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Fr_ cmetadataT)rcaddcommitrarbr_rlcreatedrUs r: get_or_createzF_get_embedding_collection_store..CollectionStore.get_or_createsYG$7J!7**$).CollectionStore.aget_or_createssG"//>>J!7**$).EmbeddingStorezEmbedding store.langchain_pg_embeddingT)rPrNz.uuidCASCADE)ondeleter})rVrJ embedding)rRix_cmetadata_ginrlginjsonb_path_ops)postgresql_usingpostgresql_opsN)r1r2r3r4ryr]r^r|idr ForeignKey collection_idr'rUr__annotations__documentrrlIndex__table_args__)rhrJvector_dimensionsr:rTrs0 Z  z00d C) ))   !J ! !"0017"  "/,O -J--f5E.FG 6G$:$$Z%6%6F%J%%ed;  J  "!& +-=>   r9rT)r<pgvector.sqlalchemyrJBase)rrTrhrJs` @@r:_get_embedding_collection_storerhsB*U'$U'n   :0H Or9c8|Dcgc]\}}| c}}Scc}}w)z!Return docs from docs and scores.r8)docs_and_scoresdoc_s r:_results_to_docsrs- .FCC .. .s cptjd}|j||jy)NzXSELECT pg_advisory_xact_lock(1573678846307946496);CREATE EXTENSION IF NOT EXISTS vector;)r]textrern)conn statements r:_create_vector_extensionrs, 1I LLKKMr9c eZdZdZddededddddddd dLdZ dMdZ dMdZe dNd Z dMd Z dMd Z dMd Z dMd ZdMdZdMdZdMdZdMdZdOdZdPdZdMdZdMdZ dQ dRdZ dQ dRdZdSdZdTdZeddeeddfdd dUdZeddeeddfdd dUdZ dV dWdZ dV dWdZ dV dXdZ dV dXd Z! dY dZd!Z" dY dZd"Z# dY d[d#Z$ dY d[d$Z%e d\d%Z& dY d]d&Z' dY d]d'Z(d^d(Z) d_d)Z*d*Z+ d`d+Z,dad,Z- dY dbd-Z. dY dcd.Z/ dY ddd/Z0 dY ddd0Z1e deeedddd1 dfd2Z2edeeddfdd dfd3Z3edeeddd4 dgd5Z4edeeddf dgd6Z5eeeddd7 dhd8Z6eeeddd7 dhd9Z7edid:Z8edeedddd; djd<Z9eeeddfdd dkd=Z:e dld>Z;dmd?Z< dn dod@Z= dn dodAZ> dn dpdBZ? dn dpdCZ@ dn dqdDZA dn dqdEZB dn drdFZC dn drdGZDeEjdsdHZGeEjdtdIZIdudJZJdudKZKy)vPGVectoruSPostgres vector store integration. Setup: Install ``langchain_postgres`` and run the docker container. .. code-block:: bash pip install -qU langchain-postgres docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 6024:5432 -d pgvector/pgvector:pg16 Key init args — indexing params: collection_name: str Name of the collection. embeddings: Embeddings Embedding function to use. Key init args — client params: connection: Union[None, DBConnection, Engine, AsyncEngine, str] Connection string or engine. Instantiate: .. code-block:: python from langchain_postgres.vectorstores import PGVector from langchain_openai import OpenAIEmbeddings # See docker command above to launch a postgres instance with pgvector enabled. connection = "postgresql+psycopg://langchain:langchain@localhost:6024/langchain" # Uses psycopg3! collection_name = "my_docs" vector_store = PGVector( embeddings=OpenAIEmbeddings(model="text-embedding-3-large"), collection_name=collection_name, connection=connection, use_jsonb=True, ) Add Documents: .. code-block:: python from langchain_core.documents import Document document_1 = Document(page_content="foo", metadata={"baz": "bar"}) document_2 = Document(page_content="thud", metadata={"bar": "baz"}) document_3 = Document(page_content="i will be deleted :(") documents = [document_1, document_2, document_3] ids = ["1", "2", "3"] vector_store.add_documents(documents=documents, ids=ids) Delete Documents: .. code-block:: python vector_store.delete(ids=["3"]) Search: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz'}] Search with filter: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"}) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz'}] Search with score: .. code-block:: python results = vector_store.similarity_search_with_score(query="qux",k=1) for doc, score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.499243] foo [{'baz': 'bar'}] Async: .. code-block:: python # add documents # await vector_store.aadd_documents(documents=documents, ids=ids) # delete documents # await vector_store.adelete(ids=["3"]) # search # results = vector_store.asimilarity_search(query="thud",k=1) # search with score results = await vector_store.asimilarity_search_with_score(query="qux",k=1) for doc,score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.499243] foo [{'baz': 'bar'}] Use as Retriever: .. code-block:: python retriever = vector_store.as_retriever( search_type="mmr", search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, ) retriever.invoke("thud") .. code-block:: python [Document(metadata={'bar': 'baz'}, page_content='thud')] NFT) connectionembedding_lengthcollection_namecollection_metadatadistance_strategypre_delete_collectionloggerrelevance_score_fn engine_args use_jsonbcreate_extension async_modec | |_||_||_||_||_||_||_|xstjt|_ | |_ d|_ d|_ d|_t|t r/| rt#|fi| xsi|_ n`t%dd|i| xsi|_ nIt|t&rd|_||_ n*t|t(rd|_||_ n t+d||jrt-|j|_n$t1t3|j|_| |_| |_| s t9d|js|j;yy) aInitialize the PGVector store. For an async version, use `PGVector.acreate()` instead. Args: connection: Postgres connection string or (async)engine. embeddings: Any embedding function implementing `langchain.embeddings.base.Embeddings` interface. embedding_length: The length of the embedding vector. (default: None) NOTE: This is not mandatory. Defining it will prevent vectors of any other size to be added to the embeddings table but, without it, the embeddings can't be indexed. collection_name: The name of the collection to use. (default: langchain) NOTE: This is not the name of the table, but the name of the collection. The tables will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. distance_strategy: The distance strategy to use. (default: COSINE) pre_delete_collection: If True, will delete the collection if it exists. (default: False). Useful for testing. engine_args: SQLAlchemy's create engine arguments. use_jsonb: Use JSONB instead of JSON for metadata. (default: True) Strongly discouraged from using JSON as it's not as efficient for querying. It's provided here for backwards compatibility with older versions, and will be removed in the future. create_extension: If True, will create the vector extension if it doesn't exist. disabling creation is useful when using ReadOnly Databases. NFurlTzconnection should be a connection string or an instance of sqlalchemy.engine.Engine or sqlalchemy.ext.asyncio.engine.AsyncEngine)bindz'use_jsonb=False is no longer supported.r8)rembedding_function_embedding_lengthrr_distance_strategyrlogging getLoggerr1roverride_relevance_score_fn_engine _async_engine _async_init isinstancertr$rr r! ValueErrorr# session_makerr(r)rrNotImplementedError __post_init__)selfr}rrrrrrrrrrrrs r:__init__zPGVector.__init__vsfZ%",!1.#6 "3%:"; 1 1( ; +=()- 48  j# &%8&#.#4"&" -SS @QrS  F +#DO%DL  K 0"DO!+D X  ??!39K9K!LD !/ $,,0O!PD " 0%&OP P    r9c|jr|jt|j\}}||_||_|j |jy)zInitialize the store.N)rcreate_vector_extensionrrrhrTcreate_tables_if_not_existscreate_collectionrrTrhs r:rzPGVector.__post_init__s[   ( ( **I  " "+ ' /, ((*  r9c4K|jryd|_t|j\}}||_||_|j r|j d{|jd{|jd{y7577 w)z/Async initialize the store (use lazy approach).NT) rrrrhrTracreate_vector_extensionacreate_tables_if_not_existsacreate_collectionrs r:__apost_init__zPGVector.__apost_init__s    *I  " "+ ' /,  //1 1 1//111%%''' 21's6ABBB4B5B B BBBc|jSrY)rrs r:r}zPGVector.embeddingss&&&r9c|jsJd |jj5}t|dddy#1swYyxYw#t$r}td||d}~wwxYw)Nzengine not foundz#Failed to create vector extension: )rconnectr Exception)rres r:rz PGVector.create_vector_extensionso||///| N%%' /4(. / / / NA!EFA M Ns3A AAA  A A A-A((A-cK|jsJd|jj4d{}|jtd{dddd{y7277 #1d{7swYyxYww)Nz_async_engine not found)rbeginrun_syncrrrs r:rz!PGVector.acreate_vector_extensionsu!!<#<<!%%++- : :-- 89 9 9 : : : 9 : : : :sV2BA'BA-A)A- B!A+"B)A-+B-A?3A6 4A?;Bc|j5}tjj|j |j dddy#1swYyxYwrY)_make_sync_sessionrmetadata create_allget_bindrnrrbs r:rz$PGVector.create_tables_if_not_existssJ  $ $ & ' MM $ $W%5%5%7 8 NN     >AA!c0K|jsJd|jj4d{}|jtjj d{dddd{y7F77 #1d{7swYyxYwwN*This method must be called with async_mode)rrrrrrrs r:rz%PGVector.acreate_tables_if_not_exists s~!!O#OO!%%++- : :-- 8 89 9 9 : : : 9 : : : :sV2BA;B-B%A=&B* B5A?6B=B?BBB BBc|j5}tjj|j |j dddy#1swYyxYwrY)rrrdrop_allrrnrs r: drop_tableszPGVector.drop_tablessJ  $ $ & ' MM " "7#3#3#5 6 NN    rcdK|jsJd|jd{|jj4d{}|jtj j d{dddd{y7i7H77 #1d{7swYyxYwwr)rrrrrrrrs r: adrop_tableszPGVector.adrop_tabless!!O#OO!!!###%%++- 8 8-- 6 67 7 7 8 8 8 $ 8 7 8 8 8 8si'B0B"B0 B B0-B=B>B B0 BB0B0BB0B-!B$ "B-)B0c |jr|j|j5}|jj ||j |j |jdddy#1swYyxYwN)rl)rdelete_collectionrrhrqrrrnrs r:rzPGVector.create_collectionsr  % %  " " $  $ $ & '  . .--9Q9Q /  NN     s AA99BcK|jd{|j4d{}|jr|j|d{|jj ||j |jd{|jd{dddd{y777k7377#1d{7swYyxYwwr) r_make_async_sessionr_adelete_collectionrhrsrrrnrs r:rzPGVector.acreate_collection#s!!###++- # #))..w777&&55--9Q9Q6  .." " "  # # # $ #7  #  # # # #sCB<CB>C!CC9CCC&C'C+ C6C7C>CCCCCCC CCc|j|}|s|jjdy|j|yNCollection not found)get_collectionrwarningrrrbrUs r:_delete_collectionzPGVector._delete_collection-s7((1  KK   6 7 z"r9cK|j|d{}|s|jjdy|j|d{y7<7wr)aget_collectionrrrrs r:rzPGVector._adelete_collection4sO//88  KK   6 7 nnZ((( 9 )s!AA6AAAAc|j5}|j|}|s%|jjd dddy|j ||j dddy#1swYyxYwr)rrrrrrnrs r:rzPGVector.delete_collection;sp  $ $ & ',,W5J ##$:;    NN: & NN     s0A4 !A44A=cK|jd{|j4d{}|j|d{}|s-|jj d dddd{y|j |d{|j d{dddd{y777y7M7577#1d{7swYyxYwwr)rrrrrrrnrs r:adelete_collectionzPGVector.adelete_collectionDs!!###++- # ##33G<C!CC!C C "C + C!6C7C!<C CC (C)C - C!8C 9C!C!C C!C C  C! CC CC!c 4|j5}||jjdt|j}|rj|j |}|s%|jj d dddy|j|jj|jk(}|j|jjj|}|j||jdddy#1swYyxYw)zDelete vectors by ids or uuids. Args: ids: List of ids to delete. collection_only: Only delete ids in the collection. NUTrying to delete vectors by ids (represented by the model using the custom ids field)r)rrdebugrrTrrrfrrzrin_rernridscollection_onlykwargsrbstmtrUs r:rzPGVector.deleteNs $ $ & ' !!2 d112"!%!4!4W!=J% ++,BC   ::++99Z__LDzz$"5"5"8"8"<"BDDcK|jd{|j4d{}||jjdt |j }|rz|j |d{}|s-|jjd dddd{y|j|j j|jk(}|j|j jj|}|j|d{|jd{dddd{y7B7,77777!7#1d{7swYyxYww)zAsync delete vectors by ids or uuids. Args: ids: List of ids to delete. collection_only: Only delete ids in the collection. Nrr)rrrrrrTrrrfrrzrrrernrs r:adeletezPGVector.adeleteqsV!!###++- # # !!2 d112"'+';';G'D!DJ% ++,BC # # # ::++99Z__LDzz$"5"5"8"8"<"G7 (G4G7< GG7G G7G!4G5$G!"G ;BG! G G!$G%G!) G74G5G7>G7G! G!G!G7!G4'G* (G4/G7c |jrJdt|}|jj|}|jd|t||r t|nd|r t|ndd|S)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 for the texts. If not provided, will generate a new id for each text. kwargs: vectorstore specific parameters Returns: List of ids from adding the texts into the vectorstore. rNrr8)rlistrembed_documentsrrrrrrtexts_r}s r: add_textszPGVector.add_texts`s}&%%V'VV%e,,<D         sA4A=c :t|tstdt|d||j drtd||j std|dt|t r~t|dk7r4tdt|d t|jd d t|jd \}}|tvrtd |dtd}|}|tvrYt|}tj|jj t#d|d|dt$t#d|it&S|dk(r|\}}tj|jj t#d|dt$t#d|it&}tj|jj t#d|dt$t#d|it&} t)j*|| S|dvr|dvrf|D]a} t| tt,t.fst1dt| d| t| t2sIt1dt| d| |jj |j4} |dvr(| j7|D cgc] } t| c} S|dvr)| j7|D cgc] } t| c} S|dvr| j9|S|dvr| j;|St1|dk(rOt|t2std |tj<|jj |} |r| S| St1cc} wcc} w)!aCreate a filter for a specific field. Args: field: name of field value: value to filter If provided as is then this will be an equality filter If provided as a dictionary then this will be a filter, the key will be the operator and the value will be the value to filter by Returns: sqlalchemy expression z"field should be a string but got: z with value: $z@Invalid filter condition. Expected a field but got an operator: zInvalid field name: z. Expected a valid identifier.zInvalid filter condition. Expected a value which is a dictionary with a single key that corresponds to an operator but got a dictionary with z keys. The first few keys are: NrzInvalid operator: z. Expected one of r?z$. z $valuerCrCz >= $valuez <= $value>r@rArDrE>r@rAzUnsupported type: for value: >r@>rA>rD>rErBz8Expected a boolean value for $exists operator, but got: )rrtrtype startswith isidentifierdictlenr keysitemsSUPPORTED_OPERATORSCOMPARISONS_TO_NATIVErjsonb_path_matchrTrlrrrr]and_intfloatrboolastextrlikeilike jsonb_exists) rfieldrCoperator filter_valuenativelowhigh lower_bound upper_boundval queried_field conditions r:_handle_field_filterzPGVector._handle_field_filter2s"%%4T%[MugV    C R'  !!#&ug-KL  eT "5zQ 114U =!!%ejjl!3BQ!7 8: &*%++-%8%; "Hl22 ( 3'':&;= H L , ,+84F((##--r%&18<g|,e4   #$IC//##--r% +X6gs^U+K //##--r% +X6gt_e,K ??; < < ; ;?*' C%cCe+<=10c <uM"#t,10c <uM !//99%@GGM7"$((l)Ks#c()KLLX%%))|*L3s8*LMMMY&$)),77Z'$**<88)++  "lD1 **69))##--I!-9 <9* <%' '+*L*Ls ;N'Ncd\}}}}}}d\} } } } } |jDcic]\}}|j|}}}|ttj|vr7|jj |j j||}|S|ttj|vr7|jj |j j||}|S|ttj|vrS|jj |j jt||dt||d}|S|ttj|vr4|jj |j t||kD}|S|ttj|vr4|jj |j t||k}|S|ttj|vr4|jj |j t||k7}|S| ttj|vr4|jj |j t|| k(}|S| ttj|vr7|jj |j j|| }|S| ttj|vr7|jj |j j|| }|S| ttj|vr6|| Dcgc]}|j||}}tj|}|S| ttj|vr6|| Dcgc]}|j||}}tj|}|Sd}|Scc}}wcc}wcc}w)aKDeprecated functionality. This is for backwards compatibility with the JSON based schema for metadata. It uses incorrect operator syntax (operators are not prefixed with $). This implementation is not efficient, and has bugs associated with the way that it handles numeric filter clauses. )inninbetweengtltne)eqrfcontainsorandrrSN)r]lowermaprtrTrlrernot_inrxrfr}_create_filter_clauser]or_ra)rkeyrCINNINBETWEENGTLTNEEQLIKECONTAINSORANDr+vvalue_case_insensitivefilter_by_metadata sub_value or_clauses and_clausess r: _create_filter_clause_deprecatedz)PGVector._create_filter_clause_deprecateds(P$C"b"&K#D(B;@;;=!I41a!'')Q,!I!I SYY& &!%!4!4!>!>s!C!J!J!N!N&r*" h"!cC 5) )!%!4!4!>!>s!C!J!J!Q!Q&s+" `"![CIIu- -!%!4!4!>!>s!C!J!J!R!R*73A67*73A67" X"!Q3syy%( (!%!4!4!>!>s!C!J!JS&r*N" N"!I3syy%( (!%!4!4!>!>s!C!J!JS&r*N" F"!A3syy%( (!%!4!4!>!>s!C!J!Jc&r*O" >"!93syy%( (!%!4!4!>!>s!C!J!Jc&r*O" 6"!1SE* *!%!4!4!>!>s!C!J!J!O!O&t," ."!)SYY. .!%!4!4!>!>s!C!J!J!S!S&x0" &"!!3syy%( ("8!;**3 :J",!< "!C 5) )"8!<**3 :K",+!>  "!"& !!m"JN sO&!O,3O1c(g}|jD]|\}}t|tr'|j||}|+|j |=|j j |jt|k(}|j |~|S)a&Convert filters from IR to SQL clauses. **DEPRECATED** This functionality will be deprecated in the future. It implements translation of filters for a schema that uses JSON for metadata rather than the JSONB field which is more efficient for querying. ) r]rrZrappendrTrlrert)rr[filter_clausesrrCrs r:%_create_filter_clause_json_deprecatedz.PGVector._create_filter_clause_json_deprecateds ,,. :JC%&%)%J%J3PU%V"%1"))*<=%)%8%8%B%B3%G%N%NRUS&"%%&89 :r9ct|tr4t|dk(rht|j d\}}|j dr |j dvr#td||j|||S|j dk(rt|tstdt|d||Dcgc]}|j|}}t|dkDrtj|St|dk(r|dStd |j d k(rt|tstdt|d||Dcgc]}|j|}}t|dkDrtj|St|dk(r|dStd |j d k(rt|trS|Dcgc]}|j|}}tj|D cgc]} tj| c} } | St|tr&|j|} tj| Std t|td|t|dkDr|jD]!}|j dstd ||j D cgc]\} } |j| | }} } t|dkDrtj|St|dk(r|dStd tdtdt|cc}wcc}wcc}wcc} wcc} } w)aConvert LangChain IR filter representation to matching SQLAlchemy clauses. At the top level, we still don't know if we're working with a field or an operator for the keys. After we've determined that we can call the appropriate logic to handle filter creation. Args: filters: Dictionary of filters to apply to the query. Returns: SQLAlchemy clause to apply to the query. rSrrR)rGrFrHz>Invalid filter condition. Expected $and, $or or $not but got: rGzExpected a list, but got rVzKInvalid filter condition. Expected a dictionary but got an empty dictionaryrFrHzCInvalid filter condition. Expected a dictionary or a list but got: z4Invalid filter condition. Expected a field but got: z$Got an empty dictionary for filters.z2Invalid type: Expected a dictionary but got type: )rrZr[r r]rXrrrtrWrr]rarnot_r\) rfiltersrrCelraritemnot_conditionsrsrr+rs r:rzPGVector._create_filter_clauses gt $7|q "'--/215 U>>#&yy{*AA(((+u.  44S'#,GG99;&(%eT2(7U }LQVPWXFKKrD66r:KDK4y1})55Ta#Aw(:YY[E)%eT2(7U }LQVPWXEJJb455b9JCJ3x!|)~~s33SQ"1v (:YY[F*!%.IN*AED66t<** *2@$-!+ :  $ #E40#99%@)t44(226u+@ %$$'5*W!"<<>C~~c*(RSVRWX ELMMOTDAq11!Q7TTt9q=%??D11Y!^7N$6 !!GHHDT']OT OLK*6UsM M"M' M,M1c.|j5}|j|}|s td|jj|j k(g}|rS|j r%|j|}|4|j|n"|j|}|j||j}|j|j|j|jdj|jt!j"dj%|j&|jj|j&j k(j)|j+} ddd| S#1swY SxYw)Query the collection.rNrO)rrrrTrrzrrrrextendrZrlabelr[order_byr]ascrBrhlimitall) rrr+r[rbrU filter_byr_typerHs r:__query_collectionzPGVector.__query_collectionrsh $ $ & ',,W5J !788,,::jooMNI>>%)%?%?%GN%1!((8&*%O%OPV%WN$$^4''E ''**95;;JG $ *..45((''559M9M9R9RRq % BC Bs E/F  FcK|j4d{}|j|d{}|s td|jj|j k(g}|rS|j r%|j|}|4|j|n"|j|}|j||j}t|j|j|jdj|jt!j"dj%|j&|jj|j&j k(j)|} |j+| d{j-} | cdddd{S777+7 #1d{7swYyxYww)rNrrO)rrrrTrrzrrrrrrrrr[rr]rrBrhrrer) rrbrr+r[rUrrrrrHs r:__aquery_collectionzPGVector.__aquery_collections++-" " #33G<>%)%?%?%GN%1!((8&*%O%OPV%WN$$^4''E''**95;;JG $ *..45((''559M9M9R9RRq -4OOD,A&A%F%F%HGE" " " <>'BA" " " " sgGF8GGF;E!GF>G& G2G3G;G>GGGG GGc f|jrJd|j|||}t|S)kReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List of Documents most similar to the query vector. rr*)rr6rrrr+r[rrs r:r-z$PGVector.similarity_search_by_vectorsC"%%V'VV%EE1VF  00r9cK|jsJd|jd{|j|||d{}t|S7+7w)rrNr*)rrr;rrs r:r2z%PGVector.asimilarity_search_by_vectorsi"!!O#OO!!!### $ L L1V!M!   00 $ s!'AAAAAA)rrrrrc n|jt|} |j|| |f||||||d| S)=Return VectorStore initialized from documents and embeddings.rrrrrr)r!r _PGVector__from rarrrrrrrrrr}s r: from_textszPGVector.from_textssW..tE{; szz      +/"7    r9c K|jt|d{} |j|| |f||||||d| d{S7(7w)rNr)r&r _PGVector__afromrs r: afrom_textszPGVector.afrom_textsso%55d5kBB  S[[      +/"7     C  s!A A #A A A  A )rrrrrc |D cgc]} | d } } |D cgc]} | d } } |j| | |f|||||d|Scc} wcc} w)aConstruct PGVector wrapper from raw documents and embeddings. Args: text_embeddings: List of tuples of text and embeddings. embedding: Embeddings object. metadatas: Optional list of metadatas associated with the texts. collection_name: Name of the collection. distance_strategy: Distance strategy to use. ids: Optional list of ids for the documents. If not provided, will generate a new id for each document. pre_delete_collection: If True, will delete the collection if it exists. **Attention**: This will delete all the documents in the existing collection. kwargs: Additional arguments. Returns: PGVector: PGVector instance. Example: .. code-block:: python from langchain_postgres.vectorstores import PGVector from langchain_openai.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() text_embeddings = embeddings.embed_documents(texts) text_embedding_pairs = list(zip(texts, text_embeddings)) vectorstore = PGVector.from_embeddings(text_embedding_pairs, embeddings) rrSrrrrr)r ratext_embeddingsrrrrrrrtrr}s r:from_embeddingszPGVector.from_embeddings2swT //!1//$34qad4 4szz      +/"7    04s A Ac K|D cgc]} | d } } |D cgc]} | d } } |j| | |f|||||d|d{Scc} wcc} w7w)aConstruct PGVector wrapper from raw documents and pre- generated embeddings. Return VectorStore initialized from documents and embeddings. Postgres connection string is required "Either pass it as a parameter or set the PGVECTOR_CONNECTION_STRING environment variable. Example: .. code-block:: python from langchain_community.vectorstores import PGVector from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() text_embeddings = embeddings.embed_documents(texts) text_embedding_pairs = list(zip(texts, text_embeddings)) faiss = PGVector.from_embeddings(text_embedding_pairs, embeddings) rrSrN)rrs r:afrom_embeddingszPGVector.afrom_embeddingsks< //!1//$34qad4 4 S[[      +/"7     04  s%A A A A AAA)rrrrc "|d|||||d|}|S) Get instance of an existing PGVector store.This method will return the instance of the store without inserting any new embeddings )rrr}rrr8r8rarrrrrrr s r:from_existing_indexzPGVector.from_existing_indexs3  !+ /"7     r9c 2Ktd|||||dd|}|Sw)rT)rrr}rrrr8)rrs r:afrom_existing_indexzPGVector.afrom_existing_indexs:  !+ /"7    sc<t|dd}|s td|S)NrPGVECTOR_CONNECTION_STRING)rrenv_keyz~Postgres connection string is requiredEither pass it as a parameteror set the PGVECTOR_CONNECTION_STRING environment variable.)rr)rarconnection_strings r:get_connection_stringzPGVector.get_connection_strings6!50"  !N  ! r9)rrrrrrc |D cgc]} | j} } |D cgc]} | j} } |jd| |||| ||||d | Scc} wcc} w)r) rrrrrrrrrr8)rNrr) ra documentsrrrrrrrrdrrs r:from_documentszPGVector.from_documentssw *33A33)23AQZZ3 3s~~  "7/!+    43s AAc K|D cgc]} | j} } |D cgc]} | j} } |j|} | |d<|jd| |||| |||d|d{Scc} wcc} w7w)z Return VectorStore initialized from documents and embeddings. Postgres connection string is required "Either pass it as a parameter or set the PGVECTOR_CONNECTION_STRING environment variable. r)rrrrrrrrNr8)rNrrr) rarrrrrrrrrrrrs r:afrom_documentszPGVector.afrom_documentss(*33A33)23AQZZ3 355f=0|$S__  "7/+     43  s%A:A.A:A36A:)A8*A:c J|dk7r tdd|d|d|d|d|d| S)z2Return connection string from database parameters.psycopgz!Only psycopg3 driver is supportedz postgresql+z://:@/)r)radriverhostportdatabaseuserpasswords r: connection_string_from_db_paramsz)PGVector.connection_string_from_db_params&sC Y %&IJ JVHCvQxj$qazRRr9cZ|j |jS|jtjk(r |jS|jtj k(r |j S|jtjk(r |jStd|jd)a8 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. z=No supported normalization function for distance_strategy of z?.Consider providing relevance_score_fn to PGVector constructor.) rrr-r6_cosine_relevance_score_fnr5_euclidean_relevance_score_fnr7%_max_inner_product_relevance_score_fnrrs r:_select_relevance_score_fnz#PGVector._select_relevance_score_fn5s  + + 733 3  " "&6&=&= =22 2  $ $(8(B(B B55 5  $ $(8(J(J J== =--1-D-D,EFQQ r9c x|jrJd|j|||}|Dcgc]}|jj} }t t j |t j| ||} |j|} t| D cgc] \} } | | vs | c} } Scc}wcc} } w)Return docs selected using the maximal marginal relevance with score to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding 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. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. rr*dtyper+ lambda_mult) rrFrTrr*nparrayfloat32rG enumerate)rrr+fetch_krr[rrHrPembedding_list mmr_selected candidatesirs r:2max_marginal_relevance_search_with_score_by_vectorz;PGVector.max_marginal_relevance_search_with_score_by_vectorPs<%%V'VV%))IQW)XHOPf&//99PP1 HHYbjj 1 #  55g> ' 3IdaqL7HIIQJsB1 B6)B6c*K|jd{|j4d{}|j||||d{}|D cgc]} | jj} } t t j|t j| ||} |j|} t| D cgc] \} }| | vs |c}} cdddd{S777cc} wcc}} w7#1d{7swYyxYww)rNrJrr) rrrKrTrr*rrrrGr)rrr+rrr[rrbrHrPrrrrrs r:3amax_marginal_relevance_search_with_score_by_vectorz C/ C>C12AC> C6C6C> D%C<&D-D/C>1 C><D>DD D Dc h|jj|}|j|f||||d|S)vReturn 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. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents selected by maximal marginal relevance. r+rrr[)r}r,'max_marginal_relevance_search_by_vectorrrZr+rrr[rrs r:max_marginal_relevance_searchz&PGVector.max_marginal_relevance_searchsK8OO//6 ;t;;  #     r9cK|jd{|jj|d{}|j|f||||d|d{S7G7&7w)rNr)rr}r1(amax_marginal_relevance_search_by_vectorrs r:amax_marginal_relevance_searchz'PGVector.amax_marginal_relevance_searchsx8!!###//66u== BTBB  #       $= s1A$A"A$A A$A"A$ A$"A$c l|jj|}|jd|||||d|}|S)Return docs selected using the maximal marginal relevance with score. 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. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. rr+rrr[r8)r}r,r rrZr+rrr[rrr8s r:(max_marginal_relevance_search_with_scorez1PGVector.max_marginal_relevance_search_with_scoresO:OO//6 FtFF #     r9c K|jd{|jj|d{}|jd|||||d|d{}|S7I7(7 w)rNrr8)rr}r1rrs r:)amax_marginal_relevance_search_with_scorez2PGVector.amax_marginal_relevance_search_with_score$s|:!!###//66u== MTMM #       $= s1A&A "A&A"A&A$A&"A&$A&c H|j|f||||d|}t|S)Return docs selected using the maximal marginal relevance to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding (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. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents selected by maximal marginal relevance. r)rrrrr+rrr[rrs r:rz0PGVector.max_marginal_relevance_search_by_vectorMsB:R$QQ  #     00r9cK|jd{|j|f||||d|d{}t|S7/7w)r Nr)rrrr s r:rz1PGVector.amax_marginal_relevance_search_by_vectorusj:!!###J$JJ'      00 $ sA AA AA A c#K|jr td|j5}tt|dddy#1swYyxYww)Make an async session.zsAttempting to use a sync method in when async mode is turned on. Please use the corresponding async method instead.N)rrrr\r%rs r:rzPGVector._make_sync_sessionsS ??E    ! 0Wgw/ / 0 0 0s(AA AA AcK|js td|j4d{}tt|dddd{y7(7#1d{7swYyxYww)rztAttempting to use an async method in when sync mode is turned on. Please use the corresponding async method instead.N)rrrr\r"rs r:rzPGVector._make_async_sessionsnE %%' 5 57lG4 4 5 5 5 5 5 5 5sD,A0AA0A A0AA0A0A-!A$ "A-)A0c Dg}|j5}|j|}|jj|jk(g}t |jj |jjj|j|}|j|jjD]=}|jt|j|j|j ? ddd|S#1swY|SxYw)Get documents by ids.rMN)rrrTrrzrrfrrr[rergrrrrrl)rrrrbrUrrrPs r: get_by_idszPGVector.get_by_idss  $ $ & ',,W5J,,::jooMNI''t**--11#67 $ "//$/779==?   !99%+__!'!1!1  &' &s C7DDc Kg}|j4d{}|j|d{}|jj|jk(g}t |jj |jjj|j|}|j|d{jj}|D]F}|jtt|j|j |j"Hdddd{|S7(777#1d{7swY|SxYww)rNrM)rrrTrrzrrfrrr[rergrrrrtrrl) rrrrbrUrrrHrPs r: aget_by_idszPGVector.aget_by_idssA ++-  #33G<%+__!'!1!1   ,- <'B    ,shE!EE!E EBE EA+E / E!:E ;E!E E  E! EE EE!)r}rrz3Union[None, DBConnection, Engine, AsyncEngine, str]r Optional[int]rrtrrwrr-rrdrzOptional[logging.Logger]rz"Optional[Callable[[float], float]]rzOptional[dict[str, Any]]rrdrrdrrdruNone)rur)rur)rbr%rur)rbr"rur)NF)rOptional[List[str]]rrdrrrur)rbr%rur)rbr"rur)r List[str]r}List[List[float]]rrrOptional[List[dict]]rrrrtrr-rz Optional[str]rrdrrdrrrur)NN) r Sequence[str]r}rrrrrrrrur) rz Iterable[str]rrrrrrrur)N) rZrtr+rbr[rwrrruList[Document])rZrtr+rbr[rwruList[Tuple[Document, float]])rur)r List[float]r+rbr[rwrur)rHrrur)rirtrCrrur)r[rruzList[SQLColumnExpression])rrrur)rrr+rbr[Optional[Dict[str, str]]ru Sequence[Any]) rbr"rrr+rbr[r rur!) rrr+rbr[rwrrrurrY)raType[PGVector]rrrrrrrrtrr-rrrrdrrdrrrur)rzList[Tuple[str, List[float]]]rrrrrrtrr-rrrrdrrrur)rar"rrrrtrr-rrdrOptional[DBConnection]rrrur)rzDict[str, Any]rurt)rar"rrrrrr#rrtrr-rrrrdrrdrrrur)rar"rrrrrrtrr-rrrrdrrdrrrur)rrtrrtrrbrrtrrtrrtrurt)ruzCallable[[float], float])rg?N)rrr+rbrrbrrcr[r rrrur)rZrtr+rbrrbrrcr[r rrrur)rZrtr+rbrrbrrcr[rwrrrur)rrr+rbrrbrrcr[r rrrur)ruzGenerator[Session, None, None])ruz"AsyncGenerator[AsyncSession, None])rrrur)Lr1r2r3r4"_LANGCHAIN_DEFAULT_COLLECTION_NAMEDEFAULT_DISTANCE_STRATEGYrrrpropertyr}rrrrrrrrrrrrrrrrr~rrrr r$r'r/r3r9r<rr6r;rGrtrrrrFrKr-r2rrrrrrrrrrrrrrrrrrr contextlibcontextmanagerrasynccontextmanagerrrrr8r9r:rrsWz@KO*.A.2.G&++/AE04!% Y!Y!H Y! ( Y!  Y!,Y!,Y! $Y!)Y!?Y!.Y!Y!Y!Y! !Y!v !  !( (&''N:  :  8 ##)#$( %! !! !  !J$( %"# "#"# "#  "#HOV  +/#'A.G$(&+###&# # ( # ! ##,#"# $### ##J +/#'A.G$(&+$$$&$ $ ( $ ! $$,$"$ $$$$ $$T+/#' 88&8( 8 ! 8  8 8|+/#' 99&9( 9 ! 9  9 9|+/#'   ( !      B+/#'   ( !      B!%           8!%           8!%    & 4!%    & .   !% 9 9  9 9 & 9!% = =  = = & = t(t(t(  t(lB"H "2k`+/ (( () (  (\+/ ***  * ) *  *^!% 11 1 1  1  14!% 11 1 1  1  10 +/  B.G#'&+    (    , !  $      : +/A.G#'&+     (    ,  !  $      8 +/A.G#'&+6 66 6 ( 6  6 ,6 !6  $6 6  6 6 p +/A.G#'&+* 6* * ( *  * , * !*  $* *  * * X B.G&+-1   ,  $ + 4 B.G&+-1   ,  $ + 6!!  .2A.G#'&+  !  +    , !  $      > B.G#'&+# # # !# #  # , # ! #  $# # #  # # J S S S S  S  S S  S S< +/ ,J,J ,J ,J  ,J ) ,J,J &,Jb +/ /N/N /N /N  /N ) /N/N &/Nh +/ $ $  $  $  $ ) $ $  $ R +/ % %  %  %  % ) % %  % T !% && & &  &  && &&V !% '' ' '  '  '' &'X +/ &1&1 &1 &1  &1 ) &1&1 &1V +/ )1)1 )1 )1  )1 ) )1)1 )1V00##5$50r9rrY)rrrur)rrrur)rrrur)T __future__rr(enumrrzwarningstypingrrrrrr r r r r rrrr\numpyrr]langchain_core.documentsrlangchain_core.embeddingsrlangchain_core.utilsrlangchain_core.vectorstoresrrrrrrsqlalchemy.dialects.postgresqlrrrrrsqlalchemy.enginerr sqlalchemy.ext.asyncior!r"r#r$sqlalchemy.ormr%r&r'r(r)langchain_postgres._utilsr* simplefilterPendingDeprecationWarningrtEnumr-r6r&rr%r<rr_SPECIAL_CASED_OPERATORSTEXT_OPERATORSLOGICAL_OPERATORSsetunionr^rrrengine DBConnectionrr8r9r:rCsc"      -053UUNN0 Af78 sDII -33&1"#          , U> U  U "# }@/ Z&&--s23 n{nr9