-expressive-heading-06-font-weight: 600;--cds-expressive-heading-06-line-height: 1.25;--cds-expressive-heading-06-letter-spacing: 0;--cds-expressive-paragraph-01-font-size: 1.5rem;--cds-expressive-paragraph-01-font-weight: 300;--cds-expressive-paragraph-01-line-height: 1.334;--cds-expressive-paragraph-01-letter-spacing: 0;--cds-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-01-font-size: 1.25rem;--cds-quotation-01-font-weight: 400;--cds-quotation-01-line-height: 1.3;--cds-quotation-01-letter-spacing: 0;--cds-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-02-font-size: 2rem;--cds-quotation-02-font-weight: 300;--cds-quotation-02-line-height: 1.25;--cds-quotation-02-letter-spacing: 0;--cds-display-01-font-size: 2.625rem;--cds-display-01-font-weight: 300;--cds-display-01-line-height: 1.19;--cds-display-01-letter-spacing: 0;--cds-display-02-font-size: 2.625rem;--cds-display-02-font-weight: 600;--cds-display-02-line-height: 1.19;--cds-display-02-letter-spacing: 0;--cds-display-03-font-size: 2.625rem;--cds-display-03-font-weight: 300;--cds-display-03-line-height: 1.19;--cds-display-03-letter-spacing: 0;--cds-display-04-font-size: 2.625rem;--cds-display-04-font-weight: 600;--cds-display-04-line-height: 1.19;--cds-display-04-letter-spacing: 0;--cds-legal-01-font-size: .75rem;--cds-legal-01-font-weight: 400;--cds-legal-01-line-height: 1.33333;--cds-legal-01-letter-spacing: .32px;--cds-legal-02-font-size: .875rem;--cds-legal-02-font-weight: 400;--cds-legal-02-line-height: 1.28572;--cds-legal-02-letter-spacing: .16px;--cds-body-compact-01-font-size: .875rem;--cds-body-compact-01-font-weight: 400;--cds-body-compact-01-line-height: 1.28572;--cds-body-compact-01-letter-spacing: .16px;--cds-body-compact-02-font-size: 1rem;--cds-body-compact-02-font-weight: 400;--cds-body-compact-02-line-height: 1.375;--cds-body-compact-02-letter-spacing: 0;--cds-body-01-font-size: .875rem;--cds-body-01-font-weight: 400;--cds-body-01-line-height: 1.42857;--cds-body-01-letter-spacing: .16px;--cds-body-02-font-size: 1rem;--cds-body-02-font-weight: 400;--cds-body-02-line-height: 1.5;--cds-body-02-letter-spacing: 0;--cds-heading-compact-01-font-size: .875rem;--cds-heading-compact-01-font-weight: 600;--cds-heading-compact-01-line-height: 1.28572;--cds-heading-compact-01-letter-spacing: .16px;--cds-heading-compact-02-font-size: 1rem;--cds-heading-compact-02-font-weight: 600;--cds-heading-compact-02-line-height: 1.375;--cds-heading-compact-02-letter-spacing: 0;--cds-heading-03-font-size: 1.25rem;--cds-heading-03-font-weight: 400;--cds-heading-03-line-height: 1.4;--cds-heading-03-letter-spacing: 0;--cds-heading-04-font-size: 1.75rem;--cds-heading-04-font-weight: 400;--cds-heading-04-line-height: 1.28572;--cds-heading-04-letter-spacing: 0;--cds-heading-05-font-size: 2rem;--cds-heading-05-font-weight: 400;--cds-heading-05-line-height: 1.25;--cds-heading-05-letter-spacing: 0;--cds-heading-06-font-size: 2.625rem;--cds-heading-06-font-weight: 300;--cds-heading-06-line-height: 1.199;--cds-heading-06-letter-spacing: 0;--cds-heading-07-font-size: 3.375rem;--cds-heading-07-font-weight: 300;--cds-heading-07-line-height: 1.199;--cds-heading-07-letter-spacing: 0;--cds-fluid-heading-03-font-size: 1.25rem;--cds-fluid-heading-03-font-weight: 400;--cds-fluid-heading-03-line-height: 1.4;--cds-fluid-heading-03-letter-spacing: 0;--cds-fluid-heading-04-font-size: 1.75rem;--cds-fluid-heading-04-font-weight: 400;--cds-fluid-heading-04-line-height: 1.28572;--cds-fluid-heading-04-letter-spacing: 0;--cds-fluid-heading-05-font-size: 2rem;--cds-fluid-heading-05-font-weight: 400;--cds-fluid-heading-05-line-height: 1.25;--cds-fluid-heading-05-letter-spacing: 0;--cds-fluid-heading-06-font-size: 2rem;--cds-fluid-heading-06-font-weight: 600;--cds-fluid-heading-06-line-height: 1.25;--cds-fluid-heading-06-letter-spacing: 0;--cds-fluid-paragraph-01-font-size: 1.5rem;--cds-fluid-paragraph-01-font-weight: 300;--cds-fluid-paragraph-01-line-height: 1.334;--cds-fluid-paragraph-01-letter-spacing: 0;--cds-fluid-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-01-font-size: 1.25rem;--cds-fluid-quotation-01-font-weight: 400;--cds-fluid-quotation-01-line-height: 1.3;--cds-fluid-quotation-01-letter-spacing: 0;--cds-fluid-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-02-font-size: 2rem;--cds-fluid-quotation-02-font-weight: 300;--cds-fluid-quotation-02-line-height: 1.25;--cds-fluid-quotation-02-letter-spacing: 0;--cds-fluid-display-01-font-size: 2.625rem;--cds-fluid-display-01-font-weight: 300;--cds-fluid-display-01-line-height: 1.19;--cds-fluid-display-01-letter-spacing: 0;--cds-fluid-display-02-font-size: 2.625rem;--cds-fluid-display-02-font-weight: 600;--cds-fluid-display-02-line-height: 1.19;--cds-fluid-display-02-letter-spacing: 0;--cds-fluid-display-03-font-size: 2.625rem;--cds-fluid-display-03-font-weight: 300;--cds-fluid-display-03-line-height: 1.19;--cds-fluid-display-03-letter-spacing: 0;--cds-fluid-display-04-font-size: 2.625rem;--cds-fluid-display-04-font-weight: 600;--cds-fluid-display-04-line-height: 1.19;--cds-fluid-display-04-letter-spacing: 0;--cds-spacing-01: .125rem;--cds-spacing-02: .25rem;--cds-spacing-03: .5rem;--cds-spacing-04: .75rem;--cds-spacing-05: 1rem;--cds-spacing-06: 1.5rem;--cds-spacing-07: 2rem;--cds-spacing-08: 2.5rem;--cds-spacing-09: 3rem;--cds-spacing-10: 4rem;--cds-spacing-11: 5rem;--cds-spacing-12: 6rem;--cds-spacing-13: 10rem;--cds-fluid-spacing-01: 0;--cds-fluid-spacing-02: 2vw;--cds-fluid-spacing-03: 5vw;--cds-fluid-spacing-04: 10vw;--cds-layout-01: 1rem;--cds-layout-02: 1.5rem;--cds-layout-03: 2rem;--cds-layout-04: 3rem;--cds-layout-05: 4rem;--cds-layout-06: 6rem;--cds-layout-07: 10rem;--cds-container-01: 1.5rem;--cds-container-02: 2rem;--cds-container-03: 2.5rem;--cds-container-04: 3rem;--cds-container-05: 4rem;--cds-size-xsmall: 1.5rem;--cds-size-small: 2rem;--cds-size-medium: 2.5rem;--cds-size-large: 3rem;--cds-size-xlarge: 4rem;--cds-size-2XLarge: 5rem;--cds-icon-size-01: 1rem;--cds-icon-size-02: 1.25rem;--cds-tag-background-red: #ffd7d9;--cds-tag-color-red: #750e13;--cds-tag-hover-red: #ffb3b8;--cds-tag-background-magenta: #ffd6e8;--cds-tag-color-magenta: #740937;--cds-tag-hover-magenta: #ffafd2;--cds-tag-background-purple: #e8daff;--cds-tag-color-purple: #491d8b;--cds-tag-hover-purple: #d4bbff;--cds-tag-background-blue: #d0e2ff;--cds-tag-color-blue: #002d9c;--cds-tag-hover-blue: #a6c8ff;--cds-tag-background-cyan: #bae6ff;--cds-tag-color-cyan: #003a6d;--cds-tag-hover-cyan: #82cfff;--cds-tag-background-teal: #9ef0f0;--cds-tag-color-teal: #004144;--cds-tag-hover-teal: #3ddbd9;--cds-tag-background-green: #a7f0ba;--cds-tag-color-green: #044317;--cds-tag-hover-green: #6fdc8c;--cds-tag-background-gray: #e0e0e0;--cds-tag-color-gray: #393939;--cds-tag-hover-gray: #c6c6c6;--cds-tag-background-cool-gray: #dde1e6;--cds-tag-color-cool-gray: #343a3f;--cds-tag-hover-cool-gray: #c1c7cd;--cds-tag-background-warm-gray: #e5e0df;--cds-tag-color-warm-gray: #3c3838;--cds-tag-hover-warm-gray: #cac5c4;--cds-notification-background-error: #fff1f1;--cds-notification-background-success: #defbe6;--cds-notification-background-info: #edf5ff;--cds-notification-background-warning: #fdf6dd;--cds-notification-action-hover: #ffffff}:root[theme=g80]{--cds-interactive-01: #0f62fe;--cds-interactive-02: #6f6f6f;--cds-interactive-03: #ffffff;--cds-interactive-04: #78a9ff;--cds-ui-background: #393939;--cds-ui-01: #525252;--cds-ui-02: #525252;--cds-ui-03: #6f6f6f;--cds-ui-04: #a8a8a8;--cds-ui-05: #f4f4f4;--cds-text-01: #f4f4f4;--cds-text-02: #c6c6c6;--cds-text-03: #6f6f6f;--cds-text-04: #ffffff;--cds-text-05: #a8a8a8;--cds-text-error: #ffb3b8;--cds-icon-01: #f4f4f4;--cds-icon-02: #c6c6c6;--cds-icon-03: #ffffff;--cds-link-01: #78a9ff;--cds-link-02: #a6c8ff;--cds-inverse-link: #0f62fe;--cds-field-01: #525252;--cds-field-02: #6f6f6f;--cds-inverse-01: #161616;--cds-inverse-02: #f4f4f4;--cds-support-01: #ff8389;--cds-support-02: #42be65;--cds-support-03: #f1c21b;--cds-support-04: #4589ff;--cds-inverse-support-01: #da1e28;--cds-inverse-support-02: #24a148;--cds-inverse-support-03: #f1c21b;--cds-inverse-support-04: #0f62fe;--cds-overlay-01: rgba(0, 0, 0, .65);--cds-danger-01: #da1e28;--cds-danger-02: #ff8389;--cds-focus: #ffffff;--cds-inverse-focus-ui: #0f62fe;--cds-hover-primary: #0151e4;--cds-active-primary: #002d9c;--cds-hover-primary-text: #a6c8ff;--cds-hover-secondary: #5e5e5e;--cds-active-secondary: #525252;--cds-hover-tertiary: #ffffff;--cds-active-tertiary: #c6c6c6;--cds-hover-ui: #474747;--cds-hover-light-ui: #5e5e5e;--cds-hover-selected-ui: #616161;--cds-active-ui: #525252;--cds-active-light-ui: #8d8d8d;--cds-selected-ui: #525252;--cds-selected-light-ui: #8d8d8d;--cds-inverse-hover-ui: #e8e8e8;--cds-hover-danger: #b81921;--cds-active-danger: #750e13;--cds-hover-row: #636363;--cds-visited-link: #be95ff;--cds-disabled-01: #525252;--cds-disabled-02: #8d8d8d;--cds-disabled-03: #c6c6c6;--cds-highlight: #0043ce;--cds-decorative-01: #6f6f6f;--cds-button-separator: #161616;--cds-skeleton-01: #474747;--cds-skeleton-02: #6f6f6f;--cds-background: #393939;--cds-layer: #525252;--cds-layer-accent: #6f6f6f;--cds-layer-accent-hover: #5e5e5e;--cds-layer-accent-active: #525252;--cds-field: #525252;--cds-background-inverse: #f4f4f4;--cds-background-brand: #0f62fe;--cds-interactive: #78a9ff;--cds-border-subtle: #6f6f6f;--cds-border-strong: #a8a8a8;--cds-border-inverse: #f4f4f4;--cds-border-interactive: #4589ff;--cds-text-primary: #f4f4f4;--cds-text-secondary: #c6c6c6;--cds-text-placeholder: #6f6f6f;--cds-text-helper: #a8a8a8;--cds-text-on-color: #ffffff;--cds-text-inverse: #161616;--cds-link-primary: #78a9ff;--cds-link-secondary: #a6c8ff;--cds-link-visited: #be95ff;--cds-link-inverse: #0f62fe;--cds-icon-primary: #f4f4f4;--cds-icon-secondary: #c6c6c6;--cds-icon-on-color: #ffffff;--cds-icon-inverse: #161616;--cds-support-error: #ff8389;--cds-support-success: #42be65;--cds-support-warning: #f1c21b;--cds-support-info: #4589ff;--cds-support-error-inverse: #da1e28;--cds-support-success-inverse: #24a148;--cds-support-warning-inverse: #f1c21b;--cds-support-info-inverse: #0f62fe;--cds-overlay: rgba(0, 0, 0, .65);--cds-toggle-off: #8d8d8d;--cds-shadow: rgba(0, 0, 0, .8);--cds-button-primary: #0f62fe;--cds-button-secondary: #6f6f6f;--cds-button-tertiary: #ffffff;--cds-button-danger-primary: #da1e28;--cds-button-danger-secondary: #ff8389;--cds-background-active: #525252;--cds-layer-active: #6f6f6f;--cds-button-danger-active: #750e13;--cds-button-primary-active: #002d9c;--cds-button-secondary-active: #525252;--cds-button-tertiary-active: #c6c6c6;--cds-focus-inset: #161616;--cds-focus-inverse: #0f62fe;--cds-background-hover: #474747;--cds-layer-hover: #636363;--cds-field-hover: #636363;--cds-background-inverse-hover: #e8e8e8;--cds-link-primary-hover: #a6c8ff;--cds-button-danger-hover: #b81921;--cds-button-primary-hover: #0151e4;--cds-button-secondary-hover: #5e5e5e;--cds-button-tertiary-hover: #ffffff;--cds-background-selected: #525252;--cds-background-selected-hover: #636363;--cds-layer-selected: #6f6f6f;--cds-layer-selected-hover: #616161;--cds-layer-selected-inverse: #f4f4f4;--cds-border-subtle-selected: #8d8d8d;--cds-border-disabled: #525252;--cds-text-disabled: #8d8d8d;--cds-button-disabled: #8d8d8d;--cds-icon-disabled: #8d8d8d;--cds-text-on-color-disabled: #c6c6c6;--cds-icon-on-color-disabled: #c6c6c6;--cds-layer-selected-disabled: #c6c6c6;--cds-skeleton-background: #474747;--cds-skeleton-element: #6f6f6f;--cds-brand-01: #0f62fe;--cds-brand-02: #6f6f6f;--cds-brand-03: #ffffff;--cds-active-01: #525252;--cds-hover-field: #474747;--cds-danger: #da1e28;--cds-caption-01-font-size: .75rem;--cds-caption-01-font-weight: 400;--cds-caption-01-line-height: 1.33333;--cds-caption-01-letter-spacing: .32px;--cds-caption-02-font-size: .875rem;--cds-caption-02-font-weight: 400;--cds-caption-02-line-height: 1.28572;--cds-caption-02-letter-spacing: .32px;--cds-label-01-font-size: .75rem;--cds-label-01-font-weight: 400;--cds-label-01-line-height: 1.33333;--cds-label-01-letter-spacing: .32px;--cds-label-02-font-size: .875rem;--cds-label-02-font-weight: 400;--cds-label-02-line-height: 1.28572;--cds-label-02-letter-spacing: .16px;--cds-helper-text-01-font-size: .75rem;--cds-helper-text-01-line-height: 1.33333;--cds-helper-text-01-letter-spacing: .32px;--cds-helper-text-02-font-size: .875rem;--cds-helper-text-02-line-height: 1.28572;--cds-helper-text-02-letter-spacing: .16px;--cds-body-short-01-font-size: .875rem;--cds-body-short-01-font-weight: 400;--cds-body-short-01-line-height: 1.28572;--cds-body-short-01-letter-spacing: .16px;--cds-body-long-01-font-size: .875rem;--cds-body-long-01-font-weight: 400;--cds-body-long-01-line-height: 1.42857;--cds-body-long-01-letter-spacing: .16px;--cds-body-short-02-font-size: 1rem;--cds-body-short-02-font-weight: 400;--cds-body-short-02-line-height: 1.375;--cds-body-short-02-letter-spacing: 0;--cds-body-long-02-font-size: 1rem;--cds-body-long-02-font-weight: 400;--cds-body-long-02-line-height: 1.5;--cds-body-long-02-letter-spacing: 0;--cds-code-01-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-01-font-size: .75rem;--cds-code-01-font-weight: 400;--cds-code-01-line-height: 1.33333;--cds-code-01-letter-spacing: .32px;--cds-code-02-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-02-font-size: .875rem;--cds-code-02-font-weight: 400;--cds-code-02-line-height: 1.42857;--cds-code-02-letter-spacing: .32px;--cds-heading-01-font-size: .875rem;--cds-heading-01-font-weight: 600;--cds-heading-01-line-height: 1.42857;--cds-heading-01-letter-spacing: .16px;--cds-productive-heading-01-font-size: .875rem;--cds-productive-heading-01-font-weight: 600;--cds-productive-heading-01-line-height: 1.28572;--cds-productive-heading-01-letter-spacing: .16px;--cds-heading-02-font-size: 1rem;--cds-heading-02-font-weight: 600;--cds-heading-02-line-height: 1.5;--cds-heading-02-letter-spacing: 0;--cds-productive-heading-02-font-size: 1rem;--cds-productive-heading-02-font-weight: 600;--cds-productive-heading-02-line-height: 1.375;--cds-productive-heading-02-letter-spacing: 0;--cds-productive-heading-03-font-size: 1.25rem;--cds-productive-heading-03-font-weight: 400;--cds-productive-heading-03-line-height: 1.4;--cds-productive-heading-03-letter-spacing: 0;--cds-productive-heading-04-font-size: 1.75rem;--cds-productive-heading-04-font-weight: 400;--cds-productive-heading-04-line-height: 1.28572;--cds-productive-heading-04-letter-spacing: 0;--cds-productive-heading-05-font-size: 2rem;--cds-productive-heading-05-font-weight: 400;--cds-productive-heading-05-line-height: 1.25;--cds-productive-heading-05-letter-spacing: 0;--cds-productive-heading-06-font-size: 2.625rem;--cds-productive-heading-06-font-weight: 300;--cds-productive-heading-06-line-height: 1.199;--cds-productive-heading-06-letter-spacing: 0;--cds-productive-heading-07-font-size: 3.375rem;--cds-productive-heading-07-font-weight: 300;--cds-productive-heading-07-line-height: 1.199;--cds-productive-heading-07-letter-spacing: 0;--cds-expressive-heading-01-font-size: .875rem;--cds-expressive-heading-01-font-weight: 600;--cds-expressive-heading-01-line-height: 1.25;--cds-expressive-heading-01-letter-spacing: .16px;--cds-expressive-heading-02-font-size: 1rem;--cds-expressive-heading-02-font-weight: 600;--cds-expressive-heading-02-line-height: 1.5;--cds-expressive-heading-02-letter-spacing: 0;--cds-expressive-heading-03-font-size: 1.25rem;--cds-expressive-heading-03-font-weight: 400;--cds-expressive-heading-03-line-height: 1.4;--cds-expressive-heading-03-letter-spacing: 0;--cds-expressive-heading-04-font-size: 1.75rem;--cds-expressive-heading-04-font-weight: 400;--cds-expressive-heading-04-line-height: 1.28572;--cds-expressive-heading-04-letter-spacing: 0;--cds-expressive-heading-05-font-size: 2rem;--cds-expressive-heading-05-font-weight: 400;--cds-expressive-heading-05-line-height: 1.25;--cds-expressive-heading-05-letter-spacing: 0;--cds-expressive-heading-06-font-size: 2rem;--cds-expressive-heading-06-font-weight: 600;--cds-expressive-heading-06-line-height: 1.25;--cds-expressive-heading-06-letter-spacing: 0;--cds-expressive-paragraph-01-font-size: 1.5rem;--cds-expressive-paragraph-01-font-weight: 300;--cds-expressive-paragraph-01-line-height: 1.334;--cds-expressive-paragraph-01-letter-spacing: 0;--cds-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-01-font-size: 1.25rem;--cds-quotation-01-font-weight: 400;--cds-quotation-01-line-height: 1.3;--cds-quotation-01-letter-spacing: 0;--cds-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-02-font-size: 2rem;--cds-quotation-02-font-weight: 300;--cds-quotation-02-line-height: 1.25;--cds-quotation-02-letter-spacing: 0;--cds-display-01-font-size: 2.625rem;--cds-display-01-font-weight: 300;--cds-display-01-line-height: 1.19;--cds-display-01-letter-spacing: 0;--cds-display-02-font-size: 2.625rem;--cds-display-02-font-weight: 600;--cds-display-02-line-height: 1.19;--cds-display-02-letter-spacing: 0;--cds-display-03-font-size: 2.625rem;--cds-display-03-font-weight: 300;--cds-display-03-line-height: 1.19;--cds-display-03-letter-spacing: 0;--cds-display-04-font-size: 2.625rem;--cds-display-04-font-weight: 600;--cds-display-04-line-height: 1.19;--cds-display-04-letter-spacing: 0;--cds-legal-01-font-size: .75rem;--cds-legal-01-font-weight: 400;--cds-legal-01-line-height: 1.33333;--cds-legal-01-letter-spacing: .32px;--cds-legal-02-font-size: .875rem;--cds-legal-02-font-weight: 400;--cds-legal-02-line-height: 1.28572;--cds-legal-02-letter-spacing: .16px;--cds-body-compact-01-font-size: .875rem;--cds-body-compact-01-font-weight: 400;--cds-body-compact-01-line-height: 1.28572;--cds-body-compact-01-letter-spacing: .16px;--cds-body-compact-02-font-size: 1rem;--cds-body-compact-02-font-weight: 400;--cds-body-compact-02-line-height: 1.375;--cds-body-compact-02-letter-spacing: 0;--cds-body-01-font-size: .875rem;--cds-body-01-font-weight: 400;--cds-body-01-line-height: 1.42857;--cds-body-01-letter-spacing: .16px;--cds-body-02-font-size: 1rem;--cds-body-02-font-weight: 400;--cds-body-02-line-height: 1.5;--cds-body-02-letter-spacing: 0;--cds-heading-compact-01-font-size: .875rem;--cds-heading-compact-01-font-weight: 600;--cds-heading-compact-01-line-height: 1.28572;--cds-heading-compact-01-letter-spacing: .16px;--cds-heading-compact-02-font-size: 1rem;--cds-heading-compact-02-font-weight: 600;--cds-heading-compact-02-line-height: 1.375;--cds-heading-compact-02-letter-spacing: 0;--cds-heading-03-font-size: 1.25rem;--cds-heading-03-font-weight: 400;--cds-heading-03-line-height: 1.4;--cds-heading-03-letter-spacing: 0;--cds-heading-04-font-size: 1.75rem;--cds-heading-04-font-weight: 400;--cds-heading-04-line-height: 1.28572;--cds-heading-04-letter-spacing: 0;--cds-heading-05-font-size: 2rem;--cds-heading-05-font-weight: 400;--cds-heading-05-line-height: 1.25;--cds-heading-05-letter-spacing: 0;--cds-heading-06-font-size: 2.625rem;--cds-heading-06-font-weight: 300;--cds-heading-06-line-height: 1.199;--cds-heading-06-letter-spacing: 0;--cds-heading-07-font-size: 3.375rem;--cds-heading-07-font-weight: 300;--cds-heading-07-line-height: 1.199;--cds-heading-07-letter-spacing: 0;--cds-fluid-heading-03-font-size: 1.25rem;--cds-fluid-heading-03-font-weight: 400;--cds-fluid-heading-03-line-height: 1.4;--cds-fluid-heading-03-letter-spacing: 0;--cds-fluid-heading-04-font-size: 1.75rem;--cds-fluid-heading-04-font-weight: 400;--cds-fluid-heading-04-line-height: 1.28572;--cds-fluid-heading-04-letter-spacing: 0;--cds-fluid-heading-05-font-size: 2rem;--cds-fluid-heading-05-font-weight: 400;--cds-fluid-heading-05-line-height: 1.25;--cds-fluid-heading-05-letter-spacing: 0;--cds-fluid-heading-06-font-size: 2rem;--cds-fluid-heading-06-font-weight: 600;--cds-fluid-heading-06-line-height: 1.25;--cds-fluid-heading-06-letter-spacing: 0;--cds-fluid-paragraph-01-font-size: 1.5rem;--cds-fluid-paragraph-01-font-weight: 300;--cds-fluid-paragraph-01-line-height: 1.334;--cds-fluid-paragraph-01-letter-spacing: 0;--cds-fluid-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-01-font-size: 1.25rem;--cds-fluid-quotation-01-font-weight: 400;--cds-fluid-quotation-01-line-height: 1.3;--cds-fluid-quotation-01-letter-spacing: 0;--cds-fluid-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-02-font-size: 2rem;--cds-fluid-quotation-02-font-weight: 300;--cds-fluid-quotation-02-line-height: 1.25;--cds-fluid-quotation-02-letter-spacing: 0;--cds-fluid-display-01-font-size: 2.625rem;--cds-fluid-display-01-font-weight: 300;--cds-fluid-display-01-line-height: 1.19;--cds-fluid-display-01-letter-spacing: 0;--cds-fluid-display-02-font-size: 2.625rem;--cds-fluid-display-02-font-weight: 600;--cds-fluid-display-02-line-height: 1.19;--cds-fluid-display-02-letter-spacing: 0;--cds-fluid-display-03-font-size: 2.625rem;--cds-fluid-display-03-font-weight: 300;--cds-fluid-display-03-line-height: 1.19;--cds-fluid-display-03-letter-spacing: 0;--cds-fluid-display-04-font-size: 2.625rem;--cds-fluid-display-04-font-weight: 600;--cds-fluid-display-04-line-height: 1.19;--cds-fluid-display-04-letter-spacing: 0;--cds-spacing-01: .125rem;--cds-spacing-02: .25rem;--cds-spacing-03: .5rem;--cds-spacing-04: .75rem;--cds-spacing-05: 1rem;--cds-spacing-06: 1.5rem;--cds-spacing-07: 2rem;--cds-spacing-08: 2.5rem;--cds-spacing-09: 3rem;--cds-spacing-10: 4rem;--cds-spacing-11: 5rem;--cds-spacing-12: 6rem;--cds-spacing-13: 10rem;--cds-fluid-spacing-01: 0;--cds-fluid-spacing-02: 2vw;--cds-fluid-spacing-03: 5vw;--cds-fluid-spacing-04: 10vw;--cds-layout-01: 1rem;--cds-layout-02: 1.5rem;--cds-layout-03: 2rem;--cds-layout-04: 3rem;--cds-layout-05: 4rem;--cds-layout-06: 6rem;--cds-layout-07: 10rem;--cds-container-01: 1.5rem;--cds-container-02: 2rem;--cds-container-03: 2.5rem;--cds-container-04: 3rem;--cds-container-05: 4rem;--cds-size-xsmall: 1.5rem;--cds-size-small: 2rem;--cds-size-medium: 2.5rem;--cds-size-large: 3rem;--cds-size-xlarge: 4rem;--cds-size-2XLarge: 5rem;--cds-icon-size-01: 1rem;--cds-icon-size-02: 1.25rem;--cds-tag-background-red: #ffd7d9;--cds-tag-color-red: #750e13;--cds-tag-hover-red: #ffb3b8;--cds-tag-background-magenta: #ffd6e8;--cds-tag-color-magenta: #740937;--cds-tag-hover-magenta: #ffafd2;--cds-tag-background-purple: #e8daff;--cds-tag-color-purple: #491d8b;--cds-tag-hover-purple: #d4bbff;--cds-tag-background-blue: #d0e2ff;--cds-tag-color-blue: #002d9c;--cds-tag-hover-blue: #a6c8ff;--cds-tag-background-cyan: #bae6ff;--cds-tag-color-cyan: #003a6d;--cds-tag-hover-cyan: #82cfff;--cds-tag-background-teal: #9ef0f0;--cds-tag-color-teal: #004144;--cds-tag-hover-teal: #3ddbd9;--cds-tag-background-green: #a7f0ba;--cds-tag-color-green: #044317;--cds-tag-hover-green: #6fdc8c;--cds-tag-background-gray: #e0e0e0;--cds-tag-color-gray: #393939;--cds-tag-hover-gray: #c6c6c6;--cds-tag-background-cool-gray: #dde1e6;--cds-tag-color-cool-gray: #343a3f;--cds-tag-hover-cool-gray: #c1c7cd;--cds-tag-background-warm-gray: #e5e0df;--cds-tag-color-warm-gray: #3c3838;--cds-tag-hover-warm-gray: #cac5c4;--cds-notification-background-error: var(--cds-layer, #f4f4f4);--cds-notification-background-success: var(--cds-layer, #f4f4f4);--cds-notification-background-info: var(--cds-layer, #f4f4f4);--cds-notification-background-warning: var(--cds-layer, #f4f4f4);--cds-notification-action-hover: var(--cds-layer-hover, #e5e5e5)}:root[theme=g90]{--cds-interactive-01: #0f62fe;--cds-interactive-02: #6f6f6f;--cds-interactive-03: #ffffff;--cds-interactive-04: #4589ff;--cds-ui-background: #262626;--cds-ui-01: #393939;--cds-ui-02: #525252;--cds-ui-03: #525252;--cds-ui-04: #8d8d8d;--cds-ui-05: #f4f4f4;--cds-text-01: #f4f4f4;--cds-text-02: #c6c6c6;--cds-text-03: #6f6f6f;--cds-text-04: #ffffff;--cds-text-05: #8d8d8d;--cds-text-error: #ffb3b8;--cds-icon-01: #f4f4f4;--cds-icon-02: #c6c6c6;--cds-icon-03: #ffffff;--cds-link-01: #78a9ff;--cds-link-02: #a6c8ff;--cds-inverse-link: #0f62fe;--cds-field-01: #393939;--cds-field-02: #525252;--cds-inverse-01: #161616;--cds-inverse-02: #f4f4f4;--cds-support-01: #ff8389;--cds-support-02: #42be65;--cds-support-03: #f1c21b;--cds-support-04: #4589ff;--cds-inverse-support-01: #da1e28;--cds-inverse-support-02: #24a148;--cds-inverse-support-03: #f1c21b;--cds-inverse-support-04: #0f62fe;--cds-overlay-01: rgba(0, 0, 0, .65);--cds-danger-01: #da1e28;--cds-danger-02: #ff8389;--cds-focus: #ffffff;--cds-inverse-focus-ui: #0f62fe;--cds-hover-primary: #0353e9;--cds-active-primary: #002d9c;--cds-hover-primary-text: #a6c8ff;--cds-hover-secondary: #606060;--cds-active-secondary: #393939;--cds-hover-tertiary: #f4f4f4;--cds-active-tertiary: #c6c6c6;--cds-hover-ui: #4c4c4c;--cds-hover-light-ui: #656565;--cds-hover-selected-ui: #656565;--cds-active-ui: #6f6f6f;--cds-active-light-ui: #8d8d8d;--cds-selected-ui: #525252;--cds-selected-light-ui: #6f6f6f;--cds-inverse-hover-ui: #e5e5e5;--cds-hover-danger: #b81921;--cds-active-danger: #750e13;--cds-hover-row: #4c4c4c;--cds-visited-link: #be95ff;--cds-disabled-01: #393939;--cds-disabled-02: #6f6f6f;--cds-disabled-03: #a8a8a8;--cds-highlight: #0043ce;--cds-decorative-01: #6f6f6f;--cds-button-separator: #161616;--cds-skeleton-01: #353535;--cds-skeleton-02: #525252;--cds-background: #262626;--cds-layer: #393939;--cds-layer-accent: #525252;--cds-layer-accent-hover: #636363;--cds-layer-accent-active: #8d8d8d;--cds-field: #393939;--cds-background-inverse: #f4f4f4;--cds-background-brand: #0f62fe;--cds-interactive: #4589ff;--cds-border-subtle: #525252;--cds-border-strong: #8d8d8d;--cds-border-inverse: #f4f4f4;--cds-border-interactive: #4589ff;--cds-text-primary: #f4f4f4;--cds-text-secondary: #c6c6c6;--cds-text-placeholder: #6f6f6f;--cds-text-helper: #8d8d8d;--cds-text-on-color: #ffffff;--cds-text-inverse: #161616;--cds-link-primary: #78a9ff;--cds-link-secondary: #a6c8ff;--cds-link-visited: #be95ff;--cds-link-inverse: #0f62fe;--cds-icon-primary: #f4f4f4;--cds-icon-secondary: #c6c6c6;--cds-icon-on-color: #ffffff;--cds-icon-inverse: #161616;--cds-support-error: #ff8389;--cds-support-success: #42be65;--cds-support-warning: #f1c21b;--cds-support-info: #4589ff;--cds-support-error-inverse: #da1e28;--cds-support-success-inverse: #24a148;--cds-support-warning-inverse: #f1c21b;--cds-support-info-inverse: #0f62fe;--cds-overlay: rgba(0, 0, 0, .65);--cds-toggle-off: #8d8d8d;--cds-shadow: rgba(0, 0, 0, .8);--cds-button-primary: #0f62fe;--cds-button-secondary: #6f6f6f;--cds-button-tertiary: #ffffff;--cds-button-danger-primary: #da1e28;--cds-button-danger-secondary: #ff8389;--cds-background-active: #6f6f6f;--cds-layer-active: #6f6f6f;--cds-button-danger-active: #750e13;--cds-button-primary-active: #002d9c;--cds-button-secondary-active: #393939;--cds-button-tertiary-active: #c6c6c6;--cds-focus-inset: #161616;--cds-focus-inverse: #0f62fe;--cds-background-hover: #4c4c4c;--cds-layer-hover: #4c4c4c;--cds-field-hover: #4c4c4c;--cds-background-inverse-hover: #e5e5e5;--cds-link-primary-hover: #a6c8ff;--cds-button-danger-hover: #b81921;--cds-button-primary-hover: #0353e9;--cds-button-secondary-hover: #606060;--cds-button-tertiary-hover: #f4f4f4;--cds-background-selected: #525252;--cds-background-selected-hover: #656565;--cds-layer-selected: #525252;--cds-layer-selected-hover: #656565;--cds-layer-selected-inverse: #f4f4f4;--cds-border-subtle-selected: #6f6f6f;--cds-border-disabled: #393939;--cds-text-disabled: #6f6f6f;--cds-button-disabled: #6f6f6f;--cds-icon-disabled: #6f6f6f;--cds-text-on-color-disabled: #a8a8a8;--cds-icon-on-color-disabled: #a8a8a8;--cds-layer-selected-disabled: #a8a8a8;--cds-skeleton-background: #353535;--cds-skeleton-element: #525252;--cds-brand-01: #0f62fe;--cds-brand-02: #6f6f6f;--cds-brand-03: #ffffff;--cds-active-01: #6f6f6f;--cds-hover-field: #4c4c4c;--cds-danger: #da1e28;--cds-caption-01-font-size: .75rem;--cds-caption-01-font-weight: 400;--cds-caption-01-line-height: 1.33333;--cds-caption-01-letter-spacing: .32px;--cds-caption-02-font-size: .875rem;--cds-caption-02-font-weight: 400;--cds-caption-02-line-height: 1.28572;--cds-caption-02-letter-spacing: .32px;--cds-label-01-font-size: .75rem;--cds-label-01-font-weight: 400;--cds-label-01-line-height: 1.33333;--cds-label-01-letter-spacing: .32px;--cds-label-02-font-size: .875rem;--cds-label-02-font-weight: 400;--cds-label-02-line-height: 1.28572;--cds-label-02-letter-spacing: .16px;--cds-helper-text-01-font-size: .75rem;--cds-helper-text-01-line-height: 1.33333;--cds-helper-text-01-letter-spacing: .32px;--cds-helper-text-02-font-size: .875rem;--cds-helper-text-02-line-height: 1.28572;--cds-helper-text-02-letter-spacing: .16px;--cds-body-short-01-font-size: .875rem;--cds-body-short-01-font-weight: 400;--cds-body-short-01-line-height: 1.28572;--cds-body-short-01-letter-spacing: .16px;--cds-body-long-01-font-size: .875rem;--cds-body-long-01-font-weight: 400;--cds-body-long-01-line-height: 1.42857;--cds-body-long-01-letter-spacing: .16px;--cds-body-short-02-font-size: 1rem;--cds-body-short-02-font-weight: 400;--cds-body-short-02-line-height: 1.375;--cds-body-short-02-letter-spacing: 0;--cds-body-long-02-font-size: 1rem;--cds-body-long-02-font-weight: 400;--cds-body-long-02-line-height: 1.5;--cds-body-long-02-letter-spacing: 0;--cds-code-01-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-01-font-size: .75rem;--cds-code-01-font-weight: 400;--cds-code-01-line-height: 1.33333;--cds-code-01-letter-spacing: .32px;--cds-code-02-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-02-font-size: .875rem;--cds-code-02-font-weight: 400;--cds-code-02-line-height: 1.42857;--cds-code-02-letter-spacing: .32px;--cds-heading-01-font-size: .875rem;--cds-heading-01-font-weight: 600;--cds-heading-01-line-height: 1.42857;--cds-heading-01-letter-spacing: .16px;--cds-productive-heading-01-font-size: .875rem;--cds-productive-heading-01-font-weight: 600;--cds-productive-heading-01-line-height: 1.28572;--cds-productive-heading-01-letter-spacing: .16px;--cds-heading-02-font-size: 1rem;--cds-heading-02-font-weight: 600;--cds-heading-02-line-height: 1.5;--cds-heading-02-letter-spacing: 0;--cds-productive-heading-02-font-size: 1rem;--cds-productive-heading-02-font-weight: 600;--cds-productive-heading-02-line-height: 1.375;--cds-productive-heading-02-letter-spacing: 0;--cds-productive-heading-03-font-size: 1.25rem;--cds-productive-heading-03-font-weight: 400;--cds-productive-heading-03-line-height: 1.4;--cds-productive-heading-03-letter-spacing: 0;--cds-productive-heading-04-font-size: 1.75rem;--cds-productive-heading-04-font-weight: 400;--cds-productive-heading-04-line-height: 1.28572;--cds-productive-heading-04-letter-spacing: 0;--cds-productive-heading-05-font-size: 2rem;--cds-productive-heading-05-font-weight: 400;--cds-productive-heading-05-line-height: 1.25;--cds-productive-heading-05-letter-spacing: 0;--cds-productive-heading-06-font-size: 2.625rem;--cds-productive-heading-06-font-weight: 300;--cds-productive-heading-06-line-height: 1.199;--cds-productive-heading-06-letter-spacing: 0;--cds-productive-heading-07-font-size: 3.375rem;--cds-productive-heading-07-font-weight: 300;--cds-productive-heading-07-line-height: 1.199;--cds-productive-heading-07-letter-spacing: 0;--cds-expressive-heading-01-font-size: .875rem;--cds-expressive-heading-01-font-weight: 600;--cds-expressive-heading-01-line-height: 1.25;--cds-expressive-heading-01-letter-spacing: .16px;--cds-expressive-heading-02-font-size: 1rem;--cds-expressive-heading-02-font-weight: 600;--cds-expressive-heading-02-line-height: 1.5;--cds-expressive-heading-02-letter-spacing: 0;--cds-expressive-heading-03-font-size: 1.25rem;--cds-expressive-heading-03-font-weight: 400;--cds-expressive-heading-03-line-height: 1.4;--cds-expressive-heading-03-letter-spacing: 0;--cds-expressive-heading-04-font-size: 1.75rem;--cds-expressive-heading-04-font-weight: 400;--cds-expressive-heading-04-line-height: 1.28572;--cds-expressive-heading-04-letter-spacing: 0;--cds-expressive-heading-05-font-size: 2rem;--cds-expressive-heading-05-font-weight: 400;--cds-expressive-heading-05-line-height: 1.25;--cds-expressive-heading-05-letter-spacing: 0;--cds-expressive-heading-06-font-size: 2rem;--cds-expressive-heading-06-font-weight: 600;--cds-expressive-heading-06-line-height: 1.25;--cds-expressive-heading-06-letter-spacing: 0;--cds-expressive-paragraph-01-font-size: 1.5rem;--cds-expressive-paragraph-01-font-weight: 300;--cds-expressive-paragraph-01-line-height: 1.334;--cds-expressive-paragraph-01-letter-spacing: 0;--cds-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-01-font-size: 1.25rem;--cds-quotation-01-font-weight: 400;--cds-quotation-01-line-height: 1.3;--cds-quotation-01-letter-spacing: 0;--cds-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-02-font-size: 2rem;--cds-quotation-02-font-weight: 300;--cds-quotation-02-line-height: 1.25;--cds-quotation-02-letter-spacing: 0;--cds-display-01-font-size: 2.625rem;--cds-display-01-font-weight: 300;--cds-display-01-line-height: 1.19;--cds-display-01-letter-spacing: 0;--cds-display-02-font-size: 2.625rem;--cds-display-02-font-weight: 600;--cds-display-02-line-height: 1.19;--cds-display-02-letter-spacing: 0;--cds-display-03-font-size: 2.625rem;--cds-display-03-font-weight: 300;--cds-display-03-line-height: 1.19;--cds-display-03-letter-spacing: 0;--cds-display-04-font-size: 2.625rem;--cds-display-04-font-weight: 600;--cds-display-04-line-height: 1.19;--cds-display-04-letter-spacing: 0;--cds-legal-01-font-size: .75rem;--cds-legal-01-font-weight: 400;--cds-legal-01-line-height: 1.33333;--cds-legal-01-letter-spacing: .32px;--cds-legal-02-font-size: .875rem;--cds-legal-02-font-weight: 400;--cds-legal-02-line-height: 1.28572;--cds-legal-02-letter-spacing: .16px;--cds-body-compact-01-font-size: .875rem;--cds-body-compact-01-font-weight: 400;--cds-body-compact-01-line-height: 1.28572;--cds-body-compact-01-letter-spacing: .16px;--cds-body-compact-02-font-size: 1rem;--cds-body-compact-02-font-weight: 400;--cds-body-compact-02-line-height: 1.375;--cds-body-compact-02-letter-spacing: 0;--cds-body-01-font-size: .875rem;--cds-body-01-font-weight: 400;--cds-body-01-line-height: 1.42857;--cds-body-01-letter-spacing: .16px;--cds-body-02-font-size: 1rem;--cds-body-02-font-weight: 400;--cds-body-02-line-height: 1.5;--cds-body-02-letter-spacing: 0;--cds-heading-compact-01-font-size: .875rem;--cds-heading-compact-01-font-weight: 600;--cds-heading-compact-01-line-height: 1.28572;--cds-heading-compact-01-letter-spacing: .16px;--cds-heading-compact-02-font-size: 1rem;--cds-heading-compact-02-font-weight: 600;--cds-heading-compact-02-line-height: 1.375;--cds-heading-compact-02-letter-spacing: 0;--cds-heading-03-font-size: 1.25rem;--cds-heading-03-font-weight: 400;--cds-heading-03-line-height: 1.4;--cds-heading-03-letter-spacing: 0;--cds-heading-04-font-size: 1.75rem;--cds-heading-04-font-weight: 400;--cds-heading-04-line-height: 1.28572;--cds-heading-04-letter-spacing: 0;--cds-heading-05-font-size: 2rem;--cds-heading-05-font-weight: 400;--cds-heading-05-line-height: 1.25;--cds-heading-05-letter-spacing: 0;--cds-heading-06-font-size: 2.625rem;--cds-heading-06-font-weight: 300;--cds-heading-06-line-height: 1.199;--cds-heading-06-letter-spacing: 0;--cds-heading-07-font-size: 3.375rem;--cds-heading-07-font-weight: 300;--cds-heading-07-line-height: 1.199;--cds-heading-07-letter-spacing: 0;--cds-fluid-heading-03-font-size: 1.25rem;--cds-fluid-heading-03-font-weight: 400;--cds-fluid-heading-03-line-height: 1.4;--cds-fluid-heading-03-letter-spacing: 0;--cds-fluid-heading-04-font-size: 1.75rem;--cds-fluid-heading-04-font-weight: 400;--cds-fluid-heading-04-line-height: 1.28572;--cds-fluid-heading-04-letter-spacing: 0;--cds-fluid-heading-05-font-size: 2rem;--cds-fluid-heading-05-font-weight: 400;--cds-fluid-heading-05-line-height: 1.25;--cds-fluid-heading-05-letter-spacing: 0;--cds-fluid-heading-06-font-size: 2rem;--cds-fluid-heading-06-font-weight: 600;--cds-fluid-heading-06-line-height: 1.25;--cds-fluid-heading-06-letter-spacing: 0;--cds-fluid-paragraph-01-font-size: 1.5rem;--cds-fluid-paragraph-01-font-weight: 300;--cds-fluid-paragraph-01-line-height: 1.334;--cds-fluid-paragraph-01-letter-spacing: 0;--cds-fluid-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-01-font-size: 1.25rem;--cds-fluid-quotation-01-font-weight: 400;--cds-fluid-quotation-01-line-height: 1.3;--cds-fluid-quotation-01-letter-spacing: 0;--cds-fluid-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-02-font-size: 2rem;--cds-fluid-quotation-02-font-weight: 300;--cds-fluid-quotation-02-line-height: 1.25;--cds-fluid-quotation-02-letter-spacing: 0;--cds-fluid-display-01-font-size: 2.625rem;--cds-fluid-display-01-font-weight: 300;--cds-fluid-display-01-line-height: 1.19;--cds-fluid-display-01-letter-spacing: 0;--cds-fluid-display-02-font-size: 2.625rem;--cds-fluid-display-02-font-weight: 600;--cds-fluid-display-02-line-height: 1.19;--cds-fluid-display-02-letter-spacing: 0;--cds-fluid-display-03-font-size: 2.625rem;--cds-fluid-display-03-font-weight: 300;--cds-fluid-display-03-line-height: 1.19;--cds-fluid-display-03-letter-spacing: 0;--cds-fluid-display-04-font-size: 2.625rem;--cds-fluid-display-04-font-weight: 600;--cds-fluid-display-04-line-height: 1.19;--cds-fluid-display-04-letter-spacing: 0;--cds-spacing-01: .125rem;--cds-spacing-02: .25rem;--cds-spacing-03: .5rem;--cds-spacing-04: .75rem;--cds-spacing-05: 1rem;--cds-spacing-06: 1.5rem;--cds-spacing-07: 2rem;--cds-spacing-08: 2.5rem;--cds-spacing-09: 3rem;--cds-spacing-10: 4rem;--cds-spacing-11: 5rem;--cds-spacing-12: 6rem;--cds-spacing-13: 10rem;--cds-fluid-spacing-01: 0;--cds-fluid-spacing-02: 2vw;--cds-fluid-spacing-03: 5vw;--cds-fluid-spacing-04: 10vw;--cds-layout-01: 1rem;--cds-layout-02: 1.5rem;--cds-layout-03: 2rem;--cds-layout-04: 3rem;--cds-layout-05: 4rem;--cds-layout-06: 6rem;--cds-layout-07: 10rem;--cds-container-01: 1.5rem;--cds-container-02: 2rem;--cds-container-03: 2.5rem;--cds-container-04: 3rem;--cds-container-05: 4rem;--cds-size-xsmall: 1.5rem;--cds-size-small: 2rem;--cds-size-medium: 2.5rem;--cds-size-large: 3rem;--cds-size-xlarge: 4rem;--cds-size-2XLarge: 5rem;--cds-icon-size-01: 1rem;--cds-icon-size-02: 1.25rem;--cds-tag-background-red: #a2191f;--cds-tag-color-red: #ffd7d9;--cds-tag-hover-red: #c21e25;--cds-tag-background-magenta: #9f1853;--cds-tag-color-magenta: #ffd6e8;--cds-tag-hover-magenta: #bf1d63;--cds-tag-background-purple: #6929c4;--cds-tag-color-purple: #e8daff;--cds-tag-hover-purple: #7c3dd6;--cds-tag-background-blue: #0043ce;--cds-tag-color-blue: #d0e2ff;--cds-tag-hover-blue: #0053ff;--cds-tag-background-cyan: #00539a;--cds-tag-color-cyan: #bae6ff;--cds-tag-hover-cyan: #0066bd;--cds-tag-background-teal: #005d5d;--cds-tag-color-teal: #9ef0f0;--cds-tag-hover-teal: #007070;--cds-tag-background-green: #0e6027;--cds-tag-color-green: #a7f0ba;--cds-tag-hover-green: #11742f;--cds-tag-background-gray: #525252;--cds-tag-color-gray: #e0e0e0;--cds-tag-hover-gray: #636363;--cds-tag-background-cool-gray: #4d5358;--cds-tag-color-cool-gray: #dde1e6;--cds-tag-hover-cool-gray: #5d646a;--cds-tag-background-warm-gray: #565151;--cds-tag-color-warm-gray: #e5e0df;--cds-tag-hover-warm-gray: #696363;--cds-notification-background-error: var(--cds-ui-01, #f4f4f4);--cds-notification-background-success: var(--cds-ui-01, #f4f4f4);--cds-notification-background-info: var(--cds-ui-01, #f4f4f4);--cds-notification-background-warning: var(--cds-ui-01, #f4f4f4);--cds-notification-action-hover: var(--cds-hover-ui, #e5e5e5)}:root[theme=g100]{--cds-interactive-01: #0f62fe;--cds-interactive-02: #6f6f6f;--cds-interactive-03: #ffffff;--cds-interactive-04: #4589ff;--cds-ui-background: #161616;--cds-ui-01: #262626;--cds-ui-02: #393939;--cds-ui-03: #393939;--cds-ui-04: #6f6f6f;--cds-ui-05: #f4f4f4;--cds-text-01: #f4f4f4;--cds-text-02: #c6c6c6;--cds-text-03: #6f6f6f;--cds-text-04: #ffffff;--cds-text-05: #8d8d8d;--cds-text-error: #ff8389;--cds-icon-01: #f4f4f4;--cds-icon-02: #c6c6c6;--cds-icon-03: #ffffff;--cds-link-01: #78a9ff;--cds-link-02: #a6c8ff;--cds-inverse-link: #0f62fe;--cds-field-01: #262626;--cds-field-02: #393939;--cds-inverse-01: #161616;--cds-inverse-02: #f4f4f4;--cds-support-01: #fa4d56;--cds-support-02: #42be65;--cds-support-03: #f1c21b;--cds-support-04: #4589ff;--cds-inverse-support-01: #da1e28;--cds-inverse-support-02: #24a148;--cds-inverse-support-03: #f1c21b;--cds-inverse-support-04: #0f62fe;--cds-overlay-01: rgba(0, 0, 0, .65);--cds-danger-01: #da1e28;--cds-danger-02: #fa4d56;--cds-focus: #ffffff;--cds-inverse-focus-ui: #0f62fe;--cds-hover-primary: #0353e9;--cds-active-primary: #002d9c;--cds-hover-primary-text: #a6c8ff;--cds-hover-secondary: #606060;--cds-active-secondary: #393939;--cds-hover-tertiary: #f4f4f4;--cds-active-tertiary: #c6c6c6;--cds-hover-ui: #353535;--cds-hover-light-ui: #4c4c4c;--cds-hover-selected-ui: #4c4c4c;--cds-active-ui: #525252;--cds-active-light-ui: #6f6f6f;--cds-selected-ui: #393939;--cds-selected-light-ui: #525252;--cds-inverse-hover-ui: #e5e5e5;--cds-hover-danger: #b81921;--cds-active-danger: #750e13;--cds-hover-row: #353535;--cds-visited-link: #be95ff;--cds-disabled-01: #262626;--cds-disabled-02: #525252;--cds-disabled-03: #8d8d8d;--cds-highlight: #002d9c;--cds-decorative-01: #525252;--cds-button-separator: #161616;--cds-skeleton-01: #353535;--cds-skeleton-02: #525252;--cds-background: #161616;--cds-layer: #262626;--cds-layer-accent: #393939;--cds-layer-accent-hover: #474747;--cds-layer-accent-active: #6f6f6f;--cds-field: #262626;--cds-background-inverse: #f4f4f4;--cds-background-brand: #0f62fe;--cds-interactive: #4589ff;--cds-border-subtle: #393939;--cds-border-strong: #6f6f6f;--cds-border-inverse: #f4f4f4;--cds-border-interactive: #4589ff;--cds-text-primary: #f4f4f4;--cds-text-secondary: #c6c6c6;--cds-text-placeholder: #6f6f6f;--cds-text-helper: #8d8d8d;--cds-text-on-color: #ffffff;--cds-text-inverse: #161616;--cds-link-primary: #78a9ff;--cds-link-secondary: #a6c8ff;--cds-link-visited: #be95ff;--cds-link-inverse: #0f62fe;--cds-icon-primary: #f4f4f4;--cds-icon-secondary: #c6c6c6;--cds-icon-on-color: #ffffff;--cds-icon-inverse: #161616;--cds-support-error: #fa4d56;--cds-support-success: #42be65;--cds-support-warning: #f1c21b;--cds-support-info: #4589ff;--cds-support-error-inverse: #da1e28;--cds-support-success-inverse: #24a148;--cds-support-warning-inverse: #f1c21b;--cds-support-info-inverse: #0f62fe;--cds-overlay: rgba(0, 0, 0, .65);--cds-toggle-off: #6f6f6f;--cds-shadow: rgba(0, 0, 0, .8);--cds-button-primary: #0f62fe;--cds-button-secondary: #6f6f6f;--cds-button-tertiary: #ffffff;--cds-button-danger-primary: #da1e28;--cds-button-danger-secondary: #fa4d56;--cds-background-active: #525252;--cds-layer-active: #525252;--cds-button-danger-active: #750e13;--cds-button-primary-active: #002d9c;--cds-button-secondary-active: #393939;--cds-button-tertiary-active: #c6c6c6;--cds-focus-inset: #161616;--cds-focus-inverse: #0f62fe;--cds-background-hover: #353535;--cds-layer-hover: #353535;--cds-field-hover: #353535;--cds-background-inverse-hover: #e5e5e5;--cds-link-primary-hover: #a6c8ff;--cds-button-danger-hover: #b81921;--cds-button-primary-hover: #0353e9;--cds-button-secondary-hover: #606060;--cds-button-tertiary-hover: #f4f4f4;--cds-background-selected: #393939;--cds-background-selected-hover: #4c4c4c;--cds-layer-selected: #393939;--cds-layer-selected-hover: #4c4c4c;--cds-layer-selected-inverse: #f4f4f4;--cds-border-subtle-selected: #525252;--cds-border-disabled: #262626;--cds-text-disabled: #525252;--cds-button-disabled: #525252;--cds-icon-disabled: #525252;--cds-text-on-color-disabled: #8d8d8d;--cds-icon-on-color-disabled: #8d8d8d;--cds-layer-selected-disabled: #8d8d8d;--cds-skeleton-background: #353535;--cds-skeleton-element: #525252;--cds-brand-01: #0f62fe;--cds-brand-02: #6f6f6f;--cds-brand-03: #ffffff;--cds-active-01: #525252;--cds-hover-field: #353535;--cds-danger: #da1e28;--cds-caption-01-font-size: .75rem;--cds-caption-01-font-weight: 400;--cds-caption-01-line-height: 1.33333;--cds-caption-01-letter-spacing: .32px;--cds-caption-02-font-size: .875rem;--cds-caption-02-font-weight: 400;--cds-caption-02-line-height: 1.28572;--cds-caption-02-letter-spacing: .32px;--cds-label-01-font-size: .75rem;--cds-label-01-font-weight: 400;--cds-label-01-line-height: 1.33333;--cds-label-01-letter-spacing: .32px;--cds-label-02-font-size: .875rem;--cds-label-02-font-weight: 400;--cds-label-02-line-height: 1.28572;--cds-label-02-letter-spacing: .16px;--cds-helper-text-01-font-size: .75rem;--cds-helper-text-01-line-height: 1.33333;--cds-helper-text-01-letter-spacing: .32px;--cds-helper-text-02-font-size: .875rem;--cds-helper-text-02-line-height: 1.28572;--cds-helper-text-02-letter-spacing: .16px;--cds-body-short-01-font-size: .875rem;--cds-body-short-01-font-weight: 400;--cds-body-short-01-line-height: 1.28572;--cds-body-short-01-letter-spacing: .16px;--cds-body-long-01-font-size: .875rem;--cds-body-long-01-font-weight: 400;--cds-body-long-01-line-height: 1.42857;--cds-body-long-01-letter-spacing: .16px;--cds-body-short-02-font-size: 1rem;--cds-body-short-02-font-weight: 400;--cds-body-short-02-line-height: 1.375;--cds-body-short-02-letter-spacing: 0;--cds-body-long-02-font-size: 1rem;--cds-body-long-02-font-weight: 400;--cds-body-long-02-line-height: 1.5;--cds-body-long-02-letter-spacing: 0;--cds-code-01-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-01-font-size: .75rem;--cds-code-01-font-weight: 400;--cds-code-01-line-height: 1.33333;--cds-code-01-letter-spacing: .32px;--cds-code-02-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-02-font-size: .875rem;--cds-code-02-font-weight: 400;--cds-code-02-line-height: 1.42857;--cds-code-02-letter-spacing: .32px;--cds-heading-01-font-size: .875rem;--cds-heading-01-font-weight: 600;--cds-heading-01-line-height: 1.42857;--cds-heading-01-letter-spacing: .16px;--cds-productive-heading-01-font-size: .875rem;--cds-productive-heading-01-font-weight: 600;--cds-productive-heading-01-line-height: 1.28572;--cds-productive-heading-01-letter-spacing: .16px;--cds-heading-02-font-size: 1rem;--cds-heading-02-font-weight: 600;--cds-heading-02-line-height: 1.5;--cds-heading-02-letter-spacing: 0;--cds-productive-heading-02-font-size: 1rem;--cds-productive-heading-02-font-weight: 600;--cds-productive-heading-02-line-height: 1.375;--cds-productive-heading-02-letter-spacing: 0;--cds-productive-heading-03-font-size: 1.25rem;--cds-productive-heading-03-font-weight: 400;--cds-productive-heading-03-line-height: 1.4;--cds-productive-heading-03-letter-spacing: 0;--cds-productive-heading-04-font-size: 1.75rem;--cds-productive-heading-04-font-weight: 400;--cds-productive-heading-04-line-height: 1.28572;--cds-productive-heading-04-letter-spacing: 0;--cds-productive-heading-05-font-size: 2rem;--cds-productive-heading-05-font-weight: 400;--cds-productive-heading-05-line-height: 1.25;--cds-productive-heading-05-letter-spacing: 0;--cds-productive-heading-06-font-size: 2.625rem;--cds-productive-heading-06-font-weight: 300;--cds-productive-heading-06-line-height: 1.199;--cds-productive-heading-06-letter-spacing: 0;--cds-productive-heading-07-font-size: 3.375rem;--cds-productive-heading-07-font-weight: 300;--cds-productive-heading-07-line-height: 1.199;--cds-productive-heading-07-letter-spacing: 0;--cds-expressive-heading-01-font-size: .875rem;--cds-expressive-heading-01-font-weight: 600;--cds-expressive-heading-01-line-height: 1.25;--cds-expressive-heading-01-letter-spacing: .16px;--cds-expressive-heading-02-font-size: 1rem;--cds-expressive-heading-02-font-weight: 600;--cds-expressive-heading-02-line-height: 1.5;--cds-expressive-heading-02-letter-spacing: 0;--cds-expressive-heading-03-font-size: 1.25rem;--cds-expressive-heading-03-font-weight: 400;--cds-expressive-heading-03-line-height: 1.4;--cds-expressive-heading-03-letter-spacing: 0;--cds-expressive-heading-04-font-size: 1.75rem;--cds-expressive-heading-04-font-weight: 400;--cds-expressive-heading-04-line-height: 1.28572;--cds-expressive-heading-04-letter-spacing: 0;--cds-expressive-heading-05-font-size: 2rem;--cds-expressive-heading-05-font-weight: 400;--cds-expressive-heading-05-line-height: 1.25;--cds-expressive-heading-05-letter-spacing: 0;--cds-expressive-heading-06-font-size: 2rem;--cds-expressive-heading-06-font-weight: 600;--cds-expressive-heading-06-line-height: 1.25;--cds-expressive-heading-06-letter-spacing: 0;--cds-expressive-paragraph-01-font-size: 1.5rem;--cds-expressive-paragraph-01-font-weight: 300;--cds-expressive-paragraph-01-line-height: 1.334;--cds-expressive-paragraph-01-letter-spacing: 0;--cds-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-01-font-size: 1.25rem;--cds-quotation-01-font-weight: 400;--cds-quotation-01-line-height: 1.3;--cds-quotation-01-letter-spacing: 0;--cds-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-02-font-size: 2rem;--cds-quotation-02-font-weight: 300;--cds-quotation-02-line-height: 1.25;--cds-quotation-02-letter-spacing: 0;--cds-display-01-font-size: 2.625rem;--cds-display-01-font-weight: 300;--cds-display-01-line-height: 1.19;--cds-display-01-letter-spacing: 0;--cds-display-02-font-size: 2.625rem;--cds-display-02-font-weight: 600;--cds-display-02-line-height: 1.19;--cds-display-02-letter-spacing: 0;--cds-display-03-font-size: 2.625rem;--cds-display-03-font-weight: 300;--cds-display-03-line-height: 1.19;--cds-display-03-letter-spacing: 0;--cds-display-04-font-size: 2.625rem;--cds-display-04-font-weight: 600;--cds-display-04-line-height: 1.19;--cds-display-04-letter-spacing: 0;--cds-legal-01-font-size: .75rem;--cds-legal-01-font-weight: 400;--cds-legal-01-line-height: 1.33333;--cds-legal-01-letter-spacing: .32px;--cds-legal-02-font-size: .875rem;--cds-legal-02-font-weight: 400;--cds-legal-02-line-height: 1.28572;--cds-legal-02-letter-spacing: .16px;--cds-body-compact-01-font-size: .875rem;--cds-body-compact-01-font-weight: 400;--cds-body-compact-01-line-height: 1.28572;--cds-body-compact-01-letter-spacing: .16px;--cds-body-compact-02-font-size: 1rem;--cds-body-compact-02-font-weight: 400;--cds-body-compact-02-line-height: 1.375;--cds-body-compact-02-letter-spacing: 0;--cds-body-01-font-size: .875rem;--cds-body-01-font-weight: 400;--cds-body-01-line-height: 1.42857;--cds-body-01-letter-spacing: .16px;--cds-body-02-font-size: 1rem;--cds-body-02-font-weight: 400;--cds-body-02-line-height: 1.5;--cds-body-02-letter-spacing: 0;--cds-heading-compact-01-font-size: .875rem;--cds-heading-compact-01-font-weight: 600;--cds-heading-compact-01-line-height: 1.28572;--cds-heading-compact-01-letter-spacing: .16px;--cds-heading-compact-02-font-size: 1rem;--cds-heading-compact-02-font-weight: 600;--cds-heading-compact-02-line-height: 1.375;--cds-heading-compact-02-letter-spacing: 0;--cds-heading-03-font-size: 1.25rem;--cds-heading-03-font-weight: 400;--cds-heading-03-line-height: 1.4;--cds-heading-03-letter-spacing: 0;--cds-heading-04-font-size: 1.75rem;--cds-heading-04-font-weight: 400;--cds-heading-04-line-height: 1.28572;--cds-heading-04-letter-spacing: 0;--cds-heading-05-font-size: 2rem;--cds-heading-05-font-weight: 400;--cds-heading-05-line-height: 1.25;--cds-heading-05-letter-spacing: 0;--cds-heading-06-font-size: 2.625rem;--cds-heading-06-font-weight: 300;--cds-heading-06-line-height: 1.199;--cds-heading-06-letter-spacing: 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.bx--btn__icon{fill:ButtonText}}.bx--fieldset{margin-bottom:2rem}.bx--fieldset--no-margin{margin-bottom:0}.bx--form-item{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);display:flex;flex:1 1 auto;flex-direction:column;align-items:flex-start}.bx--label{font-size:var(--cds-label-01-font-size, .75rem);font-weight:var(--cds-label-01-font-weight, 400);line-height:var(--cds-label-01-line-height, 1.33333);letter-spacing:var(--cds-label-01-letter-spacing, .32px);display:inline-block;margin-bottom:.5rem;color:var(--cds-text-02, #525252);font-weight:400;line-height:1rem;vertical-align:baseline}.bx--label .bx--tooltip__trigger{font-size:var(--cds-label-01-font-size, .75rem);font-weight:var(--cds-label-01-font-weight, 400);line-height:var(--cds-label-01-line-height, 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pre{white-space:pre-wrap;word-wrap:break-word}.bx--snippet--multi .bx--snippet-container pre{padding-right:2.5rem;padding-bottom:1.5rem;overflow-x:auto}.bx--snippet--multi.bx--snippet--no-copy .bx--snippet-container pre{padding-right:0}.bx--snippet--multi.bx--snippet--expand .bx--snippet-container pre{overflow-x:auto}.bx--snippet--multi .bx--snippet-container pre:after{position:absolute;top:0;right:0;width:1rem;height:100%;background-image:linear-gradient(to right,rgba(var(--cds-field-01, #f4f4f4),0),var(--cds-field-01, #f4f4f4));content:""}.bx--snippet--multi .bx--snippet-container pre code{overflow:hidden}.bx--snippet__icon{width:1rem;height:1rem;fill:var(--cds-icon-01, #161616);transition:all 70ms cubic-bezier(.2,0,.38,.9)}.bx--snippet-button{position:absolute;top:0;right:0;display:flex;overflow:visible;width:2.5rem;height:2.5rem;align-items:center;justify-content:center;padding:0;border:none;background-color:var(--cds-field-01, #f4f4f4);cursor:pointer;outline:none}.bx--snippet-button:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px;outline-color:var(--cds-focus, #0f62fe)}@media screen and (prefers-contrast){.bx--snippet-button:focus{outline-style:dotted}}.bx--snippet--multi .bx--snippet-button{top:var(--cds-spacing-03, .5rem);right:var(--cds-spacing-03, .5rem);width:2rem;height:2rem}.bx--snippet-button:hover{background:var(--cds-hover-ui, #e5e5e5)}.bx--snippet-button:active{background-color:var(--cds-active-ui, #c6c6c6)}.bx--btn--copy__feedback{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);font-family:IBM Plex Sans,Helvetica Neue,Arial,sans-serif;z-index:6000;top:.75rem;right:1.25rem;left:inherit;font-weight:400}.bx--btn--copy__feedback:before,.bx--btn--copy__feedback:after{background:var(--cds-inverse-02, #393939)}.bx--btn--copy__feedback:after{border:none}.bx--snippet .bx--copy-btn{position:absolute;top:0;right:0;font-family:IBM Plex Sans,Helvetica Neue,Arial,sans-serif}.bx--snippet-btn--expand{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);font-family:IBM Plex Sans,Helvetica Neue,Arial,sans-serif;position:absolute;z-index:10;right:0;bottom:0;display:inline-flex;align-items:center;padding:var(--cds-spacing-03, .5rem) var(--cds-spacing-05, 1rem);border:0;background-color:var(--cds-field-01, #f4f4f4);color:var(--cds-text-01, #161616)}.bx--snippet-btn--expand .bx--snippet-btn--text{position:relative;top:-.0625rem}.bx--snippet-btn--expand--hide.bx--snippet-btn--expand{display:none}.bx--snippet-btn--expand .bx--icon-chevron--down{margin-left:var(--cds-spacing-03, .5rem);fill:var(--cds-icon-primary, #161616);transform:rotate(0);transition:.15s cubic-bezier(.2,0,.38,.9)}.bx--snippet-btn--expand:hover{background:var(--cds-hover-ui, #e5e5e5);color:var(--cds-text-01, #161616)}.bx--snippet-btn--expand:active{background-color:var(--cds-active-ui, #c6c6c6)}.bx--snippet-btn--expand:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px;border-color:#0000}@media screen and (prefers-contrast){.bx--snippet-btn--expand:focus{outline-style:dotted}}.bx--snippet--expand .bx--snippet-btn--expand .bx--icon-chevron--down{transform:rotate(180deg);transition:transform .24s}.bx--snippet--light,.bx--snippet--light .bx--snippet-button,.bx--snippet--light .bx--btn.bx--snippet-btn--expand,.bx--snippet--light .bx--copy-btn{background-color:var(--cds-field-02, #ffffff)}.bx--snippet--light.bx--snippet--inline:hover,.bx--snippet--light .bx--snippet-button:hover,.bx--snippet--light .bx--btn.bx--snippet-btn--expand:hover,.bx--snippet--light .bx--copy-btn:hover{background-color:var(--cds-hover-light-ui, #e5e5e5)}.bx--snippet--light.bx--snippet--inline:active,.bx--snippet--light .bx--snippet-button:active,.bx--snippet--light .bx--btn.bx--snippet-btn--expand:active,.bx--snippet--light .bx--copy-btn:active{background-color:var(--cds-active-light-ui, #c6c6c6)}.bx--snippet--light.bx--snippet--single:after,.bx--snippet--light.bx--snippet--multi .bx--snippet-container pre:after{background-image:linear-gradient(to right,rgba(var(--cds-field-02, #ffffff),0),var(--cds-field-02, #ffffff))}.bx--snippet.bx--skeleton .bx--snippet-container{width:100%;height:100%}.bx--snippet-button .bx--btn--copy__feedback{top:3.175rem;right:auto;left:50%}.bx--snippet-button .bx--btn--copy__feedback:before{top:0}.bx--snippet-button .bx--btn--copy__feedback:after{top:-.25rem}.bx--snippet--multi .bx--copy-btn{z-index:10;top:.5rem;right:.5rem;width:2rem;height:2rem}.bx--snippet--multi .bx--snippet-button .bx--btn--copy__feedback{top:2.675rem}.bx--snippet--inline .bx--btn--copy__feedback{top:calc(100% - .25rem);right:auto;left:50%}.bx--snippet__overflow-indicator--left,.bx--snippet__overflow-indicator--right{z-index:1;width:1rem;flex:1 0 auto}.bx--snippet__overflow-indicator--left{order:0;margin-right:-1rem;background-image:linear-gradient(to left,transparent,var(--cds-field-01, #f4f4f4))}.bx--snippet__overflow-indicator--right{order:2;margin-left:-1rem;background-image:linear-gradient(to right,transparent,var(--cds-field-01, #f4f4f4))}.bx--snippet--single .bx--snippet__overflow-indicator--right,.bx--snippet--single .bx--snippet__overflow-indicator--left{position:absolute;width:2rem;height:calc(100% - .25rem)}.bx--snippet--single .bx--snippet__overflow-indicator--right{right:2.5rem}.bx--snippet--single.bx--snippet--no-copy .bx--snippet__overflow-indicator--right{right:0}.bx--snippet--single .bx--snippet-container:focus~.bx--snippet__overflow-indicator--right{right:2.625rem}.bx--snippet--single .bx--snippet-container:focus+.bx--snippet__overflow-indicator--left{left:.125rem}.bx--snippet--light .bx--snippet__overflow-indicator--left{background-image:linear-gradient(to left,transparent,var(--cds-field-02, #ffffff))}.bx--snippet--light .bx--snippet__overflow-indicator--right{background-image:linear-gradient(to right,transparent,var(--cds-field-02, #ffffff))}@media not all and (-webkit-min-device-pixel-ratio: 0),not all and (min-resolution: .001dpcm){@supports (-webkit-appearance: none) and (stroke-color: transparent){.bx--snippet__overflow-indicator--left{background-image:linear-gradient(to left,rgba(var(--cds-field-01, #f4f4f4),0),var(--cds-field-01, #f4f4f4))}.bx--snippet__overflow-indicator--right{background-image:linear-gradient(to right,rgba(var(--cds-field-01, #f4f4f4),0),var(--cds-field-01, #f4f4f4))}}}.bx--snippet--multi.bx--skeleton{height:6.125rem}.bx--snippet--single.bx--skeleton{height:3.5rem}.bx--snippet.bx--skeleton span{position:relative;padding:0;border:none;background:var(--cds-skeleton-01, #e5e5e5);box-shadow:none;pointer-events:none;display:block;width:100%;height:1rem;margin-top:.5rem}.bx--snippet.bx--skeleton span:hover,.bx--snippet.bx--skeleton span:focus,.bx--snippet.bx--skeleton span:active{border:none;cursor:default;outline:none}.bx--snippet.bx--skeleton span:before{position:absolute;top:0;left:0;width:100%;height:100%;animation:3s ease-in-out skeleton infinite;background:var(--cds-skeleton-02, #c6c6c6);content:"";will-change:transform-origin,transform,opacity}@media (prefers-reduced-motion: reduce){.bx--snippet.bx--skeleton span:before{animation:none}}.bx--snippet.bx--skeleton span:first-child{margin:0}.bx--snippet.bx--skeleton span:nth-child(2){width:85%}.bx--snippet.bx--skeleton span:nth-child(3){width:95%}.bx--snippet--single.bx--skeleton .bx--snippet-container{padding-bottom:0}@media screen and (-ms-high-contrast: active),(forced-colors: active),(prefers-contrast){.bx--snippet__icon{fill:ButtonText}.bx--snippet--inline:focus{color:Highlight;outline:1px solid Highlight}.bx--snippet--single,.bx--snippet--multi{outline:1px solid rgba(0,0,0,0)}}.bx--list-box__wrapper--inline{display:inline-grid;align-items:center;grid-gap:.25rem;grid-template:auto auto/auto auto}.bx--list-box__wrapper--inline .bx--label{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px)}.bx--list-box__wrapper--inline .bx--label,.bx--list-box__wrapper--inline .bx--form__helper-text,.bx--list-box__wrapper--inline .bx--form-requirement{margin:0}.bx--list-box__wrapper--inline .bx--form__helper-text{max-width:none}.bx--list-box__wrapper--inline .bx--form-requirement{grid-column:2}.bx--list-box{position:relative;width:100%;height:2.5rem;max-height:2.5rem;border:none;border-bottom:1px solid var(--cds-ui-04, #8d8d8d);background-color:var(--cds-field-01, #f4f4f4);color:var(--cds-text-01, #161616);cursor:pointer;transition:all 70ms cubic-bezier(.2,0,.38,.9)}.bx--list-box:hover{background-color:var(--cds-hover-ui, #e5e5e5)}.bx--list-box--xl,.bx--list-box--lg{height:3rem;max-height:3rem}.bx--list-box--sm{height:2rem;max-height:2rem}.bx--list-box--expanded{border-bottom-color:var(--cds-ui-03, #e0e0e0)}.bx--list-box--expanded:hover{background-color:var(--cds-field-01, #f4f4f4)}.bx--list-box--expanded:hover.bx--list-box--light:hover{background-color:var(--cds-field-02, #ffffff)}.bx--list-box .bx--text-input{min-width:0;height:100%}.bx--list-box__invalid-icon{position:absolute;top:50%;right:2.5rem;fill:var(--cds-support-01, #da1e28);transform:translateY(-50%)}.bx--list-box__invalid-icon--warning{fill:var(--cds-support-03, #f1c21b)}.bx--list-box__invalid-icon--warning path[fill]{fill:#000;opacity:1}.bx--list-box[data-invalid] .bx--list-box__field,.bx--list-box.bx--list-box--warning .bx--list-box__field{padding-right:4rem;border-bottom:0}.bx--list-box[data-invalid].bx--list-box--inline .bx--list-box__field{padding-right:3.5rem}.bx--list-box--light{background-color:var(--cds-field-02, #ffffff)}.bx--list-box--light:hover{background-color:var(--cds-hover-light-ui, #e5e5e5)}.bx--list-box--light .bx--list-box__menu{background:var(--cds-field-02, #ffffff)}.bx--list-box--light .bx--list-box__menu-item__option{border-top-color:var(--cds-decorative-01, #e0e0e0)}.bx--list-box--light.bx--list-box--expanded{border-bottom-color:#0000}.bx--list-box--disabled:hover{background-color:var(--cds-field-01, #f4f4f4)}.bx--list-box--light.bx--list-box--disabled{background-color:var(--cds-field-02, #ffffff)}.bx--list-box--disabled,.bx--list-box--disabled .bx--list-box__field,.bx--list-box--disabled .bx--list-box__field:focus{border-bottom-color:#0000;outline:none}.bx--list-box--disabled .bx--list-box__label,.bx--list-box--disabled.bx--list-box--inline .bx--list-box__label{color:var(--cds-disabled-02, #c6c6c6)}.bx--list-box--disabled .bx--list-box__menu-icon>svg,.bx--list-box--disabled .bx--list-box__selection>svg{fill:var(--cds-disabled-02, #c6c6c6)}.bx--list-box--disabled,.bx--list-box--disabled .bx--list-box__field,.bx--list-box--disabled .bx--list-box__menu-icon{cursor:not-allowed}.bx--list-box--disabled .bx--list-box__menu-item,.bx--list-box--disabled .bx--list-box__menu-item:hover,.bx--list-box--disabled .bx--list-box__menu-item--highlighted{color:var(--cds-disabled-02, #c6c6c6);text-decoration:none}.bx--list-box--disabled .bx--list-box__selection:hover{cursor:not-allowed}.bx--list-box--disabled.bx--list-box[data-invalid] .bx--list-box__field{padding-right:3rem}.bx--list-box--disabled.bx--list-box[data-invalid].bx--list-box--inline .bx--list-box__field{padding-right:2rem}.bx--list-box.bx--list-box--inline{border-width:0;background-color:#0000}.bx--list-box.bx--list-box--inline:hover{background-color:var(--cds-hover-ui, #e5e5e5)}.bx--list-box.bx--list-box--inline.bx--list-box--expanded{border-bottom-width:0}.bx--list-box.bx--list-box--inline.bx--list-box--expanded .bx--list-box__field[aria-expanded=true]{border-width:0}.bx--list-box.bx--list-box--inline.bx--list-box--disabled:hover{background-color:#0000}.bx--list-box.bx--list-box--inline.bx--list-box--expanded:hover{background-color:var(--cds-field-02, #ffffff)}.bx--list-box.bx--list-box--inline .bx--list-box__field{padding:0 2rem 0 .5rem}.bx--list-box.bx--list-box--inline .bx--list-box__menu-icon{right:.5rem}.bx--list-box.bx--list-box--inline .bx--list-box__invalid-icon{right:2rem}.bx--list-box--inline .bx--list-box__label{color:var(--cds-text-01, #161616)}.bx--list-box--inline .bx--list-box__field{height:100%}.bx--dropdown--inline .bx--list-box__field{max-width:30rem}.bx--dropdown--inline .bx--list-box__menu{min-width:18rem;max-width:30rem}.bx--list-box__field{display:inline-block;padding:0;border:0;-webkit-appearance:none;-moz-appearance:none;appearance:none;background:none;width:100%;position:relative;display:inline-flex;overflow:hidden;height:calc(100% + 1px);align-items:center;padding:0 3rem 0 1rem;cursor:pointer;outline:none;text-overflow:ellipsis;vertical-align:top;white-space:nowrap}.bx--list-box__field::-moz-focus-inner{border:0}.bx--list-box__field:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--list-box__field:focus{outline-style:dotted}}.bx--list-box__field[disabled]{color:var(--cds-disabled-02, #c6c6c6);outline:none}.bx--list-box__field .bx--text-input{padding-right:4.5rem}.bx--list-box[data-invalid] .bx--list-box__field .bx--text-input,.bx--list-box--warning .bx--list-box__field .bx--text-input{padding-right:6.125rem}.bx--list-box[data-invalid] .bx--list-box__field .bx--text-input+.bx--list-box__invalid-icon,.bx--list-box--warning .bx--list-box__field .bx--text-input+.bx--list-box__invalid-icon{right:4.125rem}.bx--list-box__field .bx--text-input--empty{padding-right:3rem}.bx--list-box[data-invalid] .bx--list-box__field .bx--text-input--empty,.bx--list-box--warning .bx--list-box__field .bx--text-input--empty{padding-right:4.5rem}.bx--list-box[data-invalid] .bx--list-box__field .bx--text-input--empty+.bx--list-box__invalid-icon,.bx--list-box--warning .bx--list-box__field .bx--text-input--empty+.bx--list-box__invalid-icon{right:2.5rem}.bx--list-box__label{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);overflow:hidden;color:var(--cds-text-01, #161616);text-overflow:ellipsis;-webkit-user-select:none;-moz-user-select:none;user-select:none;white-space:nowrap}.bx--list-box__menu-icon{display:inline-block;padding:0;border:0;-webkit-appearance:none;-moz-appearance:none;appearance:none;background:none;position:absolute;right:1rem;display:flex;width:1.5rem;height:1.5rem;align-items:center;justify-content:center;cursor:pointer;outline:none;transition:transform 70ms cubic-bezier(.2,0,.38,.9)}.bx--list-box__menu-icon::-moz-focus-inner{border:0}.bx--list-box__menu-icon>svg{fill:var(--cds-icon-01, #161616)}.bx--list-box__menu-icon--open{width:1.5rem;justify-content:center;transform:rotate(180deg)}.bx--list-box__selection{display:inline-block;padding:0;border:0;-webkit-appearance:none;-moz-appearance:none;appearance:none;background:none;position:absolute;top:50%;right:2.5rem;display:flex;width:1.5rem;height:1.5rem;align-items:center;justify-content:center;cursor:pointer;transform:translateY(-50%);transition:background-color 70ms cubic-bezier(.2,0,.38,.9);-webkit-user-select:none;-moz-user-select:none;user-select:none}.bx--list-box__selection::-moz-focus-inner{border:0}.bx--list-box__selection:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--list-box__selection:focus{outline-style:dotted}}.bx--list-box__selection:focus:hover{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--list-box__selection:focus:hover{outline-style:dotted}}.bx--list-box__selection>svg{fill:var(--cds-icon-01, #161616)}.bx--list-box--disabled .bx--list-box__selection:focus{outline:none}.bx--list-box__selection--multi{font-size:var(--cds-label-01-font-size, .75rem);font-weight:var(--cds-label-01-font-weight, 400);line-height:var(--cds-label-01-line-height, 1.33333);letter-spacing:var(--cds-label-01-letter-spacing, 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#c6c6c6);outline:none;text-decoration:none}.bx--file-browse-btn--disabled .bx--file__drop-container{border:1px dashed var(--cds-disabled-02, #c6c6c6)}.bx--label-description{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);margin-bottom:1rem;color:var(--cds-text-02, #525252)}.bx--label-description--disabled{color:var(--cds-disabled-02, #c6c6c6)}.bx--file-btn~.bx--file-container{margin-top:1.5rem}.bx--btn~.bx--file-container{margin-top:1rem}.bx--file .bx--file-container,.bx--file~.bx--file-container{margin-top:.5rem}.bx--file__selected-file{display:grid;max-width:20rem;min-height:3rem;align-items:center;margin-bottom:.5rem;background-color:var(--cds-field-01, #f4f4f4);gap:.75rem 1rem;grid-auto-rows:auto;grid-template-columns:1fr auto;word-break:break-word}.bx--file__selected-file:last-child{margin-bottom:0}.bx--file__selected-file .bx--form-requirement{display:block;max-height:none;margin:0;grid-column:1/-1}.bx--file__selected-file .bx--inline-loading__animation .bx--loading{margin-right:0}.bx--file__selected-file .bx--file-filename{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);overflow:hidden;margin-left:1rem;text-overflow:ellipsis;white-space:nowrap}.bx--file__selected-file--field,.bx--file__selected-file--md{min-height:2.5rem;gap:.5rem 1rem}.bx--file__selected-file--sm{min-height:2rem;gap:.25rem 1rem}.bx--file__selected-file--invalid__wrapper{outline:2px solid var(--cds-support-01, #da1e28);outline-offset:-2px;max-width:20rem;margin-bottom:.5rem;background-color:var(--cds-field-01, #f4f4f4);outline-width:1px}@media screen and (prefers-contrast){.bx--file__selected-file--invalid__wrapper{outline-style:dotted}}.bx--file__selected-file--invalid{outline:2px solid var(--cds-support-01, #da1e28);outline-offset:-2px;padding:.75rem 0}@media screen and (prefers-contrast){.bx--file__selected-file--invalid{outline-style:dotted}}.bx--file__selected-file--invalid.bx--file__selected-file--sm{padding:.25rem 0}.bx--file__selected-file--invalid.bx--file__selected-file--field,.bx--file__selected-file--invalid.bx--file__selected-file--md{padding:.5rem 0}.bx--file__selected-file--invalid .bx--form-requirement{padding-top:1rem;border-top:1px solid var(--cds-ui-03, #e0e0e0)}.bx--file__selected-file--invalid.bx--file__selected-file--sm .bx--form-requirement{padding-top:.4375rem}.bx--file__selected-file--invalid.bx--file__selected-file--field .bx--form-requirement,.bx--file__selected-file--invalid.bx--file__selected-file--md .bx--form-requirement{padding-top:.6875rem}.bx--file__selected-file--invalid .bx--form-requirement__title,.bx--file__selected-file--invalid .bx--form-requirement__supplement{font-size:var(--cds-label-01-font-size, .75rem);font-weight:var(--cds-label-01-font-weight, 400);line-height:var(--cds-label-01-line-height, 1.33333);letter-spacing:var(--cds-label-01-letter-spacing, .32px);padding:0 1rem}.bx--file__selected-file--invalid .bx--form-requirement__title{color:var(--cds-text-error, #da1e28)}.bx--file__selected-file--invalid .bx--form-requirement__supplement{color:var(--cds-text-01, #161616)}.bx--file__selected-file--invalid+.bx--form-requirement{font-size:var(--cds-caption-01-font-size, .75rem);font-weight:var(--cds-caption-01-font-weight, 400);line-height:var(--cds-caption-01-line-height, 1.33333);letter-spacing:var(--cds-caption-01-letter-spacing, .32px);display:block;overflow:visible;max-height:12.5rem;padding:.5rem 1rem;color:var(--cds-text-error, #da1e28);font-weight:400}.bx--file__selected-file--invalid+.bx--form-requirement .bx--form-requirement__supplement{padding-bottom:.5rem;color:var(--cds-text-01, #161616)}.bx--file__state-container{display:flex;min-width:1.5rem;align-items:center;justify-content:center;padding-right:1rem}.bx--file__state-container .bx--loading__svg{stroke:var(--cds-ui-05, #161616)}.bx--file__state-container .bx--file-complete{cursor:pointer;fill:var(--cds-interactive-04, #0f62fe)}.bx--file__state-container .bx--file-complete:focus{outline:1px solid var(--cds-focus, #0f62fe)}@media screen and (prefers-contrast){.bx--file__state-container .bx--file-complete:focus{outline-style:dotted}}.bx--file__state-container .bx--file-complete [data-icon-path=inner-path]{fill:var(--cds-icon-03, #ffffff);opacity:1}.bx--file__state-container .bx--file-invalid{width:1rem;height:1rem;fill:var(--cds-support-01, #da1e28)}.bx--file__state-container .bx--file-close{display:flex;width:1.5rem;height:1.5rem;align-items:center;justify-content:center;padding:0;border:none;background-color:#0000;cursor:pointer;fill:var(--cds-icon-01, #161616)}.bx--file__state-container .bx--file-close:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--file__state-container .bx--file-close:focus{outline-style:dotted}}.bx--file__state-container .bx--file-close svg path{fill:var(--cds-icon-01, #161616)}.bx--file__state-container .bx--inline-loading__animation{margin-right:-.5rem}.bx--file__drop-container{display:flex;overflow:hidden;height:6rem;align-items:flex-start;justify-content:space-between;padding:1rem;border:1px dashed var(--cds-ui-04, #8d8d8d)}.bx--file__drop-container--drag-over{background:none;outline:2px solid var(--cds-interactive-03, #0f62fe);outline-offset:-2px}@media screen and (-ms-high-contrast: active),(forced-colors: active),(prefers-contrast){.bx--file__selected-file{outline:1px solid rgba(0,0,0,0)}.bx--file__state-container .bx--file-close svg path{fill:ButtonText}}@keyframes stroke{to{stroke-dashoffset:0}}.bx--inline-loading{display:flex;width:100%;min-height:2rem;align-items:center}.bx--inline-loading__text{font-size:var(--cds-label-01-font-size, .75rem);font-weight:var(--cds-label-01-font-weight, 400);line-height:var(--cds-label-01-line-height, 1.33333);letter-spacing:var(--cds-label-01-letter-spacing, .32px);color:var(--cds-text-02, #525252)}.bx--inline-loading__animation{position:relative;display:flex;align-items:center;justify-content:center;margin-right:.5rem}.bx--inline-loading__checkmark-container{fill:var(--cds-support-02, #198038)}.bx--inline-loading__checkmark-container.bx--inline-loading__svg{position:absolute;top:.75rem;width:.75rem}.bx--inline-loading__checkmark-container[hidden]{display:none}.bx--inline-loading__checkmark{animation-duration:.25s;animation-fill-mode:forwards;animation-name:stroke;fill:none;stroke:var(--cds-interactive-04, #0f62fe);stroke-dasharray:12;stroke-dashoffset:12;stroke-width:1.8;transform-origin:50% 50%}.bx--inline-loading--error{width:1rem;height:1rem;fill:var(--cds-support-01, #da1e28)}.bx--inline-loading--error[hidden]{display:none}.bx--loading--small .bx--inline-loading__svg{stroke:var(--cds-interactive-04, #0f62fe)}@media screen and (-ms-high-contrast: active),screen and (-ms-high-contrast: none){.bx--inline-loading__checkmark-container{top:1px;right:.5rem}.bx--inline-loading__checkmark{animation:none;stroke-dasharray:0;stroke-dashoffset:0}}.bx--list--nested,.bx--list--unordered,.bx--list--ordered,.bx--list--ordered--native{font-size:var(--cds-body-long-01-font-size, .875rem);font-weight:var(--cds-body-long-01-font-weight, 400);line-height:var(--cds-body-long-01-line-height, 1.42857);letter-spacing:var(--cds-body-long-01-letter-spacing, .16px);list-style:none}.bx--list--expressive,.bx--list--expressive .bx--list--nested{font-size:var(--cds-body-long-02-font-size, 1rem);font-weight:var(--cds-body-long-02-font-weight, 400);line-height:var(--cds-body-long-02-line-height, 1.5);letter-spacing:var(--cds-body-long-02-letter-spacing, 0)}.bx--list--ordered--native{list-style:decimal}.bx--list__item{color:var(--cds-text-01, #161616)}.bx--list--nested{margin-left:2rem}.bx--list--nested .bx--list__item{padding-left:.25rem}.bx--list--ordered:not(.bx--list--nested){counter-reset:item}.bx--list--ordered:not(.bx--list--nested)>.bx--list__item{position:relative}.bx--list--ordered:not(.bx--list--nested)>.bx--list__item:before{position:absolute;left:-1.5rem;content:counter(item) ".";counter-increment:item}.bx--list--ordered.bx--list--nested,.bx--list--ordered--native.bx--list--nested{list-style-type:lower-latin}.bx--list--unordered>.bx--list__item{position:relative}.bx--list--unordered>.bx--list__item:before{position:absolute;left:-1rem;content:"–"}.bx--list--unordered.bx--list--nested>.bx--list__item:before{left:-.75rem;content:"▪"}@keyframes rotate{0%{transform:rotate(0)}to{transform:rotate(360deg)}}@keyframes rotate-end-p1{to{transform:rotate(360deg)}}@keyframes rotate-end-p2{to{transform:rotate(-360deg)}}@keyframes init-stroke{0%{stroke-dashoffset:276.4608}to{stroke-dashoffset:52.527552}}@keyframes stroke-end{0%{stroke-dashoffset:52.527552}to{stroke-dashoffset:276.4608}}.bx--menu{box-shadow:0 2px 6px var(--cds-shadow, rgba(0, 0, 0, .3));position:fixed;z-index:9000;min-width:13rem;max-width:18rem;padding:var(--cds-spacing-02, .25rem) 0;background-color:var(--cds-layer, #f4f4f4);visibility:hidden}.bx--menu--open{visibility:visible}.bx--menu--open:focus{outline:1px solid var(--cds-focus, #0f62fe)}@media screen and (prefers-contrast){.bx--menu--open:focus{outline-style:dotted}}.bx--menu--invisible{opacity:0;pointer-events:none}.bx--menu-option{position:relative;height:2rem;background-color:var(--cds-layer, #f4f4f4);color:var(--cds-text-primary, #161616);cursor:pointer;transition:background-color 70ms cubic-bezier(.2,0,.38,.9)}.bx--menu-option:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--menu-option:focus{outline-style:dotted}}.bx--menu-option--active,.bx--menu-option:hover{background-color:var(--cds-layer-hover, #e5e5e5)}.bx--menu-option--danger:hover,.bx--menu-option--danger:focus{background-color:var(--cds-button-danger-primary, #da1e28);color:var(--cds-text-on-color, #ffffff)}.bx--menu-option>.bx--menu{margin-top:calc(var(--cds-spacing-02, .25rem) * -1)}.bx--menu-option__content{display:flex;height:100%;align-items:center;justify-content:space-between;padding:0 var(--cds-spacing-05, 1rem)}.bx--menu-option__content--disabled{background-color:var(--cds-layer, #f4f4f4);color:var(--cds-text-disabled, #c6c6c6);cursor:not-allowed}.bx--menu-option__content--disabled .bx--menu-option__label,.bx--menu-option__content--disabled .bx--menu-option__info,.bx--menu-option__content--disabled .bx--menu-option__icon{color:var(--cds-text-disabled, #c6c6c6)}.bx--menu-option__content--indented .bx--menu-option__label{margin-left:var(--cds-spacing-05, 1rem)}.bx--menu-option__label{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);overflow:hidden;flex-grow:1;padding:var(--cds-spacing-02, .25rem) 0;text-align:start;text-overflow:ellipsis;white-space:nowrap}.bx--menu-option__info{display:inline-flex;margin-left:var(--cds-spacing-05, 1rem)}.bx--menu-option__icon{display:flex;width:1rem;height:1rem;align-items:center;margin-right:var(--cds-spacing-03, .5rem)}.bx--menu-divider{width:100%;height:1px;margin:var(--cds-spacing-02, .25rem) 0;background-color:var(--cds-border-subtle, #e0e0e0)}.bx--menu--md .bx--menu-option{height:2.5rem}.bx--menu--lg .bx--menu-option{height:3rem}.bx--modal{position:fixed;z-index:9000;top:0;left:0;display:flex;width:100vw;height:100vh;align-items:center;justify-content:center;background-color:var(--cds-overlay-01, rgba(22, 22, 22, .5));content:"";opacity:0;transition:opacity .24s cubic-bezier(.4,.14,1,1),visibility 0ms linear .24s;visibility:hidden}.bx--modal.is-visible{opacity:1;transition:opacity .24s cubic-bezier(0,0,.3,1),visibility 0ms linear;visibility:inherit}@media screen and (prefers-reduced-motion: reduce){.bx--modal.is-visible{transition:none}}.bx--modal .bx--pagination,.bx--modal .bx--pagination__control-buttons,.bx--modal .bx--text-input,.bx--modal .bx--text-area,.bx--modal .bx--search-input,.bx--modal .bx--select-input,.bx--modal .bx--dropdown,.bx--modal .bx--dropdown-list,.bx--modal .bx--number input[type=number],.bx--modal .bx--date-picker__input,.bx--modal .bx--multi-select{background-color:var(--cds-field-02, #ffffff)}.bx--modal.is-visible .bx--modal-container{transform:translateZ(0);transition:transform .24s cubic-bezier(0,0,.3,1)}.bx--modal-container{position:fixed;top:0;display:grid;overflow:hidden;width:100%;height:100%;max-height:100%;background-color:var(--cds-ui-01, #f4f4f4);grid-template-columns:100%;grid-template-rows:auto 1fr auto;outline:3px solid rgba(0,0,0,0);outline-offset:-3px;transform:translate3d(0,-24px,0);transform-origin:top center;transition:transform .24s cubic-bezier(.4,.14,1,1)}@media (min-width: 42rem){.bx--modal-container{position:static;width:84%;height:auto;max-height:90%}}@media (min-width: 66rem){.bx--modal-container{width:60%;max-height:84%}}@media (min-width: 82rem){.bx--modal-container{width:48%}}.bx--modal-content{font-size:var(--cds-body-long-01-font-size, .875rem);font-weight:var(--cds-body-long-01-font-weight, 400);line-height:var(--cds-body-long-01-line-height, 1.42857);letter-spacing:var(--cds-body-long-01-letter-spacing, .16px);position:relative;padding-top:var(--cds-spacing-03, .5rem);padding-right:var(--cds-spacing-05, 1rem);padding-left:var(--cds-spacing-05, 1rem);margin-bottom:var(--cds-spacing-09, 3rem);color:var(--cds-text-01, #161616);font-weight:400;grid-column:1/-1;grid-row:2/-2;overflow-y:auto}.bx--modal-content:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--modal-content:focus{outline-style:dotted}}.bx--modal-content p,.bx--modal-content__regular-content{padding-right:20%;font-size:var(--cds-body-long-01-font-size, .875rem);font-weight:var(--cds-body-long-01-font-weight, 400);line-height:var(--cds-body-long-01-line-height, 1.42857);letter-spacing:var(--cds-body-long-01-letter-spacing, .16px)}.bx--modal-content--with-form{padding-right:var(--cds-spacing-05, 1rem)}.bx--modal-header{padding-top:var(--cds-spacing-05, 1rem);padding-right:var(--cds-spacing-09, 3rem);padding-left:var(--cds-spacing-05, 1rem);margin-bottom:var(--cds-spacing-03, .5rem);grid-column:1/-1;grid-row:1/1}.bx--modal-header__label{font-size:var(--cds-label-01-font-size, .75rem);font-weight:var(--cds-label-01-font-weight, 400);line-height:var(--cds-label-01-line-height, 1.33333);letter-spacing:var(--cds-label-01-letter-spacing, .32px);margin-bottom:var(--cds-spacing-02, .25rem);color:var(--cds-text-02, #525252)}.bx--modal-header__heading{font-size:var(--cds-productive-heading-03-font-size, 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.bx--tile-content__below-the-fold{opacity:1;transition:opacity .11s cubic-bezier(.2,0,.38,.9),visibility .11s cubic-bezier(.2,0,.38,.9);visibility:inherit}@media not all and (-webkit-min-device-pixel-ratio: 0),not all and (min-resolution: .001dpcm){@supports (-webkit-appearance: none) and (stroke-color: transparent){.bx--tile--is-expanded .bx--tile-content__below-the-fold{overflow-y:auto}}}.bx--tile--is-selected{border:1px solid var(--cds-ui-05, #161616)}.bx--tile--is-selected .bx--tile__checkmark{opacity:1}.bx--tile--is-selected .bx--tile__checkmark svg{fill:var(--cds-ui-05, #161616)}@media screen and (-ms-high-contrast: active),screen and (prefers-contrast){.bx--tile--is-selected .bx--tile__checkmark svg{fill:ButtonText}}.bx--tile-content{width:100%;height:100%}.bx--tile-input{position:absolute;overflow:hidden;width:1px;height:1px;padding:0;border:0;margin:-1px;clip:rect(0,0,0,0);visibility:inherit;white-space:nowrap}.bx--tile-input:focus+.bx--tile{outline:2px solid var(--cds-focus, 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(prefers-contrast){.bx--side-nav__select:focus{outline-style:dotted}}.bx--side-nav__footer{width:100%;flex:0 0 3rem;background-color:#fff}.bx--side-nav__toggle{outline:2px solid rgba(0,0,0,0);outline-offset:-2px;display:inline-block;padding:0;border:0;-webkit-appearance:none;-moz-appearance:none;appearance:none;background:none;cursor:pointer;width:100%;height:100%;padding-left:1rem;text-align:left;transition:outline .11s}.bx--side-nav__toggle::-moz-focus-inner{border:0}.bx--side-nav__toggle:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--side-nav__toggle:focus{outline-style:dotted}}.bx--side-nav__items{overflow:hidden;flex:1 1 0%;padding:1rem 0 0}.bx--side-nav:hover .bx--side-nav__items,.bx--side-nav--fixed .bx--side-nav__items,.bx--side-nav--expanded .bx--side-nav__items,.bx--side-nav--ux .bx--side-nav__items{overflow-y:auto}.bx--side-nav__item{overflow:hidden;width:auto;height:auto}.bx--side-nav--ux 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#ffffff;--cds-text-inverse: #161616;--cds-link-primary: #78a9ff;--cds-link-secondary: #a6c8ff;--cds-link-visited: #be95ff;--cds-link-inverse: #0f62fe;--cds-icon-primary: #f4f4f4;--cds-icon-secondary: #c6c6c6;--cds-icon-on-color: #ffffff;--cds-icon-inverse: #161616;--cds-support-error: #ff8389;--cds-support-success: #42be65;--cds-support-warning: #f1c21b;--cds-support-info: #4589ff;--cds-support-error-inverse: #da1e28;--cds-support-success-inverse: #24a148;--cds-support-warning-inverse: #f1c21b;--cds-support-info-inverse: #0f62fe;--cds-overlay: rgba(0, 0, 0, .65);--cds-toggle-off: #8d8d8d;--cds-shadow: rgba(0, 0, 0, .8);--cds-button-primary: #0f62fe;--cds-button-secondary: #6f6f6f;--cds-button-tertiary: #ffffff;--cds-button-danger-primary: #da1e28;--cds-button-danger-secondary: #ff8389;--cds-background-active: #525252;--cds-layer-active: #6f6f6f;--cds-button-danger-active: #750e13;--cds-button-primary-active: #002d9c;--cds-button-secondary-active: #525252;--cds-button-tertiary-active: 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#525252;--cds-hover-field: #474747;--cds-danger: #da1e28;--cds-caption-01-font-size: .75rem;--cds-caption-01-font-weight: 400;--cds-caption-01-line-height: 1.33333;--cds-caption-01-letter-spacing: .32px;--cds-caption-02-font-size: .875rem;--cds-caption-02-font-weight: 400;--cds-caption-02-line-height: 1.28572;--cds-caption-02-letter-spacing: .32px;--cds-label-01-font-size: .75rem;--cds-label-01-font-weight: 400;--cds-label-01-line-height: 1.33333;--cds-label-01-letter-spacing: .32px;--cds-label-02-font-size: .875rem;--cds-label-02-font-weight: 400;--cds-label-02-line-height: 1.28572;--cds-label-02-letter-spacing: .16px;--cds-helper-text-01-font-size: .75rem;--cds-helper-text-01-line-height: 1.33333;--cds-helper-text-01-letter-spacing: .32px;--cds-helper-text-02-font-size: .875rem;--cds-helper-text-02-line-height: 1.28572;--cds-helper-text-02-letter-spacing: .16px;--cds-body-short-01-font-size: .875rem;--cds-body-short-01-font-weight: 400;--cds-body-short-01-line-height: 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400;--cds-productive-heading-04-line-height: 1.28572;--cds-productive-heading-04-letter-spacing: 0;--cds-productive-heading-05-font-size: 2rem;--cds-productive-heading-05-font-weight: 400;--cds-productive-heading-05-line-height: 1.25;--cds-productive-heading-05-letter-spacing: 0;--cds-productive-heading-06-font-size: 2.625rem;--cds-productive-heading-06-font-weight: 300;--cds-productive-heading-06-line-height: 1.199;--cds-productive-heading-06-letter-spacing: 0;--cds-productive-heading-07-font-size: 3.375rem;--cds-productive-heading-07-font-weight: 300;--cds-productive-heading-07-line-height: 1.199;--cds-productive-heading-07-letter-spacing: 0;--cds-expressive-heading-01-font-size: .875rem;--cds-expressive-heading-01-font-weight: 600;--cds-expressive-heading-01-line-height: 1.25;--cds-expressive-heading-01-letter-spacing: .16px;--cds-expressive-heading-02-font-size: 1rem;--cds-expressive-heading-02-font-weight: 600;--cds-expressive-heading-02-line-height: 1.5;--cds-expressive-heading-02-letter-spacing: 0;--cds-expressive-heading-03-font-size: 1.25rem;--cds-expressive-heading-03-font-weight: 400;--cds-expressive-heading-03-line-height: 1.4;--cds-expressive-heading-03-letter-spacing: 0;--cds-expressive-heading-04-font-size: 1.75rem;--cds-expressive-heading-04-font-weight: 400;--cds-expressive-heading-04-line-height: 1.28572;--cds-expressive-heading-04-letter-spacing: 0;--cds-expressive-heading-05-font-size: 2rem;--cds-expressive-heading-05-font-weight: 400;--cds-expressive-heading-05-line-height: 1.25;--cds-expressive-heading-05-letter-spacing: 0;--cds-expressive-heading-06-font-size: 2rem;--cds-expressive-heading-06-font-weight: 600;--cds-expressive-heading-06-line-height: 1.25;--cds-expressive-heading-06-letter-spacing: 0;--cds-expressive-paragraph-01-font-size: 1.5rem;--cds-expressive-paragraph-01-font-weight: 300;--cds-expressive-paragraph-01-line-height: 1.334;--cds-expressive-paragraph-01-letter-spacing: 0;--cds-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-01-font-size: 1.25rem;--cds-quotation-01-font-weight: 400;--cds-quotation-01-line-height: 1.3;--cds-quotation-01-letter-spacing: 0;--cds-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-02-font-size: 2rem;--cds-quotation-02-font-weight: 300;--cds-quotation-02-line-height: 1.25;--cds-quotation-02-letter-spacing: 0;--cds-display-01-font-size: 2.625rem;--cds-display-01-font-weight: 300;--cds-display-01-line-height: 1.19;--cds-display-01-letter-spacing: 0;--cds-display-02-font-size: 2.625rem;--cds-display-02-font-weight: 600;--cds-display-02-line-height: 1.19;--cds-display-02-letter-spacing: 0;--cds-display-03-font-size: 2.625rem;--cds-display-03-font-weight: 300;--cds-display-03-line-height: 1.19;--cds-display-03-letter-spacing: 0;--cds-display-04-font-size: 2.625rem;--cds-display-04-font-weight: 600;--cds-display-04-line-height: 1.19;--cds-display-04-letter-spacing: 0;--cds-legal-01-font-size: .75rem;--cds-legal-01-font-weight: 400;--cds-legal-01-line-height: 1.33333;--cds-legal-01-letter-spacing: .32px;--cds-legal-02-font-size: .875rem;--cds-legal-02-font-weight: 400;--cds-legal-02-line-height: 1.28572;--cds-legal-02-letter-spacing: .16px;--cds-body-compact-01-font-size: .875rem;--cds-body-compact-01-font-weight: 400;--cds-body-compact-01-line-height: 1.28572;--cds-body-compact-01-letter-spacing: .16px;--cds-body-compact-02-font-size: 1rem;--cds-body-compact-02-font-weight: 400;--cds-body-compact-02-line-height: 1.375;--cds-body-compact-02-letter-spacing: 0;--cds-body-01-font-size: .875rem;--cds-body-01-font-weight: 400;--cds-body-01-line-height: 1.42857;--cds-body-01-letter-spacing: .16px;--cds-body-02-font-size: 1rem;--cds-body-02-font-weight: 400;--cds-body-02-line-height: 1.5;--cds-body-02-letter-spacing: 0;--cds-heading-compact-01-font-size: .875rem;--cds-heading-compact-01-font-weight: 600;--cds-heading-compact-01-line-height: 1.28572;--cds-heading-compact-01-letter-spacing: .16px;--cds-heading-compact-02-font-size: 1rem;--cds-heading-compact-02-font-weight: 600;--cds-heading-compact-02-line-height: 1.375;--cds-heading-compact-02-letter-spacing: 0;--cds-heading-03-font-size: 1.25rem;--cds-heading-03-font-weight: 400;--cds-heading-03-line-height: 1.4;--cds-heading-03-letter-spacing: 0;--cds-heading-04-font-size: 1.75rem;--cds-heading-04-font-weight: 400;--cds-heading-04-line-height: 1.28572;--cds-heading-04-letter-spacing: 0;--cds-heading-05-font-size: 2rem;--cds-heading-05-font-weight: 400;--cds-heading-05-line-height: 1.25;--cds-heading-05-letter-spacing: 0;--cds-heading-06-font-size: 2.625rem;--cds-heading-06-font-weight: 300;--cds-heading-06-line-height: 1.199;--cds-heading-06-letter-spacing: 0;--cds-heading-07-font-size: 3.375rem;--cds-heading-07-font-weight: 300;--cds-heading-07-line-height: 1.199;--cds-heading-07-letter-spacing: 0;--cds-fluid-heading-03-font-size: 1.25rem;--cds-fluid-heading-03-font-weight: 400;--cds-fluid-heading-03-line-height: 1.4;--cds-fluid-heading-03-letter-spacing: 0;--cds-fluid-heading-04-font-size: 1.75rem;--cds-fluid-heading-04-font-weight: 400;--cds-fluid-heading-04-line-height: 1.28572;--cds-fluid-heading-04-letter-spacing: 0;--cds-fluid-heading-05-font-size: 2rem;--cds-fluid-heading-05-font-weight: 400;--cds-fluid-heading-05-line-height: 1.25;--cds-fluid-heading-05-letter-spacing: 0;--cds-fluid-heading-06-font-size: 2rem;--cds-fluid-heading-06-font-weight: 600;--cds-fluid-heading-06-line-height: 1.25;--cds-fluid-heading-06-letter-spacing: 0;--cds-fluid-paragraph-01-font-size: 1.5rem;--cds-fluid-paragraph-01-font-weight: 300;--cds-fluid-paragraph-01-line-height: 1.334;--cds-fluid-paragraph-01-letter-spacing: 0;--cds-fluid-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-01-font-size: 1.25rem;--cds-fluid-quotation-01-font-weight: 400;--cds-fluid-quotation-01-line-height: 1.3;--cds-fluid-quotation-01-letter-spacing: 0;--cds-fluid-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-02-font-size: 2rem;--cds-fluid-quotation-02-font-weight: 300;--cds-fluid-quotation-02-line-height: 1.25;--cds-fluid-quotation-02-letter-spacing: 0;--cds-fluid-display-01-font-size: 2.625rem;--cds-fluid-display-01-font-weight: 300;--cds-fluid-display-01-line-height: 1.19;--cds-fluid-display-01-letter-spacing: 0;--cds-fluid-display-02-font-size: 2.625rem;--cds-fluid-display-02-font-weight: 600;--cds-fluid-display-02-line-height: 1.19;--cds-fluid-display-02-letter-spacing: 0;--cds-fluid-display-03-font-size: 2.625rem;--cds-fluid-display-03-font-weight: 300;--cds-fluid-display-03-line-height: 1.19;--cds-fluid-display-03-letter-spacing: 0;--cds-fluid-display-04-font-size: 2.625rem;--cds-fluid-display-04-font-weight: 600;--cds-fluid-display-04-line-height: 1.19;--cds-fluid-display-04-letter-spacing: 0;--cds-spacing-01: .125rem;--cds-spacing-02: .25rem;--cds-spacing-03: .5rem;--cds-spacing-04: .75rem;--cds-spacing-05: 1rem;--cds-spacing-06: 1.5rem;--cds-spacing-07: 2rem;--cds-spacing-08: 2.5rem;--cds-spacing-09: 3rem;--cds-spacing-10: 4rem;--cds-spacing-11: 5rem;--cds-spacing-12: 6rem;--cds-spacing-13: 10rem;--cds-fluid-spacing-01: 0;--cds-fluid-spacing-02: 2vw;--cds-fluid-spacing-03: 5vw;--cds-fluid-spacing-04: 10vw;--cds-layout-01: 1rem;--cds-layout-02: 1.5rem;--cds-layout-03: 2rem;--cds-layout-04: 3rem;--cds-layout-05: 4rem;--cds-layout-06: 6rem;--cds-layout-07: 10rem;--cds-container-01: 1.5rem;--cds-container-02: 2rem;--cds-container-03: 2.5rem;--cds-container-04: 3rem;--cds-container-05: 4rem;--cds-size-xsmall: 1.5rem;--cds-size-small: 2rem;--cds-size-medium: 2.5rem;--cds-size-large: 3rem;--cds-size-xlarge: 4rem;--cds-size-2XLarge: 5rem;--cds-icon-size-01: 1rem;--cds-icon-size-02: 1.25rem;--cds-tag-background-red: #ffd7d9;--cds-tag-color-red: #750e13;--cds-tag-hover-red: #ffb3b8;--cds-tag-background-magenta: #ffd6e8;--cds-tag-color-magenta: #740937;--cds-tag-hover-magenta: #ffafd2;--cds-tag-background-purple: #e8daff;--cds-tag-color-purple: #491d8b;--cds-tag-hover-purple: #d4bbff;--cds-tag-background-blue: #d0e2ff;--cds-tag-color-blue: #002d9c;--cds-tag-hover-blue: #a6c8ff;--cds-tag-background-cyan: #bae6ff;--cds-tag-color-cyan: #003a6d;--cds-tag-hover-cyan: #82cfff;--cds-tag-background-teal: #9ef0f0;--cds-tag-color-teal: #004144;--cds-tag-hover-teal: #3ddbd9;--cds-tag-background-green: #a7f0ba;--cds-tag-color-green: #044317;--cds-tag-hover-green: #6fdc8c;--cds-tag-background-gray: #e0e0e0;--cds-tag-color-gray: #393939;--cds-tag-hover-gray: #c6c6c6;--cds-tag-background-cool-gray: #dde1e6;--cds-tag-color-cool-gray: #343a3f;--cds-tag-hover-cool-gray: #c1c7cd;--cds-tag-background-warm-gray: #e5e0df;--cds-tag-color-warm-gray: #3c3838;--cds-tag-hover-warm-gray: #cac5c4;--cds-notification-background-error: var(--cds-layer, #f4f4f4);--cds-notification-background-success: var(--cds-layer, #f4f4f4);--cds-notification-background-info: var(--cds-layer, #f4f4f4);--cds-notification-background-warning: var(--cds-layer, #f4f4f4);--cds-notification-action-hover: var(--cds-layer-hover, #e5e5e5)}:root[theme=g90]{--cds-interactive-01: #0f62fe;--cds-interactive-02: #6f6f6f;--cds-interactive-03: #ffffff;--cds-interactive-04: #4589ff;--cds-ui-background: #262626;--cds-ui-01: #393939;--cds-ui-02: #525252;--cds-ui-03: #525252;--cds-ui-04: #8d8d8d;--cds-ui-05: #f4f4f4;--cds-text-01: #f4f4f4;--cds-text-02: #c6c6c6;--cds-text-03: #6f6f6f;--cds-text-04: #ffffff;--cds-text-05: #8d8d8d;--cds-text-error: #ffb3b8;--cds-icon-01: #f4f4f4;--cds-icon-02: #c6c6c6;--cds-icon-03: #ffffff;--cds-link-01: #78a9ff;--cds-link-02: #a6c8ff;--cds-inverse-link: #0f62fe;--cds-field-01: #393939;--cds-field-02: #525252;--cds-inverse-01: #161616;--cds-inverse-02: #f4f4f4;--cds-support-01: #ff8389;--cds-support-02: #42be65;--cds-support-03: #f1c21b;--cds-support-04: #4589ff;--cds-inverse-support-01: #da1e28;--cds-inverse-support-02: #24a148;--cds-inverse-support-03: #f1c21b;--cds-inverse-support-04: #0f62fe;--cds-overlay-01: rgba(0, 0, 0, .65);--cds-danger-01: #da1e28;--cds-danger-02: #ff8389;--cds-focus: #ffffff;--cds-inverse-focus-ui: #0f62fe;--cds-hover-primary: #0353e9;--cds-active-primary: #002d9c;--cds-hover-primary-text: #a6c8ff;--cds-hover-secondary: #606060;--cds-active-secondary: #393939;--cds-hover-tertiary: #f4f4f4;--cds-active-tertiary: #c6c6c6;--cds-hover-ui: #4c4c4c;--cds-hover-light-ui: #656565;--cds-hover-selected-ui: #656565;--cds-active-ui: #6f6f6f;--cds-active-light-ui: #8d8d8d;--cds-selected-ui: #525252;--cds-selected-light-ui: #6f6f6f;--cds-inverse-hover-ui: #e5e5e5;--cds-hover-danger: #b81921;--cds-active-danger: #750e13;--cds-hover-row: #4c4c4c;--cds-visited-link: #be95ff;--cds-disabled-01: #393939;--cds-disabled-02: #6f6f6f;--cds-disabled-03: #a8a8a8;--cds-highlight: #0043ce;--cds-decorative-01: #6f6f6f;--cds-button-separator: #161616;--cds-skeleton-01: #353535;--cds-skeleton-02: #525252;--cds-background: #262626;--cds-layer: #393939;--cds-layer-accent: #525252;--cds-layer-accent-hover: #636363;--cds-layer-accent-active: #8d8d8d;--cds-field: #393939;--cds-background-inverse: #f4f4f4;--cds-background-brand: #0f62fe;--cds-interactive: #4589ff;--cds-border-subtle: #525252;--cds-border-strong: #8d8d8d;--cds-border-inverse: #f4f4f4;--cds-border-interactive: #4589ff;--cds-text-primary: #f4f4f4;--cds-text-secondary: #c6c6c6;--cds-text-placeholder: #6f6f6f;--cds-text-helper: #8d8d8d;--cds-text-on-color: #ffffff;--cds-text-inverse: #161616;--cds-link-primary: #78a9ff;--cds-link-secondary: #a6c8ff;--cds-link-visited: #be95ff;--cds-link-inverse: #0f62fe;--cds-icon-primary: #f4f4f4;--cds-icon-secondary: #c6c6c6;--cds-icon-on-color: #ffffff;--cds-icon-inverse: #161616;--cds-support-error: #ff8389;--cds-support-success: #42be65;--cds-support-warning: #f1c21b;--cds-support-info: #4589ff;--cds-support-error-inverse: #da1e28;--cds-support-success-inverse: #24a148;--cds-support-warning-inverse: #f1c21b;--cds-support-info-inverse: #0f62fe;--cds-overlay: rgba(0, 0, 0, .65);--cds-toggle-off: #8d8d8d;--cds-shadow: rgba(0, 0, 0, .8);--cds-button-primary: #0f62fe;--cds-button-secondary: #6f6f6f;--cds-button-tertiary: #ffffff;--cds-button-danger-primary: #da1e28;--cds-button-danger-secondary: #ff8389;--cds-background-active: #6f6f6f;--cds-layer-active: #6f6f6f;--cds-button-danger-active: #750e13;--cds-button-primary-active: #002d9c;--cds-button-secondary-active: #393939;--cds-button-tertiary-active: #c6c6c6;--cds-focus-inset: #161616;--cds-focus-inverse: #0f62fe;--cds-background-hover: #4c4c4c;--cds-layer-hover: #4c4c4c;--cds-field-hover: #4c4c4c;--cds-background-inverse-hover: #e5e5e5;--cds-link-primary-hover: #a6c8ff;--cds-button-danger-hover: #b81921;--cds-button-primary-hover: #0353e9;--cds-button-secondary-hover: #606060;--cds-button-tertiary-hover: #f4f4f4;--cds-background-selected: #525252;--cds-background-selected-hover: #656565;--cds-layer-selected: #525252;--cds-layer-selected-hover: #656565;--cds-layer-selected-inverse: #f4f4f4;--cds-border-subtle-selected: #6f6f6f;--cds-border-disabled: #393939;--cds-text-disabled: #6f6f6f;--cds-button-disabled: #6f6f6f;--cds-icon-disabled: #6f6f6f;--cds-text-on-color-disabled: #a8a8a8;--cds-icon-on-color-disabled: #a8a8a8;--cds-layer-selected-disabled: #a8a8a8;--cds-skeleton-background: #353535;--cds-skeleton-element: #525252;--cds-brand-01: #0f62fe;--cds-brand-02: #6f6f6f;--cds-brand-03: #ffffff;--cds-active-01: #6f6f6f;--cds-hover-field: #4c4c4c;--cds-danger: #da1e28;--cds-caption-01-font-size: .75rem;--cds-caption-01-font-weight: 400;--cds-caption-01-line-height: 1.33333;--cds-caption-01-letter-spacing: .32px;--cds-caption-02-font-size: .875rem;--cds-caption-02-font-weight: 400;--cds-caption-02-line-height: 1.28572;--cds-caption-02-letter-spacing: .32px;--cds-label-01-font-size: .75rem;--cds-label-01-font-weight: 400;--cds-label-01-line-height: 1.33333;--cds-label-01-letter-spacing: .32px;--cds-label-02-font-size: .875rem;--cds-label-02-font-weight: 400;--cds-label-02-line-height: 1.28572;--cds-label-02-letter-spacing: .16px;--cds-helper-text-01-font-size: .75rem;--cds-helper-text-01-line-height: 1.33333;--cds-helper-text-01-letter-spacing: .32px;--cds-helper-text-02-font-size: .875rem;--cds-helper-text-02-line-height: 1.28572;--cds-helper-text-02-letter-spacing: .16px;--cds-body-short-01-font-size: .875rem;--cds-body-short-01-font-weight: 400;--cds-body-short-01-line-height: 1.28572;--cds-body-short-01-letter-spacing: .16px;--cds-body-long-01-font-size: .875rem;--cds-body-long-01-font-weight: 400;--cds-body-long-01-line-height: 1.42857;--cds-body-long-01-letter-spacing: .16px;--cds-body-short-02-font-size: 1rem;--cds-body-short-02-font-weight: 400;--cds-body-short-02-line-height: 1.375;--cds-body-short-02-letter-spacing: 0;--cds-body-long-02-font-size: 1rem;--cds-body-long-02-font-weight: 400;--cds-body-long-02-line-height: 1.5;--cds-body-long-02-letter-spacing: 0;--cds-code-01-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-01-font-size: .75rem;--cds-code-01-font-weight: 400;--cds-code-01-line-height: 1.33333;--cds-code-01-letter-spacing: .32px;--cds-code-02-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-02-font-size: .875rem;--cds-code-02-font-weight: 400;--cds-code-02-line-height: 1.42857;--cds-code-02-letter-spacing: .32px;--cds-heading-01-font-size: .875rem;--cds-heading-01-font-weight: 600;--cds-heading-01-line-height: 1.42857;--cds-heading-01-letter-spacing: .16px;--cds-productive-heading-01-font-size: .875rem;--cds-productive-heading-01-font-weight: 600;--cds-productive-heading-01-line-height: 1.28572;--cds-productive-heading-01-letter-spacing: .16px;--cds-heading-02-font-size: 1rem;--cds-heading-02-font-weight: 600;--cds-heading-02-line-height: 1.5;--cds-heading-02-letter-spacing: 0;--cds-productive-heading-02-font-size: 1rem;--cds-productive-heading-02-font-weight: 600;--cds-productive-heading-02-line-height: 1.375;--cds-productive-heading-02-letter-spacing: 0;--cds-productive-heading-03-font-size: 1.25rem;--cds-productive-heading-03-font-weight: 400;--cds-productive-heading-03-line-height: 1.4;--cds-productive-heading-03-letter-spacing: 0;--cds-productive-heading-04-font-size: 1.75rem;--cds-productive-heading-04-font-weight: 400;--cds-productive-heading-04-line-height: 1.28572;--cds-productive-heading-04-letter-spacing: 0;--cds-productive-heading-05-font-size: 2rem;--cds-productive-heading-05-font-weight: 400;--cds-productive-heading-05-line-height: 1.25;--cds-productive-heading-05-letter-spacing: 0;--cds-productive-heading-06-font-size: 2.625rem;--cds-productive-heading-06-font-weight: 300;--cds-productive-heading-06-line-height: 1.199;--cds-productive-heading-06-letter-spacing: 0;--cds-productive-heading-07-font-size: 3.375rem;--cds-productive-heading-07-font-weight: 300;--cds-productive-heading-07-line-height: 1.199;--cds-productive-heading-07-letter-spacing: 0;--cds-expressive-heading-01-font-size: .875rem;--cds-expressive-heading-01-font-weight: 600;--cds-expressive-heading-01-line-height: 1.25;--cds-expressive-heading-01-letter-spacing: .16px;--cds-expressive-heading-02-font-size: 1rem;--cds-expressive-heading-02-font-weight: 600;--cds-expressive-heading-02-line-height: 1.5;--cds-expressive-heading-02-letter-spacing: 0;--cds-expressive-heading-03-font-size: 1.25rem;--cds-expressive-heading-03-font-weight: 400;--cds-expressive-heading-03-line-height: 1.4;--cds-expressive-heading-03-letter-spacing: 0;--cds-expressive-heading-04-font-size: 1.75rem;--cds-expressive-heading-04-font-weight: 400;--cds-expressive-heading-04-line-height: 1.28572;--cds-expressive-heading-04-letter-spacing: 0;--cds-expressive-heading-05-font-size: 2rem;--cds-expressive-heading-05-font-weight: 400;--cds-expressive-heading-05-line-height: 1.25;--cds-expressive-heading-05-letter-spacing: 0;--cds-expressive-heading-06-font-size: 2rem;--cds-expressive-heading-06-font-weight: 600;--cds-expressive-heading-06-line-height: 1.25;--cds-expressive-heading-06-letter-spacing: 0;--cds-expressive-paragraph-01-font-size: 1.5rem;--cds-expressive-paragraph-01-font-weight: 300;--cds-expressive-paragraph-01-line-height: 1.334;--cds-expressive-paragraph-01-letter-spacing: 0;--cds-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-01-font-size: 1.25rem;--cds-quotation-01-font-weight: 400;--cds-quotation-01-line-height: 1.3;--cds-quotation-01-letter-spacing: 0;--cds-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-quotation-02-font-size: 2rem;--cds-quotation-02-font-weight: 300;--cds-quotation-02-line-height: 1.25;--cds-quotation-02-letter-spacing: 0;--cds-display-01-font-size: 2.625rem;--cds-display-01-font-weight: 300;--cds-display-01-line-height: 1.19;--cds-display-01-letter-spacing: 0;--cds-display-02-font-size: 2.625rem;--cds-display-02-font-weight: 600;--cds-display-02-line-height: 1.19;--cds-display-02-letter-spacing: 0;--cds-display-03-font-size: 2.625rem;--cds-display-03-font-weight: 300;--cds-display-03-line-height: 1.19;--cds-display-03-letter-spacing: 0;--cds-display-04-font-size: 2.625rem;--cds-display-04-font-weight: 600;--cds-display-04-line-height: 1.19;--cds-display-04-letter-spacing: 0;--cds-legal-01-font-size: .75rem;--cds-legal-01-font-weight: 400;--cds-legal-01-line-height: 1.33333;--cds-legal-01-letter-spacing: .32px;--cds-legal-02-font-size: .875rem;--cds-legal-02-font-weight: 400;--cds-legal-02-line-height: 1.28572;--cds-legal-02-letter-spacing: .16px;--cds-body-compact-01-font-size: .875rem;--cds-body-compact-01-font-weight: 400;--cds-body-compact-01-line-height: 1.28572;--cds-body-compact-01-letter-spacing: .16px;--cds-body-compact-02-font-size: 1rem;--cds-body-compact-02-font-weight: 400;--cds-body-compact-02-line-height: 1.375;--cds-body-compact-02-letter-spacing: 0;--cds-body-01-font-size: .875rem;--cds-body-01-font-weight: 400;--cds-body-01-line-height: 1.42857;--cds-body-01-letter-spacing: .16px;--cds-body-02-font-size: 1rem;--cds-body-02-font-weight: 400;--cds-body-02-line-height: 1.5;--cds-body-02-letter-spacing: 0;--cds-heading-compact-01-font-size: .875rem;--cds-heading-compact-01-font-weight: 600;--cds-heading-compact-01-line-height: 1.28572;--cds-heading-compact-01-letter-spacing: .16px;--cds-heading-compact-02-font-size: 1rem;--cds-heading-compact-02-font-weight: 600;--cds-heading-compact-02-line-height: 1.375;--cds-heading-compact-02-letter-spacing: 0;--cds-heading-03-font-size: 1.25rem;--cds-heading-03-font-weight: 400;--cds-heading-03-line-height: 1.4;--cds-heading-03-letter-spacing: 0;--cds-heading-04-font-size: 1.75rem;--cds-heading-04-font-weight: 400;--cds-heading-04-line-height: 1.28572;--cds-heading-04-letter-spacing: 0;--cds-heading-05-font-size: 2rem;--cds-heading-05-font-weight: 400;--cds-heading-05-line-height: 1.25;--cds-heading-05-letter-spacing: 0;--cds-heading-06-font-size: 2.625rem;--cds-heading-06-font-weight: 300;--cds-heading-06-line-height: 1.199;--cds-heading-06-letter-spacing: 0;--cds-heading-07-font-size: 3.375rem;--cds-heading-07-font-weight: 300;--cds-heading-07-line-height: 1.199;--cds-heading-07-letter-spacing: 0;--cds-fluid-heading-03-font-size: 1.25rem;--cds-fluid-heading-03-font-weight: 400;--cds-fluid-heading-03-line-height: 1.4;--cds-fluid-heading-03-letter-spacing: 0;--cds-fluid-heading-04-font-size: 1.75rem;--cds-fluid-heading-04-font-weight: 400;--cds-fluid-heading-04-line-height: 1.28572;--cds-fluid-heading-04-letter-spacing: 0;--cds-fluid-heading-05-font-size: 2rem;--cds-fluid-heading-05-font-weight: 400;--cds-fluid-heading-05-line-height: 1.25;--cds-fluid-heading-05-letter-spacing: 0;--cds-fluid-heading-06-font-size: 2rem;--cds-fluid-heading-06-font-weight: 600;--cds-fluid-heading-06-line-height: 1.25;--cds-fluid-heading-06-letter-spacing: 0;--cds-fluid-paragraph-01-font-size: 1.5rem;--cds-fluid-paragraph-01-font-weight: 300;--cds-fluid-paragraph-01-line-height: 1.334;--cds-fluid-paragraph-01-letter-spacing: 0;--cds-fluid-quotation-01-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-01-font-size: 1.25rem;--cds-fluid-quotation-01-font-weight: 400;--cds-fluid-quotation-01-line-height: 1.3;--cds-fluid-quotation-01-letter-spacing: 0;--cds-fluid-quotation-02-font-family: "IBM Plex Serif", "Georgia", Times, serif;--cds-fluid-quotation-02-font-size: 2rem;--cds-fluid-quotation-02-font-weight: 300;--cds-fluid-quotation-02-line-height: 1.25;--cds-fluid-quotation-02-letter-spacing: 0;--cds-fluid-display-01-font-size: 2.625rem;--cds-fluid-display-01-font-weight: 300;--cds-fluid-display-01-line-height: 1.19;--cds-fluid-display-01-letter-spacing: 0;--cds-fluid-display-02-font-size: 2.625rem;--cds-fluid-display-02-font-weight: 600;--cds-fluid-display-02-line-height: 1.19;--cds-fluid-display-02-letter-spacing: 0;--cds-fluid-display-03-font-size: 2.625rem;--cds-fluid-display-03-font-weight: 300;--cds-fluid-display-03-line-height: 1.19;--cds-fluid-display-03-letter-spacing: 0;--cds-fluid-display-04-font-size: 2.625rem;--cds-fluid-display-04-font-weight: 600;--cds-fluid-display-04-line-height: 1.19;--cds-fluid-display-04-letter-spacing: 0;--cds-spacing-01: .125rem;--cds-spacing-02: .25rem;--cds-spacing-03: .5rem;--cds-spacing-04: .75rem;--cds-spacing-05: 1rem;--cds-spacing-06: 1.5rem;--cds-spacing-07: 2rem;--cds-spacing-08: 2.5rem;--cds-spacing-09: 3rem;--cds-spacing-10: 4rem;--cds-spacing-11: 5rem;--cds-spacing-12: 6rem;--cds-spacing-13: 10rem;--cds-fluid-spacing-01: 0;--cds-fluid-spacing-02: 2vw;--cds-fluid-spacing-03: 5vw;--cds-fluid-spacing-04: 10vw;--cds-layout-01: 1rem;--cds-layout-02: 1.5rem;--cds-layout-03: 2rem;--cds-layout-04: 3rem;--cds-layout-05: 4rem;--cds-layout-06: 6rem;--cds-layout-07: 10rem;--cds-container-01: 1.5rem;--cds-container-02: 2rem;--cds-container-03: 2.5rem;--cds-container-04: 3rem;--cds-container-05: 4rem;--cds-size-xsmall: 1.5rem;--cds-size-small: 2rem;--cds-size-medium: 2.5rem;--cds-size-large: 3rem;--cds-size-xlarge: 4rem;--cds-size-2XLarge: 5rem;--cds-icon-size-01: 1rem;--cds-icon-size-02: 1.25rem;--cds-tag-background-red: #a2191f;--cds-tag-color-red: #ffd7d9;--cds-tag-hover-red: #c21e25;--cds-tag-background-magenta: #9f1853;--cds-tag-color-magenta: #ffd6e8;--cds-tag-hover-magenta: #bf1d63;--cds-tag-background-purple: #6929c4;--cds-tag-color-purple: #e8daff;--cds-tag-hover-purple: #7c3dd6;--cds-tag-background-blue: #0043ce;--cds-tag-color-blue: #d0e2ff;--cds-tag-hover-blue: #0053ff;--cds-tag-background-cyan: #00539a;--cds-tag-color-cyan: #bae6ff;--cds-tag-hover-cyan: #0066bd;--cds-tag-background-teal: #005d5d;--cds-tag-color-teal: #9ef0f0;--cds-tag-hover-teal: #007070;--cds-tag-background-green: #0e6027;--cds-tag-color-green: #a7f0ba;--cds-tag-hover-green: #11742f;--cds-tag-background-gray: #525252;--cds-tag-color-gray: #e0e0e0;--cds-tag-hover-gray: #636363;--cds-tag-background-cool-gray: #4d5358;--cds-tag-color-cool-gray: #dde1e6;--cds-tag-hover-cool-gray: #5d646a;--cds-tag-background-warm-gray: #565151;--cds-tag-color-warm-gray: #e5e0df;--cds-tag-hover-warm-gray: #696363;--cds-notification-background-error: var(--cds-ui-01, #f4f4f4);--cds-notification-background-success: var(--cds-ui-01, #f4f4f4);--cds-notification-background-info: var(--cds-ui-01, #f4f4f4);--cds-notification-background-warning: var(--cds-ui-01, #f4f4f4);--cds-notification-action-hover: var(--cds-hover-ui, #e5e5e5)}:root[theme=g100]{--cds-interactive-01: #0f62fe;--cds-interactive-02: #6f6f6f;--cds-interactive-03: #ffffff;--cds-interactive-04: #4589ff;--cds-ui-background: #161616;--cds-ui-01: #262626;--cds-ui-02: #393939;--cds-ui-03: #393939;--cds-ui-04: #6f6f6f;--cds-ui-05: #f4f4f4;--cds-text-01: #f4f4f4;--cds-text-02: #c6c6c6;--cds-text-03: #6f6f6f;--cds-text-04: #ffffff;--cds-text-05: #8d8d8d;--cds-text-error: #ff8389;--cds-icon-01: #f4f4f4;--cds-icon-02: #c6c6c6;--cds-icon-03: #ffffff;--cds-link-01: #78a9ff;--cds-link-02: #a6c8ff;--cds-inverse-link: #0f62fe;--cds-field-01: #262626;--cds-field-02: #393939;--cds-inverse-01: #161616;--cds-inverse-02: #f4f4f4;--cds-support-01: #fa4d56;--cds-support-02: #42be65;--cds-support-03: #f1c21b;--cds-support-04: #4589ff;--cds-inverse-support-01: #da1e28;--cds-inverse-support-02: #24a148;--cds-inverse-support-03: #f1c21b;--cds-inverse-support-04: #0f62fe;--cds-overlay-01: rgba(0, 0, 0, .65);--cds-danger-01: #da1e28;--cds-danger-02: #fa4d56;--cds-focus: #ffffff;--cds-inverse-focus-ui: #0f62fe;--cds-hover-primary: #0353e9;--cds-active-primary: #002d9c;--cds-hover-primary-text: #a6c8ff;--cds-hover-secondary: #606060;--cds-active-secondary: #393939;--cds-hover-tertiary: #f4f4f4;--cds-active-tertiary: #c6c6c6;--cds-hover-ui: #353535;--cds-hover-light-ui: #4c4c4c;--cds-hover-selected-ui: #4c4c4c;--cds-active-ui: #525252;--cds-active-light-ui: #6f6f6f;--cds-selected-ui: #393939;--cds-selected-light-ui: #525252;--cds-inverse-hover-ui: #e5e5e5;--cds-hover-danger: #b81921;--cds-active-danger: #750e13;--cds-hover-row: #353535;--cds-visited-link: #be95ff;--cds-disabled-01: #262626;--cds-disabled-02: #525252;--cds-disabled-03: #8d8d8d;--cds-highlight: #002d9c;--cds-decorative-01: #525252;--cds-button-separator: #161616;--cds-skeleton-01: #353535;--cds-skeleton-02: #525252;--cds-background: #161616;--cds-layer: #262626;--cds-layer-accent: #393939;--cds-layer-accent-hover: #474747;--cds-layer-accent-active: #6f6f6f;--cds-field: #262626;--cds-background-inverse: #f4f4f4;--cds-background-brand: #0f62fe;--cds-interactive: #4589ff;--cds-border-subtle: #393939;--cds-border-strong: #6f6f6f;--cds-border-inverse: #f4f4f4;--cds-border-interactive: #4589ff;--cds-text-primary: #f4f4f4;--cds-text-secondary: #c6c6c6;--cds-text-placeholder: #6f6f6f;--cds-text-helper: #8d8d8d;--cds-text-on-color: #ffffff;--cds-text-inverse: #161616;--cds-link-primary: #78a9ff;--cds-link-secondary: #a6c8ff;--cds-link-visited: #be95ff;--cds-link-inverse: #0f62fe;--cds-icon-primary: #f4f4f4;--cds-icon-secondary: #c6c6c6;--cds-icon-on-color: #ffffff;--cds-icon-inverse: #161616;--cds-support-error: #fa4d56;--cds-support-success: #42be65;--cds-support-warning: #f1c21b;--cds-support-info: #4589ff;--cds-support-error-inverse: #da1e28;--cds-support-success-inverse: #24a148;--cds-support-warning-inverse: #f1c21b;--cds-support-info-inverse: #0f62fe;--cds-overlay: rgba(0, 0, 0, .65);--cds-toggle-off: #6f6f6f;--cds-shadow: rgba(0, 0, 0, .8);--cds-button-primary: #0f62fe;--cds-button-secondary: #6f6f6f;--cds-button-tertiary: #ffffff;--cds-button-danger-primary: #da1e28;--cds-button-danger-secondary: #fa4d56;--cds-background-active: #525252;--cds-layer-active: #525252;--cds-button-danger-active: #750e13;--cds-button-primary-active: #002d9c;--cds-button-secondary-active: #393939;--cds-button-tertiary-active: #c6c6c6;--cds-focus-inset: #161616;--cds-focus-inverse: #0f62fe;--cds-background-hover: #353535;--cds-layer-hover: #353535;--cds-field-hover: #353535;--cds-background-inverse-hover: #e5e5e5;--cds-link-primary-hover: #a6c8ff;--cds-button-danger-hover: #b81921;--cds-button-primary-hover: #0353e9;--cds-button-secondary-hover: #606060;--cds-button-tertiary-hover: #f4f4f4;--cds-background-selected: #393939;--cds-background-selected-hover: #4c4c4c;--cds-layer-selected: #393939;--cds-layer-selected-hover: #4c4c4c;--cds-layer-selected-inverse: #f4f4f4;--cds-border-subtle-selected: #525252;--cds-border-disabled: #262626;--cds-text-disabled: #525252;--cds-button-disabled: #525252;--cds-icon-disabled: #525252;--cds-text-on-color-disabled: #8d8d8d;--cds-icon-on-color-disabled: #8d8d8d;--cds-layer-selected-disabled: #8d8d8d;--cds-skeleton-background: #353535;--cds-skeleton-element: #525252;--cds-brand-01: #0f62fe;--cds-brand-02: #6f6f6f;--cds-brand-03: #ffffff;--cds-active-01: #525252;--cds-hover-field: #353535;--cds-danger: #da1e28;--cds-caption-01-font-size: .75rem;--cds-caption-01-font-weight: 400;--cds-caption-01-line-height: 1.33333;--cds-caption-01-letter-spacing: .32px;--cds-caption-02-font-size: .875rem;--cds-caption-02-font-weight: 400;--cds-caption-02-line-height: 1.28572;--cds-caption-02-letter-spacing: .32px;--cds-label-01-font-size: .75rem;--cds-label-01-font-weight: 400;--cds-label-01-line-height: 1.33333;--cds-label-01-letter-spacing: .32px;--cds-label-02-font-size: .875rem;--cds-label-02-font-weight: 400;--cds-label-02-line-height: 1.28572;--cds-label-02-letter-spacing: .16px;--cds-helper-text-01-font-size: .75rem;--cds-helper-text-01-line-height: 1.33333;--cds-helper-text-01-letter-spacing: .32px;--cds-helper-text-02-font-size: .875rem;--cds-helper-text-02-line-height: 1.28572;--cds-helper-text-02-letter-spacing: .16px;--cds-body-short-01-font-size: .875rem;--cds-body-short-01-font-weight: 400;--cds-body-short-01-line-height: 1.28572;--cds-body-short-01-letter-spacing: .16px;--cds-body-long-01-font-size: .875rem;--cds-body-long-01-font-weight: 400;--cds-body-long-01-line-height: 1.42857;--cds-body-long-01-letter-spacing: .16px;--cds-body-short-02-font-size: 1rem;--cds-body-short-02-font-weight: 400;--cds-body-short-02-line-height: 1.375;--cds-body-short-02-letter-spacing: 0;--cds-body-long-02-font-size: 1rem;--cds-body-long-02-font-weight: 400;--cds-body-long-02-line-height: 1.5;--cds-body-long-02-letter-spacing: 0;--cds-code-01-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace;--cds-code-01-font-size: .75rem;--cds-code-01-font-weight: 400;--cds-code-01-line-height: 1.33333;--cds-code-01-letter-spacing: .32px;--cds-code-02-font-family: "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream 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1.4;--cds-productive-heading-03-letter-spacing: 0;--cds-productive-heading-04-font-size: 1.75rem;--cds-productive-heading-04-font-weight: 400;--cds-productive-heading-04-line-height: 1.28572;--cds-productive-heading-04-letter-spacing: 0;--cds-productive-heading-05-font-size: 2rem;--cds-productive-heading-05-font-weight: 400;--cds-productive-heading-05-line-height: 1.25;--cds-productive-heading-05-letter-spacing: 0;--cds-productive-heading-06-font-size: 2.625rem;--cds-productive-heading-06-font-weight: 300;--cds-productive-heading-06-line-height: 1.199;--cds-productive-heading-06-letter-spacing: 0;--cds-productive-heading-07-font-size: 3.375rem;--cds-productive-heading-07-font-weight: 300;--cds-productive-heading-07-line-height: 1.199;--cds-productive-heading-07-letter-spacing: 0;--cds-expressive-heading-01-font-size: .875rem;--cds-expressive-heading-01-font-weight: 600;--cds-expressive-heading-01-line-height: 1.25;--cds-expressive-heading-01-letter-spacing: 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#ffffff);font-weight:400;text-align:left;transform:translate(-50%);font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);display:none;overflow:visible;box-sizing:content-box;margin:auto;clip:auto}@media all and (-ms-high-contrast: none),(-ms-high-contrast: active){.bx--snippet--inline .bx--copy-btn__feedback{width:auto}}@supports (-ms-accelerator: true){.bx--snippet--inline .bx--copy-btn__feedback{width:auto}}@supports (-ms-ime-align: auto){.bx--snippet--inline .bx--copy-btn__feedback{width:auto}}@media screen and (-ms-high-contrast: active),screen and (prefers-contrast){.bx--snippet--inline .bx--copy-btn__feedback{border:1px solid rgba(0,0,0,0)}}.bx--snippet--inline 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.32px);position:relative;width:100%;max-width:48rem;background-color:var(--cds-field-01, #f4f4f4);display:flex;height:2.5rem;align-items:center;padding-right:2.5rem}.bx--snippet--single.bx--snippet--no-copy{padding:0}.bx--snippet--single.bx--snippet--no-copy:after{right:1rem}.bx--snippet--single .bx--snippet-container{position:relative;display:flex;height:100%;align-items:center;padding-left:1rem;overflow-x:auto}.bx--snippet--single .bx--snippet-container:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--snippet--single .bx--snippet-container:focus{outline-style:dotted}}.bx--snippet--single pre{font-family:var(--cds-code-01-font-family, "IBM Plex Mono", "Menlo", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", Courier, monospace);font-size:var(--cds-code-01-font-size, .75rem);font-weight:var(--cds-code-01-font-weight, 400);line-height:var(--cds-code-01-line-height, 1.33333);letter-spacing:var(--cds-code-01-letter-spacing, 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pre{white-space:pre-wrap;word-wrap:break-word}.bx--snippet--multi .bx--snippet-container pre{padding-right:2.5rem;padding-bottom:1.5rem;overflow-x:auto}.bx--snippet--multi.bx--snippet--no-copy .bx--snippet-container pre{padding-right:0}.bx--snippet--multi.bx--snippet--expand .bx--snippet-container pre{overflow-x:auto}.bx--snippet--multi .bx--snippet-container pre:after{position:absolute;top:0;right:0;width:1rem;height:100%;background-image:linear-gradient(to right,rgba(var(--cds-field-01, #f4f4f4),0),var(--cds-field-01, #f4f4f4));content:""}.bx--snippet--multi .bx--snippet-container pre code{overflow:hidden}.bx--snippet__icon{width:1rem;height:1rem;fill:var(--cds-icon-01, #161616);transition:all 70ms cubic-bezier(.2,0,.38,.9)}.bx--snippet-button{position:absolute;top:0;right:0;display:flex;overflow:visible;width:2.5rem;height:2.5rem;align-items:center;justify-content:center;padding:0;border:none;background-color:var(--cds-field-01, #f4f4f4);cursor:pointer;outline:none}.bx--snippet-button:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px;outline-color:var(--cds-focus, #0f62fe)}@media screen and (prefers-contrast){.bx--snippet-button:focus{outline-style:dotted}}.bx--snippet--multi .bx--snippet-button{top:var(--cds-spacing-03, .5rem);right:var(--cds-spacing-03, .5rem);width:2rem;height:2rem}.bx--snippet-button:hover{background:var(--cds-hover-ui, #e5e5e5)}.bx--snippet-button:active{background-color:var(--cds-active-ui, #c6c6c6)}.bx--btn--copy__feedback{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);font-family:IBM Plex Sans,Helvetica Neue,Arial,sans-serif;z-index:6000;top:.75rem;right:1.25rem;left:inherit;font-weight:400}.bx--btn--copy__feedback:before,.bx--btn--copy__feedback:after{background:var(--cds-inverse-02, #393939)}.bx--btn--copy__feedback:after{border:none}.bx--snippet .bx--copy-btn{position:absolute;top:0;right:0;font-family:IBM Plex Sans,Helvetica Neue,Arial,sans-serif}.bx--snippet-btn--expand{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px);font-family:IBM Plex Sans,Helvetica Neue,Arial,sans-serif;position:absolute;z-index:10;right:0;bottom:0;display:inline-flex;align-items:center;padding:var(--cds-spacing-03, .5rem) var(--cds-spacing-05, 1rem);border:0;background-color:var(--cds-field-01, #f4f4f4);color:var(--cds-text-01, #161616)}.bx--snippet-btn--expand .bx--snippet-btn--text{position:relative;top:-.0625rem}.bx--snippet-btn--expand--hide.bx--snippet-btn--expand{display:none}.bx--snippet-btn--expand .bx--icon-chevron--down{margin-left:var(--cds-spacing-03, .5rem);fill:var(--cds-icon-primary, 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.bx--btn--copy__feedback{top:2.675rem}.bx--snippet--inline .bx--btn--copy__feedback{top:calc(100% - .25rem);right:auto;left:50%}.bx--snippet__overflow-indicator--left,.bx--snippet__overflow-indicator--right{z-index:1;width:1rem;flex:1 0 auto}.bx--snippet__overflow-indicator--left{order:0;margin-right:-1rem;background-image:linear-gradient(to left,transparent,var(--cds-field-01, #f4f4f4))}.bx--snippet__overflow-indicator--right{order:2;margin-left:-1rem;background-image:linear-gradient(to right,transparent,var(--cds-field-01, #f4f4f4))}.bx--snippet--single .bx--snippet__overflow-indicator--right,.bx--snippet--single .bx--snippet__overflow-indicator--left{position:absolute;width:2rem;height:calc(100% - .25rem)}.bx--snippet--single .bx--snippet__overflow-indicator--right{right:2.5rem}.bx--snippet--single.bx--snippet--no-copy .bx--snippet__overflow-indicator--right{right:0}.bx--snippet--single .bx--snippet-container:focus~.bx--snippet__overflow-indicator--right{right:2.625rem}.bx--snippet--single .bx--snippet-container:focus+.bx--snippet__overflow-indicator--left{left:.125rem}.bx--snippet--light .bx--snippet__overflow-indicator--left{background-image:linear-gradient(to left,transparent,var(--cds-field-02, #ffffff))}.bx--snippet--light .bx--snippet__overflow-indicator--right{background-image:linear-gradient(to right,transparent,var(--cds-field-02, #ffffff))}@media not all and (-webkit-min-device-pixel-ratio: 0),not all and (min-resolution: .001dpcm){@supports (-webkit-appearance: none) and (stroke-color: transparent){.bx--snippet__overflow-indicator--left{background-image:linear-gradient(to left,rgba(var(--cds-field-01, #f4f4f4),0),var(--cds-field-01, #f4f4f4))}.bx--snippet__overflow-indicator--right{background-image:linear-gradient(to right,rgba(var(--cds-field-01, #f4f4f4),0),var(--cds-field-01, #f4f4f4))}}}.bx--snippet--multi.bx--skeleton{height:6.125rem}.bx--snippet--single.bx--skeleton{height:3.5rem}.bx--snippet.bx--skeleton span{position:relative;padding:0;border:none;background:var(--cds-skeleton-01, #e5e5e5);box-shadow:none;pointer-events:none;display:block;width:100%;height:1rem;margin-top:.5rem}.bx--snippet.bx--skeleton span:hover,.bx--snippet.bx--skeleton span:focus,.bx--snippet.bx--skeleton span:active{border:none;cursor:default;outline:none}.bx--snippet.bx--skeleton span:before{position:absolute;top:0;left:0;width:100%;height:100%;animation:3s ease-in-out skeleton infinite;background:var(--cds-skeleton-02, #c6c6c6);content:"";will-change:transform-origin,transform,opacity}@media (prefers-reduced-motion: reduce){.bx--snippet.bx--skeleton span:before{animation:none}}.bx--snippet.bx--skeleton span:first-child{margin:0}.bx--snippet.bx--skeleton span:nth-child(2){width:85%}.bx--snippet.bx--skeleton span:nth-child(3){width:95%}.bx--snippet--single.bx--skeleton .bx--snippet-container{padding-bottom:0}@media screen and (-ms-high-contrast: active),(forced-colors: active),(prefers-contrast){.bx--snippet__icon{fill:ButtonText}.bx--snippet--inline:focus{color:Highlight;outline:1px solid Highlight}.bx--snippet--single,.bx--snippet--multi{outline:1px solid rgba(0,0,0,0)}}.bx--list-box__wrapper--inline{display:inline-grid;align-items:center;grid-gap:.25rem;grid-template:auto auto/auto auto}.bx--list-box__wrapper--inline .bx--label{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, .16px)}.bx--list-box__wrapper--inline .bx--label,.bx--list-box__wrapper--inline .bx--form__helper-text,.bx--list-box__wrapper--inline .bx--form-requirement{margin:0}.bx--list-box__wrapper--inline .bx--form__helper-text{max-width:none}.bx--list-box__wrapper--inline .bx--form-requirement{grid-column:2}.bx--list-box{position:relative;width:100%;height:2.5rem;max-height:2.5rem;border:none;border-bottom:1px solid var(--cds-ui-04, #8d8d8d);background-color:var(--cds-field-01, #f4f4f4);color:var(--cds-text-01, #161616);cursor:pointer;transition:all 70ms cubic-bezier(.2,0,.38,.9)}.bx--list-box:hover{background-color:var(--cds-hover-ui, #e5e5e5)}.bx--list-box--xl,.bx--list-box--lg{height:3rem;max-height:3rem}.bx--list-box--sm{height:2rem;max-height:2rem}.bx--list-box--expanded{border-bottom-color:var(--cds-ui-03, #e0e0e0)}.bx--list-box--expanded:hover{background-color:var(--cds-field-01, #f4f4f4)}.bx--list-box--expanded:hover.bx--list-box--light:hover{background-color:var(--cds-field-02, #ffffff)}.bx--list-box .bx--text-input{min-width:0;height:100%}.bx--list-box__invalid-icon{position:absolute;top:50%;right:2.5rem;fill:var(--cds-support-01, #da1e28);transform:translateY(-50%)}.bx--list-box__invalid-icon--warning{fill:var(--cds-support-03, 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0;border:1px solid rgba(0,0,0,0);border-top-color:var(--cds-border-subtle, #e0e0e0);margin:0 1rem;color:var(--cds-text-secondary, #525252);font-weight:400;line-height:1rem;text-decoration:none;text-overflow:ellipsis;white-space:nowrap}.bx--dropdown-link:hover{border-color:#0000;color:var(--cds-text-01, #161616)}.bx--dropdown--light .bx--dropdown-link{border-top-color:var(--cds-decorative-01, #e0e0e0)}.bx--dropdown--sm .bx--dropdown-link{height:2rem;padding-top:.4375rem;padding-bottom:.4375rem}.bx--dropdown--xl .bx--dropdown-link{height:3rem;padding-top:.9375rem;padding-bottom:.9375rem}.bx--dropdown--focused,.bx--dropdown-link:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px;padding:.6875rem 1rem;margin:0}@media screen and (prefers-contrast){.bx--dropdown--focused,.bx--dropdown-link:focus{outline-style:dotted}}.bx--dropdown-list[aria-activedescendant] .bx--dropdown-link:focus{padding:.6875rem 0;margin:0 1rem;outline:none}.bx--dropdown-list[aria-activedescendant] 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reduce){.bx--dropdown-v2.bx--skeleton:before,.bx--dropdown.bx--skeleton:before{animation:none}}@media screen and (-ms-high-contrast: active),(forced-colors: active),(prefers-contrast){.bx--dropdown .bx--list-box__field{outline:1px solid rgba(0,0,0,0)}.bx--list-box__menu-item__option{outline:none}.bx--list-box__menu-item__selected-icon{fill:ButtonText}}.bx--loading{animation-duration:.69s;animation-fill-mode:forwards;animation-iteration-count:infinite;animation-name:rotate;animation-timing-function:linear;width:5.5rem;height:5.5rem}.bx--loading svg circle{animation-duration:10ms;animation-name:init-stroke;animation-timing-function:cubic-bezier(.2,0,.38,.9)}@media screen and (prefers-reduced-motion: reduce){.bx--loading svg circle{animation:none}}.bx--loading__svg{fill:#0000}.bx--loading__svg circle{stroke-dasharray:276.4608 276.4608;stroke-linecap:butt;stroke-width:10}.bx--loading__stroke{stroke:var(--cds-interactive-04, #0f62fe);stroke-dashoffset:52.527552}.bx--loading--small 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.bx--form-requirement__supplement{padding-bottom:.5rem;color:var(--cds-text-01, #161616)}.bx--file__state-container{display:flex;min-width:1.5rem;align-items:center;justify-content:center;padding-right:1rem}.bx--file__state-container .bx--loading__svg{stroke:var(--cds-ui-05, #161616)}.bx--file__state-container .bx--file-complete{cursor:pointer;fill:var(--cds-interactive-04, #0f62fe)}.bx--file__state-container .bx--file-complete:focus{outline:1px solid var(--cds-focus, #0f62fe)}@media screen and (prefers-contrast){.bx--file__state-container .bx--file-complete:focus{outline-style:dotted}}.bx--file__state-container .bx--file-complete [data-icon-path=inner-path]{fill:var(--cds-icon-03, #ffffff);opacity:1}.bx--file__state-container .bx--file-invalid{width:1rem;height:1rem;fill:var(--cds-support-01, #da1e28)}.bx--file__state-container .bx--file-close{display:flex;width:1.5rem;height:1.5rem;align-items:center;justify-content:center;padding:0;border:none;background-color:#0000;cursor:pointer;fill:var(--cds-icon-01, #161616)}.bx--file__state-container .bx--file-close:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--file__state-container .bx--file-close:focus{outline-style:dotted}}.bx--file__state-container .bx--file-close svg path{fill:var(--cds-icon-01, #161616)}.bx--file__state-container .bx--inline-loading__animation{margin-right:-.5rem}.bx--file__drop-container{display:flex;overflow:hidden;height:6rem;align-items:flex-start;justify-content:space-between;padding:1rem;border:1px dashed var(--cds-ui-04, #8d8d8d)}.bx--file__drop-container--drag-over{background:none;outline:2px solid var(--cds-interactive-03, #0f62fe);outline-offset:-2px}@media screen and (-ms-high-contrast: active),(forced-colors: active),(prefers-contrast){.bx--file__selected-file{outline:1px solid rgba(0,0,0,0)}.bx--file__state-container .bx--file-close svg path{fill:ButtonText}}@keyframes stroke{to{stroke-dashoffset:0}}.bx--inline-loading{display:flex;width:100%;min-height:2rem;align-items:center}.bx--inline-loading__text{font-size:var(--cds-label-01-font-size, .75rem);font-weight:var(--cds-label-01-font-weight, 400);line-height:var(--cds-label-01-line-height, 1.33333);letter-spacing:var(--cds-label-01-letter-spacing, .32px);color:var(--cds-text-02, #525252)}.bx--inline-loading__animation{position:relative;display:flex;align-items:center;justify-content:center;margin-right:.5rem}.bx--inline-loading__checkmark-container{fill:var(--cds-support-02, 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reduce){.bx--pagination-nav__page:not(.bx--pagination-nav__page--direction):after{transition:none}}.bx--pagination-nav__page--active+.bx--pagination-nav__page:after,.bx--pagination-nav__page.bx--pagination-nav__page--active:after{left:calc(50% - .5rem);width:1rem;opacity:1}.bx--pagination-nav__page.bx--pagination-nav__page--active{background-color:initial;color:var(--cds-text-02, #525252);font-weight:600}.bx--pagination-nav__page .bx--pagination-nav__icon{fill:currentColor;pointer-events:none}.bx--pagination-nav__page--direction{display:flex;width:3rem;height:3rem;align-items:center;justify-content:center;line-height:0}.bx--pagination-nav__select{position:relative}.bx--pagination-nav__page--select{max-height:3rem;-webkit-appearance:none;-moz-appearance:none;appearance:none;text-indent:calc(50% - 4.5px)}@-moz-document url-prefix(){.bx--pagination-nav__page--select{text-indent:0}}.bx--pagination-nav__select-icon-wrapper{position:absolute;top:0;width:100%;height:100%;pointer-events:none}.bx--pagination-nav__select-icon-wrapper:not(.bx--pagination-nav__page--direction):after{position:absolute;bottom:0;left:50%;display:block;width:0;height:.25rem;background-color:var(--cds-interactive-01, #0f62fe);content:"";opacity:0;transition:width .11s cubic-bezier(.2,0,.38,.9)}@media screen and (prefers-reduced-motion: reduce){.bx--pagination-nav__select-icon-wrapper:not(.bx--pagination-nav__page--direction):after{transition:none}}.bx--pagination-nav__page--active+.bx--pagination-nav__select-icon-wrapper:after,.bx--pagination-nav__select-icon-wrapper.bx--pagination-nav__page--active:after{left:calc(50% - .5rem);width:1rem;opacity:1}.bx--pagination-nav__page--active+.bx--pagination-nav__select-icon-wrapper .bx--pagination-nav__select-icon{display:none}.bx--pagination-nav__select-icon{position:absolute;top:calc(50% - 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#f4f4f4);border-radius:0;color:var(--cds-text-01, #161616);cursor:pointer;font-family:inherit;opacity:1;transition:outline 70ms cubic-bezier(.2,0,.38,.9)}.bx--select-input:hover{background-color:var(--cds-hover-ui, #e5e5e5)}.bx--select-input::-ms-expand{display:none}@-moz-document url-prefix(){.bx--select-input:-moz-focusring,.bx--select-input::-moz-focus-inner{background-image:none;color:#0000;text-shadow:0 0 0 #000}}.bx--select-input:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px;color:var(--cds-text-01, #161616)}@media screen and (prefers-contrast){.bx--select-input:focus{outline-style:dotted}}.bx--select-input:disabled,.bx--select-input:hover:disabled{border-bottom-color:var(--cds-disabled-01, #f4f4f4);background-color:var(--cds-disabled-01, #f4f4f4);color:var(--cds-disabled-02, #c6c6c6);cursor:not-allowed}.bx--select-input--sm{height:2rem;max-height:2rem}.bx--select-input--xl,.bx--select-input--lg{height:3rem;max-height:3rem}.bx--select--disabled 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.11s;-webkit-user-select:none;-moz-user-select:none;user-select:none}a.bx--header__name:focus{border-color:#fff}.bx--header__name--prefix{font-weight:400}a.bx--header__name,a.bx--header__name:hover{color:#f4f4f4}.bx--header__menu-toggle:not(.bx--header__menu-toggle__hidden)~.bx--header__name{padding-left:.5rem}.bx--header__nav{position:relative;display:none;height:100%;padding-left:1rem}@media (min-width: 66rem){.bx--header__nav{display:block}}.bx--header__nav:before{position:absolute;top:50%;left:0;display:block;width:.0625rem;height:1.5rem;background-color:#393939;content:"";transform:translateY(-50%)}.bx--header__menu-bar{display:flex;height:100%;padding:0;margin:0;list-style:none}a.bx--header__menu-item{position:relative;display:flex;height:100%;align-items:center;padding:0 1rem;border:2px solid rgba(0,0,0,0);color:#c6c6c6;font-size:.875rem;font-weight:400;letter-spacing:0;line-height:1.125rem;text-decoration:none;transition:background-color .11s,border-color .11s,color 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.bx--header__menu-item{height:3rem}.bx--header__menu .bx--header__menu-item:hover{background-color:#262626;color:#f4f4f4}.bx--header__menu-arrow{margin-left:.5rem;fill:#c6c6c6;transition:transform .11s,fill .11s}.bx--header__global{display:flex;height:100%;flex:1 1 0%;justify-content:flex-end}.bx--skip-to-content{position:absolute;overflow:hidden;width:1px;height:1px;padding:0;border:0;margin:-1px;clip:rect(0,0,0,0);visibility:inherit;white-space:nowrap}.bx--skip-to-content:focus{z-index:9999;top:0;left:0;display:flex;width:auto;height:3rem;align-items:center;padding:0 1rem;border:4px solid #0f62fe;background-color:#161616;clip:auto;color:#f4f4f4;outline:none}.bx--header-panel{transition-timing-function:cubic-bezier(.2,0,1,.9);position:fixed;z-index:8000;top:3rem;right:0;bottom:0;overflow:hidden;width:0;border:none;background-color:#161616;color:#c6c6c6;transition:width .11s;will-change:width}.bx--header-panel--expanded{width:16rem;border-right:1px solid #393939;border-left:1px solid #393939}.bx--panel--overlay{position:fixed;z-index:1000;top:3rem;right:0;bottom:0;width:16rem;height:100%;padding:1rem 0;background-color:#161616;overflow-x:hidden;overflow-y:auto;transform:translate3d(100%,0,0);transition:transform .11s cubic-bezier(.2,0,.38,.9);will-change:transform}.bx--panel--expanded{box-shadow:0 8px 16px #00000040;transform:translateZ(0)}.bx--product-switcher__search{padding:0 1rem;margin-bottom:1.5rem}.bx--search--shell input{background-color:#e0e0e0}.bx--product-switcher__subheader,.bx--product-switcher__all-btn{font-size:var(--cds-body-short-01-font-size, .875rem);font-weight:var(--cds-body-short-01-font-weight, 400);line-height:var(--cds-body-short-01-line-height, 1.28572);letter-spacing:var(--cds-body-short-01-letter-spacing, 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48px)}.bx--side-nav--fixed{width:16rem}.bx--side-nav--collapsed{width:16rem;transform:translate(-16rem)}.bx--side-nav__navigation{display:flex;height:100%;flex-direction:column}.bx--side-nav__header{display:flex;width:100%;max-width:100%;height:3rem;border-bottom:1px solid #393939}.bx--side-nav:hover .bx--side-nav__header,.bx--side-nav--fixed .bx--side-nav__header,.bx--side-nav--expanded .bx--side-nav__header,.bx--side-nav--ux .bx--side-nav__header{height:auto}.bx--side-nav__details{display:flex;min-width:0;flex:1;flex-direction:column;padding-right:1rem;opacity:0;visibility:hidden}.bx--side-nav:hover .bx--side-nav__details,.bx--side-nav--fixed .bx--side-nav__details,.bx--side-nav--expanded .bx--side-nav__details{visibility:inherit;opacity:1}.bx--side-nav--ux 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(prefers-contrast){.bx--side-nav__select:focus{outline-style:dotted}}.bx--side-nav__footer{width:100%;flex:0 0 3rem;background-color:#fff}.bx--side-nav__toggle{outline:2px solid rgba(0,0,0,0);outline-offset:-2px;display:inline-block;padding:0;border:0;-webkit-appearance:none;-moz-appearance:none;appearance:none;background:none;cursor:pointer;width:100%;height:100%;padding-left:1rem;text-align:left;transition:outline .11s}.bx--side-nav__toggle::-moz-focus-inner{border:0}.bx--side-nav__toggle:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and (prefers-contrast){.bx--side-nav__toggle:focus{outline-style:dotted}}.bx--side-nav__items{overflow:hidden;flex:1 1 0%;padding:1rem 0 0}.bx--side-nav:hover .bx--side-nav__items,.bx--side-nav--fixed .bx--side-nav__items,.bx--side-nav--expanded .bx--side-nav__items,.bx--side-nav--ux .bx--side-nav__items{overflow-y:auto}.bx--side-nav__item{overflow:hidden;width:auto;height:auto}.bx--side-nav--ux .bx--side-nav__item{width:auto;height:auto}.bx--side-nav__item:not(.bx--side-nav__item--active):hover .bx--side-nav__item:not(.bx--side-nav__item--active)>.bx--side-nav__submenu:hover,.bx--side-nav__item:not(.bx--side-nav__item--active)>.bx--side-nav__link:hover,.bx--side-nav__menu a.bx--side-nav__link:not(.bx--side-nav__link--current):not([aria-current=page]):hover,.bx--side-nav a.bx--header__menu-item:hover,.bx--side-nav .bx--header__menu-title[aria-expanded=true]:hover{background-color:#e5e5e5;color:#161616}.bx--side-nav__item:not(.bx--side-nav__item--active)>.bx--side-nav__link:hover>span,.bx--side-nav__item:not(.bx--side-nav__item--active) .bx--side-nav__menu-item>.bx--side-nav__link:hover>span{color:#161616}.bx--side-nav__item--large{height:3rem}.bx--side-nav__divider{height:1px;margin:var(--cds-spacing-03, .5rem) var(--cds-spacing-05, 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(prefers-contrast){.bx--side-nav__submenu:focus{outline-style:dotted}}.bx--side-nav__submenu-title{overflow:hidden;text-overflow:ellipsis;white-space:nowrap;text-align:left}.bx--side-nav__icon.bx--side-nav__submenu-chevron{display:flex;flex:1;justify-content:flex-end}.bx--side-nav__submenu-chevron>svg{width:1rem;height:1rem;transition:transform .11s}.bx--side-nav__submenu[aria-expanded=true] .bx--side-nav__submenu-chevron>svg{transform:rotate(180deg)}.bx--side-nav__item--large .bx--side-nav__submenu{height:3rem}.bx--side-nav__item--active .bx--side-nav__submenu:hover{background-color:#e5e5e5;color:#161616}.bx--side-nav__item--active .bx--side-nav__submenu[aria-expanded=false]{position:relative;background-color:#e5e5e5;color:#161616}.bx--side-nav__item--active .bx--side-nav__submenu[aria-expanded=false]:before{position:absolute;top:0;bottom:0;left:0;width:4px;background-color:#0f62fe;content:""}.bx--side-nav__item--active .bx--side-nav__submenu-title{color:#161616;font-weight:600}.bx--side-nav__menu{display:block;max-height:0;visibility:hidden}.bx--side-nav__submenu[aria-expanded=true]+.bx--side-nav__menu{max-height:93.75rem;visibility:inherit}.bx--side-nav__menu a.bx--side-nav__link{height:2rem;min-height:2rem;padding-left:2rem;font-weight:400}.bx--side-nav__item.bx--side-nav__item--icon a.bx--side-nav__link{padding-left:4.5rem}.bx--side-nav__menu a.bx--side-nav__link--current,.bx--side-nav__menu a.bx--side-nav__link[aria-current=page],a.bx--side-nav__link--current{background-color:#e0e0e0}.bx--side-nav__menu a.bx--side-nav__link--current>span,.bx--side-nav__menu a.bx--side-nav__link[aria-current=page]>span,a.bx--side-nav__link--current>span{color:#161616;font-weight:600}a.bx--side-nav__link,.bx--side-nav a.bx--header__menu-item,.bx--side-nav .bx--header__menu-title[aria-expanded=true]+.bx--header__menu{outline:2px solid rgba(0,0,0,0);outline-offset:-2px;font-size:var(--cds-productive-heading-01-font-size, .875rem);font-weight:var(--cds-productive-heading-01-font-weight, 600);line-height:var(--cds-productive-heading-01-line-height, 1.28572);letter-spacing:var(--cds-productive-heading-01-letter-spacing, .16px);position:relative;display:flex;min-height:2rem;align-items:center;padding:0 1rem;text-decoration:none;transition:color .11s,background-color .11s,outline .11s}.bx--side-nav__item--large a.bx--side-nav__link{height:3rem}a.bx--side-nav__link>.bx--side-nav__link-text,.bx--side-nav a.bx--header__menu-item .bx--text-truncate-end{overflow:hidden;text-overflow:ellipsis;white-space:nowrap;color:#525252;font-size:.875rem;letter-spacing:.1px;line-height:1.25rem;-webkit-user-select:none;-moz-user-select:none;user-select:none}a.bx--side-nav__link:focus,.bx--side-nav a.bx--header__menu-item:focus{outline:2px solid var(--cds-focus, #0f62fe);outline-offset:-2px}@media screen and 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diff --git a/web/blueprint/build/_app/immutable/assets/_page.77bdd972.css b/web/blueprint/build/_app/immutable/assets/_page.77bdd972.css
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+`),this.blockTokens(e,this.tokens);let t;for(;t=this.inlineQueue.shift();)this.inlineTokens(t.src,t.tokens);return this.tokens}blockTokens(e,t=[]){this.options.pedantic?e=e.replace(/\t/g," ").replace(/^ +$/gm,""):e=e.replace(/^( *)(\t+)/gm,(o,f,a)=>f+" ".repeat(a.length));let r,n,l,s;for(;e;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some(o=>(r=o.call({lexer:this},e,t))?(e=e.substring(r.raw.length),t.push(r),!0):!1))){if(r=this.tokenizer.space(e)){e=e.substring(r.raw.length),r.raw.length===1&&t.length>0?t[t.length-1].raw+=`
+`:t.push(r);continue}if(r=this.tokenizer.code(e)){e=e.substring(r.raw.length),n=t[t.length-1],n&&(n.type==="paragraph"||n.type==="text")?(n.raw+=`
+`+r.raw,n.text+=`
+`+r.text,this.inlineQueue[this.inlineQueue.length-1].src=n.text):t.push(r);continue}if(r=this.tokenizer.fences(e)){e=e.substring(r.raw.length),t.push(r);continue}if(r=this.tokenizer.heading(e)){e=e.substring(r.raw.length),t.push(r);continue}if(r=this.tokenizer.hr(e)){e=e.substring(r.raw.length),t.push(r);continue}if(r=this.tokenizer.blockquote(e)){e=e.substring(r.raw.length),t.push(r);continue}if(r=this.tokenizer.list(e)){e=e.substring(r.raw.length),t.push(r);continue}if(r=this.tokenizer.html(e)){e=e.substring(r.raw.length),t.push(r);continue}if(r=this.tokenizer.def(e)){e=e.substring(r.raw.length),n=t[t.length-1],n&&(n.type==="paragraph"||n.type==="text")?(n.raw+=`
+`+r.raw,n.text+=`
+`+r.raw,this.inlineQueue[this.inlineQueue.length-1].src=n.text):this.tokens.links[r.tag]||(this.tokens.links[r.tag]={href:r.href,title:r.title});continue}if(r=this.tokenizer.table(e)){e=e.substring(r.raw.length),t.push(r);continue}if(r=this.tokenizer.lheading(e)){e=e.substring(r.raw.length),t.push(r);continue}if(l=e,this.options.extensions&&this.options.extensions.startBlock){let o=1/0;const f=e.slice(1);let a;this.options.extensions.startBlock.forEach(function(u){a=u.call({lexer:this},f),typeof a=="number"&&a>=0&&(o=Math.min(o,a))}),o<1/0&&o>=0&&(l=e.substring(0,o+1))}if(this.state.top&&(r=this.tokenizer.paragraph(l))){n=t[t.length-1],s&&n.type==="paragraph"?(n.raw+=`
+`+r.raw,n.text+=`
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diff --git a/web/blueprint/build/_app/immutable/chunks/NotificationIcon.9209ca8c.js b/web/blueprint/build/_app/immutable/chunks/NotificationIcon.9209ca8c.js
new file mode 100644
index 0000000000000000000000000000000000000000..0b63ac53ee5f5051ab2b8ba7d8bdaf57d4970510
--- /dev/null
+++ b/web/blueprint/build/_app/immutable/chunks/NotificationIcon.9209ca8c.js
@@ -0,0 +1 @@
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index 0000000000000000000000000000000000000000..2aa34ba427b36a57ec17f836b01d4a3b9b0879e3
--- /dev/null
+++ b/web/blueprint/build/_app/immutable/chunks/Toggle.b9cf6836.js
@@ -0,0 +1 @@
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Promise((p,O)=>{n=p,u=O}),l=p=>{s||(d(new qt(p)),i.abort==null||i.abort())},o=()=>{e=!0},h=()=>{e=!1},y=()=>!Fe.isFocused()||i.networkMode!=="always"&&!Qe.isOnline(),b=p=>{s||(s=!0,i.onSuccess==null||i.onSuccess(p),r==null||r(),n(p))},d=p=>{s||(s=!0,i.onError==null||i.onError(p),r==null||r(),u(p))},c=()=>new Promise(p=>{r=O=>{const T=s||!y();return T&&p(O),T},i.onPause==null||i.onPause()}).then(()=>{r=void 0,s||i.onContinue==null||i.onContinue()}),f=()=>{if(s)return;let p;try{p=i.fn()}catch(O){p=Promise.reject(O)}Promise.resolve(p).then(b).catch(O=>{var T,S;if(s)return;const w=(T=i.retry)!=null?T:3,U=(S=i.retryDelay)!=null?S:Js,C=typeof U=="function"?U(t,O):U,_=w===!0||typeof w=="number"&&t{if(y())return c()}).then(()=>{e?d(O):f()})})};return qe(i.networkMode)?f():c().then(f),{promise:a,cancel:l,continue:()=>(r==null?void 0:r())?a:Promise.resolve(),cancelRetry:o,continueRetry:h}}const Xe=console;function Xs(){let i=[],e=0,t=h=>{h()},s=h=>{h()};const r=h=>{let y;e++;try{y=h()}finally{e--,e||a()}return y},n=h=>{e?i.push(h):vt(()=>{t(h)})},u=h=>(...y)=>{n(()=>{h(...y)})},a=()=>{const h=i;i=[],h.length&&vt(()=>{s(()=>{h.forEach(y=>{t(y)})})})};return{batch:r,batchCalls:u,schedule:n,setNotifyFunction:h=>{t=h},setBatchNotifyFunction:h=>{s=h}}}const M=Xs();class Tt{destroy(){this.clearGcTimeout()}scheduleGc(){this.clearGcTimeout(),Ie(this.cacheTime)&&(this.gcTimeout=setTimeout(()=>{this.optionalRemove()},this.cacheTime))}updateCacheTime(e){this.cacheTime=Math.max(this.cacheTime||0,e??(_e?1/0:5*60*1e3))}clearGcTimeout(){this.gcTimeout&&(clearTimeout(this.gcTimeout),this.gcTimeout=void 0)}}class $s extends Tt{constructor(e){super(),this.abortSignalConsumed=!1,this.defaultOptions=e.defaultOptions,this.setOptions(e.options),this.observers=[],this.cache=e.cache,this.logger=e.logger||Xe,this.queryKey=e.queryKey,this.queryHash=e.queryHash,this.initialState=e.state||ei(this.options),this.state=this.initialState,this.scheduleGc()}get meta(){return this.options.meta}setOptions(e){this.options={...this.defaultOptions,...e},this.updateCacheTime(this.options.cacheTime)}optionalRemove(){!this.observers.length&&this.state.fetchStatus==="idle"&&this.cache.remove(this)}setData(e,t){const s=je(this.state.data,e,this.options);return this.dispatch({data:s,type:"success",dataUpdatedAt:t==null?void 0:t.updatedAt,manual:t==null?void 0:t.manual}),s}setState(e,t){this.dispatch({type:"setState",state:e,setStateOptions:t})}cancel(e){var t;const s=this.promise;return(t=this.retryer)==null||t.cancel(e),s?s.then(N).catch(N):Promise.resolve()}destroy(){super.destroy(),this.cancel({silent:!0})}reset(){this.destroy(),this.setState(this.initialState)}isActive(){return this.observers.some(e=>e.options.enabled!==!1)}isDisabled(){return this.getObserversCount()>0&&!this.isActive()}isStale(){return this.state.isInvalidated||!this.state.dataUpdatedAt||this.observers.some(e=>e.getCurrentResult().isStale)}isStaleByTime(e=0){return this.state.isInvalidated||!this.state.dataUpdatedAt||!At(this.state.dataUpdatedAt,e)}onFocus(){var e;const t=this.observers.find(s=>s.shouldFetchOnWindowFocus());t&&t.refetch({cancelRefetch:!1}),(e=this.retryer)==null||e.continue()}onOnline(){var e;const t=this.observers.find(s=>s.shouldFetchOnReconnect());t&&t.refetch({cancelRefetch:!1}),(e=this.retryer)==null||e.continue()}addObserver(e){this.observers.includes(e)||(this.observers.push(e),this.clearGcTimeout(),this.cache.notify({type:"observerAdded",query:this,observer:e}))}removeObserver(e){this.observers.includes(e)&&(this.observers=this.observers.filter(t=>t!==e),this.observers.length||(this.retryer&&(this.abortSignalConsumed?this.retryer.cancel({revert:!0}):this.retryer.cancelRetry()),this.scheduleGc()),this.cache.notify({type:"observerRemoved",query:this,observer:e}))}getObserversCount(){return this.observers.length}invalidate(){this.state.isInvalidated||this.dispatch({type:"invalidate"})}fetch(e,t){var s,r;if(this.state.fetchStatus!=="idle"){if(this.state.dataUpdatedAt&&t!=null&&t.cancelRefetch)this.cancel({silent:!0});else if(this.promise){var n;return(n=this.retryer)==null||n.continueRetry(),this.promise}}if(e&&this.setOptions(e),!this.options.queryFn){const d=this.observers.find(c=>c.options.queryFn);d&&this.setOptions(d.options)}Array.isArray(this.options.queryKey);const u=Zs(),a={queryKey:this.queryKey,pageParam:void 0,meta:this.meta},l=d=>{Object.defineProperty(d,"signal",{enumerable:!0,get:()=>{if(u)return this.abortSignalConsumed=!0,u.signal}})};l(a);const o=()=>this.options.queryFn?(this.abortSignalConsumed=!1,this.options.queryFn(a)):Promise.reject("Missing queryFn"),h={fetchOptions:t,options:this.options,queryKey:this.queryKey,state:this.state,fetchFn:o};if(l(h),(s=this.options.behavior)==null||s.onFetch(h),this.revertState=this.state,this.state.fetchStatus==="idle"||this.state.fetchMeta!==((r=h.fetchOptions)==null?void 0:r.meta)){var y;this.dispatch({type:"fetch",meta:(y=h.fetchOptions)==null?void 0:y.meta})}const b=d=>{if(Ee(d)&&d.silent||this.dispatch({type:"error",error:d}),!Ee(d)){var c,f,p,O;(c=(f=this.cache.config).onError)==null||c.call(f,d,this),(p=(O=this.cache.config).onSettled)==null||p.call(O,this.state.data,d,this)}this.isFetchingOptimistic||this.scheduleGc(),this.isFetchingOptimistic=!1};return this.retryer=Dt({fn:h.fetchFn,abort:u==null?void 0:u.abort.bind(u),onSuccess:d=>{var c,f,p,O;if(typeof d>"u"){b(new Error(this.queryHash+" data is undefined"));return}this.setData(d),(c=(f=this.cache.config).onSuccess)==null||c.call(f,d,this),(p=(O=this.cache.config).onSettled)==null||p.call(O,d,this.state.error,this),this.isFetchingOptimistic||this.scheduleGc(),this.isFetchingOptimistic=!1},onError:b,onFail:(d,c)=>{this.dispatch({type:"failed",failureCount:d,error:c})},onPause:()=>{this.dispatch({type:"pause"})},onContinue:()=>{this.dispatch({type:"continue"})},retry:h.options.retry,retryDelay:h.options.retryDelay,networkMode:h.options.networkMode}),this.promise=this.retryer.promise,this.promise}dispatch(e){const t=s=>{var r,n;switch(e.type){case"failed":return{...s,fetchFailureCount:e.failureCount,fetchFailureReason:e.error};case"pause":return{...s,fetchStatus:"paused"};case"continue":return{...s,fetchStatus:"fetching"};case"fetch":return{...s,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:(r=e.meta)!=null?r:null,fetchStatus:qe(this.options.networkMode)?"fetching":"paused",...!s.dataUpdatedAt&&{error:null,status:"loading"}};case"success":return{...s,data:e.data,dataUpdateCount:s.dataUpdateCount+1,dataUpdatedAt:(n=e.dataUpdatedAt)!=null?n:Date.now(),error:null,isInvalidated:!1,status:"success",...!e.manual&&{fetchStatus:"idle",fetchFailureCount:0,fetchFailureReason:null}};case"error":const u=e.error;return Ee(u)&&u.revert&&this.revertState?{...this.revertState}:{...s,error:u,errorUpdateCount:s.errorUpdateCount+1,errorUpdatedAt:Date.now(),fetchFailureCount:s.fetchFailureCount+1,fetchFailureReason:u,fetchStatus:"idle",status:"error"};case"invalidate":return{...s,isInvalidated:!0};case"setState":return{...s,...e.state}}};this.state=t(this.state),M.batch(()=>{this.observers.forEach(s=>{s.onQueryUpdate(e)}),this.cache.notify({query:this,type:"updated",action:e})})}}function ei(i){const e=typeof i.initialData=="function"?i.initialData():i.initialData,t=typeof e<"u",s=t?typeof i.initialDataUpdatedAt=="function"?i.initialDataUpdatedAt():i.initialDataUpdatedAt:0;return{data:e,dataUpdateCount:0,dataUpdatedAt:t?s??Date.now():0,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:null,isInvalidated:!1,status:t?"success":"loading",fetchStatus:"idle"}}class ti extends he{constructor(e){super(),this.config=e||{},this.queries=[],this.queriesMap={}}build(e,t,s){var r;const n=t.queryKey,u=(r=t.queryHash)!=null?r:Je(n,t);let a=this.get(u);return a||(a=new $s({cache:this,logger:e.getLogger(),queryKey:n,queryHash:u,options:e.defaultQueryOptions(t),state:s,defaultOptions:e.getQueryDefaults(n)}),this.add(a)),a}add(e){this.queriesMap[e.queryHash]||(this.queriesMap[e.queryHash]=e,this.queries.push(e),this.notify({type:"added",query:e}))}remove(e){const t=this.queriesMap[e.queryHash];t&&(e.destroy(),this.queries=this.queries.filter(s=>s!==e),t===e&&delete this.queriesMap[e.queryHash],this.notify({type:"removed",query:e}))}clear(){M.batch(()=>{this.queries.forEach(e=>{this.remove(e)})})}get(e){return this.queriesMap[e]}getAll(){return this.queries}find(e,t){const[s]=$(e,t);return typeof s.exact>"u"&&(s.exact=!0),this.queries.find(r=>mt(s,r))}findAll(e,t){const[s]=$(e,t);return Object.keys(s).length>0?this.queries.filter(r=>mt(s,r)):this.queries}notify(e){M.batch(()=>{this.listeners.forEach(({listener:t})=>{t(e)})})}onFocus(){M.batch(()=>{this.queries.forEach(e=>{e.onFocus()})})}onOnline(){M.batch(()=>{this.queries.forEach(e=>{e.onOnline()})})}}class si extends Tt{constructor(e){super(),this.defaultOptions=e.defaultOptions,this.mutationId=e.mutationId,this.mutationCache=e.mutationCache,this.logger=e.logger||Xe,this.observers=[],this.state=e.state||It(),this.setOptions(e.options),this.scheduleGc()}setOptions(e){this.options={...this.defaultOptions,...e},this.updateCacheTime(this.options.cacheTime)}get meta(){return this.options.meta}setState(e){this.dispatch({type:"setState",state:e})}addObserver(e){this.observers.includes(e)||(this.observers.push(e),this.clearGcTimeout(),this.mutationCache.notify({type:"observerAdded",mutation:this,observer:e}))}removeObserver(e){this.observers=this.observers.filter(t=>t!==e),this.scheduleGc(),this.mutationCache.notify({type:"observerRemoved",mutation:this,observer:e})}optionalRemove(){this.observers.length||(this.state.status==="loading"?this.scheduleGc():this.mutationCache.remove(this))}continue(){var e,t;return(e=(t=this.retryer)==null?void 0:t.continue())!=null?e:this.execute()}async execute(){const e=()=>{var _;return this.retryer=Dt({fn:()=>this.options.mutationFn?this.options.mutationFn(this.state.variables):Promise.reject("No mutationFn found"),onFail:(v,z)=>{this.dispatch({type:"failed",failureCount:v,error:z})},onPause:()=>{this.dispatch({type:"pause"})},onContinue:()=>{this.dispatch({type:"continue"})},retry:(_=this.options.retry)!=null?_:0,retryDelay:this.options.retryDelay,networkMode:this.options.networkMode}),this.retryer.promise},t=this.state.status==="loading";try{var s,r,n,u,a,l,o,h;if(!t){var y,b,d,c;this.dispatch({type:"loading",variables:this.options.variables}),await((y=(b=this.mutationCache.config).onMutate)==null?void 0:y.call(b,this.state.variables,this));const v=await((d=(c=this.options).onMutate)==null?void 0:d.call(c,this.state.variables));v!==this.state.context&&this.dispatch({type:"loading",context:v,variables:this.state.variables})}const _=await e();return await((s=(r=this.mutationCache.config).onSuccess)==null?void 0:s.call(r,_,this.state.variables,this.state.context,this)),await((n=(u=this.options).onSuccess)==null?void 0:n.call(u,_,this.state.variables,this.state.context)),await((a=(l=this.mutationCache.config).onSettled)==null?void 0:a.call(l,_,null,this.state.variables,this.state.context,this)),await((o=(h=this.options).onSettled)==null?void 0:o.call(h,_,null,this.state.variables,this.state.context)),this.dispatch({type:"success",data:_}),_}catch(_){try{var f,p,O,T,S,w,U,C;throw await((f=(p=this.mutationCache.config).onError)==null?void 0:f.call(p,_,this.state.variables,this.state.context,this)),await((O=(T=this.options).onError)==null?void 0:O.call(T,_,this.state.variables,this.state.context)),await((S=(w=this.mutationCache.config).onSettled)==null?void 0:S.call(w,void 0,_,this.state.variables,this.state.context,this)),await((U=(C=this.options).onSettled)==null?void 0:U.call(C,void 0,_,this.state.variables,this.state.context)),_}finally{this.dispatch({type:"error",error:_})}}}dispatch(e){const t=s=>{switch(e.type){case"failed":return{...s,failureCount:e.failureCount,failureReason:e.error};case"pause":return{...s,isPaused:!0};case"continue":return{...s,isPaused:!1};case"loading":return{...s,context:e.context,data:void 0,failureCount:0,failureReason:null,error:null,isPaused:!qe(this.options.networkMode),status:"loading",variables:e.variables};case"success":return{...s,data:e.data,failureCount:0,failureReason:null,error:null,status:"success",isPaused:!1};case"error":return{...s,data:void 0,error:e.error,failureCount:s.failureCount+1,failureReason:e.error,isPaused:!1,status:"error"};case"setState":return{...s,...e.state}}};this.state=t(this.state),M.batch(()=>{this.observers.forEach(s=>{s.onMutationUpdate(e)}),this.mutationCache.notify({mutation:this,type:"updated",action:e})})}}function It(){return{context:void 0,data:void 0,error:null,failureCount:0,failureReason:null,isPaused:!1,status:"idle",variables:void 0}}class ii extends he{constructor(e){super(),this.config=e||{},this.mutations=[],this.mutationId=0}build(e,t,s){const r=new si({mutationCache:this,logger:e.getLogger(),mutationId:++this.mutationId,options:e.defaultMutationOptions(t),state:s,defaultOptions:t.mutationKey?e.getMutationDefaults(t.mutationKey):void 0});return this.add(r),r}add(e){this.mutations.push(e),this.notify({type:"added",mutation:e})}remove(e){this.mutations=this.mutations.filter(t=>t!==e),this.notify({type:"removed",mutation:e})}clear(){M.batch(()=>{this.mutations.forEach(e=>{this.remove(e)})})}getAll(){return this.mutations}find(e){return typeof e.exact>"u"&&(e.exact=!0),this.mutations.find(t=>yt(e,t))}findAll(e){return this.mutations.filter(t=>yt(e,t))}notify(e){M.batch(()=>{this.listeners.forEach(({listener:t})=>{t(e)})})}resumePausedMutations(){var e;return this.resuming=((e=this.resuming)!=null?e:Promise.resolve()).then(()=>{const t=this.mutations.filter(s=>s.state.isPaused);return M.batch(()=>t.reduce((s,r)=>s.then(()=>r.continue().catch(N)),Promise.resolve()))}).then(()=>{this.resuming=void 0}),this.resuming}}function ri(){return{onFetch:i=>{i.fetchFn=()=>{var e,t,s,r,n,u;const a=(e=i.fetchOptions)==null||(t=e.meta)==null?void 0:t.refetchPage,l=(s=i.fetchOptions)==null||(r=s.meta)==null?void 0:r.fetchMore,o=l==null?void 0:l.pageParam,h=(l==null?void 0:l.direction)==="forward",y=(l==null?void 0:l.direction)==="backward",b=((n=i.state.data)==null?void 0:n.pages)||[],d=((u=i.state.data)==null?void 0:u.pageParams)||[];let c=d,f=!1;const p=C=>{Object.defineProperty(C,"signal",{enumerable:!0,get:()=>{var _;if((_=i.signal)!=null&&_.aborted)f=!0;else{var v;(v=i.signal)==null||v.addEventListener("abort",()=>{f=!0})}return i.signal}})},O=i.options.queryFn||(()=>Promise.reject("Missing queryFn")),T=(C,_,v,z)=>(c=z?[_,...c]:[...c,_],z?[v,...C]:[...C,v]),S=(C,_,v,z)=>{if(f)return Promise.reject("Cancelled");if(typeof v>"u"&&!_&&C.length)return Promise.resolve(C);const re={queryKey:i.queryKey,pageParam:v,meta:i.options.meta};p(re);const be=O(re);return Promise.resolve(be).then(De=>T(C,v,De,z))};let w;if(!b.length)w=S([]);else if(h){const C=typeof o<"u",_=C?o:Be(i.options,b);w=S(b,C,_)}else if(y){const C=typeof o<"u",_=C?o:Ut(i.options,b);w=S(b,C,_,!0)}else{c=[];const C=typeof i.options.getNextPageParam>"u";w=(a&&b[0]?a(b[0],0,b):!0)?S([],C,d[0]):Promise.resolve(T([],d[0],b[0]));for(let v=1;v{if(a&&b[v]?a(b[v],v,b):!0){const be=C?d[v]:Be(i.options,z);return S(z,C,be)}return Promise.resolve(T(z,d[v],b[v]))})}return w.then(C=>({pages:C,pageParams:c}))}}}}function Be(i,e){return i.getNextPageParam==null?void 0:i.getNextPageParam(e[e.length-1],e)}function Ut(i,e){return i.getPreviousPageParam==null?void 0:i.getPreviousPageParam(e[0],e)}function Ei(i,e){if(i.getNextPageParam&&Array.isArray(e)){const t=Be(i,e);return typeof t<"u"&&t!==null&&t!==!1}}function Pi(i,e){if(i.getPreviousPageParam&&Array.isArray(e)){const t=Ut(i,e);return typeof t<"u"&&t!==null&&t!==!1}}class ni{constructor(e={}){this.queryCache=e.queryCache||new ti,this.mutationCache=e.mutationCache||new ii,this.logger=e.logger||Xe,this.defaultOptions=e.defaultOptions||{},this.queryDefaults=[],this.mutationDefaults=[],this.mountCount=0}mount(){this.mountCount++,this.mountCount===1&&(this.unsubscribeFocus=Fe.subscribe(()=>{Fe.isFocused()&&(this.resumePausedMutations(),this.queryCache.onFocus())}),this.unsubscribeOnline=Qe.subscribe(()=>{Qe.isOnline()&&(this.resumePausedMutations(),this.queryCache.onOnline())}))}unmount(){var e,t;this.mountCount--,this.mountCount===0&&((e=this.unsubscribeFocus)==null||e.call(this),this.unsubscribeFocus=void 0,(t=this.unsubscribeOnline)==null||t.call(this),this.unsubscribeOnline=void 0)}isFetching(e,t){const[s]=$(e,t);return s.fetchStatus="fetching",this.queryCache.findAll(s).length}isMutating(e){return this.mutationCache.findAll({...e,fetching:!0}).length}getQueryData(e,t){var s;return(s=this.queryCache.find(e,t))==null?void 0:s.state.data}ensureQueryData(e,t,s){const r=pe(e,t,s),n=this.getQueryData(r.queryKey);return n?Promise.resolve(n):this.fetchQuery(r)}getQueriesData(e){return this.getQueryCache().findAll(e).map(({queryKey:t,state:s})=>{const r=s.data;return[t,r]})}setQueryData(e,t,s){const r=this.queryCache.find(e),n=r==null?void 0:r.state.data,u=xs(t,n);if(typeof u>"u")return;const a=pe(e),l=this.defaultQueryOptions(a);return this.queryCache.build(this,l).setData(u,{...s,manual:!0})}setQueriesData(e,t,s){return M.batch(()=>this.getQueryCache().findAll(e).map(({queryKey:r})=>[r,this.setQueryData(r,t,s)]))}getQueryState(e,t){var s;return(s=this.queryCache.find(e,t))==null?void 0:s.state}removeQueries(e,t){const[s]=$(e,t),r=this.queryCache;M.batch(()=>{r.findAll(s).forEach(n=>{r.remove(n)})})}resetQueries(e,t,s){const[r,n]=$(e,t,s),u=this.queryCache,a={type:"active",...r};return M.batch(()=>(u.findAll(r).forEach(l=>{l.reset()}),this.refetchQueries(a,n)))}cancelQueries(e,t,s){const[r,n={}]=$(e,t,s);typeof n.revert>"u"&&(n.revert=!0);const u=M.batch(()=>this.queryCache.findAll(r).map(a=>a.cancel(n)));return Promise.all(u).then(N).catch(N)}invalidateQueries(e,t,s){const[r,n]=$(e,t,s);return M.batch(()=>{var u,a;if(this.queryCache.findAll(r).forEach(o=>{o.invalidate()}),r.refetchType==="none")return Promise.resolve();const l={...r,type:(u=(a=r.refetchType)!=null?a:r.type)!=null?u:"active"};return this.refetchQueries(l,n)})}refetchQueries(e,t,s){const[r,n]=$(e,t,s),u=M.batch(()=>this.queryCache.findAll(r).filter(l=>!l.isDisabled()).map(l=>{var o;return l.fetch(void 0,{...n,cancelRefetch:(o=n==null?void 0:n.cancelRefetch)!=null?o:!0,meta:{refetchPage:r.refetchPage}})}));let a=Promise.all(u).then(N);return n!=null&&n.throwOnError||(a=a.catch(N)),a}fetchQuery(e,t,s){const r=pe(e,t,s),n=this.defaultQueryOptions(r);typeof n.retry>"u"&&(n.retry=!1);const u=this.queryCache.build(this,n);return u.isStaleByTime(n.staleTime)?u.fetch(n):Promise.resolve(u.state.data)}prefetchQuery(e,t,s){return this.fetchQuery(e,t,s).then(N).catch(N)}fetchInfiniteQuery(e,t,s){const r=pe(e,t,s);return r.behavior=ri(),this.fetchQuery(r)}prefetchInfiniteQuery(e,t,s){return this.fetchInfiniteQuery(e,t,s).then(N).catch(N)}resumePausedMutations(){return this.mutationCache.resumePausedMutations()}getQueryCache(){return this.queryCache}getMutationCache(){return this.mutationCache}getLogger(){return this.logger}getDefaultOptions(){return this.defaultOptions}setDefaultOptions(e){this.defaultOptions=e}setQueryDefaults(e,t){const s=this.queryDefaults.find(r=>ne(e)===ne(r.queryKey));s?s.defaultOptions=t:this.queryDefaults.push({queryKey:e,defaultOptions:t})}getQueryDefaults(e){if(!e)return;const t=this.queryDefaults.find(s=>Ae(e,s.queryKey));return t==null?void 0:t.defaultOptions}setMutationDefaults(e,t){const s=this.mutationDefaults.find(r=>ne(e)===ne(r.mutationKey));s?s.defaultOptions=t:this.mutationDefaults.push({mutationKey:e,defaultOptions:t})}getMutationDefaults(e){if(!e)return;const t=this.mutationDefaults.find(s=>Ae(e,s.mutationKey));return t==null?void 0:t.defaultOptions}defaultQueryOptions(e){if(e!=null&&e._defaulted)return e;const t={...this.defaultOptions.queries,...this.getQueryDefaults(e==null?void 0:e.queryKey),...e,_defaulted:!0};return!t.queryHash&&t.queryKey&&(t.queryHash=Je(t.queryKey,t)),typeof t.refetchOnReconnect>"u"&&(t.refetchOnReconnect=t.networkMode!=="always"),typeof t.useErrorBoundary>"u"&&(t.useErrorBoundary=!!t.suspense),t}defaultMutationOptions(e){return e!=null&&e._defaulted?e:{...this.defaultOptions.mutations,...this.getMutationDefaults(e==null?void 0:e.mutationKey),...e,_defaulted:!0}}clear(){this.queryCache.clear(),this.mutationCache.clear()}}class zt extends he{constructor(e,t){super(),this.client=e,this.options=t,this.trackedProps=new Set,this.selectError=null,this.bindMethods(),this.setOptions(t)}bindMethods(){this.remove=this.remove.bind(this),this.refetch=this.refetch.bind(this)}onSubscribe(){this.listeners.size===1&&(this.currentQuery.addObserver(this),Ot(this.currentQuery,this.options)&&this.executeFetch(),this.updateTimers())}onUnsubscribe(){this.hasListeners()||this.destroy()}shouldFetchOnReconnect(){return Ne(this.currentQuery,this.options,this.options.refetchOnReconnect)}shouldFetchOnWindowFocus(){return Ne(this.currentQuery,this.options,this.options.refetchOnWindowFocus)}destroy(){this.listeners=new Set,this.clearStaleTimeout(),this.clearRefetchInterval(),this.currentQuery.removeObserver(this)}setOptions(e,t){const s=this.options,r=this.currentQuery;if(this.options=this.client.defaultQueryOptions(e),Ue(s,this.options)||this.client.getQueryCache().notify({type:"observerOptionsUpdated",query:this.currentQuery,observer:this}),typeof this.options.enabled<"u"&&typeof this.options.enabled!="boolean")throw new Error("Expected enabled to be a boolean");this.options.queryKey||(this.options.queryKey=s.queryKey),this.updateQuery();const n=this.hasListeners();n&&_t(this.currentQuery,r,this.options,s)&&this.executeFetch(),this.updateResult(t),n&&(this.currentQuery!==r||this.options.enabled!==s.enabled||this.options.staleTime!==s.staleTime)&&this.updateStaleTimeout();const u=this.computeRefetchInterval();n&&(this.currentQuery!==r||this.options.enabled!==s.enabled||u!==this.currentRefetchInterval)&&this.updateRefetchInterval(u)}getOptimisticResult(e){const t=this.client.getQueryCache().build(this.client,e);return this.createResult(t,e)}getCurrentResult(){return this.currentResult}trackResult(e){const t={};return Object.keys(e).forEach(s=>{Object.defineProperty(t,s,{configurable:!1,enumerable:!0,get:()=>(this.trackedProps.add(s),e[s])})}),t}getCurrentQuery(){return this.currentQuery}remove(){this.client.getQueryCache().remove(this.currentQuery)}refetch({refetchPage:e,...t}={}){return this.fetch({...t,meta:{refetchPage:e}})}fetchOptimistic(e){const t=this.client.defaultQueryOptions(e),s=this.client.getQueryCache().build(this.client,t);return s.isFetchingOptimistic=!0,s.fetch().then(()=>this.createResult(s,t))}fetch(e){var t;return this.executeFetch({...e,cancelRefetch:(t=e.cancelRefetch)!=null?t:!0}).then(()=>(this.updateResult(),this.currentResult))}executeFetch(e){this.updateQuery();let t=this.currentQuery.fetch(this.options,e);return e!=null&&e.throwOnError||(t=t.catch(N)),t}updateStaleTimeout(){if(this.clearStaleTimeout(),_e||this.currentResult.isStale||!Ie(this.options.staleTime))return;const t=At(this.currentResult.dataUpdatedAt,this.options.staleTime)+1;this.staleTimeoutId=setTimeout(()=>{this.currentResult.isStale||this.updateResult()},t)}computeRefetchInterval(){var e;return typeof this.options.refetchInterval=="function"?this.options.refetchInterval(this.currentResult.data,this.currentQuery):(e=this.options.refetchInterval)!=null?e:!1}updateRefetchInterval(e){this.clearRefetchInterval(),this.currentRefetchInterval=e,!(_e||this.options.enabled===!1||!Ie(this.currentRefetchInterval)||this.currentRefetchInterval===0)&&(this.refetchIntervalId=setInterval(()=>{(this.options.refetchIntervalInBackground||Fe.isFocused())&&this.executeFetch()},this.currentRefetchInterval))}updateTimers(){this.updateStaleTimeout(),this.updateRefetchInterval(this.computeRefetchInterval())}clearStaleTimeout(){this.staleTimeoutId&&(clearTimeout(this.staleTimeoutId),this.staleTimeoutId=void 0)}clearRefetchInterval(){this.refetchIntervalId&&(clearInterval(this.refetchIntervalId),this.refetchIntervalId=void 0)}createResult(e,t){const s=this.currentQuery,r=this.options,n=this.currentResult,u=this.currentResultState,a=this.currentResultOptions,l=e!==s,o=l?e.state:this.currentQueryInitialState,h=l?this.currentResult:this.previousQueryResult,{state:y}=e;let{dataUpdatedAt:b,error:d,errorUpdatedAt:c,fetchStatus:f,status:p}=y,O=!1,T=!1,S;if(t._optimisticResults){const v=this.hasListeners(),z=!v&&Ot(e,t),re=v&&_t(e,s,t,r);(z||re)&&(f=qe(e.options.networkMode)?"fetching":"paused",b||(p="loading")),t._optimisticResults==="isRestoring"&&(f="idle")}if(t.keepPreviousData&&!y.dataUpdatedAt&&h!=null&&h.isSuccess&&p!=="error")S=h.data,b=h.dataUpdatedAt,p=h.status,O=!0;else if(t.select&&typeof y.data<"u")if(n&&y.data===(u==null?void 0:u.data)&&t.select===this.selectFn)S=this.selectResult;else try{this.selectFn=t.select,S=t.select(y.data),S=je(n==null?void 0:n.data,S,t),this.selectResult=S,this.selectError=null}catch(v){this.selectError=v}else S=y.data;if(typeof t.placeholderData<"u"&&typeof S>"u"&&p==="loading"){let v;if(n!=null&&n.isPlaceholderData&&t.placeholderData===(a==null?void 0:a.placeholderData))v=n.data;else if(v=typeof t.placeholderData=="function"?t.placeholderData():t.placeholderData,t.select&&typeof v<"u")try{v=t.select(v),this.selectError=null}catch(z){this.selectError=z}typeof v<"u"&&(p="success",S=je(n==null?void 0:n.data,v,t),T=!0)}this.selectError&&(d=this.selectError,S=this.selectResult,c=Date.now(),p="error");const w=f==="fetching",U=p==="loading",C=p==="error";return{status:p,fetchStatus:f,isLoading:U,isSuccess:p==="success",isError:C,isInitialLoading:U&&w,data:S,dataUpdatedAt:b,error:d,errorUpdatedAt:c,failureCount:y.fetchFailureCount,failureReason:y.fetchFailureReason,errorUpdateCount:y.errorUpdateCount,isFetched:y.dataUpdateCount>0||y.errorUpdateCount>0,isFetchedAfterMount:y.dataUpdateCount>o.dataUpdateCount||y.errorUpdateCount>o.errorUpdateCount,isFetching:w,isRefetching:w&&!U,isLoadingError:C&&y.dataUpdatedAt===0,isPaused:f==="paused",isPlaceholderData:T,isPreviousData:O,isRefetchError:C&&y.dataUpdatedAt!==0,isStale:$e(e,t),refetch:this.refetch,remove:this.remove}}updateResult(e){const t=this.currentResult,s=this.createResult(this.currentQuery,this.options);if(this.currentResultState=this.currentQuery.state,this.currentResultOptions=this.options,Ue(s,t))return;this.currentResult=s;const r={cache:!0},n=()=>{if(!t)return!0;const{notifyOnChangeProps:u}=this.options;if(u==="all"||!u&&!this.trackedProps.size)return!0;const a=new Set(u??this.trackedProps);return this.options.useErrorBoundary&&a.add("error"),Object.keys(this.currentResult).some(l=>{const o=l;return this.currentResult[o]!==t[o]&&a.has(o)})};(e==null?void 0:e.listeners)!==!1&&n()&&(r.listeners=!0),this.notify({...r,...e})}updateQuery(){const e=this.client.getQueryCache().build(this.client,this.options);if(e===this.currentQuery)return;const t=this.currentQuery;this.currentQuery=e,this.currentQueryInitialState=e.state,this.previousQueryResult=this.currentResult,this.hasListeners()&&(t==null||t.removeObserver(this),e.addObserver(this))}onQueryUpdate(e){const t={};e.type==="success"?t.onSuccess=!e.manual:e.type==="error"&&!Ee(e.error)&&(t.onError=!0),this.updateResult(t),this.hasListeners()&&this.updateTimers()}notify(e){M.batch(()=>{if(e.onSuccess){var t,s,r,n;(t=(s=this.options).onSuccess)==null||t.call(s,this.currentResult.data),(r=(n=this.options).onSettled)==null||r.call(n,this.currentResult.data,null)}else if(e.onError){var u,a,l,o;(u=(a=this.options).onError)==null||u.call(a,this.currentResult.error),(l=(o=this.options).onSettled)==null||l.call(o,void 0,this.currentResult.error)}e.listeners&&this.listeners.forEach(({listener:h})=>{h(this.currentResult)}),e.cache&&this.client.getQueryCache().notify({query:this.currentQuery,type:"observerResultsUpdated"})})}}function ai(i,e){return e.enabled!==!1&&!i.state.dataUpdatedAt&&!(i.state.status==="error"&&e.retryOnMount===!1)}function Ot(i,e){return ai(i,e)||i.state.dataUpdatedAt>0&&Ne(i,e,e.refetchOnMount)}function Ne(i,e,t){if(e.enabled!==!1){const s=typeof t=="function"?t(i):t;return s==="always"||s!==!1&&$e(i,e)}return!1}function _t(i,e,t,s){return t.enabled!==!1&&(i!==e||s.enabled===!1)&&(!t.suspense||i.state.status!=="error")&&$e(i,t)}function $e(i,e){return i.isStaleByTime(e.staleTime)}class ui extends he{constructor(e,t){super(),this.client=e,this.queries=[],this.result=[],this.observers=[],this.observersMap={},t&&this.setQueries(t)}onSubscribe(){this.listeners.size===1&&this.observers.forEach(e=>{e.subscribe(t=>{this.onUpdate(e,t)})})}onUnsubscribe(){this.listeners.size||this.destroy()}destroy(){this.listeners=new Set,this.observers.forEach(e=>{e.destroy()})}setQueries(e,t){this.queries=e,M.batch(()=>{const s=this.observers,r=this.findMatchingObservers(this.queries);r.forEach(o=>o.observer.setOptions(o.defaultedQueryOptions,t));const n=r.map(o=>o.observer),u=Object.fromEntries(n.map(o=>[o.options.queryHash,o])),a=n.map(o=>o.getCurrentResult()),l=n.some((o,h)=>o!==s[h]);s.length===n.length&&!l||(this.observers=n,this.observersMap=u,this.result=a,this.hasListeners()&&(dt(s,n).forEach(o=>{o.destroy()}),dt(n,s).forEach(o=>{o.subscribe(h=>{this.onUpdate(o,h)})}),this.notify()))})}getCurrentResult(){return this.result}getQueries(){return this.observers.map(e=>e.getCurrentQuery())}getObservers(){return this.observers}getOptimisticResult(e){return this.findMatchingObservers(e).map(t=>t.observer.getOptimisticResult(t.defaultedQueryOptions))}findMatchingObservers(e){const t=this.observers,s=new Map(t.map(d=>[d.options.queryHash,d])),r=e.map(d=>this.client.defaultQueryOptions(d)),n=r.flatMap(d=>{const c=s.get(d.queryHash);return c!=null?[{defaultedQueryOptions:d,observer:c}]:[]}),u=new Set(n.map(d=>d.defaultedQueryOptions.queryHash)),a=r.filter(d=>!u.has(d.queryHash)),l=new Set(n.map(d=>d.observer)),o=t.filter(d=>!l.has(d)),h=d=>{const c=this.client.defaultQueryOptions(d),f=this.observersMap[c.queryHash];return f??new zt(this.client,c)},y=a.map((d,c)=>{if(d.keepPreviousData){const f=o[c];if(f!==void 0)return{defaultedQueryOptions:d,observer:f}}return{defaultedQueryOptions:d,observer:h(d)}}),b=(d,c)=>r.indexOf(d.defaultedQueryOptions)-r.indexOf(c.defaultedQueryOptions);return n.concat(y).sort(b)}onUpdate(e,t){const s=this.observers.indexOf(e);s!==-1&&(this.result=Gs(this.result,s,t),this.notify())}notify(){M.batch(()=>{this.listeners.forEach(({listener:e})=>{e(this.result)})})}}class li extends he{constructor(e,t){super(),this.client=e,this.setOptions(t),this.bindMethods(),this.updateResult()}bindMethods(){this.mutate=this.mutate.bind(this),this.reset=this.reset.bind(this)}setOptions(e){var t;const s=this.options;this.options=this.client.defaultMutationOptions(e),Ue(s,this.options)||this.client.getMutationCache().notify({type:"observerOptionsUpdated",mutation:this.currentMutation,observer:this}),(t=this.currentMutation)==null||t.setOptions(this.options)}onUnsubscribe(){if(!this.hasListeners()){var e;(e=this.currentMutation)==null||e.removeObserver(this)}}onMutationUpdate(e){this.updateResult();const t={listeners:!0};e.type==="success"?t.onSuccess=!0:e.type==="error"&&(t.onError=!0),this.notify(t)}getCurrentResult(){return this.currentResult}reset(){this.currentMutation=void 0,this.updateResult(),this.notify({listeners:!0})}mutate(e,t){return this.mutateOptions=t,this.currentMutation&&this.currentMutation.removeObserver(this),this.currentMutation=this.client.getMutationCache().build(this.client,{...this.options,variables:typeof e<"u"?e:this.options.variables}),this.currentMutation.addObserver(this),this.currentMutation.execute()}updateResult(){const e=this.currentMutation?this.currentMutation.state:It(),t={...e,isLoading:e.status==="loading",isSuccess:e.status==="success",isError:e.status==="error",isIdle:e.status==="idle",mutate:this.mutate,reset:this.reset};this.currentResult=t}notify(e){M.batch(()=>{if(this.mutateOptions&&this.hasListeners()){if(e.onSuccess){var t,s,r,n;(t=(s=this.mutateOptions).onSuccess)==null||t.call(s,this.currentResult.data,this.currentResult.variables,this.currentResult.context),(r=(n=this.mutateOptions).onSettled)==null||r.call(n,this.currentResult.data,null,this.currentResult.variables,this.currentResult.context)}else if(e.onError){var u,a,l,o;(u=(a=this.mutateOptions).onError)==null||u.call(a,this.currentResult.error,this.currentResult.variables,this.currentResult.context),(l=(o=this.mutateOptions).onSettled)==null||l.call(o,void 0,this.currentResult.error,this.currentResult.variables,this.currentResult.context)}}e.listeners&&this.listeners.forEach(({listener:h})=>{h(this.currentResult)})})}}const jt="$$_queryClient",oi=()=>{const i=Rt(jt);if(!i)throw new Error("No QueryClient was found in Svelte context. Did you forget to wrap your component with QueryClientProvider?");return i},Mi=i=>{es(jt,i)};function et(){return oi()}function ci(i,e){const t=et(),s=t.defaultQueryOptions(i);s._optimisticResults="optimistic";let r=new e(t,s);s.onError&&(s.onError=M.batchCalls(s.onError)),s.onSuccess&&(s.onSuccess=M.batchCalls(s.onSuccess)),s.onSettled&&(s.onSettled=M.batchCalls(s.onSettled)),de(r).subscribe(a=>{r=a,r.setOptions(s,{listeners:!1})});const n=de(r.getCurrentResult(),a=>r.subscribe(M.batchCalls(a))),{subscribe:u}=Et(n,a=>(a=r.getOptimisticResult(s),s.notifyOnChangeProps?a:r.trackResult(a)));return{subscribe:u}}function hi(i,e,t){const s=pe(i,e,t);return ci(s,zt)}function fi(i){const e=et();function t(u){return u.map(a=>{const l=e.defaultQueryOptions(a);return l._optimisticResults="optimistic",l})}const s=t(i);let r=new ui(e,s);de(r).subscribe(u=>{r=u,r.setQueries(s,{listeners:!1})});const{subscribe:n}=de(r.getOptimisticResult(s),u=>r.subscribe(M.batchCalls(u)));return{subscribe:n}}function di(i,e,t){const s=Ws(i,e,t),r=et();let n=new li(r,s),u;de(n).subscribe(o=>{n=o,u=(h,y)=>{n.mutate(h,y).catch(mi)},n.setOptions(s)});const a=de(n.getCurrentResult(),o=>n.subscribe(M.batchCalls(h=>o(h)))),{subscribe:l}=Et(a,o=>({...o,mutate:u,mutateAsync:o.mutate}));return{subscribe:l}}function mi(){}const Bt=(i,e,...t)=>[...i,e,...t];function yi(i,e,t={}){return e=Array.isArray(e)?e:[e],(...s)=>hi({queryKey:Bt(e,i.name,...s),queryFn:()=>i(...s),...t})}function Ai(i,e,t=[]){return e=Array.isArray(e)?e:[e],s=>{const r=s.map(n=>({queryKey:Bt(e,i.name,...n),queryFn:()=>i(...n),...t}));return fi(r)}}function Fi(i,e={}){return()=>di({mutationFn:t=>i(...t),...e})}const bi="tasks",Qi=yi(Hs.getTaskManifest,bi,{staleTime:3e3,refetchInterval:3e3,refetchIntervalInBackground:!1,refetchOnWindowFocus:!0}),Ct=Pt([]),ki=new ni({defaultOptions:{queries:{refetchOnWindowFocus:!1,staleTime:1/0,retry:!1,onError:i=>{var e;console.error((e=i.body)==null?void 0:e.detail),Ct.update(t=>[...t,i])}},mutations:{onError:i=>{var e;console.error((e=i.body)==null?void 0:e.detail),Ct.update(t=>[...t,i])}}}}),Nt=Pt({taskCallbacks:new Map});function qi(i,e){Nt.update(t=>(t.taskCallbacks.set(i,e),t))}function Di(i){Nt.update(e=>{var t;for(const s of e.taskCallbacks.keys()){const r=i.tasks[s];((r==null?void 0:r.status)=="error"||r.status==="completed")&&((t=e.taskCallbacks.get(s))==null||t(r),e.taskCallbacks.delete(s))}return e})}export{Oi as B,Si as C,wi as E,ks as O,ni as Q,bi as T,_i as W,ki as a,Ct as b,Ri as c,Ci as d,yi as e,Fi as f,hi as g,zt as h,ri as i,Ei as j,Pi as k,ci as l,Ai as m,Di as o,pe as p,Qi as q,Ks as r,Mi as s,qi as w};
diff --git a/web/blueprint/build/_app/immutable/chunks/urlHashStore.b3058edb.js b/web/blueprint/build/_app/immutable/chunks/urlHashStore.b3058edb.js
new file mode 100644
index 0000000000000000000000000000000000000000..3bcaa4b695ce22d9f059207843027376d01984ca
--- /dev/null
+++ b/web/blueprint/build/_app/immutable/chunks/urlHashStore.b3058edb.js
@@ -0,0 +1 @@
+import{S as G,i as K,s as Q,e as F,b as I,$ as J,h as b,D as M,E as H,F as T,k as O,q as S,a as R,a2 as p,l as D,m as y,r as V,c as j,a3 as L,n,H as c,I as q,J as m,u as C,O as z}from"./index.224851d7.js";import{w as U}from"./index.fd964a74.js";function W(l){let e,i,s,u,t,d,g,v,_,f=l[0]&&N(l),r=[{"aria-atomic":"true"},{"aria-labelledby":l[4]},{"aria-live":_=l[1]?"assertive":"off"},l[6]],k={};for(let o=0;o{e=H(H({},e),T(r)),i(6,t=M(e,u)),"small"in r&&i(0,d=r.small),"active"in r&&i(1,g=r.active),"withOverlay"in r&&i(2,v=r.withOverlay),"description"in r&&i(3,_=r.description),"id"in r&&i(4,f=r.id)},l.$$.update=()=>{l.$$.dirty&1&&i(5,s=d?"42":"44")},[d,g,v,_,f,s,t]}class x extends G{constructor(e){super(),K(this,e,Z,Y,Q,{small:0,active:1,withOverlay:2,description:3,id:4})}}const ae=x,{subscribe:$,update:ee}=U({hash:"",onHashChange(l,e){const i=this.hash.replace(/^#!/,"").match(l);i!=null&&e(i.groups||{})}}),re={subscribe:$,set(l){ee(e=>(e.hash=l,e))}};export{ae as L,re as u};
diff --git a/web/blueprint/build/_app/immutable/chunks/utils.2fa053fd.js b/web/blueprint/build/_app/immutable/chunks/utils.2fa053fd.js
new file mode 100644
index 0000000000000000000000000000000000000000..6725c822d755ee1a9f56f5e9ec59414b4d606389
--- /dev/null
+++ b/web/blueprint/build/_app/immutable/chunks/utils.2fa053fd.js
@@ -0,0 +1 @@
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--- /dev/null
+++ b/web/blueprint/build/_app/immutable/nodes/1.d02cbd87.js
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--- /dev/null
+++ b/web/blueprint/build/_app/immutable/nodes/2.e3dcc6bf.js
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diff --git a/web/blueprint/build/_app/immutable/nodes/3.d9dd4803.js b/web/blueprint/build/_app/immutable/nodes/3.d9dd4803.js
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index 0000000000000000000000000000000000000000..487322d9393f8bc43e2a1592fa9e6f286c59523e
--- /dev/null
+++ b/web/blueprint/build/_app/immutable/nodes/3.d9dd4803.js
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new file mode 100644
index 0000000000000000000000000000000000000000..7a6c8033a7a2ed0f9513f969419c6c1ba88ae0dd
--- /dev/null
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