ha@ddlmZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZddlZddlmZddlmZddlmZddlmZerddlZGd d eZy) ) annotationsN)repeat) TYPE_CHECKINGAnyDictIterableListOptionalTupleTypeUnion)Document) Embeddings) VectorStore)maximal_marginal_relevancec*eZdZdZ d ddZeddZ d ddZe d ddZ ddZ d ddZ d dd Z d dd Z dd Z d d!d Z d d"d Ze d# d$dZe d%dZ d& d'dZ d& d(dZd#d)dZy)*SupabaseVectorStorea`Supabase Postgres` vector store. It assumes you have the `pgvector` extension installed and a `match_documents` (or similar) function. For more details: https://integrations.langchain.com/vectorstores?integration_name=SupabaseVectorStore You can implement your own `match_documents` function in order to limit the search space to a subset of documents based on your own authorization or business logic. Note that the Supabase Python client does not yet support async operations. If you'd like to use `max_marginal_relevance_search`, please review the instructions below on modifying the `match_documents` function to return matched embeddings. Examples: .. code-block:: python from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_core.documents import Document from langchain_community.vectorstores import SupabaseVectorStore from supabase.client import create_client docs = [ Document(page_content="foo", metadata={"id": 1}), ] embeddings = OpenAIEmbeddings() supabase_client = create_client("my_supabase_url", "my_supabase_key") vector_store = SupabaseVectorStore.from_documents( docs, embeddings, client=supabase_client, table_name="documents", query_name="match_documents", chunk_size=500, ) To load from an existing table: .. code-block:: python from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.vectorstores import SupabaseVectorStore from supabase.client import create_client embeddings = OpenAIEmbeddings() supabase_client = create_client("my_supabase_url", "my_supabase_key") vector_store = SupabaseVectorStore( client=supabase_client, embedding=embeddings, table_name="documents", query_name="match_documents", ) Nc ddl}||_||_|xsd|_|xsd|_|xsd|_y#t$r tdwxYw)z Initialize with supabase client.rNzXCould not import supabase python package. Please install it with `pip install supabase`. documentsmatch_documents)supabase ImportError_client _embedding table_name query_name chunk_size)selfclient embeddingrrrrs g/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/langchain_community/vectorstores/supabase.py__init__zSupabaseVectorStore.__init__Xsc   &/$3 $9(9$+ A  s 6A c|jSN)r)rs r" embeddingszSupabaseVectorStore.embeddingsqs c |xs+|Dcgc]}ttj!c}}|j||}|jj t |}|j|||Scc}wr%)struuiduuid4_texts_to_documentsrembed_documentslist add_vectors)rtexts metadatasidskwargs_docsvectorss r" add_textszSupabaseVectorStore.add_textsush77Ac$**,'7''y9//11$u+>s33 8s$A9c  $|s td|s td|j|} |D cgc]} ttj!}} |j ||} |j ||| | ||fi| ||||||Scc} w)z9Return VectorStore initialized from texts and embeddings.zSupabase client is required.z)Supabase document table_name is required.)r r!rrr) ValueErrorr-r)r*r+r, _add_vectors) clsr0r!r1r rrrr2r3r&r4r5s r" from_textszSupabaseVectorStore.from_textss;< <HI I..u5 */0Qs4::< 00&&ui8 J D#z EK !!!   1s$B cj|j|j|j||||jSr%)r:rrr)rr6rr2s r"r/zSupabaseVectorStore.add_vectorss0   LL$//7IsDOO  r'c d|jj|}|j|f||d|SN)kfilter)r embed_querysimilarity_search_by_vectorrqueryr@rAr3vectors r"similarity_searchz%SupabaseVectorStore.similarity_searchs7,,U3/t//U!FUfUUr'c f|j|f||d|}|Dcgc]\}}| }}}|Scc}}wr?)1similarity_search_by_vector_with_relevance_scores) rr!r@rAr3resultdocr4rs r"rCz/SupabaseVectorStore.similarity_search_by_vectorsPHGG  6 -3 (..VS!S. ./s -c d|jj|}|j|f||d|Sr?)rrBrIrDs r"'similarity_search_with_relevance_scoresz;SupabaseVectorStore.similarity_search_with_relevance_scoressB,,U3EtEE   *0  r'c,t|}|r||d<|S)N)query_embeddingrA)dict)rrErArets r" match_argszSupabaseVectorStore.match_argss#59 "CM r'c X|rb|jD]O\}}t|tsd|vs|d}djd|D} d|d| d} |r d|d| d}N| }Q|j ||} |j j |j| } |r%| jjd d|d| _| jjd || _| j} | jDcgc]R}|jd r?t|jd i|jd d |jddfT}}|A|Dcgc]\}}||k\r||f}}}t|dk(rtj d||Scc}wcc}}w)Nz$in,c3:K|]}dt|dyw)'N)r)).0vs r" zXSupabaseVectorStore.similarity_search_by_vector_with_relevance_scores..s)KAAc!fXQ-)Ksz metadata->>z IN ()(z) and (andlimitcontentmetadatar_ page_content similarityrzDNo relevant docs were retrieved using the relevance score threshold )items isinstancerPjoinrRrrpcrparamssetexecutedatagetrlenwarningswarn)rrEr@rApostgrest_filterscore_thresholdkeyvalue in_values values_str new_filtermatch_documents_params query_builderressearch match_resultrKrcs r"rIzESupabaseVectorStore.similarity_search_by_vector_with_relevance_scoress $lln 6 UeT*u~ %e I!$)K)K!KJ#.se5 A!FJ(-./?.@ |ST+U(+5( 6"&!? ((:PQ #0#7#7#;#;+,A.$M  -3377C ##%((  zz)$#ZZ B7!'Ir!: <-       &(4#C0j!L < A% ""1!24 1  s AF!#F&c |j||}|jj|j|}|r%|jj dd|d|_|jj d||_|j }|jDcgc]}|jdrt|jdi|jdd|jd d tj|jd djd tjd f} }| Scc}w)Nr\r[rZr]r^r_r`rarcrdr!z[]rT)sep)rRrrhrrirjrkrlrmrnp fromstringstripfloat32) rrEr@rArqrxryrzr{r|s r"0similarity_search_by_vector_returning_embeddingszDSupabaseVectorStore.similarity_search_by_vector_returning_embeddingss*"&!? ((:PQ #0#7#7#;#;+,A.$M  -3377C ##%(( zz)$#ZZ B7!'Ir!: <- JJ{B/55d;RZZS   "# sBD>c~| ti}t||Dcgc]\}}t||}}}|Scc}}w)z:Return list of Documents from list of texts and metadatas.)rbr_)rzipr)r0r1textr_r5s r"r,z'SupabaseVectorStore._texts_to_documentsAsP  r I#&eY"7 h $ :    s9c 2t|Dcgc]+\}}||||j|||jd|-} }}g} tdt | |D]} | | | |z} |j |j | j} t | jdk(r td| jD cgc].} | jdst| jd0}} | j|| Scc}}wcc} w)zAdd vectors to Supabase table.)idr^r!r_rzError inserting: No rows addedr) enumeraterbr_rangernfrom_upsertrkrl Exceptionrmr)extend)r rr6rr2rr3idxr!rowsid_listichunkrJs r"r:z SupabaseVectorStore._add_vectorsQs(#,G"4 & Y #h$S>66&%cN33      &  &  q#d)Z0 AQ^,E\\*-44U;CCEF6;;1$ @AA.4[[HAEE$K3quuT{#HCH NN3  1 & (Is0DDDc |j||}|Dcgc]}|d }}|Dcgc]}|d } }ttj|gtj| ||} | D cgc]} ||  } } | Scc}wcc}wcc} w)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. r)dtype)r@ lambda_mult)rrrarrayr) rr!r@fetch_krr3rJ doc_tuplematched_documentsmatched_embeddings mmr_selectedrfiltered_documentss r"'max_marginal_relevance_search_by_vectorz;SupabaseVectorStore.max_marginal_relevance_search_by_vectorws0FF w  B. Bc f|jj|}|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)rrBr)rrEr@rrr3r!r5s r"max_marginal_relevance_searchz1SupabaseVectorStore.max_marginal_relevance_searchs>rOO//6 ;; q'{<  r'c | td|Dcgc]}d|i}}|D]V}|jj|jj j d|dj Xycc}w)zUDelete by vector IDs. Args: ids: List of ids to delete. 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