j/ddlmZddlZddlmZmZmZmZmZm Z m Z m Z ddl m Z ddlmZddlmZddlmZerddlmZ d d d ZGd d eZy)) annotationsN) TYPE_CHECKINGAnyDictIterableListOptionalTupleType)Document) Embeddings) get_from_env) VectorStore)Clientc ddl}|s5|xs tdd} |xs tdd}|j ||}n-t ||jst dt| |j|S#t$r tdwxYw#t$rYwwxYw#t $r}t d |d}~wwxYw) Nrz^Could not import meilisearch python package. Please install it with `pip install meilisearch`.urlMEILI_HTTP_ADDRapi_keyMEILI_MASTER_KEY)rrz8client should be an instance of meilisearch.Client, got z"Failed to connect to Meilisearch: ) meilisearch ImportErrorr Exceptionr isinstance ValueErrortypeversion)clientrrres v/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/vectorstores/meilisearch.py_create_clientr s   ;\%):; Li9K!LG##W#=  2 2 3FtF|n U  C M)   @       C=aSABBCs4A=B+B$=B B! B!$ C-B;;Cc jeZdZdZ ddd ddZ d dd Z d dd Z d dd Z d dd Z d dd Z e ddddddddidf ddZ y) Meilisearcha`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) embeddersct|||}||_||_||_||_||_||_|jjt|jj||_ y)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__zMeilisearch.__init__Qsq v3H %#!)##'<<#5#5   !$  9 %  defaultc  t|}g}|+|Dcgc] }tjj"}}||Dcgc]}i}}|jj |}t |D]H\} } || } || } | | |j<|| } |jd| d|| i|j| iJ|jjt|jj||Scc}wcc}w)a!Run 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)r/r0r* add_documents)r3texts metadatasids embedder_namekwargsdocs_embedding_vectorsir$r<r%r4s r add_textszMeilisearch.add_textsjs *U  ;-23U4::<##UC3  %*+UUI+ OO;;EB 'GAtQB |H'+HT^^ $)!,I KK"M?Y ?))*X  ( 3t//01??E )4+s %C< Dc d|j|||||}|Dcgc]\}}| c}}Scc}}w)aReturn meilisearch documents most similar to the query. Args: query (str): Query text for which to find similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. )queryrIkfilterrJ)similarity_search_with_score) r3rQrRrSrIrJdocs_and_scoresdocrLs rsimilarity_searchzMeilisearch.similarity_searchsF*;;' < #22/Q/222 ,c h|jj|}|j|||||}|S)a%Return meilisearch documents most similar to the query, along with scores. Args: query (str): Query text for which to find similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. r4rIrRrSrJ)r+ embed_query'similarity_search_by_vector_with_scores)r3rQrRrSrIrJ_queryrKs rrTz(Meilisearch.similarity_search_with_scoresC*,,U3;;' <  r9c dg}|jjt|jj d|d|d||dd}|dD]^}||j } |j | vs!| j|j } |d} |jt| | | f`|S) #Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. g?) semanticRatioembedderT)vectorhybridlimitrSshowRankingScorehits _rankingScore) page_contentr%) r)r/r0r*searchr-r,poprDr ) r3r4rIrRrSrJrKresultsresultr%r$semantic_scores rr\z3Meilisearch.similarity_search_by_vector_with_scoress*,,$$S)9)9%:;BB #,/]K $(    foFd001H~~)||DNN3!'!8  dXF& & r9c d|j|||||}|Dcgc]\}}| c}}Scc}}w)r_rZ)r\) r3r4rRrSrIrJrKrVrLs rsimilarity_search_by_vectorz'Meilisearch.similarity_search_by_vectorsF*;;' < #''$Q$'''rXc  ht|||}||| ||}|j|| ||| | |S)aConstruct 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()r4r&rr5)rFrIrGrHr6r7)r rO)clsrFr4rGrrrr5rHr6r7r&rIrJ vectorstores r from_textszMeilisearch.from_texts%sXV v3H!  '%  r9)NNNr#r$r%)r4r rOptional[Client]r Optional[str]rrvr5r0r6r0r7r0r&Optional[Dict[str, Any]])NNr:) rFz Iterable[str]rGOptional[List[dict]]rHOptional[List[str]]rIrvrJrreturn List[str])Nr:) rQr0rRintrSOptional[Dict[str, str]]rIrvrJrrzList[Document]) rQr0rRr}rSr~rIrvrJrrzList[Tuple[Document, float]])r:r|N) r4 List[float]rIrvrRr}rSrwrJrrzr) r4rrRr}rSr~rIrvrJrrzr)rrzType[Meilisearch]rFr{r4r rGrxrrurrvrrvr5r0rHryr6rvr7rvr&zDict[str, Any]rIrvrJrrzr") __name__ __module__ __qualname____doc__r8rOrWrTr\rp classmethodrtr9rr"r"-s!L$(!!%*&&/3&&!& &  &  &&&,&8+/#''0 ..(.! . % .  . .f+/'0 33 3) 3 % 3  3 3B+/'0  )  %    &F(1+/ --%-  - ) -  - &-d+/'0 (( () ( % (  ( (< +/#'!!%*#'"(&0$&'0: :::( : ! :  :::!: :$:":%:: ::r9r")NNN)rrurrvrrvrzr) __future__rr?typingrrrrrr r r langchain_core.documentsr langchain_core.embeddingsr langchain_core.utilsrlangchain_core.vectorstoresrrrr r"rr9rrsh" RRR-0-3" $!