from __future__ import annotations import uuid from collections.abc import Callable from enum import Enum from itertools import islice from operator import itemgetter from typing import ( TYPE_CHECKING, Any, ) from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore from qdrant_client import QdrantClient, models if TYPE_CHECKING: from collections.abc import Generator, Iterable, Sequence from langchain_qdrant.sparse_embeddings import SparseEmbeddings class QdrantVectorStoreError(Exception): """`QdrantVectorStore` related exceptions.""" class RetrievalMode(str, Enum): """Modes for retrieving vectors from Qdrant.""" DENSE = "dense" SPARSE = "sparse" HYBRID = "hybrid" class QdrantVectorStore(VectorStore): """Qdrant vector store integration. Setup: Install `langchain-qdrant` package. ```bash pip install -qU langchain-qdrant ``` Key init args — indexing params: collection_name: Name of the collection. embedding: Embedding function to use. sparse_embedding: Optional sparse embedding function to use. Key init args — client params: client: Qdrant client to use. retrieval_mode: Retrieval mode to use. Instantiate: ```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: ```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: ```python vector_store.delete(ids=[ids[-1]]) ``` Search: ```python results = vector_store.similarity_search( query="thud", k=1, ) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") ``` ```python *thud[ { "bar": "baz", "_id": "0d706099-6dd9-412a-9df6-a71043e020de", "_collection_name": "demo_collection", } ] ``` Search with filter: ```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}]") ``` ```python *thud[ { "bar": "baz", "_id": "0d706099-6dd9-412a-9df6-a71043e020de", "_collection_name": "demo_collection", } ] ``` Search with score: ```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}]") ``` ```python * [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}] ``` Async: ```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}]") ``` ```python * [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}] ``` Use as Retriever: ```python retriever = vector_store.as_retriever( search_type="mmr", search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, ) retriever.invoke("thud") ``` ```python [ Document( metadata={ "bar": "baz", "_id": "0d706099-6dd9-412a-9df6-a71043e020de", "_collection_name": "demo_collection", }, page_content="thud", ) ] ``` """ # noqa: E501 CONTENT_KEY: str = "page_content" METADATA_KEY: str = "metadata" VECTOR_NAME: str = "" # The default/unnamed vector - https://qdrant.tech/documentation/concepts/collections/#create-a-collection SPARSE_VECTOR_NAME: str = "langchain-sparse" def __init__( self, client: QdrantClient, collection_name: str, embedding: Embeddings | None = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, vector_name: str = VECTOR_NAME, content_payload_key: str = CONTENT_KEY, metadata_payload_key: str = METADATA_KEY, distance: models.Distance = models.Distance.COSINE, sparse_embedding: SparseEmbeddings | None = None, sparse_vector_name: str = SPARSE_VECTOR_NAME, validate_embeddings: bool = True, # noqa: FBT001, FBT002 validate_collection_config: bool = True, # noqa: FBT001, FBT002 ) -> None: """Initialize a new instance of `QdrantVectorStore`. ```python qdrant = Qdrant( client=client, collection_name="my-collection", embedding=OpenAIEmbeddings(), retrieval_mode=RetrievalMode.HYBRID, sparse_embedding=FastEmbedSparse(), ) ``` """ if validate_embeddings: self._validate_embeddings(retrieval_mode, embedding, sparse_embedding) if validate_collection_config: self._validate_collection_config( client, collection_name, retrieval_mode, vector_name, sparse_vector_name, distance, embedding, ) self._client = client self.collection_name = collection_name self._embeddings = embedding self.retrieval_mode = retrieval_mode self.vector_name = vector_name self.content_payload_key = content_payload_key self.metadata_payload_key = metadata_payload_key self.distance = distance self._sparse_embeddings = sparse_embedding self.sparse_vector_name = sparse_vector_name @property def client(self) -> QdrantClient: """Get the Qdrant client instance that is being used. Returns: QdrantClient: An instance of `QdrantClient`. """ return self._client @property def embeddings(self) -> Embeddings | None: """Get the dense embeddings instance that is being used. Returns: Embeddings: An instance of `Embeddings`, or None for SPARSE mode. """ return self._embeddings def _get_retriever_tags(self) -> list[str]: """Get tags for retriever. Override the base class method to handle SPARSE mode where embeddings can be None. In SPARSE mode, embeddings is None, so we don't include embeddings class name in tags. In DENSE/HYBRID modes, embeddings is not None, so we include embeddings class name. """ tags = [self.__class__.__name__] # Handle different retrieval modes if self.retrieval_mode == RetrievalMode.SPARSE: # SPARSE mode: no dense embeddings, so no embeddings class name in tags pass # DENSE/HYBRID modes: include embeddings class name if available elif self.embeddings is not None: tags.append(self.embeddings.__class__.__name__) return tags def _require_embeddings(self, operation: str) -> Embeddings: """Require embeddings for operations that need them. Args: operation: Description of the operation requiring embeddings. Returns: The embeddings instance. Raises: ValueError: If embeddings are None and required for the operation. """ if self.embeddings is None: msg = f"Embeddings are required for {operation}" raise ValueError(msg) return self.embeddings @property def sparse_embeddings(self) -> SparseEmbeddings: """Get the sparse embeddings instance that is being used. Raises: ValueError: If sparse embeddings are `None`. Returns: SparseEmbeddings: An instance of `SparseEmbeddings`. """ if self._sparse_embeddings is None: msg = ( "Sparse embeddings are `None`. " "Please set using the `sparse_embedding` parameter." ) raise ValueError(msg) return self._sparse_embeddings @classmethod def from_texts( cls: type[QdrantVectorStore], texts: list[str], embedding: Embeddings | None = None, metadatas: list[dict] | None = None, ids: Sequence[str | int] | None = None, collection_name: str | None = None, location: str | None = None, url: str | None = None, port: int | None = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, # noqa: FBT001, FBT002 https: bool | None = None, # noqa: FBT001 api_key: str | None = None, prefix: str | None = None, timeout: int | None = None, host: str | None = None, path: str | None = None, distance: models.Distance = models.Distance.COSINE, content_payload_key: str = CONTENT_KEY, metadata_payload_key: str = METADATA_KEY, vector_name: str = VECTOR_NAME, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, sparse_embedding: SparseEmbeddings | None = None, sparse_vector_name: str = SPARSE_VECTOR_NAME, collection_create_options: dict[str, Any] | None = None, vector_params: dict[str, Any] | None = None, sparse_vector_params: dict[str, Any] | None = None, batch_size: int = 64, force_recreate: bool = False, # noqa: FBT001, FBT002 validate_embeddings: bool = True, # noqa: FBT001, FBT002 validate_collection_config: bool = True, # noqa: FBT001, FBT002 **kwargs: Any, ) -> QdrantVectorStore: """Construct 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. ```python from langchain_qdrant import Qdrant from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() qdrant = Qdrant.from_texts(texts, embeddings, url="http://localhost:6333") ``` """ if sparse_vector_params is None: sparse_vector_params = {} if vector_params is None: vector_params = {} if collection_create_options is None: collection_create_options = {} client_options = { "location": location, "url": url, "port": port, "grpc_port": grpc_port, "prefer_grpc": prefer_grpc, "https": https, "api_key": api_key, "prefix": prefix, "timeout": timeout, "host": host, "path": path, **kwargs, } qdrant = cls.construct_instance( embedding, retrieval_mode, sparse_embedding, client_options, collection_name, distance, content_payload_key, metadata_payload_key, vector_name, sparse_vector_name, force_recreate, collection_create_options, vector_params, sparse_vector_params, validate_embeddings, validate_collection_config, ) qdrant.add_texts(texts, metadatas, ids, batch_size) return qdrant @classmethod def from_existing_collection( cls: type[QdrantVectorStore], collection_name: str, embedding: Embeddings | None = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, location: str | None = None, url: str | None = None, port: int | None = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, # noqa: FBT001, FBT002 https: bool | None = None, # noqa: FBT001 api_key: str | None = None, prefix: str | None = None, timeout: int | None = None, host: str | None = None, path: str | None = None, distance: models.Distance = models.Distance.COSINE, content_payload_key: str = CONTENT_KEY, metadata_payload_key: str = METADATA_KEY, vector_name: str = VECTOR_NAME, sparse_vector_name: str = SPARSE_VECTOR_NAME, sparse_embedding: SparseEmbeddings | None = None, validate_embeddings: bool = True, # noqa: FBT001, FBT002 validate_collection_config: bool = True, # noqa: FBT001, FBT002 **kwargs: Any, ) -> QdrantVectorStore: """Construct `QdrantVectorStore` from existing collection without adding data. Returns: QdrantVectorStore: A new instance of `QdrantVectorStore`. """ client = QdrantClient( location=location, url=url, port=port, grpc_port=grpc_port, prefer_grpc=prefer_grpc, https=https, api_key=api_key, prefix=prefix, timeout=timeout, host=host, path=path, **kwargs, ) return cls( client=client, collection_name=collection_name, embedding=embedding, retrieval_mode=retrieval_mode, content_payload_key=content_payload_key, metadata_payload_key=metadata_payload_key, distance=distance, vector_name=vector_name, sparse_embedding=sparse_embedding, sparse_vector_name=sparse_vector_name, validate_embeddings=validate_embeddings, validate_collection_config=validate_collection_config, ) def add_texts( # type: ignore[override] self, texts: Iterable[str], metadatas: list[dict] | None = None, ids: Sequence[str | int] | None = None, batch_size: int = 64, **kwargs: Any, ) -> list[str | int]: """Add texts with embeddings to the `VectorStore`. Returns: List of ids from adding the texts into the `VectorStore`. """ added_ids = [] for batch_ids, points in self._generate_batches( texts, metadatas, ids, batch_size ): self.client.upsert( collection_name=self.collection_name, points=points, **kwargs ) added_ids.extend(batch_ids) return added_ids def similarity_search( self, query: str, k: int = 4, filter: models.Filter | None = None, # noqa: A002 search_params: models.SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, hybrid_fusion: models.FusionQuery | None = None, **kwargs: Any, ) -> list[Document]: """Return docs most similar to query. Returns: List of `Document` objects most similar to the query. """ results = self.similarity_search_with_score( query, k, filter=filter, search_params=search_params, offset=offset, score_threshold=score_threshold, consistency=consistency, hybrid_fusion=hybrid_fusion, **kwargs, ) return list(map(itemgetter(0), results)) def similarity_search_with_score( self, query: str, k: int = 4, filter: models.Filter | None = None, # noqa: A002 search_params: models.SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, hybrid_fusion: models.FusionQuery | None = None, **kwargs: Any, ) -> list[tuple[Document, float]]: """Return docs most similar to query. Returns: List of documents most similar to the query text and distance for each. """ query_options = { "collection_name": self.collection_name, "query_filter": filter, "search_params": search_params, "limit": k, "offset": offset, "with_payload": True, "with_vectors": False, "score_threshold": score_threshold, "consistency": consistency, **kwargs, } if self.retrieval_mode == RetrievalMode.DENSE: embeddings = self._require_embeddings("DENSE mode") query_dense_embedding = embeddings.embed_query(query) results = self.client.query_points( query=query_dense_embedding, using=self.vector_name, **query_options, ).points elif self.retrieval_mode == RetrievalMode.SPARSE: query_sparse_embedding = self.sparse_embeddings.embed_query(query) results = self.client.query_points( query=models.SparseVector( indices=query_sparse_embedding.indices, values=query_sparse_embedding.values, ), using=self.sparse_vector_name, **query_options, ).points elif self.retrieval_mode == RetrievalMode.HYBRID: embeddings = self._require_embeddings("HYBRID mode") query_dense_embedding = embeddings.embed_query(query) query_sparse_embedding = self.sparse_embeddings.embed_query(query) results = self.client.query_points( prefetch=[ models.Prefetch( using=self.vector_name, query=query_dense_embedding, filter=filter, limit=k, params=search_params, ), models.Prefetch( using=self.sparse_vector_name, query=models.SparseVector( indices=query_sparse_embedding.indices, values=query_sparse_embedding.values, ), filter=filter, limit=k, params=search_params, ), ], query=hybrid_fusion or models.FusionQuery(fusion=models.Fusion.RRF), **query_options, ).points else: msg = f"Invalid retrieval mode. {self.retrieval_mode}." raise ValueError(msg) return [ ( self._document_from_point( result, self.collection_name, self.content_payload_key, self.metadata_payload_key, ), result.score, ) for result in results ] def similarity_search_with_score_by_vector( self, embedding: list[float], k: int = 4, filter: models.Filter | None = None, # noqa: A002 search_params: models.SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, **kwargs: Any, ) -> list[tuple[Document, float]]: """Return docs most similar to embedding vector. Returns: List of `Document` objects most similar to the query and distance for each. """ qdrant_filter = filter self._validate_collection_for_dense( client=self.client, collection_name=self.collection_name, vector_name=self.vector_name, distance=self.distance, dense_embeddings=embedding, ) results = self.client.query_points( collection_name=self.collection_name, query=embedding, using=self.vector_name, query_filter=qdrant_filter, search_params=search_params, limit=k, offset=offset, with_payload=True, with_vectors=False, score_threshold=score_threshold, consistency=consistency, **kwargs, ).points return [ ( self._document_from_point( result, self.collection_name, self.content_payload_key, self.metadata_payload_key, ), result.score, ) for result in results ] def similarity_search_by_vector( self, embedding: list[float], k: int = 4, filter: models.Filter | None = None, # noqa: A002 search_params: models.SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, **kwargs: Any, ) -> list[Document]: """Return docs most similar to embedding vector. Returns: List of `Document` objects most similar to the query. """ results = self.similarity_search_with_score_by_vector( embedding, k, filter=filter, search_params=search_params, offset=offset, score_threshold=score_threshold, consistency=consistency, **kwargs, ) return list(map(itemgetter(0), results)) def max_marginal_relevance_search( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: models.Filter | None = None, # noqa: A002 search_params: models.SearchParams | None = None, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, **kwargs: Any, ) -> list[Document]: """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 `Document` objects selected by maximal marginal relevance. """ self._validate_collection_for_dense( self.client, self.collection_name, self.vector_name, self.distance, self.embeddings, ) embeddings = self._require_embeddings("max_marginal_relevance_search") query_embedding = embeddings.embed_query(query) return self.max_marginal_relevance_search_by_vector( query_embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter, search_params=search_params, score_threshold=score_threshold, consistency=consistency, **kwargs, ) def max_marginal_relevance_search_by_vector( self, embedding: list[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: models.Filter | None = None, # noqa: A002 search_params: models.SearchParams | None = None, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, **kwargs: Any, ) -> list[Document]: """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 `Document` objects selected by maximal marginal relevance. """ results = self.max_marginal_relevance_search_with_score_by_vector( embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter, search_params=search_params, score_threshold=score_threshold, consistency=consistency, **kwargs, ) return list(map(itemgetter(0), results)) def max_marginal_relevance_search_with_score_by_vector( self, embedding: list[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: models.Filter | None = None, # noqa: A002 search_params: models.SearchParams | None = None, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, **kwargs: Any, ) -> list[tuple[Document, float]]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Returns: List of `Document` objects selected by maximal marginal relevance and distance for each. """ results = self.client.query_points( collection_name=self.collection_name, query=models.NearestQuery( nearest=embedding, mmr=models.Mmr(diversity=lambda_mult, candidates_limit=fetch_k), ), query_filter=filter, search_params=search_params, limit=k, with_payload=True, with_vectors=True, score_threshold=score_threshold, consistency=consistency, using=self.vector_name, **kwargs, ).points return [ ( self._document_from_point( result, self.collection_name, self.content_payload_key, self.metadata_payload_key, ), result.score, ) for result in results ] def delete( # type: ignore[override] self, ids: list[str | int] | None = None, **kwargs: Any, ) -> bool | None: """Delete 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. """ result = self.client.delete( collection_name=self.collection_name, points_selector=ids, ) return result.status == models.UpdateStatus.COMPLETED def get_by_ids(self, ids: Sequence[str | int], /) -> list[Document]: results = self.client.retrieve(self.collection_name, ids, with_payload=True) return [ self._document_from_point( result, self.collection_name, self.content_payload_key, self.metadata_payload_key, ) for result in results ] @classmethod def construct_instance( cls: type[QdrantVectorStore], embedding: Embeddings | None = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, sparse_embedding: SparseEmbeddings | None = None, client_options: dict[str, Any] | None = None, collection_name: str | None = None, distance: models.Distance = models.Distance.COSINE, content_payload_key: str = CONTENT_KEY, metadata_payload_key: str = METADATA_KEY, vector_name: str = VECTOR_NAME, sparse_vector_name: str = SPARSE_VECTOR_NAME, force_recreate: bool = False, # noqa: FBT001, FBT002 collection_create_options: dict[str, Any] | None = None, vector_params: dict[str, Any] | None = None, sparse_vector_params: dict[str, Any] | None = None, validate_embeddings: bool = True, # noqa: FBT001, FBT002 validate_collection_config: bool = True, # noqa: FBT001, FBT002 ) -> QdrantVectorStore: if sparse_vector_params is None: sparse_vector_params = {} if vector_params is None: vector_params = {} if collection_create_options is None: collection_create_options = {} if client_options is None: client_options = {} if validate_embeddings: cls._validate_embeddings(retrieval_mode, embedding, sparse_embedding) collection_name = collection_name or uuid.uuid4().hex client = QdrantClient(**client_options) collection_exists = client.collection_exists(collection_name) if collection_exists and force_recreate: client.delete_collection(collection_name) collection_exists = False if collection_exists: if validate_collection_config: cls._validate_collection_config( client, collection_name, retrieval_mode, vector_name, sparse_vector_name, distance, embedding, ) else: vectors_config, sparse_vectors_config = {}, {} if retrieval_mode == RetrievalMode.DENSE: partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore[union-attr] vector_params["size"] = len(partial_embeddings[0]) vector_params["distance"] = distance vectors_config = { vector_name: models.VectorParams( **vector_params, ) } elif retrieval_mode == RetrievalMode.SPARSE: sparse_vectors_config = { sparse_vector_name: models.SparseVectorParams( **sparse_vector_params ) } elif retrieval_mode == RetrievalMode.HYBRID: partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore[union-attr] vector_params["size"] = len(partial_embeddings[0]) vector_params["distance"] = distance vectors_config = { vector_name: models.VectorParams( **vector_params, ) } sparse_vectors_config = { sparse_vector_name: models.SparseVectorParams( **sparse_vector_params ) } collection_create_options["collection_name"] = collection_name collection_create_options["vectors_config"] = vectors_config collection_create_options["sparse_vectors_config"] = sparse_vectors_config client.create_collection(**collection_create_options) return cls( client=client, collection_name=collection_name, embedding=embedding, retrieval_mode=retrieval_mode, content_payload_key=content_payload_key, metadata_payload_key=metadata_payload_key, distance=distance, vector_name=vector_name, sparse_embedding=sparse_embedding, sparse_vector_name=sparse_vector_name, validate_embeddings=False, validate_collection_config=False, ) @staticmethod def _cosine_relevance_score_fn(distance: float) -> float: """Normalize the distance to a score on a scale `[0, 1]`.""" return (distance + 1.0) / 2.0 def _select_relevance_score_fn(self) -> Callable[[float], float]: """Your "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. """ if self.distance == models.Distance.COSINE: return self._cosine_relevance_score_fn if self.distance == models.Distance.DOT: return self._max_inner_product_relevance_score_fn if self.distance == models.Distance.EUCLID: return self._euclidean_relevance_score_fn msg = "Unknown distance strategy, must be COSINE, DOT, or EUCLID." raise ValueError(msg) @classmethod def _document_from_point( cls, scored_point: Any, collection_name: str, content_payload_key: str, metadata_payload_key: str, ) -> Document: metadata = scored_point.payload.get(metadata_payload_key) or {} metadata["_id"] = scored_point.id metadata["_collection_name"] = collection_name return Document( page_content=scored_point.payload.get(content_payload_key, ""), metadata=metadata, ) def _generate_batches( self, texts: Iterable[str], metadatas: list[dict] | None = None, ids: Sequence[str | int] | None = None, batch_size: int = 64, ) -> Generator[tuple[list[str | int], list[models.PointStruct]], Any, None]: texts_iterator = iter(texts) metadatas_iterator = iter(metadatas or []) ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)]) while batch_texts := list(islice(texts_iterator, batch_size)): batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None batch_ids = list(islice(ids_iterator, batch_size)) points = [ models.PointStruct( id=point_id, vector=vector, payload=payload, ) for point_id, vector, payload in zip( batch_ids, self._build_vectors(batch_texts), self._build_payloads( batch_texts, batch_metadatas, self.content_payload_key, self.metadata_payload_key, ), strict=False, ) ] yield batch_ids, points @staticmethod def _build_payloads( texts: Iterable[str], metadatas: list[dict] | None, content_payload_key: str, metadata_payload_key: str, ) -> list[dict]: payloads = [] for i, text in enumerate(texts): if text is None: msg = ( "At least one of the texts is None. Please remove it before " "calling .from_texts or .add_texts." ) raise ValueError(msg) metadata = metadatas[i] if metadatas is not None else None payloads.append( { content_payload_key: text, metadata_payload_key: metadata, } ) return payloads def _build_vectors( self, texts: Iterable[str], ) -> list[models.VectorStruct]: if self.retrieval_mode == RetrievalMode.DENSE: embeddings = self._require_embeddings("DENSE mode") batch_embeddings = embeddings.embed_documents(list(texts)) return [ { self.vector_name: vector, } for vector in batch_embeddings ] if self.retrieval_mode == RetrievalMode.SPARSE: batch_sparse_embeddings = self.sparse_embeddings.embed_documents( list(texts) ) return [ { self.sparse_vector_name: models.SparseVector( values=vector.values, indices=vector.indices ) } for vector in batch_sparse_embeddings ] if self.retrieval_mode == RetrievalMode.HYBRID: embeddings = self._require_embeddings("HYBRID mode") dense_embeddings = embeddings.embed_documents(list(texts)) sparse_embeddings = self.sparse_embeddings.embed_documents(list(texts)) if len(dense_embeddings) != len(sparse_embeddings): msg = "Mismatched length between dense and sparse embeddings." raise ValueError(msg) return [ { self.vector_name: dense_vector, self.sparse_vector_name: models.SparseVector( values=sparse_vector.values, indices=sparse_vector.indices ), } for dense_vector, sparse_vector in zip( dense_embeddings, sparse_embeddings, strict=False ) ] msg = f"Unknown retrieval mode. {self.retrieval_mode} to build vectors." raise ValueError(msg) @classmethod def _validate_collection_config( cls: type[QdrantVectorStore], client: QdrantClient, collection_name: str, retrieval_mode: RetrievalMode, vector_name: str, sparse_vector_name: str, distance: models.Distance, embedding: Embeddings | None, ) -> None: if retrieval_mode == RetrievalMode.DENSE: cls._validate_collection_for_dense( client, collection_name, vector_name, distance, embedding ) elif retrieval_mode == RetrievalMode.SPARSE: cls._validate_collection_for_sparse( client, collection_name, sparse_vector_name ) elif retrieval_mode == RetrievalMode.HYBRID: cls._validate_collection_for_dense( client, collection_name, vector_name, distance, embedding ) cls._validate_collection_for_sparse( client, collection_name, sparse_vector_name ) @classmethod def _validate_collection_for_dense( cls: type[QdrantVectorStore], client: QdrantClient, collection_name: str, vector_name: str, distance: models.Distance, dense_embeddings: Embeddings | list[float] | None, ) -> None: collection_info = client.get_collection(collection_name=collection_name) vector_config = collection_info.config.params.vectors if isinstance(vector_config, dict): # vector_config is a Dict[str, VectorParams] if vector_name not in vector_config: msg = ( f"Existing Qdrant collection {collection_name} does not " f"contain dense vector named {vector_name}. " "Did you mean one of the " f"existing vectors: {', '.join(vector_config.keys())}? " # type: ignore[union-attr] f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) raise QdrantVectorStoreError(msg) # Get the VectorParams object for the specified vector_name vector_config = vector_config[vector_name] # type: ignore[assignment, index] # vector_config is an instance of VectorParams # Case of a collection with single/unnamed vector. elif vector_name != "": msg = ( f"Existing Qdrant collection {collection_name} is built " "with unnamed dense vector. " f"If you want to reuse it, set `vector_name` to ''(empty string)." f"If you want to recreate the collection, " "set `force_recreate` to `True`." ) raise QdrantVectorStoreError(msg) if vector_config is None: msg = "VectorParams is None" raise ValueError(msg) if isinstance(dense_embeddings, Embeddings): vector_size = len(dense_embeddings.embed_documents(["dummy_text"])[0]) elif isinstance(dense_embeddings, list): vector_size = len(dense_embeddings) else: msg = "Invalid `embeddings` type." raise TypeError(msg) if vector_config.size != vector_size: msg = ( f"Existing Qdrant collection is configured for dense vectors with " f"{vector_config.size} dimensions. " f"Selected embeddings are {vector_size}-dimensional. " f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) raise QdrantVectorStoreError(msg) if vector_config.distance != distance: msg = ( f"Existing Qdrant collection is configured for " f"{vector_config.distance.name} similarity, but requested " f"{distance.upper()}. Please set `distance` parameter to " f"`{vector_config.distance.name}` if you want to reuse it. " f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) raise QdrantVectorStoreError(msg) @classmethod def _validate_collection_for_sparse( cls: type[QdrantVectorStore], client: QdrantClient, collection_name: str, sparse_vector_name: str, ) -> None: collection_info = client.get_collection(collection_name=collection_name) sparse_vector_config = collection_info.config.params.sparse_vectors if ( sparse_vector_config is None or sparse_vector_name not in sparse_vector_config ): msg = ( f"Existing Qdrant collection {collection_name} does not " f"contain sparse vectors named {sparse_vector_name}. " f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) raise QdrantVectorStoreError(msg) @classmethod def _validate_embeddings( cls: type[QdrantVectorStore], retrieval_mode: RetrievalMode, embedding: Embeddings | None, sparse_embedding: SparseEmbeddings | None, ) -> None: if retrieval_mode == RetrievalMode.DENSE and embedding is None: msg = "'embedding' cannot be None when retrieval mode is 'dense'" raise ValueError(msg) if retrieval_mode == RetrievalMode.SPARSE and sparse_embedding is None: msg = "'sparse_embedding' cannot be None when retrieval mode is 'sparse'" raise ValueError(msg) if retrieval_mode == RetrievalMode.HYBRID and any( [embedding is None, sparse_embedding is None] ): msg = ( "Both 'embedding' and 'sparse_embedding' cannot be None " "when retrieval mode is 'hybrid'" ) raise ValueError(msg)