3fi-ddlmZddlmZmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZmZmZmZGd d e Zy )) annotations)AnyCallableIterableOptionalSequence)Document) Embeddings) VectorStore)AsyncPGVectorStore)PGEngine)HybridSearchConfig)DEFAULT_DISTANCE_STRATEGY BaseIndexDistanceStrategy QueryOptionsceZdZdZeZd7dZeddddddded d d ddf d8d Z eddddddded d d ddf d9d Z e d:dZ d; d d?dZ d; d d?dZ d> d@dZ d> d@dZeddddddddded d d ddf dAdZedddddddded d d ddf dBdZeddddddddded d d ddf dAdZedddddddded d d ddf dBdZ d; dCdZ d; dCdZdDdZ d; dEdZ d; dFdZ d; dGd Z dH dId!Z dH dJd"Z dH dKd#Z! d; dEd$Z" d; dFd%Z# d; dGd&Z$ dH dId'Z% dH dJd(Z& dH dKd)Z' dL dMd*Z( dL dMd+Z) dN dOd,Z* dN dOd-Z+d>dPd.Z,d>dPd/Z- d> dPd0Z. d> dPd1Z/ d> dQd2Z0 d> dQd3Z1dRd4Z2dRd5Z3dSd6Z4y)T PGVectorStorezPostgres Vector Store classc\|tjk7r td||_||_y)afPGVectorStore constructor. Args: key (object): Prevent direct constructor usage. engine (PGEngine): Connection pool engine for managing connections to Postgres database. vs (AsyncPGVectorStore): The async only VectorStore implementation Raises: Exception: If called directly by user. z`: if the `ids` data type does not match that of the `id_column`. N)rr9r aadd_textsrrJrKrLrMs rrPzPGVectorStore.aadd_textssF\\// DII  3 A& A    `: if the `ids` data type does not match that of the `id_column`. N)rr9raadd_documentsr documentsrLrMs rrUzPGVectorStore.aadd_documentssD\\// $DII $ $Y >v >    ;AAAc t|jj|jj||||fi|S)rG)rr@rrHrIs radd_embeddingszPGVectorStore.add_embeddingss:||(( %DII % %eZC R6 R  r!c r|jj|jj|||fi|S)rO)rr@rrPrQs r add_textszPGVectorStore.add_textss8||(( DII  3 A& A  r!c p|jj|jj||fi|S)rT)rr@rrUrVs r add_documentszPGVectorStore.add_documentss6||(( $DII $ $Y >v >  r!cK|jj|jj|fi|d{S7w)Delete records from the table. Raises: :class:`InvalidTextRepresentationError `: if the `ids` data type does not match that of the `id_column`. N)rr9radeleterrLrMs rrazPGVectorStore.adelete s:\\//0A 0A0A#0P0PQQQQs:AAAc n|jj|jj|fi|S)r`)rr@rrarbs rdeletezPGVectorStore.deletes0||(():):):3)I&)IJJr!cK|j|||||| | | | | ||||||d{}|j|||d{|S7"7w)aA Create an PGVectorStore instance from texts. Args: texts (list[str]): Texts to add to the vector store. embedding (Embeddings): Text embedding model to use. engine (PGEngine): Connection pool engine for managing connections to postgres database. table_name (str): Name of an existing table. schema_name (str, optional): Name of the database schema. Defaults to "public". metadatas (Optional[list[dict]], optional): List of metadatas to add to table records. Defaults to None. ids: (Optional[list]): List of IDs to add to table records. Defaults to None. content_column (str, optional): Column that represent a Document's page_content. Defaults to "content". embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding". metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to an empty list. ignore_metadata_columns (Optional[list[str]], optional): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None. id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id". metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata". distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE. k (int): Number of Documents to return from search. 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. index_query_options (QueryOptions): Index query option. hybrid_search_config (HybridSearchConfig): Hybrid search configuration. Defaults to None. Raises: :class:`InvalidTextRepresentationError `: if the `ids` data type does not match that of the `id_column`. Returns: PGVectorStore r*NrKrL)r8rPr;rJr$rr=r+rKrLr,r-r.r/r1r0r2r3r4r5r6r7rMrs r afrom_textszPGVectorStore.afrom_texts"s|j::   #)--$;!5/# 3!5!  $mmEYCm@@@ ' $ As!%AA AA A AcK|j||||||| | | | | |||||d{}|j||d{|S7!7w)Create an PGVectorStore instance from documents. Args: documents (list[Document]): Documents to add to the vector store. embedding (Embeddings): Text embedding model to use. engine (PGEngine): Connection pool engine for managing connections to postgres database. table_name (str): Name of an existing table. schema_name (str, optional): Name of the database schema. Defaults to "public". ids: (Optional[list]): List of IDs to add to table records. Defaults to None. content_column (str, optional): Column that represent a Document's page_content. Defaults to "content". embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding". metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to an empty list. ignore_metadata_columns (Optional[list[str]], optional): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None. id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id". metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata". distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE. k (int): Number of Documents to return from search. 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. index_query_options (QueryOptions): Index query option. hybrid_search_config (HybridSearchConfig): Hybrid search configuration. Defaults to None. Raises: :class:`InvalidTextRepresentationError `: if the `ids` data type does not match that of the `id_column`. Returns: PGVectorStore r*NrL)r8rUr;rWr$rr=r+rLr,r-r.r/r1r0r2r3r4r5r6r7rMrs rafrom_documentszPGVectorStore.afrom_documentsls|h::   #)--$;!5/# 3!5!  $ s333 ' $ 4s!%A A A A A  A c t|j|||f||| | | | | ||||||d |}|j||||S)a> Create an PGVectorStore instance from texts. Args: texts (list[str]): Texts to add to the vector store. embedding (Embeddings): Text embedding model to use. engine (PGEngine): Connection pool engine for managing connections to postgres database. table_name (str): Name of an existing table. schema_name (str, optional): Name of the database schema. Defaults to "public". metadatas (Optional[list[dict]], optional): List of metadatas to add to table records. Defaults to None. ids: (Optional[list]): List of IDs to add to table records. Defaults to None. content_column (str, optional): Column that represent a Document's page_content. Defaults to "content". embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding". metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to empty list. ignore_metadata_columns (Optional[list[str]], optional): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None. id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id". metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata". distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE. k (int): Number of Documents to return from search. 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. index_query_options (QueryOptions): Index query option. hybrid_search_config (HybridSearchConfig): Hybrid search configuration. Defaults to None. Raises: :class:`InvalidTextRepresentationError `: if the `ids` data type does not match that of the `id_column`. Returns: PGVectorStore r*rf)rAr\rgs r from_textszPGVectorStore.from_textssojS__    $)--$;!5/# 3!5! "# & UiS 9 r!c r|j|||f|||| | | | | |||||d |}|j|||S)rjr*rk)rAr^rls rfrom_documentszPGVectorStore.from_documentsspfS__    $)--$;!5/# 3!5! "# & , r!c r|jj|jj|||fi|S)3Return docs selected by similarity search on query.)rr@rasimilarity_searchrqueryr3filterrMs rsimilarity_searchzPGVectorStore.similarity_searchIs8||(( (DII ( (6 DV D  r!cK|jj|jj|||fi|d{S7w)rsN)rr9rrtrus rrtz PGVectorStore.asimilarity_searchUsF\\// (DII ( (6 DV D    rRc6|jjtjk(r |jS|jjtj k(r |j S|jjtjk(r |jSy)z7Select a relevance function based on distance strategy.N) rr2rCOSINE_DISTANCE_cosine_relevance_score_fn INNER_PRODUCT%_max_inner_product_relevance_score_fn EUCLIDEAN_euclidean_relevance_score_fnrDs r_select_relevance_score_fnz(PGVectorStore._select_relevance_score_fnbsy 99 & &*:*J*J J22 2 99 & &*:*H*H H== = YY ( (,<,F,F F55 5Gr!cK|jj|jj|||fi|d{S7w)GReturn docs and distance scores selected by similarity search on query.N)rr9rasimilarity_search_with_scorerus rrz+PGVectorStore.asimilarity_search_with_scorelsF\\// 3DII 3 3E1f O O    rRcK|jj|jj|||fi|d{S7w)1Return docs selected by vector similarity search.N)rr9rasimilarity_search_by_vectorrr$r3rwrMs rrz*PGVectorStore.asimilarity_search_by_vectorxsF\\// 2DII 2 29a R6 R    rRcK|jj|jj|||fi|d{S7w)zEReturn docs and distance scores selected by vector similarity search.N)rr9r'asimilarity_search_with_score_by_vectorrs rrz5PGVectorStore.asimilarity_search_with_score_by_vectorsL\\// =DII = =1f (.     rRc K|jj|jj|||||fi|d{S7w:Return docs selected using the maximal marginal relevance.N)rr9ramax_marginal_relevance_searchrrvr3r4r5rwrMs rrz,PGVectorStore.amax_marginal_relevance_searchsP\\// 4DII 4 4q'; :@     >AAAc K|jj|jj|||||fi|d{S7wr)rr9r(amax_marginal_relevance_search_by_vectorrr$r3r4r5rwrMs rrz6PGVectorStore.amax_marginal_relevance_search_by_vectorsP\\// >DII > >1g{F >D     rc K|jj|jj|||||fi|d{S7w)NReturn docs and distance scores selected using the maximal marginal relevance.N)rr9r3amax_marginal_relevance_search_with_score_by_vectorrs rrzAPGVectorStore.amax_marginal_relevance_search_with_score_by_vectorsP\\// IDII I I1g{F >D     rc r|jj|jj|||fi|S)r)rr@rrrus rsimilarity_search_with_scorez*PGVectorStore.similarity_search_with_scores8||(( 3DII 3 3E1f O O  r!c r|jj|jj|||fi|S)r)rr@rrrs rsimilarity_search_by_vectorz)PGVectorStore.similarity_search_by_vectors8||(( 2DII 2 29a R6 R  r!c r|jj|jj|||fi|S)zHReturn docs and distance scores selected by similarity search on vector.)rr@rrrs r&similarity_search_with_score_by_vectorz4PGVectorStore.similarity_search_with_score_by_vectors>||(( =DII = =1f (.   r!c v|jj|jj|||||fi|Sr)rr@rrrs rmax_marginal_relevance_searchz+PGVectorStore.max_marginal_relevance_searchsB||(( 4DII 4 4q'; :@   r!c v|jj|jj|||||fi|Sr)rr@rrrs r'max_marginal_relevance_search_by_vectorz5PGVectorStore.max_marginal_relevance_search_by_vectorsB||(( >DII > >1g{F >D   r!c v|jj|jj|||||fi|S)r)rr@rrrs r2max_marginal_relevance_search_with_score_by_vectorz@PGVectorStore.max_marginal_relevance_search_with_score_by_vectorsB||(( IDII I I1g{F >D   r!cK|jj|jj|d{S7w):Creates a TSV index in the vector store table if possible. concurrentlyN)rr9raapply_hybrid_search_indexrrs rrz(PGVectorStore.aapply_hybrid_search_indexs> \\// II 0 0l 0 K    9AAAcl|jj|jj|S)rr)rr@rrrs rapply_hybrid_search_indexz'PGVectorStore.apply_hybrid_search_index!s0 ||(( II 0 0l 0 K  r!cK|jj|jj|||d{S7w)*Create an index on the vector store table.rN)rr9raapply_vector_indexrindexnamers rrz!PGVectorStore.aapply_vector_index*sB\\// II ) )%L ) Q    rXcp|jj|jj|||S)rr)rr@rrrs rapply_vector_indexz PGVectorStore.apply_vector_index5s4||(( II ) )%L ) Q  r!cK|jj|jj|d{S7w) Re-index the vector store table.N)rr9rareindexr index_names rrzPGVectorStore.areindex@s0\\// 0B0B:0NOOOO8A?Acj|jj|jj|S)r)rr@rrrs rreindexzPGVectorStore.reindexDs&||((););J)GHHr!cK|jj|jj|d{S7w)Drop the vector index.N)rr9radrop_vector_indexrs rrz PGVectorStore.adrop_vector_indexHs; \\// II ( ( 4    rcj|jj|jj|S)r)rr@rrrs rdrop_vector_indexzPGVectorStore.drop_vector_indexQs( ||(()E)Ej)QRRr!cK|jj|jj|d{S7w)#Check if index exists in the table.N)rr9ris_valid_indexrs rais_valid_indexzPGVectorStore.ais_valid_indexXs2 \\// 0H0H0TUUUUrcj|jj|jj|S)r)rr@rrrs rrzPGVectorStore.is_valid_index_s( ||(()A)A*)MNNr!cK|jj|jj|d{S7w)Get documents by ids.rkN)rr9r aget_by_idsrrLs rrzPGVectorStore.aget_by_idsfs3\\// 0E0E#0E0NOOOOrcl|jj|jj|S)rrk)rr@rrrs r get_by_idszPGVectorStore.get_by_idsjs)||(()>)>3)>)GHHr!c.|jjSrC)rr=rDs rget_table_namezPGVectorStore.get_table_namensyy###r!)robjectrrrr )$r;type[PGVectorStore]rrr<r r=strr+rr,rr-rr.Optional[list[str]]r/rr1rr0 Optional[str]r2rr3intr4rr5floatr6Optional[QueryOptions]r7Optional[HybridSearchConfig]returnr)"rrr<r r=rr+rr,rr-rr.rr/rr1rr0rr2rr3rr4rr5rr6rr7rrr)rr )NN) rJ Iterable[str]rEzlist[list[float]]rKOptional[list[dict]]rLrrMrr list[str]) rJrrKrrLOptional[list]rMrrrrC)rWlist[Document]rLrrMrrr)rLrrMrrzOptional[bool]),r;rrJrr$r rrr=rr+rrKrrLrr,rr-rr.rr/rr1rr0rr2rr3rr4rr5rr6rr7rrMrrr)*r;rrWrr$r rrr=rr+rrLrr,rr-rr.rr/rr1rr0rr2rr3rr4rr5rr6rr7rrMrrr) rvrr3 Optional[int]rwOptional[dict]rMrrr)rzCallable[[float], float]) rvrr3rrwrrMrrlist[tuple[Document, float]]) r$ list[float]r3rrwrrMrrr) r$rr3rrwrrMrrr)NNNN)rvrr3rr4rr5Optional[float]rwrrMrrr)r$rr3rr4rr5rrwrrMrrr)r$rr3rr4rr5rrwrrMrrr)F)rboolrNone)NF)rrrrrrrr)rrrr)rrrr)rLz Sequence[str]rr)rr)5__name__ __module__ __qualname____doc__rrr classmethodrr8rApropertyrErHrPrUrZr\r^rardrhrmrorqrxrtrrrrrrrrrrrrrrrrrrrrrrrrrrr!rrrs %8L& $' +047;'.B.G 6:=A#=1 =1=1&=1 =1  =1  =1=1.=1"5=1=1,=1,=1 =1=1=1 4!=1";#=1$ %=1=1~ $' +047;'$8.G 6:=A#>1>1&>1 >1  >1  >1>1.>1"5>1>1">1,>1 >1>1>1 4!>1";#>1$ %>1>1@+++/#'    &  (  !       +/"   (       &#  !        &+/#'    &  (  !       +/"   (       &#  !        "# R  R R  R# K  K K  K$*."' +047;'$8.G 6:=A)G GGG G  G  G(GGGG.G"5GG"G,G !G"#G$%G&4'G(;)G*+G, -GGR$"' +047;'$8.G 6:=A'F F!FF F  F  FFFF.F"5FF"F,F F !F"#F$4%F&;'F()F* +FFP$*."' +047;'$8.G 6:=A)H HHH H  H  H(HHHH.H"5HH"H,H !H"#H$%H&4'H(;)H*+H, -HHT$"' +047;'$8.G 6:=A'F F!FF F  F  FFFF.F"5FF"F,F F !F"#F$4%F&;'F()F* +FFV !%              !%             6 !%           &   !%              !%           &  " !%'+!%       %       & !%'+!%       %       & !%'+!%       %      & & !%           &   !%              !%           &  " !%'+!%       %       & !%'+!%       %       & !%'+!%       %      & $#    #    #"          #"          PI %) !   %)S!S S%)V!V V%)O!O OPI$r!rN) __future__rtypingrrrrrlangchain_core.documentsr langchain_core.embeddingsr langchain_core.vectorstoresr async_vectorstorer rrr7rindexesrrrrrrr!rrs8">>-0314Z $KZ $r!