dhSSKJr SSKJrJrJrJrJrJrJ r J r SSK r SSK Jr SSKJr SSKJr SSKJr SSKJrJr SS KJr S S jr"S S \5rg)) annotations)AnyDictIterableListOptionalTupleUnioncastN)Document) Embeddings guard_import) VectorStore) AddableMixinDocstore)InMemoryDocstorec[S5$)z5 Import usearch if available, otherwise raise error. usearch.indexr`/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/usearch.pydependable_usearch_importrs  ((rc\rSrSrSrS SjrS S SjjrS SSjjrS SSjjr\ SSSjj5r S r g)USearchz[`USearch` vector store. To use, you should have the ``usearch`` python package installed. c4XlX lX0lX@lg)z%Initialize with necessary components.N) embeddingindexdocstoreids)selfrrr r!s r__init__USearch.__init__s#  rNc V[UR[5(d[SURS35eURR [ U55n/n[U5H*upxU(aX'O0n UR[XS95 M, UcUR(aZ[URS5S-n [R"[U5V V s/sHup[X-5PM sn n 5nOh[R"[U5V V s/sHup[U 5PM sn n 5nO+[U[ 5(a[R"U5nURR![R"U5[R"U55 URR![#[%X6555 URR'U5 [)[*[UR-55$s sn n fs sn n f)a4Run 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_contentmetadata) isinstancer r ValueErrorrembed_documentslist enumerateappendr r!intnparraystrradddictzipextendr rtolist) r"texts metadatasr!kwargs embeddings documentsitextr(last_idid_s r add_textsUSearch.add_texts)s"$--66''+}}oY@  ^^33DK@   'GA'0y|bH   X4K L( ;xxdhhrl+a/hhy?OP?OebGL 1?OPQhhYu5EF5EEBB5EFG T " "((3-C rxx}bhhz&:; $s3234 DIszz|,, QFs %H $H% cURRU5nURR[R "U5U5n/n[ URUR5HaupgURR[U55n[U[5(d[SUSU35eURX45 Mc U$)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 with distance. Could not find document for id , got )r embed_queryrsearchr2r3r7keys distancesr r4r+r r,r0) r"querykquery_embeddingmatchesdocs_with_scoresrBscoredocs rsimilarity_search_with_score$USearch.similarity_search_with_scoreTs..44U;**##BHH_$=qA9;W\\7+<+<=IB--&&s2w/Cc8,, #B2$fSE!RSS  # #SL 1 >  rc |URRU5nURR[R "U5U5n/nUR H^nURR[U55n[U[5(d[SUSU35eURU5 M` U$)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. rGrH) rrIrrJr2r3rKr r4r+r r,r0) r"rMrNr<rOrPdocsrBrSs rsimilarity_searchUSearch.similarity_searchns..44U;**##BHH_$=qA!,,B--&&s2w/Cc8,, #B2$fSE!RSS KK    rc URU5n/nUc=[R"[U5V V s/sHup[ U 5PM sn n 5nO+[ U[ 5(a[R"U5n[U5H*upU(aX;O0n UR[XS95 M, [[[XH555n[S5nUR[US5US9nUR[R"U5[R"U55 U"UUU[!["[UR%555$s sn n f)aConstruct 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) r&rr)ndimmetric)r-r2r3r/r4r+r.r0r rr6r7rIndexlenr5r rr9)clsr:rr;r!r\r<r=r>rBrCr?r@r(r usearchrs r from_textsUSearch.from_textss4..u5 $& ;((51AB1ACG1ABCC T " "((3-C 'GA'0y|bH   X4K L($DS)<$=>/ 3z!}#5f E "((3-*!569eXtDIszz|/LMMCsE$ )r rr!r)rr rrr rr! List[str])NN) r:z Iterable[str]r;Optional[List[Dict]]r!&Optional[Union[np.ndarray, list[str]]]r<rreturnrc))rMr4rNr1rfzList[Tuple[Document, float]])rMr4rNr1r<rrfzList[Document])NNcos)r:rcrr r;rdr!rer\r4r<rrfr) __name__ __module__ __qualname____firstlineno____doc__r#rDrTrX classmethodra__static_attributes__rrrrrs2       +/6: )-)-()-4 )-  )-  )-\     &  :    6 +/6: (N(N(N( (N 4 (N  (N(N (N(Nrr)rfr) __future__rtypingrrrrrr r r numpyr2langchain_core.documentsr langchain_core.embeddingsr langchain_core.utilsrlangchain_core.vectorstoresr!langchain_community.docstore.baserr&langchain_community.docstore.in_memoryrrrrrrrys;"JJJ-0-3DC)\Nk\Nr