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c34ddfeb-037f-497d-be30-e58c938750c0 | Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
"Represent the question for retrieving supporting documents: "
)
[docs]class HuggingFaceEmbeddings(BaseModel, Embeddings):
"""Wrapper around sentence_transformers embedding models.
To use, you should have the ``sentence_transformers`` python package installed.
Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
hf = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
client: Any #: :meta private:
model_name: str = DEFAULT_MODEL_NAME
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Key word arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Key word arguments to pass when calling the `encode` method of the model."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers
except ImportError as exc:
raise ImportError(
"Could not import sentence_transformers python package. "
"Please install it with `pip install sentence_transformers`."
) from exc
self.client = sentence_transformers.SentenceTransformer(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.client.encode(texts, **self.encode_kwargs)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embedding = self.client.encode(text, **self.encode_kwargs)
return embedding.tolist()
[docs]class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
"""Wrapper around sentence_transformers embedding models.
To use, you should have the ``sentence_transformers``
and ``InstructorEmbedding`` python packages installed.
Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceInstructEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
client: Any #: :meta private:
model_name: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Key word arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Key word arguments to pass when calling the `encode` method of the model."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding query."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
from InstructorEmbedding import INSTRUCTOR
self.client = INSTRUCTOR(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
except ImportError as e:
raise ValueError("Dependencies for InstructorEmbedding not found.") from e
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace instruct model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = [[self.embed_instruction, text] for text in texts]
embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace instruct model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0]
return embedding.tolist() | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
a50cc9d8-7a6c-47e4-9bd9-7aff79de458d | Source code for langchain.embeddings.tensorflow_hub
"""Wrapper around TensorflowHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]class TensorflowHubEmbeddings(BaseModel, Embeddings):
"""Wrapper around tensorflow_hub embedding models.
To use, you should have the ``tensorflow_text`` python package installed.
Example:
.. code-block:: python
from langchain.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)
"""
embed: Any #: :meta private:
model_url: str = DEFAULT_MODEL_URL
"""Model name to use."""
def __init__(self, **kwargs: Any):
"""Initialize the tensorflow_hub and tensorflow_text."""
super().__init__(**kwargs)
try:
import tensorflow_hub
except ImportError:
raise ImportError(
"Could not import tensorflow-hub python package. "
"Please install it with `pip install tensorflow-hub``."
)
try:
import tensorflow_text # noqa
except ImportError:
raise ImportError(
"Could not import tensorflow_text python package. "
"Please install it with `pip install tensorflow_text``."
)
self.embed = tensorflow_hub.load(self.model_url)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a TensorflowHub embedding model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.embed(texts).numpy()
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a TensorflowHub embedding model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embedding = self.embed([text]).numpy()[0]
return embedding.tolist() | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
a677b4c7-a40b-49c6-9bee-ee6d4aa2e15f | Source code for langchain.embeddings.fake
from typing import List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
size: int
def _get_embedding(self) -> List[float]:
return list(np.random.normal(size=self.size))
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
return [self._get_embedding() for _ in texts]
[docs] def embed_query(self, text: str) -> List[float]:
return self._get_embedding() | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html |
d8d1d067-0a66-4dcb-b7d7-80a2fe901bcb | Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
AsyncRetrying,
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]:
import openai
min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
import openai
min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
async_retrying = AsyncRetrying(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def wrap(func: Callable) -> Callable:
async def wrapped_f(*args: Any, **kwargs: Any) -> Callable:
async for _ in async_retrying:
return await func(*args, **kwargs)
raise AssertionError("this is unreachable")
return wrapped_f
return wrap
def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
return embeddings.client.create(**kwargs)
return _embed_with_retry(**kwargs)
async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
@_async_retry_decorator(embeddings)
async def _async_embed_with_retry(**kwargs: Any) -> Any:
return await embeddings.client.acreate(**kwargs)
return await _async_embed_with_retry(**kwargs)
[docs]class OpenAIEmbeddings(BaseModel, Embeddings):
"""Wrapper around OpenAI embedding models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example:
.. code-block:: python
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
openai_api_base="https://your-endpoint.openai.azure.com/",
openai_api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
"""
client: Any #: :meta private:
model: str = "text-embedding-ada-002"
deployment: str = model # to support Azure OpenAI Service custom deployment names
openai_api_version: Optional[str] = None
# to support Azure OpenAI Service custom endpoints
openai_api_base: Optional[str] = None
# to support Azure OpenAI Service custom endpoints
openai_api_type: Optional[str] = None
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = None
embedding_ctx_length: int = 8191
openai_api_key: Optional[str] = None
openai_organization: Optional[str] = None
allowed_special: Union[Literal["all"], Set[str]] = set()
disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
chunk_size: int = 1000
"""Maximum number of texts to embed in each batch"""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout in seconds for the OpenAPI request."""
headers: Any = None
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["openai_api_base"] = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
values["openai_api_type"] = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
default="",
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
default_api_version = "2022-12-01"
else:
default_api_version = ""
values["openai_api_version"] = get_from_dict_or_env(
values,
"openai_api_version",
"OPENAI_API_VERSION",
default=default_api_version,
)
values["openai_organization"] = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
values["client"] = openai.Embedding
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
return values
@property
def _invocation_params(self) -> Dict:
openai_args = {
"engine": self.deployment,
"request_timeout": self.request_timeout,
"headers": self.headers,
"api_key": self.openai_api_key,
"organization": self.openai_organization,
"api_base": self.openai_api_base,
"api_type": self.openai_api_type,
"api_version": self.openai_api_version,
}
if self.openai_proxy:
import openai
openai.proxy = {
"http": self.openai_proxy,
"https": self.openai_proxy,
} # type: ignore[assignment] # noqa: E501
return openai_args
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
def _get_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please install it with `pip install tiktoken`."
)
tokens = []
indices = []
model_name = self.tiktoken_model_name or self.model
try:
encoding = tiktoken.encoding_for_model(model_name)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
token = encoding.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
for j in range(0, len(token), self.embedding_ctx_length):
tokens += [token[j : j + self.embedding_ctx_length]]
indices += [i]
batched_embeddings = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
response = embed_with_retry(
self,
input=tokens[i : i + _chunk_size],
**self._invocation_params,
)
batched_embeddings += [r["embedding"] for r in response["data"]]
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average = embed_with_retry(
self,
input="",
**self._invocation_params,
)[
"data"
][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist()
return embeddings
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
async def _aget_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please install it with `pip install tiktoken`."
)
tokens = []
indices = []
model_name = self.tiktoken_model_name or self.model
try:
encoding = tiktoken.encoding_for_model(model_name)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
token = encoding.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
for j in range(0, len(token), self.embedding_ctx_length):
tokens += [token[j : j + self.embedding_ctx_length]]
indices += [i]
batched_embeddings = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
response = await async_embed_with_retry(
self,
input=tokens[i : i + _chunk_size],
**self._invocation_params,
)
batched_embeddings += [r["embedding"] for r in response["data"]]
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average = (
await async_embed_with_retry(
self,
input="",
**self._invocation_params,
)
)["data"][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist()
return embeddings
def _embedding_func(self, text: str, *, engine: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint."""
# handle large input text
if len(text) > self.embedding_ctx_length:
return self._get_len_safe_embeddings([text], engine=engine)[0]
else:
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
return embed_with_retry(
self,
input=[text],
**self._invocation_params,
)[
"data"
][0]["embedding"]
async def _aembedding_func(self, text: str, *, engine: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint."""
# handle large input text
if len(text) > self.embedding_ctx_length:
return (await self._aget_len_safe_embeddings([text], engine=engine))[0]
else:
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
return (
await async_embed_with_retry(
self,
input=[text],
**self._invocation_params,
)
)["data"][0]["embedding"]
[docs] def embed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
# NOTE: to keep things simple, we assume the list may contain texts longer
# than the maximum context and use length-safe embedding function.
return self._get_len_safe_embeddings(texts, engine=self.deployment)
[docs] async def aembed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint async for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
# NOTE: to keep things simple, we assume the list may contain texts longer
# than the maximum context and use length-safe embedding function.
return await self._aget_len_safe_embeddings(texts, engine=self.deployment)
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embedding = self._embedding_func(text, engine=self.deployment)
return embedding
[docs] async def aembed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint async for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embedding = await self._aembedding_func(text, engine=self.deployment)
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
37f5b390-a5ca-4939-8641-0dc4991a67ec | Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
VALID_TASKS = ("feature-extraction",)
[docs]class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
"""Wrapper around HuggingFaceHub embedding models.
To use, you should have the ``huggingface_hub`` python package installed, and the
environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceHubEmbeddings
repo_id = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceHubEmbeddings(
repo_id=repo_id,
task="feature-extraction",
huggingfacehub_api_token="my-api-key",
)
"""
client: Any #: :meta private:
repo_id: str = DEFAULT_REPO_ID
"""Model name to use."""
task: Optional[str] = "feature-extraction"
"""Task to call the model with."""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
huggingfacehub_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
from huggingface_hub.inference_api import InferenceApi
repo_id = values["repo_id"]
if not repo_id.startswith("sentence-transformers"):
raise ValueError(
"Currently only 'sentence-transformers' embedding models "
f"are supported. Got invalid 'repo_id' {repo_id}."
)
client = InferenceApi(
repo_id=repo_id,
token=huggingfacehub_api_token,
task=values.get("task"),
)
if client.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {client.task}, "
f"currently only {VALID_TASKS} are supported"
)
values["client"] = client
except ImportError:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
# replace newlines, which can negatively affect performance.
texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
responses = self.client(inputs=texts, params=_model_kwargs)
return responses
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to HuggingFaceHub's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
response = self.embed_documents([text])[0]
return response | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
4665ba45-e917-4766-a2b1-b0ee5a40df7d | Source code for langchain.embeddings.deepinfra
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"
[docs]class DeepInfraEmbeddings(BaseModel, Embeddings):
"""Wrapper around Deep Infra's embedding inference service.
To use, you should have the
environment variable ``DEEPINFRA_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
There are multiple embeddings models available,
see https://deepinfra.com/models?type=embeddings.
Example:
.. code-block:: python
from langchain.embeddings import DeepInfraEmbeddings
deepinfra_emb = DeepInfraEmbeddings(
model_id="sentence-transformers/clip-ViT-B-32",
deepinfra_api_token="my-api-key"
)
r1 = deepinfra_emb.embed_documents(
[
"Alpha is the first letter of Greek alphabet",
"Beta is the second letter of Greek alphabet",
]
)
r2 = deepinfra_emb.embed_query(
"What is the second letter of Greek alphabet"
)
"""
model_id: str = DEFAULT_MODEL_ID
"""Embeddings model to use."""
normalize: bool = False
"""whether to normalize the computed embeddings"""
embed_instruction: str = "passage: "
"""Instruction used to embed documents."""
query_instruction: str = "query: "
"""Instruction used to embed the query."""
model_kwargs: Optional[dict] = None
"""Other model keyword args"""
deepinfra_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
deepinfra_api_token = get_from_dict_or_env(
values, "deepinfra_api_token", "DEEPINFRA_API_TOKEN"
)
values["deepinfra_api_token"] = deepinfra_api_token
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"model_id": self.model_id}
def _embed(self, input: List[str]) -> List[List[float]]:
_model_kwargs = self.model_kwargs or {}
# HTTP headers for authorization
headers = {
"Authorization": f"bearer {self.deepinfra_api_token}",
"Content-Type": "application/json",
}
# send request
try:
res = requests.post(
f"https://api.deepinfra.com/v1/inference/{self.model_id}",
headers=headers,
json={"inputs": input, "normalize": self.normalize, **_model_kwargs},
)
except requests.exceptions.RequestException as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
if res.status_code != 200:
raise ValueError(
"Error raised by inference API HTTP code: %s, %s"
% (res.status_code, res.text)
)
try:
t = res.json()
embeddings = t["embeddings"]
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f"Error raised by inference API: {e}.\nResponse: {res.text}"
)
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a Deep Infra deployed embedding model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = [f"{self.query_instruction}{text}" for text in texts]
embeddings = self._embed(instruction_pairs)
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using a Deep Infra deployed embedding model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = f"{self.query_instruction}{text}"
embedding = self._embed([instruction_pair])[0]
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
e9d60ae6-6e15-43f0-b599-33c2340c27c8 | Source code for langchain.embeddings.elasticsearch
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional
from langchain.utils import get_from_env
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
from langchain.embeddings.base import Embeddings
[docs]class ElasticsearchEmbeddings(Embeddings):
"""
Wrapper around Elasticsearch embedding models.
This class provides an interface to generate embeddings using a model deployed
in an Elasticsearch cluster. It requires an Elasticsearch connection object
and the model_id of the model deployed in the cluster.
In Elasticsearch you need to have an embedding model loaded and deployed.
- https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html
- https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html
""" # noqa: E501
def __init__(
self,
client: MlClient,
model_id: str,
*,
input_field: str = "text_field",
):
"""
Initialize the ElasticsearchEmbeddings instance.
Args:
client (MlClient): An Elasticsearch ML client object.
model_id (str): The model_id of the model deployed in the Elasticsearch
cluster.
input_field (str): The name of the key for the input text field in the
document. Defaults to 'text_field'.
"""
self.client = client
self.model_id = model_id
self.input_field = input_field
[docs] @classmethod
def from_credentials(
cls,
model_id: str,
*,
es_cloud_id: Optional[str] = None,
es_user: Optional[str] = None,
es_password: Optional[str] = None,
input_field: str = "text_field",
) -> ElasticsearchEmbeddings:
"""Instantiate embeddings from Elasticsearch credentials.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch
cluster.
input_field (str): The name of the key for the input text field in the
document. Defaults to 'text_field'.
es_cloud_id: (str, optional): The Elasticsearch cloud ID to connect to.
es_user: (str, optional): Elasticsearch username.
es_password: (str, optional): Elasticsearch password.
Example:
.. code-block:: python
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Credentials can be passed in two ways. Either set the env vars
# ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically
# pulled in, or pass them in directly as kwargs.
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id,
input_field=input_field,
# es_cloud_id="foo",
# es_user="bar",
# es_password="baz",
)
documents = [
"This is an example document.",
"Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
"""
try:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
except ImportError:
raise ImportError(
"elasticsearch package not found, please install with 'pip install "
"elasticsearch'"
)
es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID")
es_user = es_user or get_from_env("es_user", "ES_USER")
es_password = es_password or get_from_env("es_password", "ES_PASSWORD")
# Connect to Elasticsearch
es_connection = Elasticsearch(
cloud_id=es_cloud_id, basic_auth=(es_user, es_password)
)
client = MlClient(es_connection)
return cls(client, model_id, input_field=input_field)
[docs] @classmethod
def from_es_connection(
cls,
model_id: str,
es_connection: Elasticsearch,
input_field: str = "text_field",
) -> ElasticsearchEmbeddings:
"""
Instantiate embeddings from an existing Elasticsearch connection.
This method provides a way to create an instance of the ElasticsearchEmbeddings
class using an existing Elasticsearch connection. The connection object is used
to create an MlClient, which is then used to initialize the
ElasticsearchEmbeddings instance.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch cluster.
es_connection (elasticsearch.Elasticsearch): An existing Elasticsearch
connection object. input_field (str, optional): The name of the key for the
input text field in the document. Defaults to 'text_field'.
Returns:
ElasticsearchEmbeddings: An instance of the ElasticsearchEmbeddings class.
Example:
.. code-block:: python
from elasticsearch import Elasticsearch
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Create Elasticsearch connection
es_connection = Elasticsearch(
hosts=["localhost:9200"], http_auth=("user", "password")
)
# Instantiate ElasticsearchEmbeddings using the existing connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
model_id,
es_connection,
input_field=input_field,
)
documents = [
"This is an example document.",
"Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
"""
# Importing MlClient from elasticsearch.client within the method to
# avoid unnecessary import if the method is not used
from elasticsearch.client import MlClient
# Create an MlClient from the given Elasticsearch connection
client = MlClient(es_connection)
# Return a new instance of the ElasticsearchEmbeddings class with
# the MlClient, model_id, and input_field
return cls(client, model_id, input_field=input_field)
def _embedding_func(self, texts: List[str]) -> List[List[float]]:
"""
Generate embeddings for the given texts using the Elasticsearch model.
Args:
texts (List[str]): A list of text strings to generate embeddings for.
Returns:
List[List[float]]: A list of embeddings, one for each text in the input
list.
"""
response = self.client.infer_trained_model(
model_id=self.model_id, docs=[{self.input_field: text} for text in texts]
)
embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Generate embeddings for a list of documents.
Args:
texts (List[str]): A list of document text strings to generate embeddings
for.
Returns:
List[List[float]]: A list of embeddings, one for each document in the input
list.
"""
return self._embedding_func(texts)
[docs] def embed_query(self, text: str) -> List[float]:
"""
Generate an embedding for a single query text.
Args:
text (str): The query text to generate an embedding for.
Returns:
List[float]: The embedding for the input query text.
"""
return self._embedding_func([text])[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
f9ac4828-265c-46f6-b559-82b97f7a1812 | Source code for langchain.embeddings.minimax
"""Wrapper around MiniMax APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
stop_after_attempt,
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator() -> Callable[[Any], Any]:
"""Returns a tenacity retry decorator."""
multiplier = 1
min_seconds = 1
max_seconds = 4
max_retries = 6
return retry(
reraise=True,
stop=stop_after_attempt(max_retries),
wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator()
@retry_decorator
def _embed_with_retry(*args: Any, **kwargs: Any) -> Any:
return embeddings.embed(*args, **kwargs)
return _embed_with_retry(*args, **kwargs)
[docs]class MiniMaxEmbeddings(BaseModel, Embeddings):
"""Wrapper around MiniMax's embedding inference service.
To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
the constructor.
Example:
.. code-block:: python
from langchain.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a test document."
document_result = embeddings.embed_documents([document_text])
"""
endpoint_url: str = "https://api.minimax.chat/v1/embeddings"
"""Endpoint URL to use."""
model: str = "embo-01"
"""Embeddings model name to use."""
embed_type_db: str = "db"
"""For embed_documents"""
embed_type_query: str = "query"
"""For embed_query"""
minimax_group_id: Optional[str] = None
"""Group ID for MiniMax API."""
minimax_api_key: Optional[str] = None
"""API Key for MiniMax API."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that group id and api key exists in environment."""
minimax_group_id = get_from_dict_or_env(
values, "minimax_group_id", "MINIMAX_GROUP_ID"
)
minimax_api_key = get_from_dict_or_env(
values, "minimax_api_key", "MINIMAX_API_KEY"
)
values["minimax_group_id"] = minimax_group_id
values["minimax_api_key"] = minimax_api_key
return values
def embed(
self,
texts: List[str],
embed_type: str,
) -> List[List[float]]:
payload = {
"model": self.model,
"type": embed_type,
"texts": texts,
}
# HTTP headers for authorization
headers = {
"Authorization": f"Bearer {self.minimax_api_key}",
"Content-Type": "application/json",
}
params = {
"GroupId": self.minimax_group_id,
}
# send request
response = requests.post(
self.endpoint_url, params=params, headers=headers, json=payload
)
parsed_response = response.json()
# check for errors
if parsed_response["base_resp"]["status_code"] != 0:
raise ValueError(
f"MiniMax API returned an error: {parsed_response['base_resp']}"
)
embeddings = parsed_response["vectors"]
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a MiniMax embedding endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
embeddings = embed_with_retry(self, texts=texts, embed_type=self.embed_type_db)
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using a MiniMax embedding endpoint.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embeddings = embed_with_retry(
self, texts=[text], embed_type=self.embed_type_query
)
return embeddings[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
495b9b34-9c46-4f1e-be66-b4cc653f7d9c | Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(BaseModel, Embeddings):
"""Wrapper around Cohere embedding models.
To use, you should have the ``cohere`` python package installed, and the
environment variable ``COHERE_API_KEY`` set with your API key or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.embeddings import CohereEmbeddings
cohere = CohereEmbeddings(
model="embed-english-light-v2.0", cohere_api_key="my-api-key"
)
"""
client: Any #: :meta private:
model: str = "embed-english-v2.0"
"""Model name to use."""
truncate: Optional[str] = None
"""Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")"""
cohere_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
cohere_api_key = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_API_KEY"
)
try:
import cohere
values["client"] = cohere.Client(cohere_api_key)
except ImportError:
raise ValueError(
"Could not import cohere python package. "
"Please install it with `pip install cohere`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Cohere's embedding endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
embeddings = self.client.embed(
model=self.model, texts=texts, truncate=self.truncate
).embeddings
return [list(map(float, e)) for e in embeddings]
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to Cohere's embedding endpoint.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embedding = self.client.embed(
model=self.model, texts=[text], truncate=self.truncate
).embeddings[0]
return list(map(float, embedding)) | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
c813c6eb-1b2b-4ec6-8c13-8939921e8d6d | Source code for langchain.embeddings.mosaicml
"""Wrapper around MosaicML APIs."""
from typing import Any, Dict, List, Mapping, Optional, Tuple
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class MosaicMLInstructorEmbeddings(BaseModel, Embeddings):
"""Wrapper around MosaicML's embedding inference service.
To use, you should have the
environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.llms import MosaicMLInstructorEmbeddings
endpoint_url = (
"https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
)
mosaic_llm = MosaicMLInstructorEmbeddings(
endpoint_url=endpoint_url,
mosaicml_api_token="my-api-key"
)
"""
endpoint_url: str = (
"https://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict"
)
"""Endpoint URL to use."""
embed_instruction: str = "Represent the document for retrieval: "
"""Instruction used to embed documents."""
query_instruction: str = (
"Represent the question for retrieving supporting documents: "
)
"""Instruction used to embed the query."""
retry_sleep: float = 1.0
"""How long to try sleeping for if a rate limit is encountered"""
mosaicml_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
mosaicml_api_token = get_from_dict_or_env(
values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
)
values["mosaicml_api_token"] = mosaicml_api_token
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"endpoint_url": self.endpoint_url}
def _embed(
self, input: List[Tuple[str, str]], is_retry: bool = False
) -> List[List[float]]:
payload = {"input_strings": input}
# HTTP headers for authorization
headers = {
"Authorization": f"{self.mosaicml_api_token}",
"Content-Type": "application/json",
}
# send request
try:
response = requests.post(self.endpoint_url, headers=headers, json=payload)
except requests.exceptions.RequestException as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
try:
parsed_response = response.json()
if "error" in parsed_response:
# if we get rate limited, try sleeping for 1 second
if (
not is_retry
and "rate limit exceeded" in parsed_response["error"].lower()
):
import time
time.sleep(self.retry_sleep)
return self._embed(input, is_retry=True)
raise ValueError(
f"Error raised by inference API: {parsed_response['error']}"
)
# The inference API has changed a couple of times, so we add some handling
# to be robust to multiple response formats.
if isinstance(parsed_response, dict):
if "data" in parsed_response:
output_item = parsed_response["data"]
elif "output" in parsed_response:
output_item = parsed_response["output"]
else:
raise ValueError(
f"No key data or output in response: {parsed_response}"
)
if isinstance(output_item, list) and isinstance(output_item[0], list):
embeddings = output_item
else:
embeddings = [output_item]
elif isinstance(parsed_response, list):
first_item = parsed_response[0]
if isinstance(first_item, list):
embeddings = parsed_response
elif isinstance(first_item, dict):
if "output" in first_item:
embeddings = [item["output"] for item in parsed_response]
else:
raise ValueError(
f"No key data or output in response: {parsed_response}"
)
else:
raise ValueError(f"Unexpected response format: {parsed_response}")
else:
raise ValueError(f"Unexpected response type: {parsed_response}")
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f"Error raised by inference API: {e}.\nResponse: {response.text}"
)
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a MosaicML deployed instructor embedding model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = [(self.embed_instruction, text) for text in texts]
embeddings = self._embed(instruction_pairs)
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using a MosaicML deployed instructor embedding model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = (self.query_instruction, text)
embedding = self._embed([instruction_pair])[0]
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
4159a0c4-ae99-42e4-aab0-e49bb651e854 | Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
"Represent the question for retrieving supporting documents: "
)
logger = logging.getLogger(__name__)
def _embed_documents(client: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
"""Inference function to send to the remote hardware.
Accepts a sentence_transformer model_id and
returns a list of embeddings for each document in the batch.
"""
return client.encode(*args, **kwargs)
def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0) -> Any:
"""Load the embedding model."""
if not instruct:
import sentence_transformers
client = sentence_transformers.SentenceTransformer(model_id)
else:
from InstructorEmbedding import INSTRUCTOR
client = INSTRUCTOR(model_id)
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
client = client.to(device)
return client
[docs]class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings):
"""Runs sentence_transformers embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the ``runhouse`` python package installed.
Example:
.. code-block:: python
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
import runhouse as rh
model_name = "sentence-transformers/all-mpnet-base-v2"
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
"""
client: Any #: :meta private:
model_id: str = DEFAULT_MODEL_NAME
"""Model name to use."""
model_reqs: List[str] = ["./", "sentence_transformers", "torch"]
"""Requirements to install on hardware to inference the model."""
hardware: Any
"""Remote hardware to send the inference function to."""
model_load_fn: Callable = load_embedding_model
"""Function to load the model remotely on the server."""
load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
inference_fn: Callable = _embed_documents
"""Inference function to extract the embeddings."""
def __init__(self, **kwargs: Any):
"""Initialize the remote inference function."""
load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
load_fn_kwargs["model_id"] = load_fn_kwargs.get("model_id", DEFAULT_MODEL_NAME)
load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", False)
load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs]class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings):
"""Runs InstructorEmbedding embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the ``runhouse`` python package installed.
Example:
.. code-block:: python
from langchain.embeddings import SelfHostedHuggingFaceInstructEmbeddings
import runhouse as rh
model_name = "hkunlp/instructor-large"
gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
hf = SelfHostedHuggingFaceInstructEmbeddings(
model_name=model_name, hardware=gpu)
"""
model_id: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding query."""
model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"]
"""Requirements to install on hardware to inference the model."""
def __init__(self, **kwargs: Any):
"""Initialize the remote inference function."""
load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
load_fn_kwargs["model_id"] = load_fn_kwargs.get(
"model_id", DEFAULT_INSTRUCT_MODEL
)
load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", True)
load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace instruct model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = []
for text in texts:
instruction_pairs.append([self.embed_instruction, text])
embeddings = self.client(self.pipeline_ref, instruction_pairs)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace instruct model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
return embedding.tolist() | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
472c5600-2e0a-4545-afb4-966ae287e371 | Source code for langchain.embeddings.embaas
"""Wrapper around embaas embeddings API."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from typing_extensions import NotRequired, TypedDict
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
# Currently supported maximum batch size for embedding requests
MAX_BATCH_SIZE = 256
EMBAAS_API_URL = "https://api.embaas.io/v1/embeddings/"
class EmbaasEmbeddingsPayload(TypedDict):
"""Payload for the embaas embeddings API."""
model: str
texts: List[str]
instruction: NotRequired[str]
[docs]class EmbaasEmbeddings(BaseModel, Embeddings):
"""Wrapper around embaas's embedding service.
To use, you should have the
environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
# Initialise with default model and instruction
from langchain.embeddings import EmbaasEmbeddings
emb = EmbaasEmbeddings()
# Initialise with custom model and instruction
from langchain.embeddings import EmbaasEmbeddings
emb_model = "instructor-large"
emb_inst = "Represent the Wikipedia document for retrieval"
emb = EmbaasEmbeddings(
model=emb_model,
instruction=emb_inst
)
"""
model: str = "e5-large-v2"
"""The model used for embeddings."""
instruction: Optional[str] = None
"""Instruction used for domain-specific embeddings."""
api_url: str = EMBAAS_API_URL
"""The URL for the embaas embeddings API."""
embaas_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
embaas_api_key = get_from_dict_or_env(
values, "embaas_api_key", "EMBAAS_API_KEY"
)
values["embaas_api_key"] = embaas_api_key
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying params."""
return {"model": self.model, "instruction": self.instruction}
def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload:
"""Generates payload for the API request."""
payload = EmbaasEmbeddingsPayload(texts=texts, model=self.model)
if self.instruction:
payload["instruction"] = self.instruction
return payload
def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]:
"""Sends a request to the Embaas API and handles the response."""
headers = {
"Authorization": f"Bearer {self.embaas_api_key}",
"Content-Type": "application/json",
}
response = requests.post(self.api_url, headers=headers, json=payload)
response.raise_for_status()
parsed_response = response.json()
embeddings = [item["embedding"] for item in parsed_response["data"]]
return embeddings
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings using the Embaas API."""
payload = self._generate_payload(texts)
try:
return self._handle_request(payload)
except requests.exceptions.RequestException as e:
if e.response is None or not e.response.text:
raise ValueError(f"Error raised by embaas embeddings API: {e}")
parsed_response = e.response.json()
if "message" in parsed_response:
raise ValueError(
"Validation Error raised by embaas embeddings API:"
f"{parsed_response['message']}"
)
raise
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Get embeddings for a list of texts.
Args:
texts: The list of texts to get embeddings for.
Returns:
List of embeddings, one for each text.
"""
batches = [
texts[i : i + MAX_BATCH_SIZE] for i in range(0, len(texts), MAX_BATCH_SIZE)
]
embeddings = [self._generate_embeddings(batch) for batch in batches]
# flatten the list of lists into a single list
return [embedding for batch in embeddings for embedding in batch]
[docs] def embed_query(self, text: str) -> List[float]:
"""Get embeddings for a single text.
Args:
text: The text to get embeddings for.
Returns:
List of embeddings.
"""
return self.embed_documents([text])[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
57912d0f-f23f-4e42-ade6-4c97ba9b4e78 | Source code for langchain.embeddings.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
class EmbeddingsContentHandler(ContentHandlerBase[List[str], List[List[float]]]):
"""Content handler for LLM class."""
[docs]class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
"""Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
"""
"""
Example:
.. code-block:: python
from langchain.embeddings import SagemakerEndpointEmbeddings
endpoint_name = (
"my-endpoint-name"
)
region_name = (
"us-west-2"
)
credentials_profile_name = (
"default"
)
se = SagemakerEndpointEmbeddings(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
"""
content_handler: EmbeddingsContentHandler
"""The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
"""
"""
Example:
.. code-block:: python
from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
class ContentHandler(EmbeddingsContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompts: List[str], model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompts: prompts, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> List[List[float]]:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["vectors"]
""" # noqa: E501
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that AWS credentials to and python package exists in environment."""
try:
import boto3
try:
if values["credentials_profile_name"] is not None:
session = boto3.Session(
profile_name=values["credentials_profile_name"]
)
else:
# use default credentials
session = boto3.Session()
values["client"] = session.client(
"sagemaker-runtime", region_name=values["region_name"]
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from e
except ImportError:
raise ValueError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
return values
def _embedding_func(self, texts: List[str]) -> List[List[float]]:
"""Call out to SageMaker Inference embedding endpoint."""
# replace newlines, which can negatively affect performance.
texts = list(map(lambda x: x.replace("\n", " "), texts))
_model_kwargs = self.model_kwargs or {}
_endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(texts, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# send request
try:
response = self.client.invoke_endpoint(
EndpointName=self.endpoint_name,
Body=body,
ContentType=content_type,
Accept=accepts,
**_endpoint_kwargs,
)
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
return self.content_handler.transform_output(response["Body"])
[docs] def embed_documents(
self, texts: List[str], chunk_size: int = 64
) -> List[List[float]]:
"""Compute doc embeddings using a SageMaker Inference Endpoint.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size defines how many input texts will
be grouped together as request. If None, will use the
chunk size specified by the class.
Returns:
List of embeddings, one for each text.
"""
results = []
_chunk_size = len(texts) if chunk_size > len(texts) else chunk_size
for i in range(0, len(texts), _chunk_size):
response = self._embedding_func(texts[i : i + _chunk_size])
results.extend(response)
return results
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a SageMaker inference endpoint.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self._embedding_func([text])[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
c735dffc-f8f6-4e54-a91e-f6f2bb7484ab | Source code for langchain.embeddings.llamacpp
"""Wrapper around llama.cpp embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
"""Wrapper around llama.cpp embedding models.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: https://github.com/abetlen/llama-cpp-python
Example:
.. code-block:: python
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
"""
client: Any #: :meta private:
model_path: str
n_ctx: int = Field(512, alias="n_ctx")
"""Token context window."""
n_parts: int = Field(-1, alias="n_parts")
"""Number of parts to split the model into.
If -1, the number of parts is automatically determined."""
seed: int = Field(-1, alias="seed")
"""Seed. If -1, a random seed is used."""
f16_kv: bool = Field(False, alias="f16_kv")
"""Use half-precision for key/value cache."""
logits_all: bool = Field(False, alias="logits_all")
"""Return logits for all tokens, not just the last token."""
vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""Number of tokens to process in parallel.
Should be a number between 1 and n_ctx."""
n_gpu_layers: Optional[int] = Field(None, alias="n_gpu_layers")
"""Number of layers to be loaded into gpu memory. Default None."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that llama-cpp-python library is installed."""
model_path = values["model_path"]
model_param_names = [
"n_ctx",
"n_parts",
"seed",
"f16_kv",
"logits_all",
"vocab_only",
"use_mlock",
"n_threads",
"n_batch",
]
model_params = {k: values[k] for k in model_param_names}
# For backwards compatibility, only include if non-null.
if values["n_gpu_layers"] is not None:
model_params["n_gpu_layers"] = values["n_gpu_layers"]
try:
from llama_cpp import Llama
values["client"] = Llama(model_path, embedding=True, **model_params)
except ImportError:
raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
raise ValueError(
f"Could not load Llama model from path: {model_path}. "
f"Received error {e}"
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of documents using the Llama model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
embeddings = [self.client.embed(text) for text in texts]
return [list(map(float, e)) for e in embeddings]
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using the Llama model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embedding = self.client.embed(text)
return list(map(float, embedding)) | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
47915982-8c25-491e-b254-13d355acdfc5 | Source code for langchain.embeddings.dashscope
"""Wrapper around DashScope embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
)
from pydantic import BaseModel, Extra, root_validator
from requests.exceptions import HTTPError
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator(embeddings: DashScopeEmbeddings) -> Callable[[Any], Any]:
multiplier = 1
min_seconds = 1
max_seconds = 4
# Wait 2^x * 1 second between each retry starting with
# 1 seconds, then up to 4 seconds, then 4 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier, min=min_seconds, max=max_seconds),
retry=(retry_if_exception_type(HTTPError)),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def embed_with_retry(embeddings: DashScopeEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
resp = embeddings.client.call(**kwargs)
if resp.status_code == 200:
return resp.output["embeddings"]
elif resp.status_code in [400, 401]:
raise ValueError(
f"status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
else:
raise HTTPError(
f"HTTP error occurred: status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
return _embed_with_retry(**kwargs)
[docs]class DashScopeEmbeddings(BaseModel, Embeddings):
"""Wrapper around DashScope embedding models.
To use, you should have the ``dashscope`` python package installed, and the
environment variable ``DASHSCOPE_API_KEY`` set with your API key or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.embeddings import DashScopeEmbeddings
embeddings = DashScopeEmbeddings(dashscope_api_key="my-api-key")
Example:
.. code-block:: python
import os
os.environ["DASHSCOPE_API_KEY"] = "your DashScope API KEY"
from langchain.embeddings.dashscope import DashScopeEmbeddings
embeddings = DashScopeEmbeddings(
model="text-embedding-v1",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
"""
client: Any #: :meta private:
model: str = "text-embedding-v1"
dashscope_api_key: Optional[str] = None
"""Maximum number of retries to make when generating."""
max_retries: int = 5
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
import dashscope
"""Validate that api key and python package exists in environment."""
values["dashscope_api_key"] = get_from_dict_or_env(
values, "dashscope_api_key", "DASHSCOPE_API_KEY"
)
dashscope.api_key = values["dashscope_api_key"]
try:
import dashscope
values["client"] = dashscope.TextEmbedding
except ImportError:
raise ImportError(
"Could not import dashscope python package. "
"Please install it with `pip install dashscope`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to DashScope's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
embeddings = embed_with_retry(
self, input=texts, text_type="document", model=self.model
)
embedding_list = [item["embedding"] for item in embeddings]
return embedding_list
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to DashScope's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embedding = embed_with_retry(
self, input=text, text_type="query", model=self.model
)[0]["embedding"]
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
cefcf16e-d695-4faf-bc91-93b69048a379 | Source code for langchain.memory.motorhead_memory
from typing import Any, Dict, List, Optional
import requests
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import get_buffer_string
MANAGED_URL = "https://api.getmetal.io/v1/motorhead"
# LOCAL_URL = "http://localhost:8080"
[docs]class MotorheadMemory(BaseChatMemory):
url: str = MANAGED_URL
timeout = 3000
memory_key = "history"
session_id: str
context: Optional[str] = None
# Managed Params
api_key: Optional[str] = None
client_id: Optional[str] = None
def __get_headers(self) -> Dict[str, str]:
is_managed = self.url == MANAGED_URL
headers = {
"Content-Type": "application/json",
}
if is_managed and not (self.api_key and self.client_id):
raise ValueError(
"""
You must provide an API key or a client ID to use the managed
version of Motorhead. Visit https://getmetal.io for more information.
"""
)
if is_managed and self.api_key and self.client_id:
headers["x-metal-api-key"] = self.api_key
headers["x-metal-client-id"] = self.client_id
return headers
[docs] async def init(self) -> None:
res = requests.get(
f"{self.url}/sessions/{self.session_id}/memory",
timeout=self.timeout,
headers=self.__get_headers(),
)
res_data = res.json()
res_data = res_data.get("data", res_data) # Handle Managed Version
messages = res_data.get("messages", [])
context = res_data.get("context", "NONE")
for message in reversed(messages):
if message["role"] == "AI":
self.chat_memory.add_ai_message(message["content"])
else:
self.chat_memory.add_user_message(message["content"])
if context and context != "NONE":
self.context = context
[docs] def load_memory_variables(self, values: Dict[str, Any]) -> Dict[str, Any]:
if self.return_messages:
return {self.memory_key: self.chat_memory.messages}
else:
return {self.memory_key: get_buffer_string(self.chat_memory.messages)}
@property
def memory_variables(self) -> List[str]:
return [self.memory_key]
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
input_str, output_str = self._get_input_output(inputs, outputs)
requests.post(
f"{self.url}/sessions/{self.session_id}/memory",
timeout=self.timeout,
json={
"messages": [
{"role": "Human", "content": f"{input_str}"},
{"role": "AI", "content": f"{output_str}"},
]
},
headers=self.__get_headers(),
)
super().save_context(inputs, outputs)
[docs] def delete_session(self) -> None:
"""Delete a session"""
requests.delete(f"{self.url}/sessions/{self.session_id}/memory") | https://api.python.langchain.com/en/latest/_modules/langchain/memory/motorhead_memory.html |
979073a4-1da4-4c31-860e-fff55f26de31 | Source code for langchain.memory.entity
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import (
ENTITY_EXTRACTION_PROMPT,
ENTITY_SUMMARIZATION_PROMPT,
)
from langchain.memory.utils import get_prompt_input_key
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseMessage, get_buffer_string
logger = logging.getLogger(__name__)
class BaseEntityStore(BaseModel, ABC):
@abstractmethod
def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
"""Get entity value from store."""
pass
@abstractmethod
def set(self, key: str, value: Optional[str]) -> None:
"""Set entity value in store."""
pass
@abstractmethod
def delete(self, key: str) -> None:
"""Delete entity value from store."""
pass
@abstractmethod
def exists(self, key: str) -> bool:
"""Check if entity exists in store."""
pass
@abstractmethod
def clear(self) -> None:
"""Delete all entities from store."""
pass
[docs]class InMemoryEntityStore(BaseEntityStore):
"""Basic in-memory entity store."""
store: Dict[str, Optional[str]] = {}
[docs] def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
return self.store.get(key, default)
[docs] def set(self, key: str, value: Optional[str]) -> None:
self.store[key] = value
[docs] def delete(self, key: str) -> None:
del self.store[key]
[docs] def exists(self, key: str) -> bool:
return key in self.store
[docs] def clear(self) -> None:
return self.store.clear()
[docs]class RedisEntityStore(BaseEntityStore):
"""Redis-backed Entity store. Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
"""
redis_client: Any
session_id: str = "default"
key_prefix: str = "memory_store"
ttl: Optional[int] = 60 * 60 * 24
recall_ttl: Optional[int] = 60 * 60 * 24 * 3
def __init__(
self,
session_id: str = "default",
url: str = "redis://localhost:6379/0",
key_prefix: str = "memory_store",
ttl: Optional[int] = 60 * 60 * 24,
recall_ttl: Optional[int] = 60 * 60 * 24 * 3,
*args: Any,
**kwargs: Any,
):
try:
import redis
except ImportError:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
super().__init__(*args, **kwargs)
try:
self.redis_client = redis.Redis.from_url(url=url, decode_responses=True)
except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
self.recall_ttl = recall_ttl or ttl
@property
def full_key_prefix(self) -> str:
return f"{self.key_prefix}:{self.session_id}"
[docs] def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
res = (
self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl)
or default
or ""
)
logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'")
return res
[docs] def set(self, key: str, value: Optional[str]) -> None:
if not value:
return self.delete(key)
self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl)
logger.debug(
f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
)
[docs] def delete(self, key: str) -> None:
self.redis_client.delete(f"{self.full_key_prefix}:{key}")
[docs] def exists(self, key: str) -> bool:
return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1
[docs] def clear(self) -> None:
# iterate a list in batches of size batch_size
def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]:
iterator = iter(iterable)
while batch := list(islice(iterator, batch_size)):
yield batch
for keybatch in batched(
self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500
):
self.redis_client.delete(*keybatch)
[docs]class SQLiteEntityStore(BaseEntityStore):
"""SQLite-backed Entity store"""
session_id: str = "default"
table_name: str = "memory_store"
def __init__(
self,
session_id: str = "default",
db_file: str = "entities.db",
table_name: str = "memory_store",
*args: Any,
**kwargs: Any,
):
try:
import sqlite3
except ImportError:
raise ImportError(
"Could not import sqlite3 python package. "
"Please install it with `pip install sqlite3`."
)
super().__init__(*args, **kwargs)
self.conn = sqlite3.connect(db_file)
self.session_id = session_id
self.table_name = table_name
self._create_table_if_not_exists()
@property
def full_table_name(self) -> str:
return f"{self.table_name}_{self.session_id}"
def _create_table_if_not_exists(self) -> None:
create_table_query = f"""
CREATE TABLE IF NOT EXISTS {self.full_table_name} (
key TEXT PRIMARY KEY,
value TEXT
)
"""
with self.conn:
self.conn.execute(create_table_query)
[docs] def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
query = f"""
SELECT value
FROM {self.full_table_name}
WHERE key = ?
"""
cursor = self.conn.execute(query, (key,))
result = cursor.fetchone()
if result is not None:
value = result[0]
return value
return default
[docs] def set(self, key: str, value: Optional[str]) -> None:
if not value:
return self.delete(key)
query = f"""
INSERT OR REPLACE INTO {self.full_table_name} (key, value)
VALUES (?, ?)
"""
with self.conn:
self.conn.execute(query, (key, value))
[docs] def delete(self, key: str) -> None:
query = f"""
DELETE FROM {self.full_table_name}
WHERE key = ?
"""
with self.conn:
self.conn.execute(query, (key,))
[docs] def exists(self, key: str) -> bool:
query = f"""
SELECT 1
FROM {self.full_table_name}
WHERE key = ?
LIMIT 1
"""
cursor = self.conn.execute(query, (key,))
result = cursor.fetchone()
return result is not None
[docs] def clear(self) -> None:
query = f"""
DELETE FROM {self.full_table_name}
"""
with self.conn:
self.conn.execute(query)
[docs]class ConversationEntityMemory(BaseChatMemory):
"""Entity extractor & summarizer memory.
Extracts named entities from the recent chat history and generates summaries.
With a swapable entity store, persisting entities across conversations.
Defaults to an in-memory entity store, and can be swapped out for a Redis,
SQLite, or other entity store.
"""
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT
# Cache of recently detected entity names, if any
# It is updated when load_memory_variables is called:
entity_cache: List[str] = []
# Number of recent message pairs to consider when updating entities:
k: int = 3
chat_history_key: str = "history"
# Store to manage entity-related data:
entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore)
@property
def buffer(self) -> List[BaseMessage]:
"""Access chat memory messages."""
return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return ["entities", self.chat_history_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""
Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet.
"""
# Create an LLMChain for predicting entity names from the recent chat history:
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
# Extract an arbitrary window of the last message pairs from
# the chat history, where the hyperparameter k is the
# number of message pairs:
buffer_string = get_buffer_string(
self.buffer[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
# Generates a comma-separated list of named entities,
# e.g. "Jane, White House, UFO"
# or "NONE" if no named entities are extracted:
output = chain.predict(
history=buffer_string,
input=inputs[prompt_input_key],
)
# If no named entities are extracted, assigns an empty list.
if output.strip() == "NONE":
entities = []
else:
# Make a list of the extracted entities:
entities = [w.strip() for w in output.split(",")]
# Make a dictionary of entities with summary if exists:
entity_summaries = {}
for entity in entities:
entity_summaries[entity] = self.entity_store.get(entity, "")
# Replaces the entity name cache with the most recently discussed entities,
# or if no entities were extracted, clears the cache:
self.entity_cache = entities
# Should we return as message objects or as a string?
if self.return_messages:
# Get last `k` pair of chat messages:
buffer: Any = self.buffer[-self.k * 2 :]
else:
# Reuse the string we made earlier:
buffer = buffer_string
return {
self.chat_history_key: buffer,
"entities": entity_summaries,
}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""
Save context from this conversation history to the entity store.
Generates a summary for each entity in the entity cache by prompting
the model, and saves these summaries to the entity store.
"""
super().save_context(inputs, outputs)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
# Extract an arbitrary window of the last message pairs from
# the chat history, where the hyperparameter k is the
# number of message pairs:
buffer_string = get_buffer_string(
self.buffer[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
input_data = inputs[prompt_input_key]
# Create an LLMChain for predicting entity summarization from the context
chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt)
# Generate new summaries for entities and save them in the entity store
for entity in self.entity_cache:
# Get existing summary if it exists
existing_summary = self.entity_store.get(entity, "")
output = chain.predict(
summary=existing_summary,
entity=entity,
history=buffer_string,
input=input_data,
)
# Save the updated summary to the entity store
self.entity_store.set(entity, output.strip())
[docs] def clear(self) -> None:
"""Clear memory contents."""
self.chat_memory.clear()
self.entity_cache.clear()
self.entity_store.clear() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
cca1e42a-b621-4737-bda1-bd74da65ab8d | Source code for langchain.memory.buffer
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import get_buffer_string
[docs]class ConversationBufferMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
memory_key: str = "history" #: :meta private:
@property
def buffer(self) -> Any:
"""String buffer of memory."""
if self.return_messages:
return self.chat_memory.messages
else:
return get_buffer_string(
self.chat_memory.messages,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
return {self.memory_key: self.buffer}
[docs]class ConversationStringBufferMemory(BaseMemory):
"""Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
"""Prefix to use for AI generated responses."""
buffer: str = ""
output_key: Optional[str] = None
input_key: Optional[str] = None
memory_key: str = "history" #: :meta private:
@root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that return messages is not True."""
if values.get("return_messages", False):
raise ValueError(
"return_messages must be False for ConversationStringBufferMemory"
)
return values
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Return history buffer."""
return {self.memory_key: self.buffer}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
if self.output_key is None:
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}")
output_key = list(outputs.keys())[0]
else:
output_key = self.output_key
human = f"{self.human_prefix}: " + inputs[prompt_input_key]
ai = f"{self.ai_prefix}: " + outputs[output_key]
self.buffer += "\n" + "\n".join([human, ai])
[docs] def clear(self) -> None:
"""Clear memory contents."""
self.buffer = "" | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
f48b481f-6b8c-466e-ad44-762f33e61ef4 | Source code for langchain.memory.vectorstore
"""Class for a VectorStore-backed memory object."""
from typing import Any, Dict, List, Optional, Union
from pydantic import Field
from langchain.memory.chat_memory import BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import Document
from langchain.vectorstores.base import VectorStoreRetriever
[docs]class VectorStoreRetrieverMemory(BaseMemory):
"""Class for a VectorStore-backed memory object."""
retriever: VectorStoreRetriever = Field(exclude=True)
"""VectorStoreRetriever object to connect to."""
memory_key: str = "history" #: :meta private:
"""Key name to locate the memories in the result of load_memory_variables."""
input_key: Optional[str] = None
"""Key name to index the inputs to load_memory_variables."""
return_docs: bool = False
"""Whether or not to return the result of querying the database directly."""
@property
def memory_variables(self) -> List[str]:
"""The list of keys emitted from the load_memory_variables method."""
return [self.memory_key]
def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str:
"""Get the input key for the prompt."""
if self.input_key is None:
return get_prompt_input_key(inputs, self.memory_variables)
return self.input_key
[docs] def load_memory_variables(
self, inputs: Dict[str, Any]
) -> Dict[str, Union[List[Document], str]]:
"""Return history buffer."""
input_key = self._get_prompt_input_key(inputs)
query = inputs[input_key]
docs = self.retriever.get_relevant_documents(query)
result: Union[List[Document], str]
if not self.return_docs:
result = "\n".join([doc.page_content for doc in docs])
else:
result = docs
return {self.memory_key: result}
def _form_documents(
self, inputs: Dict[str, Any], outputs: Dict[str, str]
) -> List[Document]:
"""Format context from this conversation to buffer."""
# Each document should only include the current turn, not the chat history
filtered_inputs = {k: v for k, v in inputs.items() if k != self.memory_key}
texts = [
f"{k}: {v}"
for k, v in list(filtered_inputs.items()) + list(outputs.items())
]
page_content = "\n".join(texts)
return [Document(page_content=page_content)]
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
documents = self._form_documents(inputs, outputs)
self.retriever.add_documents(documents)
[docs] def clear(self) -> None:
"""Nothing to clear.""" | https://api.python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
ac94fae5-3be7-4d75-8f5c-b984d27c081e | Source code for langchain.memory.buffer_window
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
memory_key: str = "history" #: :meta private:
k: int = 5
@property
def buffer(self) -> List[BaseMessage]:
"""String buffer of memory."""
return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Return history buffer."""
buffer: Any = self.buffer[-self.k * 2 :] if self.k > 0 else []
if not self.return_messages:
buffer = get_buffer_string(
buffer,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
return {self.memory_key: buffer} | https://api.python.langchain.com/en/latest/_modules/langchain/memory/buffer_window.html |
38d5fd16-7003-46fb-bb52-21419ab9eb6b | Source code for langchain.memory.summary
from __future__ import annotations
from typing import Any, Dict, List, Type
from pydantic import BaseModel, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import SUMMARY_PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
SystemMessage,
get_buffer_string,
)
class SummarizerMixin(BaseModel):
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
prompt: BasePromptTemplate = SUMMARY_PROMPT
summary_message_cls: Type[BaseMessage] = SystemMessage
def predict_new_summary(
self, messages: List[BaseMessage], existing_summary: str
) -> str:
new_lines = get_buffer_string(
messages,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
chain = LLMChain(llm=self.llm, prompt=self.prompt)
return chain.predict(summary=existing_summary, new_lines=new_lines)
[docs]class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin):
"""Conversation summarizer to memory."""
buffer: str = ""
memory_key: str = "history" #: :meta private:
[docs] @classmethod
def from_messages(
cls,
llm: BaseLanguageModel,
chat_memory: BaseChatMessageHistory,
*,
summarize_step: int = 2,
**kwargs: Any,
) -> ConversationSummaryMemory:
obj = cls(llm=llm, chat_memory=chat_memory, **kwargs)
for i in range(0, len(obj.chat_memory.messages), summarize_step):
obj.buffer = obj.predict_new_summary(
obj.chat_memory.messages[i : i + summarize_step], obj.buffer
)
return obj
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
if self.return_messages:
buffer: Any = [self.summary_message_cls(content=self.buffer)]
else:
buffer = self.buffer
return {self.memory_key: buffer}
@root_validator()
def validate_prompt_input_variables(cls, values: Dict) -> Dict:
"""Validate that prompt input variables are consistent."""
prompt_variables = values["prompt"].input_variables
expected_keys = {"summary", "new_lines"}
if expected_keys != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but it should have {expected_keys}."
)
return values
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self.buffer = self.predict_new_summary(
self.chat_memory.messages[-2:], self.buffer
)
[docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.buffer = "" | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
bbc6f8cb-1337-40de-af72-590ebd306c4f | Source code for langchain.memory.combined
import warnings
from typing import Any, Dict, List, Set
from pydantic import validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMemory
[docs]class CombinedMemory(BaseMemory):
"""Class for combining multiple memories' data together."""
memories: List[BaseMemory]
"""For tracking all the memories that should be accessed."""
@validator("memories")
def check_repeated_memory_variable(
cls, value: List[BaseMemory]
) -> List[BaseMemory]:
all_variables: Set[str] = set()
for val in value:
overlap = all_variables.intersection(val.memory_variables)
if overlap:
raise ValueError(
f"The same variables {overlap} are found in multiple"
"memory object, which is not allowed by CombinedMemory."
)
all_variables |= set(val.memory_variables)
return value
@validator("memories")
def check_input_key(cls, value: List[BaseMemory]) -> List[BaseMemory]:
"""Check that if memories are of type BaseChatMemory that input keys exist."""
for val in value:
if isinstance(val, BaseChatMemory):
if val.input_key is None:
warnings.warn(
"When using CombinedMemory, "
"input keys should be so the input is known. "
f" Was not set on {val}"
)
return value
@property
def memory_variables(self) -> List[str]:
"""All the memory variables that this instance provides."""
"""Collected from the all the linked memories."""
memory_variables = []
for memory in self.memories:
memory_variables.extend(memory.memory_variables)
return memory_variables
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load all vars from sub-memories."""
memory_data: Dict[str, Any] = {}
# Collect vars from all sub-memories
for memory in self.memories:
data = memory.load_memory_variables(inputs)
memory_data = {
**memory_data,
**data,
}
return memory_data
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this session for every memory."""
# Save context for all sub-memories
for memory in self.memories:
memory.save_context(inputs, outputs)
[docs] def clear(self) -> None:
"""Clear context from this session for every memory."""
for memory in self.memories:
memory.clear() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
fb575b19-d593-4e86-82f4-d1c48dfa8596 | Source code for langchain.memory.readonly
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class ReadOnlySharedMemory(BaseMemory):
"""A memory wrapper that is read-only and cannot be changed."""
memory: BaseMemory
@property
def memory_variables(self) -> List[str]:
"""Return memory variables."""
return self.memory.memory_variables
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load memory variables from memory."""
return self.memory.load_memory_variables(inputs)
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Nothing should be saved or changed"""
pass
[docs] def clear(self) -> None:
"""Nothing to clear, got a memory like a vault."""
pass | https://api.python.langchain.com/en/latest/_modules/langchain/memory/readonly.html |
684d6476-7561-4976-95f6-67c2930138ca | Source code for langchain.memory.simple
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other bits of information that shouldn't
ever change between prompts.
"""
memories: Dict[str, Any] = dict()
@property
def memory_variables(self) -> List[str]:
return list(self.memories.keys())
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
return self.memories
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Nothing should be saved or changed, my memory is set in stone."""
pass
[docs] def clear(self) -> None:
"""Nothing to clear, got a memory like a vault."""
pass | https://api.python.langchain.com/en/latest/_modules/langchain/memory/simple.html |
145a190f-8dc7-44b5-9328-372137064a47 | Source code for langchain.memory.token_buffer
from typing import Any, Dict, List
from langchain.base_language import BaseLanguageModel
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationTokenBufferMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
memory_key: str = "history"
max_token_limit: int = 2000
@property
def buffer(self) -> List[BaseMessage]:
"""String buffer of memory."""
return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
buffer: Any = self.buffer
if self.return_messages:
final_buffer: Any = buffer
else:
final_buffer = get_buffer_string(
buffer,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
return {self.memory_key: final_buffer}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer. Pruned."""
super().save_context(inputs, outputs)
# Prune buffer if it exceeds max token limit
buffer = self.chat_memory.messages
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
if curr_buffer_length > self.max_token_limit:
pruned_memory = []
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
06076791-1cad-40da-be4c-20a5b55755f7 | Source code for langchain.memory.summary_buffer
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.summary import SummarizerMixin
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin):
"""Buffer with summarizer for storing conversation memory."""
max_token_limit: int = 2000
moving_summary_buffer: str = ""
memory_key: str = "history"
@property
def buffer(self) -> List[BaseMessage]:
return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
buffer = self.buffer
if self.moving_summary_buffer != "":
first_messages: List[BaseMessage] = [
self.summary_message_cls(content=self.moving_summary_buffer)
]
buffer = first_messages + buffer
if self.return_messages:
final_buffer: Any = buffer
else:
final_buffer = get_buffer_string(
buffer, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix
)
return {self.memory_key: final_buffer}
@root_validator()
def validate_prompt_input_variables(cls, values: Dict) -> Dict:
"""Validate that prompt input variables are consistent."""
prompt_variables = values["prompt"].input_variables
expected_keys = {"summary", "new_lines"}
if expected_keys != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but it should have {expected_keys}."
)
return values
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self.prune()
[docs] def prune(self) -> None:
"""Prune buffer if it exceeds max token limit"""
buffer = self.chat_memory.messages
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
if curr_buffer_length > self.max_token_limit:
pruned_memory = []
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
self.moving_summary_buffer = self.predict_new_summary(
pruned_memory, self.moving_summary_buffer
)
[docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.moving_summary_buffer = "" | https://api.python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
492e5e14-96b5-4cb9-9733-0b4f9ea97b8d | Source code for langchain.memory.kg
from typing import Any, Dict, List, Type, Union
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.graphs import NetworkxEntityGraph
from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import (
ENTITY_EXTRACTION_PROMPT,
KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
)
from langchain.memory.utils import get_prompt_input_key
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import (
BaseMessage,
SystemMessage,
get_buffer_string,
)
[docs]class ConversationKGMemory(BaseChatMemory):
"""Knowledge graph memory for storing conversation memory.
Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
"""
k: int = 2
human_prefix: str = "Human"
ai_prefix: str = "AI"
kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph)
knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
llm: BaseLanguageModel
summary_message_cls: Type[BaseMessage] = SystemMessage
"""Number of previous utterances to include in the context."""
memory_key: str = "history" #: :meta private:
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
entities = self._get_current_entities(inputs)
summary_strings = []
for entity in entities:
knowledge = self.kg.get_entity_knowledge(entity)
if knowledge:
summary = f"On {entity}: {'. '.join(knowledge)}."
summary_strings.append(summary)
context: Union[str, List]
if not summary_strings:
context = [] if self.return_messages else ""
elif self.return_messages:
context = [
self.summary_message_cls(content=text) for text in summary_strings
]
else:
context = "\n".join(summary_strings)
return {self.memory_key: context}
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str:
"""Get the input key for the prompt."""
if self.input_key is None:
return get_prompt_input_key(inputs, self.memory_variables)
return self.input_key
def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str:
"""Get the output key for the prompt."""
if self.output_key is None:
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}")
return list(outputs.keys())[0]
return self.output_key
[docs] def get_current_entities(self, input_string: str) -> List[str]:
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
buffer_string = get_buffer_string(
self.chat_memory.messages[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=input_string,
)
return get_entities(output)
def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
"""Get the current entities in the conversation."""
prompt_input_key = self._get_prompt_input_key(inputs)
return self.get_current_entities(inputs[prompt_input_key])
[docs] def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]:
chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt)
buffer_string = get_buffer_string(
self.chat_memory.messages[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=input_string,
verbose=True,
)
knowledge = parse_triples(output)
return knowledge
def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
"""Get and update knowledge graph from the conversation history."""
prompt_input_key = self._get_prompt_input_key(inputs)
knowledge = self.get_knowledge_triplets(inputs[prompt_input_key])
for triple in knowledge:
self.kg.add_triple(triple)
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self._get_and_update_kg(inputs)
[docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.kg.clear() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
ca3c5d42-2115-4571-b2e0-bbd8daa179bc | Source code for langchain.memory.chat_message_histories.zep
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Dict, List, Optional
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
)
if TYPE_CHECKING:
from zep_python import Memory, MemorySearchResult, Message, NotFoundError
logger = logging.getLogger(__name__)
[docs]class ZepChatMessageHistory(BaseChatMessageHistory):
"""A ChatMessageHistory implementation that uses Zep as a backend.
Recommended usage::
# Set up Zep Chat History
zep_chat_history = ZepChatMessageHistory(
session_id=session_id,
url=ZEP_API_URL,
)
# Use a standard ConversationBufferMemory to encapsulate the Zep chat history
memory = ConversationBufferMemory(
memory_key="chat_history", chat_memory=zep_chat_history
)
Zep provides long-term conversation storage for LLM apps. The server stores,
summarizes, embeds, indexes, and enriches conversational AI chat
histories, and exposes them via simple, low-latency APIs.
For server installation instructions and more, see: https://getzep.github.io/
This class is a thin wrapper around the zep-python package. Additional
Zep functionality is exposed via the `zep_summary` and `zep_messages`
properties.
For more information on the zep-python package, see:
https://github.com/getzep/zep-python
"""
def __init__(
self,
session_id: str,
url: str = "http://localhost:8000",
) -> None:
try:
from zep_python import ZepClient
except ImportError:
raise ValueError(
"Could not import zep-python package. "
"Please install it with `pip install zep-python`."
)
self.zep_client = ZepClient(base_url=url)
self.session_id = session_id
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve messages from Zep memory"""
zep_memory: Optional[Memory] = self._get_memory()
if not zep_memory:
return []
messages: List[BaseMessage] = []
# Extract summary, if present, and messages
if zep_memory.summary:
if len(zep_memory.summary.content) > 0:
messages.append(HumanMessage(content=zep_memory.summary.content))
if zep_memory.messages:
msg: Message
for msg in zep_memory.messages:
if msg.role == "ai":
messages.append(AIMessage(content=msg.content))
else:
messages.append(HumanMessage(content=msg.content))
return messages
@property
def zep_messages(self) -> List[Message]:
"""Retrieve summary from Zep memory"""
zep_memory: Optional[Memory] = self._get_memory()
if not zep_memory:
return []
return zep_memory.messages
@property
def zep_summary(self) -> Optional[str]:
"""Retrieve summary from Zep memory"""
zep_memory: Optional[Memory] = self._get_memory()
if not zep_memory or not zep_memory.summary:
return None
return zep_memory.summary.content
def _get_memory(self) -> Optional[Memory]:
"""Retrieve memory from Zep"""
from zep_python import NotFoundError
try:
zep_memory: Memory = self.zep_client.get_memory(self.session_id)
except NotFoundError:
logger.warning(
f"Session {self.session_id} not found in Zep. Returning None"
)
return None
return zep_memory
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the Zep memory history"""
from zep_python import Memory, Message
zep_message: Message
if isinstance(message, HumanMessage):
zep_message = Message(content=message.content, role="human")
else:
zep_message = Message(content=message.content, role="ai")
zep_memory = Memory(messages=[zep_message])
self.zep_client.add_memory(self.session_id, zep_memory)
[docs] def search(
self, query: str, metadata: Optional[Dict] = None, limit: Optional[int] = None
) -> List[MemorySearchResult]:
"""Search Zep memory for messages matching the query"""
from zep_python import MemorySearchPayload
payload: MemorySearchPayload = MemorySearchPayload(
text=query, metadata=metadata
)
return self.zep_client.search_memory(self.session_id, payload, limit=limit)
[docs] def clear(self) -> None:
"""Clear session memory from Zep. Note that Zep is long-term storage for memory
and this is not advised unless you have specific data retention requirements.
"""
try:
self.zep_client.delete_memory(self.session_id)
except NotFoundError:
logger.warning(
f"Session {self.session_id} not found in Zep. Skipping delete."
) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/zep.html |
5ea01f8e-4ea6-4512-9f0f-4db66c483e3e | Source code for langchain.memory.chat_message_histories.cosmos_db
"""Azure CosmosDB Memory History."""
from __future__ import annotations
import logging
from types import TracebackType
from typing import TYPE_CHECKING, Any, List, Optional, Type
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from azure.cosmos import ContainerProxy
[docs]class CosmosDBChatMessageHistory(BaseChatMessageHistory):
"""Chat history backed by Azure CosmosDB."""
def __init__(
self,
cosmos_endpoint: str,
cosmos_database: str,
cosmos_container: str,
session_id: str,
user_id: str,
credential: Any = None,
connection_string: Optional[str] = None,
ttl: Optional[int] = None,
cosmos_client_kwargs: Optional[dict] = None,
):
"""
Initializes a new instance of the CosmosDBChatMessageHistory class.
Make sure to call prepare_cosmos or use the context manager to make
sure your database is ready.
Either a credential or a connection string must be provided.
:param cosmos_endpoint: The connection endpoint for the Azure Cosmos DB account.
:param cosmos_database: The name of the database to use.
:param cosmos_container: The name of the container to use.
:param session_id: The session ID to use, can be overwritten while loading.
:param user_id: The user ID to use, can be overwritten while loading.
:param credential: The credential to use to authenticate to Azure Cosmos DB.
:param connection_string: The connection string to use to authenticate.
:param ttl: The time to live (in seconds) to use for documents in the container.
:param cosmos_client_kwargs: Additional kwargs to pass to the CosmosClient.
"""
self.cosmos_endpoint = cosmos_endpoint
self.cosmos_database = cosmos_database
self.cosmos_container = cosmos_container
self.credential = credential
self.conn_string = connection_string
self.session_id = session_id
self.user_id = user_id
self.ttl = ttl
self.messages: List[BaseMessage] = []
try:
from azure.cosmos import ( # pylint: disable=import-outside-toplevel # noqa: E501
CosmosClient,
)
except ImportError as exc:
raise ImportError(
"You must install the azure-cosmos package to use the CosmosDBChatMessageHistory." # noqa: E501
) from exc
if self.credential:
self._client = CosmosClient(
url=self.cosmos_endpoint,
credential=self.credential,
**cosmos_client_kwargs or {},
)
elif self.conn_string:
self._client = CosmosClient.from_connection_string(
conn_str=self.conn_string,
**cosmos_client_kwargs or {},
)
else:
raise ValueError("Either a connection string or a credential must be set.")
self._container: Optional[ContainerProxy] = None
[docs] def prepare_cosmos(self) -> None:
"""Prepare the CosmosDB client.
Use this function or the context manager to make sure your database is ready.
"""
try:
from azure.cosmos import ( # pylint: disable=import-outside-toplevel # noqa: E501
PartitionKey,
)
except ImportError as exc:
raise ImportError(
"You must install the azure-cosmos package to use the CosmosDBChatMessageHistory." # noqa: E501
) from exc
database = self._client.create_database_if_not_exists(self.cosmos_database)
self._container = database.create_container_if_not_exists(
self.cosmos_container,
partition_key=PartitionKey("/user_id"),
default_ttl=self.ttl,
)
self.load_messages()
def __enter__(self) -> "CosmosDBChatMessageHistory":
"""Context manager entry point."""
self._client.__enter__()
self.prepare_cosmos()
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_val: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
"""Context manager exit"""
self.upsert_messages()
self._client.__exit__(exc_type, exc_val, traceback)
[docs] def load_messages(self) -> None:
"""Retrieve the messages from Cosmos"""
if not self._container:
raise ValueError("Container not initialized")
try:
from azure.cosmos.exceptions import ( # pylint: disable=import-outside-toplevel # noqa: E501
CosmosHttpResponseError,
)
except ImportError as exc:
raise ImportError(
"You must install the azure-cosmos package to use the CosmosDBChatMessageHistory." # noqa: E501
) from exc
try:
item = self._container.read_item(
item=self.session_id, partition_key=self.user_id
)
except CosmosHttpResponseError:
logger.info("no session found")
return
if "messages" in item and len(item["messages"]) > 0:
self.messages = messages_from_dict(item["messages"])
[docs] def add_message(self, message: BaseMessage) -> None:
"""Add a self-created message to the store"""
self.messages.append(message)
self.upsert_messages()
[docs] def upsert_messages(self) -> None:
"""Update the cosmosdb item."""
if not self._container:
raise ValueError("Container not initialized")
self._container.upsert_item(
body={
"id": self.session_id,
"user_id": self.user_id,
"messages": messages_to_dict(self.messages),
}
)
[docs] def clear(self) -> None:
"""Clear session memory from this memory and cosmos."""
self.messages = []
if self._container:
self._container.delete_item(
item=self.session_id, partition_key=self.user_id
) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
a2daaf33-bd55-4180-b8d7-4ce98cf352ac | Source code for langchain.memory.chat_message_histories.in_memory
from typing import List
from pydantic import BaseModel
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
)
[docs]class ChatMessageHistory(BaseChatMessageHistory, BaseModel):
messages: List[BaseMessage] = []
[docs] def add_message(self, message: BaseMessage) -> None:
"""Add a self-created message to the store"""
self.messages.append(message)
[docs] def clear(self) -> None:
self.messages = [] | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/in_memory.html |
eb77c1be-16b1-43a4-997f-d48282ad1ed3 | Source code for langchain.memory.chat_message_histories.cassandra
import json
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_KEYSPACE_NAME = "chat_history"
DEFAULT_TABLE_NAME = "message_store"
DEFAULT_USERNAME = "cassandra"
DEFAULT_PASSWORD = "cassandra"
DEFAULT_PORT = 9042
[docs]class CassandraChatMessageHistory(BaseChatMessageHistory):
"""Chat message history that stores history in Cassandra.
Args:
contact_points: list of ips to connect to Cassandra cluster
session_id: arbitrary key that is used to store the messages
of a single chat session.
port: port to connect to Cassandra cluster
username: username to connect to Cassandra cluster
password: password to connect to Cassandra cluster
keyspace_name: name of the keyspace to use
table_name: name of the table to use
"""
def __init__(
self,
contact_points: List[str],
session_id: str,
port: int = DEFAULT_PORT,
username: str = DEFAULT_USERNAME,
password: str = DEFAULT_PASSWORD,
keyspace_name: str = DEFAULT_KEYSPACE_NAME,
table_name: str = DEFAULT_TABLE_NAME,
):
self.contact_points = contact_points
self.session_id = session_id
self.port = port
self.username = username
self.password = password
self.keyspace_name = keyspace_name
self.table_name = table_name
try:
from cassandra import (
AuthenticationFailed,
OperationTimedOut,
UnresolvableContactPoints,
)
from cassandra.cluster import Cluster, PlainTextAuthProvider
except ImportError:
raise ValueError(
"Could not import cassandra-driver python package. "
"Please install it with `pip install cassandra-driver`."
)
self.cluster: Cluster = Cluster(
contact_points,
port=port,
auth_provider=PlainTextAuthProvider(
username=self.username, password=self.password
),
)
try:
self.session = self.cluster.connect()
except (
AuthenticationFailed,
UnresolvableContactPoints,
OperationTimedOut,
) as error:
logger.error(
"Unable to establish connection with \
cassandra chat message history database"
)
raise error
self._prepare_cassandra()
def _prepare_cassandra(self) -> None:
"""Create the keyspace and table if they don't exist yet"""
from cassandra import OperationTimedOut, Unavailable
try:
self.session.execute(
f"""CREATE KEYSPACE IF NOT EXISTS
{self.keyspace_name} WITH REPLICATION =
{{ 'class' : 'SimpleStrategy', 'replication_factor' : 1 }};"""
)
except (OperationTimedOut, Unavailable) as error:
logger.error(
f"Unable to create cassandra \
chat message history keyspace: {self.keyspace_name}."
)
raise error
self.session.set_keyspace(self.keyspace_name)
try:
self.session.execute(
f"""CREATE TABLE IF NOT EXISTS
{self.table_name} (id UUID, session_id varchar,
history text, PRIMARY KEY ((session_id), id) );"""
)
except (OperationTimedOut, Unavailable) as error:
logger.error(
f"Unable to create cassandra \
chat message history table: {self.table_name}"
)
raise error
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from Cassandra"""
from cassandra import ReadFailure, ReadTimeout, Unavailable
try:
rows = self.session.execute(
f"""SELECT * FROM {self.table_name}
WHERE session_id = '{self.session_id}' ;"""
)
except (Unavailable, ReadTimeout, ReadFailure) as error:
logger.error("Unable to Retreive chat history messages from cassadra")
raise error
if rows:
items = [json.loads(row.history) for row in rows]
else:
items = []
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in Cassandra"""
import uuid
from cassandra import Unavailable, WriteFailure, WriteTimeout
try:
self.session.execute(
"""INSERT INTO message_store
(id, session_id, history) VALUES (%s, %s, %s);""",
(uuid.uuid4(), self.session_id, json.dumps(_message_to_dict(message))),
)
except (Unavailable, WriteTimeout, WriteFailure) as error:
logger.error("Unable to write chat history messages to cassandra")
raise error
[docs] def clear(self) -> None:
"""Clear session memory from Cassandra"""
from cassandra import OperationTimedOut, Unavailable
try:
self.session.execute(
f"DELETE FROM {self.table_name} WHERE session_id = '{self.session_id}';"
)
except (Unavailable, OperationTimedOut) as error:
logger.error("Unable to clear chat history messages from cassandra")
raise error
def __del__(self) -> None:
if self.session:
self.session.shutdown()
if self.cluster:
self.cluster.shutdown() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
b7c4954f-dba0-4932-9a60-7101c749febd | Source code for langchain.memory.chat_message_histories.momento
from __future__ import annotations
import json
from datetime import timedelta
from typing import TYPE_CHECKING, Any, Optional
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
from langchain.utils import get_from_env
if TYPE_CHECKING:
import momento
def _ensure_cache_exists(cache_client: momento.CacheClient, cache_name: str) -> None:
"""Create cache if it doesn't exist.
Raises:
SdkException: Momento service or network error
Exception: Unexpected response
"""
from momento.responses import CreateCache
create_cache_response = cache_client.create_cache(cache_name)
if isinstance(create_cache_response, CreateCache.Success) or isinstance(
create_cache_response, CreateCache.CacheAlreadyExists
):
return None
elif isinstance(create_cache_response, CreateCache.Error):
raise create_cache_response.inner_exception
else:
raise Exception(f"Unexpected response cache creation: {create_cache_response}")
[docs]class MomentoChatMessageHistory(BaseChatMessageHistory):
"""Chat message history cache that uses Momento as a backend.
See https://gomomento.com/"""
def __init__(
self,
session_id: str,
cache_client: momento.CacheClient,
cache_name: str,
*,
key_prefix: str = "message_store:",
ttl: Optional[timedelta] = None,
ensure_cache_exists: bool = True,
):
"""Instantiate a chat message history cache that uses Momento as a backend.
Note: to instantiate the cache client passed to MomentoChatMessageHistory,
you must have a Momento account at https://gomomento.com/.
Args:
session_id (str): The session ID to use for this chat session.
cache_client (CacheClient): The Momento cache client.
cache_name (str): The name of the cache to use to store the messages.
key_prefix (str, optional): The prefix to apply to the cache key.
Defaults to "message_store:".
ttl (Optional[timedelta], optional): The TTL to use for the messages.
Defaults to None, ie the default TTL of the cache will be used.
ensure_cache_exists (bool, optional): Create the cache if it doesn't exist.
Defaults to True.
Raises:
ImportError: Momento python package is not installed.
TypeError: cache_client is not of type momento.CacheClientObject
"""
try:
from momento import CacheClient
from momento.requests import CollectionTtl
except ImportError:
raise ImportError(
"Could not import momento python package. "
"Please install it with `pip install momento`."
)
if not isinstance(cache_client, CacheClient):
raise TypeError("cache_client must be a momento.CacheClient object.")
if ensure_cache_exists:
_ensure_cache_exists(cache_client, cache_name)
self.key = key_prefix + session_id
self.cache_client = cache_client
self.cache_name = cache_name
if ttl is not None:
self.ttl = CollectionTtl.of(ttl)
else:
self.ttl = CollectionTtl.from_cache_ttl()
[docs] @classmethod
def from_client_params(
cls,
session_id: str,
cache_name: str,
ttl: timedelta,
*,
configuration: Optional[momento.config.Configuration] = None,
auth_token: Optional[str] = None,
**kwargs: Any,
) -> MomentoChatMessageHistory:
"""Construct cache from CacheClient parameters."""
try:
from momento import CacheClient, Configurations, CredentialProvider
except ImportError:
raise ImportError(
"Could not import momento python package. "
"Please install it with `pip install momento`."
)
if configuration is None:
configuration = Configurations.Laptop.v1()
auth_token = auth_token or get_from_env("auth_token", "MOMENTO_AUTH_TOKEN")
credentials = CredentialProvider.from_string(auth_token)
cache_client = CacheClient(configuration, credentials, default_ttl=ttl)
return cls(session_id, cache_client, cache_name, ttl=ttl, **kwargs)
@property
def messages(self) -> list[BaseMessage]: # type: ignore[override]
"""Retrieve the messages from Momento.
Raises:
SdkException: Momento service or network error
Exception: Unexpected response
Returns:
list[BaseMessage]: List of cached messages
"""
from momento.responses import CacheListFetch
fetch_response = self.cache_client.list_fetch(self.cache_name, self.key)
if isinstance(fetch_response, CacheListFetch.Hit):
items = [json.loads(m) for m in fetch_response.value_list_string]
return messages_from_dict(items)
elif isinstance(fetch_response, CacheListFetch.Miss):
return []
elif isinstance(fetch_response, CacheListFetch.Error):
raise fetch_response.inner_exception
else:
raise Exception(f"Unexpected response: {fetch_response}")
[docs] def add_message(self, message: BaseMessage) -> None:
"""Store a message in the cache.
Args:
message (BaseMessage): The message object to store.
Raises:
SdkException: Momento service or network error.
Exception: Unexpected response.
"""
from momento.responses import CacheListPushBack
item = json.dumps(_message_to_dict(message))
push_response = self.cache_client.list_push_back(
self.cache_name, self.key, item, ttl=self.ttl
)
if isinstance(push_response, CacheListPushBack.Success):
return None
elif isinstance(push_response, CacheListPushBack.Error):
raise push_response.inner_exception
else:
raise Exception(f"Unexpected response: {push_response}")
[docs] def clear(self) -> None:
"""Remove the session's messages from the cache.
Raises:
SdkException: Momento service or network error.
Exception: Unexpected response.
"""
from momento.responses import CacheDelete
delete_response = self.cache_client.delete(self.cache_name, self.key)
if isinstance(delete_response, CacheDelete.Success):
return None
elif isinstance(delete_response, CacheDelete.Error):
raise delete_response.inner_exception
else:
raise Exception(f"Unexpected response: {delete_response}") | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/momento.html |
30dfbb16-6add-4830-8da5-441c23babefd | Source code for langchain.memory.chat_message_histories.sql
import json
import logging
from typing import List
from sqlalchemy import Column, Integer, Text, create_engine
try:
from sqlalchemy.orm import declarative_base
except ImportError:
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
def create_message_model(table_name, DynamicBase): # type: ignore
"""
Create a message model for a given table name.
Args:
table_name: The name of the table to use.
DynamicBase: The base class to use for the model.
Returns:
The model class.
"""
# Model decleared inside a function to have a dynamic table name
class Message(DynamicBase):
__tablename__ = table_name
id = Column(Integer, primary_key=True)
session_id = Column(Text)
message = Column(Text)
return Message
[docs]class SQLChatMessageHistory(BaseChatMessageHistory):
"""Chat message history stored in an SQL database."""
def __init__(
self,
session_id: str,
connection_string: str,
table_name: str = "message_store",
):
self.table_name = table_name
self.connection_string = connection_string
self.engine = create_engine(connection_string, echo=False)
self._create_table_if_not_exists()
self.session_id = session_id
self.Session = sessionmaker(self.engine)
def _create_table_if_not_exists(self) -> None:
DynamicBase = declarative_base()
self.Message = create_message_model(self.table_name, DynamicBase)
# Create all does the check for us in case the table exists.
DynamicBase.metadata.create_all(self.engine)
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve all messages from db"""
with self.Session() as session:
result = session.query(self.Message).where(
self.Message.session_id == self.session_id
)
items = [json.loads(record.message) for record in result]
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in db"""
with self.Session() as session:
jsonstr = json.dumps(_message_to_dict(message))
session.add(self.Message(session_id=self.session_id, message=jsonstr))
session.commit()
[docs] def clear(self) -> None:
"""Clear session memory from db"""
with self.Session() as session:
session.query(self.Message).filter(
self.Message.session_id == self.session_id
).delete()
session.commit() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/sql.html |
ee107192-8807-4f72-8fc3-392db19e8367 | Source code for langchain.memory.chat_message_histories.file
import json
import logging
from pathlib import Path
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
[docs]class FileChatMessageHistory(BaseChatMessageHistory):
"""
Chat message history that stores history in a local file.
Args:
file_path: path of the local file to store the messages.
"""
def __init__(self, file_path: str):
self.file_path = Path(file_path)
if not self.file_path.exists():
self.file_path.touch()
self.file_path.write_text(json.dumps([]))
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from the local file"""
items = json.loads(self.file_path.read_text())
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in the local file"""
messages = messages_to_dict(self.messages)
messages.append(messages_to_dict([message])[0])
self.file_path.write_text(json.dumps(messages))
[docs] def clear(self) -> None:
"""Clear session memory from the local file"""
self.file_path.write_text(json.dumps([])) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/file.html |
619f0a10-d6f5-4f2f-85b0-cdb4f8d71c41 | Source code for langchain.memory.chat_message_histories.dynamodb
import logging
from typing import List, Optional
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
[docs]class DynamoDBChatMessageHistory(BaseChatMessageHistory):
"""Chat message history that stores history in AWS DynamoDB.
This class expects that a DynamoDB table with name `table_name`
and a partition Key of `SessionId` is present.
Args:
table_name: name of the DynamoDB table
session_id: arbitrary key that is used to store the messages
of a single chat session.
endpoint_url: URL of the AWS endpoint to connect to. This argument
is optional and useful for test purposes, like using Localstack.
If you plan to use AWS cloud service, you normally don't have to
worry about setting the endpoint_url.
"""
def __init__(
self, table_name: str, session_id: str, endpoint_url: Optional[str] = None
):
import boto3
if endpoint_url:
client = boto3.resource("dynamodb", endpoint_url=endpoint_url)
else:
client = boto3.resource("dynamodb")
self.table = client.Table(table_name)
self.session_id = session_id
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from DynamoDB"""
from botocore.exceptions import ClientError
response = None
try:
response = self.table.get_item(Key={"SessionId": self.session_id})
except ClientError as error:
if error.response["Error"]["Code"] == "ResourceNotFoundException":
logger.warning("No record found with session id: %s", self.session_id)
else:
logger.error(error)
if response and "Item" in response:
items = response["Item"]["History"]
else:
items = []
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in DynamoDB"""
from botocore.exceptions import ClientError
messages = messages_to_dict(self.messages)
_message = _message_to_dict(message)
messages.append(_message)
try:
self.table.put_item(
Item={"SessionId": self.session_id, "History": messages}
)
except ClientError as err:
logger.error(err)
[docs] def clear(self) -> None:
"""Clear session memory from DynamoDB"""
from botocore.exceptions import ClientError
try:
self.table.delete_item(Key={"SessionId": self.session_id})
except ClientError as err:
logger.error(err) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/dynamodb.html |
321a2c6d-b2c8-47c6-8343-b7b1f4c70219 | Source code for langchain.memory.chat_message_histories.mongodb
import json
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_DBNAME = "chat_history"
DEFAULT_COLLECTION_NAME = "message_store"
[docs]class MongoDBChatMessageHistory(BaseChatMessageHistory):
"""Chat message history that stores history in MongoDB.
Args:
connection_string: connection string to connect to MongoDB
session_id: arbitrary key that is used to store the messages
of a single chat session.
database_name: name of the database to use
collection_name: name of the collection to use
"""
def __init__(
self,
connection_string: str,
session_id: str,
database_name: str = DEFAULT_DBNAME,
collection_name: str = DEFAULT_COLLECTION_NAME,
):
from pymongo import MongoClient, errors
self.connection_string = connection_string
self.session_id = session_id
self.database_name = database_name
self.collection_name = collection_name
try:
self.client: MongoClient = MongoClient(connection_string)
except errors.ConnectionFailure as error:
logger.error(error)
self.db = self.client[database_name]
self.collection = self.db[collection_name]
self.collection.create_index("SessionId")
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from MongoDB"""
from pymongo import errors
try:
cursor = self.collection.find({"SessionId": self.session_id})
except errors.OperationFailure as error:
logger.error(error)
if cursor:
items = [json.loads(document["History"]) for document in cursor]
else:
items = []
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in MongoDB"""
from pymongo import errors
try:
self.collection.insert_one(
{
"SessionId": self.session_id,
"History": json.dumps(_message_to_dict(message)),
}
)
except errors.WriteError as err:
logger.error(err)
[docs] def clear(self) -> None:
"""Clear session memory from MongoDB"""
from pymongo import errors
try:
self.collection.delete_many({"SessionId": self.session_id})
except errors.WriteError as err:
logger.error(err) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/mongodb.html |
5f8eec07-562f-44f0-936b-035ee82f3b10 | Source code for langchain.memory.chat_message_histories.redis
import json
import logging
from typing import List, Optional
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
[docs]class RedisChatMessageHistory(BaseChatMessageHistory):
"""Chat message history stored in a Redis database."""
def __init__(
self,
session_id: str,
url: str = "redis://localhost:6379/0",
key_prefix: str = "message_store:",
ttl: Optional[int] = None,
):
try:
import redis
except ImportError:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
self.redis_client = redis.Redis.from_url(url=url)
except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
@property
def key(self) -> str:
"""Construct the record key to use"""
return self.key_prefix + self.session_id
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from Redis"""
_items = self.redis_client.lrange(self.key, 0, -1)
items = [json.loads(m.decode("utf-8")) for m in _items[::-1]]
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in Redis"""
self.redis_client.lpush(self.key, json.dumps(_message_to_dict(message)))
if self.ttl:
self.redis_client.expire(self.key, self.ttl)
[docs] def clear(self) -> None:
"""Clear session memory from Redis"""
self.redis_client.delete(self.key) | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/redis.html |
dd4eb5f7-72de-4efd-a2af-38c9eba0e01f | Source code for langchain.memory.chat_message_histories.postgres
import json
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_CONNECTION_STRING = "postgresql://postgres:mypassword@localhost/chat_history"
[docs]class PostgresChatMessageHistory(BaseChatMessageHistory):
"""Chat message history stored in a Postgres database."""
def __init__(
self,
session_id: str,
connection_string: str = DEFAULT_CONNECTION_STRING,
table_name: str = "message_store",
):
import psycopg
from psycopg.rows import dict_row
try:
self.connection = psycopg.connect(connection_string)
self.cursor = self.connection.cursor(row_factory=dict_row)
except psycopg.OperationalError as error:
logger.error(error)
self.session_id = session_id
self.table_name = table_name
self._create_table_if_not_exists()
def _create_table_if_not_exists(self) -> None:
create_table_query = f"""CREATE TABLE IF NOT EXISTS {self.table_name} (
id SERIAL PRIMARY KEY,
session_id TEXT NOT NULL,
message JSONB NOT NULL
);"""
self.cursor.execute(create_table_query)
self.connection.commit()
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from PostgreSQL"""
query = f"SELECT message FROM {self.table_name} WHERE session_id = %s;"
self.cursor.execute(query, (self.session_id,))
items = [record["message"] for record in self.cursor.fetchall()]
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in PostgreSQL"""
from psycopg import sql
query = sql.SQL("INSERT INTO {} (session_id, message) VALUES (%s, %s);").format(
sql.Identifier(self.table_name)
)
self.cursor.execute(
query, (self.session_id, json.dumps(_message_to_dict(message)))
)
self.connection.commit()
[docs] def clear(self) -> None:
"""Clear session memory from PostgreSQL"""
query = f"DELETE FROM {self.table_name} WHERE session_id = %s;"
self.cursor.execute(query, (self.session_id,))
self.connection.commit()
def __del__(self) -> None:
if self.cursor:
self.cursor.close()
if self.connection:
self.connection.close() | https://api.python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
ea873e47-11b5-4a8d-97ac-d7befe3484df | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import BaseMultiActionAgent, BaseSingleActionAgent
from langchain.agents.tools import Tool
from langchain.agents.types import AGENT_TO_CLASS
from langchain.base_language import BaseLanguageModel
from langchain.chains.loading import load_chain, load_chain_from_config
from langchain.utilities.loading import try_load_from_hub
logger = logging.getLogger(__file__)
URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"
def _load_agent_from_tools(
config: dict, llm: BaseLanguageModel, tools: List[Tool], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
config_type = config.pop("_type")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
agent_cls = AGENT_TO_CLASS[config_type]
combined_config = {**config, **kwargs}
return agent_cls.from_llm_and_tools(llm, tools, **combined_config)
def load_agent_from_config(
config: dict,
llm: Optional[BaseLanguageModel] = None,
tools: Optional[List[Tool]] = None,
**kwargs: Any,
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Load agent from Config Dict."""
if "_type" not in config:
raise ValueError("Must specify an agent Type in config")
load_from_tools = config.pop("load_from_llm_and_tools", False)
if load_from_tools:
if llm is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
"then LLM must be provided"
)
if tools is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
"then tools must be provided"
)
return _load_agent_from_tools(config, llm, tools, **kwargs)
config_type = config.pop("_type")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
agent_cls = AGENT_TO_CLASS[config_type]
if "llm_chain" in config:
config["llm_chain"] = load_chain_from_config(config.pop("llm_chain"))
elif "llm_chain_path" in config:
config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
if "output_parser" in config:
logger.warning(
"Currently loading output parsers on agent is not supported, "
"will just use the default one."
)
del config["output_parser"]
combined_config = {**config, **kwargs}
return agent_cls(**combined_config) # type: ignore
[docs]def load_agent(
path: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Unified method for loading a agent from LangChainHub or local fs."""
if hub_result := try_load_from_hub(
path, _load_agent_from_file, "agents", {"json", "yaml"}
):
return hub_result
else:
return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(
file: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Load agent from file."""
# Convert file to Path object.
if isinstance(file, str):
file_path = Path(file)
else:
file_path = file
# Load from either json or yaml.
if file_path.suffix == ".json":
with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
else:
raise ValueError("File type must be json or yaml")
# Load the agent from the config now.
return load_agent_from_config(config, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
c7c1a909-faa3-44e9-9fae-ef8a3a289b7b | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.tools.base import BaseTool
[docs]def initialize_agent(
tools: Sequence[BaseTool],
llm: BaseLanguageModel,
agent: Optional[AgentType] = None,
callback_manager: Optional[BaseCallbackManager] = None,
agent_path: Optional[str] = None,
agent_kwargs: Optional[dict] = None,
*,
tags: Optional[Sequence[str]] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Load an agent executor given tools and LLM.
Args:
tools: List of tools this agent has access to.
llm: Language model to use as the agent.
agent: Agent type to use. If None and agent_path is also None, will default to
AgentType.ZERO_SHOT_REACT_DESCRIPTION.
callback_manager: CallbackManager to use. Global callback manager is used if
not provided. Defaults to None.
agent_path: Path to serialized agent to use.
agent_kwargs: Additional key word arguments to pass to the underlying agent
tags: Tags to apply to the traced runs.
**kwargs: Additional key word arguments passed to the agent executor
Returns:
An agent executor
"""
tags_ = list(tags) if tags else []
if agent is None and agent_path is None:
agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION
if agent is not None and agent_path is not None:
raise ValueError(
"Both `agent` and `agent_path` are specified, "
"but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
tags_.append(agent.value if isinstance(agent, AgentType) else agent)
agent_cls = AGENT_TO_CLASS[agent]
agent_kwargs = agent_kwargs or {}
agent_obj = agent_cls.from_llm_and_tools(
llm, tools, callback_manager=callback_manager, **agent_kwargs
)
elif agent_path is not None:
agent_obj = load_agent(
agent_path, llm=llm, tools=tools, callback_manager=callback_manager
)
try:
# TODO: Add tags from the serialized object directly.
tags_.append(agent_obj._agent_type)
except NotImplementedError:
pass
else:
raise ValueError(
"Somehow both `agent` and `agent_path` are None, "
"this should never happen."
)
return AgentExecutor.from_agent_and_tools(
agent=agent_obj,
tools=tools,
callback_manager=callback_manager,
tags=tags_,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
350c9e3b-e748-4aa6-a5c3-410883fffcc1 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import Callbacks
from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
from langchain.chains.api.base import APIChain
from langchain.chains.llm_math.base import LLMMathChain
from langchain.chains.pal.base import PALChain
from langchain.requests import TextRequestsWrapper
from langchain.tools.arxiv.tool import ArxivQueryRun
from langchain.tools.pubmed.tool import PubmedQueryRun
from langchain.tools.base import BaseTool
from langchain.tools.bing_search.tool import BingSearchRun
from langchain.tools.ddg_search.tool import DuckDuckGoSearchRun
from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
from langchain.tools.metaphor_search.tool import MetaphorSearchResults
from langchain.tools.google_serper.tool import GoogleSerperResults, GoogleSerperRun
from langchain.tools.graphql.tool import BaseGraphQLTool
from langchain.tools.human.tool import HumanInputRun
from langchain.tools.python.tool import PythonREPLTool
from langchain.tools.requests.tool import (
RequestsDeleteTool,
RequestsGetTool,
RequestsPatchTool,
RequestsPostTool,
RequestsPutTool,
)
from langchain.tools.scenexplain.tool import SceneXplainTool
from langchain.tools.searx_search.tool import SearxSearchResults, SearxSearchRun
from langchain.tools.shell.tool import ShellTool
from langchain.tools.sleep.tool import SleepTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.tools.openweathermap.tool import OpenWeatherMapQueryRun
from langchain.utilities import ArxivAPIWrapper
from langchain.utilities import PubMedAPIWrapper
from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.metaphor_search import MetaphorSearchAPIWrapper
from langchain.utilities.awslambda import LambdaWrapper
from langchain.utilities.graphql import GraphQLAPIWrapper
from langchain.utilities.searx_search import SearxSearchWrapper
from langchain.utilities.serpapi import SerpAPIWrapper
from langchain.utilities.twilio import TwilioAPIWrapper
from langchain.utilities.wikipedia import WikipediaAPIWrapper
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
def _get_python_repl() -> BaseTool:
return PythonREPLTool()
def _get_tools_requests_get() -> BaseTool:
return RequestsGetTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_post() -> BaseTool:
return RequestsPostTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_patch() -> BaseTool:
return RequestsPatchTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_put() -> BaseTool:
return RequestsPutTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_delete() -> BaseTool:
return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
return ShellTool()
def _get_sleep() -> BaseTool:
return SleepTool()
_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
"python_repl": _get_python_repl,
"requests": _get_tools_requests_get, # preserved for backwards compatability
"requests_get": _get_tools_requests_get,
"requests_post": _get_tools_requests_post,
"requests_patch": _get_tools_requests_patch,
"requests_put": _get_tools_requests_put,
"requests_delete": _get_tools_requests_delete,
"terminal": _get_terminal,
"sleep": _get_sleep,
}
def _get_pal_math(llm: BaseLanguageModel) -> BaseTool:
return Tool(
name="PAL-MATH",
description="A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",
func=PALChain.from_math_prompt(llm).run,
)
def _get_pal_colored_objects(llm: BaseLanguageModel) -> BaseTool:
return Tool(
name="PAL-COLOR-OBJ",
description="A language model that is really good at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning problem. Make sure to include all information about the objects AND the final question you want to answer.",
func=PALChain.from_colored_object_prompt(llm).run,
)
def _get_llm_math(llm: BaseLanguageModel) -> BaseTool:
return Tool(
name="Calculator",
description="Useful for when you need to answer questions about math.",
func=LLMMathChain.from_llm(llm=llm).run,
coroutine=LLMMathChain.from_llm(llm=llm).arun,
)
def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS)
return Tool(
name="Open Meteo API",
description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
_LLM_TOOLS: Dict[str, Callable[[BaseLanguageModel], BaseTool]] = {
"pal-math": _get_pal_math,
"pal-colored-objects": _get_pal_colored_objects,
"llm-math": _get_llm_math,
"open-meteo-api": _get_open_meteo_api,
}
def _get_news_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
news_api_key = kwargs["news_api_key"]
chain = APIChain.from_llm_and_api_docs(
llm, news_docs.NEWS_DOCS, headers={"X-Api-Key": news_api_key}
)
return Tool(
name="News API",
description="Use this when you want to get information about the top headlines of current news stories. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
tmdb_bearer_token = kwargs["tmdb_bearer_token"]
chain = APIChain.from_llm_and_api_docs(
llm,
tmdb_docs.TMDB_DOCS,
headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
)
return Tool(
name="TMDB API",
description="Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
def _get_podcast_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
listen_api_key = kwargs["listen_api_key"]
chain = APIChain.from_llm_and_api_docs(
llm,
podcast_docs.PODCAST_DOCS,
headers={"X-ListenAPI-Key": listen_api_key},
)
return Tool(
name="Podcast API",
description="Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
def _get_lambda_api(**kwargs: Any) -> BaseTool:
return Tool(
name=kwargs["awslambda_tool_name"],
description=kwargs["awslambda_tool_description"],
func=LambdaWrapper(**kwargs).run,
)
def _get_wolfram_alpha(**kwargs: Any) -> BaseTool:
return WolframAlphaQueryRun(api_wrapper=WolframAlphaAPIWrapper(**kwargs))
def _get_google_search(**kwargs: Any) -> BaseTool:
return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_wikipedia(**kwargs: Any) -> BaseTool:
return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
def _get_arxiv(**kwargs: Any) -> BaseTool:
return ArxivQueryRun(api_wrapper=ArxivAPIWrapper(**kwargs))
def _get_pupmed(**kwargs: Any) -> BaseTool:
return PubmedQueryRun(api_wrapper=PubMedAPIWrapper(**kwargs))
def _get_google_serper(**kwargs: Any) -> BaseTool:
return GoogleSerperRun(api_wrapper=GoogleSerperAPIWrapper(**kwargs))
def _get_google_serper_results_json(**kwargs: Any) -> BaseTool:
return GoogleSerperResults(api_wrapper=GoogleSerperAPIWrapper(**kwargs))
def _get_google_search_results_json(**kwargs: Any) -> BaseTool:
return GoogleSearchResults(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_serpapi(**kwargs: Any) -> BaseTool:
return Tool(
name="Search",
description="A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
func=SerpAPIWrapper(**kwargs).run,
coroutine=SerpAPIWrapper(**kwargs).arun,
)
def _get_twilio(**kwargs: Any) -> BaseTool:
return Tool(
name="Text Message",
description="Useful for when you need to send a text message to a provided phone number.",
func=TwilioAPIWrapper(**kwargs).run,
)
def _get_searx_search(**kwargs: Any) -> BaseTool:
return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))
def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapper_kwargs), **kwargs)
def _get_bing_search(**kwargs: Any) -> BaseTool:
return BingSearchRun(api_wrapper=BingSearchAPIWrapper(**kwargs))
def _get_metaphor_search(**kwargs: Any) -> BaseTool:
return MetaphorSearchResults(api_wrapper=MetaphorSearchAPIWrapper(**kwargs))
def _get_ddg_search(**kwargs: Any) -> BaseTool:
return DuckDuckGoSearchRun(api_wrapper=DuckDuckGoSearchAPIWrapper(**kwargs))
def _get_human_tool(**kwargs: Any) -> BaseTool:
return HumanInputRun(**kwargs)
def _get_scenexplain(**kwargs: Any) -> BaseTool:
return SceneXplainTool(**kwargs)
def _get_graphql_tool(**kwargs: Any) -> BaseTool:
graphql_endpoint = kwargs["graphql_endpoint"]
wrapper = GraphQLAPIWrapper(graphql_endpoint=graphql_endpoint)
return BaseGraphQLTool(graphql_wrapper=wrapper)
def _get_openweathermap(**kwargs: Any) -> BaseTool:
return OpenWeatherMapQueryRun(api_wrapper=OpenWeatherMapAPIWrapper(**kwargs))
_EXTRA_LLM_TOOLS: Dict[
str,
Tuple[Callable[[Arg(BaseLanguageModel, "llm"), KwArg(Any)], BaseTool], List[str]],
] = {
"news-api": (_get_news_api, ["news_api_key"]),
"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
"podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
"wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha_appid"]),
"google-search": (_get_google_search, ["google_api_key", "google_cse_id"]),
"google-search-results-json": (
_get_google_search_results_json,
["google_api_key", "google_cse_id", "num_results"],
),
"searx-search-results-json": (
_get_searx_search_results_json,
["searx_host", "engines", "num_results", "aiosession"],
),
"bing-search": (_get_bing_search, ["bing_subscription_key", "bing_search_url"]),
"metaphor-search": (_get_metaphor_search, ["metaphor_api_key"]),
"ddg-search": (_get_ddg_search, []),
"google-serper": (_get_google_serper, ["serper_api_key", "aiosession"]),
"google-serper-results-json": (
_get_google_serper_results_json,
["serper_api_key", "aiosession"],
),
"serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
"twilio": (_get_twilio, ["account_sid", "auth_token", "from_number"]),
"searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
"wikipedia": (_get_wikipedia, ["top_k_results", "lang"]),
"arxiv": (
_get_arxiv,
["top_k_results", "load_max_docs", "load_all_available_meta"],
),
"pupmed": (
_get_pupmed,
["top_k_results", "load_max_docs", "load_all_available_meta"],
),
"human": (_get_human_tool, ["prompt_func", "input_func"]),
"awslambda": (
_get_lambda_api,
["awslambda_tool_name", "awslambda_tool_description", "function_name"],
),
"sceneXplain": (_get_scenexplain, []),
"graphql": (_get_graphql_tool, ["graphql_endpoint"]),
"openweathermap-api": (_get_openweathermap, ["openweathermap_api_key"]),
}
def _handle_callbacks(
callback_manager: Optional[BaseCallbackManager], callbacks: Callbacks
) -> Callbacks:
if callback_manager is not None:
warnings.warn(
"callback_manager is deprecated. Please use callbacks instead.",
DeprecationWarning,
)
if callbacks is not None:
raise ValueError(
"Cannot specify both callback_manager and callbacks arguments."
)
return callback_manager
return callbacks
[docs]def load_huggingface_tool(
task_or_repo_id: str,
model_repo_id: Optional[str] = None,
token: Optional[str] = None,
remote: bool = False,
**kwargs: Any,
) -> BaseTool:
"""Loads a tool from the HuggingFace Hub.
Args:
task_or_repo_id: Task or model repo id.
model_repo_id: Optional model repo id.
token: Optional token.
remote: Optional remote. Defaults to False.
**kwargs:
Returns:
A tool.
"""
try:
from transformers import load_tool
except ImportError:
raise ImportError(
"HuggingFace tools require the libraries `transformers>=4.29.0`"
" and `huggingface_hub>=0.14.1` to be installed."
" Please install it with"
" `pip install --upgrade transformers huggingface_hub`."
)
hf_tool = load_tool(
task_or_repo_id,
model_repo_id=model_repo_id,
token=token,
remote=remote,
**kwargs,
)
outputs = hf_tool.outputs
if set(outputs) != {"text"}:
raise NotImplementedError("Multimodal outputs not supported yet.")
inputs = hf_tool.inputs
if set(inputs) != {"text"}:
raise NotImplementedError("Multimodal inputs not supported yet.")
return Tool.from_function(
hf_tool.__call__, name=hf_tool.name, description=hf_tool.description
)
[docs]def load_tools(
tool_names: List[str],
llm: Optional[BaseLanguageModel] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> List[BaseTool]:
"""Load tools based on their name.
Args:
tool_names: name of tools to load.
llm: Optional language model, may be needed to initialize certain tools.
callbacks: Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
Returns:
List of tools.
"""
tools = []
callbacks = _handle_callbacks(
callback_manager=kwargs.get("callback_manager"), callbacks=callbacks
)
for name in tool_names:
if name == "requests":
warnings.warn(
"tool name `requests` is deprecated - "
"please use `requests_all` or specify the requests method"
)
if name == "requests_all":
# expand requests into various methods
requests_method_tools = [
_tool for _tool in _BASE_TOOLS if _tool.startswith("requests_")
]
tool_names.extend(requests_method_tools)
elif name in _BASE_TOOLS:
tools.append(_BASE_TOOLS[name]())
elif name in _LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {name} requires an LLM to be provided")
tool = _LLM_TOOLS[name](llm)
tools.append(tool)
elif name in _EXTRA_LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {name} requires an LLM to be provided")
_get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[name]
missing_keys = set(extra_keys).difference(kwargs)
if missing_keys:
raise ValueError(
f"Tool {name} requires some parameters that were not "
f"provided: {missing_keys}"
)
sub_kwargs = {k: kwargs[k] for k in extra_keys}
tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
tools.append(tool)
elif name in _EXTRA_OPTIONAL_TOOLS:
_get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
sub_kwargs = {k: kwargs[k] for k in extra_keys if k in kwargs}
tool = _get_tool_func(**sub_kwargs)
tools.append(tool)
else:
raise ValueError(f"Got unknown tool {name}")
if callbacks is not None:
for tool in tools:
tool.callbacks = callbacks
return tools
[docs]def get_all_tool_names() -> List[str]:
"""Get a list of all possible tool names."""
return (
list(_BASE_TOOLS)
+ list(_EXTRA_OPTIONAL_TOOLS)
+ list(_EXTRA_LLM_TOOLS)
+ list(_LLM_TOOLS)
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
bb45c035-b32d-43d3-a535-3fae66c7d868 | Source code for langchain.agents.agent_types
from enum import Enum
[docs]class AgentType(str, Enum):
"""Enumerator with the Agent types."""
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
REACT_DOCSTORE = "react-docstore"
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
CONVERSATIONAL_REACT_DESCRIPTION = "conversational-react-description"
CHAT_ZERO_SHOT_REACT_DESCRIPTION = "chat-zero-shot-react-description"
CHAT_CONVERSATIONAL_REACT_DESCRIPTION = "chat-conversational-react-description"
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION = (
"structured-chat-zero-shot-react-description"
)
OPENAI_FUNCTIONS = "openai-functions"
OPENAI_MULTI_FUNCTIONS = "openai-multi-functions" | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html |
21d3fc3d-d6d8-466f-8ecd-d200eb94c852 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import yaml
from pydantic import BaseModel, root_validator
from langchain.agents.agent_types import AgentType
from langchain.agents.tools import InvalidTool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
AsyncCallbackManagerForToolRun,
CallbackManagerForChainRun,
CallbackManagerForToolRun,
Callbacks,
)
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.input import get_color_mapping
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
AgentAction,
AgentFinish,
BaseMessage,
BaseOutputParser,
OutputParserException,
)
from langchain.tools.base import BaseTool
from langchain.utilities.asyncio import asyncio_timeout
logger = logging.getLogger(__name__)
[docs]class BaseSingleActionAgent(BaseModel):
"""Base Agent class."""
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
return ["output"]
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return None
[docs] @abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
[docs] @abstractmethod
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
@property
@abstractmethod
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
[docs] def return_stopped_response(
self,
early_stopping_method: str,
intermediate_steps: List[Tuple[AgentAction, str]],
**kwargs: Any,
) -> AgentFinish:
"""Return response when agent has been stopped due to max iterations."""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> BaseSingleActionAgent:
raise NotImplementedError
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
raise NotImplementedError
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
_type = self._agent_type
if isinstance(_type, AgentType):
_dict["_type"] = str(_type.value)
else:
_dict["_type"] = _type
return _dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the agent.
Args:
file_path: Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path="path/agent.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
agent_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(agent_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(file_path, "w") as f:
yaml.dump(agent_dict, f, default_flow_style=False)
else:
raise ValueError(f"{save_path} must be json or yaml")
[docs] def tool_run_logging_kwargs(self) -> Dict:
return {}
[docs]class BaseMultiActionAgent(BaseModel):
"""Base Agent class."""
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
return ["output"]
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return None
[docs] @abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Actions specifying what tool to use.
"""
[docs] @abstractmethod
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Actions specifying what tool to use.
"""
@property
@abstractmethod
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
[docs] def return_stopped_response(
self,
early_stopping_method: str,
intermediate_steps: List[Tuple[AgentAction, str]],
**kwargs: Any,
) -> AgentFinish:
"""Return response when agent has been stopped due to max iterations."""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
raise NotImplementedError
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
_dict["_type"] = str(self._agent_type)
return _dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the agent.
Args:
file_path: Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path="path/agent.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
agent_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(agent_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(file_path, "w") as f:
yaml.dump(agent_dict, f, default_flow_style=False)
else:
raise ValueError(f"{save_path} must be json or yaml")
[docs] def tool_run_logging_kwargs(self) -> Dict:
return {}
[docs]class AgentOutputParser(BaseOutputParser):
[docs] @abstractmethod
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Parse text into agent action/finish."""
[docs]class LLMSingleActionAgent(BaseSingleActionAgent):
llm_chain: LLMChain
output_parser: AgentOutputParser
stop: List[str]
@property
def input_keys(self) -> List[str]:
return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
del _dict["output_parser"]
return _dict
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
output = self.llm_chain.run(
intermediate_steps=intermediate_steps,
stop=self.stop,
callbacks=callbacks,
**kwargs,
)
return self.output_parser.parse(output)
[docs] async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
output = await self.llm_chain.arun(
intermediate_steps=intermediate_steps,
stop=self.stop,
callbacks=callbacks,
**kwargs,
)
return self.output_parser.parse(output)
[docs] def tool_run_logging_kwargs(self) -> Dict:
return {
"llm_prefix": "",
"observation_prefix": "" if len(self.stop) == 0 else self.stop[0],
}
[docs]class Agent(BaseSingleActionAgent):
"""Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called "agent_scratchpad" where the agent can put its
intermediary work.
"""
llm_chain: LLMChain
output_parser: AgentOutputParser
allowed_tools: Optional[List[str]] = None
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
del _dict["output_parser"]
return _dict
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return self.allowed_tools
@property
def return_values(self) -> List[str]:
return ["output"]
def _fix_text(self, text: str) -> str:
"""Fix the text."""
raise ValueError("fix_text not implemented for this agent.")
@property
def _stop(self) -> List[str]:
return [
f"\n{self.observation_prefix.rstrip()}",
f"\n\t{self.observation_prefix.rstrip()}",
]
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> Union[str, List[BaseMessage]]:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
return thoughts
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
return self.output_parser.parse(full_output)
[docs] async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)
return self.output_parser.parse(full_output)
[docs] def get_full_inputs(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Dict[str, Any]:
"""Create the full inputs for the LLMChain from intermediate steps."""
thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
return full_inputs
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})
@root_validator()
def validate_prompt(cls, values: Dict) -> Dict:
"""Validate that prompt matches format."""
prompt = values["llm_chain"].prompt
if "agent_scratchpad" not in prompt.input_variables:
logger.warning(
"`agent_scratchpad` should be a variable in prompt.input_variables."
" Did not find it, so adding it at the end."
)
prompt.input_variables.append("agent_scratchpad")
if isinstance(prompt, PromptTemplate):
prompt.template += "\n{agent_scratchpad}"
elif isinstance(prompt, FewShotPromptTemplate):
prompt.suffix += "\n{agent_scratchpad}"
else:
raise ValueError(f"Got unexpected prompt type {type(prompt)}")
return values
@property
@abstractmethod
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
@property
@abstractmethod
def llm_prefix(self) -> str:
"""Prefix to append the LLM call with."""
[docs] @classmethod
@abstractmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Create a prompt for this class."""
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in."""
pass
@classmethod
@abstractmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
"""Get default output parser for this class."""
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
llm_chain = LLMChain(
llm=llm,
prompt=cls.create_prompt(tools),
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser()
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
[docs] def return_stopped_response(
self,
early_stopping_method: str,
intermediate_steps: List[Tuple[AgentAction, str]],
**kwargs: Any,
) -> AgentFinish:
"""Return response when agent has been stopped due to max iterations."""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
elif early_stopping_method == "generate":
# Generate does one final forward pass
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += (
f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
)
# Adding to the previous steps, we now tell the LLM to make a final pred
thoughts += (
"\n\nI now need to return a final answer based on the previous steps:"
)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
full_output = self.llm_chain.predict(**full_inputs)
# We try to extract a final answer
parsed_output = self.output_parser.parse(full_output)
if isinstance(parsed_output, AgentFinish):
# If we can extract, we send the correct stuff
return parsed_output
else:
# If we can extract, but the tool is not the final tool,
# we just return the full output
return AgentFinish({"output": full_output}, full_output)
else:
raise ValueError(
"early_stopping_method should be one of `force` or `generate`, "
f"got {early_stopping_method}"
)
[docs] def tool_run_logging_kwargs(self) -> Dict:
return {
"llm_prefix": self.llm_prefix,
"observation_prefix": self.observation_prefix,
}
class ExceptionTool(BaseTool):
name = "_Exception"
description = "Exception tool"
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
return query
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
return query
[docs]class AgentExecutor(Chain):
"""Consists of an agent using tools."""
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
"""The agent to run for creating a plan and determining actions
to take at each step of the execution loop."""
tools: Sequence[BaseTool]
"""The valid tools the agent can call."""
return_intermediate_steps: bool = False
"""Whether to return the agent's trajectory of intermediate steps
at the end in addition to the final output."""
max_iterations: Optional[int] = 15
"""The maximum number of steps to take before ending the execution
loop.
Setting to 'None' could lead to an infinite loop."""
max_execution_time: Optional[float] = None
"""The maximum amount of wall clock time to spend in the execution
loop.
"""
early_stopping_method: str = "force"
"""The method to use for early stopping if the agent never
returns `AgentFinish`. Either 'force' or 'generate'.
`"force"` returns a string saying that it stopped because it met a
time or iteration limit.
`"generate"` calls the agent's LLM Chain one final time to generate
a final answer based on the previous steps.
"""
handle_parsing_errors: Union[
bool, str, Callable[[OutputParserException], str]
] = False
"""How to handle errors raised by the agent's output parser.
Defaults to `False`, which raises the error.
s
If `true`, the error will be sent back to the LLM as an observation.
If a string, the string itself will be sent to the LLM as an observation.
If a callable function, the function will be called with the exception
as an argument, and the result of that function will be passed to the agent
as an observation.
"""
[docs] @classmethod
def from_agent_and_tools(
cls,
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent],
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Create from agent and tools."""
return cls(
agent=agent, tools=tools, callback_manager=callback_manager, **kwargs
)
@root_validator()
def validate_tools(cls, values: Dict) -> Dict:
"""Validate that tools are compatible with agent."""
agent = values["agent"]
tools = values["tools"]
allowed_tools = agent.get_allowed_tools()
if allowed_tools is not None:
if set(allowed_tools) != set([tool.name for tool in tools]):
raise ValueError(
f"Allowed tools ({allowed_tools}) different than "
f"provided tools ({[tool.name for tool in tools]})"
)
return values
@root_validator()
def validate_return_direct_tool(cls, values: Dict) -> Dict:
"""Validate that tools are compatible with agent."""
agent = values["agent"]
tools = values["tools"]
if isinstance(agent, BaseMultiActionAgent):
for tool in tools:
if tool.return_direct:
raise ValueError(
"Tools that have `return_direct=True` are not allowed "
"in multi-action agents"
)
return values
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Raise error - saving not supported for Agent Executors."""
raise ValueError(
"Saving not supported for agent executors. "
"If you are trying to save the agent, please use the "
"`.save_agent(...)`"
)
[docs] def save_agent(self, file_path: Union[Path, str]) -> None:
"""Save the underlying agent."""
return self.agent.save(file_path)
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return self.agent.input_keys
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
if self.return_intermediate_steps:
return self.agent.return_values + ["intermediate_steps"]
else:
return self.agent.return_values
[docs] def lookup_tool(self, name: str) -> BaseTool:
"""Lookup tool by name."""
return {tool.name: tool for tool in self.tools}[name]
def _should_continue(self, iterations: int, time_elapsed: float) -> bool:
if self.max_iterations is not None and iterations >= self.max_iterations:
return False
if (
self.max_execution_time is not None
and time_elapsed >= self.max_execution_time
):
return False
return True
def _return(
self,
output: AgentFinish,
intermediate_steps: list,
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
if run_manager:
run_manager.on_agent_finish(output, color="green", verbose=self.verbose)
final_output = output.return_values
if self.return_intermediate_steps:
final_output["intermediate_steps"] = intermediate_steps
return final_output
async def _areturn(
self,
output: AgentFinish,
intermediate_steps: list,
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
if run_manager:
await run_manager.on_agent_finish(
output, color="green", verbose=self.verbose
)
final_output = output.return_values
if self.return_intermediate_steps:
final_output["intermediate_steps"] = intermediate_steps
return final_output
def _take_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
# Call the LLM to see what to do.
output = self.agent.plan(
intermediate_steps,
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
else:
raise_error = False
if raise_error:
raise e
text = str(e)
if isinstance(self.handle_parsing_errors, bool):
if e.send_to_llm:
observation = str(e.observation)
text = str(e.llm_output)
else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_errors
elif callable(self.handle_parsing_errors):
observation = self.handle_parsing_errors(e)
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
result = []
for agent_action in actions:
if run_manager:
run_manager.on_agent_action(agent_action, color="green")
# Otherwise we lookup the tool
if agent_action.tool in name_to_tool_map:
tool = name_to_tool_map[agent_action.tool]
return_direct = tool.return_direct
color = color_mapping[agent_action.tool]
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
if return_direct:
tool_run_kwargs["llm_prefix"] = ""
# We then call the tool on the tool input to get an observation
observation = tool.run(
agent_action.tool_input,
verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
else:
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = InvalidTool().run(
agent_action.tool,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
result.append((agent_action, observation))
return result
async def _atake_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
# Call the LLM to see what to do.
output = await self.agent.aplan(
intermediate_steps,
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
else:
raise_error = False
if raise_error:
raise e
text = str(e)
if isinstance(self.handle_parsing_errors, bool):
if e.send_to_llm:
observation = str(e.observation)
text = str(e.llm_output)
else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_errors
elif callable(self.handle_parsing_errors):
observation = self.handle_parsing_errors(e)
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = await ExceptionTool().arun(
output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
async def _aperform_agent_action(
agent_action: AgentAction,
) -> Tuple[AgentAction, str]:
if run_manager:
await run_manager.on_agent_action(
agent_action, verbose=self.verbose, color="green"
)
# Otherwise we lookup the tool
if agent_action.tool in name_to_tool_map:
tool = name_to_tool_map[agent_action.tool]
return_direct = tool.return_direct
color = color_mapping[agent_action.tool]
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
if return_direct:
tool_run_kwargs["llm_prefix"] = ""
# We then call the tool on the tool input to get an observation
observation = await tool.arun(
agent_action.tool_input,
verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
else:
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = await InvalidTool().arun(
agent_action.tool,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return agent_action, observation
# Use asyncio.gather to run multiple tool.arun() calls concurrently
result = await asyncio.gather(
*[_aperform_agent_action(agent_action) for agent_action in actions]
)
return list(result)
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run text through and get agent response."""
# Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name for tool in self.tools], excluded_colors=["green", "red"]
)
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
while self._should_continue(iterations, time_elapsed):
next_step_output = self._take_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return self._return(
next_step_output, intermediate_steps, run_manager=run_manager
)
intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_manager
)
iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return self._return(output, intermediate_steps, run_manager=run_manager)
async def _acall(
self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Run text through and get agent response."""
# Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name for tool in self.tools], excluded_colors=["green"]
)
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
async with asyncio_timeout(self.max_execution_time):
try:
while self._should_continue(iterations, time_elapsed):
next_step_output = await self._atake_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return await self._areturn(
next_step_output,
intermediate_steps,
run_manager=run_manager,
)
intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return await self._areturn(
tool_return, intermediate_steps, run_manager=run_manager
)
iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return await self._areturn(
output, intermediate_steps, run_manager=run_manager
)
except TimeoutError:
# stop early when interrupted by the async timeout
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return await self._areturn(
output, intermediate_steps, run_manager=run_manager
)
def _get_tool_return(
self, next_step_output: Tuple[AgentAction, str]
) -> Optional[AgentFinish]:
"""Check if the tool is a returning tool."""
agent_action, observation = next_step_output
name_to_tool_map = {tool.name: tool for tool in self.tools}
# Invalid tools won't be in the map, so we return False.
if agent_action.tool in name_to_tool_map:
if name_to_tool_map[agent_action.tool].return_direct:
return AgentFinish(
{self.agent.return_values[0]: observation},
"",
)
return None | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
123063bd-790f-45ff-b357-9ffa255e556f | Source code for langchain.agents.structured_chat.base
import re
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.structured_chat.output_parser import (
StructuredChatOutputParserWithRetries,
)
from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import AgentAction
from langchain.tools import BaseTool
HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
[docs]class StructuredChatAgent(Agent):
output_parser: AgentOutputParser = Field(
default_factory=StructuredChatOutputParserWithRetries
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> str:
agent_scratchpad = super()._construct_scratchpad(intermediate_steps)
if not isinstance(agent_scratchpad, str):
raise ValueError("agent_scratchpad should be of type string.")
if agent_scratchpad:
return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
pass
@classmethod
def _get_default_output_parser(
cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
) -> AgentOutputParser:
return StructuredChatOutputParserWithRetries.from_llm(llm=llm)
@property
def _stop(self) -> List[str]:
return ["Observation:"]
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
memory_prompts: Optional[List[BasePromptTemplate]] = None,
) -> BasePromptTemplate:
tool_strings = []
for tool in tools:
args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
formatted_tools = "\n".join(tool_strings)
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = format_instructions.format(tool_names=tool_names)
template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
_memory_prompts = memory_prompts or []
messages = [
SystemMessagePromptTemplate.from_template(template),
*_memory_prompts,
HumanMessagePromptTemplate.from_template(human_message_template),
]
return ChatPromptTemplate(input_variables=input_variables, messages=messages)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
memory_prompts: Optional[List[BasePromptTemplate]] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
prompt = cls.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
human_message_template=human_message_template,
format_instructions=format_instructions,
input_variables=input_variables,
memory_prompts=memory_prompts,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser(llm=llm)
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@property
def _agent_type(self) -> str:
raise ValueError | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
c68770d7-e7ce-442e-85cc-08b337c692fd | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain.agents.agent_types import AgentType
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
from langchain.schema import AIMessage, SystemMessage
[docs]def create_sql_agent(
llm: BaseLanguageModel,
toolkit: SQLDatabaseToolkit,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = SQL_PREFIX,
suffix: Optional[str] = None,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
top_k: int = 10,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a sql agent from an LLM and tools."""
tools = toolkit.get_tools()
prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix or SQL_SUFFIX,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
elif agent_type == AgentType.OPENAI_FUNCTIONS:
messages = [
SystemMessage(content=prefix),
HumanMessagePromptTemplate.from_template("{input}"),
AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
input_variables = ["input", "agent_scratchpad"]
_prompt = ChatPromptTemplate(input_variables=input_variables, messages=messages)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_manager=callback_manager,
**kwargs,
)
else:
raise ValueError(f"Agent type {agent_type} not supported at the moment.")
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
096a7039-ca3e-4e16-8221-58a0d8034c23 | Source code for langchain.agents.agent_toolkits.sql.toolkit
"""Toolkit for interacting with a SQL database."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.sql_database import SQLDatabase
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import (
InfoSQLDatabaseTool,
ListSQLDatabaseTool,
QuerySQLCheckerTool,
QuerySQLDataBaseTool,
)
[docs]class SQLDatabaseToolkit(BaseToolkit):
"""Toolkit for interacting with SQL databases."""
db: SQLDatabase = Field(exclude=True)
llm: BaseLanguageModel = Field(exclude=True)
@property
def dialect(self) -> str:
"""Return string representation of dialect to use."""
return self.db.dialect
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
query_sql_database_tool_description = (
"Input to this tool is a detailed and correct SQL query, output is a "
"result from the database. If the query is not correct, an error message "
"will be returned. If an error is returned, rewrite the query, check the "
"query, and try again. If you encounter an issue with Unknown column "
"'xxxx' in 'field list', using schema_sql_db to query the correct table "
"fields."
)
info_sql_database_tool_description = (
"Input to this tool is a comma-separated list of tables, output is the "
"schema and sample rows for those tables. "
"Be sure that the tables actually exist by calling list_tables_sql_db "
"first! Example Input: 'table1, table2, table3'"
)
return [
QuerySQLDataBaseTool(
db=self.db, description=query_sql_database_tool_description
),
InfoSQLDatabaseTool(
db=self.db, description=info_sql_database_tool_description
),
ListSQLDatabaseTool(db=self.db),
QuerySQLCheckerTool(db=self.db, llm=self.llm),
] | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
e120f654-ac10-45ef-90fe-4243ad8367ab | Source code for langchain.agents.agent_toolkits.python.base
"""Python agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.python.prompt import PREFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.agents.types import AgentType
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.schema import SystemMessage
from langchain.tools.python.tool import PythonREPLTool
[docs]def create_python_agent(
llm: BaseLanguageModel,
tool: PythonREPLTool,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[BaseCallbackManager] = None,
verbose: bool = False,
prefix: str = PREFIX,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a python agent from an LLM and tool."""
tools = [tool]
agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
elif agent_type == AgentType.OPENAI_FUNCTIONS:
system_message = SystemMessage(content=prefix)
_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_manager=callback_manager,
**kwargs,
)
else:
raise ValueError(f"Agent type {agent_type} not supported at the moment.")
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
37ee2390-a1eb-405b-b9fb-76704d9925b3 | Source code for langchain.agents.agent_toolkits.nla.toolkit
"""Toolkit for interacting with API's using natural language."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.nla.tool import NLATool
from langchain.base_language import BaseLanguageModel
from langchain.requests import Requests
from langchain.tools.base import BaseTool
from langchain.tools.openapi.utils.openapi_utils import OpenAPISpec
from langchain.tools.plugin import AIPlugin
[docs]class NLAToolkit(BaseToolkit):
"""Natural Language API Toolkit Definition."""
nla_tools: Sequence[NLATool] = Field(...)
"""List of API Endpoint Tools."""
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools for all the API operations."""
return list(self.nla_tools)
@staticmethod
def _get_http_operation_tools(
llm: BaseLanguageModel,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> List[NLATool]:
"""Get the tools for all the API operations."""
if not spec.paths:
return []
http_operation_tools = []
for path in spec.paths:
for method in spec.get_methods_for_path(path):
endpoint_tool = NLATool.from_llm_and_method(
llm=llm,
path=path,
method=method,
spec=spec,
requests=requests,
verbose=verbose,
**kwargs,
)
http_operation_tools.append(endpoint_tool)
return http_operation_tools
[docs] @classmethod
def from_llm_and_spec(
cls,
llm: BaseLanguageModel,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> NLAToolkit:
"""Instantiate the toolkit by creating tools for each operation."""
http_operation_tools = cls._get_http_operation_tools(
llm=llm, spec=spec, requests=requests, verbose=verbose, **kwargs
)
return cls(nla_tools=http_operation_tools)
[docs] @classmethod
def from_llm_and_url(
cls,
llm: BaseLanguageModel,
open_api_url: str,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> NLAToolkit:
"""Instantiate the toolkit from an OpenAPI Spec URL"""
spec = OpenAPISpec.from_url(open_api_url)
return cls.from_llm_and_spec(
llm=llm, spec=spec, requests=requests, verbose=verbose, **kwargs
)
[docs] @classmethod
def from_llm_and_ai_plugin(
cls,
llm: BaseLanguageModel,
ai_plugin: AIPlugin,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> NLAToolkit:
"""Instantiate the toolkit from an OpenAPI Spec URL"""
spec = OpenAPISpec.from_url(ai_plugin.api.url)
# TODO: Merge optional Auth information with the `requests` argument
return cls.from_llm_and_spec(
llm=llm,
spec=spec,
requests=requests,
verbose=verbose,
**kwargs,
)
[docs] @classmethod
def from_llm_and_ai_plugin_url(
cls,
llm: BaseLanguageModel,
ai_plugin_url: str,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> NLAToolkit:
"""Instantiate the toolkit from an OpenAPI Spec URL"""
plugin = AIPlugin.from_url(ai_plugin_url)
return cls.from_llm_and_ai_plugin(
llm=llm, ai_plugin=plugin, requests=requests, verbose=verbose, **kwargs
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
38aedfcd-0337-47d5-877e-90183131a036 | Source code for langchain.agents.agent_toolkits.powerbi.base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
from langchain.agents.agent_toolkits.powerbi.toolkit import PowerBIToolkit
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.utilities.powerbi import PowerBIDataset
[docs]def create_pbi_agent(
llm: BaseLanguageModel,
toolkit: Optional[PowerBIToolkit],
powerbi: Optional[PowerBIDataset] = None,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = POWERBI_PREFIX,
suffix: str = POWERBI_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
examples: Optional[str] = None,
input_variables: Optional[List[str]] = None,
top_k: int = 10,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a pbi agent from an LLM and tools."""
if toolkit is None:
if powerbi is None:
raise ValueError("Must provide either a toolkit or powerbi dataset")
toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
agent = ZeroShotAgent(
llm_chain=LLMChain(
llm=llm,
prompt=ZeroShotAgent.create_prompt(
tools,
prefix=prefix.format(top_k=top_k),
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
),
callback_manager=callback_manager, # type: ignore
verbose=verbose,
),
allowed_tools=[tool.name for tool in tools],
**kwargs,
)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
899a52c0-9787-4085-8a7e-09c61c7e39b7 | Source code for langchain.agents.agent_toolkits.powerbi.toolkit
"""Toolkit for interacting with a Power BI dataset."""
from typing import List, Optional
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain.tools import BaseTool
from langchain.tools.powerbi.prompt import QUESTION_TO_QUERY
from langchain.tools.powerbi.tool import (
InfoPowerBITool,
ListPowerBITool,
QueryPowerBITool,
)
from langchain.utilities.powerbi import PowerBIDataset
[docs]class PowerBIToolkit(BaseToolkit):
"""Toolkit for interacting with PowerBI dataset."""
powerbi: PowerBIDataset = Field(exclude=True)
llm: BaseLanguageModel = Field(exclude=True)
examples: Optional[str] = None
max_iterations: int = 5
callback_manager: Optional[BaseCallbackManager] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
if self.callback_manager:
chain = LLMChain(
llm=self.llm,
callback_manager=self.callback_manager,
prompt=PromptTemplate(
template=QUESTION_TO_QUERY,
input_variables=["tool_input", "tables", "schemas", "examples"],
),
)
else:
chain = LLMChain(
llm=self.llm,
prompt=PromptTemplate(
template=QUESTION_TO_QUERY,
input_variables=["tool_input", "tables", "schemas", "examples"],
),
)
return [
QueryPowerBITool(
llm_chain=chain,
powerbi=self.powerbi,
examples=self.examples,
max_iterations=self.max_iterations,
),
InfoPowerBITool(powerbi=self.powerbi),
ListPowerBITool(powerbi=self.powerbi),
] | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
c2ed783a-3152-4b59-9709-aed785a70889 | Source code for langchain.agents.agent_toolkits.powerbi.chat_base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent import AgentOutputParser
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
POWERBI_CHAT_SUFFIX,
)
from langchain.agents.agent_toolkits.powerbi.toolkit import PowerBIToolkit
from langchain.agents.conversational_chat.base import ConversationalChatAgent
from langchain.callbacks.base import BaseCallbackManager
from langchain.chat_models.base import BaseChatModel
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_memory import BaseChatMemory
from langchain.utilities.powerbi import PowerBIDataset
[docs]def create_pbi_chat_agent(
llm: BaseChatModel,
toolkit: Optional[PowerBIToolkit],
powerbi: Optional[PowerBIDataset] = None,
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = POWERBI_CHAT_PREFIX,
suffix: str = POWERBI_CHAT_SUFFIX,
examples: Optional[str] = None,
input_variables: Optional[List[str]] = None,
memory: Optional[BaseChatMemory] = None,
top_k: int = 10,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
"""
if toolkit is None:
if powerbi is None:
raise ValueError("Must provide either a toolkit or powerbi dataset")
toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
agent = ConversationalChatAgent.from_llm_and_tools(
llm=llm,
tools=tools,
system_message=prefix.format(top_k=top_k),
human_message=suffix,
input_variables=input_variables,
callback_manager=callback_manager,
output_parser=output_parser,
verbose=verbose,
**kwargs,
)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
memory=memory
or ConversationBufferMemory(memory_key="chat_history", return_messages=True),
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
7689fa53-e0fc-4df5-8f36-2369b1675d66 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
[docs]def create_json_agent(
llm: BaseLanguageModel,
toolkit: JsonToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = JSON_PREFIX,
suffix: str = JSON_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a json agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
10251219-5ef0-4b30-a5a9-48f86dfe1555 | Source code for langchain.agents.agent_toolkits.json.toolkit
"""Toolkit for interacting with a JSON spec."""
from __future__ import annotations
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.json.tool import JsonGetValueTool, JsonListKeysTool, JsonSpec
[docs]class JsonToolkit(BaseToolkit):
"""Toolkit for interacting with a JSON spec."""
spec: JsonSpec
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return [
JsonListKeysTool(spec=self.spec),
JsonGetValueTool(spec=self.spec),
] | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
fa410ba1-d68c-4778-ba4e-818fd449e0b0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional, Tuple
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.pandas.prompt import (
FUNCTIONS_WITH_DF,
FUNCTIONS_WITH_MULTI_DF,
MULTI_DF_PREFIX,
MULTI_DF_PREFIX_FUNCTIONS,
PREFIX,
PREFIX_FUNCTIONS,
SUFFIX_NO_DF,
SUFFIX_WITH_DF,
SUFFIX_WITH_MULTI_DF,
)
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.agents.types import AgentType
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import SystemMessage
from langchain.tools.python.tool import PythonAstREPLTool
def _get_multi_prompt(
dfs: List[Any],
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Optional[bool] = True,
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
num_dfs = len(dfs)
if suffix is not None:
suffix_to_use = suffix
include_dfs_head = True
elif include_df_in_prompt:
suffix_to_use = SUFFIX_WITH_MULTI_DF
include_dfs_head = True
else:
suffix_to_use = SUFFIX_NO_DF
include_dfs_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad", "num_dfs"]
if include_dfs_head:
input_variables += ["dfs_head"]
if prefix is None:
prefix = MULTI_DF_PREFIX
df_locals = {}
for i, dataframe in enumerate(dfs):
df_locals[f"df{i + 1}"] = dataframe
tools = [PythonAstREPLTool(locals=df_locals)]
prompt = ZeroShotAgent.create_prompt(
tools, prefix=prefix, suffix=suffix_to_use, input_variables=input_variables
)
partial_prompt = prompt.partial()
if "dfs_head" in input_variables:
dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
partial_prompt = partial_prompt.partial(num_dfs=str(num_dfs), dfs_head=dfs_head)
if "num_dfs" in input_variables:
partial_prompt = partial_prompt.partial(num_dfs=str(num_dfs))
return partial_prompt, tools
def _get_single_prompt(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Optional[bool] = True,
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
if suffix is not None:
suffix_to_use = suffix
include_df_head = True
elif include_df_in_prompt:
suffix_to_use = SUFFIX_WITH_DF
include_df_head = True
else:
suffix_to_use = SUFFIX_NO_DF
include_df_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
if include_df_head:
input_variables += ["df_head"]
if prefix is None:
prefix = PREFIX
tools = [PythonAstREPLTool(locals={"df": df})]
prompt = ZeroShotAgent.create_prompt(
tools, prefix=prefix, suffix=suffix_to_use, input_variables=input_variables
)
partial_prompt = prompt.partial()
if "df_head" in input_variables:
partial_prompt = partial_prompt.partial(df_head=str(df.head().to_markdown()))
return partial_prompt, tools
def _get_prompt_and_tools(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Optional[bool] = True,
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
try:
import pandas as pd
except ImportError:
raise ValueError(
"pandas package not found, please install with `pip install pandas`"
)
if include_df_in_prompt is not None and suffix is not None:
raise ValueError("If suffix is specified, include_df_in_prompt should not be.")
if isinstance(df, list):
for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_multi_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
else:
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_single_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
def _get_functions_single_prompt(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
include_df_in_prompt: Optional[bool] = True,
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
if suffix is not None:
suffix_to_use = suffix
if include_df_in_prompt:
suffix_to_use = suffix_to_use.format(df_head=str(df.head().to_markdown()))
elif include_df_in_prompt:
suffix_to_use = FUNCTIONS_WITH_DF.format(df_head=str(df.head().to_markdown()))
else:
suffix_to_use = ""
if prefix is None:
prefix = PREFIX_FUNCTIONS
tools = [PythonAstREPLTool(locals={"df": df})]
system_message = SystemMessage(content=prefix + suffix_to_use)
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
return prompt, tools
def _get_functions_multi_prompt(
dfs: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
include_df_in_prompt: Optional[bool] = True,
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
if suffix is not None:
suffix_to_use = suffix
if include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
suffix_to_use = suffix_to_use.format(
dfs_head=dfs_head,
)
elif include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
suffix_to_use = FUNCTIONS_WITH_MULTI_DF.format(
dfs_head=dfs_head,
)
else:
suffix_to_use = ""
if prefix is None:
prefix = MULTI_DF_PREFIX_FUNCTIONS
prefix = prefix.format(num_dfs=str(len(dfs)))
df_locals = {}
for i, dataframe in enumerate(dfs):
df_locals[f"df{i + 1}"] = dataframe
tools = [PythonAstREPLTool(locals=df_locals)]
system_message = SystemMessage(content=prefix + suffix_to_use)
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
return prompt, tools
def _get_functions_prompt_and_tools(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Optional[bool] = True,
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
try:
import pandas as pd
except ImportError:
raise ValueError(
"pandas package not found, please install with `pip install pandas`"
)
if input_variables is not None:
raise ValueError("`input_variables` is not supported at the moment.")
if include_df_in_prompt is not None and suffix is not None:
raise ValueError("If suffix is specified, include_df_in_prompt should not be.")
if isinstance(df, list):
for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_functions_multi_prompt(
df,
prefix=prefix,
suffix=suffix,
include_df_in_prompt=include_df_in_prompt,
)
else:
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_functions_single_prompt(
df,
prefix=prefix,
suffix=suffix,
include_df_in_prompt=include_df_in_prompt,
)
[docs]def create_pandas_dataframe_agent(
llm: BaseLanguageModel,
df: Any,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
verbose: bool = False,
return_intermediate_steps: bool = False,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
include_df_in_prompt: Optional[bool] = True,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a pandas agent from an LLM and dataframe."""
agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt, tools = _get_prompt_and_tools(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
callback_manager=callback_manager,
**kwargs,
)
elif agent_type == AgentType.OPENAI_FUNCTIONS:
_prompt, tools = _get_functions_prompt_and_tools(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_manager=callback_manager,
**kwargs,
)
else:
raise ValueError(f"Agent type {agent_type} not supported at the moment.")
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ed9771b1-aa28-4b6d-ab59-03da6ae8af10 | Source code for langchain.agents.agent_toolkits.gmail.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.gmail.create_draft import GmailCreateDraft
from langchain.tools.gmail.get_message import GmailGetMessage
from langchain.tools.gmail.get_thread import GmailGetThread
from langchain.tools.gmail.search import GmailSearch
from langchain.tools.gmail.send_message import GmailSendMessage
from langchain.tools.gmail.utils import build_resource_service
if TYPE_CHECKING:
# This is for linting and IDE typehints
from googleapiclient.discovery import Resource
else:
try:
# We do this so pydantic can resolve the types when instantiating
from googleapiclient.discovery import Resource
except ImportError:
pass
SCOPES = ["https://mail.google.com/"]
[docs]class GmailToolkit(BaseToolkit):
"""Toolkit for interacting with Gmail."""
api_resource: Resource = Field(default_factory=build_resource_service)
class Config:
"""Pydantic config."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return [
GmailCreateDraft(api_resource=self.api_resource),
GmailSendMessage(api_resource=self.api_resource),
GmailSearch(api_resource=self.api_resource),
GmailGetMessage(api_resource=self.api_resource),
GmailGetThread(api_resource=self.api_resource),
] | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/gmail/toolkit.html |
dde961ca-3446-41ab-a8f3-44a829829486 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit import (
VectorStoreRouterToolkit,
VectorStoreToolkit,
)
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
[docs]def create_vectorstore_agent(
llm: BaseLanguageModel,
toolkit: VectorStoreToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = PREFIX,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a vectorstore agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
)
[docs]def create_vectorstore_router_agent(
llm: BaseLanguageModel,
toolkit: VectorStoreRouterToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = ROUTER_PREFIX,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a vectorstore router agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
5c910677-c358-45f9-8a84-8f5432ebe8db | Source code for langchain.agents.agent_toolkits.vectorstore.toolkit
"""Toolkit for interacting with a vector store."""
from typing import List
from pydantic import BaseModel, Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.llms.openai import OpenAI
from langchain.tools import BaseTool
from langchain.tools.vectorstore.tool import (
VectorStoreQATool,
VectorStoreQAWithSourcesTool,
)
from langchain.vectorstores.base import VectorStore
[docs]class VectorStoreInfo(BaseModel):
"""Information about a vectorstore."""
vectorstore: VectorStore = Field(exclude=True)
name: str
description: str
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs]class VectorStoreToolkit(BaseToolkit):
"""Toolkit for interacting with a vector store."""
vectorstore_info: VectorStoreInfo = Field(exclude=True)
llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
description = VectorStoreQATool.get_description(
self.vectorstore_info.name, self.vectorstore_info.description
)
qa_tool = VectorStoreQATool(
name=self.vectorstore_info.name,
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
)
description = VectorStoreQAWithSourcesTool.get_description(
self.vectorstore_info.name, self.vectorstore_info.description
)
qa_with_sources_tool = VectorStoreQAWithSourcesTool(
name=f"{self.vectorstore_info.name}_with_sources",
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
)
return [qa_tool, qa_with_sources_tool]
[docs]class VectorStoreRouterToolkit(BaseToolkit):
"""Toolkit for routing between vector stores."""
vectorstores: List[VectorStoreInfo] = Field(exclude=True)
llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
tools: List[BaseTool] = []
for vectorstore_info in self.vectorstores:
description = VectorStoreQATool.get_description(
vectorstore_info.name, vectorstore_info.description
)
qa_tool = VectorStoreQATool(
name=vectorstore_info.name,
description=description,
vectorstore=vectorstore_info.vectorstore,
llm=self.llm,
)
tools.append(qa_tool)
return tools | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
52ed7f1a-8ca7-4c55-86b3-2c73407c383e | Source code for langchain.agents.agent_toolkits.spark.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.tools.python.tool import PythonAstREPLTool
def _validate_spark_df(df: Any) -> bool:
try:
from pyspark.sql import DataFrame as SparkLocalDataFrame
return isinstance(df, SparkLocalDataFrame)
except ImportError:
return False
def _validate_spark_connect_df(df: Any) -> bool:
try:
from pyspark.sql.connect.dataframe import DataFrame as SparkConnectDataFrame
return isinstance(df, SparkConnectDataFrame)
except ImportError:
return False
[docs]def create_spark_dataframe_agent(
llm: BaseLLM,
df: Any,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
input_variables: Optional[List[str]] = None,
verbose: bool = False,
return_intermediate_steps: bool = False,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a spark agent from an LLM and dataframe."""
if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
raise ValueError("Spark is not installed. run `pip install pyspark`.")
if input_variables is None:
input_variables = ["df", "input", "agent_scratchpad"]
tools = [PythonAstREPLTool(locals={"df": df})]
prompt = ZeroShotAgent.create_prompt(
tools, prefix=prefix, suffix=suffix, input_variables=input_variables
)
partial_prompt = prompt.partial(df=str(df.first()))
llm_chain = LLMChain(
llm=llm,
prompt=partial_prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
callback_manager=callback_manager,
**kwargs,
)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
53826aad-e2f5-4be1-85f0-a8a58e22287b | Source code for langchain.agents.agent_toolkits.playwright.toolkit
"""Playwright web browser toolkit."""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Type, cast
from pydantic import Extra, root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools.base import BaseTool
from langchain.tools.playwright.base import (
BaseBrowserTool,
lazy_import_playwright_browsers,
)
from langchain.tools.playwright.click import ClickTool
from langchain.tools.playwright.current_page import CurrentWebPageTool
from langchain.tools.playwright.extract_hyperlinks import ExtractHyperlinksTool
from langchain.tools.playwright.extract_text import ExtractTextTool
from langchain.tools.playwright.get_elements import GetElementsTool
from langchain.tools.playwright.navigate import NavigateTool
from langchain.tools.playwright.navigate_back import NavigateBackTool
if TYPE_CHECKING:
from playwright.async_api import Browser as AsyncBrowser
from playwright.sync_api import Browser as SyncBrowser
else:
try:
# We do this so pydantic can resolve the types when instantiating
from playwright.async_api import Browser as AsyncBrowser
from playwright.sync_api import Browser as SyncBrowser
except ImportError:
pass
[docs]class PlayWrightBrowserToolkit(BaseToolkit):
"""Toolkit for web browser tools."""
sync_browser: Optional["SyncBrowser"] = None
async_browser: Optional["AsyncBrowser"] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator
def validate_imports_and_browser_provided(cls, values: dict) -> dict:
"""Check that the arguments are valid."""
lazy_import_playwright_browsers()
if values.get("async_browser") is None and values.get("sync_browser") is None:
raise ValueError("Either async_browser or sync_browser must be specified.")
return values
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
tool_classes: List[Type[BaseBrowserTool]] = [
ClickTool,
NavigateTool,
NavigateBackTool,
ExtractTextTool,
ExtractHyperlinksTool,
GetElementsTool,
CurrentWebPageTool,
]
tools = [
tool_cls.from_browser(
sync_browser=self.sync_browser, async_browser=self.async_browser
)
for tool_cls in tool_classes
]
return cast(List[BaseTool], tools)
[docs] @classmethod
def from_browser(
cls,
sync_browser: Optional[SyncBrowser] = None,
async_browser: Optional[AsyncBrowser] = None,
) -> PlayWrightBrowserToolkit:
"""Instantiate the toolkit."""
# This is to raise a better error than the forward ref ones Pydantic would have
lazy_import_playwright_browsers()
return cls(sync_browser=sync_browser, async_browser=async_browser) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
f72080c7-3760-4ddb-a5ec-d0d93bc5c1b9 | Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, List, Optional, Union
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.base_language import BaseLanguageModel
[docs]def create_csv_agent(
llm: BaseLanguageModel,
path: Union[str, List[str]],
pandas_kwargs: Optional[dict] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Create csv agent by loading to a dataframe and using pandas agent."""
try:
import pandas as pd
except ImportError:
raise ValueError(
"pandas package not found, please install with `pip install pandas`"
)
_kwargs = pandas_kwargs or {}
if isinstance(path, str):
df = pd.read_csv(path, **_kwargs)
elif isinstance(path, list):
df = []
for item in path:
if not isinstance(item, str):
raise ValueError(f"Expected str, got {type(path)}")
df.append(pd.read_csv(item, **_kwargs))
else:
raise ValueError(f"Expected str or list, got {type(path)}")
return create_pandas_dataframe_agent(llm, df, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
d56dd031-2cc6-4424-80fa-4fb422c35f3b | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
[docs]def create_openapi_agent(
llm: BaseLanguageModel,
toolkit: OpenAPIToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = OPENAPI_PREFIX,
suffix: str = OPENAPI_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
verbose: bool = False,
return_intermediate_steps: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a json agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
61a4a007-851b-44da-8aef-f7fa1bf28d6e | Source code for langchain.agents.agent_toolkits.openapi.toolkit
"""Requests toolkit."""
from __future__ import annotations
from typing import Any, List
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.json.base import create_json_agent
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
from langchain.agents.agent_toolkits.openapi.prompt import DESCRIPTION
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.requests import TextRequestsWrapper
from langchain.tools import BaseTool
from langchain.tools.json.tool import JsonSpec
from langchain.tools.requests.tool import (
RequestsDeleteTool,
RequestsGetTool,
RequestsPatchTool,
RequestsPostTool,
RequestsPutTool,
)
class RequestsToolkit(BaseToolkit):
"""Toolkit for making requests."""
requests_wrapper: TextRequestsWrapper
def get_tools(self) -> List[BaseTool]:
"""Return a list of tools."""
return [
RequestsGetTool(requests_wrapper=self.requests_wrapper),
RequestsPostTool(requests_wrapper=self.requests_wrapper),
RequestsPatchTool(requests_wrapper=self.requests_wrapper),
RequestsPutTool(requests_wrapper=self.requests_wrapper),
RequestsDeleteTool(requests_wrapper=self.requests_wrapper),
]
[docs]class OpenAPIToolkit(BaseToolkit):
"""Toolkit for interacting with a OpenAPI api."""
json_agent: AgentExecutor
requests_wrapper: TextRequestsWrapper
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
json_agent_tool = Tool(
name="json_explorer",
func=self.json_agent.run,
description=DESCRIPTION,
)
request_toolkit = RequestsToolkit(requests_wrapper=self.requests_wrapper)
return [*request_toolkit.get_tools(), json_agent_tool]
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
json_spec: JsonSpec,
requests_wrapper: TextRequestsWrapper,
**kwargs: Any,
) -> OpenAPIToolkit:
"""Create json agent from llm, then initialize."""
json_agent = create_json_agent(llm, JsonToolkit(spec=json_spec), **kwargs)
return cls(json_agent=json_agent, requests_wrapper=requests_wrapper) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
d7d7b6a8-923b-4d35-b09e-90d3e3c761b7 | Source code for langchain.agents.agent_toolkits.jira.toolkit
"""Jira Toolkit."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.jira.tool import JiraAction
from langchain.utilities.jira import JiraAPIWrapper
[docs]class JiraToolkit(BaseToolkit):
"""Jira Toolkit."""
tools: List[BaseTool] = []
[docs] @classmethod
def from_jira_api_wrapper(cls, jira_api_wrapper: JiraAPIWrapper) -> "JiraToolkit":
actions = jira_api_wrapper.list()
tools = [
JiraAction(
name=action["name"],
description=action["description"],
mode=action["mode"],
api_wrapper=jira_api_wrapper,
)
for action in actions
]
return cls(tools=tools)
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return self.tools | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
8d522601-dc79-427b-bfc1-346c5b6e9136 | Source code for langchain.agents.agent_toolkits.azure_cognitive_services.toolkit
from __future__ import annotations
import sys
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools.azure_cognitive_services import (
AzureCogsFormRecognizerTool,
AzureCogsImageAnalysisTool,
AzureCogsSpeech2TextTool,
AzureCogsText2SpeechTool,
)
from langchain.tools.base import BaseTool
[docs]class AzureCognitiveServicesToolkit(BaseToolkit):
"""Toolkit for Azure Cognitive Services."""
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
tools = [
AzureCogsFormRecognizerTool(),
AzureCogsSpeech2TextTool(),
AzureCogsText2SpeechTool(),
]
# TODO: Remove check once azure-ai-vision supports MacOS.
if sys.platform.startswith("linux") or sys.platform.startswith("win"):
tools.append(AzureCogsImageAnalysisTool())
return tools | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/azure_cognitive_services/toolkit.html |
5dcc5750-8d46-41a1-a2a5-a0211f73f2ae | Source code for langchain.agents.agent_toolkits.spark_sql.base
"""Spark SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark_sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.spark_sql.toolkit import SparkSQLToolkit
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
[docs]def create_spark_sql_agent(
llm: BaseLanguageModel,
toolkit: SparkSQLToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = SQL_PREFIX,
suffix: str = SQL_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
top_k: int = 10,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a sql agent from an LLM and tools."""
tools = toolkit.get_tools()
prefix = prefix.format(top_k=top_k)
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
8c22f64b-2ad7-48ca-804d-4d3ebe8a0d95 | Source code for langchain.agents.agent_toolkits.spark_sql.toolkit
"""Toolkit for interacting with Spark SQL."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.tools import BaseTool
from langchain.tools.spark_sql.tool import (
InfoSparkSQLTool,
ListSparkSQLTool,
QueryCheckerTool,
QuerySparkSQLTool,
)
from langchain.utilities.spark_sql import SparkSQL
[docs]class SparkSQLToolkit(BaseToolkit):
"""Toolkit for interacting with Spark SQL."""
db: SparkSQL = Field(exclude=True)
llm: BaseLanguageModel = Field(exclude=True)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return [
QuerySparkSQLTool(db=self.db),
InfoSparkSQLTool(db=self.db),
ListSparkSQLTool(db=self.db),
QueryCheckerTool(db=self.db, llm=self.llm),
] | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/toolkit.html |
71943ddd-8743-4cda-bfe6-839ae2fc66a4 | Source code for langchain.agents.agent_toolkits.zapier.toolkit
"""Zapier Toolkit."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.zapier.tool import ZapierNLARunAction
from langchain.utilities.zapier import ZapierNLAWrapper
[docs]class ZapierToolkit(BaseToolkit):
"""Zapier Toolkit."""
tools: List[BaseTool] = []
[docs] @classmethod
def from_zapier_nla_wrapper(
cls, zapier_nla_wrapper: ZapierNLAWrapper
) -> "ZapierToolkit":
"""Create a toolkit from a ZapierNLAWrapper."""
actions = zapier_nla_wrapper.list()
tools = [
ZapierNLARunAction(
action_id=action["id"],
zapier_description=action["description"],
params_schema=action["params"],
api_wrapper=zapier_nla_wrapper,
)
for action in actions
]
return cls(tools=tools)
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return self.tools | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
f5f3e4a2-2a38-453d-b5fe-ea2be556148f | Source code for langchain.agents.agent_toolkits.file_management.toolkit
"""Toolkit for interacting with the local filesystem."""
from __future__ import annotations
from typing import List, Optional
from pydantic import root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.file_management.copy import CopyFileTool
from langchain.tools.file_management.delete import DeleteFileTool
from langchain.tools.file_management.file_search import FileSearchTool
from langchain.tools.file_management.list_dir import ListDirectoryTool
from langchain.tools.file_management.move import MoveFileTool
from langchain.tools.file_management.read import ReadFileTool
from langchain.tools.file_management.write import WriteFileTool
_FILE_TOOLS = {
tool_cls.__fields__["name"].default: tool_cls
for tool_cls in [
CopyFileTool,
DeleteFileTool,
FileSearchTool,
MoveFileTool,
ReadFileTool,
WriteFileTool,
ListDirectoryTool,
]
}
[docs]class FileManagementToolkit(BaseToolkit):
"""Toolkit for interacting with a Local Files."""
root_dir: Optional[str] = None
"""If specified, all file operations are made relative to root_dir."""
selected_tools: Optional[List[str]] = None
"""If provided, only provide the selected tools. Defaults to all."""
@root_validator
def validate_tools(cls, values: dict) -> dict:
selected_tools = values.get("selected_tools") or []
for tool_name in selected_tools:
if tool_name not in _FILE_TOOLS:
raise ValueError(
f"File Tool of name {tool_name} not supported."
f" Permitted tools: {list(_FILE_TOOLS)}"
)
return values
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
allowed_tools = self.selected_tools or _FILE_TOOLS.keys()
tools: List[BaseTool] = []
for tool in allowed_tools:
tool_cls = _FILE_TOOLS[tool]
tools.append(tool_cls(root_dir=self.root_dir)) # type: ignore
return tools
__all__ = ["FileManagementToolkit"] | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
01de31af-8de3-4c44-8f51-0133e321bb92 | Source code for langchain.agents.conversational_chat.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.conversational_chat.output_parser import ConvoOutputParser
from langchain.agents.conversational_chat.prompt import (
PREFIX,
SUFFIX,
TEMPLATE_TOOL_RESPONSE,
)
from langchain.agents.utils import validate_tools_single_input
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
)
from langchain.schema import (
AgentAction,
AIMessage,
BaseMessage,
BaseOutputParser,
HumanMessage,
)
from langchain.tools.base import BaseTool
[docs]class ConversationalChatAgent(Agent):
"""An agent designed to hold a conversation in addition to using tools."""
output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
template_tool_response: str = TEMPLATE_TOOL_RESPONSE
@classmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
return ConvoOutputParser()
@property
def _agent_type(self) -> str:
raise NotImplementedError
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
system_message: str = PREFIX,
human_message: str = SUFFIX,
input_variables: Optional[List[str]] = None,
output_parser: Optional[BaseOutputParser] = None,
) -> BasePromptTemplate:
tool_strings = "\n".join(
[f"> {tool.name}: {tool.description}" for tool in tools]
)
tool_names = ", ".join([tool.name for tool in tools])
_output_parser = output_parser or cls._get_default_output_parser()
format_instructions = human_message.format(
format_instructions=_output_parser.get_format_instructions()
)
final_prompt = format_instructions.format(
tool_names=tool_names, tools=tool_strings
)
if input_variables is None:
input_variables = ["input", "chat_history", "agent_scratchpad"]
messages = [
SystemMessagePromptTemplate.from_template(system_message),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template(final_prompt),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
return ChatPromptTemplate(input_variables=input_variables, messages=messages)
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> List[BaseMessage]:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts: List[BaseMessage] = []
for action, observation in intermediate_steps:
thoughts.append(AIMessage(content=action.log))
human_message = HumanMessage(
content=self.template_tool_response.format(observation=observation)
)
thoughts.append(human_message)
return thoughts
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
system_message: str = PREFIX,
human_message: str = SUFFIX,
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
_output_parser = output_parser or cls._get_default_output_parser()
prompt = cls.create_prompt(
tools,
system_message=system_message,
human_message=human_message,
input_variables=input_variables,
output_parser=_output_parser,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
1840f8a9-54d5-4f89-b3fb-e97adcab5227 | Source code for langchain.agents.openai_functions_agent.base
"""Module implements an agent that uses OpenAI's APIs function enabled API."""
import json
from dataclasses import dataclass
from json import JSONDecodeError
from typing import Any, List, Optional, Sequence, Tuple, Union
from pydantic import root_validator
from langchain.agents import BaseSingleActionAgent
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import Callbacks
from langchain.chat_models.openai import ChatOpenAI
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.chat import (
BaseMessagePromptTemplate,
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
from langchain.schema import (
AgentAction,
AgentFinish,
AIMessage,
BaseMessage,
FunctionMessage,
OutputParserException,
SystemMessage,
)
from langchain.tools import BaseTool
from langchain.tools.convert_to_openai import format_tool_to_openai_function
@dataclass
class _FunctionsAgentAction(AgentAction):
message_log: List[BaseMessage]
def _convert_agent_action_to_messages(
agent_action: AgentAction, observation: str
) -> List[BaseMessage]:
"""Convert an agent action to a message.
This code is used to reconstruct the original AI message from the agent action.
Args:
agent_action: Agent action to convert.
Returns:
AIMessage that corresponds to the original tool invocation.
"""
if isinstance(agent_action, _FunctionsAgentAction):
return agent_action.message_log + [
_create_function_message(agent_action, observation)
]
else:
return [AIMessage(content=agent_action.log)]
def _create_function_message(
agent_action: AgentAction, observation: str
) -> FunctionMessage:
"""Convert agent action and observation into a function message.
Args:
agent_action: the tool invocation request from the agent
observation: the result of the tool invocation
Returns:
FunctionMessage that corresponds to the original tool invocation
"""
if not isinstance(observation, str):
try:
content = json.dumps(observation, ensure_ascii=False)
except Exception:
content = str(observation)
else:
content = observation
return FunctionMessage(
name=agent_action.tool,
content=content,
)
def _format_intermediate_steps(
intermediate_steps: List[Tuple[AgentAction, str]],
) -> List[BaseMessage]:
"""Format intermediate steps.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations
Returns:
list of messages to send to the LLM for the next prediction
"""
messages = []
for intermediate_step in intermediate_steps:
agent_action, observation = intermediate_step
messages.extend(_convert_agent_action_to_messages(agent_action, observation))
return messages
def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
"""Parse an AI message."""
if not isinstance(message, AIMessage):
raise TypeError(f"Expected an AI message got {type(message)}")
function_call = message.additional_kwargs.get("function_call", {})
if function_call:
function_call = message.additional_kwargs["function_call"]
function_name = function_call["name"]
try:
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
raise OutputParserException(
f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
)
# HACK HACK HACK:
# The code that encodes tool input into Open AI uses a special variable
# name called `__arg1` to handle old style tools that do not expose a
# schema and expect a single string argument as an input.
# We unpack the argument here if it exists.
# Open AI does not support passing in a JSON array as an argument.
if "__arg1" in _tool_input:
tool_input = _tool_input["__arg1"]
else:
tool_input = _tool_input
content_msg = "responded: {content}\n" if message.content else "\n"
return _FunctionsAgentAction(
tool=function_name,
tool_input=tool_input,
log=f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n",
message_log=[message],
)
return AgentFinish(return_values={"output": message.content}, log=message.content)
[docs]class OpenAIFunctionsAgent(BaseSingleActionAgent):
"""An Agent driven by OpenAIs function powered API.
Args:
llm: This should be an instance of ChatOpenAI, specifically a model
that supports using `functions`.
tools: The tools this agent has access to.
prompt: The prompt for this agent, should support agent_scratchpad as one
of the variables. For an easy way to construct this prompt, use
`OpenAIFunctionsAgent.create_prompt(...)`
"""
llm: BaseLanguageModel
tools: Sequence[BaseTool]
prompt: BasePromptTemplate
[docs] def get_allowed_tools(self) -> List[str]:
"""Get allowed tools."""
return list([t.name for t in self.tools])
@root_validator
def validate_llm(cls, values: dict) -> dict:
if not isinstance(values["llm"], ChatOpenAI):
raise ValueError("Only supported with ChatOpenAI models.")
return values
@root_validator
def validate_prompt(cls, values: dict) -> dict:
prompt: BasePromptTemplate = values["prompt"]
if "agent_scratchpad" not in prompt.input_variables:
raise ValueError(
"`agent_scratchpad` should be one of the variables in the prompt, "
f"got {prompt.input_variables}"
)
return values
@property
def input_keys(self) -> List[str]:
"""Get input keys. Input refers to user input here."""
return ["input"]
@property
def functions(self) -> List[dict]:
return [dict(format_tool_to_openai_function(t)) for t in self.tools]
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
selected_inputs = {
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
}
full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
prompt = self.prompt.format_prompt(**full_inputs)
messages = prompt.to_messages()
predicted_message = self.llm.predict_messages(
messages, functions=self.functions, callbacks=callbacks
)
agent_decision = _parse_ai_message(predicted_message)
return agent_decision
[docs] async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
selected_inputs = {
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
}
full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
prompt = self.prompt.format_prompt(**full_inputs)
messages = prompt.to_messages()
predicted_message = await self.llm.apredict_messages(
messages, functions=self.functions, callbacks=callbacks
)
agent_decision = _parse_ai_message(predicted_message)
return agent_decision
[docs] @classmethod
def create_prompt(
cls,
system_message: Optional[SystemMessage] = SystemMessage(
content="You are a helpful AI assistant."
),
extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
) -> BasePromptTemplate:
"""Create prompt for this agent.
Args:
system_message: Message to use as the system message that will be the
first in the prompt.
extra_prompt_messages: Prompt messages that will be placed between the
system message and the new human input.
Returns:
A prompt template to pass into this agent.
"""
_prompts = extra_prompt_messages or []
messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
if system_message:
messages = [system_message]
else:
messages = []
messages.extend(
[
*_prompts,
HumanMessagePromptTemplate.from_template("{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
return ChatPromptTemplate(messages=messages)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
system_message: Optional[SystemMessage] = SystemMessage(
content="You are a helpful AI assistant."
),
**kwargs: Any,
) -> BaseSingleActionAgent:
"""Construct an agent from an LLM and tools."""
if not isinstance(llm, ChatOpenAI):
raise ValueError("Only supported with ChatOpenAI models.")
prompt = cls.create_prompt(
extra_prompt_messages=extra_prompt_messages,
system_message=system_message,
)
return cls(
llm=llm,
prompt=prompt,
tools=tools,
callback_manager=callback_manager,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
0a3930a4-24e1-4e8b-8272-95ebfcf6eeb7 | Source code for langchain.agents.mrkl.base
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.mrkl.output_parser import MRKLOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.agents.tools import Tool
from langchain.agents.utils import validate_tools_single_input
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.tools.base import BaseTool
class ChainConfig(NamedTuple):
"""Configuration for chain to use in MRKL system.
Args:
action_name: Name of the action.
action: Action function to call.
action_description: Description of the action.
"""
action_name: str
action: Callable
action_description: str
[docs]class ZeroShotAgent(Agent):
"""Agent for the MRKL chain."""
output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser)
@classmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
return MRKLOutputParser()
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
return AgentType.ZERO_SHOT_REACT_DESCRIPTION
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
) -> PromptTemplate:
"""Create prompt in the style of the zero shot agent.
Args:
tools: List of tools the agent will have access to, used to format the
prompt.
prefix: String to put before the list of tools.
suffix: String to put after the list of tools.
input_variables: List of input variables the final prompt will expect.
Returns:
A PromptTemplate with the template assembled from the pieces here.
"""
tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = format_instructions.format(tool_names=tool_names)
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
return PromptTemplate(template=template, input_variables=input_variables)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
prompt = cls.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser()
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
validate_tools_single_input(cls.__name__, tools)
if len(tools) == 0:
raise ValueError(
f"Got no tools for {cls.__name__}. At least one tool must be provided."
)
for tool in tools:
if tool.description is None:
raise ValueError(
f"Got a tool {tool.name} without a description. For this agent, "
f"a description must always be provided."
)
super()._validate_tools(tools)
[docs]class MRKLChain(AgentExecutor):
"""Chain that implements the MRKL system.
Example:
.. code-block:: python
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
"""
[docs] @classmethod
def from_chains(
cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any
) -> AgentExecutor:
"""User friendly way to initialize the MRKL chain.
This is intended to be an easy way to get up and running with the
MRKL chain.
Args:
llm: The LLM to use as the agent LLM.
chains: The chains the MRKL system has access to.
**kwargs: parameters to be passed to initialization.
Returns:
An initialized MRKL chain.
Example:
.. code-block:: python
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm)
chains = [
ChainConfig(
action_name = "Search",
action=search.search,
action_description="useful for searching"
),
ChainConfig(
action_name="Calculator",
action=llm_math_chain.run,
action_description="useful for doing math"
)
]
mrkl = MRKLChain.from_chains(llm, chains)
"""
tools = [
Tool(
name=c.action_name,
func=c.action,
description=c.action_description,
)
for c in chains
]
agent = ZeroShotAgent.from_llm_and_tools(llm, tools)
return cls(agent=agent, tools=tools, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
e6caa44f-2503-412b-ac91-377a919da1f5 | Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.react.output_parser import ReActOutputParser
from langchain.agents.react.textworld_prompt import TEXTWORLD_PROMPT
from langchain.agents.react.wiki_prompt import WIKI_PROMPT
from langchain.agents.tools import Tool
from langchain.agents.utils import validate_tools_single_input
from langchain.base_language import BaseLanguageModel
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
from langchain.tools.base import BaseTool
class ReActDocstoreAgent(Agent):
"""Agent for the ReAct chain."""
output_parser: AgentOutputParser = Field(default_factory=ReActOutputParser)
@classmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
return ReActOutputParser()
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
return AgentType.REACT_DOCSTORE
@classmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Return default prompt."""
return WIKI_PROMPT
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
validate_tools_single_input(cls.__name__, tools)
super()._validate_tools(tools)
if len(tools) != 2:
raise ValueError(f"Exactly two tools must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Lookup", "Search"}:
raise ValueError(
f"Tool names should be Lookup and Search, got {tool_names}"
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def _stop(self) -> List[str]:
return ["\nObservation:"]
@property
def llm_prefix(self) -> str:
"""Prefix to append the LLM call with."""
return "Thought:"
class DocstoreExplorer:
"""Class to assist with exploration of a document store."""
def __init__(self, docstore: Docstore):
"""Initialize with a docstore, and set initial document to None."""
self.docstore = docstore
self.document: Optional[Document] = None
self.lookup_str = ""
self.lookup_index = 0
def search(self, term: str) -> str:
"""Search for a term in the docstore, and if found save."""
result = self.docstore.search(term)
if isinstance(result, Document):
self.document = result
return self._summary
else:
self.document = None
return result
def lookup(self, term: str) -> str:
"""Lookup a term in document (if saved)."""
if self.document is None:
raise ValueError("Cannot lookup without a successful search first")
if term.lower() != self.lookup_str:
self.lookup_str = term.lower()
self.lookup_index = 0
else:
self.lookup_index += 1
lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
if len(lookups) == 0:
return "No Results"
elif self.lookup_index >= len(lookups):
return "No More Results"
else:
result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})"
return f"{result_prefix} {lookups[self.lookup_index]}"
@property
def _summary(self) -> str:
return self._paragraphs[0]
@property
def _paragraphs(self) -> List[str]:
if self.document is None:
raise ValueError("Cannot get paragraphs without a document")
return self.document.page_content.split("\n\n")
[docs]class ReActTextWorldAgent(ReActDocstoreAgent):
"""Agent for the ReAct TextWorld chain."""
[docs] @classmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Return default prompt."""
return TEXTWORLD_PROMPT
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
validate_tools_single_input(cls.__name__, tools)
super()._validate_tools(tools)
if len(tools) != 1:
raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Play"}:
raise ValueError(f"Tool name should be Play, got {tool_names}")
[docs]class ReActChain(AgentExecutor):
"""Chain that implements the ReAct paper.
Example:
.. code-block:: python
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
"""
def __init__(self, llm: BaseLanguageModel, docstore: Docstore, **kwargs: Any):
"""Initialize with the LLM and a docstore."""
docstore_explorer = DocstoreExplorer(docstore)
tools = [
Tool(
name="Search",
func=docstore_explorer.search,
description="Search for a term in the docstore.",
),
Tool(
name="Lookup",
func=docstore_explorer.lookup,
description="Lookup a term in the docstore.",
),
]
agent = ReActDocstoreAgent.from_llm_and_tools(llm, tools)
super().__init__(agent=agent, tools=tools, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
70479637-8dd0-494c-996b-e2d9f08e89e8 | Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self ask with search."""
from typing import Any, Sequence, Union
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.self_ask_with_search.output_parser import SelfAskOutputParser
from langchain.agents.self_ask_with_search.prompt import PROMPT
from langchain.agents.tools import Tool
from langchain.agents.utils import validate_tools_single_input
from langchain.base_language import BaseLanguageModel
from langchain.prompts.base import BasePromptTemplate
from langchain.tools.base import BaseTool
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.serpapi import SerpAPIWrapper
class SelfAskWithSearchAgent(Agent):
"""Agent for the self-ask-with-search paper."""
output_parser: AgentOutputParser = Field(default_factory=SelfAskOutputParser)
@classmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
return SelfAskOutputParser()
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
return AgentType.SELF_ASK_WITH_SEARCH
@classmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Prompt does not depend on tools."""
return PROMPT
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
validate_tools_single_input(cls.__name__, tools)
super()._validate_tools(tools)
if len(tools) != 1:
raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Intermediate Answer, got {tool_names}"
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Intermediate answer: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the LLM call with."""
return ""
[docs]class SelfAskWithSearchChain(AgentExecutor):
"""Chain that does self ask with search.
Example:
.. code-block:: python
from langchain import SelfAskWithSearchChain, OpenAI, GoogleSerperAPIWrapper
search_chain = GoogleSerperAPIWrapper()
self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)
"""
def __init__(
self,
llm: BaseLanguageModel,
search_chain: Union[GoogleSerperAPIWrapper, SerpAPIWrapper],
**kwargs: Any,
):
"""Initialize with just an LLM and a search chain."""
search_tool = Tool(
name="Intermediate Answer",
func=search_chain.run,
coroutine=search_chain.arun,
description="Search",
)
agent = SelfAskWithSearchAgent.from_llm_and_tools(llm, [search_tool])
super().__init__(agent=agent, tools=[search_tool], **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
8ac54ebe-55e3-4dbb-a672-5df44012403b | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.conversational.output_parser import ConvoOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.agents.utils import validate_tools_single_input
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.tools.base import BaseTool
[docs]class ConversationalAgent(Agent):
"""An agent designed to hold a conversation in addition to using tools."""
ai_prefix: str = "AI"
output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
@classmethod
def _get_default_output_parser(
cls, ai_prefix: str = "AI", **kwargs: Any
) -> AgentOutputParser:
return ConvoOutputParser(ai_prefix=ai_prefix)
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
return AgentType.CONVERSATIONAL_REACT_DESCRIPTION
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str]] = None,
) -> PromptTemplate:
"""Create prompt in the style of the zero shot agent.
Args:
tools: List of tools the agent will have access to, used to format the
prompt.
prefix: String to put before the list of tools.
suffix: String to put after the list of tools.
ai_prefix: String to use before AI output.
human_prefix: String to use before human output.
input_variables: List of input variables the final prompt will expect.
Returns:
A PromptTemplate with the template assembled from the pieces here.
"""
tool_strings = "\n".join(
[f"> {tool.name}: {tool.description}" for tool in tools]
)
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = format_instructions.format(
tool_names=tool_names, ai_prefix=ai_prefix, human_prefix=human_prefix
)
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "chat_history", "agent_scratchpad"]
return PromptTemplate(template=template, input_variables=input_variables)
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
prompt = cls.create_prompt(
tools,
ai_prefix=ai_prefix,
human_prefix=human_prefix,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser(
ai_prefix=ai_prefix
)
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
ai_prefix=ai_prefix,
output_parser=_output_parser,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
3f92002d-98c6-4f1a-87bf-bf812633f187 | Source code for langchain.chains.loading
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
from langchain.chains.combine_documents.refine import RefineDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.graph_qa.cypher import GraphCypherQAChain
from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
from langchain.chains.llm import LLMChain
from langchain.chains.llm_bash.base import LLMBashChain
from langchain.chains.llm_checker.base import LLMCheckerChain
from langchain.chains.llm_math.base import LLMMathChain
from langchain.chains.llm_requests import LLMRequestsChain
from langchain.chains.pal.base import PALChain
from langchain.chains.qa_with_sources.base import QAWithSourcesChain
from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain
from langchain.chains.retrieval_qa.base import RetrievalQA, VectorDBQA
from langchain.chains.sql_database.base import SQLDatabaseChain
from langchain.llms.loading import load_llm, load_llm_from_config
from langchain.prompts.loading import (
_load_output_parser,
load_prompt,
load_prompt_from_config,
)
from langchain.utilities.loading import try_load_from_hub
URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/chains/"
def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain:
"""Load LLM chain from config dict."""
if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
else:
raise ValueError("One of `prompt` or `prompt_path` must be present.")
_load_output_parser(config)
return LLMChain(llm=llm, prompt=prompt, **config)
def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedder:
"""Load hypothetical document embedder chain from config dict."""
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
if "embeddings" in kwargs:
embeddings = kwargs.pop("embeddings")
else:
raise ValueError("`embeddings` must be present.")
return HypotheticalDocumentEmbedder(
llm_chain=llm_chain, base_embeddings=embeddings, **config
)
def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "document_prompt" in config:
prompt_config = config.pop("document_prompt")
document_prompt = load_prompt_from_config(prompt_config)
elif "document_prompt_path" in config:
document_prompt = load_prompt(config.pop("document_prompt_path"))
else:
raise ValueError(
"One of `document_prompt` or `document_prompt_path` must be present."
)
return StuffDocumentsChain(
llm_chain=llm_chain, document_prompt=document_prompt, **config
)
def _load_map_reduce_documents_chain(
config: dict, **kwargs: Any
) -> MapReduceDocumentsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "combine_document_chain" in config:
combine_document_chain_config = config.pop("combine_document_chain")
combine_document_chain = load_chain_from_config(combine_document_chain_config)
elif "combine_document_chain_path" in config:
combine_document_chain = load_chain(config.pop("combine_document_chain_path"))
else:
raise ValueError(
"One of `combine_document_chain` or "
"`combine_document_chain_path` must be present."
)
if "collapse_document_chain" in config:
collapse_document_chain_config = config.pop("collapse_document_chain")
if collapse_document_chain_config is None:
collapse_document_chain = None
else:
collapse_document_chain = load_chain_from_config(
collapse_document_chain_config
)
elif "collapse_document_chain_path" in config:
collapse_document_chain = load_chain(config.pop("collapse_document_chain_path"))
return MapReduceDocumentsChain(
llm_chain=llm_chain,
combine_document_chain=combine_document_chain,
collapse_document_chain=collapse_document_chain,
**config,
)
def _load_llm_bash_chain(config: dict, **kwargs: Any) -> LLMBashChain:
llm_chain = None
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
# llm attribute is deprecated in favor of llm_chain, here to support old configs
elif "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
# llm_path attribute is deprecated in favor of llm_chain_path,
# its to support old configs
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
if llm_chain:
return LLMBashChain(llm_chain=llm_chain, prompt=prompt, **config)
else:
return LLMBashChain(llm=llm, prompt=prompt, **config)
def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain:
if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "create_draft_answer_prompt" in config:
create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt")
create_draft_answer_prompt = load_prompt_from_config(
create_draft_answer_prompt_config
)
elif "create_draft_answer_prompt_path" in config:
create_draft_answer_prompt = load_prompt(
config.pop("create_draft_answer_prompt_path")
)
if "list_assertions_prompt" in config:
list_assertions_prompt_config = config.pop("list_assertions_prompt")
list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config)
elif "list_assertions_prompt_path" in config:
list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path"))
if "check_assertions_prompt" in config:
check_assertions_prompt_config = config.pop("check_assertions_prompt")
check_assertions_prompt = load_prompt_from_config(
check_assertions_prompt_config
)
elif "check_assertions_prompt_path" in config:
check_assertions_prompt = load_prompt(
config.pop("check_assertions_prompt_path")
)
if "revised_answer_prompt" in config:
revised_answer_prompt_config = config.pop("revised_answer_prompt")
revised_answer_prompt = load_prompt_from_config(revised_answer_prompt_config)
elif "revised_answer_prompt_path" in config:
revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path"))
return LLMCheckerChain(
llm=llm,
create_draft_answer_prompt=create_draft_answer_prompt,
list_assertions_prompt=list_assertions_prompt,
check_assertions_prompt=check_assertions_prompt,
revised_answer_prompt=revised_answer_prompt,
**config,
)
def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain:
llm_chain = None
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
# llm attribute is deprecated in favor of llm_chain, here to support old configs
elif "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
# llm_path attribute is deprecated in favor of llm_chain_path,
# its to support old configs
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
if llm_chain:
return LLMMathChain(llm_chain=llm_chain, prompt=prompt, **config)
else:
return LLMMathChain(llm=llm, prompt=prompt, **config)
def _load_map_rerank_documents_chain(
config: dict, **kwargs: Any
) -> MapRerankDocumentsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain:
llm_chain = None
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
# llm attribute is deprecated in favor of llm_chain, here to support old configs
elif "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
# llm_path attribute is deprecated in favor of llm_chain_path,
# its to support old configs
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
else:
raise ValueError("One of `prompt` or `prompt_path` must be present.")
if llm_chain:
return PALChain(llm_chain=llm_chain, prompt=prompt, **config)
else:
return PALChain(llm=llm, prompt=prompt, **config)
def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
if "initial_llm_chain" in config:
initial_llm_chain_config = config.pop("initial_llm_chain")
initial_llm_chain = load_chain_from_config(initial_llm_chain_config)
elif "initial_llm_chain_path" in config:
initial_llm_chain = load_chain(config.pop("initial_llm_chain_path"))
else:
raise ValueError(
"One of `initial_llm_chain` or `initial_llm_chain_config` must be present."
)
if "refine_llm_chain" in config:
refine_llm_chain_config = config.pop("refine_llm_chain")
refine_llm_chain = load_chain_from_config(refine_llm_chain_config)
elif "refine_llm_chain_path" in config:
refine_llm_chain = load_chain(config.pop("refine_llm_chain_path"))
else:
raise ValueError(
"One of `refine_llm_chain` or `refine_llm_chain_config` must be present."
)
if "document_prompt" in config:
prompt_config = config.pop("document_prompt")
document_prompt = load_prompt_from_config(prompt_config)
elif "document_prompt_path" in config:
document_prompt = load_prompt(config.pop("document_prompt_path"))
return RefineDocumentsChain(
initial_llm_chain=initial_llm_chain,
refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
**config,
)
def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain:
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config)
def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain:
if "database" in kwargs:
database = kwargs.pop("database")
else:
raise ValueError("`database` must be present.")
if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
else:
prompt = None
return SQLDatabaseChain.from_llm(llm, database, prompt=prompt, **config)
def _load_vector_db_qa_with_sources_chain(
config: dict, **kwargs: Any
) -> VectorDBQAWithSourcesChain:
if "vectorstore" in kwargs:
vectorstore = kwargs.pop("vectorstore")
else:
raise ValueError("`vectorstore` must be present.")
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return VectorDBQAWithSourcesChain(
combine_documents_chain=combine_documents_chain,
vectorstore=vectorstore,
**config,
)
def _load_retrieval_qa(config: dict, **kwargs: Any) -> RetrievalQA:
if "retriever" in kwargs:
retriever = kwargs.pop("retriever")
else:
raise ValueError("`retriever` must be present.")
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return RetrievalQA(
combine_documents_chain=combine_documents_chain,
retriever=retriever,
**config,
)
def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
if "vectorstore" in kwargs:
vectorstore = kwargs.pop("vectorstore")
else:
raise ValueError("`vectorstore` must be present.")
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return VectorDBQA(
combine_documents_chain=combine_documents_chain,
vectorstore=vectorstore,
**config,
)
def _load_graph_cypher_chain(config: dict, **kwargs: Any) -> GraphCypherQAChain:
if "graph" in kwargs:
graph = kwargs.pop("graph")
else:
raise ValueError("`graph` must be present.")
if "cypher_generation_chain" in config:
cypher_generation_chain_config = config.pop("cypher_generation_chain")
cypher_generation_chain = load_chain_from_config(cypher_generation_chain_config)
else:
raise ValueError("`cypher_generation_chain` must be present.")
if "qa_chain" in config:
qa_chain_config = config.pop("qa_chain")
qa_chain = load_chain_from_config(qa_chain_config)
else:
raise ValueError("`qa_chain` must be present.")
return GraphCypherQAChain(
graph=graph,
cypher_generation_chain=cypher_generation_chain,
qa_chain=qa_chain,
**config,
)
def _load_api_chain(config: dict, **kwargs: Any) -> APIChain:
if "api_request_chain" in config:
api_request_chain_config = config.pop("api_request_chain")
api_request_chain = load_chain_from_config(api_request_chain_config)
elif "api_request_chain_path" in config:
api_request_chain = load_chain(config.pop("api_request_chain_path"))
else:
raise ValueError(
"One of `api_request_chain` or `api_request_chain_path` must be present."
)
if "api_answer_chain" in config:
api_answer_chain_config = config.pop("api_answer_chain")
api_answer_chain = load_chain_from_config(api_answer_chain_config)
elif "api_answer_chain_path" in config:
api_answer_chain = load_chain(config.pop("api_answer_chain_path"))
else:
raise ValueError(
"One of `api_answer_chain` or `api_answer_chain_path` must be present."
)
if "requests_wrapper" in kwargs:
requests_wrapper = kwargs.pop("requests_wrapper")
else:
raise ValueError("`requests_wrapper` must be present.")
return APIChain(
api_request_chain=api_request_chain,
api_answer_chain=api_answer_chain,
requests_wrapper=requests_wrapper,
**config,
)
def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
if "requests_wrapper" in kwargs:
requests_wrapper = kwargs.pop("requests_wrapper")
return LLMRequestsChain(
llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config
)
else:
return LLMRequestsChain(llm_chain=llm_chain, **config)
type_to_loader_dict = {
"api_chain": _load_api_chain,
"hyde_chain": _load_hyde_chain,
"llm_chain": _load_llm_chain,
"llm_bash_chain": _load_llm_bash_chain,
"llm_checker_chain": _load_llm_checker_chain,
"llm_math_chain": _load_llm_math_chain,
"llm_requests_chain": _load_llm_requests_chain,
"pal_chain": _load_pal_chain,
"qa_with_sources_chain": _load_qa_with_sources_chain,
"stuff_documents_chain": _load_stuff_documents_chain,
"map_reduce_documents_chain": _load_map_reduce_documents_chain,
"map_rerank_documents_chain": _load_map_rerank_documents_chain,
"refine_documents_chain": _load_refine_documents_chain,
"sql_database_chain": _load_sql_database_chain,
"vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
"vector_db_qa": _load_vector_db_qa,
"retrieval_qa": _load_retrieval_qa,
"graph_cypher_chain": _load_graph_cypher_chain,
}
def load_chain_from_config(config: dict, **kwargs: Any) -> Chain:
"""Load chain from Config Dict."""
if "_type" not in config:
raise ValueError("Must specify a chain Type in config")
config_type = config.pop("_type")
if config_type not in type_to_loader_dict:
raise ValueError(f"Loading {config_type} chain not supported")
chain_loader = type_to_loader_dict[config_type]
return chain_loader(config, **kwargs)
[docs]def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain:
"""Unified method for loading a chain from LangChainHub or local fs."""
if hub_result := try_load_from_hub(
path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs
):
return hub_result
else:
return _load_chain_from_file(path, **kwargs)
def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain:
"""Load chain from file."""
# Convert file to Path object.
if isinstance(file, str):
file_path = Path(file)
else:
file_path = file
# Load from either json or yaml.
if file_path.suffix == ".json":
with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
else:
raise ValueError("File type must be json or yaml")
# Override default 'verbose' and 'memory' for the chain
if "verbose" in kwargs:
config["verbose"] = kwargs.pop("verbose")
if "memory" in kwargs:
config["memory"] = kwargs.pop("memory")
# Load the chain from the config now.
return load_chain_from_config(config, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
35afffd0-a42a-42ee-ac6f-92b5491183fb | Source code for langchain.chains.llm
"""Chain that just formats a prompt and calls an LLM."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from pydantic import Extra, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManager,
AsyncCallbackManagerForChainRun,
CallbackManager,
CallbackManagerForChainRun,
Callbacks,
)
from langchain.chains.base import Chain
from langchain.input import get_colored_text
from langchain.load.dump import dumpd
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
BaseLLMOutputParser,
LLMResult,
NoOpOutputParser,
PromptValue,
)
[docs]class LLMChain(Chain):
"""Chain to run queries against LLMs.
Example:
.. code-block:: python
from langchain import LLMChain, OpenAI, PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)
llm = LLMChain(llm=OpenAI(), prompt=prompt)
"""
@property
def lc_serializable(self) -> bool:
return True
prompt: BasePromptTemplate
"""Prompt object to use."""
llm: BaseLanguageModel
"""Language model to call."""
output_key: str = "text" #: :meta private:
output_parser: BaseLLMOutputParser = Field(default_factory=NoOpOutputParser)
"""Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise."""
return_final_only: bool = True
"""Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation."""
llm_kwargs: dict = Field(default_factory=dict)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Will be whatever keys the prompt expects.
:meta private:
"""
return self.prompt.input_variables
@property
def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
if self.return_final_only:
return [self.output_key]
else:
return [self.output_key, "full_generation"]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = self.generate([inputs], run_manager=run_manager)
return self.create_outputs(response)[0]
[docs] def generate(
self,
input_list: List[Dict[str, Any]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> LLMResult:
"""Generate LLM result from inputs."""
prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
return self.llm.generate_prompt(
prompts,
stop,
callbacks=run_manager.get_child() if run_manager else None,
**self.llm_kwargs,
)
[docs] async def agenerate(
self,
input_list: List[Dict[str, Any]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> LLMResult:
"""Generate LLM result from inputs."""
prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)
return await self.llm.agenerate_prompt(
prompts,
stop,
callbacks=run_manager.get_child() if run_manager else None,
**self.llm_kwargs,
)
[docs] def prep_prompts(
self,
input_list: List[Dict[str, Any]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Tuple[List[PromptValue], Optional[List[str]]]:
"""Prepare prompts from inputs."""
stop = None
if "stop" in input_list[0]:
stop = input_list[0]["stop"]
prompts = []
for inputs in input_list:
selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
prompt = self.prompt.format_prompt(**selected_inputs)
_colored_text = get_colored_text(prompt.to_string(), "green")
_text = "Prompt after formatting:\n" + _colored_text
if run_manager:
run_manager.on_text(_text, end="\n", verbose=self.verbose)
if "stop" in inputs and inputs["stop"] != stop:
raise ValueError(
"If `stop` is present in any inputs, should be present in all."
)
prompts.append(prompt)
return prompts, stop
[docs] async def aprep_prompts(
self,
input_list: List[Dict[str, Any]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Tuple[List[PromptValue], Optional[List[str]]]:
"""Prepare prompts from inputs."""
stop = None
if "stop" in input_list[0]:
stop = input_list[0]["stop"]
prompts = []
for inputs in input_list:
selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
prompt = self.prompt.format_prompt(**selected_inputs)
_colored_text = get_colored_text(prompt.to_string(), "green")
_text = "Prompt after formatting:\n" + _colored_text
if run_manager:
await run_manager.on_text(_text, end="\n", verbose=self.verbose)
if "stop" in inputs and inputs["stop"] != stop:
raise ValueError(
"If `stop` is present in any inputs, should be present in all."
)
prompts.append(prompt)
return prompts, stop
[docs] def apply(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> List[Dict[str, str]]:
"""Utilize the LLM generate method for speed gains."""
callback_manager = CallbackManager.configure(
callbacks, self.callbacks, self.verbose
)
run_manager = callback_manager.on_chain_start(
dumpd(self),
{"input_list": input_list},
)
try:
response = self.generate(input_list, run_manager=run_manager)
except (KeyboardInterrupt, Exception) as e:
run_manager.on_chain_error(e)
raise e
outputs = self.create_outputs(response)
run_manager.on_chain_end({"outputs": outputs})
return outputs
[docs] async def aapply(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> List[Dict[str, str]]:
"""Utilize the LLM generate method for speed gains."""
callback_manager = AsyncCallbackManager.configure(
callbacks, self.callbacks, self.verbose
)
run_manager = await callback_manager.on_chain_start(
dumpd(self),
{"input_list": input_list},
)
try:
response = await self.agenerate(input_list, run_manager=run_manager)
except (KeyboardInterrupt, Exception) as e:
await run_manager.on_chain_error(e)
raise e
outputs = self.create_outputs(response)
await run_manager.on_chain_end({"outputs": outputs})
return outputs
@property
def _run_output_key(self) -> str:
return self.output_key
[docs] def create_outputs(self, llm_result: LLMResult) -> List[Dict[str, Any]]:
"""Create outputs from response."""
result = [
# Get the text of the top generated string.
{
self.output_key: self.output_parser.parse_result(generation),
"full_generation": generation,
}
for generation in llm_result.generations
]
if self.return_final_only:
result = [{self.output_key: r[self.output_key]} for r in result]
return result
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = await self.agenerate([inputs], run_manager=run_manager)
return self.create_outputs(response)[0]
[docs] def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
"""Format prompt with kwargs and pass to LLM.
Args:
callbacks: Callbacks to pass to LLMChain
**kwargs: Keys to pass to prompt template.
Returns:
Completion from LLM.
Example:
.. code-block:: python
completion = llm.predict(adjective="funny")
"""
return self(kwargs, callbacks=callbacks)[self.output_key]
[docs] async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
"""Format prompt with kwargs and pass to LLM.
Args:
callbacks: Callbacks to pass to LLMChain
**kwargs: Keys to pass to prompt template.
Returns:
Completion from LLM.
Example:
.. code-block:: python
completion = llm.predict(adjective="funny")
"""
return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]
[docs] def predict_and_parse(
self, callbacks: Callbacks = None, **kwargs: Any
) -> Union[str, List[str], Dict[str, Any]]:
"""Call predict and then parse the results."""
warnings.warn(
"The predict_and_parse method is deprecated, "
"instead pass an output parser directly to LLMChain."
)
result = self.predict(callbacks=callbacks, **kwargs)
if self.prompt.output_parser is not None:
return self.prompt.output_parser.parse(result)
else:
return result
[docs] async def apredict_and_parse(
self, callbacks: Callbacks = None, **kwargs: Any
) -> Union[str, List[str], Dict[str, str]]:
"""Call apredict and then parse the results."""
warnings.warn(
"The apredict_and_parse method is deprecated, "
"instead pass an output parser directly to LLMChain."
)
result = await self.apredict(callbacks=callbacks, **kwargs)
if self.prompt.output_parser is not None:
return self.prompt.output_parser.parse(result)
else:
return result
[docs] def apply_and_parse(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> Sequence[Union[str, List[str], Dict[str, str]]]:
"""Call apply and then parse the results."""
warnings.warn(
"The apply_and_parse method is deprecated, "
"instead pass an output parser directly to LLMChain."
)
result = self.apply(input_list, callbacks=callbacks)
return self._parse_generation(result)
def _parse_generation(
self, generation: List[Dict[str, str]]
) -> Sequence[Union[str, List[str], Dict[str, str]]]:
if self.prompt.output_parser is not None:
return [
self.prompt.output_parser.parse(res[self.output_key])
for res in generation
]
else:
return generation
[docs] async def aapply_and_parse(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> Sequence[Union[str, List[str], Dict[str, str]]]:
"""Call apply and then parse the results."""
warnings.warn(
"The aapply_and_parse method is deprecated, "
"instead pass an output parser directly to LLMChain."
)
result = await self.aapply(input_list, callbacks=callbacks)
return self._parse_generation(result)
@property
def _chain_type(self) -> str:
return "llm_chain"
[docs] @classmethod
def from_string(cls, llm: BaseLanguageModel, template: str) -> LLMChain:
"""Create LLMChain from LLM and template."""
prompt_template = PromptTemplate.from_template(template)
return cls(llm=llm, prompt=prompt_template) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
98123f88-6ba0-4e8b-8788-535a5f24213d | Source code for langchain.chains.moderation
"""Pass input through a moderation endpoint."""
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.utils import get_from_dict_or_env
[docs]class OpenAIModerationChain(Chain):
"""Pass input through a moderation endpoint.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.chains import OpenAIModerationChain
moderation = OpenAIModerationChain()
"""
client: Any #: :meta private:
model_name: Optional[str] = None
"""Moderation model name to use."""
error: bool = False
"""Whether or not to error if bad content was found."""
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
openai_api_key: Optional[str] = None
openai_organization: Optional[str] = None
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
openai_organization = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = openai.Moderation
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
return values
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
def _moderate(self, text: str, results: dict) -> str:
if results["flagged"]:
error_str = "Text was found that violates OpenAI's content policy."
if self.error:
raise ValueError(error_str)
else:
return error_str
return text
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
text = inputs[self.input_key]
results = self.client.create(text)
output = self._moderate(text, results["results"][0])
return {self.output_key: output} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
8ac39ea1-1c45-4fb3-a96f-93470fcd4087 | Source code for langchain.chains.transform
"""Chain that runs an arbitrary python function."""
from typing import Callable, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
[docs]class TransformChain(Chain):
"""Chain transform chain output.
Example:
.. code-block:: python
from langchain import TransformChain
transform_chain = TransformChain(input_variables=["text"],
output_variables["entities"], transform=func())
"""
input_variables: List[str]
output_variables: List[str]
transform: Callable[[Dict[str, str]], Dict[str, str]]
@property
def input_keys(self) -> List[str]:
"""Expect input keys.
:meta private:
"""
return self.input_variables
@property
def output_keys(self) -> List[str]:
"""Return output keys.
:meta private:
"""
return self.output_variables
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
return self.transform(inputs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/transform.html |
802fc638-0fb2-410b-b35e-b83a6e540a8d | Source code for langchain.chains.sequential
"""Chain pipeline where the outputs of one step feed directly into next."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.input import get_color_mapping
[docs]class SequentialChain(Chain):
"""Chain where the outputs of one chain feed directly into next."""
chains: List[Chain]
input_variables: List[str]
output_variables: List[str] #: :meta private:
return_all: bool = False
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Return expected input keys to the chain.
:meta private:
"""
return self.input_variables
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return self.output_variables
@root_validator(pre=True)
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that the correct inputs exist for all chains."""
chains = values["chains"]
input_variables = values["input_variables"]
memory_keys = list()
if "memory" in values and values["memory"] is not None:
"""Validate that prompt input variables are consistent."""
memory_keys = values["memory"].memory_variables
if set(input_variables).intersection(set(memory_keys)):
overlapping_keys = set(input_variables) & set(memory_keys)
raise ValueError(
f"The the input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
)
known_variables = set(input_variables + memory_keys)
for chain in chains:
missing_vars = set(chain.input_keys).difference(known_variables)
if missing_vars:
raise ValueError(
f"Missing required input keys: {missing_vars}, "
f"only had {known_variables}"
)
overlapping_keys = known_variables.intersection(chain.output_keys)
if overlapping_keys:
raise ValueError(
f"Chain returned keys that already exist: {overlapping_keys}"
)
known_variables |= set(chain.output_keys)
if "output_variables" not in values:
if values.get("return_all", False):
output_keys = known_variables.difference(input_variables)
else:
output_keys = chains[-1].output_keys
values["output_variables"] = output_keys
else:
missing_vars = set(values["output_variables"]).difference(known_variables)
if missing_vars:
raise ValueError(
f"Expected output variables that were not found: {missing_vars}."
)
return values
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
known_values = inputs.copy()
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
for i, chain in enumerate(self.chains):
callbacks = _run_manager.get_child()
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
known_values = inputs.copy()
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
for i, chain in enumerate(self.chains):
outputs = await chain.acall(
known_values, return_only_outputs=True, callbacks=callbacks
)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
[docs]class SimpleSequentialChain(Chain):
"""Simple chain where the outputs of one step feed directly into next."""
chains: List[Chain]
strip_outputs: bool = False
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
@root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that chains are all single input/output."""
for chain in values["chains"]:
if len(chain.input_keys) != 1:
raise ValueError(
"Chains used in SimplePipeline should all have one input, got "
f"{chain} with {len(chain.input_keys)} inputs."
)
if len(chain.output_keys) != 1:
raise ValueError(
"Chains used in SimplePipeline should all have one output, got "
f"{chain} with {len(chain.output_keys)} outputs."
)
return values
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.chains):
_input = chain.run(_input, callbacks=_run_manager.get_child(f"step_{i+1}"))
if self.strip_outputs:
_input = _input.strip()
_run_manager.on_text(
_input, color=color_mapping[str(i)], end="\n", verbose=self.verbose
)
return {self.output_key: _input}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.chains):
_input = await chain.arun(_input, callbacks=callbacks)
if self.strip_outputs:
_input = _input.strip()
await _run_manager.on_text(
_input, color=color_mapping[str(i)], end="\n", verbose=self.verbose
)
return {self.output_key: _input} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
5b5f9db4-1384-40f4-83b9-d1d0bd40b96c | Source code for langchain.chains.llm_requests
"""Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain.requests import TextRequestsWrapper
DEFAULT_HEADERS = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36" # noqa: E501
}
[docs]class LLMRequestsChain(Chain):
"""Chain that hits a URL and then uses an LLM to parse results."""
llm_chain: LLMChain
requests_wrapper: TextRequestsWrapper = Field(
default_factory=lambda: TextRequestsWrapper(headers=DEFAULT_HEADERS),
exclude=True,
)
text_length: int = 8000
requests_key: str = "requests_result" #: :meta private:
input_key: str = "url" #: :meta private:
output_key: str = "output" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Will be whatever keys the prompt expects.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
return [self.output_key]
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from bs4 import BeautifulSoup # noqa: F401
except ImportError:
raise ValueError(
"Could not import bs4 python package. "
"Please install it with `pip install bs4`."
)
return values
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
from bs4 import BeautifulSoup
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
url = inputs[self.input_key]
res = self.requests_wrapper.get(url)
# extract the text from the html
soup = BeautifulSoup(res, "html.parser")
other_keys[self.requests_key] = soup.get_text()[: self.text_length]
result = self.llm_chain.predict(
callbacks=_run_manager.get_child(), **other_keys
)
return {self.output_key: result}
@property
def _chain_type(self) -> str:
return "llm_requests_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
a97ada2d-e6d4-4be2-b1d2-c6b6b7a51a94 | Source code for langchain.chains.mapreduce
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
from langchain.text_splitter import TextSplitter
[docs]class MapReduceChain(Chain):
"""Map-reduce chain."""
combine_documents_chain: BaseCombineDocumentsChain
"""Chain to use to combine documents."""
text_splitter: TextSplitter
"""Text splitter to use."""
input_key: str = "input_text" #: :meta private:
output_key: str = "output_text" #: :meta private:
[docs] @classmethod
def from_params(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate,
text_splitter: TextSplitter,
callbacks: Callbacks = None,
combine_chain_kwargs: Optional[Mapping[str, Any]] = None,
reduce_chain_kwargs: Optional[Mapping[str, Any]] = None,
**kwargs: Any,
) -> MapReduceChain:
"""Construct a map-reduce chain that uses the chain for map and reduce."""
llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks)
reduce_chain = StuffDocumentsChain(
llm_chain=llm_chain,
callbacks=callbacks,
**(reduce_chain_kwargs if reduce_chain_kwargs else {}),
)
combine_documents_chain = MapReduceDocumentsChain(
llm_chain=llm_chain,
combine_document_chain=reduce_chain,
callbacks=callbacks,
**(combine_chain_kwargs if combine_chain_kwargs else {}),
)
return cls(
combine_documents_chain=combine_documents_chain,
text_splitter=text_splitter,
callbacks=callbacks,
**kwargs,
)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
# Split the larger text into smaller chunks.
doc_text = inputs.pop(self.input_key)
texts = self.text_splitter.split_text(doc_text)
docs = [Document(page_content=text) for text in texts]
_inputs: Dict[str, Any] = {
**inputs,
self.combine_documents_chain.input_key: docs,
}
outputs = self.combine_documents_chain.run(
_inputs, callbacks=_run_manager.get_child()
)
return {self.output_key: outputs} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
efa491c5-1dc2-45ff-9713-cbc0d548e516 | Source code for langchain.chains.router.multi_retrieval_qa
"""Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.base_language import BaseLanguageModel
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
from langchain.chains.conversation.prompt import DEFAULT_TEMPLATE
from langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA
from langchain.chains.router.base import MultiRouteChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_retrieval_prompt import (
MULTI_RETRIEVAL_ROUTER_TEMPLATE,
)
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schema import BaseRetriever
[docs]class MultiRetrievalQAChain(MultiRouteChain):
"""A multi-route chain that uses an LLM router chain to choose amongst retrieval
qa chains."""
router_chain: LLMRouterChain
"""Chain for deciding a destination chain and the input to it."""
destination_chains: Mapping[str, BaseRetrievalQA]
"""Map of name to candidate chains that inputs can be routed to."""
default_chain: Chain
"""Default chain to use when router doesn't map input to one of the destinations."""
@property
def output_keys(self) -> List[str]:
return ["result"]
[docs] @classmethod
def from_retrievers(
cls,
llm: BaseLanguageModel,
retriever_infos: List[Dict[str, Any]],
default_retriever: Optional[BaseRetriever] = None,
default_prompt: Optional[PromptTemplate] = None,
default_chain: Optional[Chain] = None,
**kwargs: Any,
) -> MultiRetrievalQAChain:
if default_prompt and not default_retriever:
raise ValueError(
"`default_retriever` must be specified if `default_prompt` is "
"provided. Received only `default_prompt`."
)
destinations = [f"{r['name']}: {r['description']}" for r in retriever_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_RETRIEVAL_ROUTER_TEMPLATE.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(next_inputs_inner_key="query"),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
destination_chains = {}
for r_info in retriever_infos:
prompt = r_info.get("prompt")
retriever = r_info["retriever"]
chain = RetrievalQA.from_llm(llm, prompt=prompt, retriever=retriever)
name = r_info["name"]
destination_chains[name] = chain
if default_chain:
_default_chain = default_chain
elif default_retriever:
_default_chain = RetrievalQA.from_llm(
llm, prompt=default_prompt, retriever=default_retriever
)
else:
prompt_template = DEFAULT_TEMPLATE.replace("input", "query")
prompt = PromptTemplate(
template=prompt_template, input_variables=["history", "query"]
)
_default_chain = ConversationChain(
llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
)
return cls(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=_default_chain,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
c13cde83-0c27-455f-b8b0-6c65d2febef6 | Source code for langchain.chains.router.base
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from typing import Any, Dict, List, Mapping, NamedTuple, Optional
from pydantic import Extra
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from langchain.chains.base import Chain
class Route(NamedTuple):
destination: Optional[str]
next_inputs: Dict[str, Any]
[docs]class RouterChain(Chain, ABC):
"""Chain that outputs the name of a destination chain and the inputs to it."""
@property
def output_keys(self) -> List[str]:
return ["destination", "next_inputs"]
[docs] def route(self, inputs: Dict[str, Any], callbacks: Callbacks = None) -> Route:
result = self(inputs, callbacks=callbacks)
return Route(result["destination"], result["next_inputs"])
[docs] async def aroute(
self, inputs: Dict[str, Any], callbacks: Callbacks = None
) -> Route:
result = await self.acall(inputs, callbacks=callbacks)
return Route(result["destination"], result["next_inputs"])
[docs]class MultiRouteChain(Chain):
"""Use a single chain to route an input to one of multiple candidate chains."""
router_chain: RouterChain
"""Chain that routes inputs to destination chains."""
destination_chains: Mapping[str, Chain]
"""Chains that return final answer to inputs."""
default_chain: Chain
"""Default chain to use when none of the destination chains are suitable."""
silent_errors: bool = False
"""If True, use default_chain when an invalid destination name is provided.
Defaults to False."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Will be whatever keys the router chain prompt expects.
:meta private:
"""
return self.router_chain.input_keys
@property
def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
return []
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
route = self.router_chain.route(inputs, callbacks=callbacks)
_run_manager.on_text(
str(route.destination) + ": " + str(route.next_inputs), verbose=self.verbose
)
if not route.destination:
return self.default_chain(route.next_inputs, callbacks=callbacks)
elif route.destination in self.destination_chains:
return self.destination_chains[route.destination](
route.next_inputs, callbacks=callbacks
)
elif self.silent_errors:
return self.default_chain(route.next_inputs, callbacks=callbacks)
else:
raise ValueError(
f"Received invalid destination chain name '{route.destination}'"
)
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
route = await self.router_chain.aroute(inputs, callbacks=callbacks)
_run_manager.on_text(
str(route.destination) + ": " + str(route.next_inputs), verbose=self.verbose
)
if not route.destination:
return await self.default_chain.acall(
route.next_inputs, callbacks=callbacks
)
elif route.destination in self.destination_chains:
return await self.destination_chains[route.destination].acall(
route.next_inputs, callbacks=callbacks
)
elif self.silent_errors:
return await self.default_chain.acall(
route.next_inputs, callbacks=callbacks
)
else:
raise ValueError(
f"Received invalid destination chain name '{route.destination}'"
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
4c05c00c-7c71-4cb8-8297-d9b361d12c2c | Source code for langchain.chains.router.multi_prompt
"""Use a single chain to route an input to one of multiple llm chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.base_language import BaseLanguageModel
from langchain.chains import ConversationChain
from langchain.chains.llm import LLMChain
from langchain.chains.router.base import MultiRouteChain, RouterChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE
from langchain.prompts import PromptTemplate
[docs]class MultiPromptChain(MultiRouteChain):
"""A multi-route chain that uses an LLM router chain to choose amongst prompts."""
router_chain: RouterChain
"""Chain for deciding a destination chain and the input to it."""
destination_chains: Mapping[str, LLMChain]
"""Map of name to candidate chains that inputs can be routed to."""
default_chain: LLMChain
"""Default chain to use when router doesn't map input to one of the destinations."""
@property
def output_keys(self) -> List[str]:
return ["text"]
[docs] @classmethod
def from_prompts(
cls,
llm: BaseLanguageModel,
prompt_infos: List[Dict[str, str]],
default_chain: Optional[LLMChain] = None,
**kwargs: Any,
) -> MultiPromptChain:
"""Convenience constructor for instantiating from destination prompts."""
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
chain = LLMChain(llm=llm, prompt=prompt)
destination_chains[name] = chain
_default_chain = default_chain or ConversationChain(llm=llm, output_key="text")
return cls(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=_default_chain,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
0ad2eaeb-1073-4ed2-bde3-0cba089418d1 | Source code for langchain.chains.router.llm_router
"""Base classes for LLM-powered router chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type, cast
from pydantic import root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains import LLMChain
from langchain.chains.router.base import RouterChain
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchain.prompts import BasePromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException
[docs]class LLMRouterChain(RouterChain):
"""A router chain that uses an LLM chain to perform routing."""
llm_chain: LLMChain
"""LLM chain used to perform routing"""
@root_validator()
def validate_prompt(cls, values: dict) -> dict:
prompt = values["llm_chain"].prompt
if prompt.output_parser is None:
raise ValueError(
"LLMRouterChain requires base llm_chain prompt to have an output"
" parser that converts LLM text output to a dictionary with keys"
" 'destination' and 'next_inputs'. Received a prompt with no output"
" parser."
)
return values
@property
def input_keys(self) -> List[str]:
"""Will be whatever keys the LLM chain prompt expects.
:meta private:
"""
return self.llm_chain.input_keys
def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
super()._validate_outputs(outputs)
if not isinstance(outputs["next_inputs"], dict):
raise ValueError
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
output = cast(
Dict[str, Any],
self.llm_chain.predict_and_parse(callbacks=callbacks, **inputs),
)
return output
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
output = cast(
Dict[str, Any],
await self.llm_chain.apredict_and_parse(callbacks=callbacks, **inputs),
)
return output
[docs] @classmethod
def from_llm(
cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, **kwargs: Any
) -> LLMRouterChain:
"""Convenience constructor."""
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs)
class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
"""Parser for output of router chain int he multi-prompt chain."""
default_destination: str = "DEFAULT"
next_inputs_type: Type = str
next_inputs_inner_key: str = "input"
def parse(self, text: str) -> Dict[str, Any]:
try:
expected_keys = ["destination", "next_inputs"]
parsed = parse_and_check_json_markdown(text, expected_keys)
if not isinstance(parsed["destination"], str):
raise ValueError("Expected 'destination' to be a string.")
if not isinstance(parsed["next_inputs"], self.next_inputs_type):
raise ValueError(
f"Expected 'next_inputs' to be {self.next_inputs_type}."
)
parsed["next_inputs"] = {self.next_inputs_inner_key: parsed["next_inputs"]}
if (
parsed["destination"].strip().lower()
== self.default_destination.lower()
):
parsed["destination"] = None
else:
parsed["destination"] = parsed["destination"].strip()
return parsed
except Exception as e:
raise OutputParserException(
f"Parsing text\n{text}\n raised following error:\n{e}"
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
2c6e1723-0320-45b5-8e15-df84faefdbb7 | Source code for langchain.chains.natbot.base
"""Implement an LLM driven browser."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.natbot.prompt import PROMPT
from langchain.llms.openai import OpenAI
[docs]class NatBotChain(Chain):
"""Implement an LLM driven browser.
Example:
.. code-block:: python
from langchain import NatBotChain
natbot = NatBotChain.from_default("Buy me a new hat.")
"""
llm_chain: LLMChain
objective: str
"""Objective that NatBot is tasked with completing."""
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
input_url_key: str = "url" #: :meta private:
input_browser_content_key: str = "browser_content" #: :meta private:
previous_command: str = "" #: :meta private:
output_key: str = "command" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an NatBotChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"] is not None:
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=PROMPT)
return values
[docs] @classmethod
def from_default(cls, objective: str, **kwargs: Any) -> NatBotChain:
"""Load with default LLMChain."""
llm = OpenAI(temperature=0.5, best_of=10, n=3, max_tokens=50)
return cls.from_llm(llm, objective, **kwargs)
[docs] @classmethod
def from_llm(
cls, llm: BaseLanguageModel, objective: str, **kwargs: Any
) -> NatBotChain:
"""Load from LLM."""
llm_chain = LLMChain(llm=llm, prompt=PROMPT)
return cls(llm_chain=llm_chain, objective=objective, **kwargs)
@property
def input_keys(self) -> List[str]:
"""Expect url and browser content.
:meta private:
"""
return [self.input_url_key, self.input_browser_content_key]
@property
def output_keys(self) -> List[str]:
"""Return command.
:meta private:
"""
return [self.output_key]
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
url = inputs[self.input_url_key]
browser_content = inputs[self.input_browser_content_key]
llm_cmd = self.llm_chain.predict(
objective=self.objective,
url=url[:100],
previous_command=self.previous_command,
browser_content=browser_content[:4500],
callbacks=_run_manager.get_child(),
)
llm_cmd = llm_cmd.strip()
self.previous_command = llm_cmd
return {self.output_key: llm_cmd}
[docs] def execute(self, url: str, browser_content: str) -> str:
"""Figure out next browser command to run.
Args:
url: URL of the site currently on.
browser_content: Content of the page as currently displayed by the browser.
Returns:
Next browser command to run.
Example:
.. code-block:: python
browser_content = "...."
llm_command = natbot.run("www.google.com", browser_content)
"""
_inputs = {
self.input_url_key: url,
self.input_browser_content_key: browser_content,
}
return self(_inputs)[self.output_key]
@property
def _chain_type(self) -> str:
return "nat_bot_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
b02cb80c-ffb1-40b8-a109-3a4ac54b643a | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs.networkx_graph import NetworkxEntityGraph, get_entities
from langchain.prompts.base import BasePromptTemplate
[docs]class GraphQAChain(Chain):
"""Chain for question-answering against a graph."""
graph: NetworkxEntityGraph = Field(exclude=True)
entity_extraction_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
qa_prompt: BasePromptTemplate = PROMPT,
entity_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT,
**kwargs: Any,
) -> GraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
return cls(
qa_chain=qa_chain,
entity_extraction_chain=entity_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Extract entities, look up info and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs[self.input_key]
entity_string = self.entity_extraction_chain.run(question)
_run_manager.on_text("Entities Extracted:", end="\n", verbose=self.verbose)
_run_manager.on_text(
entity_string, color="green", end="\n", verbose=self.verbose
)
entities = get_entities(entity_string)
context = ""
for entity in entities:
triplets = self.graph.get_entity_knowledge(entity)
context += "\n".join(triplets)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(context, color="green", end="\n", verbose=self.verbose)
result = self.qa_chain(
{"question": question, "context": context},
callbacks=_run_manager.get_child(),
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
361e42c3-d313-4701-aaaf-e8d537b5efec | Source code for langchain.chains.graph_qa.cypher
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs.neo4j_graph import Neo4jGraph
from langchain.prompts.base import BasePromptTemplate
INTERMEDIATE_STEPS_KEY = "intermediate_steps"
def extract_cypher(text: str) -> str:
"""
Extract Cypher code from a text.
Args:
text: Text to extract Cypher code from.
Returns:
Cypher code extracted from the text.
"""
# The pattern to find Cypher code enclosed in triple backticks
pattern = r"```(.*?)```"
# Find all matches in the input text
matches = re.findall(pattern, text, re.DOTALL)
return matches[0] if matches else text
[docs]class GraphCypherQAChain(Chain):
"""Chain for question-answering against a graph by generating Cypher statements."""
graph: Neo4jGraph = Field(exclude=True)
cypher_generation_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
top_k: int = 10
"""Number of results to return from the query"""
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the graph directly."""
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
@property
def _chain_type(self) -> str:
return "graph_cypher_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
**kwargs: Any,
) -> GraphCypherQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
return cls(
qa_chain=qa_chain,
cypher_generation_chain=cypher_generation_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
intermediate_steps: List = []
generated_cypher = self.cypher_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
# Extract Cypher code if it is wrapped in backticks
generated_cypher = extract_cypher(generated_cypher)
_run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_cypher, color="green", end="\n", verbose=self.verbose
)
intermediate_steps.append({"query": generated_cypher})
# Retrieve and limit the number of results
context = self.graph.query(generated_cypher)[: self.top_k]
if self.return_direct:
final_result = context
else:
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
intermediate_steps.append({"context": context})
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
final_result = result[self.qa_chain.output_key]
chain_result: Dict[str, Any] = {self.output_key: final_result}
if self.return_intermediate_steps:
chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
return chain_result | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
a277bb58-8178-41cc-86d0-4acd256cc251 | Source code for langchain.chains.graph_qa.nebulagraph
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import CYPHER_QA_PROMPT, NGQL_GENERATION_PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs.nebula_graph import NebulaGraph
from langchain.prompts.base import BasePromptTemplate
[docs]class NebulaGraphQAChain(Chain):
"""Chain for question-answering against a graph by generating nGQL statements."""
graph: NebulaGraph = Field(exclude=True)
ngql_generation_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
ngql_prompt: BasePromptTemplate = NGQL_GENERATION_PROMPT,
**kwargs: Any,
) -> NebulaGraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
ngql_generation_chain = LLMChain(llm=llm, prompt=ngql_prompt)
return cls(
qa_chain=qa_chain,
ngql_generation_chain=ngql_generation_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Generate nGQL statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
generated_ngql = self.ngql_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
_run_manager.on_text("Generated nGQL:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_ngql, color="green", end="\n", verbose=self.verbose
)
context = self.graph.query(generated_ngql)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
1c812f77-f67c-4ad0-a5ef-4c4814c5ddc1 | Source code for langchain.chains.graph_qa.kuzu
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import CYPHER_QA_PROMPT, KUZU_GENERATION_PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs.kuzu_graph import KuzuGraph
from langchain.prompts.base import BasePromptTemplate
[docs]class KuzuQAChain(Chain):
"""Chain for question-answering against a graph by generating Cypher statements for
Kùzu.
"""
graph: KuzuGraph = Field(exclude=True)
cypher_generation_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
cypher_prompt: BasePromptTemplate = KUZU_GENERATION_PROMPT,
**kwargs: Any,
) -> KuzuQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
return cls(
qa_chain=qa_chain,
cypher_generation_chain=cypher_generation_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
generated_cypher = self.cypher_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
_run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_cypher, color="green", end="\n", verbose=self.verbose
)
context = self.graph.query(generated_cypher)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
689a7d15-ab66-4a4f-a183-ac2a68289449 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_bash.prompt import PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import OutputParserException
from langchain.utilities.bash import BashProcess
logger = logging.getLogger(__name__)
[docs]class LLMBashChain(Chain):
"""Chain that interprets a prompt and executes bash code to perform bash operations.
Example:
.. code-block:: python
from langchain import LLMBashChain, OpenAI
llm_bash = LLMBashChain.from_llm(OpenAI())
"""
llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
input_key: str = "question" #: :meta private:
output_key: str = "answer" #: :meta private:
prompt: BasePromptTemplate = PROMPT
"""[Deprecated]"""
bash_process: BashProcess = Field(default_factory=BashProcess) #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMBashChain with an llm is deprecated. "
"Please instantiate with llm_chain or using the from_llm class method."
)
if "llm_chain" not in values and values["llm"] is not None:
prompt = values.get("prompt", PROMPT)
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
return values
@root_validator
def validate_prompt(cls, values: Dict) -> Dict:
if values["llm_chain"].prompt.output_parser is None:
raise ValueError(
"The prompt used by llm_chain is expected to have an output_parser."
)
return values
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
return [self.output_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_run_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = self.llm_chain.predict(
question=inputs[self.input_key], callbacks=_run_manager.get_child()
)
_run_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
try:
parser = self.llm_chain.prompt.output_parser
command_list = parser.parse(t) # type: ignore[union-attr]
except OutputParserException as e:
_run_manager.on_chain_error(e, verbose=self.verbose)
raise e
if self.verbose:
_run_manager.on_text("\nCode: ", verbose=self.verbose)
_run_manager.on_text(
str(command_list), color="yellow", verbose=self.verbose
)
output = self.bash_process.run(command_list)
_run_manager.on_text("\nAnswer: ", verbose=self.verbose)
_run_manager.on_text(output, color="yellow", verbose=self.verbose)
return {self.output_key: output}
@property
def _chain_type(self) -> str:
return "llm_bash_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMBashChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
6693f9cb-84c4-45be-9a2b-c1b658a41cb5 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.question_answering.stuff_prompt import PROMPT_SELECTOR
from langchain.prompts import PromptTemplate
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores.base import VectorStore
class BaseRetrievalQA(Chain):
combine_documents_chain: BaseCombineDocumentsChain
"""Chain to use to combine the documents."""
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
return_source_documents: bool = False
"""Return the source documents."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
allow_population_by_field_name = True
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> BaseRetrievalQA:
"""Initialize from LLM."""
_prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
llm_chain = LLMChain(llm=llm, prompt=_prompt)
document_prompt = PromptTemplate(
input_variables=["page_content"], template="Context:\n{page_content}"
)
combine_documents_chain = StuffDocumentsChain(
llm_chain=llm_chain,
document_variable_name="context",
document_prompt=document_prompt,
)
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
@classmethod
def from_chain_type(
cls,
llm: BaseLanguageModel,
chain_type: str = "stuff",
chain_type_kwargs: Optional[dict] = None,
**kwargs: Any,
) -> BaseRetrievalQA:
"""Load chain from chain type."""
_chain_type_kwargs = chain_type_kwargs or {}
combine_documents_chain = load_qa_chain(
llm, chain_type=chain_type, **_chain_type_kwargs
)
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
@abstractmethod
def _get_docs(self, question: str) -> List[Document]:
"""Get documents to do question answering over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run get_relevant_text and llm on input query.
If chain has 'return_source_documents' as 'True', returns
the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = indexqa({'query': 'This is my query'})
answer, docs = res['result'], res['source_documents']
"""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs[self.input_key]
docs = self._get_docs(question)
answer = self.combine_documents_chain.run(
input_documents=docs, question=question, callbacks=_run_manager.get_child()
)
if self.return_source_documents:
return {self.output_key: answer, "source_documents": docs}
else:
return {self.output_key: answer}
@abstractmethod
async def _aget_docs(self, question: str) -> List[Document]:
"""Get documents to do question answering over."""
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run get_relevant_text and llm on input query.
If chain has 'return_source_documents' as 'True', returns
the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = indexqa({'query': 'This is my query'})
answer, docs = res['result'], res['source_documents']
"""
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = inputs[self.input_key]
docs = await self._aget_docs(question)
answer = await self.combine_documents_chain.arun(
input_documents=docs, question=question, callbacks=_run_manager.get_child()
)
if self.return_source_documents:
return {self.output_key: answer, "source_documents": docs}
else:
return {self.output_key: answer}
[docs]class RetrievalQA(BaseRetrievalQA):
"""Chain for question-answering against an index.
Example:
.. code-block:: python
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorstores.base import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS(...))
retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever)
"""
retriever: BaseRetriever = Field(exclude=True)
def _get_docs(self, question: str) -> List[Document]:
return self.retriever.get_relevant_documents(question)
async def _aget_docs(self, question: str) -> List[Document]:
return await self.retriever.aget_relevant_documents(question)
@property
def _chain_type(self) -> str:
"""Return the chain type."""
return "retrieval_qa"
[docs]class VectorDBQA(BaseRetrievalQA):
"""Chain for question-answering against a vector database."""
vectorstore: VectorStore = Field(exclude=True, alias="vectorstore")
"""Vector Database to connect to."""
k: int = 4
"""Number of documents to query for."""
search_type: str = "similarity"
"""Search type to use over vectorstore. `similarity` or `mmr`."""
search_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Extra search args."""
@root_validator()
def raise_deprecation(cls, values: Dict) -> Dict:
warnings.warn(
"`VectorDBQA` is deprecated - "
"please use `from langchain.chains import RetrievalQA`"
)
return values
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
search_type = values["search_type"]
if search_type not in ("similarity", "mmr"):
raise ValueError(f"search_type of {search_type} not allowed.")
return values
def _get_docs(self, question: str) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(
question, k=self.k, **self.search_kwargs
)
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(
question, k=self.k, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_docs(self, question: str) -> List[Document]:
raise NotImplementedError("VectorDBQA does not support async")
@property
def _chain_type(self) -> str:
"""Return the chain type."""
return "vector_db_qa" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
7b5e1334-5b96-4bb4-9377-6b5d41b2c3d4 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
from __future__ import annotations
import math
import re
import warnings
from typing import Any, Dict, List, Optional
import numexpr
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_math.prompt import PROMPT
from langchain.prompts.base import BasePromptTemplate
[docs]class LLMMathChain(Chain):
"""Chain that interprets a prompt and executes python code to do math.
Example:
.. code-block:: python
from langchain import LLMMathChain, OpenAI
llm_math = LLMMathChain.from_llm(OpenAI())
"""
llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
prompt: BasePromptTemplate = PROMPT
"""[Deprecated] Prompt to use to translate to python if necessary."""
input_key: str = "question" #: :meta private:
output_key: str = "answer" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMMathChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"] is not None:
prompt = values.get("prompt", PROMPT)
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
return values
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
return [self.output_key]
def _evaluate_expression(self, expression: str) -> str:
try:
local_dict = {"pi": math.pi, "e": math.e}
output = str(
numexpr.evaluate(
expression.strip(),
global_dict={}, # restrict access to globals
local_dict=local_dict, # add common mathematical functions
)
)
except Exception as e:
raise ValueError(
f'LLMMathChain._evaluate("{expression}") raised error: {e}.'
" Please try again with a valid numerical expression"
)
# Remove any leading and trailing brackets from the output
return re.sub(r"^\[|\]$", "", output)
def _process_llm_result(
self, llm_output: str, run_manager: CallbackManagerForChainRun
) -> Dict[str, str]:
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1)
output = self._evaluate_expression(expression)
run_manager.on_text("\nAnswer: ", verbose=self.verbose)
run_manager.on_text(output, color="yellow", verbose=self.verbose)
answer = "Answer: " + output
elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = "Answer: " + llm_output.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
async def _aprocess_llm_result(
self,
llm_output: str,
run_manager: AsyncCallbackManagerForChainRun,
) -> Dict[str, str]:
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1)
output = self._evaluate_expression(expression)
await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
await run_manager.on_text(output, color="yellow", verbose=self.verbose)
answer = "Answer: " + output
elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = "Answer: " + llm_output.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_run_manager.on_text(inputs[self.input_key])
llm_output = self.llm_chain.predict(
question=inputs[self.input_key],
stop=["```output"],
callbacks=_run_manager.get_child(),
)
return self._process_llm_result(llm_output, _run_manager)
async def _acall(
self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
await _run_manager.on_text(inputs[self.input_key])
llm_output = await self.llm_chain.apredict(
question=inputs[self.input_key],
stop=["```output"],
callbacks=_run_manager.get_child(),
)
return await self._aprocess_llm_result(llm_output, _run_manager)
@property
def _chain_type(self) -> str:
return "llm_math_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMMathChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
cb400598-0e82-47a5-aa27-d44577b4a457 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.hyde.prompts import PROMPT_MAP
from langchain.chains.llm import LLMChain
from langchain.embeddings.base import Embeddings
[docs]class HypotheticalDocumentEmbedder(Chain, Embeddings):
"""Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
"""
base_embeddings: Embeddings
llm_chain: LLMChain
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Input keys for Hyde's LLM chain."""
return self.llm_chain.input_keys
@property
def output_keys(self) -> List[str]:
"""Output keys for Hyde's LLM chain."""
return self.llm_chain.output_keys
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call the base embeddings."""
return self.base_embeddings.embed_documents(texts)
[docs] def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
"""Combine embeddings into final embeddings."""
return list(np.array(embeddings).mean(axis=0))
[docs] def embed_query(self, text: str) -> List[float]:
"""Generate a hypothetical document and embedded it."""
var_name = self.llm_chain.input_keys[0]
result = self.llm_chain.generate([{var_name: text}])
documents = [generation.text for generation in result.generations[0]]
embeddings = self.embed_documents(documents)
return self.combine_embeddings(embeddings)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Call the internal llm chain."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
return self.llm_chain(inputs, callbacks=_run_manager.get_child())
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
base_embeddings: Embeddings,
prompt_key: str,
**kwargs: Any,
) -> HypotheticalDocumentEmbedder:
"""Load and use LLMChain for a specific prompt key."""
prompt = PROMPT_MAP[prompt_key]
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(base_embeddings=base_embeddings, llm_chain=llm_chain, **kwargs)
@property
def _chain_type(self) -> str:
return "hyde_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
3cd419ea-5991-4686-b5ce-ec95abf33566 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_checker.prompt import (
CHECK_ASSERTIONS_PROMPT,
CREATE_DRAFT_ANSWER_PROMPT,
LIST_ASSERTIONS_PROMPT,
REVISED_ANSWER_PROMPT,
)
from langchain.chains.sequential import SequentialChain
from langchain.prompts import PromptTemplate
def _load_question_to_checked_assertions_chain(
llm: BaseLanguageModel,
create_draft_answer_prompt: PromptTemplate,
list_assertions_prompt: PromptTemplate,
check_assertions_prompt: PromptTemplate,
revised_answer_prompt: PromptTemplate,
) -> SequentialChain:
create_draft_answer_chain = LLMChain(
llm=llm,
prompt=create_draft_answer_prompt,
output_key="statement",
)
list_assertions_chain = LLMChain(
llm=llm,
prompt=list_assertions_prompt,
output_key="assertions",
)
check_assertions_chain = LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
)
revised_answer_chain = LLMChain(
llm=llm,
prompt=revised_answer_prompt,
output_key="revised_statement",
)
chains = [
create_draft_answer_chain,
list_assertions_chain,
check_assertions_chain,
revised_answer_chain,
]
question_to_checked_assertions_chain = SequentialChain(
chains=chains,
input_variables=["question"],
output_variables=["revised_statement"],
verbose=True,
)
return question_to_checked_assertions_chain
[docs]class LLMCheckerChain(Chain):
"""Chain for question-answering with self-verification.
Example:
.. code-block:: python
from langchain import OpenAI, LLMCheckerChain
llm = OpenAI(temperature=0.7)
checker_chain = LLMCheckerChain.from_llm(llm)
"""
question_to_checked_assertions_chain: SequentialChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
create_draft_answer_prompt: PromptTemplate = CREATE_DRAFT_ANSWER_PROMPT
"""[Deprecated]"""
list_assertions_prompt: PromptTemplate = LIST_ASSERTIONS_PROMPT
"""[Deprecated]"""
check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT
"""[Deprecated]"""
revised_answer_prompt: PromptTemplate = REVISED_ANSWER_PROMPT
"""[Deprecated] Prompt to use when questioning the documents."""
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMCheckerChain with an llm is deprecated. "
"Please instantiate with question_to_checked_assertions_chain "
"or using the from_llm class method."
)
if (
"question_to_checked_assertions_chain" not in values
and values["llm"] is not None
):
question_to_checked_assertions_chain = (
_load_question_to_checked_assertions_chain(
values["llm"],
values.get(
"create_draft_answer_prompt", CREATE_DRAFT_ANSWER_PROMPT
),
values.get("list_assertions_prompt", LIST_ASSERTIONS_PROMPT),
values.get("check_assertions_prompt", CHECK_ASSERTIONS_PROMPT),
values.get("revised_answer_prompt", REVISED_ANSWER_PROMPT),
)
)
values[
"question_to_checked_assertions_chain"
] = question_to_checked_assertions_chain
return values
@property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
return [self.output_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs[self.input_key]
output = self.question_to_checked_assertions_chain(
{"question": question}, callbacks=_run_manager.get_child()
)
return {self.output_key: output["revised_statement"]}
@property
def _chain_type(self) -> str:
return "llm_checker_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
create_draft_answer_prompt: PromptTemplate = CREATE_DRAFT_ANSWER_PROMPT,
list_assertions_prompt: PromptTemplate = LIST_ASSERTIONS_PROMPT,
check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT,
revised_answer_prompt: PromptTemplate = REVISED_ANSWER_PROMPT,
**kwargs: Any,
) -> LLMCheckerChain:
question_to_checked_assertions_chain = (
_load_question_to_checked_assertions_chain(
llm,
create_draft_answer_prompt,
list_assertions_prompt,
check_assertions_prompt,
revised_answer_prompt,
)
)
return cls(
question_to_checked_assertions_chain=question_to_checked_assertions_chain,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
993db45b-4e3b-431d-a2a6-48ed5944912a | Source code for langchain.chains.constitutional_ai.base
"""Chain for applying constitutional principles to the outputs of another chain."""
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
from langchain.chains.constitutional_ai.principles import PRINCIPLES
from langchain.chains.constitutional_ai.prompts import CRITIQUE_PROMPT, REVISION_PROMPT
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
[docs]class ConstitutionalChain(Chain):
"""Chain for applying constitutional principles.
Example:
.. code-block:: python
from langchain.llms import OpenAI
from langchain.chains import LLMChain, ConstitutionalChain
from langchain.chains.constitutional_ai.models \
import ConstitutionalPrinciple
llm = OpenAI()
qa_prompt = PromptTemplate(
template="Q: {question} A:",
input_variables=["question"],
)
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
constitutional_chain = ConstitutionalChain.from_llm(
llm=llm,
chain=qa_chain,
constitutional_principles=[
ConstitutionalPrinciple(
critique_request="Tell if this answer is good.",
revision_request="Give a better answer.",
)
],
)
constitutional_chain.run(question="What is the meaning of life?")
"""
chain: LLMChain
constitutional_principles: List[ConstitutionalPrinciple]
critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
return [PRINCIPLES[name] for name in names]
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
chain: LLMChain,
critique_prompt: BasePromptTemplate = CRITIQUE_PROMPT,
revision_prompt: BasePromptTemplate = REVISION_PROMPT,
**kwargs: Any,
) -> "ConstitutionalChain":
"""Create a chain from an LLM."""
critique_chain = LLMChain(llm=llm, prompt=critique_prompt)
revision_chain = LLMChain(llm=llm, prompt=revision_prompt)
return cls(
chain=chain,
critique_chain=critique_chain,
revision_chain=revision_chain,
**kwargs,
)
@property
def input_keys(self) -> List[str]:
"""Defines the input keys."""
return self.chain.input_keys
@property
def output_keys(self) -> List[str]:
"""Defines the output keys."""
if self.return_intermediate_steps:
return ["output", "critiques_and_revisions", "initial_output"]
return ["output"]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child("original"),
)
initial_response = response
input_prompt = self.chain.prompt.format(**inputs)
_run_manager.on_text(
text="Initial response: " + response + "\n\n",
verbose=self.verbose,
color="yellow",
)
critiques_and_revisions = []
for constitutional_principle in self.constitutional_principles:
# Do critique
raw_critique = self.critique_chain.run(
input_prompt=input_prompt,
output_from_model=response,
critique_request=constitutional_principle.critique_request,
callbacks=_run_manager.get_child("critique"),
)
critique = self._parse_critique(
output_string=raw_critique,
).strip()
# if the critique contains "No critique needed", then we're done
# in this case, initial_output is the same as output,
# but we'll keep it for consistency
if "no critique needed" in critique.lower():
critiques_and_revisions.append((critique, ""))
continue
# Do revision
revision = self.revision_chain.run(
input_prompt=input_prompt,
output_from_model=response,
critique_request=constitutional_principle.critique_request,
critique=critique,
revision_request=constitutional_principle.revision_request,
callbacks=_run_manager.get_child("revision"),
).strip()
response = revision
critiques_and_revisions.append((critique, revision))
_run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + critique + "\n\n",
verbose=self.verbose,
color="blue",
)
_run_manager.on_text(
text="Updated response: " + revision + "\n\n",
verbose=self.verbose,
color="yellow",
)
final_output: Dict[str, Any] = {"output": response}
if self.return_intermediate_steps:
final_output["initial_output"] = initial_response
final_output["critiques_and_revisions"] = critiques_and_revisions
return final_output
@staticmethod
def _parse_critique(output_string: str) -> str:
if "Revision request:" not in output_string:
return output_string
output_string = output_string.split("Revision request:")[0]
if "\n\n" in output_string:
output_string = output_string.split("\n\n")[0]
return output_string | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
adea5d40-2691-4bc9-9403-3360345bc25e | Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from pydantic import Extra, Field, root_validator
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer import ConversationBufferMemory
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseMemory
[docs]class ConversationChain(LLMChain):
"""Chain to have a conversation and load context from memory.
Example:
.. code-block:: python
from langchain import ConversationChain, OpenAI
conversation = ConversationChain(llm=OpenAI())
"""
memory: BaseMemory = Field(default_factory=ConversationBufferMemory)
"""Default memory store."""
prompt: BasePromptTemplate = PROMPT
"""Default conversation prompt to use."""
input_key: str = "input" #: :meta private:
output_key: str = "response" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Use this since so some prompt vars come from history."""
return [self.input_key]
@root_validator()
def validate_prompt_input_variables(cls, values: Dict) -> Dict:
"""Validate that prompt input variables are consistent."""
memory_keys = values["memory"].memory_variables
input_key = values["input_key"]
if input_key in memory_keys:
raise ValueError(
f"The input key {input_key} was also found in the memory keys "
f"({memory_keys}) - please provide keys that don't overlap."
)
prompt_variables = values["prompt"].input_variables
expected_keys = memory_keys + [input_key]
if set(expected_keys) != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but got {memory_keys} as inputs from "
f"memory, and {input_key} as the normal input key."
)
return values | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
65384a39-7c49-4bd1-abfb-04ab08aeeb03 | Source code for langchain.chains.qa_with_sources.retrieval
"""Question-answering with sources over an index."""
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain):
"""Question-answering with sources over an index."""
retriever: BaseRetriever = Field(exclude=True)
"""Index to connect to."""
reduce_k_below_max_tokens: bool = False
"""Reduce the number of results to return from store based on tokens limit"""
max_tokens_limit: int = 3375
"""Restrict the docs to return from store based on tokens,
enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
num_docs = len(docs)
if self.reduce_k_below_max_tokens and isinstance(
self.combine_documents_chain, StuffDocumentsChain
):
tokens = [
self.combine_documents_chain.llm_chain.llm.get_num_tokens(
doc.page_content
)
for doc in docs
]
token_count = sum(tokens[:num_docs])
while token_count > self.max_tokens_limit:
num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = self.retriever.get_relevant_documents(question)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = await self.retriever.aget_relevant_documents(question)
return self._reduce_tokens_below_limit(docs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
438ee92d-42f9-44b8-8b67-ea3c88e039bd | Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.chains.qa_with_sources.map_reduce_prompt import (
COMBINE_PROMPT,
EXAMPLE_PROMPT,
QUESTION_PROMPT,
)
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
class BaseQAWithSourcesChain(Chain, ABC):
"""Question answering with sources over documents."""
combine_documents_chain: BaseCombineDocumentsChain
"""Chain to use to combine documents."""
question_key: str = "question" #: :meta private:
input_docs_key: str = "docs" #: :meta private:
answer_key: str = "answer" #: :meta private:
sources_answer_key: str = "sources" #: :meta private:
return_source_documents: bool = False
"""Return the source documents."""
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
question_prompt: BasePromptTemplate = QUESTION_PROMPT,
combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)
combine_results_chain = StuffDocumentsChain(
llm_chain=llm_combine_chain,
document_prompt=document_prompt,
document_variable_name="summaries",
)
combine_document_chain = MapReduceDocumentsChain(
llm_chain=llm_question_chain,
combine_document_chain=combine_results_chain,
document_variable_name="context",
)
return cls(
combine_documents_chain=combine_document_chain,
**kwargs,
)
@classmethod
def from_chain_type(
cls,
llm: BaseLanguageModel,
chain_type: str = "stuff",
chain_type_kwargs: Optional[dict] = None,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Load chain from chain type."""
_chain_kwargs = chain_type_kwargs or {}
combine_document_chain = load_qa_with_sources_chain(
llm, chain_type=chain_type, **_chain_kwargs
)
return cls(combine_documents_chain=combine_document_chain, **kwargs)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.question_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
_output_keys = [self.answer_key, self.sources_answer_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@root_validator(pre=True)
def validate_naming(cls, values: Dict) -> Dict:
"""Fix backwards compatability in naming."""
if "combine_document_chain" in values:
values["combine_documents_chain"] = values.pop("combine_document_chain")
return values
@abstractmethod
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
"""Get docs to run questioning over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
docs = self._get_docs(inputs)
answer = self.combine_documents_chain.run(
input_documents=docs, callbacks=_run_manager.get_child(), **inputs
)
if re.search(r"SOURCES:\s", answer):
answer, sources = re.split(r"SOURCES:\s", answer)
else:
sources = ""
result: Dict[str, Any] = {
self.answer_key: answer,
self.sources_answer_key: sources,
}
if self.return_source_documents:
result["source_documents"] = docs
return result
@abstractmethod
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
"""Get docs to run questioning over."""
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
docs = await self._aget_docs(inputs)
answer = await self.combine_documents_chain.arun(
input_documents=docs, callbacks=_run_manager.get_child(), **inputs
)
if re.search(r"SOURCES:\s", answer):
answer, sources = re.split(r"SOURCES:\s", answer)
else:
sources = ""
result: Dict[str, Any] = {
self.answer_key: answer,
self.sources_answer_key: sources,
}
if self.return_source_documents:
result["source_documents"] = docs
return result
[docs]class QAWithSourcesChain(BaseQAWithSourcesChain):
"""Question answering with sources over documents."""
input_docs_key: str = "docs" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_docs_key, self.question_key]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
return inputs.pop(self.input_docs_key)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
return inputs.pop(self.input_docs_key)
@property
def _chain_type(self) -> str:
return "qa_with_sources_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
456351ea-3a7f-41c7-bf63-a755634f158f | Source code for langchain.chains.qa_with_sources.vector_db
"""Question-answering with sources over a vector database."""
import warnings
from typing import Any, Dict, List
from pydantic import Field, root_validator
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
from langchain.docstore.document import Document
from langchain.vectorstores.base import VectorStore
[docs]class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain):
"""Question-answering with sources over a vector database."""
vectorstore: VectorStore = Field(exclude=True)
"""Vector Database to connect to."""
k: int = 4
"""Number of results to return from store"""
reduce_k_below_max_tokens: bool = False
"""Reduce the number of results to return from store based on tokens limit"""
max_tokens_limit: int = 3375
"""Restrict the docs to return from store based on tokens,
enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
search_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Extra search args."""
def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
num_docs = len(docs)
if self.reduce_k_below_max_tokens and isinstance(
self.combine_documents_chain, StuffDocumentsChain
):
tokens = [
self.combine_documents_chain.llm_chain.llm.get_num_tokens(
doc.page_content
)
for doc in docs
]
token_count = sum(tokens[:num_docs])
while token_count > self.max_tokens_limit:
num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = self.vectorstore.similarity_search(
question, k=self.k, **self.search_kwargs
)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
raise NotImplementedError("VectorDBQAWithSourcesChain does not support async")
@root_validator()
def raise_deprecation(cls, values: Dict) -> Dict:
warnings.warn(
"`VectorDBQAWithSourcesChain` is deprecated - "
"please use `from langchain.chains import RetrievalQAWithSourcesChain`"
)
return values
@property
def _chain_type(self) -> str:
return "vector_db_qa_with_sources_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |