tjddlmZddlmZmZmZmZmZmZm Z m Z ddl Z ddl mZddlmZddlmZddlmZddlmZmZdd lmZdd ZGd deZdS)) annotations)AnyDictIterableListOptionalTupleUnioncastN)Document) Embeddings guard_import) VectorStore) AddableMixinDocstore)InMemoryDocstorereturnrc tdS)z= Import usearch if available, otherwise raise error. usearch.indexrf/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/vectorstores/usearch.pydependable_usearch_importrs  ( ((rc`eZdZdZd d Z d!d"dZ d#d$dZ d#d%dZe d&d'dZ d S)(USearchzc`USearch` vector store. To use, you should have the ``usearch`` python package installed. embeddingr indexrdocstorerids List[str]c>||_||_||_||_dS)z%Initialize with necessary components.N)rrrr )selfrrrr s r__init__zUSearch.__init__s$#   rNtexts Iterable[str] metadatasOptional[List[Dict]]&Optional[Union[np.ndarray, list[str]]]kwargsrc  t|jtstd|jd|jt |}g}t|D]5\}}|r||ni} |t|| 6|~|j rKt|j ddz tj fdt|D}nUtj dt|D}n)t|t rtj |}|jtj |tj ||jt!t#|||j |t't(t*|S) alRun 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 unique IDs. Returns: List of ids from adding the texts into the vectorstore. zSIf trying to add texts, the underlying docstore should support adding items, which z does not page_contentmetadataNc:g|]\}}t|zSrstr).0id_last_ids r z%USearch.add_texts..Is)PPPeb!GbL 1 1PPPrc2g|]\}}t|Srr2r4r5r6s rr8z%USearch.add_texts..Ks"FFFEBBFFFr) isinstancerr ValueErrorrembed_documentslist enumerateappendr r intnparrayradddictzipextendr rr3tolist) r#r%r'r r* embeddings documentsitextr.r7s @r add_textszUSearch.add_texts)s"$-66 @'+}@@@  ^33DKK@@   '' M MGAt'08y||bH   X4(KKK L L L L ;x Hdhrl++a/hPPPPy?O?OPPPQQhFFYu5E5EFFFGG T " " (3--C rx}}bhz&:&:;;; $s3 2233444 DIszz||,,,rqueryr3krAList[Tuple[Document, float]]c|j|}|jt j||}g}t |j|jD]m\}}|j t|}t|tstd|d||||fn|S)a Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of documents most similar to the query with distance. Could not find document for id , got )r embed_queryrsearchrBrCrFkeys distancesrr3r;r r<r@) r#rOrPquery_embeddingmatchesdocs_with_scoresr5scoredocs rsimilarity_search_with_scorez$USearch.similarity_search_with_scoreTs.44U;;*##BH_$=$=qAA9;W\7+<== 2 2IB-&&s2ww//Cc8,, T !R2!R!RS!R!RSSS  # #S%L 1 1 1 1rList[Document]c x|j|}|jt j||}g}|jD]h}|jt|}t|tstd|d|| |i|S)zReturn docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of Documents most similar to the query. rSrT) rrUrrVrBrCrWrr3r;r r<r@) r#rOrPr*rYrZdocsr5r]s rsimilarity_searchzUSearch.similarity_searchns.44U;;*##BH_$=$=qAA!,  B-&&s2ww//Cc8,, T !R2!R!RS!R!RSSS KK     rcosmetricc ||}g}|,tjdt|D}n)t |t rtj|}t|D]5\} } |r|| ni} |t| | 6ttt||} td} | t|d|}|tj|tj||||| tt t"|S)aWConstruct USearch wrapper from raw documents. This is a user friendly interface that: 1. Embeds documents. 2. Creates an in memory docstore 3. Initializes the USearch database This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import USearch from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() usearch = USearch.from_texts(texts, embeddings) Nc2g|]\}}t|Srr2r:s rr8z&USearch.from_texts..s"BBBACGGBBBrr,rr)ndimrd)r=rBrCr?r;r>r@r rrErFrIndexlenrDr rr3rH)clsr%rr'r rdr*rIrJrKrLr.rusearchrs r from_textszUSearch.from_textssZ4..u55 $& ;(BB51A1ABBBCCCC T " " (3--C '' M MGAt'08y||bH   X4(KKK L L L L#DS))<)<$=$=>>// 3z!}#5#5f EE "(3--*!5!5666s9eXtDIszz||/L/LMMMr)rr rrrrr r!)NN) r%r&r'r(r r)r*rrr!)rN)rOr3rPrArrQ)rOr3rPrAr*rrr_)NNrc)r%r!rr r'r(r r)rdr3r*rrr) __name__ __module__ __qualname____doc__r$rMr^rb classmethodrlrrrrrs     +/6: )-)-)-)-)-\     :6 +/6: (N(N(N(N[(N(N(Nrr)rr) __future__rtypingrrrrrr r r numpyrBlangchain_core.documentsr langchain_core.embeddingsr langchain_core.utilsrlangchain_core.vectorstoresr!langchain_community.docstore.baserr&langchain_community.docstore.in_memoryrrrrrrr{s-""""""JJJJJJJJJJJJJJJJJJJJ------000000------333333DDDDDDDDCCCCCC))))\N\N\N\N\Nk\N\N\N\N\Nr