tjRddlmZddlZddlZddlZddlmZmZmZm Z m Z m Z m Z m Z mZddlZddlmZddlmZddlmZddlmZmZerddlmZdd lmZmZeje Z!dd Z"e d dZ#GddeZ$dS)) annotationsN) TYPE_CHECKINGAnyCallableIterableListOptionalTupleTypeVarUnion)Document) Embeddings) VectorStore)DistanceStrategymaximal_marginal_relevanceClient)NeighborVectorDistanceMetricreturnrcZ ddlm}n"#t$r}td|d}~wwxYw|S)NrrzoCould not import aerospike_vector_search python package. Please install it with `pip install aerospike_vector`.)aerospike_vector_searchr ImportError)res h/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/vectorstores/aerospike.py_import_aerospiker#s_2222222  E    Ms (#(AVST Aerospike)boundcFeZdZdZdddddejfdMdZedNdZdOdZ dPdZ e dQd!Z dRdSd/Z dTdUd1Z dVdWd7Z dVdXd8Z dVdYd:Z dVdZd;Zd[d=Ze d\d@Z d]d^dEZ d]d_dFZe d`dadLZdS)brzu`Aerospike` vector store. To use, you should have the ``aerospike_vector_search`` python package installed. N_vector_text_idclientr embeddingUnion[Embeddings, Callable] namespacestr index_name Optional[str] vector_keytext_keyid_keyset_namedistance_strategy7Optional[Union[DistanceStrategy, VectorDistanceMetric]]c vt} t|tstjdt|| st dt |||_||_||_ ||_ ||_ ||_ ||_ ||_|| |_dS)aInitialize with Aerospike client. Args: client: Aerospike client. embedding: Embeddings object or Callable (deprecated) to embed text. namespace: Namespace to use for storing vectors. This should match index_name: Name of the index previously created in Aerospike. This vector_key: Key to use for vector in metadata. This should match the key used during index creation. text_key: Key to use for text in metadata. id_key: Key to use for id in metadata. set_name: Default set name to use for storing vectors. distance_strategy: Distance strategy to use for similarity search This should match the distance strategy used during index creation. z`Passing in `embedding` as a Callable is deprecated. Please pass in an Embeddings object instead.zDclient should be an instance of aerospike_vector_search.Client, got N)r isinstancerwarningswarn ValueErrortype_client _embedding _text_key _vector_key_id_key _index_name _namespace _set_nameconvert_distance_strategy_distance_strategy) selfr$r%r'r)r+r,r-r.r/ aerospikes r__init__zAerospike.__init__7s<&'' )Z00  M.    &),, &F||&&   #!% %#!"&"@"@AR"S"SrOptional[Embeddings]cHt|jtr|jSdS)z/Access the query embedding object if available.N)r2r8rrAs r embeddingszAerospike.embeddingsms% doz 2 2 #? "trDtexts Iterable[str]List[List[float]]ctjtr'jt |Sfd|DS)zEmbed search docs.c:g|]}|S)r8).0trAs r z.Aerospike._embed_documents..xs%222q""222rD)r2r8rembed_documentslist)rArIs` r_embed_documentszAerospike._embed_documentstsN doz 2 2 @?224;;?? ?2222E2222rDtext List[float]ct|jtr|j|S||S)zEmbed query text.)r2r8r embed_query)rArUs r _embed_queryzAerospike._embed_queryzs> doz 2 2 5?..t44 4t$$$rD-Union[VectorDistanceMetric, DistanceStrategy]rcddlm}t|tr|S||jkr tjS||jkr tjS||jkr tjStd)z Convert Aerospikes distance strategy to langchains DistanceStrategy enum. This is a convenience method to allow users to pass in the same distance metric used to create the index. r)rDUnknown distance strategy, must be cosine, dot_product, or euclidean) aerospike_vector_search.typesrr2rCOSINE DOT_PRODUCTSQUARED_EUCLIDEANEUCLIDEAN_DISTANCEr5)r/rs rr?z#Aerospike.convert_distance_strategys GFFFFF ')9 : : %$ $  4 ; ; ;#* *  4 @ @ @#/ /  4 F F F#6 6 R   rDT metadatasOptional[List[dict]]idsOptional[List[str]]embedding_chunk_sizeintwait_for_indexboolkwargsr List[str]c ||j}||j}|r|tdt|}|p d|D}|r d|D}n|p d|D}t dt ||D]} || | |z} || | |z} || | |z} || } t| | | D]\}}}|||j<|||j <t| | D]+\}}|||j <|j j d |j |||d|,|r!|j |j ||S) aRun more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadata associated with the texts. ids: Optional list of ids to associate with the texts. set_name: Optional aerospike set name to add the texts to. batch_size: Batch size to use when adding the texts to the vectorstore. embedding_chunk_size: Chunk size to use when embedding the texts. index_name: Optional aerospike index name used for waiting for index completion. If not provided, the default index_name will be used. wait_for_index: If True, wait for the all the texts to be indexed before returning. Requires index_name to be provided. Defaults to True. kwargs: Additional keyword arguments to pass to the client upsert call. Returns: List of ids from adding the texts into the vectorstore. Nz6if wait_for_index is True, index_name must be providedcNg|]"}ttj#SrN)r(uuiduuid4rO_s rrQz'Aerospike.add_texts..s&777Ac$*,,''777rDc6g|]}|SrN)copy)rOms rrQz'Aerospike.add_texts..s 555a555rDcg|]}iSrNrNrqs rrQz'Aerospike.add_texts..s%8%8%8Qb%8%8%8rDr)r'keyr. record_data)r'namerN)r>r<r5rSrangelenrTzipr:r9r;r7upsertr=wait_for_index_completion)rArIrcrer.rgr)rirki chunk_texts chunk_idschunk_metadatasrHmetadatar%rUids r add_textszAerospike.add_textss@  ~H  )J  Wj0UVV VU 777777  9559555II!8%8%8%%8%8%8Iq#e**&:;;  AA(<$< <=KA$8 889I'A0D,D(DEO..{;;J-0[.. 0 0))T.7)*+/(( #I ? ?   H)+&# #"o% (       L 2 2/ 3     rDOptional[bool]c rddlm}|r.|D]+} |jjd|j||d|#|$rYdSwxYwdS)a7Delete by vector ID or other criteria. Args: ids: List of ids to delete. **kwargs: Other keyword arguments to pass to client delete call. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. r)AVSServerError)r'rwr.FTrN)rrr7deleter=)rArer.rkrrs rrzAerospike.deletes ;:::::  ! ! !!'DL'"&/!)!  &!!! 555!ts *44queryk metadata_keysList[Tuple[Document, float]]c L|j||f|||d|S)aReturn aerospike documents most similar to query, along with scores. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Name of the index to search. Overrides the default index_name. kwargs: Additional keyword arguments to pass to the search method. Returns: List of Documents most similar to the query and associated scores. rrr))&similarity_search_by_vector_with_scorerY)rArrrr)rks rsimilarity_search_with_scorez&Aerospike.similarity_search_with_score sI.;t:   e $ $ '!       rDc g}|r|j|vr |jg|z}||j}|td|jjd||j|||d|}|D]}}|j} |j| vrH| |j} |j} | t| | | fZt d|jd~|S)aReturn aerospike documents most similar to embedding, along with scores. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Name of the index to search. Overrides the default index_name. kwargs: Additional keyword arguments to pass to the client vector_search method. Returns: List of Documents most similar to the query and associated scores. Nzindex_name must be provided)r)r'rlimit field_names) page_contentrzFound document with no `z` key. Skipping.rN) r9r<r5r7 vector_searchr=fieldspopdistanceappendr loggerwarning) rAr%rrr)rkdocsresultsresultrrUscores rrz0Aerospike.similarity_search_by_vector_with_score*s 2  =T^=@@!^,}.}s,    Q     rDr)r)rAr%rrr)rks rsimilarity_search_by_vectorz%Aerospike.similarity_search_by_vectorfsR.   E$E+%       rDc >|j|f|||d|}d|DS)a*Return aerospike documents most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Optional name of the index to search. Overrides the default index_name. Returns: List of Documents most similar to the query and score for each rcg|]\}}|SrNrNrs rrQz/Aerospike.similarity_search..s222Q222rD)r)rArrrr)rkdocs_and_scoress rsimilarity_searchzAerospike.similarity_searchsJ*<$;  m   NT  32/2222rDCallable[[float], float]c|jtjkr|jS|jtjkr|jS|jtjkr|jStd)a The 'correct' relevance function may differ depending on a few things, including: - the distance / similarity metric used by the VectorStore - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) - embedding dimensionality - etc. 0 is dissimilar, 1 is similar. Aerospike's relevance_fn assume euclidean and dot product embeddings are normalized to unit norm. r\) r@rr^_cosine_relevance_score_fnr_%_max_inner_product_relevance_score_fnra_euclidean_relevance_score_fnr5rGs r_select_relevance_score_fnz$Aerospike._select_relevance_score_fnsj  "&6&= = =2 2  $(8(D D D= =  $(8(K K K5 5V rDrfloatcd|dz z S)zgAerospike returns cosine distance scores between [0,2] 0 is dissimilar, 1 is similar. rN)rs rrz$Aerospike._cosine_relevance_score_fns EAIrD?fetch_k lambda_multc X |rj|vr jg|z}j|f|||d| ttj|gtjfd D||}|r3j|vr*|D]'} | jj( fd|DS)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. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Optional name of the index to search. Overrides the default index_name. Returns: List of Documents selected by maximal marginal relevance. r)dtypec4g|]}|jjSrN)rr:)rOrrAs rrQzEAerospike.max_marginal_relevance_search_by_vector..s# < < <S\$* + < < .s...AQ...rD)r:rrnparrayfloat32rr) rAr%rrrrr)rk mmr_selectedrrs ` @r'max_marginal_relevance_search_by_vectorz1Aerospike.max_marginal_relevance_search_by_vectors <  ?T-]BB!-.>M/t/  '!      2 Hi[ 3 3 3 < < < >! 7 7Q $$T%56666........rDc T||}|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. index_name: Name of the index to search. Returns: List of Documents selected by maximal marginal relevance. )rr))rYr) rArrrrrr)rkr%s rmax_marginal_relevance_searchz'Aerospike.max_marginal_relevance_searchsV6%%e,, ;t;     (!      rDtestrembeddings_chunk_size client_kwargsOptional[dict]c  F||||fi| } | j|f||||d| pi| S)a This is a user friendly interface that: 1. Embeds text. 2. Converts the texts into documents. 3. Adds the documents to a provided Aerospike index This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Aerospike from langchain_openai import OpenAIEmbeddings from aerospike_vector_search import Client, HostPort client = Client(seeds=HostPort(host="localhost", port=5000)) aerospike = Aerospike.from_texts( ["foo", "bar", "baz"], embedder, client, "namespace", index_name="index", vector_key="vector", distance_strategy=MODEL_DISTANCE_CALC, ) )rcrer)rg)r) clsrIr%rcr$r'r)rerrrkrBs r from_textszAerospike.from_textsswPC            !!6   "    rD)r$rr%r&r'r(r)r*r+r(r,r(r-r(r.r*r/r0)rrE)rIrJrrK)rUr(rrV)r/rZrr)NNNrbNT)rIrJrcrdrerfr.r*rgrhr)r*rirjrkrrrl)NN)rerfr.r*rkrrr)rNN) rr(rrhrrfr)r*rkrrr) r%rVrrhrrfr)r*rkrrr) r%rVrrhrrfr)r*rkrrr) rr(rrhrrfr)r*rkrrr)rr)rrrr)rrrNN)r%rVrrhrrhrrrrfr)r*rkrrr)rr(rrhrrhrrrrfr)r*rkrrr)NNrNNrbN)rIrlr%rrcrdr$rr'r(r)r*rerfrrhrrrkrrr)__name__ __module__ __qualname____doc__rrarCpropertyrHrTrY staticmethodr?rrrrrrrrrr classmethodrrNrDrrr1sl%)#"&  /4T4T4T4T4TlX 3333 %%%%    \ :+/#'"&$($(#NNNNNd$("&F-1$(      D-1$( :::::~-1$(      J-1$( 3333342\ -1$(3/3/3/3/3/p -1$($ $ $ $ $ L +/$(#'%)(,6666[666rD)rr)% __future__rloggingror3typingrrrrrr r r r numpyrlangchain_core.documentsr langchain_core.embeddingsrlangchain_core.vectorstoresr&langchain_community.vectorstores.utilsrrrrr]rr getLoggerrrrrrrNrDrrs""""""                       ------000000333333 M......LLLLLLLL  8 $ $wv[)))ddddd dddddrD