a bg@sdZddlZddlZddlmZmZmZmZmZm Z ddl Z ddl m Z ddl mZddlmZGdddZGd d d eZdS) ah Pathway Vector Store client. The Pathway Vector Server is a pipeline written in the Pathway framweork which indexes all files in a given folder, embeds them, and builds a vector index. The pipeline reacts to changes in source files, automatically updating appropriate index entries. The PathwayVectorClient implements the LangChain VectorStore interface and queries the PathwayVectorServer to retrieve up-to-date documents. You can use the client with managed instances of Pathway Vector Store, or run your own instance as described at https://pathway.com/developers/user-guide/llm-xpack/vectorstore_pipeline/ N)AnyCallableIterableListOptionalTupleDocument) Embeddings) VectorStorec@sxeZdZdeeeeeedddZdeeeeeedddZ e Z ed d d Z deeeee d d dZ dS)_VectorStoreClientN)hostporturlcCsRd}|dur$|s|rt|||_n*|dur4t||p:d}d|d||_dS)av A client you can use to query :py:class:`VectorStoreServer`. Please provide aither the `url`, or `host` and `port`. Args: - host: host on which `:py:class:`VectorStoreServer` listens - port: port on which `:py:class:`VectorStoreServer` listens - url: url at which `:py:class:`VectorStoreServer` listens zCEither (`host` and `port`) or `url` must be provided, but not both.NPzhttp://:) ValueErrorr)selfr rrerrrv/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/vectorstores/pathway.py__init__sz_VectorStoreClient.__init__querykmetadata_filterreturncCsX||d}|dur||d<|jd}tj|t|ddidd}|}t|d d d S) av Perform a query to the vector store and fetch results. Args: - query: - k: number of documents to be returned - metadata_filter: optional string representing the metadata filtering query in the JMESPath format. The search will happen only for documents satisfying this filtering. )rrNrz /v1/retrieve Content-Typeapplication/jsonr)dataheaderstimeoutcSs|dS)Ndistr)xrrrRz*_VectorStoreClient.query..)key)rrequestspostjsondumpssorted)rrrrr rresponse responsesrrrr9s  z_VectorStoreClient.queryrcCs*|jd}tj|iddid}|}|S)z.Fetch basic statistics about the vector store.z/v1/statisticsrrr*r!rr(r)r*)rrr-r.rrrget_vectorstore_statisticsWs z-_VectorStoreClient.get_vectorstore_statisticsrfilepath_globpatternrcCs0|jd}tj|||dddid}|}|S)a Fetch information on documents in the vector store. Args: metadata_filter: optional string representing the metadata filtering query in the JMESPath format. The search will happen only for documents satisfying this filtering. filepath_globpattern: optional glob pattern specifying which documents will be searched for this query. z /v1/inputs)rr4rrr0r1)rrr4rr-r.rrrget_input_filescs z"_VectorStoreClient.get_input_files)NNN)rN)NN)__name__ __module__ __qualname__rstrintrrdictr__call__r2listr5rrrrr s,   r c@seZdZdZdeeeeeeddddZdeeee e e e edddZ e de eeee e e dd d d Zdeee e ed ddZdeeeee eeefdddZeegefdddZe dddZd eeeeedddZdS)!PathwayVectorClientz9 VectorStore connecting to Pathway Vector Store. N)r rrrcCst||||_dS)aO A client you can use to query Pathway Vector Store. Please provide aither the `url`, or `host` and `port`. Args: - host: host on which Pathway Vector Store listens - port: port on which Pathway Vector Store listens - url: url at which Pathway Vector Store listens N)r client)rr rrrrrrszPathwayVectorClient.__init__)texts metadataskwargsrcKs tddS)z(Pathway is not suitable for this method.zKPathway vector store does not support adding or removing texts from client.NNotImplementedError)rr@rArBrrr add_textsszPathwayVectorClient.add_texts)r@ embeddingrArBrcKs tddS)Nz>Pathway vector store does not support initializing from_texts.rC)clsr@rFrArBrrr from_textsszPathwayVectorClient.from_texts)rrrBrcKs:|dd}|rtd||j|||d}dd|DS)NrzBUnknown kwargs passed to PathwayVectorClient.similarity_search: %srrrcSs g|]}t|d|ddqS)textmetadataZ page_contentrLr.0retrrr sz9PathwayVectorClient.similarity_search..)poploggingwarningr?)rrrrBrretsrrrsimilarity_searchs z%PathwayVectorClient.similarity_searchrcCs|j|||d}dd|DS)a(Run similarity search with Pathway with distance. Args: - query (str): Query text to search for. - k (int): Number of results to return. Defaults to 4. - metadata_filter (Optional[str]): Filter by metadata. Filtering query should be in JMESPath format. Defaults to None. Returns: List[Tuple[Document, float]]: List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity. rJcSs(g|] }t|d|dd|dfqS)rKrLrMr#rrNrrrrQszDPathwayVectorClient.similarity_search_with_score..)r?)rrrrrUrrrsimilarity_search_with_scoresz0PathwayVectorClient.similarity_search_with_scorer/cCs|jS)N)Z_cosine_relevance_score_fnrrrr_select_relevance_score_fnsz.PathwayVectorClient._select_relevance_score_fncCs |jS)z.Fetch basic statistics about the Vector Store.)r?r2rXrrrr2sz.PathwayVectorClient.get_vectorstore_statisticsr3cCs|j||S)z'List files indexed by the Vector Store.)r?r5)rrr4rrrr5sz#PathwayVectorClient.get_input_files)NNN)N)N)rI)rIN)NN)r6r7r8__doc__rr9r:rrrr;rrE classmethodr rHr rVrfloatrWrrYr2r=r5rrrrr>s`       r>)rZr*rStypingrrrrrrr(Zlangchain_core.documentsr Zlangchain_core.embeddingsr Zlangchain_core.vectorstoresr r r>rrrrs    b