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dd�ZdS )�VectorStoreIndexWrapperz-Wrapper around a vectorstore for easy access.� vectorstoreT�forbid��arbitrary_types_allowed�extraN�question�llm�retriever_kwargs�kwargsr c K sR |du rt d��|p i }tj|fd| jjdi |��i|��}|�|j|i�|j S )a� Query the vectorstore using the provided LLM.
Args:
question: The question or prompt to query.
llm: The language model to use. Must not be None.
retriever_kwargs: Optional keyword arguments for the retriever.
**kwargs: Additional keyword arguments forwarded to the chain.
Returns:
The result string from the RetrievalQA chain.
N��This API has been changed to require an LLM. Please provide an llm to use for querying the vectorstore.
For example,
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)� retrieverr )�NotImplementedErrorr �from_chain_typer � as_retriever�invoke� input_key�
output_key��selfr r r! r" �chainr r r �query s ����z VectorStoreIndexWrapper.queryc � sZ �|du r t d��|p i }tj|fd| jjdi |��i|��}|�|j|i�I dH |j S )a� Asynchronously query the vectorstore using the provided LLM.
Args:
question: The question or prompt to query.
llm: The language model to use. Must not be None.
retriever_kwargs: Optional keyword arguments for the retriever.
**kwargs: Additional keyword arguments forwarded to the chain.
Returns:
The asynchronous result string from the RetrievalQA chain.
Nr# r$ r )r% r r& r r' �ainvoker) r* r+ r r r �aquery@ s ����� z VectorStoreIndexWrapper.aqueryc K sL |du rt d��|p i }tj|fd| jjdi |��i|��}|�|j|i�S )a� Query the vectorstore and retrieve the answer along with sources.
Args:
question: The question or prompt to query.
llm: The language model to use. Must not be None.
retriever_kwargs: Optional keyword arguments for the retriever.
**kwargs: Additional keyword arguments forwarded to the chain.
Returns:
A dictionary containing the answer and source documents.
Nr# r$ r )r% r r& r r' r( � question_keyr+ r r r �query_with_sources` s ����z*VectorStoreIndexWrapper.query_with_sourcesc � sT �|du r t d��|p i }tj|fd| jjdi |��i|��}|�|j|i�I dH S )a� Asynchronously query the vectorstore and retrieve the answer and sources.
Args:
question: The question or prompt to query.
llm: The language model to use. Must not be None.
retriever_kwargs: Optional keyword arguments for the retriever.
**kwargs: Additional keyword arguments forwarded to the chain.
Returns:
A dictionary containing the answer and source documents.
Nr# r$ r )r% r r& r r' r/ r1 r+ r r r �aquery_with_sources� s �����z+VectorStoreIndexWrapper.aquery_with_sources)NN)�__name__�
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�#������r c C s> ddl } zddlm} W n ty td��w | �d� |S )z Get the InMemoryVectorStore.r N)�InMemoryVectorStorezBPlease install langchain-community to use the InMemoryVectorStore.z�Using InMemoryVectorStore as the default vectorstore.This memory store won't persist data. You should explicitlyspecify a vectorstore when using VectorstoreIndexCreator)�warnings�)langchain_community.vectorstores.inmemoryr< � ImportError�warn)r= r< r r r �_get_in_memory_vectorstore� s ���rA c @ s� e Zd ZU dZeed�Zee e d< e
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ee d efdd�Zdee d efdd�Zdee d efdd�ZdS )�VectorstoreIndexCreatorzLogic for creating indexes.)�default_factory�vectorstore_cls� embedding�
text_splitter�vectorstore_kwargsTr r �loadersr c C s& g }|D ] }|� |�� � q| �|�S )z�Create a vectorstore index from a list of loaders.
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loaders: A list of `BaseLoader` instances to load documents.
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z$VectorstoreIndexCreator.from_loadersc � sB �g }|D ]}|� � 2 z 3 dH W }|�|� q 6 q| �|�I dH S )a Asynchronously create a vectorstore index from a list of loaders.
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loaders: A list of `BaseLoader` instances to load documents.
Returns:
A `VectorStoreIndexWrapper` containing the constructed vectorstore.
N)�
alazy_load�append�afrom_documents)r, rH rL rM �docr r r �
afrom_loaders� s � �z%VectorstoreIndexCreator.afrom_loaders� documentsc C s0 | j �|�}| jj|| jfi | j��}t|d�S )z�Create a vectorstore index from a list of documents.
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documents: A list of `Document` objects.
Returns:
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�r )rF �split_documentsrD rK rE rG r �r, rT �sub_docsr r r r rK � s ��
z&VectorstoreIndexCreator.from_documentsc � s8 �| j �|�}| jj|| jfi | j��I dH }t|d�S )z�Asynchronously create a vectorstore index from a list of documents.
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documents: A list of `Document` objects.
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A `VectorStoreIndexWrapper` containing the constructed vectorstore.
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�pydanticr r r �*langchain.chains.qa_with_sources.retrievalr �"langchain.chains.retrieval_qa.baser r r rA rB r r r r �<module> s