langchain/libs/community/langchain_community/retrievers/bedrock.py

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


class VectorSearchConfig(BaseModel, extra="allow"):  # type: ignore[call-arg]
    """Configuration for vector search."""

    numberOfResults: int = 4


class RetrievalConfig(BaseModel, extra="allow"):  # type: ignore[call-arg]
    """Configuration for retrieval."""

    vectorSearchConfiguration: VectorSearchConfig


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