Ng1ddlmZddlZddlmZddlmZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZmZddlZddlmZddlmZdd lmZdd lmZmZdd l m!Z!dd l"m#Z#Gd de$Z%Gdde&eZ'GddeZ(dS)) annotationsN)Enum)islice) itemgetter) AnyCallableDict GeneratorIterableListOptionalSequenceTupleTypeUnion)Document) Embeddings) VectorStore) QdrantClientmodels)maximal_marginal_relevance)SparseEmbeddingsceZdZdZdS)QdrantVectorStoreErrorz'`QdrantVectorStore` related exceptions.N)__name__ __module__ __qualname____doc__S/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_qdrant/qdrant.pyrrs1111r rceZdZdZdZdZdS) RetrievalModedensesparsehybridN)rrrDENSESPARSEHYBRIDrr r!r#r##s E F FFFr r#cfeZdZUdZdZded<dZded<dZded<d Zded <d e j eeee j j d ed d f ddZedd!Zedd#Zedd%Zed d d d d d d&d'd(d d d d d d e j j eeee j d eiiid)d(d d fddIZed e j d d d&d'd(d d d d d d e j j eeeed d d fddJZ dddMZ ddd^Z ddd`Z dddbZ dddhZ dddiZ dddjZ dddlZddnZed e j d id e j j eeeed(iiid d fddpZe ddqZ!ddsZ"eddvZ# dddxZ$ddzZ%dd|Z&edd~Z'eddZ(eddZ)eddZ*d S)QdrantVectorStoreuQdrant 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_NAMENTclientrcollection_name embeddingOptional[Embeddings]retrieval_moder# vector_namecontent_payload_keymetadata_payload_keydistancemodels.Distancesparse_embeddingOptional[SparseEmbeddings]sparse_vector_namevalidate_embeddingsboolvalidate_collection_configc | r|||| | r|||||| ||||_||_||_||_||_||_||_||_ | |_ | |_ dS)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_clientr5 _embeddingsr8r9r:r;r<_sparse_embeddingsr@) selfr4r5r6r8r9r:r;r<r>r@rArCs r!__init__zQdrantVectorStore.__init__s6  S  % %niAQ R R R %   , ,"    .$,&#6 $8!  "2"4r returnc|jS)zGet the Qdrant client instance that is being used. Returns: QdrantClient: An instance of `QdrantClient`. )rGrJs r!r4zQdrantVectorStore.clients |r rc<|jtd|jS)zGet the dense embeddings instance that is being used. Raises: ValueError: If embeddings are `None`. Returns: Embeddings: An instance of `Embeddings`. NzBEmbeddings are `None`. Please set using the `embedding` parameter.)rH ValueErrorrNs r! embeddingszQdrantVectorStore.embeddingss-   #T r rc<|jtd|jS)zGet the sparse embeddings instance that is being used. Raises: ValueError: If sparse embeddings are `None`. Returns: SparseEmbeddings: An instance of `SparseEmbeddings`. NzPSparse embeddings are `None`. Please set using the `sparse_embedding` parameter.)rIrPrNs r!sparse_embeddingsz#QdrantVectorStore.sparse_embeddingss0  " *E &&r iiF@clsType[QdrantVectorStore]texts List[str] metadatasOptional[List[dict]]idsOptional[Sequence[str | int]] Optional[str]locationurlport Optional[int] grpc_portint prefer_grpchttpsOptional[bool]api_keyprefixtimeouthostpathcollection_create_optionsDict[str, Any] vector_paramssparse_vector_params batch_sizeforce_recreatekwargsrc |||| | | | | |||d |} ||||| ||||||||||||}!|!|||||!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") r^r_r`rbrdrergrhrirjrk)construct_instance add_texts)"rUrWr6rYr[r5r^r_r`rbrdrergrhrirjrkr<r:r;r9r8r>r@rlrnrorprqrArCrrclient_optionsqdrants" r! from_textszQdrantVectorStore.from_textssj!"&     ''           %   &!  $  3 ;;; r c `td|||||| | | | | |d |}||||||||||||| S)zConstruct an instance of `QdrantVectorStore` from an existing collection without adding any data. Returns: QdrantVectorStore: A new instance of `QdrantVectorStore`. rt r4r5r6r8r:r;r<r9r>r@rArCr)r)rUr5r6r8r^r_r`rbrdrergrhrirjrkr<r:r;r9r@r>rArCrrr4s r!from_existing_collectionz*QdrantVectorStore.from_existing_collectionlsB  #      s+) 3!5#-1 3'A    r Iterable[str]List[str | int]c g}|||||D]4\}}|jjd|j|d|||5|S)zAdd texts with embeddings to the vectorstore. Returns: List of ids from adding the texts into the vectorstore. )r5pointsr)_generate_batchesr4upsertr5extend) rJrWrYr[rprr added_ids batch_idsrs r!rvzQdrantVectorStore.add_textss !%!7!7 9c:" "  ( ( Iv DK   $ 4V  GM      Y ' ' ' 'r rquerykfilterOptional[models.Filter] search_paramsOptional[models.SearchParams]offsetscore_thresholdOptional[float] consistency Optional[models.ReadConsistency] hybrid_fusionOptional[models.FusionQuery]List[Document]c |j||f||||||d| } tttd| S)zvReturn docs most similar to query. Returns: List of Documents most similar to the query. )rrrrrrr)similarity_search_with_scorelistmapr) rJrrrrrrrrrrresultss r!similarity_searchz#QdrantVectorStore.similarity_searchsf"4$3    '+#'      C 1 w//000r List[Tuple[Document, float]]c j||||dd||d | } jtjkr;j|} jjd | jd| j } nejtj krXj |} jjd tj | j| jjd| j } njtjkrˉj|} j |} jjd tjj| |||tjjtj | j| j|||g|p#tjtjjd| j } nt-d jd fd | DS) zReturn docs most similar to query. Returns: List of documents most similar to the query text and distance for each. TF) r5 query_filterrlimitr with_payload with_vectorsrr)rusing)indicesvalues)rrrrparams)fusion)prefetchrzInvalid retrieval mode. .clg|]0}|jjj|jf1Sr_document_from_pointr5r:r;score.0resultrJs r! zBQdrantVectorStore.similarity_search_with_score...s\    ))(,-        r r)r5r8r#r'rQ embed_queryr4 query_pointsr9rr(rSr SparseVectorrrr@r)Prefetch FusionQueryFusionRRFrP)rJrrrrrrrrrr query_optionsquery_dense_embeddingrquery_sparse_embeddings` r!rz.QdrantVectorStore.similarity_search_with_scoresv$ $3"* !.&       -"5 5 5$(O$?$?$F$F !.dk.+&  G  M$8 8 8%)%;%G%G%N%N ".dk.)2:18-    G M$8 8 8$(O$?$?$F$F !%)%;%G%G%N%N ".dk.O".3%, O"5$1$:$B#9#@ &,   &$Sv'9AR'S'S'S)* +,- G2N8KNNNOO O     "    r List[float]c |} jjjj|jjdj|j| |||dd||d |j} fd| DS)zReturn docs most similar to embedding vector. Returns: List of Documents most similar to the query. )r4r5r9r<dense_embeddingsTF) r5rrrrrrrrrrc^g|])}|jjj*Srrr5r:r;rs r!rzAQdrantVectorStore.similarity_search_by_vector..cP     % %$()      r r)_validate_collection_for_denser4r5r9r<rr) rJr6rrrrrrrr qdrant_filterrs ` r!similarity_search_by_vectorz-QdrantVectorStore.similarity_search_by_vector;s   ++; 0(]& ,   +$+*   0"&'+#            "    r ?fetch_k lambda_multfloatc ||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. rrrrrrr)rr4r5r9r<rQr'max_marginal_relevance_search_by_vector) rJrrrrrrrrrrquery_embeddings r!max_marginal_relevance_searchz/QdrantVectorStore.max_marginal_relevance_searchms* ++ K    M O    /55e<<;t;   #'+#      r c |j|f|||||||d| } tttd| S)a$Return docs selected using the maximal marginal relevance with dense vectors. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Returns: List of Documents selected by maximal marginal relevance. rr)2max_marginal_relevance_search_with_score_by_vectorrrr) rJr6rrrrrrrrrrs r!rz9QdrantVectorStore.max_marginal_relevance_search_by_vectorse(J$I   #'+#      C 1 w//000r c   jjdj||||dd||jd | j fd D} t t j|| ||} fd| DS)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) r5rrrrrrrrrcg|]B}t|jtr|jn|jjCSr) isinstancevectorrgetr9rs r!rzXQdrantVectorStore.max_marginal_relevance_search_with_score_by_vector..sX   &-.. 5FMM""4#344   r )rrcg|]<}|jjj|jf=Srr)rirrJs r!rzXQdrantVectorStore.max_marginal_relevance_search_with_score_by_vector..se    ))AJ(,-         r r)r4rr5r9rrnparray) rJr6rrrrrrrrrrQ mmr_selectedrs ` @r!rzDQdrantVectorStore.max_marginal_relevance_search_with_score_by_vectors(+$+*   0'+#"            "    2 HY  qk         "    r Optional[List[str | int]]c x|j|j|}|jtjjkS)zDelete documents by their ids. Args: ids: List of ids to delete. **kwargs: Other keyword arguments that subclasses might use. Returns: True if deletion is successful, False otherwise. )r5points_selector)r4deleter5statusr UpdateStatus COMPLETED)rJr[rrrs r!rzQdrantVectorStore.deletes>## 0$  } 3 ===r Sequence[str | int]cdjj|d}fd|DS)NT)rc^g|])}|jjj*Srrrs r!rz0QdrantVectorStore.get_by_ids..rr )r4retriever5)rJr[rs` r! get_by_idszQdrantVectorStore.get_by_idssO+&&t';St&TT    "    r rwc\|r|||||ptjj}t d i|}||}|r| r||d}|r|r||||| | ||nii}}|tj krG| dg}t|d| d<|| d<| tj d i| i}n|tjkr| tjd i|i}ni|tjkrY| dg}t|d| d<|| d<| tj d i| i}| tjd i|i}|| d<|| d<|| d<|jd i| ||||||||| || dd }|S) NF dummy_textrsizer<r5vectors_configsparse_vectors_configr{r)rEuuiduuid4hexrcollection_existsdelete_collectionrFr#r'embed_documentslenr VectorParamsr(SparseVectorParamsr)create_collection)rUr6r8r>rwr5r<r:r;r9r@rqrlrnrorArCr4rrrpartial_embeddingsrxs r!ruz$QdrantVectorStore.construct_instances(  R  $ $^Y@P Q Q Q)=TZ\\-=////"44_EE  & &  $ $_ 5 5 5 %  6 B) //#"&571N!444%.%>%> ~%N%N"(+,>q,A(B(B f%,4 j) !4""'"""  =#777&(A)).)))%%  =#777%.%>%> ~%N%N"(+,>q,A(B(B f%,4 j) !4""'"""'(A)).)))% $F $ A A'@ A 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 Callable[[float], float]c|jtjjkr|jS|jtjjkr|jS|jtjjkr|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_fnrPrNs r!_select_relevance_score_fnz,QdrantVectorStore._select_relevance_score_fn|sj =FO2 2 22 2 ]fo1 1 1= = ]fo4 4 45 5L r scored_pointrc|j|pi}|j|d<||d<t|j|d|S)N_id_collection_namer1)r,r/)payloadridr)rUrr5r:r;r/s r!rz&QdrantVectorStore._document_from_pointsg '++,@AAGR&/'6#$%-112ErJJ    r FGenerator[tuple[list[str | int], list[models.PointStruct]], Any, None]c #:Kt|}t|pg}t|pdt|D}tt||x}rtt||pd} tt||} dt| ||||| |j|jD} | | fVtt||x}dSdS)Nc>g|]}tjjSr)rrr)r_s r!rz7QdrantVectorStore._generate_batches..s!#J#J#JDJLL$4#J#J#Jr cDg|]\}}}tj|||S))rrr)r PointStruct)rpoint_idrrs r!rz7QdrantVectorStore._generate_batches..sK .Hfg "!#r )iterrrzip_build_vectors_build_payloadsr:r;) rJrWrYr[rptexts_iteratormetadatas_iterator ids_iterator batch_textsbatch_metadatasrrs r!rz#QdrantVectorStore._generate_batchessJe!)/r22CJ#J#Jd5kk#J#J#JKK !&"D"DEEEk $"6*>I 25'' 44((#'01  2 2 F$V# # # #+"&"D"DEEEk $ $ $ $ $r List[dict]cg}t|D];\}}|td|||nd}|||||i<|S)Nz]At least one of the texts is None. Please remove it before calling .from_texts or .add_texts.) enumeraterPappend) rJrWrYr:r;payloadsrtextr/s r!rz!QdrantVectorStore._build_payloadss ''  GAt| 9(1'.s5$fr c^g|])}jtj|j|ji*S)rr)r@rrrrrs r!rz4QdrantVectorStore._build_vectors..sP  +V-@%}fn...r z6Mismatched length between dense and sparse embeddings.c rg|]3\}}j|jtj|j|ji4Sr!)r9r@rrrr)r dense_vector sparse_vectorrJs r!rz4QdrantVectorStore._build_vectors..s^   0L- $l+V-@,3]=R...   r zUnknown retrieval mode. z to build vectors.) r8r#r'rQrrr(rSr)rrrP)rJrWbatch_embeddingsbatch_sparse_embeddingsrrSs` r!rz QdrantVectorStore._build_vectorss  -"5 5 5#>>tE{{KK /   M$8 8 8&*&<&L&LU '' # 6   M$8 8 8#>>tE{{KK  $ 6 F FtE{{ S S '((C!--G    47$&744    R4+>RRR r Nonec2|tjkr||||||dS|tjkr||||dS|tjkr2||||||||||dSdSN)r#r'rr(_validate_collection_for_sparser))rUr4r5r8r9r@r<r6s r!rFz-QdrantVectorStore._validate_collection_configs ]0 0 0  . .h      }3 3 3  / /);     }3 3 3  . .h      / /);      4 3r r$Union[Embeddings, List[float], None]c $||}|jjj}t |t rK||vr>t d|d|dd|d||}n|dkrt d|d| Jd t |tr*t| d gd }n4t |trt|}ntd |j|krt d |jd|d|j|kr?t d|jjd|d|jjddS)Nr5Existing Qdrant collection z% does not contain dense vector named z,. Did you mean one of the existing vectors: z, zS? If you want to recreate the collection, set `force_recreate` parameter to `True`.r1z is built with unnamed dense vector. If you want to reuse it, set `vector_name` to ''(empty string).If you want to recreate the collection, set `force_recreate` to `True`.zVectorParams is NonerrzInvalid `embeddings` type.z@Existing Qdrant collection is configured for dense vectors with z% dimensions. Selected embeddings are z_-dimensional. If you want to recreate the collection, set `force_recreate` parameter to `True`.z-Existing Qdrant collection is configured for z similarity, but requested z&. Please set `distance` parameter to `zl` if you want to reuse it. If you want to recreate the collection, set `force_recreate` parameter to `True`.)get_collectionconfigrvectorsrr rjoinkeysrrrrrPrr<nameupper) rUr4r5r9r<rcollection_info vector_config vector_sizes r!rz0QdrantVectorStore._validate_collection_for_dense+s!///PP'.5= mT * * -//,,/,,2=,,*.=3E3E3G3G)H)H,,,*+6MM b  ,6/666((*@((( & 3 3 ;.>> ~NNqQRRKK ($ / / ;.//KK9:: :   , ,(( %((+6(((   !X - -(( ).((>>##(("*/(((  . -r c||}|jjj}|||vrt d|d|ddS)Nr-r.z' does not contain sparse vectors named zS. If you want to recreate the collection, set `force_recreate` parameter to `True`.)r/r0rsparse_vectorsr)rUr4r5r@r6sparse_vector_configs r!r*z1QdrantVectorStore._validate_collection_for_sparsensz!///PP.5<K ! 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