From 374f414c919fa1f1505866f1fdcd69b5086d278f Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Mon, 11 Aug 2025 12:43:41 -0400 Subject: [PATCH] feat(qdrant): ruff fixes and rules (#32500) --- .../qdrant/langchain_qdrant/__init__.py | 4 +- .../qdrant/langchain_qdrant/_utils.py | 6 +- .../langchain_qdrant/fastembed_sparse.py | 20 +- .../qdrant/langchain_qdrant/qdrant.py | 206 +- .../langchain_qdrant/sparse_embeddings.py | 4 +- .../qdrant/langchain_qdrant/vectorstores.py | 306 ++- libs/partners/qdrant/pyproject.toml | 59 +- .../async_api/test_add_texts.py | 14 +- .../async_api/test_from_texts.py | 25 +- .../async_api/test_max_marginal_relevance.py | 2 + .../async_api/test_similarity_search.py | 8 +- .../qdrant/tests/integration_tests/common.py | 19 +- .../tests/integration_tests/fixtures.py | 2 +- .../qdrant_vector_store/test_add_texts.py | 17 +- .../qdrant_vector_store/test_from_existing.py | 3 +- .../qdrant_vector_store/test_from_texts.py | 27 +- .../qdrant_vector_store/test_mmr.py | 4 +- .../qdrant_vector_store/test_search.py | 4 +- .../tests/integration_tests/test_add_texts.py | 14 +- .../tests/integration_tests/test_compile.py | 1 - .../test_embedding_interface.py | 2 + .../test_from_existing_collection.py | 2 +- .../integration_tests/test_from_texts.py | 24 +- .../test_max_marginal_relevance.py | 6 +- .../test_similarity_search.py | 8 +- libs/partners/qdrant/uv.lock | 2361 ++++++++--------- 26 files changed, 1659 insertions(+), 1489 deletions(-) diff --git a/libs/partners/qdrant/langchain_qdrant/__init__.py b/libs/partners/qdrant/langchain_qdrant/__init__.py index f8266308489..1a3bf857404 100644 --- a/libs/partners/qdrant/langchain_qdrant/__init__.py +++ b/libs/partners/qdrant/langchain_qdrant/__init__.py @@ -4,10 +4,10 @@ from langchain_qdrant.sparse_embeddings import SparseEmbeddings, SparseVector from langchain_qdrant.vectorstores import Qdrant __all__ = [ + "FastEmbedSparse", "Qdrant", "QdrantVectorStore", + "RetrievalMode", "SparseEmbeddings", "SparseVector", - "FastEmbedSparse", - "RetrievalMode", ] diff --git a/libs/partners/qdrant/langchain_qdrant/_utils.py b/libs/partners/qdrant/langchain_qdrant/_utils.py index df2c5d6039e..91fc77442ad 100644 --- a/libs/partners/qdrant/langchain_qdrant/_utils.py +++ b/libs/partners/qdrant/langchain_qdrant/_utils.py @@ -47,17 +47,17 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: X = np.array(X) Y = np.array(Y) if X.shape[1] != Y.shape[1]: - raise ValueError( + msg = ( f"Number of columns in X and Y must be the same. X has shape {X.shape} " f"and Y has shape {Y.shape}." ) + raise ValueError(msg) try: import simsimd as simd X = np.array(X, dtype=np.float32) Y = np.array(Y, dtype=np.float32) - Z = 1 - np.array(simd.cdist(X, Y, metric="cosine")) - return Z + return 1 - np.array(simd.cdist(X, Y, metric="cosine")) except ImportError: X_norm = np.linalg.norm(X, axis=1) Y_norm = np.linalg.norm(Y, axis=1) diff --git a/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py b/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py index 84371986395..c7b5fa3b140 100644 --- a/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py +++ b/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from collections.abc import Sequence from typing import Any, Optional @@ -17,9 +19,11 @@ class FastEmbedSparse(SparseEmbeddings): parallel: Optional[int] = None, **kwargs: Any, ) -> None: - """ - Sparse encoder implementation using FastEmbed - https://qdrant.github.io/fastembed/ - For a list of available models, see https://qdrant.github.io/fastembed/examples/Supported_Models/ + """Sparse encoder implementation using FastEmbed. + + Uses `FastEmbed `__ for sparse text + embeddings. + For a list of available models, see `the Qdrant docs `__. Args: model_name (str): The name of the model to use. Defaults to `"Qdrant/bm25"`. @@ -38,15 +42,19 @@ class FastEmbedSparse(SparseEmbeddings): kwargs: Additional options to pass to fastembed.SparseTextEmbedding Raises: ValueError: If the model_name is not supported in SparseTextEmbedding. + """ try: - from fastembed import SparseTextEmbedding # type: ignore - except ImportError: - raise ValueError( + from fastembed import ( # type: ignore[import-not-found] + SparseTextEmbedding, + ) + except ImportError as err: + msg = ( "The 'fastembed' package is not installed. " "Please install it with " "`pip install fastembed` or `pip install fastembed-gpu`." ) + raise ValueError(msg) from err self._batch_size = batch_size self._parallel = parallel self._model = SparseTextEmbedding( diff --git a/libs/partners/qdrant/langchain_qdrant/qdrant.py b/libs/partners/qdrant/langchain_qdrant/qdrant.py index d45f4083e16..38f8e76060e 100644 --- a/libs/partners/qdrant/langchain_qdrant/qdrant.py +++ b/libs/partners/qdrant/langchain_qdrant/qdrant.py @@ -185,8 +185,8 @@ class QdrantVectorStore(VectorStore): distance: models.Distance = models.Distance.COSINE, sparse_embedding: Optional[SparseEmbeddings] = None, sparse_vector_name: str = SPARSE_VECTOR_NAME, - validate_embeddings: bool = True, - validate_collection_config: bool = True, + validate_embeddings: bool = True, # noqa: FBT001, FBT002 + validate_collection_config: bool = True, # noqa: FBT001, FBT002 ): """Initialize a new instance of `QdrantVectorStore`. @@ -232,6 +232,7 @@ class QdrantVectorStore(VectorStore): Returns: QdrantClient: An instance of ``QdrantClient``. + """ return self._client @@ -244,11 +245,11 @@ class QdrantVectorStore(VectorStore): Returns: Embeddings: An instance of ``Embeddings``. + """ if self._embeddings is None: - raise ValueError( - "Embeddings are `None`. Please set using the `embedding` parameter." - ) + msg = "Embeddings are `None`. Please set using the `embedding` parameter." + raise ValueError(msg) return self._embeddings @property @@ -260,12 +261,14 @@ class QdrantVectorStore(VectorStore): Returns: SparseEmbeddings: An instance of ``SparseEmbeddings``. + """ if self._sparse_embeddings is None: - raise ValueError( + msg = ( "Sparse embeddings are `None`. " "Please set using the `sparse_embedding` parameter." ) + raise ValueError(msg) return self._sparse_embeddings @classmethod @@ -280,8 +283,8 @@ class QdrantVectorStore(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, @@ -294,13 +297,13 @@ class QdrantVectorStore(VectorStore): retrieval_mode: RetrievalMode = RetrievalMode.DENSE, sparse_embedding: Optional[SparseEmbeddings] = None, sparse_vector_name: str = SPARSE_VECTOR_NAME, - collection_create_options: dict[str, Any] = {}, - vector_params: dict[str, Any] = {}, - sparse_vector_params: dict[str, Any] = {}, + collection_create_options: Optional[dict[str, Any]] = None, + vector_params: Optional[dict[str, Any]] = None, + sparse_vector_params: Optional[dict[str, Any]] = None, batch_size: int = 64, - force_recreate: bool = False, - validate_embeddings: bool = True, - validate_collection_config: bool = True, + 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. @@ -321,6 +324,12 @@ class QdrantVectorStore(VectorStore): 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, @@ -367,8 +376,8 @@ class QdrantVectorStore(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, @@ -380,8 +389,8 @@ class QdrantVectorStore(VectorStore): vector_name: str = VECTOR_NAME, sparse_vector_name: str = SPARSE_VECTOR_NAME, sparse_embedding: Optional[SparseEmbeddings] = None, - validate_embeddings: bool = True, - validate_collection_config: bool = True, + validate_embeddings: bool = True, # noqa: FBT001, FBT002 + validate_collection_config: bool = True, # noqa: FBT001, FBT002 **kwargs: Any, ) -> QdrantVectorStore: """Construct an instance of ``QdrantVectorStore`` from an existing collection @@ -389,7 +398,8 @@ class QdrantVectorStore(VectorStore): Returns: QdrantVectorStore: A new instance of ``QdrantVectorStore``. - """ + + """ # noqa: D205 client = QdrantClient( location=location, url=url, @@ -420,7 +430,7 @@ class QdrantVectorStore(VectorStore): validate_collection_config=validate_collection_config, ) - def add_texts( # type: ignore + def add_texts( # type: ignore[override] self, texts: Iterable[str], metadatas: Optional[list[dict]] = None, @@ -432,6 +442,7 @@ class QdrantVectorStore(VectorStore): Returns: List of ids from adding the texts into the vectorstore. + """ added_ids = [] for batch_ids, points in self._generate_batches( @@ -448,7 +459,7 @@ class QdrantVectorStore(VectorStore): self, query: str, k: int = 4, - filter: Optional[models.Filter] = None, + filter: Optional[models.Filter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -460,6 +471,7 @@ class QdrantVectorStore(VectorStore): Returns: List of Documents most similar to the query. + """ results = self.similarity_search_with_score( query, @@ -478,7 +490,7 @@ class QdrantVectorStore(VectorStore): self, query: str, k: int = 4, - filter: Optional[models.Filter] = None, + filter: Optional[models.Filter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -490,6 +502,7 @@ class QdrantVectorStore(VectorStore): Returns: List of documents most similar to the query text and distance for each. + """ query_options = { "collection_name": self.collection_name, @@ -550,7 +563,8 @@ class QdrantVectorStore(VectorStore): ).points else: - raise ValueError(f"Invalid retrieval mode. {self.retrieval_mode}.") + msg = f"Invalid retrieval mode. {self.retrieval_mode}." + raise ValueError(msg) return [ ( self._document_from_point( @@ -568,7 +582,7 @@ class QdrantVectorStore(VectorStore): self, embedding: list[float], k: int = 4, - filter: Optional[models.Filter] = None, + filter: Optional[models.Filter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -579,6 +593,7 @@ class QdrantVectorStore(VectorStore): Returns: List of Documents most similar to the query and distance for each. + """ qdrant_filter = filter @@ -621,7 +636,7 @@ class QdrantVectorStore(VectorStore): self, embedding: list[float], k: int = 4, - filter: Optional[models.Filter] = None, + filter: Optional[models.Filter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -632,6 +647,7 @@ class QdrantVectorStore(VectorStore): Returns: List of Documents most similar to the query. + """ results = self.similarity_search_with_score_by_vector( embedding, @@ -651,7 +667,7 @@ class QdrantVectorStore(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[models.Filter] = None, + filter: Optional[models.Filter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, @@ -664,6 +680,7 @@ class QdrantVectorStore(VectorStore): Returns: List of Documents selected by maximal marginal relevance. + """ self._validate_collection_for_dense( self.client, @@ -692,7 +709,7 @@ class QdrantVectorStore(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[models.Filter] = None, + filter: Optional[models.Filter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, @@ -705,6 +722,7 @@ class QdrantVectorStore(VectorStore): Returns: List of Documents selected by maximal marginal relevance. + """ results = self.max_marginal_relevance_search_with_score_by_vector( embedding, @@ -725,19 +743,21 @@ class QdrantVectorStore(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[models.Filter] = None, + filter: Optional[models.Filter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = 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 Documents selected by maximal marginal relevance and distance for each. + """ results = self.client.query_points( collection_name=self.collection_name, @@ -756,7 +776,7 @@ class QdrantVectorStore(VectorStore): embeddings = [ result.vector if isinstance(result.vector, list) - else result.vector.get(self.vector_name) # type: ignore + else result.vector.get(self.vector_name) # type: ignore[union-attr] for result in results ] mmr_selected = maximal_marginal_relevance( @@ -775,7 +795,7 @@ class QdrantVectorStore(VectorStore): for i in mmr_selected ] - def delete( # type: ignore + def delete( # type: ignore[override] self, ids: Optional[list[str | int]] = None, **kwargs: Any, @@ -788,6 +808,7 @@ class QdrantVectorStore(VectorStore): Returns: True if deletion is successful, False otherwise. + """ result = self.client.delete( collection_name=self.collection_name, @@ -814,20 +835,28 @@ class QdrantVectorStore(VectorStore): embedding: Optional[Embeddings] = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, sparse_embedding: Optional[SparseEmbeddings] = None, - client_options: dict[str, Any] = {}, + client_options: Optional[dict[str, Any]] = None, collection_name: Optional[str] = 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, - collection_create_options: dict[str, Any] = {}, - vector_params: dict[str, Any] = {}, - sparse_vector_params: dict[str, Any] = {}, - validate_embeddings: bool = True, - validate_collection_config: bool = True, + force_recreate: bool = False, # noqa: FBT001, FBT002 + collection_create_options: Optional[dict[str, Any]] = None, + vector_params: Optional[dict[str, Any]] = None, + sparse_vector_params: Optional[dict[str, Any]] = 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 @@ -852,7 +881,7 @@ class QdrantVectorStore(VectorStore): else: vectors_config, sparse_vectors_config = {}, {} if retrieval_mode == RetrievalMode.DENSE: - partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore + partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore[union-attr] vector_params["size"] = len(partial_embeddings[0]) vector_params["distance"] = distance @@ -871,7 +900,7 @@ class QdrantVectorStore(VectorStore): } elif retrieval_mode == RetrievalMode.HYBRID: - partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore + partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore[union-attr] vector_params["size"] = len(partial_embeddings[0]) vector_params["distance"] = distance @@ -894,7 +923,7 @@ class QdrantVectorStore(VectorStore): client.create_collection(**collection_create_options) - qdrant = cls( + return cls( client=client, collection_name=collection_name, embedding=embedding, @@ -908,7 +937,6 @@ class QdrantVectorStore(VectorStore): validate_embeddings=False, validate_collection_config=False, ) - return qdrant @staticmethod def _cosine_relevance_score_fn(distance: float) -> float: @@ -916,25 +944,22 @@ class QdrantVectorStore(VectorStore): return (distance + 1.0) / 2.0 def _select_relevance_score_fn(self) -> Callable[[float], float]: - """ - The "correct" relevance function may differ depending on a few things, - including: + """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 - elif self.distance == models.Distance.DOT: + if self.distance == models.Distance.DOT: return self._max_inner_product_relevance_score_fn - elif self.distance == models.Distance.EUCLID: + if self.distance == models.Distance.EUCLID: return self._euclidean_relevance_score_fn - else: - raise ValueError( - "Unknown distance strategy, must be COSINE, DOT, or EUCLID." - ) + msg = "Unknown distance strategy, must be COSINE, DOT, or EUCLID." + raise ValueError(msg) @classmethod def _document_from_point( @@ -996,10 +1021,11 @@ class QdrantVectorStore(VectorStore): payloads = [] for i, text in enumerate(texts): if text is None: - raise ValueError( + 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( { @@ -1023,7 +1049,7 @@ class QdrantVectorStore(VectorStore): for vector in batch_embeddings ] - elif self.retrieval_mode == RetrievalMode.SPARSE: + if self.retrieval_mode == RetrievalMode.SPARSE: batch_sparse_embeddings = self.sparse_embeddings.embed_documents( list(texts) ) @@ -1036,14 +1062,13 @@ class QdrantVectorStore(VectorStore): for vector in batch_sparse_embeddings ] - elif self.retrieval_mode == RetrievalMode.HYBRID: + if self.retrieval_mode == RetrievalMode.HYBRID: dense_embeddings = self.embeddings.embed_documents(list(texts)) sparse_embeddings = self.sparse_embeddings.embed_documents(list(texts)) if len(dense_embeddings) != len(sparse_embeddings): - raise ValueError( - "Mismatched length between dense and sparse embeddings." - ) + msg = "Mismatched length between dense and sparse embeddings." + raise ValueError(msg) return [ { @@ -1057,10 +1082,8 @@ class QdrantVectorStore(VectorStore): ) ] - else: - raise ValueError( - f"Unknown retrieval mode. {self.retrieval_mode} to build vectors." - ) + msg = f"Unknown retrieval mode. {self.retrieval_mode} to build vectors." + raise ValueError(msg) @classmethod def _validate_collection_config( @@ -1106,51 +1129,55 @@ class QdrantVectorStore(VectorStore): if isinstance(vector_config, dict): # vector_config is a Dict[str, VectorParams] if vector_name not in vector_config: - raise QdrantVectorStoreError( + 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 + 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 + vector_config = vector_config[vector_name] # type: ignore[assignment, index] - else: - # vector_config is an instance of VectorParams - # Case of a collection with single/unnamed vector. - if vector_name != "": - raise QdrantVectorStoreError( - 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`." - ) + # 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: - raise ValueError("VectorParams 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: - raise ValueError("Invalid `embeddings` type.") + msg = "Invalid `embeddings` type." + raise ValueError(msg) if vector_config.size != vector_size: - raise QdrantVectorStoreError( + 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: - raise QdrantVectorStoreError( + msg = ( f"Existing Qdrant collection is configured for " f"{vector_config.distance.name} similarity, but requested " f"{distance.upper()}. Please set `distance` parameter to " @@ -1158,6 +1185,7 @@ class QdrantVectorStore(VectorStore): f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantVectorStoreError(msg) @classmethod def _validate_collection_for_sparse( @@ -1173,12 +1201,13 @@ class QdrantVectorStore(VectorStore): sparse_vector_config is None or sparse_vector_name not in sparse_vector_config ): - raise QdrantVectorStoreError( + 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( @@ -1188,19 +1217,18 @@ class QdrantVectorStore(VectorStore): sparse_embedding: Optional[SparseEmbeddings], ) -> None: if retrieval_mode == RetrievalMode.DENSE and embedding is None: - raise ValueError( - "'embedding' cannot be None when retrieval mode is 'dense'" - ) + msg = "'embedding' cannot be None when retrieval mode is 'dense'" + raise ValueError(msg) - elif retrieval_mode == RetrievalMode.SPARSE and sparse_embedding is None: - raise ValueError( - "'sparse_embedding' cannot be None when retrieval mode is 'sparse'" - ) + if retrieval_mode == RetrievalMode.SPARSE and sparse_embedding is None: + msg = "'sparse_embedding' cannot be None when retrieval mode is 'sparse'" + raise ValueError(msg) - elif retrieval_mode == RetrievalMode.HYBRID and any( + if retrieval_mode == RetrievalMode.HYBRID and any( [embedding is None, sparse_embedding is None] ): - raise ValueError( + msg = ( "Both 'embedding' and 'sparse_embedding' cannot be None " "when retrieval mode is 'hybrid'" ) + raise ValueError(msg) diff --git a/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py b/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py index 15a8002e501..bec4df8e5ca 100644 --- a/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py +++ b/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py @@ -5,9 +5,7 @@ from pydantic import BaseModel, Field class SparseVector(BaseModel, extra="forbid"): - """ - Sparse vector structure - """ + """Sparse vector structure.""" indices: list[int] = Field(..., description="indices must be unique") values: list[float] = Field( diff --git a/libs/partners/qdrant/langchain_qdrant/vectorstores.py b/libs/partners/qdrant/langchain_qdrant/vectorstores.py index b7bf3422741..d4df2459199 100644 --- a/libs/partners/qdrant/langchain_qdrant/vectorstores.py +++ b/libs/partners/qdrant/langchain_qdrant/vectorstores.py @@ -7,13 +7,7 @@ import warnings from collections.abc import AsyncGenerator, Generator, Iterable, Sequence from itertools import islice from operator import itemgetter -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Optional, - Union, -) +from typing import TYPE_CHECKING, Any, Callable, Optional, Union import numpy as np from langchain_core._api.deprecation import deprecated @@ -37,10 +31,11 @@ class QdrantException(Exception): def sync_call_fallback(method: Callable) -> Callable: - """ - Decorator to call the synchronous method of the class if the async method is not - implemented. This decorator might be only used for the methods that are defined - as async in the class. + """Call the synchronous method if the async method is not implemented. + + This decorator should only be used for methods that are defined as async in the + class. + """ @functools.wraps(method) @@ -93,27 +88,29 @@ class Qdrant(VectorStore): ): """Initialize with necessary components.""" if not isinstance(client, QdrantClient): - raise ValueError( + msg = ( f"client should be an instance of qdrant_client.QdrantClient, " f"got {type(client)}" ) + raise ValueError(msg) if async_client is not None and not isinstance(async_client, AsyncQdrantClient): - raise ValueError( + msg = ( f"async_client should be an instance of qdrant_client.AsyncQdrantClient" f"got {type(async_client)}" ) + raise ValueError(msg) if embeddings is None and embedding_function is None: - raise ValueError( - "`embeddings` value can't be None. Pass `embeddings` instance." - ) + msg = "`embeddings` value can't be None. Pass `embeddings` instance." + raise ValueError(msg) if embeddings is not None and embedding_function is not None: - raise ValueError( + msg = ( "Both `embeddings` and `embedding_function` are passed. " "Use `embeddings` only." ) + raise ValueError(msg) self._embeddings = embeddings self._embeddings_function = embedding_function @@ -127,13 +124,15 @@ class Qdrant(VectorStore): if embedding_function is not None: warnings.warn( "Using `embedding_function` is deprecated. " - "Pass `Embeddings` instance to `embeddings` instead." + "Pass `Embeddings` instance to `embeddings` instead.", + stacklevel=2, ) if not isinstance(embeddings, Embeddings): warnings.warn( "`embeddings` should be an instance of `Embeddings`." - "Using `embeddings` as `embedding_function` which is deprecated" + "Using `embeddings` as `embedding_function` which is deprecated", + stacklevel=2, ) self._embeddings_function = embeddings self._embeddings = None @@ -163,9 +162,11 @@ class Qdrant(VectorStore): batch_size: How many vectors upload per-request. Default: ``64`` + **kwargs: Additional keyword arguments. Returns: List of ids from adding the texts into the vectorstore. + """ added_ids = [] for batch_ids, points in self._generate_rest_batches( @@ -198,16 +199,17 @@ class Qdrant(VectorStore): batch_size: How many vectors upload per-request. Default: ``64`` + **kwargs: Additional keyword arguments. Returns: List of ids from adding the texts into the vectorstore. + """ if self.async_client is None or isinstance( self.async_client._client, AsyncQdrantLocal ): - raise NotImplementedError( - "QdrantLocal cannot interoperate with sync and async clients" - ) + msg = "QdrantLocal cannot interoperate with sync and async clients" + raise NotImplementedError(msg) added_ids = [] async for batch_ids, points in self._agenerate_rest_batches( @@ -224,7 +226,7 @@ class Qdrant(VectorStore): self, query: str, k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -264,6 +266,7 @@ class Qdrant(VectorStore): Returns: List of Documents most similar to the query. + """ results = self.similarity_search_with_score( query, @@ -282,16 +285,20 @@ class Qdrant(VectorStore): self, query: str, k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 **kwargs: Any, ) -> list[Document]: """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 by metadata. Defaults to None. + **kwargs: Additional keyword arguments. + Returns: List of Documents most similar to the query. + """ results = await self.asimilarity_search_with_score(query, k, filter, **kwargs) return list(map(itemgetter(0), results)) @@ -300,7 +307,7 @@ class Qdrant(VectorStore): self, query: str, k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -340,6 +347,7 @@ class Qdrant(VectorStore): Returns: List of documents most similar to the query text and distance for each. + """ return self.similarity_search_with_score_by_vector( self._embed_query(query), @@ -357,7 +365,7 @@ class Qdrant(VectorStore): self, query: str, k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -398,6 +406,7 @@ class Qdrant(VectorStore): Returns: List of documents most similar to the query text and distance for each. + """ query_embedding = await self._aembed_query(query) return await self.asimilarity_search_with_score_by_vector( @@ -415,7 +424,7 @@ class Qdrant(VectorStore): self, embedding: list[float], k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -455,6 +464,7 @@ class Qdrant(VectorStore): Returns: List of Documents most similar to the query. + """ results = self.similarity_search_with_score_by_vector( embedding, @@ -473,7 +483,7 @@ class Qdrant(VectorStore): self, embedding: list[float], k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -514,6 +524,7 @@ class Qdrant(VectorStore): Returns: List of Documents most similar to the query. + """ results = await self.asimilarity_search_with_score_by_vector( embedding, @@ -531,7 +542,7 @@ class Qdrant(VectorStore): self, embedding: list[float], k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -571,6 +582,7 @@ class Qdrant(VectorStore): Returns: List of documents most similar to the query text and distance for each. + """ if filter is not None and isinstance(filter, dict): warnings.warn( @@ -578,6 +590,7 @@ class Qdrant(VectorStore): "filters directly: " "https://qdrant.tech/documentation/concepts/filtering/", DeprecationWarning, + stacklevel=2, ) qdrant_filter = self._qdrant_filter_from_dict(filter) else: @@ -618,7 +631,7 @@ class Qdrant(VectorStore): self, embedding: list[float], k: int = 4, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, @@ -659,20 +672,20 @@ class Qdrant(VectorStore): Returns: List of documents most similar to the query text and distance for each. - """ + """ if self.async_client is None or isinstance( self.async_client._client, AsyncQdrantLocal ): - raise NotImplementedError( - "QdrantLocal cannot interoperate with sync and async clients" - ) + msg = "QdrantLocal cannot interoperate with sync and async clients" + raise NotImplementedError(msg) if filter is not None and isinstance(filter, dict): warnings.warn( "Using dict as a `filter` is deprecated. Please use qdrant-client " "filters directly: " "https://qdrant.tech/documentation/concepts/filtering/", DeprecationWarning, + stacklevel=2, ) qdrant_filter = self._qdrant_filter_from_dict(filter) else: @@ -714,7 +727,7 @@ class Qdrant(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, @@ -755,8 +768,10 @@ class Qdrant(VectorStore): - 'all' - query all replicas, and return values present in all replicas **kwargs: Any other named arguments to pass through to QdrantClient.search() + Returns: List of Documents selected by maximal marginal relevance. + """ query_embedding = self._embed_query(query) return self.max_marginal_relevance_search_by_vector( @@ -778,7 +793,7 @@ class Qdrant(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, @@ -820,8 +835,10 @@ class Qdrant(VectorStore): **kwargs: Any other named arguments to pass through to AsyncQdrantClient.Search(). + Returns: List of Documents selected by maximal marginal relevance. + """ query_embedding = await self._aembed_query(query) return await self.amax_marginal_relevance_search_by_vector( @@ -842,7 +859,7 @@ class Qdrant(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, @@ -882,8 +899,10 @@ class Qdrant(VectorStore): - 'all' - query all replicas, and return values present in all replicas **kwargs: Any other named arguments to pass through to QdrantClient.search() + Returns: List of Documents selected by maximal marginal relevance. + """ results = self.max_marginal_relevance_search_with_score_by_vector( embedding, @@ -905,15 +924,17 @@ class Qdrant(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **kwargs: Any, ) -> list[Document]: """Return docs selected using the maximal marginal relevance. + Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. + Args: embedding: Embedding vector to look up documents similar to. k: Number of Documents to return. Defaults to 4. @@ -945,9 +966,11 @@ class Qdrant(VectorStore): **kwargs: Any other named arguments to pass through to AsyncQdrantClient.Search(). + Returns: List of Documents selected by maximal marginal relevance and distance for each. + """ results = await self.amax_marginal_relevance_search_with_score_by_vector( embedding, @@ -968,15 +991,17 @@ class Qdrant(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = 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. + Args: embedding: Embedding vector to look up documents similar to. k: Number of Documents to return. Defaults to 4. @@ -1007,9 +1032,11 @@ class Qdrant(VectorStore): - 'all' - query all replicas, and return values present in all replicas **kwargs: Any other named arguments to pass through to QdrantClient.search() + Returns: List of Documents selected by maximal marginal relevance and distance for each. + """ query_vector = embedding if self.vector_name is not None: @@ -1056,15 +1083,17 @@ class Qdrant(VectorStore): k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, - filter: Optional[MetadataFilter] = None, + filter: Optional[MetadataFilter] = None, # noqa: A002 search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = 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. + Args: embedding: Embedding vector to look up documents similar to. k: Number of Documents to return. Defaults to 4. @@ -1074,16 +1103,22 @@ class Qdrant(VectorStore): of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. + filter: Filter by metadata. Defaults to None. + search_params: Additional search params. + score_threshold: Define a minimal score threshold for the result. + consistency: Read consistency of the search. + **kwargs: Additional keyword arguments. + Returns: List of Documents selected by maximal marginal relevance and distance for each. + """ if self.async_client is None or isinstance( self.async_client._client, AsyncQdrantLocal ): - raise NotImplementedError( - "QdrantLocal cannot interoperate with sync and async clients" - ) + msg = "QdrantLocal cannot interoperate with sync and async clients" + raise NotImplementedError(msg) query_vector = embedding if self.vector_name is not None: query_vector = (self.vector_name, query_vector) # type: ignore[assignment] @@ -1131,8 +1166,8 @@ class Qdrant(VectorStore): Returns: True if deletion is successful, False otherwise. - """ + """ result = self.client.delete( collection_name=self.collection_name, points_selector=ids, @@ -1151,13 +1186,13 @@ class Qdrant(VectorStore): Returns: True if deletion is successful, False otherwise. + """ if self.async_client is None or isinstance( self.async_client._client, AsyncQdrantLocal ): - raise NotImplementedError( - "QdrantLocal cannot interoperate with sync and async clients" - ) + msg = "QdrantLocal cannot interoperate with sync and async clients" + raise NotImplementedError(msg) result = await self.async_client.delete( collection_name=self.collection_name, @@ -1177,8 +1212,8 @@ class Qdrant(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, @@ -1193,14 +1228,14 @@ class Qdrant(VectorStore): shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, - on_disk_payload: Optional[bool] = None, + on_disk_payload: Optional[bool] = None, # noqa: FBT001 hnsw_config: Optional[models.HnswConfigDiff] = None, optimizers_config: Optional[models.OptimizersConfigDiff] = None, wal_config: Optional[models.WalConfigDiff] = None, quantization_config: Optional[models.QuantizationConfig] = None, init_from: Optional[models.InitFrom] = None, - on_disk: Optional[bool] = None, - force_recreate: bool = False, + on_disk: Optional[bool] = None, # noqa: FBT001 + force_recreate: bool = False, # noqa: FBT001, FBT002 **kwargs: Any, ) -> Qdrant: """Construct Qdrant wrapper from a list of texts. @@ -1287,6 +1322,8 @@ class Qdrant(VectorStore): Params for quantization, if None - quantization will be disabled init_from: Use data stored in another collection to initialize this collection + on_disk: + If true - vectors will be stored on disk, reducing memory usage. force_recreate: Force recreating the collection **kwargs: @@ -1354,8 +1391,8 @@ class Qdrant(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, @@ -1366,14 +1403,14 @@ class Qdrant(VectorStore): vector_name: Optional[str] = VECTOR_NAME, **kwargs: Any, ) -> Qdrant: - """ - Get instance of an existing Qdrant collection. - This method will return the instance of the store without inserting any new - embeddings - """ + """Get instance of an existing Qdrant collection. + This method will return the instance of the store without inserting any new + embeddings. + """ if collection_name is None: - raise ValueError("Must specify collection_name. Received None.") + msg = "Must specify collection_name. Received None." + raise ValueError(msg) client, async_client = cls._generate_clients( location=location, @@ -1412,8 +1449,8 @@ class Qdrant(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, @@ -1428,14 +1465,14 @@ class Qdrant(VectorStore): shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, - on_disk_payload: Optional[bool] = None, + on_disk_payload: Optional[bool] = None, # noqa: FBT001 hnsw_config: Optional[models.HnswConfigDiff] = None, optimizers_config: Optional[models.OptimizersConfigDiff] = None, wal_config: Optional[models.WalConfigDiff] = None, quantization_config: Optional[models.QuantizationConfig] = None, init_from: Optional[models.InitFrom] = None, - on_disk: Optional[bool] = None, - force_recreate: bool = False, + on_disk: Optional[bool] = None, # noqa: FBT001 + force_recreate: bool = False, # noqa: FBT001, FBT002 **kwargs: Any, ) -> Qdrant: """Construct Qdrant wrapper from a list of texts. @@ -1522,6 +1559,12 @@ class Qdrant(VectorStore): Params for quantization, if None - quantization will be disabled init_from: Use data stored in another collection to initialize this collection + on_disk: + If true - point`s payload will not be stored in memory. + It will be read from the disk every time it is requested. + This setting saves RAM by (slightly) increasing the response time. + Note: those payload values that are involved in filtering and are + indexed - remain in RAM. force_recreate: Force recreating the collection **kwargs: @@ -1588,8 +1631,8 @@ class Qdrant(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, @@ -1603,14 +1646,14 @@ class Qdrant(VectorStore): shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, - on_disk_payload: Optional[bool] = None, + on_disk_payload: Optional[bool] = None, # noqa: FBT001 hnsw_config: Optional[models.HnswConfigDiff] = None, optimizers_config: Optional[models.OptimizersConfigDiff] = None, wal_config: Optional[models.WalConfigDiff] = None, quantization_config: Optional[models.QuantizationConfig] = None, init_from: Optional[models.InitFrom] = None, - on_disk: Optional[bool] = None, - force_recreate: bool = False, + on_disk: Optional[bool] = None, # noqa: FBT001 + force_recreate: bool = False, # noqa: FBT001, FBT002 **kwargs: Any, ) -> Qdrant: # Just do a single quick embedding to get vector size @@ -1646,16 +1689,17 @@ class Qdrant(VectorStore): current_vector_config = collection_info.config.params.vectors if isinstance(current_vector_config, dict) and vector_name is not None: if vector_name not in current_vector_config: - raise QdrantException( + msg = ( f"Existing Qdrant collection {collection_name} does not " f"contain vector named {vector_name}. Did you mean one of the " f"existing vectors: {', '.join(current_vector_config.keys())}? " f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantException(msg) current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment] elif isinstance(current_vector_config, dict) and vector_name is None: - raise QdrantException( + msg = ( f"Existing Qdrant collection {collection_name} uses named vectors. " f"If you want to reuse it, please set `vector_name` to any of the " f"existing named vectors: " @@ -1663,35 +1707,39 @@ class Qdrant(VectorStore): f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantException(msg) elif ( not isinstance(current_vector_config, dict) and vector_name is not None ): - raise QdrantException( + msg = ( f"Existing Qdrant collection {collection_name} doesn't use named " f"vectors. If you want to reuse it, please set `vector_name` to " f"`None`. If you want to recreate the collection, set " f"`force_recreate` parameter to `True`." ) + raise QdrantException(msg) if not isinstance(current_vector_config, models.VectorParams): - raise ValueError( + msg = ( "Expected current_vector_config to be an instance of " f"models.VectorParams, but got {type(current_vector_config)}" ) + raise ValueError(msg) # Check if the vector configuration has the same dimensionality. if current_vector_config.size != vector_size: - raise QdrantException( + msg = ( f"Existing Qdrant collection is configured for vectors with " f"{current_vector_config.size} " f"dimensions. Selected embeddings are {vector_size}-dimensional. " f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantException(msg) current_distance_func = ( current_vector_config.distance.name.upper() # type: ignore[union-attr] ) if current_distance_func != distance_func: - raise QdrantException( + msg = ( f"Existing Qdrant collection is configured for " f"{current_distance_func} similarity, but requested " f"{distance_func}. Please set `distance_func` parameter to " @@ -1699,6 +1747,7 @@ class Qdrant(VectorStore): f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantException(msg) else: vectors_config = models.VectorParams( size=vector_size, @@ -1727,7 +1776,7 @@ class Qdrant(VectorStore): init_from=init_from, timeout=timeout, # type: ignore[arg-type] ) - qdrant = cls( + return cls( client=client, collection_name=collection_name, embeddings=embedding, @@ -1737,7 +1786,6 @@ class Qdrant(VectorStore): vector_name=vector_name, async_client=async_client, ) - return qdrant @classmethod async def aconstruct_instance( @@ -1748,8 +1796,8 @@ class Qdrant(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, @@ -1763,14 +1811,14 @@ class Qdrant(VectorStore): shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, - on_disk_payload: Optional[bool] = None, + on_disk_payload: Optional[bool] = None, # noqa: FBT001 hnsw_config: Optional[models.HnswConfigDiff] = None, optimizers_config: Optional[models.OptimizersConfigDiff] = None, wal_config: Optional[models.WalConfigDiff] = None, quantization_config: Optional[models.QuantizationConfig] = None, init_from: Optional[models.InitFrom] = None, - on_disk: Optional[bool] = None, - force_recreate: bool = False, + on_disk: Optional[bool] = None, # noqa: FBT001 + force_recreate: bool = False, # noqa: FBT001, FBT002 **kwargs: Any, ) -> Qdrant: # Just do a single quick embedding to get vector size @@ -1807,16 +1855,17 @@ class Qdrant(VectorStore): current_vector_config = collection_info.config.params.vectors if isinstance(current_vector_config, dict) and vector_name is not None: if vector_name not in current_vector_config: - raise QdrantException( + msg = ( f"Existing Qdrant collection {collection_name} does not " f"contain vector named {vector_name}. Did you mean one of the " f"existing vectors: {', '.join(current_vector_config.keys())}? " f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantException(msg) current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment] elif isinstance(current_vector_config, dict) and vector_name is None: - raise QdrantException( + msg = ( f"Existing Qdrant collection {collection_name} uses named vectors. " f"If you want to reuse it, please set `vector_name` to any of the " f"existing named vectors: " @@ -1824,36 +1873,40 @@ class Qdrant(VectorStore): f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantException(msg) elif ( not isinstance(current_vector_config, dict) and vector_name is not None ): - raise QdrantException( + msg = ( f"Existing Qdrant collection {collection_name} doesn't use named " f"vectors. If you want to reuse it, please set `vector_name` to " f"`None`. If you want to recreate the collection, set " f"`force_recreate` parameter to `True`." ) + raise QdrantException(msg) if not isinstance(current_vector_config, models.VectorParams): - raise ValueError( + msg = ( "Expected current_vector_config to be an instance of " f"models.VectorParams, but got {type(current_vector_config)}" ) + raise ValueError(msg) # Check if the vector configuration has the same dimensionality. if current_vector_config.size != vector_size: - raise QdrantException( + msg = ( f"Existing Qdrant collection is configured for vectors with " f"{current_vector_config.size} " f"dimensions. Selected embeddings are {vector_size}-dimensional. " f"If you want to recreate the collection, set `force_recreate` " f"parameter to `True`." ) + raise QdrantException(msg) current_distance_func = ( current_vector_config.distance.name.upper() # type: ignore[union-attr] ) if current_distance_func != distance_func: - raise QdrantException( + msg = ( f"Existing Qdrant collection is configured for " f"{current_vector_config.distance} " # type: ignore[union-attr] f"similarity. Please set `distance_func` parameter to " @@ -1861,6 +1914,7 @@ class Qdrant(VectorStore): f"recreate the collection, set `force_recreate` parameter to " f"`True`." ) + raise QdrantException(msg) else: vectors_config = models.VectorParams( size=vector_size, @@ -1889,7 +1943,7 @@ class Qdrant(VectorStore): init_from=init_from, timeout=timeout, # type: ignore[arg-type] ) - qdrant = cls( + return cls( client=client, collection_name=collection_name, embeddings=embedding, @@ -1899,7 +1953,6 @@ class Qdrant(VectorStore): vector_name=vector_name, async_client=async_client, ) - return qdrant @staticmethod def _cosine_relevance_score_fn(distance: float) -> float: @@ -1907,26 +1960,24 @@ class Qdrant(VectorStore): return (distance + 1.0) / 2.0 def _select_relevance_score_fn(self) -> Callable[[float], float]: - """ - 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 + """Your 'correct' relevance function may differ depending on a few things. + + For example: + - 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_strategy == "COSINE": return self._cosine_relevance_score_fn - elif self.distance_strategy == "DOT": + if self.distance_strategy == "DOT": return self._max_inner_product_relevance_score_fn - elif self.distance_strategy == "EUCLID": + if self.distance_strategy == "EUCLID": return self._euclidean_relevance_score_fn - else: - raise ValueError( - "Unknown distance strategy, must be cosine, " - "max_inner_product, or euclidean" - ) + msg = ( + "Unknown distance strategy, must be cosine, max_inner_product, or euclidean" + ) + raise ValueError(msg) def _similarity_search_with_relevance_scores( self, @@ -1947,6 +1998,7 @@ class Qdrant(VectorStore): Returns: List of Tuples of (doc, similarity_score) + """ return self.similarity_search_with_score(query, k, **kwargs) @@ -1970,6 +2022,7 @@ class Qdrant(VectorStore): Returns: List of Tuples of (doc, similarity_score) + """ return await self.asimilarity_search_with_score(query, k, **kwargs) @@ -1984,10 +2037,11 @@ class Qdrant(VectorStore): payloads = [] for i, text in enumerate(texts): if text is None: - raise ValueError( + msg = ( "At least one of the texts is None. Please remove it before " "calling .from_texts or .add_texts on Qdrant instance." ) + raise ValueError(msg) metadata = metadatas[i] if metadatas is not None else None payloads.append( { @@ -2018,8 +2072,8 @@ class Qdrant(VectorStore): out = [] if isinstance(value, dict): - for _key, value in value.items(): - out.extend(self._build_condition(f"{key}.{_key}", value)) + for _key, _value in value.items(): + out.extend(self._build_condition(f"{key}.{_key}", _value)) elif isinstance(value, list): for _value in value: if isinstance(_value, dict): @@ -2037,15 +2091,15 @@ class Qdrant(VectorStore): return out def _qdrant_filter_from_dict( - self, filter: Optional[DictFilter] + self, filter_: Optional[DictFilter] ) -> Optional[models.Filter]: - if not filter: + if not filter_: return None return models.Filter( must=[ condition - for key, value in filter.items() + for key, value in filter_.items() # type: ignore[union-attr] for condition in self._build_condition(key, value) ] ) @@ -2060,14 +2114,15 @@ class Qdrant(VectorStore): Returns: List of floats representing the query embedding. + """ if self.embeddings is not None: embedding = self.embeddings.embed_query(query) + elif self._embeddings_function is not None: + embedding = self._embeddings_function(query) else: - if self._embeddings_function is not None: - embedding = self._embeddings_function(query) - else: - raise ValueError("Neither of embeddings or embedding_function is set") + msg = "Neither of embeddings or embedding_function is set" + raise ValueError(msg) return embedding.tolist() if hasattr(embedding, "tolist") else embedding async def _aembed_query(self, query: str) -> list[float]: @@ -2080,14 +2135,15 @@ class Qdrant(VectorStore): Returns: List of floats representing the query embedding. + """ if self.embeddings is not None: embedding = await self.embeddings.aembed_query(query) + elif self._embeddings_function is not None: + embedding = self._embeddings_function(query) else: - if self._embeddings_function is not None: - embedding = self._embeddings_function(query) - else: - raise ValueError("Neither of embeddings or embedding_function is set") + msg = "Neither of embeddings or embedding_function is set" + raise ValueError(msg) return embedding.tolist() if hasattr(embedding, "tolist") else embedding def _embed_texts(self, texts: Iterable[str]) -> list[list[float]]: @@ -2100,6 +2156,7 @@ class Qdrant(VectorStore): Returns: List of floats representing the texts embedding. + """ if self.embeddings is not None: embeddings = self.embeddings.embed_documents(list(texts)) @@ -2113,7 +2170,8 @@ class Qdrant(VectorStore): embedding = embedding.tolist() embeddings.append(embedding) else: - raise ValueError("Neither of embeddings or embedding_function is set") + msg = "Neither of embeddings or embedding_function is set" + raise ValueError(msg) return embeddings @@ -2127,6 +2185,7 @@ class Qdrant(VectorStore): Returns: List of floats representing the texts embedding. + """ if self.embeddings is not None: embeddings = await self.embeddings.aembed_documents(list(texts)) @@ -2140,7 +2199,8 @@ class Qdrant(VectorStore): embedding = embedding.tolist() embeddings.append(embedding) else: - raise ValueError("Neither of embeddings or embedding_function is set") + msg = "Neither of embeddings or embedding_function is set" + raise ValueError(msg) return embeddings @@ -2230,8 +2290,8 @@ class Qdrant(VectorStore): url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, - prefer_grpc: bool = False, - https: Optional[bool] = None, + prefer_grpc: bool = False, # noqa: FBT001, FBT002 + https: Optional[bool] = None, # noqa: FBT001 api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, diff --git a/libs/partners/qdrant/pyproject.toml b/libs/partners/qdrant/pyproject.toml index 8fbfe03677a..8cd7ebf9de7 100644 --- a/libs/partners/qdrant/pyproject.toml +++ b/libs/partners/qdrant/pyproject.toml @@ -51,8 +51,63 @@ langchain-core = { path = "../../core", editable = true } target-version = "py39" [tool.ruff.lint] -select = ["E", "F", "I", "T201", "UP", "S"] -ignore = [ "UP007", ] +select = [ + "A", # flake8-builtins + "B", # flake8-bugbear + "ASYNC", # flake8-async + "C4", # flake8-comprehensions + "COM", # flake8-commas + "D", # pydocstyle + "E", # pycodestyle error + "EM", # flake8-errmsg + "F", # pyflakes + "FA", # flake8-future-annotations + "FBT", # flake8-boolean-trap + "FLY", # flake8-flynt + "I", # isort + "ICN", # flake8-import-conventions + "INT", # flake8-gettext + "ISC", # isort-comprehensions + "PGH", # pygrep-hooks + "PIE", # flake8-pie + "PERF", # flake8-perf + "PYI", # flake8-pyi + "Q", # flake8-quotes + "RET", # flake8-return + "RSE", # flake8-rst-docstrings + "RUF", # ruff + "S", # flake8-bandit + "SLF", # flake8-self + "SLOT", # flake8-slots + "SIM", # flake8-simplify + "T10", # flake8-debugger + "T20", # flake8-print + "TID", # flake8-tidy-imports + "UP", # pyupgrade + "W", # pycodestyle warning + "YTT", # flake8-2020 +] +ignore = [ + "D100", # pydocstyle: Missing docstring in public module + "D101", # pydocstyle: Missing docstring in public class + "D102", # pydocstyle: Missing docstring in public method + "D103", # pydocstyle: Missing docstring in public function + "D104", # pydocstyle: Missing docstring in public package + "D105", # pydocstyle: Missing docstring in magic method + "D107", # pydocstyle: Missing docstring in __init__ + "D203", # Messes with the formatter + "D213", # pydocstyle: Multi-line docstring summary should start at the second line (incompatible with D212) + "D407", # pydocstyle: Missing-dashed-underline-after-section + "COM812", # Messes with the formatter + "ISC001", # Messes with the formatter + "PERF203", # Rarely useful + "S112", # Rarely useful + "RUF012", # Doesn't play well with Pydantic + "SLF001", # Private member access + "UP007", # pyupgrade: non-pep604-annotation-union + "UP045", # pyupgrade: non-pep604-annotation-optional +] +unfixable = ["B028"] # People should intentionally tune the stacklevel [tool.mypy] disallow_untyped_defs = true diff --git a/libs/partners/qdrant/tests/integration_tests/async_api/test_add_texts.py b/libs/partners/qdrant/tests/integration_tests/async_api/test_add_texts.py index 87a4033eb57..23465e8b2e8 100644 --- a/libs/partners/qdrant/tests/integration_tests/async_api/test_add_texts.py +++ b/libs/partners/qdrant/tests/integration_tests/async_api/test_add_texts.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os import uuid from typing import Optional @@ -25,8 +27,8 @@ async def test_qdrant_aadd_texts_returns_all_ids( ) ids = await docsearch.aadd_texts(["foo", "bar", "baz"]) - assert 3 == len(ids) - assert 3 == len(set(ids)) + assert len(ids) == 3 + assert len(set(ids)) == 3 @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -53,8 +55,8 @@ async def test_qdrant_aadd_texts_stores_duplicated_texts( ) ids = await vec_store.aadd_texts(["abc", "abc"], [{"a": 1}, {"a": 2}]) - assert 2 == len(set(ids)) - assert 2 == client.count(collection_name).count + assert len(set(ids)) == 2 + assert client.count(collection_name).count == 2 @pytest.mark.parametrize("batch_size", [1, 64]) @@ -84,7 +86,7 @@ async def test_qdrant_aadd_texts_stores_ids( ) assert all(first == second for first, second in zip(ids, returned_ids)) - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 stored_ids = [point.id for point in client.scroll(collection_name)[0]] assert set(ids) == set(stored_ids) @@ -116,7 +118,7 @@ async def test_qdrant_aadd_texts_stores_embeddings_as_named_vectors( ) await vec_store.aadd_texts(["lorem", "ipsum", "dolor", "sit", "amet"]) - assert 5 == client.count(collection_name).count + assert client.count(collection_name).count == 5 assert all( vector_name in point.vector # type: ignore[operator] for point in client.scroll(collection_name, with_vectors=True)[0] diff --git a/libs/partners/qdrant/tests/integration_tests/async_api/test_from_texts.py b/libs/partners/qdrant/tests/integration_tests/async_api/test_from_texts.py index 4495f7e5726..bd3289a0ebf 100644 --- a/libs/partners/qdrant/tests/integration_tests/async_api/test_from_texts.py +++ b/libs/partners/qdrant/tests/integration_tests/async_api/test_from_texts.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os import uuid from typing import Optional @@ -29,7 +31,7 @@ async def test_qdrant_from_texts_stores_duplicated_texts(qdrant_location: str) - ) client = vec_store.client - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 @pytest.mark.parametrize("batch_size", [1, 64]) @@ -55,7 +57,7 @@ async def test_qdrant_from_texts_stores_ids( ) client = vec_store.client - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 stored_ids = [point.id for point in client.scroll(collection_name)[0]] assert set(ids) == set(stored_ids) @@ -78,7 +80,7 @@ async def test_qdrant_from_texts_stores_embeddings_as_named_vectors( ) client = vec_store.client - assert 5 == client.count(collection_name).count + assert client.count(collection_name).count == 5 assert all( vector_name in point.vector # type: ignore[operator] for point in client.scroll(collection_name, with_vectors=True)[0] @@ -90,7 +92,7 @@ async def test_qdrant_from_texts_stores_embeddings_as_named_vectors( async def test_qdrant_from_texts_reuses_same_collection( location: str, vector_name: Optional[str] ) -> None: - """Test if Qdrant.afrom_texts reuses the same collection""" + """Test if Qdrant.afrom_texts reuses the same collection.""" collection_name = uuid.uuid4().hex embeddings = ConsistentFakeEmbeddings() @@ -111,7 +113,7 @@ async def test_qdrant_from_texts_reuses_same_collection( ) client = vec_store.client - assert 7 == client.count(collection_name).count + assert client.count(collection_name).count == 7 @pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False)) @@ -121,7 +123,8 @@ async def test_qdrant_from_texts_raises_error_on_different_dimensionality( vector_name: Optional[str], ) -> None: """Test if Qdrant.afrom_texts raises an exception if dimensionality does not - match""" + match. + """ # noqa: D205 collection_name = uuid.uuid4().hex await Qdrant.afrom_texts( @@ -156,7 +159,7 @@ async def test_qdrant_from_texts_raises_error_on_different_vector_name( first_vector_name: Optional[str], second_vector_name: Optional[str], ) -> None: - """Test if Qdrant.afrom_texts raises an exception if vector name does not match""" + """Test if Qdrant.afrom_texts raises an exception if vector name does not match.""" collection_name = uuid.uuid4().hex await Qdrant.afrom_texts( @@ -181,7 +184,7 @@ async def test_qdrant_from_texts_raises_error_on_different_vector_name( async def test_qdrant_from_texts_raises_error_on_different_distance( location: str, ) -> None: - """Test if Qdrant.afrom_texts raises an exception if distance does not match""" + """Test if Qdrant.afrom_texts raises an exception if distance does not match.""" collection_name = uuid.uuid4().hex await Qdrant.afrom_texts( @@ -208,7 +211,7 @@ async def test_qdrant_from_texts_recreates_collection_on_force_recreate( location: str, vector_name: Optional[str], ) -> None: - """Test if Qdrant.afrom_texts recreates the collection even if config mismatches""" + """Test if Qdrant.afrom_texts recreates the collection even if config mismatches.""" from qdrant_client import QdrantClient collection_name = uuid.uuid4().hex @@ -231,11 +234,11 @@ async def test_qdrant_from_texts_recreates_collection_on_force_recreate( ) client = QdrantClient(location=location, api_key=os.getenv("QDRANT_API_KEY")) - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 vector_params = client.get_collection(collection_name).config.params.vectors if vector_name is not None: vector_params = vector_params[vector_name] # type: ignore[index] - assert 5 == vector_params.size # type: ignore[union-attr] + assert vector_params.size == 5 # type: ignore[union-attr] @pytest.mark.parametrize("batch_size", [1, 64]) diff --git a/libs/partners/qdrant/tests/integration_tests/async_api/test_max_marginal_relevance.py b/libs/partners/qdrant/tests/integration_tests/async_api/test_max_marginal_relevance.py index 15b140b5554..896d5e2b77a 100644 --- a/libs/partners/qdrant/tests/integration_tests/async_api/test_max_marginal_relevance.py +++ b/libs/partners/qdrant/tests/integration_tests/async_api/test_max_marginal_relevance.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from typing import Optional import pytest # type: ignore[import-not-found] diff --git a/libs/partners/qdrant/tests/integration_tests/async_api/test_similarity_search.py b/libs/partners/qdrant/tests/integration_tests/async_api/test_similarity_search.py index 7563f64a940..ba1d41361d3 100644 --- a/libs/partners/qdrant/tests/integration_tests/async_api/test_similarity_search.py +++ b/libs/partners/qdrant/tests/integration_tests/async_api/test_similarity_search.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from typing import Optional import numpy as np @@ -187,7 +189,7 @@ async def test_qdrant_similarity_search_with_relevance_score_with_threshold( "foo", k=3, **kwargs ) assert len(output) == 1 - assert all([score >= score_threshold for _, score in output]) + assert all(score >= score_threshold for _, score in output) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -222,7 +224,7 @@ async def test_similarity_search_with_relevance_score_with_threshold_and_filter( "foo", k=3, **kwargs ) assert len(output) == 1 - assert all([score >= score_threshold for _, score in output]) + assert all(score >= score_threshold for _, score in output) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -301,5 +303,5 @@ async def test_qdrant_similarity_search_with_relevance_scores( output = await docsearch.asimilarity_search_with_relevance_scores("foo", k=3) assert all( - (1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output + (score <= 1 or np.isclose(score, 1)) and score >= 0 for _, score in output ) diff --git a/libs/partners/qdrant/tests/integration_tests/common.py b/libs/partners/qdrant/tests/integration_tests/common.py index b63a6e18618..7e8ae21a477 100644 --- a/libs/partners/qdrant/tests/integration_tests/common.py +++ b/libs/partners/qdrant/tests/integration_tests/common.py @@ -1,4 +1,4 @@ -import requests # type: ignore +import requests # type: ignore[import-untyped] from langchain_core.documents import Document from langchain_core.embeddings import Embeddings @@ -7,7 +7,6 @@ from langchain_qdrant import SparseEmbeddings, SparseVector def qdrant_running_locally() -> bool: """Check if Qdrant is running at http://localhost:6333.""" - try: response = requests.get("http://localhost:6333", timeout=10.0) response_json = response.json() @@ -33,7 +32,8 @@ def assert_documents_equals(actual: list[Document], expected: list[Document]): class ConsistentFakeEmbeddings(Embeddings): """Fake embeddings which remember all the texts seen so far to return consistent - vectors for the same texts.""" + vectors for the same texts. + """ # noqa: D205 def __init__(self, dimensionality: int = 10) -> None: self.known_texts: list[str] = [] @@ -53,13 +53,15 @@ class ConsistentFakeEmbeddings(Embeddings): def embed_query(self, text: str) -> list[float]: """Return consistent embeddings for the text, if seen before, or a constant - one if the text is unknown.""" + one if the text is unknown. + """ # noqa: D205 return self.embed_documents([text])[0] class ConsistentFakeSparseEmbeddings(SparseEmbeddings): - """Fake sparse embeddings which remembers all the texts seen so far " - "to return consistent vectors for the same texts.""" + """Fake sparse embeddings which remembers all the texts seen so far + "to return consistent vectors for the same texts. + """ # noqa: D205 def __init__(self, dimensionality: int = 25) -> None: self.known_texts: list[str] = [] @@ -78,6 +80,7 @@ class ConsistentFakeSparseEmbeddings(SparseEmbeddings): return out_vectors def embed_query(self, text: str) -> SparseVector: - """Return consistent embeddings for the text, " - "if seen before, or a constant one if the text is unknown.""" + """Return consistent embeddings for the text, if seen before, or a constant + one if the text is unknown. + """ # noqa: D205 return self.embed_documents([text])[0] diff --git a/libs/partners/qdrant/tests/integration_tests/fixtures.py b/libs/partners/qdrant/tests/integration_tests/fixtures.py index 6e059cbc671..0cc4d267ca6 100644 --- a/libs/partners/qdrant/tests/integration_tests/fixtures.py +++ b/libs/partners/qdrant/tests/integration_tests/fixtures.py @@ -7,7 +7,7 @@ from tests.integration_tests.common import qdrant_running_locally logger = logging.getLogger(__name__) -def qdrant_locations(use_in_memory: bool = True) -> list[str]: +def qdrant_locations(use_in_memory: bool = True) -> list[str]: # noqa: FBT001, FBT002 locations = [] if use_in_memory: diff --git a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_add_texts.py b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_add_texts.py index 5908ba4b7e2..25d0554b42e 100644 --- a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_add_texts.py +++ b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_add_texts.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import uuid from typing import Union @@ -71,9 +73,9 @@ def test_qdrant_add_texts_returns_all_ids( ) ids = docsearch.add_texts(["foo", "bar", "baz"]) - assert 3 == len(ids) - assert 3 == len(set(ids)) - assert 3 == len(docsearch.get_by_ids(ids)) + assert len(ids) == 3 + assert len(set(ids)) == 3 + assert len(docsearch.get_by_ids(ids)) == 3 @pytest.mark.parametrize("location", qdrant_locations()) @@ -83,7 +85,6 @@ def test_qdrant_add_texts_stores_duplicated_texts( vector_name: str, ) -> None: """Test end to end Qdrant.add_texts stores duplicated texts separately.""" - client = QdrantClient(location) collection_name = uuid.uuid4().hex vectors_config = { @@ -99,8 +100,8 @@ def test_qdrant_add_texts_stores_duplicated_texts( ) ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}]) - assert 2 == len(set(ids)) - assert 2 == client.count(collection_name).count + assert len(set(ids)) == 2 + assert client.count(collection_name).count == 2 @pytest.mark.parametrize("location", qdrant_locations()) @@ -137,7 +138,7 @@ def test_qdrant_add_texts_stores_ids( batch_size=batch_size, ) - assert 3 == vec_store.client.count(collection_name).count + assert vec_store.client.count(collection_name).count == 3 stored_ids = [point.id for point in vec_store.client.scroll(collection_name)[0]] assert set(ids) == set(stored_ids) - assert 3 == len(vec_store.get_by_ids(ids)) + assert len(vec_store.get_by_ids(ids)) == 3 diff --git a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_existing.py b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_existing.py index 4e4a78d517c..f25cc5a1226 100644 --- a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_existing.py +++ b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_existing.py @@ -23,7 +23,6 @@ def test_qdrant_from_existing_collection_uses_same_collection( sparse_vector_name: str, ) -> None: """Test if the QdrantVectorStore.from_existing_collection reuses the collection.""" - collection_name = uuid.uuid4().hex docs = ["foo"] QdrantVectorStore.from_texts( @@ -48,4 +47,4 @@ def test_qdrant_from_existing_collection_uses_same_collection( ) qdrant.add_texts(["baz", "bar"]) - assert 3 == qdrant.client.count(collection_name).count + assert qdrant.client.count(collection_name).count == 3 diff --git a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py index 437d9525e4e..cfb98e823d9 100644 --- a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py +++ b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import uuid from typing import Union @@ -30,7 +32,7 @@ def test_vectorstore_from_texts(location: str, retrieval_mode: RetrievalMode) -> sparse_embedding=ConsistentFakeSparseEmbeddings(), ) - assert 2 == vec_store.client.count(collection_name).count + assert vec_store.client.count(collection_name).count == 2 @pytest.mark.parametrize("batch_size", [1, 64]) @@ -66,7 +68,7 @@ def test_qdrant_from_texts_stores_ids( sparse_vector_name=sparse_vector_name, ) - assert 2 == vec_store.client.count(collection_name).count + assert vec_store.client.count(collection_name).count == 2 stored_ids = [point.id for point in vec_store.client.retrieve(collection_name, ids)] assert set(ids) == set(stored_ids) @@ -84,7 +86,6 @@ def test_qdrant_from_texts_stores_embeddings_as_named_vectors( sparse_vector_name: str, ) -> None: """Test end to end Qdrant.from_texts stores named vectors if name is provided.""" - collection_name = uuid.uuid4().hex vec_store = QdrantVectorStore.from_texts( ["lorem", "ipsum", "dolor", "sit", "amet"], @@ -97,15 +98,15 @@ def test_qdrant_from_texts_stores_embeddings_as_named_vectors( sparse_embedding=ConsistentFakeSparseEmbeddings(), ) - assert 5 == vec_store.client.count(collection_name).count + assert vec_store.client.count(collection_name).count == 5 if retrieval_mode in retrieval_modes(sparse=False): assert all( - (vector_name in point.vector or isinstance(point.vector, list)) # type: ignore + (vector_name in point.vector or isinstance(point.vector, list)) # type: ignore[operator] for point in vec_store.client.scroll(collection_name, with_vectors=True)[0] ) if retrieval_mode in retrieval_modes(dense=False): assert all( - sparse_vector_name in point.vector # type: ignore + sparse_vector_name in point.vector # type: ignore[operator] for point in vec_store.client.scroll(collection_name, with_vectors=True)[0] ) @@ -122,7 +123,7 @@ def test_qdrant_from_texts_reuses_same_collection( vector_name: str, sparse_vector_name: str, ) -> None: - """Test if Qdrant.from_texts reuses the same collection""" + """Test if Qdrant.from_texts reuses the same collection.""" collection_name = uuid.uuid4().hex embeddings = ConsistentFakeEmbeddings() sparse_embeddings = ConsistentFakeSparseEmbeddings() @@ -149,7 +150,7 @@ def test_qdrant_from_texts_reuses_same_collection( sparse_embedding=sparse_embeddings, ) - assert 7 == vec_store.client.count(collection_name).count + assert vec_store.client.count(collection_name).count == 7 @pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False)) @@ -160,7 +161,7 @@ def test_qdrant_from_texts_raises_error_on_different_dimensionality( vector_name: str, retrieval_mode: RetrievalMode, ) -> None: - """Test if Qdrant.from_texts raises an exception if dimensionality does not match""" + """Test if Qdrant.from_texts raises an exception if dimensionality doesn't match.""" collection_name = uuid.uuid4().hex QdrantVectorStore.from_texts( ["lorem", "ipsum", "dolor", "sit", "amet"], @@ -204,7 +205,7 @@ def test_qdrant_from_texts_raises_error_on_different_vector_name( second_vector_name: str, retrieval_mode: RetrievalMode, ) -> None: - """Test if Qdrant.from_texts raises an exception if vector name does not match""" + """Test if Qdrant.from_texts raises an exception if vector name does not match.""" collection_name = uuid.uuid4().hex QdrantVectorStore.from_texts( ["lorem", "ipsum", "dolor", "sit", "amet"], @@ -237,7 +238,7 @@ def test_qdrant_from_texts_raises_error_on_different_vector_name( def test_qdrant_from_texts_raises_error_on_different_distance( location: str, vector_name: str, retrieval_mode: RetrievalMode ) -> None: - """Test if Qdrant.from_texts raises an exception if distance does not match""" + """Test if Qdrant.from_texts raises an exception if distance does not match.""" collection_name = uuid.uuid4().hex QdrantVectorStore.from_texts( ["lorem", "ipsum", "dolor", "sit", "amet"], @@ -302,7 +303,7 @@ def test_qdrant_from_texts_recreates_collection_on_force_recreate( force_recreate=True, ) - assert 2 == vec_store.client.count(collection_name).count + assert vec_store.client.count(collection_name).count == 2 @pytest.mark.parametrize("location", qdrant_locations()) @@ -380,6 +381,6 @@ def test_from_texts_passed_optimizers_config_and_on_disk_payload( ) collection_info = vec_store.client.get_collection(collection_name) - assert collection_info.config.params.vectors[vector_name].on_disk is True # type: ignore + assert collection_info.config.params.vectors[vector_name].on_disk is True # type: ignore[index] assert collection_info.config.optimizer_config.memmap_threshold == 1000 assert collection_info.config.params.on_disk_payload is True diff --git a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_mmr.py b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_mmr.py index 78f784a2c47..8e96fc70993 100644 --- a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_mmr.py +++ b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_mmr.py @@ -31,7 +31,7 @@ def test_qdrant_mmr_search( vector_name: str, ) -> None: """Test end to end construction and MRR search.""" - filter = models.Filter( + filter_ = models.Filter( must=[ models.FieldCondition( key=f"{metadata_payload_key}.page", @@ -68,7 +68,7 @@ def test_qdrant_mmr_search( ) output = docsearch.max_marginal_relevance_search( - "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter + "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter_ ) assert_documents_equals( output, diff --git a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py index 162c819afdd..d95a923c726 100644 --- a/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py +++ b/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py @@ -197,7 +197,7 @@ def test_relevance_search_with_threshold( kwargs = {"score_threshold": score_threshold} output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs) assert len(output) == 1 - assert all([score >= score_threshold for _, score in output]) + assert all(score >= score_threshold for _, score in output) @pytest.mark.parametrize("location", qdrant_locations()) @@ -248,7 +248,7 @@ def test_relevance_search_with_threshold_and_filter( kwargs = {"filter": positive_filter, "score_threshold": score_threshold} output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs) assert len(output) == 1 - assert all([score >= score_threshold for _, score in output]) + assert all(score >= score_threshold for _, score in output) @pytest.mark.parametrize("location", qdrant_locations()) diff --git a/libs/partners/qdrant/tests/integration_tests/test_add_texts.py b/libs/partners/qdrant/tests/integration_tests/test_add_texts.py index 177677d9eaa..4547668a671 100644 --- a/libs/partners/qdrant/tests/integration_tests/test_add_texts.py +++ b/libs/partners/qdrant/tests/integration_tests/test_add_texts.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import uuid from typing import Optional @@ -48,8 +50,8 @@ def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None: ) ids = docsearch.add_texts(["foo", "bar", "baz"]) - assert 3 == len(ids) - assert 3 == len(set(ids)) + assert len(ids) == 3 + assert len(set(ids)) == 3 @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -73,8 +75,8 @@ def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> ) ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}]) - assert 2 == len(set(ids)) - assert 2 == client.count(collection_name).count + assert len(set(ids)) == 2 + assert client.count(collection_name).count == 2 @pytest.mark.parametrize("batch_size", [1, 64]) @@ -99,7 +101,7 @@ def test_qdrant_add_texts_stores_ids(batch_size: int) -> None: returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size) assert all(first == second for first, second in zip(ids, returned_ids)) - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 stored_ids = [point.id for point in client.scroll(collection_name)[0]] assert set(ids) == set(stored_ids) @@ -128,7 +130,7 @@ def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) - ) vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"]) - assert 5 == client.count(collection_name).count + assert client.count(collection_name).count == 5 assert all( vector_name in point.vector # type: ignore[operator] for point in client.scroll(collection_name, with_vectors=True)[0] diff --git a/libs/partners/qdrant/tests/integration_tests/test_compile.py b/libs/partners/qdrant/tests/integration_tests/test_compile.py index 5196f4c0f8c..5c812af3144 100644 --- a/libs/partners/qdrant/tests/integration_tests/test_compile.py +++ b/libs/partners/qdrant/tests/integration_tests/test_compile.py @@ -4,4 +4,3 @@ import pytest # type: ignore[import-not-found] @pytest.mark.compile def test_placeholder() -> None: """Used for compiling integration tests without running any real tests.""" - pass diff --git a/libs/partners/qdrant/tests/integration_tests/test_embedding_interface.py b/libs/partners/qdrant/tests/integration_tests/test_embedding_interface.py index ee5fcb1f471..487cca3aacf 100644 --- a/libs/partners/qdrant/tests/integration_tests/test_embedding_interface.py +++ b/libs/partners/qdrant/tests/integration_tests/test_embedding_interface.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import uuid from typing import Callable, Optional diff --git a/libs/partners/qdrant/tests/integration_tests/test_from_existing_collection.py b/libs/partners/qdrant/tests/integration_tests/test_from_existing_collection.py index 622b8f81619..792fe8a4e69 100644 --- a/libs/partners/qdrant/tests/integration_tests/test_from_existing_collection.py +++ b/libs/partners/qdrant/tests/integration_tests/test_from_existing_collection.py @@ -34,4 +34,4 @@ def test_qdrant_from_existing_collection_uses_same_collection(vector_name: str) del qdrant client = QdrantClient(path=str(tmpdir)) - assert 3 == client.count(collection_name).count + assert client.count(collection_name).count == 3 diff --git a/libs/partners/qdrant/tests/integration_tests/test_from_texts.py b/libs/partners/qdrant/tests/integration_tests/test_from_texts.py index 391ddf8a042..b2eaa8ee6bd 100644 --- a/libs/partners/qdrant/tests/integration_tests/test_from_texts.py +++ b/libs/partners/qdrant/tests/integration_tests/test_from_texts.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import tempfile import uuid from typing import Optional @@ -30,7 +32,7 @@ def test_qdrant_from_texts_stores_duplicated_texts() -> None: del vec_store client = QdrantClient(path=str(tmpdir)) - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 @pytest.mark.parametrize("batch_size", [1, 64]) @@ -59,7 +61,7 @@ def test_qdrant_from_texts_stores_ids( del vec_store client = QdrantClient(path=str(tmpdir)) - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 stored_ids = [point.id for point in client.scroll(collection_name)[0]] assert set(ids) == set(stored_ids) @@ -81,7 +83,7 @@ def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str) del vec_store client = QdrantClient(path=str(tmpdir)) - assert 5 == client.count(collection_name).count + assert client.count(collection_name).count == 5 assert all( vector_name in point.vector # type: ignore[operator] for point in client.scroll(collection_name, with_vectors=True)[0] @@ -90,7 +92,7 @@ def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str) @pytest.mark.parametrize("vector_name", [None, "custom-vector"]) def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) -> None: - """Test if Qdrant.from_texts reuses the same collection""" + """Test if Qdrant.from_texts reuses the same collection.""" from qdrant_client import QdrantClient collection_name = uuid.uuid4().hex @@ -115,14 +117,14 @@ def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) -> del vec_store client = QdrantClient(path=str(tmpdir)) - assert 7 == client.count(collection_name).count + assert client.count(collection_name).count == 7 @pytest.mark.parametrize("vector_name", [None, "custom-vector"]) def test_qdrant_from_texts_raises_error_on_different_dimensionality( vector_name: Optional[str], ) -> None: - """Test if Qdrant.from_texts raises an exception if dimensionality does not match""" + """Test if Qdrant.from_texts raises an exception if dimensionality doesn't match.""" collection_name = uuid.uuid4().hex with tempfile.TemporaryDirectory() as tmpdir: vec_store = Qdrant.from_texts( @@ -156,7 +158,7 @@ def test_qdrant_from_texts_raises_error_on_different_vector_name( first_vector_name: Optional[str], second_vector_name: Optional[str], ) -> None: - """Test if Qdrant.from_texts raises an exception if vector name does not match""" + """Test if Qdrant.from_texts raises an exception if vector name does not match.""" collection_name = uuid.uuid4().hex with tempfile.TemporaryDirectory() as tmpdir: vec_store = Qdrant.from_texts( @@ -179,7 +181,7 @@ def test_qdrant_from_texts_raises_error_on_different_vector_name( def test_qdrant_from_texts_raises_error_on_different_distance() -> None: - """Test if Qdrant.from_texts raises an exception if distance does not match""" + """Test if Qdrant.from_texts raises an exception if distance does not match.""" collection_name = uuid.uuid4().hex with tempfile.TemporaryDirectory() as tmpdir: vec_store = Qdrant.from_texts( @@ -211,7 +213,7 @@ def test_qdrant_from_texts_raises_error_on_different_distance() -> None: def test_qdrant_from_texts_recreates_collection_on_force_recreate( vector_name: Optional[str], ) -> None: - """Test if Qdrant.from_texts recreates the collection even if config mismatches""" + """Test if Qdrant.from_texts recreates the collection even if config mismatches.""" from qdrant_client import QdrantClient collection_name = uuid.uuid4().hex @@ -236,7 +238,7 @@ def test_qdrant_from_texts_recreates_collection_on_force_recreate( del vec_store client = QdrantClient(path=str(tmpdir)) - assert 2 == client.count(collection_name).count + assert client.count(collection_name).count == 2 @pytest.mark.parametrize("batch_size", [1, 64]) @@ -283,6 +285,6 @@ def test_from_texts_passed_optimizers_config_and_on_disk_payload(location: str) ) collection_info = vec_store.client.get_collection(collection_name) - assert collection_info.config.params.vectors.on_disk is True # type: ignore + assert collection_info.config.params.vectors.on_disk is True # type: ignore[union-attr] assert collection_info.config.optimizer_config.memmap_threshold == 1000 assert collection_info.config.params.on_disk_payload is True diff --git a/libs/partners/qdrant/tests/integration_tests/test_max_marginal_relevance.py b/libs/partners/qdrant/tests/integration_tests/test_max_marginal_relevance.py index 5a531a8306c..252dc621ad5 100644 --- a/libs/partners/qdrant/tests/integration_tests/test_max_marginal_relevance.py +++ b/libs/partners/qdrant/tests/integration_tests/test_max_marginal_relevance.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from typing import Optional import pytest # type: ignore[import-not-found] @@ -23,7 +25,7 @@ def test_qdrant_max_marginal_relevance_search( """Test end to end construction and MRR search.""" from qdrant_client import models - filter = models.Filter( + filter_ = models.Filter( must=[ models.FieldCondition( key=f"{metadata_payload_key}.page", @@ -59,7 +61,7 @@ def test_qdrant_max_marginal_relevance_search( ) output = docsearch.max_marginal_relevance_search( - "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter + "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter_ ) assert_documents_equals( output, diff --git a/libs/partners/qdrant/tests/integration_tests/test_similarity_search.py b/libs/partners/qdrant/tests/integration_tests/test_similarity_search.py index 2c8c87a22a6..91ec974dfe6 100644 --- a/libs/partners/qdrant/tests/integration_tests/test_similarity_search.py +++ b/libs/partners/qdrant/tests/integration_tests/test_similarity_search.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from typing import Optional import numpy as np @@ -174,7 +176,7 @@ def test_qdrant_similarity_search_with_relevance_score_with_threshold( kwargs = {"score_threshold": score_threshold} output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs) assert len(output) == 1 - assert all([score >= score_threshold for _, score in output]) + assert all(score >= score_threshold for _, score in output) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -205,7 +207,7 @@ def test_qdrant_similarity_search_with_relevance_score_with_threshold_and_filter kwargs = {"filter": positive_filter, "score_threshold": score_threshold} output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs) assert len(output) == 1 - assert all([score >= score_threshold for _, score in output]) + assert all(score >= score_threshold for _, score in output) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -280,5 +282,5 @@ def test_qdrant_similarity_search_with_relevance_scores( output = docsearch.similarity_search_with_relevance_scores("foo", k=3) assert all( - (1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output + (score <= 1 or np.isclose(score, 1)) and score >= 0 for _, score in output ) diff --git a/libs/partners/qdrant/uv.lock b/libs/partners/qdrant/uv.lock index 20e9e93fba2..e65aa034ce0 100644 --- a/libs/partners/qdrant/uv.lock +++ b/libs/partners/qdrant/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.13'", @@ -13,9 +13,9 @@ resolution-markers = [ name = "annotated-types" version = "0.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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