a bg:#@s|ddlmZddlZddlmZddlmZmZmZm Z m Z m Z m Z ddl mZddlmZddlmZGdd d eZdS) ) annotationsN)repeat)AnyDictIterableListOptionalTupleTypeDocument) Embeddings) VectorStorec @s2eZdZdZddddddddZeddd d Zd@d d dddddZdAddddddddZdBd d dddddZ e dCddd dddZ e dDd ddd!d"d"dddd#d$ d%d&Z dEdd(d)dd d*d+d,ZdFdd(d)dd-d*d.d/Zd0d0d1d2d3ZdGdd4ddd5d6d7Zddd8d9ZdHdd?Zd S)IXataVectorStorez`Xata` vector store. It assumes you have a Xata database created with the right schema. See the guide at: https://integrations.langchain.com/vectorstores?integration_name=XataVectorStore strr None)api_keydb_url embedding table_namereturncCsNzddlm}Wnty*tdYn0|||d|_||_|pFd|_dS)zInitialize with Xata client.r) XataClientzPCould not import xata python package. Please install it with `pip install xata`.)rrvectorsN)Z xata.clientr ImportError_client _embedding _table_name)selfrrrrrrs/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/vectorstores/xata.py__init__s  zXataVectorStore.__init__)rcCs|jSN)rrrrr embeddings(szXataVectorStore.embeddingsNzList[List[float]]zList[Document]zOptional[List[str]]z List[str])r documentsidsrcCs||||Sr!) _add_vectors)rrr$r%rrr add_vectors,szXataVectorStore.add_vectorsz Iterable[str]zOptional[List[Dict[Any, Any]]]r)texts metadatasr%kwargsrcKs.|}|||}|jt|}||||Sr!)_texts_to_documentsrembed_documentslistr')rr(r)r%r*docsrrrr add_texts4s zXataVectorStore.add_textscCsg}t|D]Z\}}||j|d}|r4|||d<||jD]\}} |dvrB| ||<qB||q d} g} tdt|| D]Z} || | | } |j |j d| i}|j dkrt d|j d || |d q| S) z!Add vectors to the Xata database.)contentrid)r1r0rirrecordszError adding vectors to Xata:  Z recordIDs) enumerate page_contentmetadataitemsappendrangelenrr2Z bulk_insertr status_code Exceptionextend)rrr$r%rowsidxrrowkeyval chunk_sizeZid_listichunkrrrrr&As(    zXataVectorStore._add_vectorsz"Optional[Iterable[Dict[Any, Any]]])r(r)rcCs(|durti}ddt||D}|S)z:Return list of Documents from list of texts and metadatas.NcSsg|]\}}t||dqS)r6r7r ).0textr7rrr lsz7XataVectorStore._texts_to_documents..)rzip)r(r)r.rrrr+cs z#XataVectorStore._texts_to_documentsrzType['XataVectorStore']zOptional[List[dict]]z Optional[str]z'XataVectorStore') clsr(rr)rrrr%r*rc KsL|r|std||} d}|||} |||||d} | | | || S)z9Return VectorStore initialized from texts and embeddings.z$Xata api_key and db_url must be set.N)rrrr) ValueErrorr,r+r&) rMr(rr)rrrr%r*r#r.Z vector_dbrrr from_textsss  zXataVectorStore.from_textsintzOptional[dict])querykfilterr*rcKs"|j|||d}dd|D}|S)zReturn docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of Documents most similar to the query. )rTcSsg|] }|dqS)rr)rIdrrrrKz5XataVectorStore.similarity_search..)similarity_search_with_score)rrRrSrTr*docs_and_scoresr$rrrsimilarity_searchs z!XataVectorStore.similarity_searchzList[Tuple[Document, float]]c  sxj|}|d|d}|r$||d<jjj|d}|jdkrZtd|jd||d}fd d |D} | S) aRun similarity search with Chroma with distance. Args: query (str): Query text to search for. k (int): Number of results to return. Defaults to 4. filter (Optional[dict]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of documents most similar to the query text with distance in float. r)Z queryVectorcolumnsizerTpayloadr3z!Error running similarity search: r4r2cs.g|]&}t|d|d|ddfqS)r0rHxataZscore)r _extractMetadata)rIhitr"rrrKs z@XataVectorStore.similarity_search_with_score..)rZ embed_queryrdataZ vector_searchrr<r=) rrRrSrTr*rr]rGhitsrXrr"rrWs    z,XataVectorStore.similarity_search_with_scoredict)recordrcCs*i}|D]\}}|dvr |||<q |S)z:Extract metadata from a record. Filters out known columns.)r1r0rr^)r8)rrdr7rBrCrrrr_s  z XataVectorStore._extractMetadatazOptional[bool])r% delete_allr*rc s|rjddnd|durvd}tdt||D]<}||||}fdd|D}jjd|idq6ntd dS) zDelete by vector IDs. Args: ids: List of ids to delete. delete_all: Delete all records in the table. r)ndocsNicsg|]}dj|diqSdelete)tabler1rrIr1r"rrrKsz*XataVectorStore.delete.. operationsr\z%Either ids or delete_all must be set.) _delete_allwait_for_indexingr:r;rr2 transactionrN)rr%rer*rDrErFrlrr"rrhs  zXataVectorStore.deletecsjjjddgid}|jdkr.r2rcsg|]}dj|diqSrgrjrkr"rrrKsrlN) rrarRrr<r=r;r2ro)rrGr%rlrr"rrms   zXataVectorStore._delete_allfloat)timeoutrfrcCs~t}|jj|jdddidd}|jdkrHtd|jd||d |krVqzt||krntd td qd S) zeWait for the search index to contain a certain number of documents. Useful in tests. r[r)rRpager\r3zError running search: r4Z totalCountz+Timed out waiting for indexing to complete.g?N)timerraZ search_tablerr<r=sleep)rrtrfstartrGrrrrns   z!XataVectorStore.wait_for_indexing)N)NN)N)N)NNNrN)rPN)rPN)NN)rqrr)__name__ __module__ __qualname____doc__r propertyr#r'r/r& staticmethodr+ classmethodrOrYrWr_rhrmrnrrrrr s> "$& r) __future__rrw itertoolsrtypingrrrrrr r Zlangchain_core.documentsr Zlangchain_core.embeddingsr Zlangchain_core.vectorstoresrrrrrrs  $