j-&dZddlmZmZddlmZddlmZddlm Z ddl m Z ddl m Z ddlmZmZdd lmZmZmZdd lmZdd lmZd efd ZGddeZd ee fdZGddeZy)z'Vectorstore stubs for the indexing api.)AnyOptional) BaseLoader)Document) Embeddings)BaseLanguageModel) VectorStore)RecursiveCharacterTextSplitter TextSplitter) BaseModel ConfigDictField)RetrievalQAWithSourcesChain) RetrievalQAreturnctddS)z=Return the default text splitter used for chunking documents.ir) chunk_size chunk_overlap)r g/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain/indexes/vectorstore.py_get_default_text_splitterrs )T KKrc eZdZUdZeed<eddZ ddede e d e e ee fd e d ef d Z ddede e d e e ee fd e d ef d Z ddede e d e e ee fd e d e f dZ ddede e d e e ee fd e d e f dZy)VectorStoreIndexWrapperz-Wrapper around a vectorstore for easy access. vectorstoreTforbidarbitrary_types_allowedextraNquestionllmretriever_kwargskwargsrc | d}t||xsi}tj|fd|jjdi|i|}|j |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. 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)NotImplementedErrorrfrom_chain_typer as_retrieverinvoke input_key output_keyselfr r!r"r#msgchains rqueryzVectorStoreIndexWrapper.query s$ ;. &c* *+1r++  3d&&33G6FG   ||U__h789I9IJJrc K| d}t||xsi}tj|fd|jjdi|i|}|j |j |id{|jS7w)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.aqueryCs$ ;. &c* *+1r++  3d&&33G6FG   mmU__h$?@@%BRBRSS@sA(A>*A<+A>c | d}t||xsi}tj|fd|jjdi|i|}|j |j |iS)aQuery the vectorstore and retrieve the answer along with sources. Args: question: The question or prompt to query. llm: The language model to use. Must not be None. retriever_kwargs: Optional keyword arguments for the retriever. **kwargs: Additional keyword arguments forwarded to the chain. Returns: A dictionary containing the answer and source documents. r%r&r)r'rr(rr)r* question_keyr-s rquery_with_sourcesz*VectorStoreIndexWrapper.query_with_sourcesfs$ ;. &c* *+1r+;;  3d&&33G6FG   ||U//:;;rc K| d}t||xsi}tj|fd|jjdi|i|}|j |j |id{S7w)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: A dictionary containing the answer and source documents. Nr%r&r)r'rr(rr)r3r6r-s raquery_with_sourcesz+VectorStoreIndexWrapper.aquery_with_sourcess$ ;. &c* *+1r+;;  3d&&33G6FG   ]]E$6$6#ABBBBsA(A1*A/+A1)NN)__name__ __module__ __qualname____doc__r __annotations__r model_configstrrrdictrr1r4r7r9rrrrrsp7 $L,059 !K!K' (!K#4S>2 !K  !K !KL,059 !T!T' (!T#4S>2 !T  !T !TL,059 !<!<' (!<#4S>2 !<  !<  !2 !C  !C  !Crrcddl} ddlm}|j dd|S#t$r}d}t||d}~wwxYw)zGet the InMemoryVectorStore.rN)InMemoryVectorStorezBPlease install langchain-community to use the InMemoryVectorStore.zUsing InMemoryVectorStore as the default vectorstore.This memory store won't persist data. You should explicitlyspecify a vectorstore when using VectorstoreIndexCreator) stacklevel)warnings)langchain_community.vectorstores.inmemoryrC ImportErrorwarn)rFrCer/s r_get_in_memory_vectorstorerKsW&Q MM C   &R#A%&s! =8=ceZdZUdZeeZeee d<e e d<ee Z e e d<eeZee d<edd Zd eed efd Zd eed efd Zdeed efdZdeed efdZy)VectorstoreIndexCreatorzLogic for creating indexes.)default_factoryvectorstore_cls embedding text_splittervectorstore_kwargsTrrloadersrctg}|D]!}|j|j#|j|S)zCreate a vectorstore index from a list of loaders. Args: loaders: A list of `BaseLoader` instances to load documents. Returns: A `VectorStoreIndexWrapper` containing the constructed vectorstore. )extendloadfrom_documents)r.rSdocsloaders r from_loadersz$VectorstoreIndexCreator.from_loaderss6F KK &""4((rc Kg}|D],}|j|j2cgc3d{}| |j|d{S7!6ncc}wc}Y7w)aAsynchronously create a vectorstore index from a list of loaders. Args: loaders: A list of `BaseLoader` instances to load documents. Returns: A `VectorStoreIndexWrapper` containing the constructed vectorstore. N)rU alazy_loadafrom_documents)r.rSrXrYdocs r afrom_loadersz%VectorstoreIndexCreator.afrom_loaderss[F KKf.?.?.ABBs))$///CBB C/sA%A"A A AA A A" A  A"A A A" documentsc|jj|}|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)rQsplit_documentsrOrWrPrRrr.r`sub_docsrs rrWz&VectorstoreIndexCreator.from_documentssY%%55i@9d**99  NN %% ';??rcK|jj|}|jj||jfi|j d{}t |S7w)zAsynchronously create a vectorstore index from a list of documents. Args: documents: A list of `Document` objects. Returns: A `VectorStoreIndexWrapper` containing the constructed vectorstore. Nrb)rQrcrOr]rPrRrrds rr]z'VectorstoreIndexCreator.afrom_documentssg%%55i@@D00@@  NN %%  ';??  sAA&A$A&N)r:r;r<r=rrKrOtyper r>rrrQr rArRr r?listrrrZr_rrWr]rrrrMrMs%).2*OT+&"'8R"SM<S$T:: $L )D$4 )9P ) 04 +; 0@W 0@X@;R@"@>@ !@rrMN)r=typingrrlangchain_core.document_loadersrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.language_modelsrlangchain_core.vectorstoresr langchain_text_splittersr r pydanticr r r*langchain.chains.qa_with_sources.retrievalr"langchain.chains.retrieval_qa.baserrrrgrKrMrrrrssi- 6-0<3Q11R:LLL TCiTCnD$5$N@iN@r