Ng=ddlmZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZddlZddlmZddlmZddlmZddlmZerddlZGd d eZdS) ) annotationsN)repeat) TYPE_CHECKINGAnyDictIterableListOptionalTupleTypeUnionDocument) Embeddings) VectorStore)maximal_marginal_relevancec"eZdZdZ dFdGdZedHdZ dIdJdZe dKdLd$Z dMd(Z dNdOd.Z dNdPd0Z dNdQd2Z dRd4Z dSdTd8Z dIdUd:Ze dVdWd<ZedXd=Z dYdZdCZ dYd[dDZdVd\dEZdS)]SupabaseVectorStorea`Supabase Postgres` vector store. It assumes you have the `pgvector` extension installed and a `match_documents` (or similar) function. For more details: https://integrations.langchain.com/vectorstores?integration_name=SupabaseVectorStore You can implement your own `match_documents` function in order to limit the search space to a subset of documents based on your own authorization or business logic. Note that the Supabase Python client does not yet support async operations. If you'd like to use `max_marginal_relevance_search`, please review the instructions below on modifying the `match_documents` function to return matched embeddings. Examples: .. code-block:: python from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_core.documents import Document from langchain_community.vectorstores import SupabaseVectorStore from supabase.client import create_client docs = [ Document(page_content="foo", metadata={"id": 1}), ] embeddings = OpenAIEmbeddings() supabase_client = create_client("my_supabase_url", "my_supabase_key") vector_store = SupabaseVectorStore.from_documents( docs, embeddings, client=supabase_client, table_name="documents", query_name="match_documents", chunk_size=500, ) To load from an existing table: .. code-block:: python from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.vectorstores import SupabaseVectorStore from supabase.client import create_client embeddings = OpenAIEmbeddings() supabase_client = create_client("my_supabase_url", "my_supabase_key") vector_store = SupabaseVectorStore( client=supabase_client, embedding=embeddings, table_name="documents", query_name="match_documents", ) Nclientsupabase.client.Client embeddingr table_namestr chunk_sizeint query_nameUnion[str, None]returnNonec ddl}n#t$rtdwxYw||_||_|pd|_|pd|_|pd|_dS)z Initialize with supabase client.rNzXCould not import supabase python package. Please install it with `pip install supabase`. documentsmatch_documentsr)supabase ImportError_client _embeddingrrr)selfrrrrrr$s e/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/supabase.py__init__zSupabaseVectorStore.__init__Xs~  OOOO   A    &/$3 $9(9$+s!c|jSN)r')r(s r) embeddingszSupabaseVectorStore.embeddingsqs texts Iterable[str] metadatasOptional[List[Dict[Any, Any]]]idsOptional[List[str]]kwargsr List[str]c |p d|D}|||}|jt|}||||S)NcNg|]"}ttj#Sruuiduuid4.0_s r) z1SupabaseVectorStore.add_texts..|s&777Ac$*,,''777r.)_texts_to_documentsr'embed_documentslist add_vectors)r(r/r1r3r5docsvectorss r) add_textszSupabaseVectorStore.add_textsuse777777''y99/11$u++>>s333r.r"r#clsType['SupabaseVectorStore']Optional[List[dict]] Optional[supabase.client.Client] Optional[str]'SupabaseVectorStore'c  |std|std||} d|D}|||} |j||| | ||fi| ||||||S)z9Return VectorStore initialized from texts and embeddings.zSupabase client is required.z)Supabase document table_name is required.cNg|]"}ttj#Sr9r:r=s r)r@z2SupabaseVectorStore.from_texts..s&000Qs4:<<  000r.)rrrrr) ValueErrorrBrA _add_vectors) rHr/rr1rrrrr3r5r-rEs r) from_textszSupabaseVectorStore.from_textss =;<< < JHII I..u55 00%000&&ui88 J D#z  EK   s!!!     r.rFList[List[float]]List[Document]cT||j|j||||jSr,)rQr&rr)r(rFr"r3s r)rDzSupabaseVectorStore.add_vectorss/   L$/7IsDO   r.querykfilterOptional[Dict[str, Any]]c X|j|}|j|f||d|SNrXrY)r' embed_querysimilarity_search_by_vectorr(rWrXrYr5vectors r)similarity_searchz%SupabaseVectorStore.similarity_searchs;,,U33/t/U!FUUfUUUr. List[float]c @|j|f||d|}d|D}|S)Nr]cg|]\}}|Sr9r9)r>docr?s r)r@zCSupabaseVectorStore.similarity_search_by_vector..s...VS!S...r.)1similarity_search_by_vector_with_relevance_scores)r(rrXrYr5resultr"s r)r_z/SupabaseVectorStore.similarity_search_by_vectorsNHG  6  -3  /.v... r.List[Tuple[Document, float]]c X|j|}|j|f||d|Sr\)r'r^rgr`s r)'similarity_search_with_relevance_scoresz;SupabaseVectorStore.similarity_search_with_relevance_scoressJ,,U33EtE    *0   r.Dict[str, Any]c4t|}|r||d<|S)N)query_embeddingrY)dict)r(rWrYrets r) match_argszSupabaseVectorStore.match_argss*#5999  #"CM r.postgrest_filterscore_thresholdOptional[float]c|||}|j|j|}|r$|jdd|d|_|jd||_|}d|jD} 8fd| D} t| dkrtj d| S) Nand()limitc g|]f}|dt|di|dd|ddfgS)contentmetadatar| page_content similarity)getrr>searchs r)r@zYSupabaseVectorStore.similarity_search_by_vector_with_relevance_scores..s    zz)$$  #ZZ B77!'Ir!:!: <--      r.c*g|]\}}|k ||fSr9r9)r>rfrrss r)r@zYSupabaseVectorStore.similarity_search_by_vector_with_relevance_scores..s7#C00j!000r.rzDNo relevant docs were retrieved using the relevance score threshold ) rqr&rpcrparamssetexecutedatalenwarningswarn) r(rWrXrYrrrsmatch_documents_params query_builderres match_results ` r)rgzESupabaseVectorStore.similarity_search_by_vector_with_relevance_scoress+"&!?!? ((:PQQ  #0#7#;#;.+...$$M  -377CC ##%%   (      &'3L <  A%% 4"144 r.(List[Tuple[Document, float, np.ndarray]]cH|||}|j|j|}|r$|jdd|d|_|jd||_|}d|jD}|S)Nrvrwrxryc dg|]}|dt|di|dd|ddtj|dddtjd fS) r{r|r}r~rrrz[],)sep)rrnp fromstringstripfloat32rs r)r@zXSupabaseVectorStore.similarity_search_by_vector_returning_embeddings..s   zz)$$ #ZZ B77!'Ir!:!: <-- JJ{B//55d;;RZS    r.)rqr&rrrrrr) r(rWrXrYrrrrrrs r)0similarity_search_by_vector_returning_embeddingszDSupabaseVectorStore.similarity_search_by_vector_returning_embeddings s"&!?!? ((:PQQ  #0#7#;#;.+...$$M  -377CC ##%%  (   "r."Optional[Iterable[Dict[Any, Any]]]c\|ti}dt||D}|S)z:Return list of Documents from list of texts and metadatas.Nc6g|]\}}t||S)rr|r)r>textr|s r)r@z;SupabaseVectorStore._texts_to_documents..:s9   h $ : : :   r.)rzip)r/r1rEs r)rAz'SupabaseVectorStore._texts_to_documents1sD  r I  "%eY"7"7     r.c fdt|D}g}tdt||D]} || | |z} ||| } t| jdkrtdd| jD||S)zAdd vectors to Supabase table.c`g|]*\}}||j||jd+S))idr{rr|r)r>idxrr"r3r5s r)r@z4SupabaseVectorStore._add_vectors..Ms^ & & & Y #h$S>6&%cN3       & & & r.rzError inserting: No rows addedc|g|]9}|dt|d:Sr)rr)r>is r)r@z4SupabaseVectorStore._add_vectors..as9HHHAEE$KKH3quuT{{##HHHr.) enumeraterangerfrom_upsertrr Exceptionextend) rrrFr"r3rr5rowsid_listrchunkrhs `` ` r)rQz SupabaseVectorStore._add_vectorsAs & & & & & & #,G"4"4 & & &  q#d))Z00 AQ^+,E\\*--44U;;CCEEF6;1$$ @AAAIHV[HHHC NN3    r.?fetch_k lambda_multfloatc  |||}d|D d|D}ttj|gtj|||} fd|D} | S)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. 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. Returns: List of Documents selected by maximal marginal relevance. cg|] }|d S)rr9r> doc_tuples r)r@zOSupabaseVectorStore.max_marginal_relevance_search_by_vector..sBBBiYq\BBBr.cg|] }|d S)r9rs r)r@zOSupabaseVectorStore.max_marginal_relevance_search_by_vector..sCCCyilCCCr.)dtype)rXrc g|] }| Sr9r9)r>rmatched_documentss r)r@zOSupabaseVectorStore.max_marginal_relevance_search_by_vector..sIIIq/2IIIr.)rrrarrayr) r(rrXrrr5rhmatched_embeddings mmr_selectedfiltered_documentsrs @r)'max_marginal_relevance_search_by_vectorz;SupabaseVectorStore.max_marginal_relevance_search_by_vectorgs0FF w  CB6BBBCCFCCC1 Hi[ 3 3 3 #    JIIILIII!!r.c l|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: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. 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. Returns: List of Documents selected by maximal marginal relevance. `max_marginal_relevance_search` requires that `query_name` returns matched embeddings alongside the match documents. The following function demonstrates how to do this: ```sql CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536), match_count int) RETURNS TABLE( id uuid, content text, metadata jsonb, embedding vector(1536), similarity float) LANGUAGE plpgsql AS $$ # variable_conflict use_column BEGIN RETURN query SELECT id, content, metadata, embedding, 1 -(docstore.embedding <=> query_embedding) AS similarity FROM docstore ORDER BY docstore.embedding <=> query_embedding LIMIT match_count; END; $$; ``` )r)r'r^r)r(rWrXrrr5rrEs r)max_marginal_relevance_searchz1SupabaseVectorStore.max_marginal_relevance_searchsDrO//66 ;; q'{<   r.c |tdd|D}|D]_}|j|jd|d`dS)zUDelete by vector IDs. Args: ids: List of ids to delete. NzNo ids provided to delete.cg|]}d|iSrr9)r>rs r)r@z.SupabaseVectorStore.delete..s1& & & b & & & r.r)rPr&rrdeleteeqr)r(r3r5rrows r)rzSupabaseVectorStore.deletes ;9:: :& &  & & &  W WC L  t / / 6 6 8 8 ; ;D#d) L L T T V V V V W Wr.)rN) rrrrrrrrrrrr )rr)NN) r/r0r1r2r3r4r5rrr6)NNr"r#rN)rHrIr/r6rrr1rJrrKrrLrrrrr3r4r5rrrM)rFrSr"rTr3r6rr6)rVN) rWrrXrrYrZr5rrrT) rrcrXrrYrZr5rrrT) rWrrXrrYrZr5rrri)rWrcrYrZrrl)NNN) rWrcrXrrYrZrrrLrsrtrri) rWrcrXrrYrZrrrLrrr,)r/r0r1rrrT)rrrrrFrSr"rTr3r6rrr5rrr6)rVrr) rrcrXrrrrrr5rrrT) rWrrXrrrrrr5rrrT)r3r4r5rrr )__name__ __module__ __qualname____doc__r*propertyr-rG classmethodrRrDrbr_rkrqrgr staticmethodrArQrrrr9r.r)rrsG88~'+ ,,,,,2X 59#' 4 4 4 4 4 +/37$/'8#'! ! ! ! [! F    +/ VVVVV+/     $+/      ,0*.+/ ,,,,,d,0*. $$$$$L9=    \ ###\#P ("("("("("Z =====~WWWWWWWr.r) __future__rr;r itertoolsrtypingrrrrr r r r r numpyrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresr&langchain_community.vectorstores.utilsrr$rr9r.r)rsX""""""                       ------000000333333MMMMMMOOOFWFWFWFWFW+FWFWFWFWFWr.