+#h1ddlmZddlZddlmZddlmZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZmZddlZddlmZddlmZdd lmZdd lmZmZdd l m!Z!dd l"m#Z#Gd de$Z%Gdde&eZ'GddeZ(y)) annotationsN)Enum)islice) itemgetter) AnyCallableDict GeneratorIterableListOptionalSequenceTupleTypeUnion)Document) Embeddings) VectorStore) QdrantClientmodels)maximal_marginal_relevance)SparseEmbeddingsceZdZdZy)QdrantVectorStoreErrorz'`QdrantVectorStore` related exceptions.N)__name__ __module__ __qualname____doc__T/var/www/html/sandstorm/venv/lib/python3.12/site-packages/langchain_qdrant/qdrant.pyrrs1r rceZdZdZdZdZy) RetrievalModedensesparsehybridN)rrrDENSESPARSEHYBRIDrr r!r#r##s E F Fr r#ceZdZUdZdZded<dZded<dZded<d Zded <d e jeeee jjd ed d f d+d Zed,dZed-dZed.dZed d d d d d dddd d d d d d e jjeeee jd eiiiddd d f d/dZed e jd d dddd d d d d d e jjeeeed d d f d0dZ d1 d2dZ d3 d4dZ d3 d5dZ d6 d7dZ d8 d9dZ d8 d:dZ d8 d;dZ d< d=dZd>dZed e jd id e jjeeeediiid d f d?d Ze d@d!Z!dAd"Z"e dBd#Z# d1 dCd$Z$ dDd%Z% dEd&Z&e dFd'Z'e dGd(Z(e dHd)Z)e dId*Z*y )JQdrantVectorStoreuQdrant vector store integration. Setup: Install ``langchain-qdrant`` package. .. code-block:: bash pip install -qU langchain-qdrant Key init args — indexing params: collection_name: str Name of the collection. embedding: Embeddings Embedding function to use. sparse_embedding: SparseEmbeddings Optional sparse embedding function to use. Key init args — client params: client: QdrantClient Qdrant client to use. retrieval_mode: RetrievalMode Retrieval mode to use. Instantiate: .. code-block:: python from langchain_qdrant import QdrantVectorStore from qdrant_client import QdrantClient from qdrant_client.http.models import Distance, VectorParams from langchain_openai import OpenAIEmbeddings client = QdrantClient(":memory:") client.create_collection( collection_name="demo_collection", vectors_config=VectorParams(size=1536, distance=Distance.COSINE), ) vector_store = QdrantVectorStore( client=client, collection_name="demo_collection", embedding=OpenAIEmbeddings(), ) Add Documents: .. code-block:: python from langchain_core.documents import Document from uuid import uuid4 document_1 = Document(page_content="foo", metadata={"baz": "bar"}) document_2 = Document(page_content="thud", metadata={"bar": "baz"}) document_3 = Document(page_content="i will be deleted :(") documents = [document_1, document_2, document_3] ids = [str(uuid4()) for _ in range(len(documents))] vector_store.add_documents(documents=documents, ids=ids) Delete Documents: .. code-block:: python vector_store.delete(ids=[ids[-1]]) Search: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}] Search with filter: .. code-block:: python from qdrant_client.http import models results = vector_store.similarity_search(query="thud",k=1,filter=models.Filter(must=[models.FieldCondition(key="metadata.bar", match=models.MatchValue(value="baz"),)])) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}] Search with score: .. code-block:: python results = vector_store.similarity_search_with_score(query="qux",k=1) for doc, score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}] Async: .. code-block:: python # add documents # await vector_store.aadd_documents(documents=documents, ids=ids) # delete documents # await vector_store.adelete(ids=["3"]) # search # results = vector_store.asimilarity_search(query="thud",k=1) # search with score results = await vector_store.asimilarity_search_with_score(query="qux",k=1) for doc,score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}] Use as Retriever: .. code-block:: python retriever = vector_store.as_retriever( search_type="mmr", search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, ) retriever.invoke("thud") .. code-block:: python [Document(metadata={'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}, page_content='thud')] page_contentstr CONTENT_KEYmetadata METADATA_KEY VECTOR_NAMEzlangchain-sparseSPARSE_VECTOR_NAMENTc | r|j||| | r|j||||| ||||_||_||_||_||_||_||_||_ | |_ | |_ y)aaInitialize a new instance of `QdrantVectorStore`. Example: .. code-block:: python qdrant = Qdrant( client=client, collection_name="my-collection", embedding=OpenAIEmbeddings(), retrieval_mode=RetrievalMode.HYBRID, sparse_embedding=FastEmbedSparse(), ) N) _validate_embeddings_validate_collection_config_clientcollection_name _embeddingsretrieval_mode vector_namecontent_payload_keymetadata_payload_keydistance_sparse_embeddingssparse_vector_name) selfclientr8 embeddingr:r;r<r=r>sparse_embeddingr@validate_embeddingsvalidate_collection_configs r!__init__zQdrantVectorStore.__init__s6   % %niAQ R %  , ,"  .$,&#6 $8!  "2"4r c|jS)zGet the Qdrant client instance that is being used. Returns: QdrantClient: An instance of `QdrantClient`. )r7rAs r!rBzQdrantVectorStore.clients||r cH|j td|jS)zGet the dense embeddings instance that is being used. Raises: ValueError: If embeddings are `None`. Returns: Embeddings: An instance of `Embeddings`. zBEmbeddings are `None`. Please set using the `embedding` parameter.)r9 ValueErrorrIs r! embeddingszQdrantVectorStore.embeddingss.    #T r cH|j td|jS)zGet the sparse embeddings instance that is being used. Raises: ValueError: If sparse embeddings are `None`. Returns: SparseEmbeddings: An instance of `SparseEmbeddings`. zPSparse embeddings are `None`. Please set using the `sparse_embedding` parameter.)r?rKrIs r!sparse_embeddingsz#QdrantVectorStore.sparse_embeddingss1  " " *E &&&r iiF@c |||| | | | | |||d |} |j|||| ||||||||||||}!|!j|||||!S)aConstruct an instance of `QdrantVectorStore` from a list of texts. This is a user-friendly interface that: 1. Creates embeddings, one for each text 2. Creates a Qdrant collection if it doesn't exist. 3. Adds the text embeddings to the Qdrant database This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_qdrant import Qdrant from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() qdrant = Qdrant.from_texts(texts, embeddings, url="http://localhost:6333") locationurlport grpc_port prefer_grpchttpsapi_keyprefixtimeouthostpath)construct_instance add_texts)"clstextsrC metadatasidsr8rRrSrTrUrVrWrXrYrZr[r\r>r<r=r;r:rDr@collection_create_options vector_paramssparse_vector_params batch_sizeforce_recreaterErFkwargsclient_optionsqdrants" r! from_textszQdrantVectorStore.from_textssj!"&    ''           %   &! $  3 ; r c Xtd|||||| | | | | |d |}||||||||||||| S)zConstruct an instance of `QdrantVectorStore` from an existing collection without adding any data. Returns: QdrantVectorStore: A new instance of `QdrantVectorStore`. rQ rBr8rCr:r<r=r>r;rDr@rErFr)r)r_r8rCr:rRrSrTrUrVrWrXrYrZr[r\r>r<r=r;r@rDrErFrhrBs r!from_existing_collectionz*QdrantVectorStore.from_existing_collectionlsrB  #    +) 3!5#-1 3'A  r c g}|j||||D]?\}}|jjd|j|d||j |A|S)zAdd texts with embeddings to the vectorstore. Returns: List of ids from adding the texts into the vectorstore. )r8pointsr)_generate_batchesrBupsertr8extend) rAr`rarbrfrh added_ids batch_idsrps r!r^zQdrantVectorStore.add_textssw !%!7!7 9c:"  ( Iv DKK    $ 4 4V GM    Y '  (r c t|j||f||||||d| } tttd| S)zvReturn docs most similar to query. Returns: List of Documents most similar to the query. )filter search_paramsoffsetscore_threshold consistency hybrid_fusionr)similarity_search_with_scorelistmapr) rAquerykrwrxryrzr{r|rhresultss r!similarity_searchz#QdrantVectorStore.similarity_searchsW"4$33    '+#'    C 1 w/00r c D|j||||dd||d | } |jtjk(rP|jj |} |j jd | |jd| j} n|jtjk(ry|jj |} |j jd tj| j| j|j d| j} n5|jtj"k(r|jj |} |jj |} |j jd tj$|j| |||tj$|j tj| j| j|||g|xs.tj&tj(j*d| j} nt-d |jd | Dcgc]@}|j/||j|j0|j2|j4fBc}Scc}w) zReturn docs most similar to query. Returns: List of documents most similar to the query text and distance for each. TF) r8 query_filterrxlimitry with_payload with_vectorsrzr{)rusing)indicesvalues)rrrwrparams)fusion)prefetchrzInvalid retrieval mode. .r)r8r:r#r'rL embed_queryrB query_pointsr;rpr(rNr SparseVectorrrr@r)Prefetch FusionQueryFusionRRFrK_document_from_pointr<r=score)rArrrwrxryrzr{r|rh query_optionsquery_dense_embeddingrquery_sparse_embeddingresults r!r}z.QdrantVectorStore.similarity_search_with_scores$ $33"* !.&       -"5"5 5$(OO$?$?$F !.dkk..+&& f   M$8$8 8%)%;%;%G%G%N ".dkk..))2::188--   f  M$8$8 8$(OO$?$?$F !%)%;%;%G%G%N ".dkk..OO"..3%, OO"55$11$:$B$B#9#@#@ &, &$Sv'9'9ARAR'S)* +,f- 278K8K7LANO O"  ))((,,--       sAJc |} |j|j|j|j|j||jj d|j||j| |||dd||d |j } | D cgc]4} |j| |j|j|j6c} Scc} w)zReturn docs most similar to embedding vector. Returns: List of Documents most similar to the query. )rBr8r;r>dense_embeddingsTF) r8rrrrxrryrrrzr{r) _validate_collection_for_denserBr8r;r>rrprr<r=) rArCrrwrxryrzr{rh qdrant_filterrrs r!similarity_search_by_vectorz-QdrantVectorStore.similarity_search_by_vector;s   ++;; 00((]]& , +$++**   00""&'+#     & ,"   % %$$(())     s 9Cc |j|j|j|j|j|j |j j |} |j| f|||||||d| S)a%Return docs selected using the maximal marginal relevance with dense vectors. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Returns: List of Documents selected by maximal marginal relevance. rfetch_k lambda_multrwrxrzr{)rrBr8r;r>rLr'max_marginal_relevance_search_by_vector) rArrrrrwrxrzr{rhquery_embeddings r!max_marginal_relevance_searchz/QdrantVectorStore.max_marginal_relevance_searchms* ++ KK     MM OO  //55e<;t;;   #'+#    r c t|j|f|||||||d| } tttd| S)a$Return docs selected using the maximal marginal relevance with dense vectors. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Returns: List of Documents selected by maximal marginal relevance. rr)2max_marginal_relevance_search_with_score_by_vectorr~rr) rArCrrrrwrxrzr{rhrs r!rz9QdrantVectorStore.max_marginal_relevance_search_by_vectorsV(J$II   #'+#    C 1 w/00r c  8|jjd|j||||dd|||jd | j} | D cgc]M} t | j tr | j n$| j j|jO} } ttj|| ||} | Dcgc]F}|j| ||j|j|j| |jfHc}Scc} wcc}w)a2Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Returns: List of Documents selected by maximal marginal relevance and distance for each. T) r8rrrxrrrrzr{r)rrr)rBrr8r;rp isinstancevectorr~getrnparrayrr<r=r)rArCrrrrwrxrzr{rhrrrL mmr_selectedis r!rzDQdrantVectorStore.max_marginal_relevance_search_with_score_by_vectors7(+$++**   00'+#""     & $"  &--. MM""4#3#34 5  2 HHY qk "  ))AJ((,,--           s ADA Dc |jj|j|}|jtj j k(S)zDelete documents by their ids. Args: ids: List of ids to delete. **kwargs: Other keyword arguments that subclasses might use. Returns: True if deletion is successful, False otherwise. )r8points_selector)rBdeleter8statusr UpdateStatus COMPLETED)rArbrhrs r!rzQdrantVectorStore.deletesF## 00$ }} 3 3 = ===r c |jj|j|d}|Dcgc]4}|j||j|j|j 6c}Scc}w)NT)r)rBretriever8rr<r=)rArbrrs r! get_by_idszQdrantVectorStore.get_by_idssp++&&t';';St&T"   % %$$(())     s9A)cV|r|j||||xstjj}t d i|}|j |}|r| r|j |d}|r|r%|j|||| | ||n ii}}|tjk(r@|jdg}t|d| d<|| d<| tjd i| i}n|tjk(r| tjd i|i}ni|tj k(rV|jdg}t|d| d<|| d<| tjd i| i}| tjd i|i}|| d<|| d<|| d<|j"d i| ||||||||| || dd }|S) NF dummy_textrsizer>r8vectors_configsparse_vectors_configrmr)r5uuiduuid4hexrcollection_existsdelete_collectionr6r#r'embed_documentslenr VectorParamsr(SparseVectorParamsr)create_collection)r_rCr:rDrir8r>r<r=r;r@rgrcrdrerErFrBrrrpartial_embeddingsrjs r!r]z$QdrantVectorStore.construct_instances"(   $ $^Y@P Q)=TZZ\-=-=//"44_E   $ $_ 5 %  )//#"&571N!4!44%.%>%> ~%N"(+,>q,A(B f%,4 j) !4!4"'""  =#7#77&(A(A).))%  =#7#77%.%>%> ~%N"(+,>q,A(B f%,4 j) !4!4"'""'(A(A).))% $F $ $ A'@ A+) 3!5#-1 %',   r c|dzdz S)z4Normalize the distance to a score on a scale [0, 1].g?g@r)r>s r!_cosine_relevance_score_fnz,QdrantVectorStore._cosine_relevance_score_fnws3#%%r cJ|jtjjk(r |jS|jtjj k(r |j S|jtjjk(r |jStd)a8 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. z:Unknown distance strategy, must be COSINE, DOT, or EUCLID.) r>rDistanceCOSINErDOT%_max_inner_product_relevance_score_fnEUCLID_euclidean_relevance_score_fnrKrIs r!_select_relevance_score_fnz,QdrantVectorStore._select_relevance_score_fn|sz ==FOO22 222 2 ]]foo11 1== = ]]foo44 455 5L r c|jj|xsi}|j|d<||d<t|jj|d|S)N_id_collection_namer1)r,r/)payloadridr)r_ scored_pointr8r<r=r/s r!rz&QdrantVectorStore._document_from_points_ ''++,@AGR&//'6#$%--112ErJ  r c #~Kt|}t|xsg}t|xs5t|Dcgc] }tjj"c}}t t ||x} rt t ||xsd} t t ||} t | |j| |j| | |j|jD cgc]\} } }tj| | | }} } }| |ft t ||x} ryycc}wcc}} } ww)N)rrr) iterrrrr~rzip_build_vectors_build_payloadsr<r=r PointStruct)rAr`rarbrftexts_iteratormetadatas_iterator_ ids_iterator batch_textsbatch_metadatasrupoint_idrrrps r!rqz#QdrantVectorStore._generate_batchess6e!)/r2CJd5k#JDJJL$4$4#JK !&"DEEkE"6*I25'' 4((#'0011  2  .Hfg ""!#F$V# #+"&"DEEkE$K s#1D=%D1BD=*#D6 !D=/D=cg}t|D]0\}}| td|||nd}|j||||i2|S)Nz]At least one of the texts is None. Please remove it before calling .from_texts or .add_texts.) enumeraterKappend) rAr`rar<r=payloadsrtextr/s r!rz!QdrantVectorStore._build_payloadssl ' GAt| 9(1'>tE{K  / $$f  M$8$8 8&*&<&<&L&LU ' #6   ++V-@-@%}}fnn.  M$8$8 8#>>tE{K  $ 6 6 F FtE{ S '(C!- HG H47$&74 0L- $$l++V-@-@,33]=R=R.  *4+>+>*??QR S" sG #>G%6A G*c|tjk(r|j|||||y|tjk(r|j |||y|tj k(r)|j||||||j |||yyN)r#r'rr(_validate_collection_for_sparser))r_rBr8r:r;r@r>rCs r!r6z-QdrantVectorStore._validate_collection_configs ]00 0  . .h  }33 3  / /); }33 3  . .h    / /);  4r c |j|}|jjj}t |t r<||vr2t d|d|ddj|jd||}n|dk7rt d|d|Jd t |trt|jd gd }n't |tr t|}n td |j|k7rt d |jd|d|j|k7rKt d|jj d|j#d|jj dy)Nr8Existing Qdrant collection z% does not contain dense vector named z,. Did you mean one of the existing vectors: z, zS? If you want to recreate the collection, set `force_recreate` parameter to `True`.r1z is built with unnamed dense vector. If you want to reuse it, set `vector_name` to ''(empty string).If you want to recreate the collection, set `force_recreate` to `True`.zVectorParams is NonerrzInvalid `embeddings` type.z@Existing Qdrant collection is configured for dense vectors with z% dimensions. Selected embeddings are z_-dimensional. If you want to recreate the collection, set `force_recreate` parameter to `True`.z-Existing Qdrant collection is configured for z similarity, but requested z&. Please set `distance` parameter to `zl` if you want to reuse it. If you want to recreate the collection, set `force_recreate` parameter to `True`.)get_collectionconfigrvectorsrr rjoinkeysrrrr~rKrr>nameupper) r_rBr8r;r>rcollection_info vector_config vector_sizes r!rz0QdrantVectorStore._validate_collection_for_dense+s!///P'..55== mT *-/,1/1BC22=?))-=3E3E3G)H(IJ+,*+6M b ,1/1BC66(@*@@( & 3.>> ~NqQRK ($ /./K9: :    ,(R %%&'++6-8'(   ! !X -(? ))..//J>>#$%!**//01'(  .r c|j|}|jjj}|||vrt d|d|dy)Nrrz' does not contain sparse vectors named zS. If you want to recreate the collection, set `force_recreate` parameter to `True`.)rrrsparse_vectorsr)r_rBr8r@rsparse_vector_configs r!rz1QdrantVectorStore._validate_collection_for_sparsensp!///P.55<<KK ! (!)==(-o->?00D/EF'( >r c|tjk(r | td|tjk(r | td|tjk(rt |du|dugr tdyy)Nz9'embedding' cannot be None when retrieval mode is 'dense'zA'sparse_embedding' cannot be None when retrieval mode is 'sparse'zVBoth 'embedding' and 'sparse_embedding' cannot be None when retrieval mode is 'hybrid')r#r'rKr(r)any)r_r:rCrDs r!r5z&QdrantVectorStore._validate_embeddingss ]00 0Y5FK }33 38H8PS }33 3 $  0D 8 99 2 9 3r )rBrr8r-rCOptional[Embeddings]r:r#r;r-r<r-r=r-r>models.DistancerDOptional[SparseEmbeddings]r@r-rEboolrFr )returnr)r r)r r)Br_Type[QdrantVectorStore]r`z List[str]rCrraOptional[List[dict]]rbOptional[Sequence[str | int]]r8 Optional[str]rRrrSrrT Optional[int]rUintrVr rWOptional[bool]rXrrYrrZrr[rr\rr>r r<r-r=r-r;r-r:r#rDr r@r-rcDict[str, Any]rdrrerrfrrgr rEr rFr rhrr r+)2r_r r8r-rCrr:r#rRrrSrrTrrUrrVr rWrrXrrYrrZrr[rr\rr>r r<r-r=r-r;r-r@r-rDr rEr rFr rhrr r+)NNrO) r` Iterable[str]rarrbrrfrrhrr zList[str | int])NNrNNN)rr-rrrwOptional[models.Filter]rxOptional[models.SearchParams]ryrrzOptional[float]r{ Optional[models.ReadConsistency]r|Optional[models.FusionQuery]rhrr List[Document])rr-rrrwrrxrryrrzrr{rr|rrhrr List[Tuple[Document, float]])rNNrNN)rC List[float]rrrwrrxrryrrzrr{rrhrr r)rg?NNNN)rr-rrrrrfloatrwrrxrrzrr{rrhrr r)rCrrrrrrr rwrrxrrzrr{rrhrr r)rCrrrrrrr rwrrxrrzrr{rrhrr rr)rbzOptional[List[str | int]]rhrr r)rbzSequence[str | int]r r)$r_r rCrr:r#rDr rirr8rr>r r<r-r=r-r;r-r@r-rgr rcrrdrrerrEr rFr r r+)r>r r r )r zCallable[[float], float]) rrr8r-r<r-r=r-r r) r`rrarrbrrfrr zFGenerator[tuple[list[str | int], list[models.PointStruct]], Any, None]) r`rrarr<r-r=r-r z List[dict])r`rr zList[models.VectorStruct])r_r rBrr8r-r:r#r;r-r@r-r>r rCrr None)r_r rBrr8r-r;r-r>r rz$Union[Embeddings, List[float], None]r r!) r_r rBrr8r-r@r-r r!) r_r r:r#rCrrDr r r!)+rrrrr.__annotations__r0r2r3r#r'rrrrGpropertyrBrLrN classmethodrkrnr^rr}rrrrrrr] staticmethodrrrrqrrr6rrr5rr r!r+r+)s% EN&K%"L#"K00 +/(5(;(;&#.$0$*OO$:$:7;"4$(+/252525( 25 & 25  25!25"25"25525 25"25%)25h    '' +/*.-1)-"&!"! $!% $!%""$*OO$:$:#.$0&(5(;(;7;"446(*/1$$(+/?U $UU(U( U + U ' U UUUUUUUUU !U"#U$"%U&!'U(")U*+U,&-U.5/U0 1U2$23U4&5U6-7U89U:;U<"=U>%)?U@AUB CUUn+/(5(;(;"&!"! $!% $!%""$*OO$:$:#.$0&"47;$(+//< $< < (< & <  <  < < < < < < < < < < "!< "!#< $"%< &'< ( )< *5+< ,"-< .%)/< 01< 2 3< < B+/-1 (+      6*.7;+/8<6:11 1( 1 5 1  1)161411 1B*.7;+/8<6:X X  X ( X 5 X  X )X 6X 4X X  &X z*.7;+/8<0 0  0 ( 0 5 0  0 )0 60 0  0 j *.7;+/8<( (  (  (  ( ( ( 5( )( 6( (  ( Z *.7;+/8<11 1 1  1 ( 151)1611 1H *.7;+/8<6 6  6  6  6 ( 6 56 )6 66 6  &6 t*.> &>>  >(  +/(5(;(;7;)+)-$*OO$:$:#.$0&"4$46(*/1$(+/#c $c'c&c5 c ' c ' c"c!c"cc cc$2c&c-c "!c"%)#c$ %ccJ&&*      !  "      $+/-1 $ $( $+ $  $ P $D(!  "   011 #1f $&     "( 8@ $@@@ @ " @ ? @ @@D $   ( $%(5   r r+)) __future__rrenumr itertoolsroperatorrtypingrrr r r r r rrrrnumpyrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresr qdrant_clientrrlangchain_qdrant._utilsr"langchain_qdrant.sparse_embeddingsr Exceptionrr-r#r+rr r!r3sg"     -03.>?2Y2C q qr