
    Ng                         d dl mZmZmZmZ d dlmZ d dlmZ d dl	m
Z
 d dlmZmZ  G d ded	          Z G d
 ded	          Z G d de
          ZdS )    )AnyDictListOptional)CallbackManagerForRetrieverRun)Document)BaseRetriever)	BaseModelmodel_validatorc                   "    e Zd ZU dZdZeed<   dS )VectorSearchConfigz Configuration for vector search.   numberOfResultsN)__name__
__module____qualname____doc__r   int__annotations__     b/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/retrievers/bedrock.pyr   r   	   s(         **OSr   r   allow)extrac                       e Zd ZU dZeed<   dS )RetrievalConfigzConfiguration for retrieval.vectorSearchConfigurationN)r   r   r   r   r   r   r   r   r   r   r      s$         &&111111r   r   c                       e Zd ZU dZeed<   dZee         ed<   dZee         ed<   dZ	ee         ed<   e
ed<   eed<    ed	
          edeee
f         de
fd                        Zdededee         fdZdS )AmazonKnowledgeBasesRetrievera	  Amazon Bedrock Knowledge Bases retriever.

    See https://aws.amazon.com/bedrock/knowledge-bases for more info.

    Setup:
        Install ``langchain-aws``:

        .. code-block:: bash

            pip install -U langchain-aws

    Key init 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.

    Instantiate:
        .. code-block:: python

            from langchain_community.retrievers import AmazonKnowledgeBasesRetriever

            retriever = AmazonKnowledgeBasesRetriever(
                knowledge_base_id="<knowledge-base-id>",
                retrieval_config={
                    "vectorSearchConfiguration": {
                        "numberOfResults": 4
                    }
                },
            )

    Usage:
        .. code-block:: python

            query = "..."

            retriever.invoke(query)

    Use within a chain:
        .. code-block:: python

            from langchain_aws import ChatBedrockConverse
            from langchain_core.output_parsers import StrOutputParser
            from langchain_core.prompts import ChatPromptTemplate
            from langchain_core.runnables import RunnablePassthrough
            from langchain_openai import ChatOpenAI

            prompt = ChatPromptTemplate.from_template(
                """Answer the question based only on the context provided.

            Context: {context}

            Question: {question}"""
            )

            llm = ChatBedrockConverse(
                model_id="anthropic.claude-3-5-sonnet-20240620-v1:0"
            )

            def format_docs(docs):
                return "\n\n".join(doc.page_content for doc in docs)

            chain = (
                {"context": retriever | format_docs, "question": RunnablePassthrough()}
                | prompt
                | llm
                | StrOutputParser()
            )

            chain.invoke("...")

    knowledge_base_idNregion_namecredentials_profile_nameendpoint_urlclientretrieval_configbefore)modevaluesreturnc                 J   |                     d          |S 	 dd l}ddlm} ddlm} |                     d          r|                    |d                   }n|                                }d |ddd	di
          i}|                     d          r|d         |d<   |                     d          r|d         |d<    |j        di ||d<   |S # t          $ r t          d          |$ r}t          d          |d }~wt          $ r}t          d          |d }~ww xY w)Nr$   r   )Config)UnknownServiceErrorr"   )profile_nameconfigx   max_attempts)connect_timeoutread_timeoutretriesr!   r#   bedrock-agent-runtimezRCould not import boto3 python package. Please install it with `pip install boto3`.zjEnsure that you have installed the latest boto3 package that contains the API for `bedrock-runtime-agent`.zCould not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid.)r4   )getboto3botocore.clientr+   botocore.exceptionsr,   Sessionr$   ImportError	Exception
ValueError)clsr(   r6   r+   r,   sessionclient_paramses           r   create_clientz+AmazonKnowledgeBasesRetriever.create_clientl   s    ::h+M(	LLL......??????zz455 *--V<V5W-XX  --// &&$'cNTUCV  M
 zz-(( E/5m/Dm,zz.)) G06~0Fn--w~WWWWF8M 	 	 	>   # 	 	 	E    	 	 	*  		s$   B9C D"0D  D"DD"queryrun_managerc                   | j                             d|                                i| j        | j                                                  }|d         }g }|D ]n}|d         d         }|                    d           d|vrd|d<   d|v r|                    d          |d<   |                    t          ||	                     o|S )
Ntext)retrievalQueryknowledgeBaseIdretrievalConfigurationretrievalResultscontentscorer   metadatasource_metadata)page_contentrL   )	r$   retrievestripr    r%   dictpopappendr   )selfrB   rC   responseresults	documentsresultrJ   s           r   _get_relevant_documentsz5AmazonKnowledgeBasesRetriever._get_relevant_documents   s     ;''"EKKMM2 2#'#8#=#=#?#? ( 
 

 -.	 	 	FY'/GJJy!!!f$$"#wV##,2JJz,B,B()!(#      r   )r   r   r   r   strr   r!   r   r"   r#   r   r   r   classmethodr   rA   r   r   r   rY   r   r   r   r   r      s         M M^ !%K#%%%.2hsm222"&L(3-&&&KKK%%%%_(###,4S> ,c , , , [ $#,\*H	h     r   r   N)typingr   r   r   r   langchain_core.callbacksr   langchain_core.documentsr   langchain_core.retrieversr	   pydanticr
   r   r   r   r   r   r   r   <module>ra      s   , , , , , , , , , , , , C C C C C C - - - - - - 3 3 3 3 3 3 / / / / / / / /    '    2 2 2 2 2iw 2 2 2 2_ _ _ _ _M _ _ _ _ _r   