NgڽddlZddlZddlmZmZddlmZmZmZm Z m Z m Z m Z m Z mZmZddlZddlmZddlmZddlmZddlmZddlmZmZerdd lmZeje Z!Gd d eZ"ed d dGdde"Z#ed d dGdde"Z$ed d dGdde"Z%ed d dGddeZ&dS)N)ABCabstractmethod) TYPE_CHECKINGAnyCallableDictIterableListLiteralOptionalTupleUnion) deprecated)Document) Embeddings) VectorStore)DistanceStrategymaximal_marginal_relevance) ElasticsearchceZdZdZedeeedfdeedfde de deded ee d ee dfd e fd Z ed ee dfded ee dfd e fdZdddeded dfdZd efdZdS)BaseRetrievalStrategyz4Base class for `Elasticsearch` retrieval strategies. query_vectorNquerykfetch_kvector_query_field text_fieldfilter similarityreturncdS)a Executes when a search is performed on the store. Args: query_vector: The query vector, or None if not using vector-based query. query: The text query, or None if not using text-based query. k: The total number of results to retrieve. fetch_k: The number of results to fetch initially. vector_query_field: The field containing the vector representations in the index. text_field: The field containing the text data in the index. filter: List of filter clauses to apply to the query. similarity: The similarity strategy to use, or None if not using one. Returns: Dict: The Elasticsearch query body. N selfrrrrrrrrs j/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/elasticsearch.pyrzBaseRetrievalStrategy.query% dims_lengthcdS)a  Executes when the index is created. Args: dims_length: Numeric length of the embedding vectors, or None if not using vector-based query. vector_query_field: The field containing the vector representations in the index. similarity: The similarity strategy to use, or None if not using one. Returns: Dict: The Elasticsearch settings and mappings for the strategy. Nr"r$r(rrs r%indexzBaseRetrievalStrategy.indexEr&r'clientrcdS)a Executes before the index is created. Used for setting up any required Elasticsearch resources like a pipeline. Args: client: The Elasticsearch client. text_field: The field containing the text data in the index. vector_query_field: The field containing the vector representations in the index. Nr"r$r,rrs r%before_index_setupz(BaseRetrievalStrategy.before_index_setup[r&r'cdS)a) Returns whether or not the strategy requires inference to be performed on the text before it is added to the index. Returns: bool: Whether or not the strategy requires inference to be performed on the text before it is added to the index. Tr"r$s r%require_inferencez'BaseRetrievalStrategy.require_inferenceis tr')__name__ __module__ __qualname____doc__rrr floatstrintdictrrrr+r/boolr2r"r'r%rr"s_>> DK-. S$Y        T  *D01     ^ > 39%   *D01      ^ *  %  36  LO        4      r'rz0.0.27z,Use class in langchain-elasticsearch packageT) alternativependingceZdZdZ ddeedeedeeeeffdZ d ee e dfd eedfd e d e d edede edee dfdefdZdee dfd edee dfdefdZdS)ApproxRetrievalStrategyz:Approximate retrieval strategy using the `HNSW` algorithm.NFTquery_model_idhybridrrfc0||_||_||_dSNr@rArB)r$r@rArBs r%__init__z ApproxRetrievalStrategy.__init__{s - r'rrrrrrrrr c \||||d} |r |js|| d<n(|r|jrd|j|di| d<ntd|jrd| dd|d |iiig|d id } t|jt r d |ji| d <n*t|jt r|jdurd ii| d <| Sd| iS)N)rfieldrnum_candidatesrtext_embeddingmodel_id model_textquery_vector_builderzZYou must provide an embedding function or a query_model_id to perform a similarity search.r;matchrmustr)knnrrBrankTrR)r@ ValueErrorrA isinstancerBr:r;) r$rrrrrrrrrR query_bodys r%rzApproxRetrievalStrategy.querysL'%      3 ".C   t*  $ 3"'##+C& ' 'B  ; !($.(/1&*"!#)   J$$(D)) 1&+TX%6 6""DHd++ 1D0@0@&+R[ 6" 3< r'r(c|tjurd}nF|tjurd}n5|tjurd}n$|tjurd}nt d|ddd|d |d |d iiiS) /Create the mapping for the Elasticsearch index.cosinel2_norm dot_productmax_inner_product Similarity  not supported.mappings properties dense_vectorT)typedimsr+r)rCOSINEEUCLIDEAN_DISTANCE DOT_PRODUCTMAX_INNER_PRODUCTrT)r$r(rrsimilarityAlgos r%r+zApproxRetrievalStrategy.indexs )0 0 0%NN +> > >&NN +7 7 7*NN += = =0NNF:FFFGG G & . +!%&4 ))   r'NFT)r3r4r5r6r r8r;rr:rFr r7r9rrrr+r"r'r%r?r?usaED)-!&+/      eD$J' (    D DK-.D S$YD   D  D  D D T D *D01D  D D D D L 39%   *D01         r'r?ceZdZdZdeeedfdeedfdedededed eee dfd ee dfd e fd Z d eedfded ee dfd e fdZ dS)ExactRetrievalStrategyz8Exact retrieval strategy using the `script_score` query.rNrrrrrrrr c |tjurd|d} n=|tjurd|d} n(|tjurd|d} nt d|dd ii} |rd d |ii} d d | | d|iddiiS)Nz'cosineSimilarity(params.query_vector, 'z') + 1.0z&1 / (1 + l2norm(params.query_vector, 'z'))z= double value = dotProduct(params.query_vector, 'z?'); return sigmoid(1, Math.E, -value); r]r^ match_allr;rr script_scorer)sourceparams)rscript)rrdrerfrT) r$rrrrrrrrrh queryBools r%rzExactRetrievalStrategy.querys )0 0 0V:LVVV N+> > >P9KPPP N+7 7 7=ONN F:FFFGG G&+  5(F!34I &"0#1<"@!!  r'r(cdd|d|ddiiiS)rXr_r`raF)rbrcr+r"r*s r%r+zExactRetrievalStrategy.index!s4 & . +!&))  r')r3r4r5r6rr r7r8r9r:rrrr+r"r'r%rkrksCB) DK-.) S$Y)   )  )  ) ) d4j$&') *D01)  ) ) ) ) V 39%   *D01         r'rkceZdZdZddeefdZdeee dfdeedfde de d ed ed ee d ee dfd e fdZd efdZddd ed ed dfdZdee dfd ed ee dfd e fdZd efdZdS)SparseRetrievalStrategyz?Sparse retrieval strategy using the `text_expansion` processor.NrLc|pd|_dS)Nz.elser_model_1rL)r$rLs r%rFz SparseRetrievalStrategy.__init__<s 4$4 r'rrrrrrrrr c 4ddd|d|j|diig|diiS)Nrr;text_expansionz.tokensrKrPrwr#s r%rzSparseRetrievalStrategy.query?s\ -#5 > > >04 27A"A"/ %    r'c|jdS)N_sparse_embeddingrwr1s r%_get_pipeline_namez*SparseRetrievalStrategy._get_pipeline_name\s-2222r'r,rc |jrD|j|dd|j||didddiidigdSdS) Nz,Embedding pipeline for langchain vectorstore inferencerry results_fieldtokens)rL target_field field_mapinference_config)id description processors)rLingest put_pipeliner|r.s r%r/z*SparseRetrievalStrategy.before_index_setup_s =  M & &**,,J$(, ,>*4l)C 0?H2M1 &&   '       r'r(cHd|ddddiiiiid|idS)Nr`rrb rank_featuresdefault_pipeline)r_settings)r|r*s r%r+zSparseRetrievalStrategy.indexusM&$x&/1J&K),T-D-D-F-FG   r'cdS)NFr"r1s r%r2z)SparseRetrievalStrategy.require_inferencesur'rD)r3r4r5r6r r8rFrr r7r9r:rrrr|r/r+r;r2r"r'r%ruru6sJI55#5555 DK-. S$Y         T  *D01      :3C3333%36LO , 39%   *D01       "4r'ruceZdZdZddddddddddedd dedeeded d eed eed eed eedeedededeee j e j e j e j fdedeeeeffdZedefdZedddddddd eedeedeedeedeedeeeefdd fdZedeefdZ dRd ed!ed"ed#eeed$edeef d%Z dSd ed!ed"ed(ed)eeed$edeefd*Zed+edefd,Zdeegeffd-Z dTd ed!ed#eeed$edee!eeff d.Z" dTdeed!ed#eeed$edee!eeff d/Z# dUd eed!ed0e$eedfd"ed)eeed#eeed1eeee$edfgefd2eeegefd$edee!eeffd3Z% dVd5eeed6ee&d$edee&fd7Z' dWded8eeddfd9Z( dXd:e)ed;eeeedeed$edeefd?Z* dXd:e)edeed$edeefd@Z+ dXdAe)e!eeefdeed$edeefdBZ,e- dYd:eedeedeed$eddf dCZ.e dWdeed$eddfdDZ/e- dZdEeedeed>eed$eddf dFZ0ed[dHZ1e d\dJeedKee&dLee$ee&fddMfdNZe dWdOeeddPfdQZ2dS)]ElasticsearchStorea`Elasticsearch` vector store. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings vectorstore = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_url="http://localhost:9200" ) Args: index_name: Name of the Elasticsearch index to create. es_url: URL of the Elasticsearch instance to connect to. cloud_id: Cloud ID of the Elasticsearch instance to connect to. es_user: Username to use when connecting to Elasticsearch. es_password: Password to use when connecting to Elasticsearch. es_api_key: API key to use when connecting to Elasticsearch. es_connection: Optional pre-existing Elasticsearch connection. vector_query_field: Optional. Name of the field to store the embedding vectors in. query_field: Optional. Name of the field to store the texts in. strategy: Optional. Retrieval strategy to use when searching the index. Defaults to ApproxRetrievalStrategy. Can be one of ExactRetrievalStrategy, ApproxRetrievalStrategy, or SparseRetrievalStrategy. distance_strategy: Optional. Distance strategy to use when searching the index. Defaults to COSINE. Can be one of COSINE, EUCLIDEAN_DISTANCE, MAX_INNER_PRODUCT or DOT_PRODUCT. If you want to use a cloud hosted Elasticsearch instance, you can pass in the cloud_id argument instead of the es_url argument. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings vectorstore = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_cloud_id="" es_user="elastic", es_password="" ) You can also connect to an existing Elasticsearch instance by passing in a pre-existing Elasticsearch connection via the es_connection argument. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings from elasticsearch import Elasticsearch es_connection = Elasticsearch("http://localhost:9200") vectorstore = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_connection=es_connection ) ElasticsearchStore by default uses the ApproxRetrievalStrategy, which uses the HNSW algorithm to perform approximate nearest neighbor search. This is the fastest and most memory efficient algorithm. If you want to use the Brute force / Exact strategy for searching vectors, you can pass in the ExactRetrievalStrategy to the ElasticsearchStore constructor. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings vectorstore = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_url="http://localhost:9200", strategy=ElasticsearchStore.ExactRetrievalStrategy() ) Both strategies require that you know the similarity metric you want to use when creating the index. The default is cosine similarity, but you can also use dot product or euclidean distance. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.vectorstores.utils import DistanceStrategy vectorstore = ElasticsearchStore( "langchain-demo", embedding=OpenAIEmbeddings(), es_url="http://localhost:9200", distance_strategy="DOT_PRODUCT" ) Nvectortext) embedding es_connectiones_url es_cloud_ides_user es_api_key es_passwordr query_fielddistance_strategystrategy es_params index_namerrrrrrrrrrrrrc ||_||_| |_| |_| tjn t| |_| |_|Zt|j }| d| i| ||_ dS||'t|||||| |_ dSt!d)N user-agent)headers)rusernamepasswordcloud_idapi_keyrz{Either provide a pre-existing Elasticsearch connection, or valid credentials for creating a new connection.)rrrrrrdrrr:_headersupdateget_user_agentoptionsr,rconnect_to_elasticsearchrT)r$rrrrrrrrrrrrrrs r%rFzElasticsearchStore.__init__s0#$&"4!(  # #!"34  !  $=122G NNL$*=*=*?*?@ A A A'///@@DKKK  ;#:,EE $$"# FDKKKG r'r cddlm}d|S)Nr __version__zlangchain-py-vs/)langchain_communityrrs r%rz!ElasticsearchStore.get_user_agent2s"333333/+///r')rrrrrrrrrrc ddl}n#t$rtdwxYw|r|rtdi}|r|g|d<n|r||d<ntd|r||d<n |r |r||f|d<||||jd i|d d t ii} |n1#t$r$} t d | | d} ~ wwxYw|S) NrbCould not import elasticsearch python package. Please install it with `pip install elasticsearch`.z>Both es_url and cloud_id are defined. Please provide only one.hostsrz4Please provide either elasticsearch_url or cloud_id.r basic_authrrz#Error connecting to Elasticsearch: r") elasticsearch ImportErrorrTrrrrinfo Exceptionloggererror) rrrrrrrconnection_params es_clientes r%rz+ElasticsearchStore.connect_to_elasticsearch8s     F    h P -/  U*0 g & &  U,4 j ) )STT T  C+2 i ( (  C( C/7.B l +   $ $Y / / //M/    !#5#D#D#F#FG     NN        LLBqBB C C CG s !.C C1 C,,C1c|jSrD)rr1s r% embeddingszElasticsearchStore.embeddingsls ~r'2rrrrkwargsc >|jd||||d|}d|DS)aReturn Elasticsearch documents most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to knn num_candidates. filter: Array of Elasticsearch filter clauses to apply to the query. Returns: List of Documents most similar to the query, in descending order of similarity. )rrrrcg|]\}}|Sr"r").0doc_s r% z8ElasticsearchStore.similarity_search..s***Q***r'r")_search)r$rrrrrresultss r%similarity_searchz$ElasticsearchStore.similarity_searchpsH*$, 1gf  @F  +*'****r'? lambda_multfieldsc  jtdd}| jg}n&j|vr|jnd}j|}jd |||d| fd D} t tj|| ||} fd| D} |r| D]} | j j=| S) aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. lambda_mult (float): 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. fields: Other fields to get from elasticsearch source. These fields will be added to the document metadata. Returns: List[Document]: A list of Documents selected by maximal marginal relevance. Nz5You must provide an embedding function to perform MMRTF)rrrc:g|]\}}|jjSr")metadatar)rrrr$s r%rzDElasticsearchStore.max_marginal_relevance_search..s'WWWFC#,t'>?WWWr')rrc,g|]}|dS)rr")rigot_docss r%rzDElasticsearchStore.max_marginal_relevance_search..s!BBBA!QBBBr'r") rrTrappend embed_queryrrnparrayr)r$rrrrrr'remove_vector_query_field_from_metadataquery_embeddinggot_embeddingsselected_indices selected_docsrrs` @r%max_marginal_relevance_searchz0ElasticsearchStore.max_marginal_relevance_searchs78 > !TUU U26/ >-.FF  $F 2 2 MM$1 2 2 2 26; 3.44U;; 4< (GF  FL   XWWWhWWW6 H_ % %~;RS   CBBB1ABBB 2 :$ : :L!899r'scorec|SrDr")rs r% _identity_fnzElasticsearchStore._identity_fns r'c|jS)a The 'correct' relevance function may differ depending on a few things, including: - the distance / similarity metric used by the VectorStore - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) - embedding dimensionality - etc. Vectorstores should define their own selection based method of relevance. )rr1s r%_select_relevance_score_fnz-ElasticsearchStore._select_relevance_score_fns   r'c t|jtr|jjrt d|jd|||d|S)aReturn Elasticsearch documents most similar to query, along with scores. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Array of Elasticsearch filter clauses to apply to the query. Returns: List of Documents most similar to the query and score for each 1scores are currently not supported in hybrid mode)rrrr"rUrr?rArTr)r$rrrrs r%similarity_search_with_scorez/ElasticsearchStore.similarity_search_with_scoresW dm%< = = R$-BV RPQQ Qt|F%1VFFvFFFr'c t|jtr|jjrt d|jd|||d|S)aReturn Elasticsearch documents most similar to query, along with scores. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Array of Elasticsearch filter clauses to apply to the query. Returns: List of Documents most similar to the embedding and score for each r)rrrr"r)r$rrrrs r%1similarity_search_by_vector_with_relevance_scoreszDElasticsearchStore.similarity_search_by_vector_with_relevance_scoressW" dm%< = = R$-BV RPQQ Qt|QaQQ&QQQr'r custom_query doc_builderc >|g}d|vr|dj|vr|jjr||j|}j||||jj|pgj} t d| |)|| |} t d| j j ddj i| ||d} dtd tffd } |p| }g} | d d D]t}|D]I}||d vr=|djfvr2d|d vr i|d d<|d ||d d|<J| |||d fu| S)a8Return Elasticsearch documents most similar to query, along with scores. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. query_vector: Embedding to look up documents similar to. fetch_k: Number of candidates to fetch from each shard. Defaults to 50. fields: List of fields to return from Elasticsearch. Defaults to only returning the text field. filter: Array of Elasticsearch filter clauses to apply to the query. custom_query: Function to modify the Elasticsearch query body before it is sent to Elasticsearch. Returns: List of Documents most similar to the query and score for each Nr)rrrrrrrrz Query body: z&Calling custom_query, Query body now: r+)sizerohitr c|t|djd|ddS)N_sourcer) page_contentr)rgetr)rr$s r%default_doc_builderz7ElasticsearchStore._search..default_doc_builderCs@ ^//0@"EEY 3 r'hitsr_scorer")rrrrrrrrrdebugr,searchrrr)r$rrrrrrrrrrVresponserdocs_and_scoresrrHs` r%rzElasticsearchStore._searchsW: >F V # # MM* % % %  6 ) ) MM$* + + + > =e/L4H>55e<.s&777Ac$*,,''777r')rr(rr+rrrT) stats_onlyrzAdded z and failed to add z texts to indexz added texts z to indexzError adding texts: rrrzNo texts to add to index)rrrrrrr enumeraterrrr,rrrr r)r$rrrrrrrrrrrequestsr(rrrrequestsuccessfailedrr s r%__addzElasticsearchStore.__adds  B B B B B B B B B   F   "'R 777777 %  #!*Q-00 "  , ,?  -   !'' % %GAt'08y||bHG$/ $Hs1v G A3=a=/0 OOG $ $ $ $ x==1   "&$K# $+ ## " ## PWPPPPP :C:::;;; !    7A77888Xa[__Wb99==grJJ  NJNN84L4LNNOOO   LL3 4 4 4Is" %,AEGBG  Gc |j(|jt|}nd}|||||||||S)a`Run more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids to associate with the texts. refresh_indices: Whether to refresh the Elasticsearch indices after adding the texts. create_index_if_not_exists: Whether to create the Elasticsearch index if it doesn't already exist. *bulk_kwargs: Additional arguments to pass to Elasticsearch bulk. - chunk_size: Optional. Number of texts to add to the index at a time. Defaults to 500. Returns: List of ids from adding the texts into the vectorstore. Nrrrrrr)rembed_documentslist_ElasticsearchStore__add) r$rrrrrrrrs r% add_textszElasticsearchStore.add_textsse6 > %77U DDJJJzz  +'A#   r'text_embeddingsc t|\}} |t|t| ||||||S)amAdd the given texts and embeddings to the vectorstore. Args: text_embeddings: Iterable pairs of string and embedding to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of unique IDs. refresh_indices: Whether to refresh the Elasticsearch indices after adding the texts. create_index_if_not_exists: Whether to create the Elasticsearch index if it doesn't already exist. *bulk_kwargs: Additional arguments to pass to Elasticsearch bulk. - chunk_size: Optional. Number of texts to add to the index at a time. Defaults to 500. Returns: List of ids from adding the texts into the vectorstore. r")zipr%r$) r$r'rrrrrrrrs r%add_embeddingsz!ElasticsearchStore.add_embeddings(sT8 1zzz KK   +'A#   r'c \tjdd|i|}|||||S)aConstruct ElasticsearchStore wrapper from raw documents. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings db = ElasticsearchStore.from_texts( texts, // embeddings optional if using // a strategy that doesn't require inference embeddings, index_name="langchain-demo", es_url="http://localhost:9200" ) Args: texts: List of texts to add to the Elasticsearch index. embedding: Embedding function to use to embed the texts. metadatas: Optional list of metadatas associated with the texts. index_name: Name of the Elasticsearch index to create. es_url: URL of the Elasticsearch instance to connect to. cloud_id: Cloud ID of the Elasticsearch instance to connect to. es_user: Username to use when connecting to Elasticsearch. es_password: Password to use when connecting to Elasticsearch. es_api_key: API key to use when connecting to Elasticsearch. es_connection: Optional pre-existing Elasticsearch connection. vector_query_field: Optional. Name of the field to store the embedding vectors in. query_field: Optional. Name of the field to store the texts in. distance_strategy: Optional. Name of the distance strategy to use. Defaults to "COSINE". can be one of "COSINE", "EUCLIDEAN_DISTANCE", "DOT_PRODUCT", "MAX_INNER_PRODUCT". bulk_kwargs: Optional. Additional arguments to pass to Elasticsearch bulk. r)rrr")r_create_cls_from_kwargsr&)clsrrrrrelasticsearchStores r% from_textszElasticsearchStore.from_textsPs[b0G   #)   $$ YK %   "!r'c p|d}|td|d}|d}|d}|d}|d}|d}|d } |d } |d } |d t} i} | | | d <| | | d <td||||||||| | d | S)NrzPlease provide an index_name.rrrrrrrrrr) rrrrrrrrrrr")rrTrr?)rrrrrrrrrrrrr optional_argss r%r,z*ElasticsearchStore._create_cls_from_kwargssQZZ --  <== = ?33 jj// H%%**Y''jj// ZZ -- #ZZ(<==jj// "JJ':;;::j*<*T*T*V*VWW  )2DM. /  "+6M- (!  !'##!/      r' documentsc Ztjdd|i|}||||S)aConstruct ElasticsearchStore wrapper from documents. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings db = ElasticsearchStore.from_documents( texts, embeddings, index_name="langchain-demo", es_url="http://localhost:9200" ) Args: texts: List of texts to add to the Elasticsearch index. embedding: Embedding function to use to embed the texts. Do not provide if using a strategy that doesn't require inference. metadatas: Optional list of metadatas associated with the texts. index_name: Name of the Elasticsearch index to create. es_url: URL of the Elasticsearch instance to connect to. cloud_id: Cloud ID of the Elasticsearch instance to connect to. es_user: Username to use when connecting to Elasticsearch. es_password: Password to use when connecting to Elasticsearch. es_api_key: API key to use when connecting to Elasticsearch. es_connection: Optional pre-existing Elasticsearch connection. vector_query_field: Optional. Name of the field to store the embedding vectors in. query_field: Optional. Name of the field to store the texts in. bulk_kwargs: Optional. Additional arguments to pass to Elasticsearch bulk. r)rr")rr, add_documents)r-r2rrrr.s r%from_documentsz!ElasticsearchStore.from_documentssNV0G   #)   (( (LLL!!r'rkctS)zUUsed to perform brute force / exact nearest neighbor search via script_score.)rkr"r'r%rkz)ElasticsearchStore.ExactRetrievalStrategys&'''r'Fr@rArBr?c&t|||S)a(Used to perform approximate nearest neighbor search using the HNSW algorithm. At build index time, this strategy will create a dense vector field in the index and store the embedding vectors in the index. At query time, the text will either be embedded using the provided embedding function or the query_model_id will be used to embed the text using the model deployed to Elasticsearch. if query_model_id is used, do not provide an embedding function. Args: query_model_id: Optional. ID of the model to use to embed the query text within the stack. Requires embedding model to be deployed to Elasticsearch. hybrid: Optional. If True, will perform a hybrid search using both the knn query and a text query. Defaults to False. rrf: Optional. rrf is Reciprocal Rank Fusion. When `hybrid` is True, and `rrf` is True, then rrf: {}. and `rrf` is False, then rrf is omitted. and isinstance(rrf, dict) is True, then pass in the dict values. rrf could be passed for adjusting 'rank_constant' and 'window_size'. rE)r?rEs r%r?z*ElasticsearchStore.ApproxRetrievalStrategys#D')&c    r'rLruc"t|S)aUsed to perform sparse vector search via text_expansion. Used for when you want to use ELSER model to perform document search. At build index time, this strategy will create a pipeline that will embed the text using the ELSER model and store the resulting tokens in the index. At query time, the text will be embedded using the ELSER model and the resulting tokens will be used to perform a text_expansion query. Args: model_id: Optional. Default is ".elser_model_1". ID of the model to use to embed the query text within the stack. Requires embedding model to be deployed to Elasticsearch. rw)rurws r%SparseVectorRetrievalStrategyz0ElasticsearchStore.SparseVectorRetrievalStrategys*'9999r')rrN)rrrN)rN)NrNrNNNN)NTrD)NNTTN)NNN)NN)r rkri)3r3r4r5r6r?r8r rr rrdrfrergrrrrF staticmethodrrpropertyrr9r r:rrr7rrrrr rrrrr;rrr r%r&r* classmethodr/r,r5rkr9r"r'r%rrs lld+/37 $%)!%$(%)"*! *A*A*C*C.2-4444J' 4  0 4  4c]4#4SM4c]4 44$  ' , 3 24  4*(+4,DcN+-4444l0C000\0 !%"&!%"&"&.2111 13-1# 1 3- 1 3- 1DcN+1 111\1fHZ0X '+ +++ + + d$ +  + h++++: &* ;;; ; ;  ; c# ;; h;;;;zEe\ !HeWe^,D ! ! ! ! FJGGG G.6tDz.BGUXG eHeO$ %GGGG*'+ RR;R Rd$ R  R eHeO$ % RRRR0 $15&*'+KO<@ZZ}Z ZDK-. 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