mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-23 03:19:38 +00:00
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
This commit is contained in:
Bagatur
GitHub
@@ -0,0 +1,211 @@
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain_community.llms.sagemaker_endpoint import ContentHandlerBase
|
||||
|
||||
|
||||
class EmbeddingsContentHandler(ContentHandlerBase[List[str], List[List[float]]]):
|
||||
"""Content handler for LLM class."""
|
||||
|
||||
|
||||
class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
|
||||
"""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_community.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
|
||||
)
|
||||
|
||||
#Use with boto3 client
|
||||
client = boto3.client(
|
||||
"sagemaker-runtime",
|
||||
region_name=region_name
|
||||
)
|
||||
se = SagemakerEndpointEmbeddings(
|
||||
endpoint_name=endpoint_name,
|
||||
client=client
|
||||
)
|
||||
"""
|
||||
client: Any = None
|
||||
|
||||
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_community.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
|
||||
"""Keyword 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:
|
||||
"""Dont do anything if client provided externally"""
|
||||
if values.get("client") is not None:
|
||||
return values
|
||||
|
||||
"""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 ImportError(
|
||||
"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"])
|
||||
|
||||
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
|
||||
|
||||
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]
|
||||
Reference in New Issue
Block a user