o Zh@@sddlmZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZddlZddlmZddlmZddlmZddlmZerLddlZGd d d eZdS) ) annotationsN)repeat) TYPE_CHECKINGAnyDictIterableListOptionalTupleTypeUnionDocument) Embeddings) VectorStore)maximal_marginal_relevancec@seZdZdZ  dVdWddZedXddZ  dYdZddZe     d[d\d'd(Z d]d,d-Z . d^d_d3d4Z . d^d`d6d7Z . d^dad9d:Z dbd|z1SupabaseVectorStore.add_texts..)_texts_to_documentsr$embed_documentslist add_vectors)r%r+r-r/r1docsvectorsr&r&r' add_textsus zSupabaseVectorStore.add_textsrr clsType['SupabaseVectorStore']Optional[List[dict]] Optional[supabase.client.Client] Optional[str]'SupabaseVectorStore'c Ksj|std|s td||} dd|D}|||} |j||| | ||fi| ||||||dS)z9Return VectorStore initialized from texts and embeddings.zSupabase client is required.z)Supabase document table_name is required.cSr3r&r4r7r&r&r'r:r;z2SupabaseVectorStore.from_texts..)rrrrr) ValueErrorr=r< _add_vectors) rCr+rr-rrrrr/r1r*r@r&r&r' from_textss&   zSupabaseVectorStore.from_textsrAList[List[float]]List[Document]cCs||j|j||||jSr))rJr#rr)r%rArr/r&r&r'r?szSupabaseVectorStore.add_vectorsquerykfilterOptional[Dict[str, Any]]cK$|j|}|j|f||d|SNrPrQ)r$ embed_querysimilarity_search_by_vectorr%rOrPrQr1Zvectorr&r&r'similarity_searchs z%SupabaseVectorStore.similarity_search List[float]cKs*|j|f||d|}dd|D}|S)NrUcSsg|]\}}|qSr&r&)r8docr9r&r&r'r:zCSupabaseVectorStore.similarity_search_by_vector..)1similarity_search_by_vector_with_relevance_scores)r%rrPrQr1resultrr&r&r'rWsz/SupabaseVectorStore.similarity_search_by_vectorList[Tuple[Document, float]]cKrSrT)r$rVr]rXr&r&r''similarity_search_with_relevance_scoress z;SupabaseVectorStore.similarity_search_with_relevance_scoresDict[str, Any]cCst|d}|r ||d<|S)N)Zquery_embeddingrQ)dict)r%rOrQretr&r&r' match_argss zSupabaseVectorStore.match_argspostgrest_filterscore_thresholdOptional[float]cs |r9|D]2\}}t|tr8d|vr8|d}ddd|D} d|d| d} |r6d|d | d}q| }q|||} |j|j| } |rU| j d d|d| _| j d || _| } d d | j D}durfdd |D}t |dkrt d|S)Nz$in,css |] }dt|dVqdS)'N)r)r8vr&r&r' szXSupabaseVectorStore.similarity_search_by_vector_with_relevance_scores..z metadata->>z IN ()(z) and (andlimitcSs>g|]}|drt|di|ddd|ddfqS)contentmetadatarq page_content similarity)getrr8searchr&r&r'r:s   zYSupabaseVectorStore.similarity_search_by_vector_with_relevance_scores..cs g|] \}}|kr||fqSr&r&)r8r[rurfr&r'r:s rzDNo relevant docs were retrieved using the relevance score threshold )items isinstancerbjoinrdr#rpcrparamssetexecutedatalenwarningswarn)r%rOrPrQrerfkeyvalueZ in_valuesZ values_strZ new_filtermatch_documents_params query_builderres match_resultr&rzr'r]s@     zESupabaseVectorStore.similarity_search_by_vector_with_relevance_scores(List[Tuple[Document, float, np.ndarray]]c Csd|||}|j|j|}|r|jdd|d|_|jd||_|}dd|jD}|S)Nrnrmrlroc Ss\g|]*}|drt|di|ddd|ddtj|dddtjdd fqS) rprqrrrsrurvrz[]rh)sep)rwrnpZ fromstringstripfloat32rxr&r&r'r:.s    zXSupabaseVectorStore.similarity_search_by_vector_returning_embeddings..)rdr#r~rrrrr) r%rOrPrQrerrrrr&r&r'0similarity_search_by_vector_returning_embeddingss   zDSupabaseVectorStore.similarity_search_by_vector_returning_embeddings"Optional[Iterable[Dict[Any, Any]]]cCs(|durti}ddt||D}|S)z:Return list of Documents from list of texts and metadatas.NcSsg|] \}}t||dqS)rtrqr )r8textrqr&r&r'r:Js z;SupabaseVectorStore._texts_to_documents..)rzip)r+r-r@r&r&r'r<As z'SupabaseVectorStore._texts_to_documentsc  sfddt|D}g}tdt||D],} || | |} ||| } t| jdkr6tddd| jD|q|S)zAdd vectors to Supabase table.cs2g|]\}}||j||jdqS))idrprrqr)r8idxrrr/r1r&r'r:]sz4SupabaseVectorStore._add_vectors..rzError inserting: No rows addedcSs$g|]}|drt|dqSr)rwrr8ir&r&r'r:qs$) enumeraterangerfrom_Zupsertrr Exceptionextend) rrrArr/rr1rowsZid_listrchunkr^r&rr'rJQs   z SupabaseVectorStore._add_vectors?fetch_k lambda_multfloatc  s\|||}dd|Ddd|D}ttj|gtjd|||d}fdd|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. cSg|]}|dqS)rr&r8Z doc_tupler&r&r'r:r\zOSupabaseVectorStore.max_marginal_relevance_search_by_vector..cSr)r&rr&r&r'r:r\)Zdtype)rPrcsg|]}|qSr&r&rZmatched_documentsr&r'r:r\)rrrarrayr) r%rrPrrr1r^Zmatched_embeddingsZ mmr_selectedZfiltered_documentsr&rr''max_marginal_relevance_search_by_vectorwsz;SupabaseVectorStore.max_marginal_relevance_search_by_vectorcKs"|j|}|j||||d}|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$rVr)r%rOrPrrr1rr@r&r&r'max_marginal_relevance_searchs 9z1SupabaseVectorStore.max_marginal_relevance_searchcKsN|durtddd|D}|D]}|j|jd|dqdS)zUDelete by vector IDs. Args: ids: List of ids to delete. NzNo ids provided to delete.cSsg|]}d|iqSrr&)r8rr&r&r'r:s z.SupabaseVectorStore.delete..r)rIr#rrdeleteeqr)r%r/r1rrowr&r&r'rs$zSupabaseVectorStore.delete)rN) rrrrrrrrrrrr)rr)NN) r+r,r-r.r/r0r1rrr2)NNrr rN)rCrDr+r2rrr-rErrFrrGrrrrr/r0r1rrrH)rArLrrMr/r2rr2)rNN) rOrrPrrQrRr1rrrM) rrZrPrrQrRr1rrrM) rOrrPrrQrRr1rrr_)rOrZrQrRrra)NNN) rOrZrPrrQrRrerGrfrgrr_) rOrZrPrrQrRrerGrrr))r+r,r-rrrM)rrrrrArLrrMr/r2rrr1rrr2)rNrr) rrZrPrrrrrr1rrrM) rOrrPrrrrrr1rrrM)r/r0r1rrr)__name__ __module__ __qualname____doc__r(propertyr*rB classmethodrKr?rYrWr`rdr]r staticmethodr<rJrrrr&r&r&r'rsd?     #       B & ( - ?r) __future__rr5r itertoolsrtypingrrrrrr r r r numpyrZlangchain_core.documentsrZlangchain_core.embeddingsrZlangchain_core.vectorstoresrZ&langchain_community.vectorstores.utilsrr!rr&r&r&r's  ,