a bg3K@sddlmZddlZddlZddlmZmZmZmZm Z m Z m Z m Z ddl mZddlZddlmZddlmZddlmZddlmZerddlZd d d d d d ZddddddddZdddddZdddddZGdddeZdS)) annotationsN) TYPE_CHECKINGAnyCallableDictIterableListOptionalTupleuuid4)Document) Embeddings) VectorStore)maximal_marginal_relevancestrr) index_nametext_keyreturncCs||dgdgdS)Ntext)nameZdataType)class properties)rrrrw/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/vectorstores/weaviate.py_default_schemas r Optional[str]rzweaviate.Client)urlapi_keykwargsrcKstz ddl}Wnty&tdYn0|p6tjd}|pFtjd}|rZ|jj|dnd}|jf||d|S)Nr_Could not import weaviate python package. Please install it with `pip install weaviate-client`Z WEAVIATE_URLZWEAVIATE_API_KEY)r)rZauth_client_secret)weaviate ImportErrorosenvirongetauthZ AuthApiKeyClient)rrrr!r&rrr_create_weaviate_client(s   r(float)valrcCsdddt|S)N)npexp)r*rrr_default_score_normalizer:sr.)valuercCst|tjr|S|SN) isinstancedatetime isoformat)r/rrr_json_serializable>s r4c@sJeZdZdZddedfddddddd d d d Zedd ddZdd ddZd?dddddddZ d@dddddddZ dAddddddd Z dBd!dddd"d#d$Z dCdddd'ddd(d)d*Z dDd!ddd'ddd+d,d-ZdEdddd.dd/d0ZedFdddddd1d2ed3dd4dd5d6d6d7d6dd dddd8 d9d:ZdGddd;d<d=d>ZdS)HWeaviatea`Weaviate` vector store. To use, you should have the ``weaviate-client`` python package installed. Example: .. code-block:: python import weaviate from langchain_community.vectorstores import Weaviate client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...) weaviate = Weaviate(client, index_name, text_key) NTrrzOptional[Embeddings]zOptional[List[str]]z"Optional[Callable[[float], float]]bool)clientrr embedding attributesrelevance_score_fnby_textc Csz ddl}Wnty&tdYn0t||jsFtdt|||_||_||_||_ |j g|_ ||_ ||_ |dur|j |dS)z Initialize with Weaviate client.rNz_Could not import weaviate python package. Please install it with `pip install weaviate-client`.z5client should be an instance of weaviate.Client, got )r!r"r1r' ValueErrortype_client _index_name _embedding _text_key _query_attrsr:_by_textextend) selfr7rrr8r9r:r;r!rrr__init__Ts&       zWeaviate.__init__)rcCs|jSr0)r@rErrr embeddingsvszWeaviate.embeddingszCallable[[float], float]cCs|jr |jStSr0)r:r.rGrrr_select_relevance_score_fnzsz#Weaviate._select_relevance_score_fnz Iterable[str]zOptional[List[dict]]z List[str])texts metadatasrrc Ksddlm}g}d}|jr8t|ts,t|}|j|}|jj}t|D]\}} |j | i} |dur|| D]\} } t | | | <qp|t } d|vr|d|} nd|vr|d|} |j | |j| |r||nd|dd|| qJWdn1s0Y|S)z4Upload texts with metadata (properties) to Weaviate.rget_valid_uuidNuuidsidstenant) data_object class_nameuuidvectorrP) weaviate.utilrMr@r1listembed_documentsr>batch enumeraterAitemsr4r add_data_objectr?r%append)rErJrKrrMrOrHrXirdata_propertieskeyr*_idrrr add_textss6       ,zWeaviate.add_textsintzList[Document])querykrrcKsP|jr|j||fi|S|jdur,td|j|}|j||fi|SdS)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. NzC_embedding cannot be None for similarity_search when _by_text=False)rCsimilarity_search_by_textr@r< embed_querysimilarity_search_by_vector)rErdrerr8rrrsimilarity_searchs   zWeaviate.similarity_searchc Ksd|gi}|dr"|d|d<|jj|j|j}|drP||d}|drj||d}|dr||d}|| | }d|vrt d|dg}|d d |jD]"}| |j } |t| |d q|S) rfconceptssearch_distance certainty where_filterrP additionalerrorsError during query: dataGetZ page_contentmetadata)r%r>rdr?rB with_where with_tenantwith_additionalwith_near_text with_limitdor<poprAr\r ) rErdrercontent query_objresultdocsresrrrrrgs$      z"Weaviate.similarity_search_by_textz List[float])r8rerrc Ksd|i}|jj|j|j}|dr6||d}|drP||d}|drj||d}|| | }d|vrt d|dg}|dd|jD]"}| |j } |t| |d q|S) z:Look up similar documents by embedding vector in Weaviate.rTrnrProrprqrrrsrt)r>rdr%r?rBrvrwrxwith_near_vectorrzr{r<r|rAr\r ) rEr8rerrTr~rrrrrrrris     z$Weaviate.similarity_search_by_vector?r))rdrefetch_k lambda_multrrcKs:|jdur|j|}ntd|j|f|||d|S)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. NzCmax_marginal_relevance_search requires a suitable Embeddings object)rerr)r@rhr<'max_marginal_relevance_search_by_vector)rErdrerrrr8rrrmax_marginal_relevance_searchs z&Weaviate.max_marginal_relevance_search)r8rerrrrcKsd|i}|jj|j|j}|dr6||d}|drP||d}|d| | }|dd|j} dd| D} t t || ||d} g} | D]<} | | |j}| | d | | }| t||d q| S) aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. rTrnrPrrrscSsg|]}|ddqS) _additionalrTr).0rrrr BzDWeaviate.max_marginal_relevance_search_by_vector..)rerrrt)r>rdr%r?rBrvrwrxrrzr{rr,arrayr|rAr\r )rEr8rerrrrTr~resultspayloadrHZ mmr_selectedridxrmetarrrrs0   z0Weaviate.max_marginal_relevance_search_by_vectorzList[Tuple[Document, float]]c Ks<|jdurtdd|gi}|dr4|d|d<|jj|j|j}|drb||d}|dr|||d}|j |}|j sd|i}| | | d}n|| | d}d |vrtd |d g} |d d |jD]:} | |j} t| d d|} | t| | d| fq| S)z Return list of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity. Nz:_embedding cannot be None for similarity_search_with_scorerkrlrmrnrPrTrprqrrrsrrt)r@r<r%r>rdr?rBrvrwrhrCrrzrxr{ryr|rAr,dotr\r ) rErdrerr}r~Zembedded_queryrTrZdocs_and_scoresrrZscorerrrsimilarity_search_with_scoreOsF         z%Weaviate.similarity_search_with_scorerF)r7 weaviate_urlweaviate_api_key batch_sizerrr;r:rzOptional[weaviate.Client]rz Optional[int]) rJr8rKr7rrrrrr;r:rrc szddlmWn.ty>} ztd| WYd} ~ n d} ~ 00|pNt||d}|rb|jj|d|prdtj}t|| }|j |s|j ||r| |nd}|rt |dnd}d| vr| d}nfd d tt|D}|j}t|D]x\}}| |i}|dur<||D]}|||||<q$||}|||d }|durf|||d <|jfi|q|Wdn1s0Y|||| f||| | d | S)avConstruct Weaviate wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Creates a new index for the embeddings in the Weaviate instance. 3. Adds the documents to the newly created Weaviate index. This is intended to be a quick way to get started. Args: texts: Texts to add to vector store. embedding: Text embedding model to use. metadatas: Metadata associated with each text. client: weaviate.Client to use. weaviate_url: The Weaviate URL. If using Weaviate Cloud Services get it from the ``Details`` tab. Can be passed in as a named param or by setting the environment variable ``WEAVIATE_URL``. Should not be specified if client is provided. weaviate_api_key: The Weaviate API key. If enabled and using Weaviate Cloud Services, get it from ``Details`` tab. Can be passed in as a named param or by setting the environment variable ``WEAVIATE_API_KEY``. Should not be specified if client is provided. batch_size: Size of batch operations. index_name: Index name. text_key: Key to use for uploading/retrieving text to/from vectorstore. by_text: Whether to search by text or by embedding. relevance_score_fn: Function for converting whatever distance function the vector store uses to a relevance score, which is a normalized similarity score (0 means dissimilar, 1 means similar). kwargs: Additional named parameters to pass to ``Weaviate.__init__()``. Example: .. code-block:: python from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import Weaviate embeddings = OpenAIEmbeddings() weaviate = Weaviate.from_texts( texts, embeddings, weaviate_url="http://localhost:8080" ) rrLr N)rr)rZ LangChain_rNcsg|]}tqSrr )r_rLrrrrz'Weaviate.from_texts..)rSrQrRrT)r8r9r:r;)rUrMr"r(rX configurer hexrschemaexistsZ create_classrWrVkeysr|rangelenrYr[flush)clsrJr8rKr7rrrrrr;r:rerrHr9rNrXr]rr^r_r`paramsrrLr from_textssf@       (zWeaviate.from_textsNone)rOrrcKs.|durtd|D]}|jjj|dqdS)zUDelete by vector IDs. Args: ids: List of ids to delete. NzNo ids provided to delete.)rS)r<r>rQdelete)rErOridrrrrszWeaviate.delete)N)rb)rb)rb)rbrr)rbrr)rb)N)N)__name__ __module__ __qualname____doc__r.rFpropertyrHrIrarjrgrirrr classmethodrrrrrrr5DsR" , '50,r5)NN) __future__rr2r#typingrrrrrrr r rSr numpyr,Zlangchain_core.documentsr Zlangchain_core.embeddingsrZlangchain_core.vectorstoresrZ&langchain_community.vectorstores.utilsrr!rr(r.r4r5rrrrs$ (