NgddlmZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZddlZddlmZddlmZddlmZddlmZdd lmZdd l m!Z!dd l"m#Z#m$Z$dd l%m&Z&er dd l'm(Z)ddl'm*Z*eddZ+edZ,edZ-e e.efZ/dZ0dZ1dZ2dZ3d"dZ4eddd Gd!de!Z(dS)#) annotationsN)ThreadPoolExecutor) TYPE_CHECKINGAny AwaitableCallableDictIterableListOptionalSetTupleTypeTypeVarUnion) deprecatedDocument) Embeddings)gather_with_concurrency) batch_iterate) VectorStore) SetupMode_AstraDBCollectionEnvironment)maximal_marginal_relevance)AstraDB) AsyncAstraDBADBVSTr)boundTU lstList[T]keyCallable[[T], U]returnct}g}|D];}||}||vr*||||<|SN)setaddappend)r%r' visited_keysnew_lstitemitem_keys d/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/astradb.py _unique_listr49sc55LG!!3t99 < ' '   X & & & NN4 Nz0.0.21z1.0z$langchain_astradb.AstraDBVectorStore)sinceremovalalternative_importc eZdZedqdZddddddddddejdd drd Zdsd"Zdsd#Z e dtd$Z edud'Z dvd)Z dwd*Zdwd+Zdxd-Zdxd.Z dydzd5Z dydzd6Zdwd7Zdwd8Ze dyd{d@Zed|dDZ dyddddEd}dIZ dyddddEd}dJZ d~ddPZ d~ddQZ d~ddSZ d~ddTZ d~ddVZ d~ddWZ d~ddYZ d~ddZZ! d~dd[Z" d~dd\Z# d~dd]Z$ d~dd^Z%eddaZ& dddeZ' dddfZ( dddgZ) dddhZ*e+ddlZ,e+ dyddmZ-e+ dyddnZ.e+dfdp Z/xZ0S)r filter_dictOptional[Dict[str, Any]]r)Dict[str, Any]c|iSi}|D]^\}}|rO|ddkrCt|trd|D||<8t|||<V||d|<_|S)Nr$cBg|]}t|S)r_filter_to_metadata).0fs r3 z/AstraDB._filter_to_metadata..Ss'-X-X-XQRg.I.I!.L.L-X-X-Xr5z metadata.)items isinstancelistrrA)r:metadata_filterkvs r3rAzAstraDB._filter_to_metadataJs  I O#))++ 9 9191!!T**L-X-XVW-X-X-X**-4-H-H-K-K**78OOOO44" "r5NF) token api_endpointastra_db_clientasync_astra_db_client namespacemetric batch_sizebulk_insert_batch_concurrency!bulk_insert_overwrite_concurrencybulk_delete_concurrency setup_modepre_delete_collection embeddingrcollection_namestrrK Optional[str]rLrMOptional[LibAstraDB]rNOptional[AsyncAstraDB]rOrPrQ Optional[int]rRrSrTrUrrVboolNonec $||_||_||_||_||_| pt |_| pt|_| pt|_ | pt|_ ||_ d}| tjkr|}n$| tjkr|}t'||||||| ||| |_|jj|_|jj|_|jj|_|jj|_dS)a Wrapper around DataStax Astra DB for vector-store workloads. For quickstart and details, visit https://docs.datastax.com/en/astra/astra-db-vector/ Example: .. code-block:: python from langchain_community.vectorstores import AstraDB from langchain_openai.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() vectorstore = AstraDB( embedding=embeddings, collection_name="my_store", token="AstraCS:...", api_endpoint="https://-.apps.astra.datastax.com" ) vectorstore.add_texts(["Giraffes", "All good here"]) results = vectorstore.similarity_search("Everything's ok", k=1) Args: embedding: embedding function to use. collection_name: name of the Astra DB collection to create/use. token: API token for Astra DB usage. api_endpoint: full URL to the API endpoint, such as `https://-us-east1.apps.astra.datastax.com`. astra_db_client: *alternative to token+api_endpoint*, you can pass an already-created 'astrapy.db.AstraDB' instance. async_astra_db_client: *alternative to token+api_endpoint*, you can pass an already-created 'astrapy.db.AsyncAstraDB' instance. namespace: namespace (aka keyspace) where the collection is created. Defaults to the database's "default namespace". metric: similarity function to use out of those available in Astra DB. If left out, it will use Astra DB API's defaults (i.e. "cosine" - but, for performance reasons, "dot_product" is suggested if embeddings are normalized to one). batch_size: Size of batches for bulk insertions. bulk_insert_batch_concurrency: Number of threads or coroutines to insert batches concurrently. bulk_insert_overwrite_concurrency: Number of threads or coroutines in a batch to insert pre-existing entries. bulk_delete_concurrency: Number of threads (for deleting multiple rows concurrently). pre_delete_collection: whether to delete the collection before creating it. If False and the collection already exists, the collection will be used as is. Note: For concurrency in synchronous :meth:`~add_texts`:, as a rule of thumb, on a typical client machine it is suggested to keep the quantity bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency much below 1000 to avoid exhausting the client multithreading/networking resources. The hardcoded defaults are somewhat conservative to meet most machines' specs, but a sensible choice to test may be: - bulk_insert_batch_concurrency = 80 - bulk_insert_overwrite_concurrency = 10 A bit of experimentation is required to nail the best results here, depending on both the machine/network specs and the expected workload (specifically, how often a write is an update of an existing id). Remember you can pass concurrency settings to individual calls to :meth:`~add_texts` and :meth:`~add_documents` as well. N) rXrKrLrMrNrOrUrVembedding_dimensionrP)rWrXrKrLrODEFAULT_BATCH_SIZErQ%DEFAULT_BULK_INSERT_BATCH_CONCURRENCYrR)DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCYrSDEFAULT_BULK_DELETE_CONCURRENCYrTrPrASYNC_aget_embedding_dimensionSYNC_get_embedding_dimensionr astra_envastra_dbasync_astra_db collectionasync_collection)selfrWrXrKrLrMrNrOrPrQrRrSrTrUrVras r3__init__zAstraDB.__init__[s.h#. (")?-? ) R-R * . 98 . $ F'F $ @D  ( ("&"@"@"B"B   9> ) )"&"?"?"A"A 6+%+"7!"7 3    / "n;.3 $ ?r5intcRt|jdSNzThis is a sample sentence.)text)lenrW embed_queryros r3riz AstraDB._get_embedding_dimensions$4>--3O-PPQQQr5cbKt|jdd{VSrs)rurW aembed_queryrws r3rgz!AstraDB._aget_embedding_dimensions:44:V4WWWWWWWWXXXr5c|jSr+)rWrws r3 embeddingszAstraDB.embeddingss ~r5similarity0to1floatc|S)z?Keep similarity from client unchanged ad it's in [0:1] already.r@)r|s r3_dont_flip_the_cos_scorez AstraDB._dont_flip_the_cos_scores r5Callable[[float], float]c|jS)z The underlying API calls 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: )rrws r3_select_relevance_score_fnz"AstraDB._select_relevance_score_fns ,,r5cl|j|jidSz/Empty the collection of all its stored entries.N)rjensure_db_setuprm delete_manyrws r3clearz AstraDB.clears2 &&((( ##B'''''r5cK|jd{V|jid{VdSr)rjaensure_db_setuprnrrws r3aclearzAstraDB.aclears[n--/////////#//33333333333r5 document_idc|j|j|}|pidpidddkS) Remove a single document from the store, given its document ID. Args: document_id: The document ID Returns True if a document has indeed been deleted, False if ID not found. status deletedCountr)rjrrm delete_onegetrordeletion_responses r3delete_by_document_idzAstraDB.delete_by_document_idsk &&((( O66{CC"(b--h77=2BB A   r5cK|jd{V|j|d{V}|pidpidddkS)rNrrrr)rjrrnrrrs r3adelete_by_document_idzAstraDB.adelete_by_document_id sn--/////////"&"7"B"B;"O"OOOOOOO"(b--h77=2BB A   r5idsOptional[List[str]] concurrencykwargsrOptional[bool]c r|rJtjddt|d|t d|p|j}t|5}t| |j |}dddn #1swxYwYdS)a+Delete by vector ids. Args: ids: List of ids to delete. concurrency: max number of threads issuing single-doc delete requests. Defaults to instance-level setting. Returns: True if deletion is successful, False otherwise. zLMethod 'delete' of AstraDB vector store invoked with unsupported arguments (, ), which will be ignored.NNo ids provided to delete. max_workersT) warningswarnjoinsortedkeys ValueErrorrTrrGmapr)rorrr _max_workerstpe_s r3deletezAstraDB.deletes "   M)*.))F6;;==4I4I*J*J)))    ;9:: :"Bd&B L 9 9 9 S.A               ts7)B,,B03B0c K|rJtjddt|d|t dt t|gfd|DRd{VS)aiDelete by vector ids. Args: ids: List of ids to delete. concurrency: max concurrency of single-doc delete requests. Defaults to instance-level setting. **kwargs: Other keyword arguments that subclasses might use. Returns: True if deletion is successful, False otherwise. zMMethod 'adelete' of AstraDB vector store invoked with unsupported arguments (rrNrc:g|]}|Sr@)r)rBdoc_idros r3rDz#AstraDB.adelete..\s'UUUvt::6BBUUUr5)rrrrrrallr)rorrrs` r3adeletezAstraDB.adelete?s"   M)*.))F6;;==4I4I*J*J)))    ;9:: :)UUUUQTUUU         r5cx|j|j|jdS)z Completely delete the collection from the database (as opposed to :meth:`~clear`, which empties it only). Stored data is lost and unrecoverable, resources are freed. Use with caution. rXN)rjrrkdelete_collectionrXrws r3rzAstraDB.delete_collection`sG &&((( '' 0 (     r5cK|jd{V|j|jd{VdS)z Completely delete the collection from the database (as opposed to :meth:`~aclear`, which empties it only). Stored data is lost and unrecoverable, resources are freed. Use with caution. Nr)rjrrlrrXrws r3adelete_collectionzAstraDB.adelete_collectionls|n--/////////!33 04           r5texts Iterable[str]embedding_vectorsList[List[float]] metadatasOptional[List[dict]] List[DocDict]c| d|D}| d|D}dt||||D}t|ddddddd}|S)Nc>g|]}tjjSr@)uuiduuid4hexrBrs r3rDz4AstraDB._get_documents_to_insert..s!3334:<<#333r5cg|]}iSr@r@rs r3rDz4AstraDB._get_documents_to_insert..s++++++r5c&g|]\}}}}||||dS))content_id$vectormetadatar@)rBb_txtb_embb_idb_mds r3rDz4AstraDB._get_documents_to_insert..sE    )udD !        r5c|dS)Nrr@)documents r3z2AstraDB._get_documents_to_insert..s Xe_r5)zipr4)rrrrdocuments_to_insertuniqued_documents_to_inserts r3_get_documents_to_insertz AstraDB._get_documents_to_insertxs ;33U333C  ++U+++I   -0! --    '3 " % , ,' '  $B$'#+*r5document_batch insert_resultTuple[List[str], List[DocDict]]cd|vrtdt||dd}d|Dt|z |dg}t |}t d|D}|t ks|rtdt|fd|D}||fS)Nrz,API Exception while running bulk insertion: insertedIdsch|] }|d Srr@)rBrs r3 z2AstraDB._get_missing_from_batch..sNNN8xNNNr5errorsc3HK|]}|ddkVdS) errorCodeDOCUMENT_ALREADY_EXISTSN)r)rBerrors r3 z2AstraDB._get_missing_from_batch..sE  DIEIIk " "&? ?      r5c(g|]}|dv |Srr@)rBrmissed_inserted_idss r3rDz3AstraDB._get_missing_from_batch..s3   "555 555r5)rrYr,rruany)rrbatch_insertedr num_errorsunexpected_errorsmissing_from_batchrs @r3_get_missing_from_batchzAstraDB._get_missing_from_batchs5 = ( (Ss=?Q?QSS 'x0?NN~NNNQT R R  ""8R00[[   MS      011 1 15F 1Ls6{{LL     *    111r5)rQbatch_concurrencyoverwrite_concurrencyrr List[str]c ,|rJtjddt|djjt|} ||||} d fd } |pj } t| 5} | | t|pj| } d d d n #1swxYwYd | DS) Run texts through the embeddings and add them to the vectorstore. If passing explicit ids, those entries whose id is in the store already will be replaced. Args: texts: Texts to add to the vectorstore. metadatas: Optional list of metadatas. ids: Optional list of ids. batch_size: Number of documents in each API call. Check the underlying Astra DB HTTP API specs for the max value (20 at the time of writing this). If not provided, defaults to the instance-level setting. batch_concurrency: number of threads to process insertion batches concurrently. Defaults to instance-level setting if not provided. overwrite_concurrency: number of threads to process pre-existing documents in each batch (which require individual API calls). Defaults to instance-level setting if not provided. Note: There are constraints on the allowed field names in the metadata dictionaries, coming from the underlying Astra DB API. For instance, the `$` (dollar sign) cannot be used in the dict keys. See this document for details: https://docs.datastax.com/en/astra/astra-db-vector/api-reference/data-api.html Returns: The list of ids of the added texts. zOMethod 'add_texts' of AstraDB vector store invoked with unsupported arguments (rrrrr)rc2 j|ddid} ||\}}d fd }p j}t | 5}t |||}dddn #1swxYwY||zS) NorderedFT documentsoptionspartial_failures_allowedmissing_documentDocDictr)rYctjd|di|}|dddSNr)filter replacementdatar)rmfind_one_and_replacerreplacement_resultros r3_handle_missing_documentzJAstraDB.add_texts.._handle_batch.._handle_missing_documentsI%)_%I%I!#3E#:; 0&J&&"*&1*=eDDr5rrrr)rY)rm insert_manyrrSrrGr) r im_resultrrr_u_max_workerstpe2batch_replacedrros r3 _handle_batchz(AstraDB.add_texts.._handle_batchs33("E*)-4I 261M1M 22 .N. E E E E E E&O)O $??? 4!%HH0*""               "N2 2s$B  B B rNcg|] }|D]}| Sr@r@rBid_listiids r3rDz%AstraDB.add_texts..%EEEWEEcEEEEr5rrr)r)rrrrrrjrrWembed_documentsrGrrRrrrrQ)rorrrrQrrrrrr _b_max_workersrall_ids_nesteds` ` r3 add_textszAstraDB.add_textssT   M)*.))F6;;==4I4I*J*J)))    &&((( N::4;;GG";; $i   3 3 3 3 3 3 3:+Pd.P N ; ; ; s WW1$/'N               FE>EEEEs,C??DDc   K|rJtjddt|djd{Vjt|d{V} ||||} d fd |pj } t| g fd t|pj| DRd{V} d | DS) rzPMethod 'aadd_texts' of AstraDB vector store invoked with unsupported arguments (rrNrrr)rcKj|ddidd{V}||\}}d fd pj}t |gfd |DRd{V}||zS) NrFTrrrr)rYcKjd|di|d{V}|dddSr)rnrrs r3rzKAstraDB.aadd_texts.._handle_batch.._handle_missing_documentWsl+/+@+U+U!#3E#:; 0,V,,&&&&&&"*&1*=eDDr5c&g|] }|Sr@r@)rBdocrs r3rDz=AstraDB.aadd_texts.._handle_batch..cs%NNNC**3//NNNr5r)rnrrrSr) rrrrrrrrros @r3r z)AstraDB.aadd_texts.._handle_batchMs "3??("E*)-@I 261M1M 22 .N. E E E E E E&O)O $;$NNNN;MNNN$$$N"N2 2r5c&g|] }|Sr@r@)rBbatchr s r3rDz&AstraDB.aadd_texts..js3 e$$r5cg|] }|D]}| Sr@r@r s r3rDz&AstraDB.aadd_texts..srr5r)rrrrrrjrrWaembed_documentsrGrrRrrrQ) rorrrrQrrrrrrrr s ` ` @r3 aadd_textszAstraDB.aadd_textssR   M)*.))F6;;==4I4I*J*J)))    n--/////////"&."A"A$u++"N"NNNNNNN";; $i   3 3 3 3 3 3 34+Pd.P6  *1$/'       FE>EEEEr5 List[float]rIr!List[Tuple[Document, float, str]]c |j||}t|j|d|i|dddddd}d|DS)rReturn docs most similar to embedding vector with score and id. 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. Returns: The list of (Document, score, id), the most similar to the query vector. rTlimitincludeSimilarityrrrrrsortr projectioncfg|].}t|d|d|d|df/S)rr page_contentr $similarityrrrBhits r3rDzEAstraDB.similarity_search_with_score_id_by_vector..s]    !$Y _M"E      r5)rjrrArGrmpaginated_find)rorWrIrmetadata_parameterhitss r3)similarity_search_with_score_id_by_vectorz1AstraDB.similarity_search_with_score_id_by_vectorus &&(((!55f== O * *)+"#$?? ! +            r5c K|jd{V||}d|j|d|i|dddddd2d{VS) r"NcxKg|3d{V }t|d|d|d|df56S)Nrrr+r-rrr.s r3rDzFAstraDB.asimilarity_search_with_score_id_by_vector..s         !$Y _M"E      s:rTr#rr&r')rjrrArnr0)rorWrIrr1s r3*asimilarity_search_with_score_id_by_vectorz2AstraDB.asimilarity_search_with_score_id_by_vectors n--/////////!55f==  "2AA)+"#$?? ! B            r5querycf|j|}||||S)\Return docs most similar to the query with score and id. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score, id), the most similar to the query. rWrIr)rWrvr3ror7rIrembedding_vectors r3similarity_search_with_score_idz'AstraDB.similarity_search_with_score_ids@  >55e<<==&>   r5cK|j|d{V}||||d{VS)r9Nr:)rWryr6r;s r3 asimilarity_search_with_score_idz(AstraDB.asimilarity_search_with_score_idsu "&!.s2   $eV%L   r5r:)r3rorWrIrs r3&similarity_search_with_score_by_vectorz.AstraDB.similarity_search_with_score_by_vectorsB   (,(V(V#)W))    r5cVKd||||d{VDS)rBcg|] \}}}||f Sr@r@rDs r3rDzCAstraDB.asimilarity_search_with_score_by_vector.. s7     %L    r5r:N)r6rFs r3'asimilarity_search_with_score_by_vectorz/AstraDB.asimilarity_search_with_score_by_vectorsf   FF#G    r5List[Document]c f|j|}||||S)5Return docs most similar to query. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of Documents most similar to the query. r)rWrvsimilarity_search_by_vectorror7rIrrr<s r3similarity_searchzAstraDB.similarity_search-s@" >55e<<//  0   r5cK|j|d{V}||||d{VS)rMNrN)rWryasimilarity_search_by_vectorrPs r3asimilarity_searchzAstraDB.asimilarity_searchEst""&!.n,   Q    r5rN)rGrorWrIrrs r3rOz#AstraDB.similarity_search_by_vector]sB"  EEF    r5cVKd||||d{VDS)rVcg|]\}}|Sr@r@rXs r3rDz8AstraDB.asimilarity_search_by_vector..rYr5rNN)rJrZs r3rSz$AstraDB.asimilarity_search_by_vectorwsd"  $ L L!M!!    r5cf|j|}||||S)TReturn docs most similar to query with score. Args: query: Query to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Filter on the metadata to apply. Returns: The list of (Document, score), the most similar to the query vector. rN)rWrvrGr;s r3similarity_search_with_scorez$AstraDB.similarity_search_with_scores@  >55e<<::  ;   r5cK|j|d{V}||||d{VS)r^NrN)rWryrJr;s r3asimilarity_search_with_scorez%AstraDB.asimilarity_search_with_scoresu "&!.s G G G\) $ G G Gr5)rIrbc"g|] \}}|v | Sr@r@)rBprefetch_indexrgmmr_chosen_indicess r3rDz)AstraDB._get_mmr_hits..s3   , !333 333r5cHg|]}t|d|d S)rrr+rr.s r3rDz)AstraDB._get_mmr_hits..sG     ^Z      r5)rnparrayfloat32 enumerate)rWrIrbrcmmr_hitsrjs @r3 _get_mmr_hitszAstraDB._get_mmr_hitss8 HYbj 1 1 1 G G G G G#         09-0H0H           r5r"?fetch_kc |j||}t|j|d|i|ddddddd}|||||S)Return 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. 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. filter: Filter on the metadata to apply. Returns: The list of Documents selected by maximal marginal relevance. rTr#rrrrrr')rjrrArGrmr0rq rorWrIrsrbrrr1rcs r3'max_marginal_relevance_search_by_vectorz/AstraDB.max_marginal_relevance_search_by_vectors4 &&(((!55f== O * *)+")EE !  +     !!)Q ]KKKr5c K|jd{V||}d|j|d|i|ddddddd2d{V}|||||S) ruNc$Kg|3d{V }| 6Sr+r@r.s r3rDzDAstraDB.amax_marginal_relevance_search_by_vector.. sO                srTr#rrvr')rjrrArnr0rqrws r3(amax_marginal_relevance_search_by_vectorz0AstraDB.amax_marginal_relevance_search_by_vectors4n--/////////!55f==   !2AA)+")EE !  B            !!)Q ]KKKr5c j|j|}||||||S)Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Query to look up documents similar to. k: Number of Documents to return. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. filter: Filter on the metadata to apply. Returns: The list of Documents selected by maximal marginal relevance. rbr)rWrvrxror7rIrsrbrrr<s r3max_marginal_relevance_searchz%AstraDB.max_marginal_relevance_search1sF4 >55e<<;;  # <   r5cK|j|d{V}||||||d{VS)r}Nr~)rWryr{rs r3amax_marginal_relevance_searchz&AstraDB.amax_marginal_relevance_searchTs{4"&! rKrPrOrQrLrMrXrrNrrTrRrSzPMethod 'from_texts' of AstraDB vector store invoked with unsupported arguments (rrrXrKrLrMrNrOrPrQrRrSrT) rWrXrKrLrMrNrOrPrQrRrSrT)r,rrrrrr) rrWr known_kwargsunknown_kwargsrXrKrLrMrNrOrPs r3 _from_kwargszAstraDB._from_kwargswsj       //,>N  -.2ii~8N8N.O.O--- &&78 7##zz.11  **%677 & +B C CJJ{++ H%%s+%+"7zz,//*0**5T*U*U.4jj3//%+JJ/H$I$I    r5c tj|fi|}|||||d|d|d|S)^Create an Astra DB vectorstore from raw texts. Args: texts: the texts to insert. embedding: the embedding function to use in the store. metadatas: metadata dicts for the texts. ids: ids to associate to the texts. **kwargs: you can pass any argument that you would to :meth:`~add_texts` and/or to the 'AstraDB' constructor (see these methods for details). These arguments will be routed to the respective methods as they are. Returns: an `AstraDb` vectorstore. rQrrrrrrQrr)rrrrrrrWrrrastra_db_stores r3 from_textszAstraDB.from_textssy0!-iBB6BB  zz,//$jj)<=="(**-D"E"E !   r5c Ktj|fi|}|||||d|d|dd{V|S)rrQrrrN)rrrrrs r3 afrom_textszAstraDB.afrom_textss0!-iBB6BB''zz,//$jj)<=="(**-D"E"E (         r5rc :tj||fi|S)a6Create an Astra DB vectorstore from a document list. Utility method that defers to 'from_texts' (see that one). Args: see 'from_texts', except here you have to supply 'documents' in place of 'texts' and 'metadatas'. Returns: an `AstraDB` vectorstore. )superfrom_documents)rrrWr __class__s r3rzAstraDB.from_documentss&"&uww%iEEfEEEr5)r:r;r)r<)rWrrXrYrKrZrLrZrMr[rNr\rOrZrPrZrQr]rRr]rSr]rTr]rUrrVr^r)r_)r)rq)r)r)r|r}r)r})r)r)r)r_)rrYr)r^)NN)rrrr]rrr)r) rrrrrrrrr)r)rrrr<r)r)rrrrrrrQr]rr]rr]rrr)r)rN)rWrrIrqrr;r)r )r7rYrIrqrr;r)r )rWrrIrqrr;r)r@) r7rYrIrqrr;rrr)rK) rWrrIrqrr;rrr)rK)r7rYrIrqrr;r)r@) rWrrIrqrbr}rcrr)rK)rr"rrN)rWrrIrqrsrqrbr}rr;rrr)rK)r7rYrIrqrsrqrbr}rr;rrr)rK)rrrWrrrr)r)rrrrrWrrrrrrrr)r) rrrrKrWrrrr)r)1__name__ __module__ __qualname__ staticmethodrArrhrprirgpropertyr{rrrrrrrrrrrrrrr3r6r=r?rGrJrQrTrOrSr_rarqrxr{rr classmethodrrrr __classcell__)rs@r3rrDs ###\#* $&*048<#' $$(7;;?15 )&+!|@|@|@|@|@|@|RRRRYYYYX\----(((( 4444  $$(%)#####N$(%)     B          +/#' ++++\+@222\2B+/#' ]F %)+//3]F]F]F]F]F]FD+/#' \F %)+//3\F\F\F\F\F\FB+/ * * * * * ^+/ & & & & & V+/      4+/      4+/      8+/      @+/      6+/      6+/      :+/      :+/      4+/      .   \ 2 +/ +L+L+L+L+L` +/ ,L,L,L,L,Lb +/ ! ! ! ! ! L +/ ! ! ! ! ! F4 4 4 [4 l +/#'    [ D +/#'    [ DFFFFF[FFFFFr5)r%r&r'r(r)r&)5 __future__rrrconcurrent.futuresrtypingrrrrr r r r r rrrrnumpyrllangchain_core._api.deprecationrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.runnables.utilsrlangchain_core.utils.iterrlangchain_core.vectorstoresr%langchain_community.utilities.astradbrr&langchain_community.vectorstores.utilsr astrapy.dbr LibAstraDBrrr r!rYrrbrcrdrer4r@r5r3rs"""""" 111111 666666------000000BBBBBB333333333333NMMMMM(000000''''''  + + + GCLL GCLL sCx. (*%,.)"$   = |F|F|F|F|Fk|F|F  |F|F|Fr5