IgddlmZddlmZmZmZmZmZmZm Z ddl Z ddl m Z ddlmZddlmZddlmZddlmZmZdd lmZd d ZGd d eZy)) annotations)AnyDictIterableListOptionalTupleUnionN)Document) Embeddings guard_import) VectorStore) AddableMixinDocstore)InMemoryDocstorectdS)z= Import usearch if available, otherwise raise error. usearch.indexr e/var/www/html/answerous/venv/lib/python3.12/site-packages/langchain_community/vectorstores/usearch.pydependable_usearch_importrs  ((rceZdZdZ ddZ d d dZ d d dZ d d dZe d ddZ y)USearchzc`USearch` vector store. To use, you should have the ``usearch`` python package installed. c<||_||_||_||_y)z%Initialize with necessary components.N) embeddingindexdocstoreids)selfrrrrs r__init__zUSearch.__init__s #   rNc ft|jtstd|jd|jj t |}g}t|D]*\}}|r||ni} |jt|| ,t|jddz} |>tjt|D cgc]\} } t| | zc} } }n%t|t rtj|}|jj!tj|tj||jj!t#t%|||jj'||j)Scc} } w)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_contentmetadata) isinstancerr ValueErrorrembed_documentslist enumerateappendr intrnparraystrradddictzipextendtolist) r texts metadatasrkwargs embeddings documentsitextr%last_idid_s r add_textszUSearch.add_texts)sS"$--6''+}}oY@  ^^33DK@   ' MGAt'0y|bH   X4(K L Mdhhrl#a' ;((9U;KL%"aC" -LMC T "((3-C rxx}bhhz&:; $s3 234 zz|Ms F- c|jj|}|jjt j ||}g}t |j|jD]]\}}|jjt|}t|tstd|d||j||f_|S)a Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of documents most similar to the query with distance. Could not find document for id , got )r embed_queryrsearchr/r0r4keys distancesrr1r(r r)r-) r querykquery_embeddingmatchesdocs_with_scoresr?scoredocs rsimilarity_search_with_scorez$USearch.similarity_search_with_scorePs..44U;**##BHH_$=qA9;W\\7+<+<= 2IB--&&s2w/Cc8, #B2$fSE!RSS  # #S%L 1  2  rc l|jj|}|jjt j ||}g}|j D]X}|jjt|}t|tstd|d||j|Z|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. rCrD) rrErrFr/r0rGrr1r(r r)r-) r rIrJr9rKrLdocsr?rOs rsimilarity_searchzUSearch.similarity_searchjs..44U;**##BHH_$=qA!,, B--&&s2w/Cc8, #B2$fSE!RSS KK     rc |j|}g}|;tjt|D cgc]\} } t | c} } }n%t |t rtj|}t|D]*\} } |r|| ni} |jt| | ,ttt||}td}|jt|d|}|jtj|tj||||||j!Scc} } w)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) r#rr)ndimmetric)r*r/r0r,r1r(r+r-r rr3r4rIndexlenr2r6)clsr7rr8rrVr9r:r;r?r@r<r=r%rusearchrs r from_textszUSearch.from_textss4..u5 $& ;((51ABACGBCC T "((3-C ' MGAt'0y|bH   X4(K L M$DS))<$=>/ 3z!}#5f E "((3-*!569eXszz|<<CsE )rr rrrrr List[str])NN) r7z Iterable[str]r8Optional[List[Dict]]r&Optional[Union[np.ndarray, list[str]]]r9rreturnr\))rIr1rJr.r_zList[Tuple[Document, float]])rIr1rJr.r9rr_zList[Document])NNcos)r7r\rr r8r]rr^rVr1r9rr_r) __name__ __module__ __qualname____doc__r!rArPrS classmethodr[rrrrrs)       +/6: %%(%4 %  %  %T     &  :    6 +/6: (=(=(=( (= 4 (=  (=(= (=(=rr)r_r) __future__rtypingrrrrrr r numpyr/langchain_core.documentsr langchain_core.embeddingsr langchain_core.utilsrlangchain_core.vectorstoresr!langchain_community.docstore.baserr&langchain_community.docstore.in_memoryrrrrrrrps9"DDD-0-3DC)X=kX=r