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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() From 213c4aca11f3de89be337158de026eef354bd17d Mon Sep 17 00:00:00 2001 From: Bagatur Date: Tue, 10 Dec 2024 10:12:47 -0800 Subject: [PATCH 4/9] fmt --- libs/core/tests/unit_tests/runnables/test_runnable.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/core/tests/unit_tests/runnables/test_runnable.py b/libs/core/tests/unit_tests/runnables/test_runnable.py index 828f4cafcabf9..259f3881ea674 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable.py @@ -4606,12 +4606,12 @@ async def af(x: int) -> int: "b": RunnableLambda(lambda x: x * 3), } ) == ( - "RunnableLambda(...)\n" + "RunnableLambda(lambda x: x * 2)\n" "| {\n" " a: RunnableLambda(...),\n" " b: RunnableLambda(...)\n" " }" - ), "repr where code string contains multiple lambdas gives up" + ) async def test_tool_from_runnable() -> None: From 0d2b3d0228e35e9cba21fc93065c47578f56b813 Mon Sep 17 00:00:00 2001 From: Bagatur Date: Tue, 10 Dec 2024 10:21:46 -0800 Subject: [PATCH 5/9] fmt --- libs/core/tests/unit_tests/runnables/test_runnable.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/tests/unit_tests/runnables/test_runnable.py b/libs/core/tests/unit_tests/runnables/test_runnable.py index 259f3881ea674..b06cb381e80e6 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable.py @@ -4600,7 +4600,7 @@ async def af(x: int) -> int: assert repr(RunnableLambda(func=f, afunc=af)) == "RunnableLambda(f)" assert repr( - RunnableLambda(lambda x: x + 2) + RunnableLambda(lambda x: x * 2) | { "a": RunnableLambda(lambda x: x * 2), "b": RunnableLambda(lambda x: x * 3), From 38c78907104ebdaf25aa99a094c813afdbf2646b Mon Sep 17 00:00:00 2001 From: Bagatur Date: Tue, 10 Dec 2024 10:25:30 -0800 Subject: [PATCH 6/9] fmt --- .../retrievers/document_compressors/embeddings_filter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py 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)) if self.k is not None: included_idxs = np.argsort(similarity)[::-1][: self.k] if self.similarity_threshold is not None: From 6bb4c3c66d3442158dfe3c1c8d36797c8b10c3e6 Mon Sep 17 00:00:00 2001 From: Bagatur Date: Tue, 10 Dec 2024 10:29:49 -0800 Subject: [PATCH 7/9] fmt --- libs/community/langchain_community/embeddings/fastembed.py | 6 +++--- libs/community/langchain_community/embeddings/laser.py | 6 +++--- libs/community/langchain_community/utilities/redis.py | 4 ++-- libs/community/langchain_community/vectorstores/semadb.py | 6 +++--- libs/community/langchain_community/vectorstores/usearch.py | 6 +++--- 5 files changed, 14 insertions(+), 14 deletions(-) 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..33d144b8d31f8 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[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())) From 4e882b9bbe4ab6f8e70b91397de16922ec636ff5 Mon Sep 17 00:00:00 2001 From: Bagatur Date: Tue, 10 Dec 2024 10:31:50 -0800 Subject: [PATCH 8/9] fmt --- libs/community/langchain_community/vectorstores/semadb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/community/langchain_community/vectorstores/semadb.py b/libs/community/langchain_community/vectorstores/semadb.py index 33d144b8d31f8..b39d46eb6bd27 100644 --- a/libs/community/langchain_community/vectorstores/semadb.py +++ b/libs/community/langchain_community/vectorstores/semadb.py @@ -111,7 +111,7 @@ def add_texts( embed_matrix = embed_matrix / np.linalg.norm( embed_matrix, axis=1, keepdims=True ) - embeddings = cast(List[float], embed_matrix.tolist()) + embeddings = cast(List[List[float]], embed_matrix.tolist()) # Create points ids: List[str] = [] points = [] From 11e84eb08d59bcb8d6e037f6b3ce75dc5ed7a9c8 Mon Sep 17 00:00:00 2001 From: Bagatur Date: Tue, 10 Dec 2024 10:37:35 -0800 Subject: [PATCH 9/9] fmt --- .../integration_tests/vectorstores/test_singlestoredb.py | 8 ++++---- .../community/tests/unit_tests/vectorstores/test_utils.py | 4 +++- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py b/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py index fe8e3ca3004bf..8308adaa7603f 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py @@ -3,7 +3,7 @@ import math import os import tempfile -from typing import List +from typing import List, cast 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