o Zh%@sVddlmZddlZddlZddlZddlZddlmZmZddl m Z ddl m Z m Z mZmZmZmZmZddlZddlmZddlmZddlmZdd lmZmZdd lmZmZdd l m!Z!e"e#Z$d Z%d Z&dZ'eGdddZ(eGdddZ)eGdddZ*eGdddZ+GdddeZ,GdddeZ-GdddeZ.dS)) annotationsN) dataclassfield)md5)AnyIterableIteratorListOptionalTupleType)CallbackManagerForRetrieverRunDocument) Embeddings)RunnableRunnableConfig) VectorStoreVectorStoreRetriever) ConfigDictivAivAivAc@sNeZdZUdZdZded<dZded<dZd ed <d Zd ed <dZ ded <dS) SummaryConfigajConfiguration for summary generation. is_enabled: True if summary is enabled, False otherwise max_results: maximum number of results to summarize response_lang: requested language for the summary prompt_name: name of the prompt to use for summarization (see https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer) Fbool is_enabledint max_resultsZengstr response_langz"vectara-summary-ext-24-05-med-omni prompt_namestreamN) __name__ __module__ __qualname____doc__r__annotations__rrrrr%r%_/var/www/html/lang_env/lib/python3.10/site-packages/langchain_community/vectorstores/vectara.pyrs    rc@s6eZdZUdZdZded<dZded<dZd ed <d S) MMRConfigaConfiguration for Maximal Marginal Relevance (MMR) search. This will soon be deprated in favor of RerankConfig. is_enabled: True if MMR is enabled, False otherwise mmr_k: number of results to fetch for MMR, defaults to 50 diversity_bias: number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to minimum diversity and 1 to maximum diversity. Defaults to 0.3. Note: diversity_bias is equivalent 1-lambda_mult where lambda_mult is the value often used in max_marginal_relevance_search() We chose to use that since we believe it's more intuitive to the user. Frr2rmmr_k333333?floatdiversity_biasN)r r!r"r#rr$r)r,r%r%r%r&r'.s   r'c@sBeZdZUdZdZded<dZded<dZd ed <d Zded <d S) RerankConfigaConfiguration for Reranker. reranker: "mmr", "rerank_multilingual_v1", "udf" or "none" rerank_k: number of results to fetch before reranking, defaults to 50 mmr_diversity_bias: for MMR only - a number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to minimum diversity and 1 to maximum diversity. Defaults to 0.3. Note: mmr_diversity_bias is equivalent 1-lambda_mult where lambda_mult is the value often used in max_marginal_relevance_search() We chose to use that since we believe it's more intuitive to the user. user_function: for UDF only - the user function to use for reranking. nonerrerankerr(rrerank_kr*r+mmr_diversity_bias user_functionN) r r!r"r#r/r$r0r1r3r%r%r%r&r-Cs    r-c@seZdZUdZdZded<dZded<dZd ed <d Zd ed <dZ ded<dZ ded<e e dZ ded<e edZded<      ddddZd S) VectaraQueryConfigaWConfiguration for Vectara query. k: Number of Documents to return. Defaults to 10. lambda_val: lexical match parameter for hybrid search. filter Dictionary of argument(s) to filter on metadata. For example a filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details. score_threshold: minimal score threshold for the result. If defined, results with score less than this value will be filtered out. n_sentence_before: number of sentences before the matching segment to add, defaults to 2 n_sentence_after: number of sentences before the matching segment to add, defaults to 2 rerank_config: RerankConfig configuration dataclass summary_config: SummaryConfig configuration dataclass rkr+ lambda_valr2rfilterNOptional[float]score_thresholdn_sentence_beforen_sentence_after)default_factoryr- rerank_configrsummary_confign_sentence_context Optional[int] mmr_configOptional[MMRConfig]Optional[SummaryConfig]Optional[RerankConfig]c Cs||_||_||_||_| r| |_nt|_|r%||_||_t dt n||_||_| r2| |_ dS|rFt d|j |jd|_ t dt dSt |_ dS)Nz[n_sentence_context is deprecated. Please use n_sentence_before and n_sentence_after insteadmmrr/r0r1z9MMRConfig is deprecated. Please use RerankConfig instead.)r6r8r9r;rArr=r>warningswarnDeprecationWarningr@r-r)r,) selfr6r8r9r;r=r>rBrDrAr@r%r%r&__init__ws:   zVectaraQueryConfig.__init__) r5r7r2Nr<r<NNNN)r6rr8r+r9rr;r:r=rr>rrBrCrDrErArFr@rG)r r!r"r#r6r$r8r9r;r=r>rr-r@rrArNr%r%r%r&r4Ys*       r4c@seZdZdZ     dXdYd dZedZddZd[ddZd\ddZd]d^ddZ d_d`d$d%Z d_dad+d,Z  dbdcd0d1Z  ddded7d8Z dfd:d;Zdgd2sz"Vectara.delete..T)all)rMrrsuccessr%rnr&delete(s zVectara.delete files_list Iterable[str] metadatasOptional[List[dict]] List[str]c Ksg}t|D]\}}tj|std|dq|r ||ni}|t|dft|d}| } | d|j j d|j d|jd|d | |jd } | jd krg| d d } td|d| dq| jdkrz| d d } || qtd|d| q|S)ac Vectara provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain Args: files_list: Iterable of strings, each representing a local file path. Files could be text, HTML, PDF, markdown, doc/docx, ppt/pptx, etc. see API docs for full list metadatas: Optional list of metadatas associated with each file Returns: List of ids associated with each of the files indexed zFile z does not exist, skippingrb)file doc_metadatarqz https://api.vectara.io/upload?c=z&o=z&d=TrueT)filesrxryrzrrZ documentIdz# already exists on Vectara (doc_id=z ), skippingr{zError indexing file z: ) enumeraterZpathexistsr`ropenr~rrrpoprfr}r]r^rVrinfoappend) rMrrrZdoc_idsZinxrmdrryrrsr%r%r& add_files7s8      zVectara.add_filestextsrOptional[dict]c Kst}|D] }||q|}|durdd|D}|r%d|d<nddi}|dd}|r3dnd } d |d t|| d dt||Di} |j| |d } | dkr`| ||| |gS| dkrht d|gS)aRun more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. doc_metadata: optional metadata for the document This function indexes all the input text strings in the Vectara corpus as a single Vectara document, where each input text is considered a "section" and the metadata are associated with each section. if 'doc_metadata' is provided, it is associated with the Vectara document. Returns: document ID of the document added NcSsg|]}iqSr%r%)r_r%r%r&rsz%Vectara.add_texts..rQrWrFpartssectionrv metadataJsoncSs g|] \}}|t|dqS))rr)r~r)rrrr%r%r&rs)rrrziNo permissions to add document to Vectara. 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Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 10. any other querying variable in VectaraQueryConfig like: - lambda_val: lexical match parameter for hybrid search. - filter: filter string - score_threshold: minimal score threshold for the result. - n_sentence_before: number of sentences before the matching segment - n_sentence_after: number of sentences after the matching segment - rerank_config: optional configuration for Reranking (see RerankConfig dataclass) - summary_config: optional configuration for summary (see SummaryConfig dataclass) Returns: List of Documents most similar to the query and score for each. Nr%)r4r)rMrrrdocsr%r%r&similarity_search_with_scoreGs z$Vectara.similarity_search_with_scoreList[Document]cKs |j|fi|}dd|DS)aReturn Vectara documents most similar to query, along with scores. Args: query: Text to look up documents similar to. any other querying variable in VectaraQueryConfig Returns: List of Documents most similar to the query cSg|]\}}|qSr%r%rrrr%r%r&ruz-Vectara.similarity_search..)r)rMrrdocs_and_scoresr%r%r&similarity_searchcs zVectara.similarity_searchr(?fetch_k lambda_multr+cKs(td|d|d|d<|j|fi|S)aSReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 5. fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 50 lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. kwargs: any other querying variable in VectaraQueryConfig Returns: List of Documents selected by maximal marginal relevance. rHrIr@)r-r)rMrrrrr%r%r&max_marginal_relevance_searchws  z%Vectara.max_marginal_relevance_searchcls Type[Vectara] embeddingcKs6|di}|di|}|j||fd|i||S)aConstruct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectara = Vectara.from_texts( texts, vectara_customer_id=customer_id, vectara_corpus_id=corpus_id, vectara_api_key=api_key, ) rNr%)rr)rrrrrrrr%r%r& from_textss zVectara.from_textsrcKs|di|}||||S)aConstruct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectara = Vectara.from_files( files_list, vectara_customer_id=customer_id, vectara_corpus_id=corpus_id, vectara_api_key=api_key, ) Nr%)r)rrrrrrr%r%r& from_filess zVectara.from_files VectaraRAGcCs t||S)zReturn a Vectara RAG runnable.rrMrr%r%r&as_rags zVectara.as_ragcCst||ddS)z'Return a Vectara RAG runnable for chat.T)rrr r%r%r&as_chatszVectara.as_chatVectaraRetrievercKst||dtdS)zreturn a retriever object.r) vectorstorer)r r\r4)rMrr%r%r& as_retrieverszVectara.as_retriever)NNNrPrQ) rRrSrTrSrUrSrVrrWr)rkrl)rkrp)rsrrkrF)rrprrrkrrm)rrrrrkr)rrrrrrrkr)NN) rrrrrrrrrkr)FN) rrrr4rrrrSrrrkrp)rrrr4rrrkr)rrrrrkr)rrrrrkr)r(r) rrrrrr+rrrkr) rrrrrrlrrrrrkrO) rrrrrrlrrrrrkrO)rr4rkr)rrrkr )r r!r"r#rNpropertyrorrrrrrrrrrrr classmethodrrr r rr%r%r%r&rOsN "     8 = S O    rOc@sDeZdZUdZded< ded< eddZdddZdddZdS)r zVectara Retriever class.rOr r4rT)Zarbitrary_types_allowedrr run_managerr rrrkrcKs&|jj||jfi|}dd|DS)NcSrr%r%rr%r%r&rrz.)r rr)rMrrrrr%r%r&_get_relevant_documentssz(VectaraRetriever._get_relevant_documentsrrcKs|jj|fi|S)zAdd documents to vectorstore.)r  add_documents)rMrrr%r%r&rszVectaraRetriever.add_documentsN)rrrr rrrkr)rrrrrkr) r r!r"r#r$rZ model_configrrr%r%r%r&r s  r c@s:eZdZdZ ddd d Z ddddZ ddddZd S)rzVectara RAG runnable. Parameters: vectara: Vectara object config: VectaraQueryConfig object chat: bool, default False FrrOrr4rrcCs||_||_||_d|_dSrm)rrrconv_id)rMrrrr%r%r&rNs zVectaraRAG.__init__NinputrOptional[RunnableConfig]rrrkIterator[dict]c +sj|jjj}jjjjdt |jj dd}|j dkr;t dd|j d|jd|jd d Sg}g}d |iV|D]}|rGt|d } | d } | d} | d ur| dd } | d urlqHt| ddkrt d| dddqH| dd } | r| dd r| d}t d||dkrd _t dqH| r| dd nd }|r|_| dd r| didd }d|iVqHt| d}d|iVqHjjrfdd| d D}n| d }| d!}g}|D]-}d"d#|d$D}|d%}d&d#||d$D}d'|vrd(|d'<||||qd)dt||D}jjjd*vrB|d jj}d+|iVqHd S),aGet streaming output from Vectara RAG. 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