id stringlengths 14 16 | source stringlengths 49 117 | text stringlengths 16 2.73k |
|---|---|---|
824167dadd07-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | query_embeddings: Optional[List[List[float]]] = None,
n_results: int = 4,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Query the chroma collection."""
try:
import chromadb
except ImportError:
raise ValueError(
... |
824167dadd07-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | # TODO: Handle the case where the user doesn't provide ids on the Collection
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = None
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(list(texts))
self... |
824167dadd07-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query vector.
"""
results = self.__query_collection(
query_embeddings=embedding, n_results=k, where=filter
)
return _results_to_docs(res... |
824167dadd07-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | [docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected u... |
824167dadd07-6 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | [docs] def max_marginal_relevance_search(
self,
query: str,
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal margi... |
824167dadd07-7 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | """Gets the collection.
Args:
include (Optional[List[str]]): List of fields to include from db.
Defaults to None.
"""
if include is not None:
return self._collection.get(include=include)
else:
return self._collection.get()
[docs] def... |
824167dadd07-8 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a raw docu... |
824167dadd07-9 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html | persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None, # Add this line
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is speci... |
2fc70cd48f6c-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
import uuid
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple
import numpy as np
from langchain.docstore.document imp... |
2fc70cd48f6c-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | nearest_indices: List, indices of nearest neighbors
"""
if data_vectors.shape[0] == 0:
return [], []
# Calculate the distance between the query_vector and all data_vectors
distances = distance_metric_map[distance_metric](query_embedding, data_vectors)
nearest_indices = np.argsort(distances)
... |
2fc70cd48f6c-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding_function: Optional[Embeddings] = None,
... |
2fc70cd48f6c-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | f"Deep Lake Dataset in {dataset_path} already exists, "
f"loading from the storage"
)
self.ds.summary()
else:
if "overwrite" in kwargs:
del kwargs["overwrite"]
self.ds = deeplake.empty(
dataset_path,
... |
2fc70cd48f6c-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | """Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
Return... |
2fc70cd48f6c-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | ingest().eval(
batched,
self.ds,
num_workers=min(self.num_workers, len(batched) // max(self.num_workers, 1)),
**kwargs,
)
self.ds.commit(allow_empty=True)
if self.verbose:
self.ds.summary()
return ids
def _search_helper(
... |
2fc70cd48f6c-6 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
List of Documents selected by the specified distance metric,
if return_score T... |
2fc70cd48f6c-7 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | lambda_mult=lambda_mult,
)
view = view[indices]
scores = [scores[i] for i in indices]
docs = [
Document(
page_content=el["text"].data()["value"],
metadata=el["metadata"].data()["value"],
)
for el ... |
2fc70cd48f6c-8 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | [docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defau... |
2fc70cd48f6c-9 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among ... |
2fc70cd48f6c-10 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults... |
2fc70cd48f6c-11 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | - AWS S3 path of the form ``s3://bucketname/path/to/dataset``.
Credentials are required in either the environment
- Google Cloud Storage path of the form
``gcs://bucketname/path/to/dataset`` Credentials are required
in either the environment
... |
2fc70cd48f6c-12 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html | filter (Optional[Dict[str, str]], optional): The filter to delete by.
Defaults to None.
delete_all (Optional[bool], optional): Whether to drop the dataset.
Defaults to None.
"""
if delete_all:
self.ds.delete(large_ok=True)
return True
... |
c0932f6b2a02-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | Source code for langchain.vectorstores.weaviate
"""Wrapper around weaviate vector database."""
from __future__ import annotations
import datetime
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from ... |
c0932f6b2a02-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | client = weaviate.Client(weaviate_url, auth_client_secret=auth)
return client
def _default_score_normalizer(val: float) -> float:
return 1 - 1 / (1 + np.exp(val))
def _json_serializable(value: Any) -> Any:
if isinstance(value, datetime.datetime):
return value.isoformat()
return value
[docs]class... |
c0932f6b2a02-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | self._index_name = index_name
self._embedding = embedding
self._text_key = text_key
self._query_attrs = [self._text_key]
self._relevance_score_fn = relevance_score_fn
self._by_text = by_text
if attributes is not None:
self._query_attrs.extend(attributes)
[docs... |
c0932f6b2a02-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | ) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
if self._by_text:
... |
c0932f6b2a02-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
[docs] def similarity_search_by_vector(
s... |
c0932f6b2a02-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 c... |
c0932f6b2a02-6 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | Returns:
List of Documents selected by maximal marginal relevance.
"""
vector = {"vector": embedding}
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter")... |
c0932f6b2a02-7 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | query_obj.with_near_vector(vector)
.with_limit(k)
.with_additional("vector")
.do()
)
else:
result = (
query_obj.with_near_text(content)
.with_limit(k)
.with_additional("vector")
... |
c0932f6b2a02-8 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> Weaviate:
"""Construct Weaviate wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Weaviate instance.
3. Adds... |
c0932f6b2a02-9 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html | # If the UUID of one of the objects already exists
# then the existing objectwill be replaced by the new object.
if "uuids" in kwargs:
_id = kwargs["uuids"][i]
else:
_id = get_valid_uuid(uuid4())
# if an embedding st... |
f41b6e1b9c30-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html | Source code for langchain.vectorstores.typesense
"""Wrapper around Typesense vector search"""
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
fro... |
f41b6e1b9c30-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html | text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "
"Please install it with `pip install typesense`."
... |
f41b6e1b9c30-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html | {"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "type": "auto"},
]
self._typesense_client.collections.create(
{"name": self._typesense_collection_name, "fields": fields}
)
[docs] def ad... |
f41b6e1b9c30-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html | """Return typesense documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: typesense filter_by expression to filter documents on
Returns:
List of Docum... |
f41b6e1b9c30-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html | docs_and_score = self.similarity_search_with_score(query, k=k, filter=filter)
return [doc for doc, _ in docs_and_score]
[docs] @classmethod
def from_client_params(
cls,
embedding: Embeddings,
*,
host: str = "localhost",
port: Union[str, int] = "8108",
proto... |
f41b6e1b9c30-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html | "connection_timeout_seconds": connection_timeout_seconds,
}
return cls(Client(client_config), embedding, **kwargs)
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[s... |
b372f973ac52-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | Source code for langchain.vectorstores.analyticdb
"""VectorStore wrapper around a Postgres/PGVector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple
import sqlalchemy
from sqlalchemy import REAL, Index
from sqlalchemy.dialects.postg... |
b372f973ac52-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | Returns [Collection, bool] where the bool is True if the collection was created.
"""
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, created
collection = cls(name=name, cmetadata=cmetadata)
session.add(collection)
... |
b372f973ac52-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | AnalyticDB is a distributed full PostgresSQL syntax cloud-native database.
- `connection_string` is a postgres connection string.
- `embedding_function` any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
- `collection_name` is the name of the collection to use. (de... |
b372f973ac52-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | [docs] def create_tables_if_not_exists(self) -> None:
Base.metadata.create_all(self._conn)
[docs] def drop_tables(self) -> None:
Base.metadata.drop_all(self._conn)
[docs] def create_collection(self) -> None:
if self.pre_delete_collection:
self.delete_collection()
wit... |
b372f973ac52-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
collection = self.get_collection(session)
if not collection:
raise ValueError("Collection not found")
for text, metadata, embedding, id in zip(texts, metadatas, ... |
b372f973ac52-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.... |
b372f973ac52-6 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | Document(
page_content=result.EmbeddingStore.document,
metadata=result.EmbeddingStore.cmetadata,
),
result.distance if self.embedding_function is not None else None,
)
for result in results
]
return docs
[doc... |
b372f973ac52-7 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | connection_string=connection_string,
collection_name=collection_name,
embedding_function=embedding,
pre_delete_collection=pre_delete_collection,
)
store.add_texts(texts=texts, metadatas=metadatas, ids=ids, **kwargs)
return store
[docs] @classmethod
def ... |
b372f973ac52-8 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html | ids=ids,
collection_name=collection_name,
**kwargs,
)
[docs] @classmethod
def connection_string_from_db_params(
cls,
driver: str,
host: str,
port: int,
database: str,
user: str,
password: str,
) -> str:
"""Return ... |
e7dded4122b5-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | Source code for langchain.vectorstores.elastic_vector_search
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from abc import ABC
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
from l... |
e7dded4122b5-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | # their own specific implementations. If you plan to subclass ElasticVectorSearch,
# you can inherit from it and define your own implementation of the necessary methods
# and attributes.
[docs]class ElasticVectorSearch(VectorStore, ABC):
"""Wrapper around Elasticsearch as a vector database.
To connect to an Ela... |
e7dded4122b5-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id... |
e7dded4122b5-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> List[str]:
... |
e7dded4122b5-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | "vector": embeddings[i],
"text": text,
"metadata": metadata,
"_id": _id,
}
ids.append(_id)
requests.append(request)
bulk(self.client, requests)
if refresh_indices:
self.client.indices.refresh(index=self.index... |
e7dded4122b5-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | docs_and_scores = [
(
Document(
page_content=hit["_source"]["text"],
metadata=hit["_source"]["metadata"],
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs] @cl... |
e7dded4122b5-6 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | return vectorsearch
[docs] def create_index(self, client: Any, index_name: str, mapping: Dict) -> None:
version_num = client.info()["version"]["number"][0]
version_num = int(version_num)
if version_num >= 8:
client.indices.create(index=index_name, mappings=mapping)
else:
... |
e7dded4122b5-7 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | index_name: The name of the Elasticsearch index.
embedding: An instance of the Embeddings class, used to generate vector
representations of text strings.
es_connection: An existing Elasticsearch connection.
es_cloud_id: The Cloud ID of the Elasticsearch instance. Requ... |
e7dded4122b5-8 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | "similarity": "dot_product",
},
}
}
@staticmethod
def _default_knn_query(
query_vector: Optional[List[float]] = None,
query: Optional[str] = None,
model_id: Optional[str] = None,
field: Optional[str] = "vector",
k: Optional[int] = 10,
... |
e7dded4122b5-9 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
) -> Dict:
"""
Performs a k-nearest neighbor (k-NN) search on the Elasticsearch index.
The search can be conducted using either a raw query vector or a model ID.
The method first generates
t... |
e7dded4122b5-10 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | source=source,
fields=fields,
)
return dict(res)
def knn_hybrid_search(
self,
query: Optional[str] = None,
k: Optional[int] = 10,
query_vector: Optional[List[float]] = None,
model_id: Optional[str] = None,
size: Optional[int] = 10,
... |
e7dded4122b5-11 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html | source: Whether to include the source of each hit in the results.
knn_boost: The boost factor for the k-NN part of the search.
query_boost: The boost factor for the text-based part of the search.
fields
The fields to include in the source of each hit. If None, all fie... |
ad46bbbb2c17-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html | Source code for langchain.vectorstores.pinecone
"""Wrapper around Pinecone vector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Callable, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings... |
ad46bbbb2c17-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html | self._embedding_function = embedding_function
self._text_key = text_key
self._namespace = namespace
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
... |
ad46bbbb2c17-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html | ) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
... |
ad46bbbb2c17-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html | namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_scores = self.similarity_search_with_score(
query, k=k, filter=filter, namespace=namespace, **kwargs
)
... |
ad46bbbb2c17-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html | "Please install it with `pip install pinecone-client`."
)
indexes = pinecone.list_indexes() # checks if provided index exists
if index_name in indexes:
index = pinecone.Index(index_name)
elif len(indexes) == 0:
raise ValueError(
"No active ind... |
ad46bbbb2c17-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html | [docs] @classmethod
def from_existing_index(
cls,
index_name: str,
embedding: Embeddings,
text_key: str = "text",
namespace: Optional[str] = None,
) -> Pinecone:
"""Load pinecone vectorstore from index name."""
try:
import pinecone
e... |
1c11890f5525-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | Source code for langchain.vectorstores.myscale
"""Wrapper around MyScale vector database."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from pydantic import BaseSettings
from langchain.... |
1c11890f5525-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | 'id': 'text_id',
'vector': 'text_embedding',
'text': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
}
Defaults to identity map.
"""
ho... |
1c11890f5525-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | """MyScale Wrapper to LangChain
embedding_function (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.myscale.com/)
"""
try:
from clickhouse_connect import get_cl... |
1c11890f5525-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | {self.config.column_map['vector']} Array(Float32),
{self.config.column_map['metadata']} JSON,
CONSTRAINT cons_vec_len CHECK length(\
{self.config.column_map['vector']}) = {dim},
VECTOR INDEX vidx {self.config.column_map['vector']} \
... |
1c11890f5525-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | {','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
_i_str = self._build_istr(transac, column_names)
self.client.command(_i_str)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: O... |
1c11890f5525-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | zip(*values), desc="Inserting data...", total=len(metadatas)
):
assert len(v[keys.index(self.config.column_map["vector"])]) == self.dim
transac.append(v)
if len(transac) == batch_size:
if t:
t.join()
... |
1c11890f5525-6 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | metadata (List[dict], optional): metadata to texts. Defaults to None.
Other keyword arguments will pass into
[clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
Returns:
MyScale Index
"""
ctx = cls(embeddi... |
1c11890f5525-7 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | q_emb_str = ",".join(map(str, q_emb))
if where_str:
where_str = f"PREWHERE {where_str}"
else:
where_str = ""
q_str = f"""
SELECT {self.config.column_map['text']},
{self.config.column_map['metadata']}, dist
FROM {self.config.databas... |
1c11890f5525-8 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | ) -> List[Document]:
"""Perform a similarity search with MyScale by vectors
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
... |
1c11890f5525-9 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html | NOTE: Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List... |
c2e132d64ec0-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html | Source code for langchain.vectorstores.tair
"""Wrapper around Tair Vector."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.... |
c2e132d64ec0-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html | index = self.client.tvs_get_index(self.index_name)
if index is not None:
logger.info("Index already exists")
return False
self.client.tvs_create_index(
self.index_name,
dim,
distance_type,
index_type,
data_type,
... |
c2e132d64ec0-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html | Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
# Creates embedding vector from user query
embedding = self.embedding_function.embed_query(query)
keys_and_scores = self.client.tvs_knnsearch(
self.index_name, k, embedding, ... |
c2e132d64ec0-3 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html | index_type = tairvector.IndexType.HNSW
if "index_type" in kwargs:
index_type = kwargs.pop("index_type")
data_type = tairvector.DataType.Float32
if "data_type" in kwargs:
data_type = kwargs.pop("data_type")
index_params = {}
if "index_params" in kwargs:
... |
c2e132d64ec0-4 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html | texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(
texts, embedding, metadatas, index_name, content_key, metadata_key, **kwargs
)
[docs] @staticmethod
def drop_index(
index_name: str = "langchain",
**k... |
c2e132d64ec0-5 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html | """Connect to an existing Tair index."""
url = get_from_dict_or_env(kwargs, "tair_url", "TAIR_URL")
search_params = {}
if "search_params" in kwargs:
search_params = kwargs.pop("search_params")
return cls(
embedding,
url,
index_name,
... |
13e191bcb303-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html | Source code for langchain.vectorstores.docarray.hnsw
"""Wrapper around Hnswlib store."""
from __future__ import annotations
from typing import Any, List, Literal, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,
)... |
13e191bcb303-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html | max_elements (int): Maximum number of vectors that can be stored.
Defaults to 1024.
index (bool): Whether an index should be built for this field.
Defaults to True.
ef_construction (int): defines a construction time/accuracy trade-off.
Defaults to ... |
13e191bcb303-2 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html | ) -> DocArrayHnswSearch:
"""Create an DocArrayHnswSearch store and insert data.
Args:
texts (List[str]): Text data.
embedding (Embeddings): Embedding function.
metadatas (Optional[List[dict]]): Metadata for each text if it exists.
Defaults to None.
... |
b6ab57e324ac-0 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html | Source code for langchain.vectorstores.docarray.in_memory
"""Wrapper around in-memory storage."""
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_doc... |
b6ab57e324ac-1 | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html | cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> DocArrayInMemorySearch:
"""Create an DocArrayInMemorySearch store and insert data.
Args:
texts (List[str]): Text data.
embedding... |
66ced19d2d4a-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html | Source code for langchain.output_parsers.regex
from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex: str
output_keys: List[str]
... |
a35fb67d736d-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html | Source code for langchain.output_parsers.retry
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from lang... |
a35fb67d736d-1 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html | return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException:
new_completion = self.retry_chain.run(
prompt=... |
a35fb67d736d-2 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html | return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException as e:
new_completion = self.retry_chain.run(
pr... |
9b6fb847859f-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Dict
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
guard: Any
@property
def _type(self) -> str:
return "guardrails"
[docs] @classme... |
3caa69652019-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html | Source code for langchain.output_parsers.list
from __future__ import annotations
from abc import abstractmethod
from typing import List
from langchain.schema import BaseOutputParser
[docs]class ListOutputParser(BaseOutputParser):
"""Class to parse the output of an LLM call to a list."""
@property
def _type(... |
6f6a3e6afd4e-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html | Source code for langchain.output_parsers.structured
from __future__ import annotations
from typing import Any, List
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import STRUCTURED_FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchai... |
a75e4e413257-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html | Source code for langchain.output_parsers.fix
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
from langchain.prompts.base import BasePromptTemplate
f... |
b672b3cb2096-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html | Source code for langchain.output_parsers.pydantic
import json
import re
from typing import Type, TypeVar
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar(... |
b672b3cb2096-1 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html | return "pydantic"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
1c5f0bcc3004-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html | Source code for langchain.output_parsers.regex_dict
from __future__ import annotations
import re
from typing import Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexDictParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex_pattern: str = r"{}:\s?([^.'\n'... |
07ee397c6255-0 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html | Source code for langchain.output_parsers.datetime
import random
from datetime import datetime, timedelta
from typing import List
from langchain.schema import BaseOutputParser, OutputParserException
from langchain.utils import comma_list
def _generate_random_datetime_strings(
pattern: str,
n: int = 3,
start_... |
07ee397c6255-1 | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html | def _type(self) -> str:
return "datetime"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
5666daa205ee-0 | https://python.langchain.com/en/latest/_modules/langchain/docstore/wikipedia.html | Source code for langchain.docstore.wikipedia
"""Wrapper around wikipedia API."""
from typing import Union
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
[docs]class Wikipedia(Docstore):
"""Wrapper around wikipedia API."""
def __init__(self) -> None:
"""Chec... |
99698d991c39-0 | https://python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html | Source code for langchain.docstore.in_memory
"""Simple in memory docstore in the form of a dict."""
from typing import Dict, Union
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
[docs]class InMemoryDocstore(Docstore, AddableMixin):
"""Simple in memory doc... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.