tj<&dZddlmZddlmZddlmZddlmZddl m Z ddl m Z ddl mZmZdd lmZmZmZdd lmZdd lmZd efd ZGddeZd ee fdZGddeZdS)z'Vectorstore stubs for the indexing api.)Any) BaseLoader)Document) Embeddings)BaseLanguageModel) VectorStore)RecursiveCharacterTextSplitter TextSplitter) BaseModel ConfigDictField)RetrievalQAWithSourcesChain) RetrievalQAreturnc$tddS)z=Return the default text splitter used for chunking documents.ir) chunk_size chunk_overlap)r c/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_classic/indexes/vectorstore.py_get_default_text_splitterrs )T K K KKrc 0eZdZUdZeed<eddZ ddede dzd e ee fdzd e d ef d Z ddede dzd e ee fdzd e d ef d Z ddede dzd e ee fdzd e d e f dZ ddede dzd e ee fdzd e d e f dZdS)VectorStoreIndexWrapperz/Wrapper around a `VectorStore` for easy access. vectorstoreTforbidarbitrary_types_allowedextraNquestionllmretriever_kwargskwargsrc |d}t||pi}tj|fd|jjdi|i|}||j|i|jS)aQuery the `VectorStore` using the provided LLM. Args: question: The question or prompt to query. llm: The language model to use. Must not be `None`. retriever_kwargs: Optional keyword arguments for the retriever. **kwargs: Additional keyword arguments forwarded to the chain. Returns: The result string from the RetrievalQA chain. NThis API has been changed to require an LLM. Please provide an llm to use for querying the vectorstore. For example, from langchain_openai import OpenAI model = OpenAI(temperature=0) retrieverr)NotImplementedErrorrfrom_chain_typer as_retrieverinvoke input_key output_keyselfrr r!r"msgchains rqueryzVectorStoreIndexWrapper.query"s$ ;0 &c** *+1r+   3d&3GG6FGG    ||U_h7889IJJrc K|d}t||pi}tj|fd|jjdi|i|}||j|id{V|jS)aAsynchronously 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$r%r)r&rr'rr(ainvoker*r+r,s raqueryzVectorStoreIndexWrapper.aqueryEs$ ;0 &c** *+1r+   3d&3GG6FGG    mmU_h$?@@@@@@@@%BRSSrc |d}t||pi}tj|fd|jjdi|i|}||j|iS)aQuery the `VectorStore` and retrieve the answer along with sources. Args: question: The question or prompt to query. llm: The language model to use. Must not be `None`. retriever_kwargs: Optional keyword arguments for the retriever. **kwargs: Additional keyword arguments forwarded to the chain. Returns: `dict` containing the answer and source documents. Nr$r%r)r&rr'rr(r) question_keyr,s rquery_with_sourcesz*VectorStoreIndexWrapper.query_with_sourceshs$ ;0 &c** *+1r+;   3d&3GG6FGG    ||U/:;;;rc K|d}t||pi}tj|fd|jjdi|i|}||j|id{VS)aAsynchronously 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: `dict` containing the answer and source documents. Nr$r%r)r&rr'rr(r2r5r,s raquery_with_sourcesz+VectorStoreIndexWrapper.aquery_with_sourcess$ ;0 &c** *+1r+;   3d&3GG6FGG    ]]E$6#ABBBBBBBBBr)NN)__name__ __module__ __qualname____doc__r__annotations__r model_configstrrdictrr0r3r6r8rrrrrs99: $L)-26 !K!K!K %!KsCx.4/ !K  !K !K!K!K!KL)-26 !T!T!T %!TsCx.4/ !T  !T !T!T!T!TL)-26 !<!<!< %!<sCx.4/ !<  !<  !<!<!<!z9VectorstoreIndexCreator.afrom_loaders..s.BBBBBBBBsBBBBsN)rT alazy_loadafrom_documentsrWs r afrom_loadersz%VectorstoreIndexCreator.afrom_loaderss D DF KKBBf.?.?.A.ABBBBBBBBB C C C C))$/////////r documentsc|j|}|jj||jfi|j}t |S)zCreate a `VectorStore` index from a list of documents. Args: documents: A list of `Document` objects. Returns: A `VectorStoreIndexWrapper` containing the constructed vectorstore. r)rPsplit_documentsrNrVrOrQrr-rcsub_docsrs rrVz&VectorstoreIndexCreator.from_documentss^%55i@@9d*9  N  %  ';????rcK|j|}|jj||jfi|jd{V}t |S)zAsynchronously create a `VectorStore` index from a list of documents. Args: documents: A list of `Document` objects. Returns: A `VectorStoreIndexWrapper` containing the constructed vectorstore. Nre)rPrfrNrarOrQrrgs rraz'VectorstoreIndexCreator.afrom_documentss%55i@@@D0@  N  %        ';????rN)r9r:r;r<r rJrNtyperr=rrrPr r@rQr r>listrrrZrbrrVrarrrrLrLs`%%).2***OT+&"'%8R"S"S"SM<SSS$uT::::::: $L )D$4 )9P ) ) ) ) 04 +; 0@W 0 0 0 0@X@;R@@@@"@>@ !@@@@@@rrLN)r<typingrlangchain_core.document_loadersrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.language_modelsrlangchain_core.vectorstoresrlangchain_text_splittersr r pydanticr r r 2langchain_classic.chains.qa_with_sources.retrievalr*langchain_classic.chains.retrieval_qa.baserrrrjrJrLrrrrvs--666666------000000<<<<<<333333QQQQQQQQ1111111111CBBBBBLLLLLL TCTCTCTCTCiTCTCTCnD$5$N@N@N@N@N@iN@N@N@N@N@r