dh/SSKJr SSKrSSKJrJrJrJrJrJ r J r J r SSK J r SSKJr SSKJr SSKJr \(aSSKJr S S S jjr"S S \5rg)) annotationsN) TYPE_CHECKINGAnyDictIterableListOptionalTupleType)Document) Embeddings) get_from_env) VectorStore)ClientcSSKnU(d;U=(d [SS5nU=(d [SS5nUR XS9nO1[ XR5(d[ S[U535eUR5 U$![a [S5ef=f![a Nyf=f![ an[ S U35eSnAff=f) Nrz^Could not import meilisearch python package. Please install it with `pip install meilisearch`.urlMEILI_HTTP_ADDRapi_keyMEILI_MASTER_KEY)rrz8client should be an instance of meilisearch.Client, got z"Failed to connect to Meilisearch: ) meilisearch ImportErrorr Exceptionr isinstance ValueErrortypeversion)clientrrres d/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/meilisearch.py_create_clientr s   ;\%):; Li9K!LG###=  2 2 3 3FtF|n U  C M)   @       C=aSABBCs4B B%:B5 B"% B21B25 C?C  Cc \rSrSrSrSSS.SSjjjrSSS jjrSSS jjrSSS jjrSSS jjr SSSjjr \ SSSSSSSS0S 4 SSjj5r Sr g) Meilisearch-a`Meilisearch` vector store. To use this, you need to have `meilisearch` python package installed, and a running Meilisearch instance. To learn more about Meilisearch Python, refer to the in-depth Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/. See the following documentation for how to run a Meilisearch instance: https://www.meilisearch.com/docs/learn/getting_started/quick_start. Example: .. code-block:: python from langchain_community.vectorstores import Meilisearch from langchain_community.embeddings.openai import OpenAIEmbeddings import meilisearch # api_key is optional; provide it if your meilisearch instance requires it client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() embedders = { "theEmbedderName": { "source": "userProvided", "dimensions": "1536" } } vectorstore = Meilisearch( embedding=embeddings, embedders=embedders, client=client, index_name='langchain_demo', text_key='text') Nlangchain-demotextmetadata) embeddersc[X#US9nX lXPlXlX`lXplXlURR[UR55RU5Ul g)z#Initialize with Meilisearch client.rrrN) r _client _index_name _embedding _text_key _metadata_key _embeddersindexstrupdate_embedders_embedders_settings) self embeddingrrr index_nametext_key metadata_keyr's r__init__Meilisearch.__init__Qsc vH %#!)##'<<#5#5   !$  9 %  defaultc [U5n/nUc.UVs/sH"n[R"5RPM$ nnUcUVs/sHn0PM nnURR U5n[ U5HDupX9n X)n XUR'Xn URSU SUU 0URU 05 MF URR[UR55RU5 U$s snfs snf)aRun more texts through the embedding and add them to the vector store. Args: texts (Iterable[str]): Iterable of strings/text to add to the vectorstore. embedder_name: Name of the embedder. Defaults to "default". metadatas (Optional[List[dict]]): Optional list of metadata. Defaults to None. ids Optional[List[str]]: Optional list of IDs. Defaults to None. Returns: List[str]: List of IDs of the texts added to the vectorstore. id_vectors)listuuiduuid4hexr,embed_documents enumerater-appendr.r*r0r1r+ add_documents)r4texts metadatasids embedder_namekwargsdocs_embedding_vectorsir%r>r&r5s r add_textsMeilisearch.add_textsjs*U  ;-23U4::<##UC3  %*+UUI+ OO;;EB 'GAB |H'+T^^ $),I KK"M?Y ?))*X  ( 3t//01??E )4+s (D Dc `URUUUUUS9nUVVs/sHupxUPM snn$s snnf)aReturn meilisearch documents most similar to the query. Args: query (str): Query text for which to find similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. )queryrKkfilterrL)similarity_search_with_score) r4rTrUrVrKrLdocs_and_scoresdocrNs rsimilarity_searchMeilisearch.similarity_searchsD*;;' < #22//222 *c bURRU5nURUUUUUS9nU$)aReturn meilisearch documents most similar to the query, along with scores. Args: query (str): Query text for which to find similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. r5rKrUrVrL)r, embed_query'similarity_search_by_vector_with_scores)r4rTrUrVrKrL_queryrMs rrW(Meilisearch.similarity_search_with_scoresC*,,U3;;' <  r;c d/nURR[UR55R SUSUS.UUSS.5nUSH]nXR n UR U ;dM#U RUR 5n USn UR[XS9U 45 M_ U$) Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. g?) semanticRatioembedderT)vectorhybridlimitrVshowRankingScorehits _rankingScore) page_contentr&) r*r0r1r+searchr.r-poprFr ) r4r5rKrUrVrLrMresultsresultr&r%semantic_scores rr`3Meilisearch.similarity_search_by_vector_with_scoress*,,$$S)9)9%:;BB #,/]K $(    foF001H~~)||DNN3!'!8  dF& & r;c `URUUUUUS9nUVVs/sHupxUPM snn$s snnf)rdr^)r`) r4r5rUrVrKrLrMrYrNs rsimilarity_search_by_vector'Meilisearch.similarity_search_by_vectorsD*;;' < #''$$'''r\c T[XEUS9nU"UU UUS9nURUU UUU U S9 U$)asConstruct Meilisearch wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Adds the documents to a provided Meilisearch index. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Meilisearch from langchain_community.embeddings import OpenAIEmbeddings import meilisearch # The environment should be the one specified next to the API key # in your Meilisearch console client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embedding = OpenAIEmbeddings() embedders: Embedders index setting. embedder_name: Name of the embedder. Defaults to "default". docsearch = Meilisearch.from_texts( client=client, embedding=embedding, ) r))r5r'rr6)rHrKrIrJr7r8)r rQ)clsrHr5rIrrrr6rJr7r8r'rKrL vectorstores r from_textsMeilisearch.from_texts%sVV vH!  '%  r;)r*r/r3r,r+r.r-)NNNr$r%r&)r5r rOptional[Client]r Optional[str]rr~r6r1r7r1r8r1r'Optional[Dict[str, Any]])NNr<) rHz Iterable[str]rIOptional[List[dict]]rJOptional[List[str]]rKr~rLrreturn List[str])Nr<) rTr1rUintrVOptional[Dict[str, str]]rKr~rLrrList[Document]) rTr1rUrrVrrKr~rLrrList[Tuple[Document, float]])r<rN) r5 List[float]rKr~rUrrVrrLrrr) r5rrUrrVrrKr~rLrrr)ryzType[Meilisearch]rHrr5r rIrrr}rr~rr~r6r1rJrr7r~r8r~r'zDict[str, Any]rKr~rLrrr")__name__ __module__ __qualname____firstlineno____doc__r9rQrZrWr`rv classmethodr{__static_attributes__r;rr"r"-s!L$(!!%*&&/3&&!& &  &  &&&,&8+/#''0 ..(.! . % .  . .f+/'0 33 3) 3 % 3  3 3B+/'0  )  %    &F(1+/ --%-  - ) -  - &-d+/'0 (( () ( % (  ( (< +/#'!!%*#'"(&0$&'0: :::( : ! :  :::!: :$:":%:: ::r;r")NNN)rr}rr~rr~rr) __future__rrAtypingrrrrrr r r langchain_core.documentsr langchain_core.embeddingsr langchain_core.utilsrlangchain_core.vectorstoresrrrr r"rr;rrsh" RRR-0-3" $!