Source code for langchain_community.retrievers.bedrock

from typing import Any, Dict, List, Optional

from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.retrievers import BaseRetriever


[docs]class VectorSearchConfig(BaseModel, extra="allow"): # type: ignore[call-arg] """Configuration for vector search.""" numberOfResults: int = 4
[docs]class RetrievalConfig(BaseModel, extra="allow"): # type: ignore[call-arg] """Configuration for retrieval.""" vectorSearchConfiguration: VectorSearchConfig
[docs]class AmazonKnowledgeBasesRetriever(BaseRetriever): """`Amazon Bedrock Knowledge Bases` retrieval. See https://aws.amazon.com/bedrock/knowledge-bases for more info. Args: knowledge_base_id: Knowledge Base ID. region_name: The aws region e.g., `us-west-2`. Fallback to AWS_DEFAULT_REGION env variable or region specified in ~/.aws/config. credentials_profile_name: 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. client: boto3 client for bedrock agent runtime. retrieval_config: Configuration for retrieval. Example: .. code-block:: python from langchain_community.retrievers import AmazonKnowledgeBasesRetriever retriever = AmazonKnowledgeBasesRetriever( knowledge_base_id="<knowledge-base-id>", retrieval_config={ "vectorSearchConfiguration": { "numberOfResults": 4 } }, ) """ knowledge_base_id: str region_name: Optional[str] = None credentials_profile_name: Optional[str] = None endpoint_url: Optional[str] = None client: Any retrieval_config: RetrievalConfig @root_validator(pre=True) def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]: if values.get("client") is not None: return values try: import boto3 from botocore.client import Config from botocore.exceptions import UnknownServiceError if values.get("credentials_profile_name"): session = boto3.Session(profile_name=values["credentials_profile_name"]) else: # use default credentials session = boto3.Session() client_params = { "config": Config( connect_timeout=120, read_timeout=120, retries={"max_attempts": 0} ) } if values.get("region_name"): client_params["region_name"] = values["region_name"] if values.get("endpoint_url"): client_params["endpoint_url"] = values["endpoint_url"] values["client"] = session.client("bedrock-agent-runtime", **client_params) return values except ImportError: raise ImportError( "Could not import boto3 python package. " "Please install it with `pip install boto3`." ) except UnknownServiceError as e: raise ImportError( "Ensure that you have installed the latest boto3 package " "that contains the API for `bedrock-runtime-agent`." ) from e 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 def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: response = self.client.retrieve( retrievalQuery={"text": query.strip()}, knowledgeBaseId=self.knowledge_base_id, retrievalConfiguration=self.retrieval_config.dict(), ) results = response["retrievalResults"] documents = [] for result in results: content = result["content"]["text"] result.pop("content") if "score" not in result: result["score"] = 0 if "metadata" in result: result["source_metadata"] = result.pop("metadata") documents.append( Document( page_content=content, metadata=result, ) ) return documents