diff --git a/libs/community/langchain_community/embeddings/fastembed.py b/libs/community/langchain_community/embeddings/fastembed.py index 28105dd72088b..e40a9d473ddc0 100644 --- a/libs/community/langchain_community/embeddings/fastembed.py +++ b/libs/community/langchain_community/embeddings/fastembed.py @@ -1,6 +1,6 @@ import importlib import importlib.metadata -from typing import Any, Dict, List, Literal, Optional +from typing import Any, Dict, List, Literal, Optional, cast import numpy as np from langchain_core.embeddings import Embeddings @@ -117,7 +117,7 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: embeddings = self.model.embed( texts, batch_size=self.batch_size, parallel=self.parallel ) - return [e.tolist() for e in embeddings] + return [cast(List[float], e.tolist()) for e in embeddings] def embed_query(self, text: str) -> List[float]: """Generate query embeddings using FastEmbed. @@ -133,4 +133,4 @@ def embed_query(self, text: str) -> List[float]: text, batch_size=self.batch_size, parallel=self.parallel ) ) - return query_embeddings.tolist() + return cast(List[float], query_embeddings.tolist()) diff --git a/libs/community/langchain_community/embeddings/laser.py b/libs/community/langchain_community/embeddings/laser.py index 1299e55e518c0..088ffbb4d1bd4 100644 --- a/libs/community/langchain_community/embeddings/laser.py +++ b/libs/community/langchain_community/embeddings/laser.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, cast import numpy as np from langchain_core.embeddings import Embeddings @@ -73,7 +73,7 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: embeddings: np.ndarray embeddings = self._encoder_pipeline.encode_sentences(texts) - return embeddings.tolist() + return cast(List[List[float]], embeddings.tolist()) def embed_query(self, text: str) -> List[float]: """Generate single query text embeddings using LASER. @@ -86,4 +86,4 @@ def embed_query(self, text: str) -> List[float]: """ query_embeddings: np.ndarray query_embeddings = self._encoder_pipeline.encode_sentences([text]) - return query_embeddings.tolist()[0] + return cast(List[List[float]], query_embeddings.tolist())[0] diff --git a/libs/community/langchain_community/utilities/redis.py b/libs/community/langchain_community/utilities/redis.py index 09aa0d274461d..7877e6ae6cb6d 100644 --- a/libs/community/langchain_community/utilities/redis.py +++ b/libs/community/langchain_community/utilities/redis.py @@ -2,7 +2,7 @@ import logging import re -from typing import TYPE_CHECKING, Any, List, Optional, Pattern +from typing import TYPE_CHECKING, Any, List, Optional, Pattern, cast from urllib.parse import urlparse import numpy as np @@ -18,7 +18,7 @@ def _array_to_buffer(array: List[float], dtype: Any = np.float32) -> bytes: def _buffer_to_array(buffer: bytes, dtype: Any = np.float32) -> List[float]: - return np.frombuffer(buffer, dtype=dtype).tolist() + return cast(List[float], np.frombuffer(buffer, dtype=dtype).tolist()) class TokenEscaper: diff --git a/libs/community/langchain_community/vectorstores/semadb.py b/libs/community/langchain_community/vectorstores/semadb.py index b54f832cee6cf..b39d46eb6bd27 100644 --- a/libs/community/langchain_community/vectorstores/semadb.py +++ b/libs/community/langchain_community/vectorstores/semadb.py @@ -1,4 +1,4 @@ -from typing import Any, Iterable, List, Optional, Tuple +from typing import Any, Iterable, List, Optional, Tuple, cast from uuid import uuid4 import numpy as np @@ -111,7 +111,7 @@ def add_texts( embed_matrix = embed_matrix / np.linalg.norm( embed_matrix, axis=1, keepdims=True ) - embeddings = embed_matrix.tolist() + embeddings = cast(List[List[float]], embed_matrix.tolist()) # Create points ids: List[str] = [] points = [] @@ -186,7 +186,7 @@ def _search_points(self, embedding: List[float], k: int = 4) -> List[dict]: if self.distance_strategy == DistanceStrategy.COSINE: vec = np.array(embedding) vec = vec / np.linalg.norm(vec) - embedding = vec.tolist() + embedding = cast(List[float], vec.tolist()) # Perform search request payload = { "vector": embedding, diff --git a/libs/community/langchain_community/vectorstores/usearch.py b/libs/community/langchain_community/vectorstores/usearch.py index c59446de54da1..4f9d69d02f7ee 100644 --- a/libs/community/langchain_community/vectorstores/usearch.py +++ b/libs/community/langchain_community/vectorstores/usearch.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union, cast import numpy as np from langchain_core.documents import Document @@ -75,7 +75,7 @@ def add_texts( self.index.add(np.array(ids), np.array(embeddings)) self.docstore.add(dict(zip(ids, documents))) self.ids.extend(ids) - return ids.tolist() + return cast(List[str], ids.tolist()) def similarity_search_with_score( self, @@ -171,4 +171,4 @@ def from_texts( usearch = guard_import("usearch.index") index = usearch.Index(ndim=len(embeddings[0]), metric=metric) index.add(np.array(ids), np.array(embeddings)) - return cls(embedding, index, docstore, ids.tolist()) + return cls(embedding, index, docstore, cast(List[str], ids.tolist())) diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index 13f550948f4d7..2e558743cf910 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -1827,7 +1827,7 @@ aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} langchain-core = "^0.3.22" langchain-text-splitters = "^0.3.0" -langsmith = "^0.1.17" +langsmith = ">=0.1.17,<0.3" numpy = [ {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""}, {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""}, @@ -1853,7 +1853,7 @@ develop = true [package.dependencies] jsonpatch = "^1.33" -langsmith = "^0.1.125" +langsmith = ">=0.1.125,<0.3" packaging = ">=23.2,<25" pydantic = [ {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""}, @@ -1869,7 +1869,7 @@ url = "../core" [[package]] name = "langchain-tests" -version = "0.3.5" +version = "0.3.6" description = "Standard tests for LangChain implementations" optional = false python-versions = ">=3.9,<4.0" @@ -1878,7 +1878,7 @@ develop = true [package.dependencies] httpx = ">=0.25.0,<1" -langchain-core = "^0.3.21" 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import numpy as np import pytest @@ -60,13 +60,13 @@ class RandomEmbeddings(Embeddings): """Fake embeddings with random vectors. For testing purposes.""" def embed_documents(self, texts: List[str]) -> List[List[float]]: - return [np.random.rand(100).tolist() for _ in texts] + return [cast(list[float], np.random.rand(100).tolist()) for _ in texts] def embed_query(self, text: str) -> List[float]: - return np.random.rand(100).tolist() + return cast(list[float], np.random.rand(100).tolist()) def embed_image(self, uris: List[str]) -> List[List[float]]: - return [np.random.rand(100).tolist() for _ in uris] + return [cast(list[float], np.random.rand(100).tolist()) for _ in uris] class IncrementalEmbeddings(Embeddings): diff --git a/libs/community/tests/unit_tests/vectorstores/test_utils.py b/libs/community/tests/unit_tests/vectorstores/test_utils.py index c9714df6aa6e7..5d4e97d068b0e 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_utils.py +++ b/libs/community/tests/unit_tests/vectorstores/test_utils.py @@ -1,5 +1,7 @@ """Test vector store utility functions.""" +from typing import cast + import numpy as np from langchain_core.documents import Document @@ -53,7 +55,7 @@ def test_maximal_marginal_relevance() -> None: def test_maximal_marginal_relevance_query_dim() -> None: query_embedding = np.random.random(size=5) query_embedding_2d = query_embedding.reshape((1, 5)) - embedding_list = np.random.random(size=(4, 5)).tolist() + embedding_list = cast(list[list[float]], np.random.random(size=(4, 5)).tolist()) first = maximal_marginal_relevance(query_embedding, embedding_list) second = maximal_marginal_relevance(query_embedding_2d, embedding_list) assert first == second diff --git a/libs/core/langchain_core/tracers/context.py b/libs/core/langchain_core/tracers/context.py index 295c68552a3a8..3694db7188b06 100644 --- a/libs/core/langchain_core/tracers/context.py +++ b/libs/core/langchain_core/tracers/context.py @@ -6,6 +6,7 @@ from typing import ( TYPE_CHECKING, Any, + Literal, Optional, Union, cast, @@ -141,7 +142,7 @@ def _get_trace_callbacks( return cb -def _tracing_v2_is_enabled() -> bool: +def _tracing_v2_is_enabled() -> Union[bool, Literal["local"]]: if tracing_v2_callback_var.get() is not None: return True return ls_utils.tracing_is_enabled() diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock index dfc6f8a05c59f..5d5d379300867 100644 --- a/libs/core/poetry.lock +++ b/libs/core/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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b/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py index d71e7f1b3c293..f7acd800ac1d6 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py +++ b/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py @@ -75,7 +75,7 @@ def compress_documents( ) embedded_query = self.embeddings.embed_query(query) similarity = self.similarity_fn([embedded_query], embedded_documents)[0] - included_idxs = np.arange(len(embedded_documents)) + included_idxs: np.ndarray = np.arange(len(embedded_documents)) if self.k is not None: included_idxs = np.argsort(similarity)[::-1][: self.k] if self.similarity_threshold is not None: @@ -110,7 +110,7 @@ async def acompress_documents( ) embedded_query = await self.embeddings.aembed_query(query) similarity = self.similarity_fn([embedded_query], embedded_documents)[0] - included_idxs = np.arange(len(embedded_documents)) + included_idxs: np.ndarray = np.arange(len(embedded_documents)) 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