Ngf(ddlmZddlZddlZddlZddlZddlZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZddlZddlmZejrddlmZddlmZddlmZddlmZm Z dd l!m"Z"dd l#m$Z$ed d Z%edZ&Gdd eZ'dS)) annotationsN) Any AwaitableCallableDictIterableListOptionalTupleTypeTypeVarUnion)Version)SessionDocument) Embeddings) VectorStoreVectorStoreRetriever) SetupMode)maximal_marginal_relevanceCVST Cassandra)boundz0.1.10c eZdZUded<ddZddZ ddejd d ddZe ddZ ddZ dd Z dd!Z dd"Zdd#Zdd%Zdd&Zddd,Z ddd-Zd.d/dd3Zd.d/dd4Z ddd;Z ddd=Zd.d/dd@Zd.dAddBZeddFZddHZddIZidJfddMZidJfddNZ dddVZ eddZZ! ddd[Z" ddd\Z# ddd^Z$ ddd_Z% dddaZ& dddbZ' ddddZ( dddeZ) dddfZ* dddgZ+ dddhZ, dddiZ-eddnZ. dddrZ/ dddsZ0 dddtZ1 ddduZ2e dddvZ3e4 dddddddd dwdd{Z5e4 dddddddd dwdd|Z6e ddd~Z7e4ddddddd dwddZ8e4ddddddd dwddZ9 dddZ:dS)rzUnion[int, None]_embedding_dimensionreturnintcv|j,t|jd|_|jSNzThis is a sample sentence.)rlen embedding embed_queryselfs f/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/cassandra.py_get_embedding_dimensionz"Cassandra._get_embedding_dimension*s<  $ ,(+**+GHH))D %((cK|j2t|jdd{V|_|jSr )rr!r" aembed_queryr$s r&_aget_embedding_dimensionz#Cassandra._aget_embedding_dimension1sR  $ ,(+n112NOOOOOOOO))D %((r(Nall)body_index_options setup_modemetadata_indexingr"rsessionOptional[Session]keyspace Optional[str] table_namestr ttl_seconds Optional[int]r.Optional[List[Tuple[str, Any]]]r/rr0%Union[Tuple[str, Iterable[str]], str]Nonec  ddlm} n$#ttf$rtdwxYwt t jd} | %| tkrdtd} t| |std||_ ||_ ||_ ||_ ||_d|_i} ||| d <|t jkrd | d <d} |t jkr|} n$|t jkr|} | d|||| |d |t jkd | |_|j |jj |_ dSdS)aApache Cassandra(R) for vector-store workloads. To use it, you need a recent installation of the `cassio` library and a Cassandra cluster / Astra DB instance supporting vector capabilities. Visit the cassio.org website for extensive quickstarts and code examples. Example: .. code-block:: python from langchain_community.vectorstores import Cassandra from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() session = ... # create your Cassandra session object keyspace = 'my_keyspace' # the keyspace should exist already table_name = 'my_vector_store' vectorstore = Cassandra(embeddings, session, keyspace, table_name) Args: embedding: Embedding function to use. session: Cassandra driver session. If not provided, it is resolved from cassio. keyspace: Cassandra keyspace. If not provided, it is resolved from cassio. table_name: Cassandra table (required). ttl_seconds: Optional time-to-live for the added texts. body_index_options: Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER] setup_mode: mode used to create the Cassandra table (SYNC, ASYNC or OFF). metadata_indexing: Optional specification of a metadata indexing policy, i.e. to fine-tune which of the metadata fields are indexed. It can be a string ("all" or "none"), or a 2-tuple. The following means that all fields except 'f1', 'f2' ... are NOT indexed: metadata_indexing=("allowlist", ["f1", "f2", ...]) The following means all fields EXCEPT 'g1', 'g2', ... are indexed: metadata_indexing("denylist", ["g1", "g2", ...]) The default is to index every metadata field. Note: if you plan to have massive unique text metadata entries, consider not indexing them for performance (and to overcome max-length limitations). r)MetadataVectorCassandraTablezTCould not import cassio python package. Please install it with `pip install cassio`.cassioNz?Cassio version not supported. Please upgrade cassio to version z or higher.z(Missing required parameter 'table_name'.r.T async_setupTEXT)r1r3tablevector_dimensionr0primary_key_typeskip_provisioning) cassio.tabler= ImportErrorModuleNotFoundErrorr importlibmetadataversionMIN_CASSIO_VERSION ValueErrorr"r1r3r5r7rrASYNCr+SYNCr'OFFrA)r%r"r1r3r5r7r.r/r0r=cassio_versionmsgkwargsembedding_dimensions r&__init__zCassandra.__init__8sl  A A A A A A A01   ?   !!3!;!;H!E!EFF  %.;M*M*M>0>>> c"" " IGHH H"   $&$(!!#  )+=F' (  ( ($(F= !@D  ( ("&"@"@"B"B   9> ) )"&"?"?"A"A 11  0/#(IM9        < :-DLLL s !*c|jSN)r"r$s r& embeddingszCassandra.embeddingss ~r(Callable[[float], float]cdS)z The underlying VectorTable already returns a "score proper", i.e. one in [0, 1] where higher means more *similar*, so here the final score transformation is not reversing the interval: c|SrWrE)scores r&z6Cassandra._select_relevance_score_fn..sUr(rEr$s r&_select_relevance_score_fnz$Cassandra._select_relevance_score_fns #""r(c.|dS)zm Just an alias for `clear` (to better align with other VectorStore implementations). N)clearr$s r&delete_collectionzCassandra.delete_collections r(c>K|d{VdS)zn Just an alias for `aclear` (to better align with other VectorStore implementations). N)aclearr$s r&adelete_collectionzCassandra.adelete_collections. kkmmr(c8|jdSzEmpty the table.N)rAr`r$s r&r`zCassandra.clears r(cHK|jd{VdSrf)rArcr$s r&rczCassandra.aclears2j!!!!!!!!!!!r( document_idc8|j|S)bDelete by document ID. Args: document_id: the document ID to delete. row_id)rAdeleter%rhs r&delete_by_document_idzCassandra.delete_by_document_ids z   444r(cHK|j|d{VS)rjrkN)rAadeleterns r&adelete_by_document_idz Cassandra.adelete_by_document_ids3 Z''{';;;;;;;;;r(idsOptional[List[str]]rSrOptional[bool]c \|td|D]}||dSzDelete by vector IDs. Args: ids: List of ids to delete. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. NzNo ids provided to delete.T)rMror%rsrSrhs r&rmzCassandra.deletesC ;9:: : 4 4K  & &{ 3 3 3 3tr(clK|td|D]}||d{VdSrw)rMrrrxs r&rqzCassandra.adeletesY ;9:: : ; ;K--k:: : : : : : : : :tr(2) batch_sizefilterdict[str, Any]r{c`|sd}t||j||S)Delete all documents matching a certain metadata filtering condition. This operation does not use the vector embeddings in any way, it simply removes all documents whose metadata match the provided condition. Args: filter: Filter on the metadata to apply. The filter cannot be empty. batch_size: amount of deletions per each batch (until exhaustion of the matching documents). Returns: A number expressing the amount of deleted documents. Method `delete_by_metadata_filter` does not accept an empty filter. Use the `clear()` method if you really want to empty the vector store.rJr{)rMrAfind_and_delete_entriesr%r|r{rRs r&delete_by_metadata_filterz#Cassandra.delete_by_metadata_filtersI& "$  S// !z11!2   r(cpK|sd}t||j||d{VS)rrrN)rMrAafind_and_delete_entriesrs r&adelete_by_metadata_filterz$Cassandra.adelete_by_metadata_filtersk& "$  S// !Z88!9         r(texts Iterable[str] metadatasOptional[List[dict]] List[str]c t|}|p d|D}|pigt|z}pjj|}t dt||D]n} || | |z} || | |z} || | |z} || | |z} fdt | | | | D}|D]}|o|S)aRun more texts through the embeddings and add to the vectorstore. Args: texts: Texts to add to the vectorstore. metadatas: Optional list of metadatas. ids: Optional list of IDs. batch_size: Number of concurrent requests to send to the server. ttl_seconds: Optional time-to-live for the added texts. Returns: List[str]: List of IDs of the added texts. c>g|]}tjjSrEuuiduuid4hex.0_s r& z'Cassandra.add_texts..P!7771djll&777r(rc \g|](\}}}}j||||pi)S)rl body_blobvectorrJr7rA put_async)rtextembedding_vectortext_idrJr%r7s r&rz'Cassandra.add_texts..[s^   >D*GX $$""+%^ + %   r()listr!r7r"embed_documentsrangezipresult)r%rrrsr{r7rS_textsembedding_vectorsi batch_textsbatch_embedding_vectors batch_idsbatch_metadatasfuturesfutures` ` r& add_textszCassandra.add_texts:sC*e7777773"F !3 !5T%5  N::6BBq#f++z22  A Q^!34K&7A N8J&K #AJ./I'A N(:;O     BE!8)_BB   G"     r( concurrencyc  Kt|}|p d|D}|pigt|z}pjj|d{V} t j| d fd } td t|D]O} t j| || || | | || g} t j | d{VP|S)aRun more texts through the embeddings and add to the vectorstore. Args: texts: Texts to add to the vectorstore. metadatas: Optional list of metadatas. ids: Optional list of IDs. concurrency: Number of concurrent queries to the database. Defaults to 16. ttl_seconds: Optional time-to-live for the added texts. Returns: List[str]: List of IDs of the added texts. c>g|]}tjjSrErrs r&rz(Cassandra.aadd_texts..rr(Nrlr6rr List[float]rJdictrr;cK4d{Vj||||pid{Vdddd{VdS#1d{VswxYwYdS)NrrAaput)rlrrrJr%semr7s r&send_concurrentlyz/Cassandra.aadd_texts..send_concurrentlys        joo!"+%^ + &                              s(A AAr) rlr6rr6rrrJrrr;) rr!r7r"aembed_documentsasyncio Semaphorer create_taskgather)r%rrrsrr7rSr _metadatasrrrtasksrs` ` @r& aadd_textszCassandra.aadd_textsks?,e777777!*!@rdS[[.@ !5T%5 "&."A"A&"I"IIIIIII ,,        q#f++&& ) )A#%%Aq +.sL #FF  $$!#%r(N)ritemsrr!r)r%rr{ids_and_metadatasr batch_i_mrrs` r&replace_metadatazCassandra.replace_metadatas0!!5!5!7!788q#/00*== A)!a*n*<=I '0 G"    r()rcKt|}tj|d fd }|D]8\}}tj|||g}tj|d{V9dS) aReplace the metadata of documents. For each document to update, identified by its ID, the new metadata dictionary completely replaces what is on the store. This includes passing empty metadata `{}` to erase the currently-stored information. Args: id_to_metadata: map from the Document IDs to modify to the new metadata for updating. Keys in this dictionary that do not correspond to an existing document will not cause an error, rather will result in new rows being written into the Cassandra table but without an associated vector: hence unreachable through vector search. concurrency: Number of concurrent queries to the database. Defaults to 50. Returns: None if the writes succeed (otherwise an error is raised). rr6rrrr;cK4d{Vj||d{Vdddd{VdS#1d{VswxYwYdS)Nrr)rrr%rs r&rz6Cassandra.areplace_metadata..send_concurrentlys         joo!#&                              s#A A A N)rr6rrrr;)rrrrrr) r%rrrrrrrrs ` @r&areplace_metadatazCassandra.areplace_metadatas2!!5!5!7!788 ,,       0 ) )NFF():):66)J)JKKLE.%( ( ( ( ( ( ( ( (r(rowDict[str, Any]rcJt|d|d|dS)NrlrrJ)id page_contentrJrrs r&_row_to_documentzCassandra._row_to_documents/8}[)_    r(Document | Nonecl|j|}|dS||SzRetrieve a single document from the store, given its document ID. Args: document_id: The document ID Returns: The the document if it exists. Otherwise None. rkNr)rAgetrr%rhrs r&get_by_document_idzCassandra.get_by_document_ids:jnnKn00 ;4$$$---r(c|K|j|d{V}|dS||Sr)rAagetrrs r&aget_by_document_idzCassandra.aget_by_document_idsPJOO;O77777777 ;4$$$---r(nIterable[Document]cXj||}fd|DS)Get documents via a metadata search. Args: filter: the metadata to query for. n: the maximum number of documents to return. rJrc@g|]}||Srrrrr%s r&rz-Cassandra.metadata_search.. s/FFF3#F%%#%..FFFr()rA find_entriesr%r|rrowss` r&metadata_searchzCassandra.metadata_searchs9z&&!&<<FFFF$FFFFr(chKj||d{V}fd|DS)rrNc<g|]}|Srrrs r&rz.Cassandra.ametadata_search...s*???3%%#%..???r()rA afind_entriesrs` r&ametadata_searchzCassandra.ametadata_search"sOZ--v-CCCCCCCC????$????r(rkOptional[Dict[str, str]] body_searchOptional[Union[str, List[str]]]'List[Tuple[Document, List[float], str]]czKi}|||d<|||d<jjd||d|d{V}fd|DS)aReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, embedding, id), the most similar to the query vector. NrJr)rrcZg|]'}||d|df(S)rrrlr)rhitr%s r&rzLCassandra.asimilarity_search_with_embedding_id_by_vector..MsO    %%#%..H H     r(rE)rA aann_searchr%r"rr|rrShitss` r&.asimilarity_search_with_embedding_id_by_vectorz8Cassandra.asimilarity_search_with_embedding_id_by_vector0s$"$  !'F:   "$/F= !+TZ+                     r(rIterable[Dict[str, Any]]!List[Tuple[Document, float, str]]cd|DS)Ncng|]2}t|dd|dzz|df3S)r?distancerlrrrrs r&rz2Cassandra._search_to_documents..\sX    **s*33cC O++H     r(rE)rs r&_search_to_documentszCassandra._search_to_documentsVs%        r(cxi}|||d<|||d<|jjd||dd|}||SaReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score, id), the most similar to the query vector. NrJrcosrrmetricrE)rAmetric_ann_searchrrs r&)similarity_search_with_score_id_by_vectorz3Cassandra.similarity_search_with_score_id_by_vectorfsr$"$  !'F:   "$/F= !+tz+       ((...r(cKi}|||d<|||d<|jjd||dd|d{V}||Sr )rAametric_ann_searchrrs r&*asimilarity_search_with_score_id_by_vectorz4Cassandra.asimilarity_search_with_score_id_by_vectors$"$  !'F:   "$/F= !2TZ2             ((...r(querych|j|}|||||S)Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score, id), the most similar to the query vector. r"rr|r)r"r#rr%rrr|rrs r&similarity_search_with_score_idz)Cassandra.similarity_search_with_score_idsC$ >55e<<==&# >   r(cK|j|d{V}|||||d{VS)rNr)r"r*rrs r& asimilarity_search_with_score_idz*Cassandra.asimilarity_search_with_score_idsx$"&!.s2   #eU%L   r(r)rr%r"rr|rs r&&similarity_search_with_score_by_vectorz0Cassandra.similarity_search_with_score_by_vectorsE$  '+'U'U#' (V((    r(cXKd|||||d{VDS)rcg|] \}}}||f SrErE)rrr\rs r&rzECassandra.asimilarity_search_with_score_by_vector..s7     %L    r(rN)rr!s r&'asimilarity_search_with_score_by_vectorz1Cassandra.asimilarity_search_with_score_by_vectorsi$   FF#' G    r(List[Document]c h|j|}|||||S)Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Document, the most similar to the query vector. r|r)r"r#similarity_search_by_vectorr%rrr|rrSrs r&similarity_searchzCassandra.similarity_searchsC& >55e<<//  # 0   r(cK|j|d{V}|||||d{VS)r(Nr))r"r*asimilarity_search_by_vectorr+s r&asimilarity_searchzCassandra.asimilarity_search1sw&"&!._,   Q    r(r))r"r%r"rr|rrSs r&r*z%Cassandra.similarity_search_by_vectorLsE&  EE' F    r(cXKd|||||d{VDS)r1cg|]\}}|SrErEr3s r&rz:Cassandra.asimilarity_search_by_vector..|r4r(r)N)r%r5s r&r.z&Cassandra.asimilarity_search_by_vectorisg&  $ L L' !M!!    r(ch|j|}|||||S)Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score), the most similar to the query vector. r))r"r#r"rs r&similarity_search_with_scorez&Cassandra.similarity_search_with_scoresC$ >55e<<::  # ;   r(cK|j|d{V}|||||d{VS)r9Nr))r"r*r%rs r&asimilarity_search_with_scorez'Cassandra.asimilarity_search_with_scoresx$"&!.s : : :&VH  : : :r()rr?c"g|] \}}|v | SrErE)rpf_indexrDmmr_chosen_indicess r&rz6Cassandra._mmr_search_to_documents..s3    &--- ---r(cDg|]}t|Srrrs r&rz6Cassandra._mmr_search_to_documents..s)HHH **s*33HHHr()rnparrayfloat32 enumerate)r=r"rr?mmr_hitsrGs @r&_mmr_search_to_documentsz"Cassandra._mmr_search_to_documentss8 HYbj 1 1 1 : :M : : :#         $-m$<$<    IHxHHHHr(rfetch_kc i}|||d<|||d<t|jjd||dd|} || |||SaReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance. NrJrr r rE)rrArrN r%r"rrPr?r|rrS_kwargsr=s r&'max_marginal_relevance_search_by_vectorz1Cassandra.max_marginal_relevance_search_by_vectors8#%  "(GJ   "%0GM " (DJ (         ,,]Iq+VVVr(c Ki}|||d<|||d<t|jjd||dd|d{V} || |||SrR)rrArrNrSs r&(amax_marginal_relevance_search_by_vectorz2Cassandra.amax_marginal_relevance_search_by_vectors8#%  "(GJ   "%0GM "/$*/          ,,]Iq+VVVr(c l|j|}|||||||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. Defaults to 20. 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. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance. r?r|r)r"r#rU r%rrrPr?r|rrSrs r&max_marginal_relevance_searchz'Cassandra.max_marginal_relevance_search'sI8 >55e<<;;  ## <   r(cK|j|d{V}|||||||d{VS)aqReturn 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. 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. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance. NrY)r"r*rWrZs r&amax_marginal_relevance_searchz(Cassandra.amax_marginal_relevance_searchMs~6"&!r"rrrr?r@rr&)rrOrNN)r"rrrrPrr?r@r|rrrrSrrr&)rr6rrrPrr?r@r|rrrrSrrr&)NN)rrrrrsrtrr&)rbrcrrr"rrrr1r2r3r4r5r6rsrtr7r8r.r9r0r:rSrrr)r`r&rsrtrr&)rbrcrhr&r"rr1r2r3r4r5r6rsrtr7r8r.r9r0r:rSrrr)rNNN) rr6rrrrtrJrrSrrr);__name__ __module__ __qualname____annotations__r'r+rrOrUpropertyrXr^rardr`rcrorrrmrqrrrrrr staticmethodrrrrrrrrrrrr"r%r,r/r*r.r:r<rNrUrWr[r]ra classmethodrkrnrprirmrrEr(r&rr'si****))))))))&*"&%) h.?C )CHh.h.h.h.h.h.TX####""""5555<<<<&*.0       H       F+/#'%) /////h+/#'%) 33333r $$$$$$T ((((((T   \  . . . . . . . . "$ G G G G G "$ @ @ @ @ @"+/7; $ $ $ $ $ L    \  $+/7; /////D+/7; /////F+/7;      :+/7;      <+/7;      >+/7;      F+/7;      <+/7;      <+/7;      @+/7;      @+/7;      :+/7;      4III\I. +/7;*W*W*W*W*W^ +/7;*W*W*W*W*W^ +/7;$ $ $ $ $ R +/7;# # # # # J+/#'\" +/ = &*"&#'%)>BCH= = = = = [= ~ +/ = &*"&#'%)>BCH= = = = = [= ~$(\ &*"&#'%)>BCHBBBBB[BH &*"&#'%)>BCHEEEEE[ER(26$(-1 H H H H H H H r()( __future__rrimportlib.metadatarItypingrrxrrrrrr r r r r rnumpyrIpackaging.versionr TYPE_CHECKINGcassandra.clusterrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresrr'langchain_community.utilities.cassandrar&langchain_community.vectorstores.utilsrrrLrrEr(r&rs""""""                           %%%%%% *))))))------000000IIIIIIII======MMMMMMwv[)))WX&&| | | | | | | | | | r(