a bg%@sVddlmZddlZddlZddlZddlZddlmZmZddl m Z ddl m Z m Z mZmZmZmZmZddlZddlmZddlmZddlmZdd lmZmZdd lmZmZdd l m!Z!e"e#Z$d Z%d Z&dZ'eGdddZ(eGdddZ)eGdddZ*eGdddZ+GdddeZ,GdddeZ-GdddeZ.dS)) annotationsN) dataclassfield)md5)AnyIterableIteratorListOptionalTupleType)CallbackManagerForRetrieverRunDocument) Embeddings)RunnableRunnableConfig) VectorStoreVectorStoreRetriever) ConfigDictivAivAivAc@sNeZdZUdZdZded<dZded<dZd ed <d Zd ed <dZ ded <dS) SummaryConfigajConfiguration for summary generation. is_enabled: True if summary is enabled, False otherwise max_results: maximum number of results to summarize response_lang: requested language for the summary prompt_name: name of the prompt to use for summarization (see https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer) Fbool is_enabledint max_resultsZengstr response_langz"vectara-summary-ext-24-05-med-omni prompt_namestreamN) __name__ __module__ __qualname____doc__r__annotations__rrrrr%r%v/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/vectorstores/vectara.pyrs     rc@s6eZdZUdZdZded<dZded<dZd ed <d S) MMRConfigaConfiguration for Maximal Marginal Relevance (MMR) search. This will soon be deprated in favor of RerankConfig. is_enabled: True if MMR is enabled, False otherwise mmr_k: number of results to fetch for MMR, defaults to 50 diversity_bias: number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to minimum diversity and 1 to maximum diversity. Defaults to 0.3. Note: diversity_bias is equivalent 1-lambda_mult where lambda_mult is the value often used in max_marginal_relevance_search() We chose to use that since we believe it's more intuitive to the user. Frr2rmmr_k333333?floatdiversity_biasN)r r!r"r#rr$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$r0r1r3r%r%r%r&r-Cs    r-c @seZdZUdZdZded<dZded<dZd ed <d Zd ed <dZ ded<dZ ded<e e dZ ded<e edZded<dddd d ddddddd ddZd S)VectaraQueryConfigaWConfiguration for Vectara query. k: Number of Documents to return. Defaults to 10. lambda_val: lexical match parameter for hybrid search. filter Dictionary of argument(s) to filter on metadata. For example a filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details. score_threshold: minimal score threshold for the result. If defined, results with score less than this value will be filtered out. n_sentence_before: number of sentences before the matching segment to add, defaults to 2 n_sentence_after: number of sentences before the matching segment to add, defaults to 2 rerank_config: RerankConfig configuration dataclass summary_config: SummaryConfig configuration dataclass rkr+ lambda_valr2rfilterNzOptional[float]score_thresholdn_sentence_beforen_sentence_after)default_factoryr- rerank_configrsummary_configz Optional[int]zOptional[MMRConfig]zOptional[SummaryConfig]zOptional[RerankConfig]) r6r8r9r:r<r=n_sentence_context mmr_configr@r?c Cs||_||_||_||_| r$| |_nt|_|rJ||_||_t dt n ||_||_| rb| |_ n.|rt d|j |jd|_ t dt nt |_ 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:r@rr<r=warningswarnDeprecationWarningr?r-r)r,) selfr6r8r9r:r<r=rArBr@r?r%r%r&__init__ws: zVectaraQueryConfig.__init__) r5r7r2Nr;r;NNNN)r r!r"r#r6r$r8r9r:r<r=rr-r?rr@rIr%r%r%r&r4Ys(       r4c @seZdZdZdOddddddd d Zed d d dZdd ddZdddddZdPddddddZ dQddddddZ dRd d!dd"d#d$d%Z dSd d!d&dd"d'd(d)Z dTdd*ddddd+d,d-Z dd*dd.d/d0d1Zddd.d2d3d4Zddd5d2d6d7ZdUddd:dd5d;dd"d d!ddd?d@dAZedWd>d"d d!dddBdCdDZd*dEdFdGdHZd*dEdFdIdJZddKdLdMdNZdS)XVectaraa`Vectara API` vector store. See (https://vectara.com). Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectorstore = Vectara( vectara_customer_id=vectara_customer_id, vectara_corpus_id=vectara_corpus_id, vectara_api_key=vectara_api_key ) Nx langchainz Optional[str]rr)vectara_customer_idvectara_corpus_idvectara_api_keyvectara_api_timeoutsourcecCs|ptjd|_|p tjd|_|p2tjd|_|jdusT|jdusT|jdur`tdntd|j||_ t |_ t j jdd}|j d |||_dS) zInitialize with Vectara API.ZVECTARA_CUSTOMER_IDZVECTARA_CORPUS_IDZVECTARA_API_KEYNzHCan't find Vectara credentials, customer_id or corpus_id in environment.zUsing corpus id ) max_retrieszhttp://)osenvironget_vectara_customer_id_vectara_corpus_id_vectara_api_keyloggerwarningdebug_sourcerequestsSession_sessionadapters HTTPAdaptermountrP)rHrMrNrOrPrQadapterr%r%r&rIs,   zVectara.__init__zOptional[Embeddings])returncCsdSNr%rHr%r%r& embeddingsszVectara.embeddingsdictcCs|j|jd|jdS)z=Returns headers that should be attached to each post request.zapplication/json)z x-api-keyz customer-id Content-TypezX-Source)rYrWr]rgr%r%r&_get_post_headerss zVectara._get_post_headersr)doc_idrec Csl|j|j|d}|jjdt|d||jd}|jdkrht d|d|jd|j d |j d SdS) z Delete a document from the Vectara corpus. Args: doc_id (str): ID of the document to delete. Returns: bool: True if deletion was successful, False otherwise. ) customer_id corpus_id document_idz$https://api.vectara.io/v1/delete-docT)dataverifyheaderstimeoutz#Delete request failed for doc_id = z with status code , reason z, text F) rWrXr`postjsondumpsrkrP status_coderZerrorreasontext)rHrlbodyresponser%r%r& _delete_docs.  zVectara._delete_docF)doc use_core_apirec Csi}|j|d<|j|d<||d<|r(dnd}|jj||t||jdd}|j}|}d|vrp|dd nd}|d ks|r|d krd S|r|d krdSdSdS)Nrmrndocumentz$https://api.vectara.io/v1/core/indexzhttps://api.vectara.io/v1/indexT)rrurlrprsrqstatuscodeZALREADY_EXISTSE_ALREADY_EXISTS FORBIDDENE_NO_PERMISSIONSZ E_SUCCEEDED) rWrXr`rvrkrwrxrPry) rHrrrequestZ api_endpointr~ryresultZ status_strr%r%r& _index_doc s.   zVectara._index_doczOptional[List[str]]rzOptional[bool])idskwargsrec s&|rfdd|D}t|SdSdS)zDelete by vector ID or other criteria. Args: ids: List of ids to delete. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. csg|]}|qSr%)r).0idrgr%r& 2z"Vectara.delete..TN)all)rHrrsuccessr%rgr&delete(s zVectara.deletez Iterable[str]zOptional[List[dict]] List[str]) files_list metadatasrrec Ksg}t|D]\}}tj|s6td|dq |rB||ni}|t|dft|d}| } | d|j j d|j d|jd|d | |jd } | jd kr| d d } td|d| dq | jdkr| d d } || q td|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_metadatarjz https://api.vectara.io/upload?c=z&o=z&d=TrueT)filesrqrrrsrrZ documentIdz# already exists on Vectara (doc_id=z ), skippingrtzError indexing file z: ) enumeraterTpathexistsrZrzopenrwrxrkpopr`rvrWrXrPryinfoappend) rHrrrZdoc_idsZinxrmdrrrr~rlr%r%r& add_files7s8      zVectara.add_fileszOptional[dict])textsrrrrec Kst}|D]}||q |}|dur.rLrQrFpartssectionro metadataJsoncSs g|]\}}|t|dqS))r|r)rwrx)rr|rr%r%r&rs)rrrziNo permissions to add document to Vectara. 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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. )r4r)rHrrrdocsr%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. 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Defaults to 0.5. kwargs: any other querying variable in VectaraQueryConfig Returns: List of Documents selected by maximal marginal relevance. rCrDr?)r-r)rHrrrrr%r%r&max_marginal_relevance_searchws z%Vectara.max_marginal_relevance_searchz Type[Vectara])clsr embeddingrrrecKs6|di}|fi|}|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, ) r)rr)rrrrrrrr%r%r& from_textss zVectara.from_texts)rrrrrrecKs|fi|}||||S)aConstruct Vectara wrapper from raw documents. This is intended to be a quick way to get started. 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