From d0c2dc06d57bb2cfd4f917f788959f8694ce4132 Mon Sep 17 00:00:00 2001 From: ccurme Date: Tue, 11 Feb 2025 10:19:59 -0800 Subject: [PATCH 01/11] mongodb[patch]: fix link in readme (#29738) --- libs/partners/mongodb/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/partners/mongodb/README.md b/libs/partners/mongodb/README.md index d99bbccd532..0889c8ac62c 100644 --- a/libs/partners/mongodb/README.md +++ b/libs/partners/mongodb/README.md @@ -1,3 +1,3 @@ This package has moved! -https://github.com/langchain-ai/langchain-mongodb/tree/main/libs/mongodb +https://github.com/langchain-ai/langchain-mongodb From 1edd27d86091d0ca64307809c7f4870f36ec5321 Mon Sep 17 00:00:00 2001 From: Christopher Menon <16004217+cmenon12@users.noreply.github.com> Date: Tue, 11 Feb 2025 19:10:12 +0000 Subject: [PATCH 02/11] docs: fix SQL-based metadata filter syntax, add link to BigQuery docs (#29736) Fix the syntax for SQL-based metadata filtering in the [Google BigQuery Vector Search docs](https://python.langchain.com/docs/integrations/vectorstores/google_bigquery_vector_search/#searching-documents-with-metadata-filters). Also add a link to learn more about BigQuery operators that can be used here. I have been using this library, and have found that this is the correct syntax to use for the SQL-based filters. **Issue**: no open issue. **Dependencies**: none. **Twitter handle**: none. No tests as this is only a change to the documentation. --- .../vectorstores/google_bigquery_vector_search.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb b/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb index f5cc0e92c4e..d8878b61bc0 100644 --- a/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb +++ b/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb @@ -331,7 +331,7 @@ "- Dictionary-based Filters\n", " - You can pass a dictionary (dict) where the keys represent metadata fields and the values specify the filter condition. This method applies an equality filter between the key and the corresponding value. When multiple key-value pairs are provided, they are combined using a logical AND operation.\n", "- SQL-based Filters\n", - " - Alternatively, you can provide a string representing an SQL WHERE clause to define more complex filtering conditions. This allows for greater flexibility, supporting SQL expressions such as comparison operators and logical operators." + " - Alternatively, you can provide a string representing an SQL WHERE clause to define more complex filtering conditions. This allows for greater flexibility, supporting SQL expressions such as comparison operators and logical operators. Learn more about [BigQuery operators](https://cloud.google.com/bigquery/docs/reference/standard-sql/operators)." ] }, { @@ -356,7 +356,7 @@ "source": [ "# SQL-based Filters\n", "# This should return \"Banana\", \"Apples and oranges\" and \"Cars and airplanes\" documents.\n", - "docs = store.similarity_search_by_vector(query_vector, filter={\"len = 6 AND len > 17\"})\n", + "docs = store.similarity_search_by_vector(query_vector, filter=\"len = 6 AND len > 17\")\n", "print(docs)" ] }, From 9477f49409ef406934a4f9d764dade67e6c7e4ca Mon Sep 17 00:00:00 2001 From: ccurme Date: Tue, 11 Feb 2025 11:13:23 -0800 Subject: [PATCH 03/11] openai, deepseek: make _convert_chunk_to_generation_chunk an instance method (#29731) 1. Make `_convert_chunk_to_generation_chunk` an instance method on BaseChatOpenAI 2. Override on ChatDeepSeek to add `"reasoning_content"` to message additional_kwargs. Resolves https://github.com/langchain-ai/langchain/issues/29513 --- .../langchain_deepseek/chat_models.py | 25 +++- .../integration_tests/test_chat_models.py | 13 ++- .../langchain_openai/chat_models/base.py | 108 +++++++++--------- 3 files changed, 90 insertions(+), 56 deletions(-) diff --git a/libs/partners/deepseek/langchain_deepseek/chat_models.py b/libs/partners/deepseek/langchain_deepseek/chat_models.py index 969e52aecff..e7f3f3e3a7b 100644 --- a/libs/partners/deepseek/langchain_deepseek/chat_models.py +++ b/libs/partners/deepseek/langchain_deepseek/chat_models.py @@ -1,9 +1,10 @@ """DeepSeek chat models.""" -from typing import Dict, Optional, Union +from typing import Dict, Optional, Type, Union import openai -from langchain_core.outputs import ChatResult +from langchain_core.messages import AIMessageChunk +from langchain_core.outputs import ChatGenerationChunk, ChatResult from langchain_core.utils import from_env, secret_from_env from langchain_openai.chat_models.base import BaseChatOpenAI from pydantic import ConfigDict, Field, SecretStr, model_validator @@ -218,3 +219,23 @@ class ChatDeepSeek(BaseChatOpenAI): ) return rtn + + def _convert_chunk_to_generation_chunk( + self, + chunk: dict, + default_chunk_class: Type, + base_generation_info: Optional[Dict], + ) -> Optional[ChatGenerationChunk]: + generation_chunk = super()._convert_chunk_to_generation_chunk( + chunk, + default_chunk_class, + base_generation_info, + ) + if (choices := chunk.get("choices")) and generation_chunk: + top = choices[0] + if reasoning_content := top.get("delta", {}).get("reasoning_content"): + if isinstance(generation_chunk.message, AIMessageChunk): + generation_chunk.message.additional_kwargs["reasoning_content"] = ( + reasoning_content + ) + return generation_chunk diff --git a/libs/partners/deepseek/tests/integration_tests/test_chat_models.py b/libs/partners/deepseek/tests/integration_tests/test_chat_models.py index cf5dce6d521..521296c3ef6 100644 --- a/libs/partners/deepseek/tests/integration_tests/test_chat_models.py +++ b/libs/partners/deepseek/tests/integration_tests/test_chat_models.py @@ -1,9 +1,10 @@ """Test ChatDeepSeek chat model.""" -from typing import Type +from typing import Optional, Type import pytest from langchain_core.language_models import BaseChatModel +from langchain_core.messages import AIMessageChunk, BaseMessageChunk from langchain_core.tools import BaseTool from langchain_tests.integration_tests import ChatModelIntegrationTests @@ -38,3 +39,13 @@ def test_reasoning_content() -> None: assert response.content assert response.additional_kwargs["reasoning_content"] raise ValueError() + + +@pytest.mark.xfail(reason="Takes > 30s to run.") +def test_reasoning_content_streaming() -> None: + chat_model = ChatDeepSeek(model="deepseek-reasoner") + full: Optional[BaseMessageChunk] = None + for chunk in chat_model.stream("What is the square root of 256256?"): + full = chunk if full is None else full + chunk + assert isinstance(full, AIMessageChunk) + assert full.additional_kwargs["reasoning_content"] diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index bbda248d0b5..cfd5131c5db 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -316,57 +316,6 @@ def _convert_delta_to_message_chunk( return default_class(content=content, id=id_) # type: ignore -def _convert_chunk_to_generation_chunk( - chunk: dict, default_chunk_class: Type, base_generation_info: Optional[Dict] -) -> Optional[ChatGenerationChunk]: - if chunk.get("type") == "content.delta": # from beta.chat.completions.stream - return None - token_usage = chunk.get("usage") - choices = ( - chunk.get("choices", []) - # from beta.chat.completions.stream - or chunk.get("chunk", {}).get("choices", []) - ) - - usage_metadata: Optional[UsageMetadata] = ( - _create_usage_metadata(token_usage) if token_usage else None - ) - if len(choices) == 0: - # logprobs is implicitly None - generation_chunk = ChatGenerationChunk( - message=default_chunk_class(content="", usage_metadata=usage_metadata) - ) - return generation_chunk - - choice = choices[0] - if choice["delta"] is None: - return None - - message_chunk = _convert_delta_to_message_chunk( - choice["delta"], default_chunk_class - ) - generation_info = {**base_generation_info} if base_generation_info else {} - - if finish_reason := choice.get("finish_reason"): - generation_info["finish_reason"] = finish_reason - if model_name := chunk.get("model"): - generation_info["model_name"] = model_name - if system_fingerprint := chunk.get("system_fingerprint"): - generation_info["system_fingerprint"] = system_fingerprint - - logprobs = choice.get("logprobs") - if logprobs: - generation_info["logprobs"] = logprobs - - if usage_metadata and isinstance(message_chunk, AIMessageChunk): - message_chunk.usage_metadata = usage_metadata - - generation_chunk = ChatGenerationChunk( - message=message_chunk, generation_info=generation_info or None - ) - return generation_chunk - - def _update_token_usage( overall_token_usage: Union[int, dict], new_usage: Union[int, dict] ) -> Union[int, dict]: @@ -692,6 +641,59 @@ class BaseChatOpenAI(BaseChatModel): combined["system_fingerprint"] = system_fingerprint return combined + def _convert_chunk_to_generation_chunk( + self, + chunk: dict, + default_chunk_class: Type, + base_generation_info: Optional[Dict], + ) -> Optional[ChatGenerationChunk]: + if chunk.get("type") == "content.delta": # from beta.chat.completions.stream + return None + token_usage = chunk.get("usage") + choices = ( + chunk.get("choices", []) + # from beta.chat.completions.stream + or chunk.get("chunk", {}).get("choices", []) + ) + + usage_metadata: Optional[UsageMetadata] = ( + _create_usage_metadata(token_usage) if token_usage else None + ) + if len(choices) == 0: + # logprobs is implicitly None + generation_chunk = ChatGenerationChunk( + message=default_chunk_class(content="", usage_metadata=usage_metadata) + ) + return generation_chunk + + choice = choices[0] + if choice["delta"] is None: + return None + + message_chunk = _convert_delta_to_message_chunk( + choice["delta"], default_chunk_class + ) + generation_info = {**base_generation_info} if base_generation_info else {} + + if finish_reason := choice.get("finish_reason"): + generation_info["finish_reason"] = finish_reason + if model_name := chunk.get("model"): + generation_info["model_name"] = model_name + if system_fingerprint := chunk.get("system_fingerprint"): + generation_info["system_fingerprint"] = system_fingerprint + + logprobs = choice.get("logprobs") + if logprobs: + generation_info["logprobs"] = logprobs + + if usage_metadata and isinstance(message_chunk, AIMessageChunk): + message_chunk.usage_metadata = usage_metadata + + generation_chunk = ChatGenerationChunk( + message=message_chunk, generation_info=generation_info or None + ) + return generation_chunk + def _stream( self, messages: List[BaseMessage], @@ -727,7 +729,7 @@ class BaseChatOpenAI(BaseChatModel): for chunk in response: if not isinstance(chunk, dict): chunk = chunk.model_dump() - generation_chunk = _convert_chunk_to_generation_chunk( + generation_chunk = self._convert_chunk_to_generation_chunk( chunk, default_chunk_class, base_generation_info if is_first_chunk else {}, @@ -895,7 +897,7 @@ class BaseChatOpenAI(BaseChatModel): async for chunk in response: if not isinstance(chunk, dict): chunk = chunk.model_dump() - generation_chunk = _convert_chunk_to_generation_chunk( + generation_chunk = self._convert_chunk_to_generation_chunk( chunk, default_chunk_class, base_generation_info if is_first_chunk else {}, From ba8f752bf57f3bf17d43b1eb97acd1780dccfaca Mon Sep 17 00:00:00 2001 From: ccurme Date: Tue, 11 Feb 2025 11:20:11 -0800 Subject: [PATCH 04/11] openai[patch]: release 0.3.5 (#29740) --- libs/partners/openai/pyproject.toml | 2 +- libs/partners/openai/uv.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml index 2b48f923fb2..f099368229a 100644 --- a/libs/partners/openai/pyproject.toml +++ b/libs/partners/openai/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "tiktoken<1,>=0.7", ] name = "langchain-openai" -version = "0.3.4" +version = "0.3.5" description = "An integration package connecting OpenAI and LangChain" readme = "README.md" diff --git a/libs/partners/openai/uv.lock b/libs/partners/openai/uv.lock index b3f23486175..c18de379832 100644 --- a/libs/partners/openai/uv.lock +++ b/libs/partners/openai/uv.lock @@ -520,7 +520,7 @@ typing = [ [[package]] name = "langchain-openai" -version = "0.3.4" +version = "0.3.5" source = { editable = "." } dependencies = [ { name = "langchain-core" }, From 001cf992533b5365ed986c2947c34dab63c395e6 Mon Sep 17 00:00:00 2001 From: ccurme Date: Tue, 11 Feb 2025 11:20:21 -0800 Subject: [PATCH 05/11] pinecone[patch]: add support for python 3.13 (#29737) --- .github/scripts/check_diff.py | 1 - libs/partners/pinecone/pyproject.toml | 4 +- libs/partners/pinecone/uv.lock | 220 +++++++++++++++++++++++++- 3 files changed, 216 insertions(+), 9 deletions(-) diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index a588779b6c4..189479b1b25 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -39,7 +39,6 @@ IGNORED_PARTNERS = [ PY_312_MAX_PACKAGES = [ "libs/partners/huggingface", # https://github.com/pytorch/pytorch/issues/130249 - "libs/partners/pinecone", "libs/partners/voyageai", ] diff --git a/libs/partners/pinecone/pyproject.toml b/libs/partners/pinecone/pyproject.toml index 1b16ed0fbc0..7be567725ab 100644 --- a/libs/partners/pinecone/pyproject.toml +++ b/libs/partners/pinecone/pyproject.toml @@ -5,12 +5,12 @@ build-backend = "pdm.backend" [project] authors = [] license = {text = "MIT"} -requires-python = "<3.13,>=3.9" +requires-python = "<3.14,>=3.9" dependencies = [ "langchain-core<1.0.0,>=0.3.29", "pinecone<6.0.0,>=5.4.0", "aiohttp<3.11,>=3.10", - "numpy<2.0.0,>=1.26.0", + "numpy<2.0.0,>=1.26.4", "langchain-tests<1.0.0,>=0.3.7", ] name = "langchain-pinecone" diff --git a/libs/partners/pinecone/uv.lock b/libs/partners/pinecone/uv.lock index 6980e33c3e2..a765e4e3706 100644 --- a/libs/partners/pinecone/uv.lock +++ b/libs/partners/pinecone/uv.lock @@ -1,5 +1,5 @@ version = 1 -requires-python = ">=3.9, <3.13" +requires-python = ">=3.9, <3.14" resolution-markers = [ "python_full_version >= '3.12.4'", "python_full_version >= '3.12' and python_full_version < '3.12.4'", @@ -75,6 +75,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ca/1a/3bd7f18e3909eabd57e5d17ecdbf5ea4c5828d91341e3676a07de7c76312/aiohttp-3.10.11-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6ce66780fa1a20e45bc753cda2a149daa6dbf1561fc1289fa0c308391c7bc0a4", size = 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"An integration package connecting Pinecone and LangChain" readme = "README.md" diff --git a/libs/partners/pinecone/uv.lock b/libs/partners/pinecone/uv.lock index a765e4e3706..c0722c134be 100644 --- a/libs/partners/pinecone/uv.lock +++ b/libs/partners/pinecone/uv.lock @@ -652,7 +652,7 @@ typing = [ [[package]] name = "langchain-openai" -version = "0.3.4" +version = "0.3.5" source = { directory = "../openai" } dependencies = [ { name = "langchain-core" }, @@ -700,7 +700,7 @@ typing = [ [[package]] name = "langchain-pinecone" -version = "0.2.2" +version = "0.2.3" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -742,7 +742,7 @@ typing = [ [package.metadata] requires-dist = [ { name = "aiohttp", specifier = ">=3.10,<3.11" }, - { name = "langchain-core", specifier = ">=0.3.29,<1.0.0" }, + { name = "langchain-core", specifier = ">=0.3.34,<1.0.0" }, { name = "langchain-tests", specifier = ">=0.3.7,<1.0.0" }, { name = "numpy", specifier = ">=1.26.4,<2.0.0" }, { name = "pinecone", specifier = ">=5.4.0,<6.0.0" }, From 42ebf6ae0ca8a00ffd62f0122b7a5dbb56681de2 Mon Sep 17 00:00:00 2001 From: ccurme Date: Tue, 11 Feb 2025 11:53:43 -0800 Subject: [PATCH 07/11] deepseek[patch]: release 0.1.2 (#29742) --- libs/partners/deepseek/pyproject.toml | 6 ++-- libs/partners/deepseek/uv.lock | 47 ++++++++++++++++++++++----- 2 files changed, 42 insertions(+), 11 deletions(-) diff --git a/libs/partners/deepseek/pyproject.toml b/libs/partners/deepseek/pyproject.toml index e12d016e265..98e0a054801 100644 --- a/libs/partners/deepseek/pyproject.toml +++ b/libs/partners/deepseek/pyproject.toml @@ -7,11 +7,11 @@ authors = [] license = {text = "MIT"} requires-python = "<4.0,>=3.9" dependencies = [ - "langchain-core<1.0.0,>=0.3.33", - "langchain-openai<1.0.0,>=0.3.3", + "langchain-core<1.0.0,>=0.3.34", + "langchain-openai<1.0.0,>=0.3.5", ] name = "langchain-deepseek" -version = "0.1.1" +version = "0.1.2" description = "An integration package connecting DeepSeek and LangChain" readme = "README.md" diff --git a/libs/partners/deepseek/uv.lock b/libs/partners/deepseek/uv.lock index da496e7bae8..9f0f48e8df3 100644 --- a/libs/partners/deepseek/uv.lock +++ b/libs/partners/deepseek/uv.lock @@ -367,7 +367,7 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.33" +version = "0.3.34" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jsonpatch" }, @@ -378,14 +378,14 @@ dependencies = [ { name = "tenacity" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/57/b3/426268e07273c395affc6dd02cdf89803888121cfc59ce60922f363aeff8/langchain_core-0.3.33.tar.gz", hash = "sha256:b5dd93a4e7f8198d2fc6048723b0bfecf7aaf128b0d268cbac19c34c1579b953", size = 331492 } +sdist = { url = "https://files.pythonhosted.org/packages/b9/c8/a4394a5bdfc820f539bd6983b1408964723ed43ce8cfafbcc7cada69c015/langchain_core-0.3.34.tar.gz", hash = "sha256:26504cf1e8e6c310adad907b890d4e3c147581cfa7434114f6dc1134fe4bc6d3", size = 524756 } wheels = [ - { url = "https://files.pythonhosted.org/packages/98/78/463bc92174555cc04b3e234faa169bb8b58f36fff77892d7b8ae2b4f58e4/langchain_core-0.3.33-py3-none-any.whl", hash = "sha256:269706408a2223f863ff1f9616f31903a5712403199d828b50aadbc4c28b553a", size = 412656 }, + { url = "https://files.pythonhosted.org/packages/9f/65/27a586c8871a0632d747059eb97855b49ac6dea12b263a79f6c1b4f18b99/langchain_core-0.3.34-py3-none-any.whl", hash = "sha256:a057ebeddd2158d3be14bde341b25640ddf958b6989bd6e47160396f5a8202ae", size = 412955 }, ] [[package]] name = "langchain-deepseek" -version = "0.1.1" +version = "0.1.2" source = { editable = "." } dependencies = [ { name = "langchain-core" }, @@ -414,8 +414,8 @@ typing = [ [package.metadata] requires-dist = [ - { name = "langchain-core", specifier = ">=0.3.33,<1.0.0" }, - { name = "langchain-openai", specifier = ">=0.3.3,<1.0.0" }, + { name = "langchain-core", specifier = ">=0.3.34,<1.0.0" }, + { name = "langchain-openai", specifier = ">=0.3.5,<1.0.0" }, ] [package.metadata.requires-dev] @@ -436,7 +436,7 @@ typing = [{ name = "mypy", specifier = ">=1.10,<2.0" }] [[package]] name = "langchain-openai" -version = "0.3.3" +version = "0.3.5" source = { directory = "../openai" } dependencies = [ { name = "langchain-core" }, @@ -446,11 +446,42 @@ dependencies = [ [package.metadata] requires-dist = [ - { name = "langchain-core", specifier = ">=0.3.33,<0.4.0" }, + { name = "langchain-core", specifier = ">=0.3.34,<1.0.0" }, { name = "openai", specifier = ">=1.58.1,<2.0.0" }, { name = "tiktoken", specifier = ">=0.7,<1" }, ] +[package.metadata.requires-dev] +codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }] +dev = [{ name = "langchain-core", directory = "../../core" }] +lint = [{ name = "ruff", specifier = ">=0.5,<1.0" }] +test = [ + { name = "freezegun", specifier = ">=1.2.2,<2.0.0" }, + { name = "langchain-core", directory = "../../core" }, + { name = "langchain-tests", directory = "../../standard-tests" }, + { name = "numpy", marker = "python_full_version < '3.12'", specifier = ">=1,<2" }, + { name = "numpy", marker = "python_full_version >= '3.12'", specifier = ">=1.26.0,<2.0.0" }, + { name = "pytest", specifier = ">=7.3.0,<8.0.0" }, + { name = "pytest-asyncio", specifier = ">=0.21.1,<1.0.0" }, + { name = "pytest-cov", specifier = ">=4.1.0,<5.0.0" }, + { name = "pytest-mock", specifier = ">=3.10.0,<4.0.0" }, + { name = "pytest-socket", specifier = ">=0.6.0,<1.0.0" }, + { name = "pytest-watcher", specifier = ">=0.3.4,<1.0.0" }, + { name = "pytest-xdist", specifier = ">=3.6.1,<4.0.0" }, + { name = "syrupy", specifier = ">=4.0.2,<5.0.0" }, +] +test-integration = [ + { name = "httpx", specifier = ">=0.27.0,<1.0.0" }, + { name = "numpy", marker = "python_full_version < '3.12'", specifier = ">=1,<2" }, + { name = "numpy", marker = "python_full_version >= '3.12'", specifier = ">=1.26.0,<2.0.0" }, + { name = "pillow", specifier = ">=10.3.0,<11.0.0" }, +] +typing = [ + { name = "langchain-core", directory = "../../core" }, + { name = "mypy", specifier = ">=1.10,<2.0" }, + { name = "types-tqdm", specifier = ">=4.66.0.5,<5.0.0.0" }, +] + [[package]] name = "langchain-tests" version = "0.3.10" From c7d74eb7a33f4684000699458fd185a672b14ef3 Mon Sep 17 00:00:00 2001 From: Shailendra Mishra Date: Tue, 11 Feb 2025 14:56:55 -0800 Subject: [PATCH 08/11] Oraclevs integration (#29723) Thank you for contributing to LangChain! - [ ] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" community: langchain_community/vectorstore/oraclevs.py - [ ] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** Refactored code to allow a connection or a connection pool. - **Issue:** Normally an idel connection is terminated by the server side listener at timeout. A user thus has to re-instantiate the vector store. The timeout in case of connection is not configurable. The solution is to use a connection pool where a user can specify a user defined timeout and the connections are managed by the pool. - **Dependencies:** None - **Twitter handle:** - [ ] **Add tests and docs**: This is not a new integration. A user can pass either a connection or a connection pool. The determination of what is passed is made at run time. Everything should work as before. - [ ] **Lint and test**: Already done. Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --------- Co-authored-by: Erick Friis --- .../vectorstores/oraclevs.py | 188 +++++++++++------- 1 file changed, 118 insertions(+), 70 deletions(-) diff --git a/libs/community/langchain_community/vectorstores/oraclevs.py b/libs/community/langchain_community/vectorstores/oraclevs.py index 6aa767927f5..0e0181c67f4 100644 --- a/libs/community/langchain_community/vectorstores/oraclevs.py +++ b/libs/community/langchain_community/vectorstores/oraclevs.py @@ -22,6 +22,8 @@ from typing import ( cast, ) +from numpy.typing import NDArray + if TYPE_CHECKING: from oracledb import Connection @@ -47,6 +49,31 @@ logging.basicConfig( T = TypeVar("T", bound=Callable[..., Any]) +def _get_connection(client: Any) -> Connection | None: + # Dynamically import oracledb and the required classes + try: + import oracledb + except ImportError as e: + raise ImportError( + "Unable to import oracledb, please install with `pip install -U oracledb`." + ) from e + + # check if ConnectionPool exists + connection_pool_class = getattr(oracledb, "ConnectionPool", None) + + if isinstance(client, oracledb.Connection): + return client + elif connection_pool_class and isinstance(client, connection_pool_class): + return client.acquire() + else: + valid_types = "oracledb.Connection" + if connection_pool_class: + valid_types += " or oracledb.ConnectionPool" + raise TypeError( + f"Expected client of type {valid_types}, got {type(client).__name__}" + ) + + def _handle_exceptions(func: T) -> T: @functools.wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Any: @@ -70,7 +97,7 @@ def _handle_exceptions(func: T) -> T: return cast(T, wrapper) -def _table_exists(client: Connection, table_name: str) -> bool: +def _table_exists(connection: Connection, table_name: str) -> bool: try: import oracledb except ImportError as e: @@ -79,7 +106,7 @@ def _table_exists(client: Connection, table_name: str) -> bool: ) from e try: - with client.cursor() as cursor: + with connection.cursor() as cursor: cursor.execute(f"SELECT COUNT(*) FROM {table_name}") return True except oracledb.DatabaseError as ex: @@ -106,7 +133,7 @@ def _compare_version(version: str, target_version: str) -> bool: @_handle_exceptions -def _index_exists(client: Connection, index_name: str) -> bool: +def _index_exists(connection: Connection, index_name: str) -> bool: # Check if the index exists query = """ SELECT index_name @@ -114,7 +141,7 @@ def _index_exists(client: Connection, index_name: str) -> bool: WHERE upper(index_name) = upper(:idx_name) """ - with client.cursor() as cursor: + with connection.cursor() as cursor: # Execute the query cursor.execute(query, idx_name=index_name.upper()) result = cursor.fetchone() @@ -146,16 +173,16 @@ def _get_index_name(base_name: str) -> str: @_handle_exceptions -def _create_table(client: Connection, table_name: str, embedding_dim: int) -> None: +def _create_table(connection: Connection, table_name: str, embedding_dim: int) -> None: cols_dict = { "id": "RAW(16) DEFAULT SYS_GUID() PRIMARY KEY", "text": "CLOB", - "metadata": "CLOB", + "metadata": "JSON", "embedding": f"vector({embedding_dim}, FLOAT32)", } - if not _table_exists(client, table_name): - with client.cursor() as cursor: + if not _table_exists(connection, table_name): + with connection.cursor() as cursor: ddl_body = ", ".join( f"{col_name} {col_type}" for col_name, col_type in cols_dict.items() ) @@ -168,43 +195,45 @@ def _create_table(client: Connection, table_name: str, embedding_dim: int) -> No @_handle_exceptions def create_index( - client: Connection, + client: Any, vector_store: OracleVS, params: Optional[dict[str, Any]] = None, ) -> None: - """Create an index on the vector store. - - Args: - client: The OracleDB connection object. - vector_store: The vector store object. - params: Optional parameters for the index creation. - - Raises: - ValueError: If an invalid parameter is provided. - """ + connection = _get_connection(client) + if connection is None: + raise ValueError("Failed to acquire a connection.") if params: if params["idx_type"] == "HNSW": _create_hnsw_index( - client, vector_store.table_name, vector_store.distance_strategy, params + connection, + vector_store.table_name, + vector_store.distance_strategy, + params, ) elif params["idx_type"] == "IVF": _create_ivf_index( - client, vector_store.table_name, vector_store.distance_strategy, params + connection, + vector_store.table_name, + vector_store.distance_strategy, + params, ) else: _create_hnsw_index( - client, vector_store.table_name, vector_store.distance_strategy, params + connection, + vector_store.table_name, + vector_store.distance_strategy, + params, ) else: _create_hnsw_index( - client, vector_store.table_name, vector_store.distance_strategy, params + connection, vector_store.table_name, vector_store.distance_strategy, params ) return @_handle_exceptions def _create_hnsw_index( - client: Connection, + connection: Connection, table_name: str, distance_strategy: DistanceStrategy, params: Optional[dict[str, Any]] = None, @@ -278,8 +307,8 @@ def _create_hnsw_index( ddl = ddl_assembly.format(**config) # Check if the index exists - if not _index_exists(client, config["idx_name"]): - with client.cursor() as cursor: + if not _index_exists(connection, config["idx_name"]): + with connection.cursor() as cursor: cursor.execute(ddl) logger.info("Index created successfully...") else: @@ -288,7 +317,7 @@ def _create_hnsw_index( @_handle_exceptions def _create_ivf_index( - client: Connection, + connection: Connection, table_name: str, distance_strategy: DistanceStrategy, params: Optional[dict[str, Any]] = None, @@ -350,8 +379,8 @@ def _create_ivf_index( ddl = ddl_assembly.format(**config) # Check if the index exists - if not _index_exists(client, config["idx_name"]): - with client.cursor() as cursor: + if not _index_exists(connection, config["idx_name"]): + with connection.cursor() as cursor: cursor.execute(ddl) logger.info("Index created successfully...") else: @@ -359,7 +388,7 @@ def _create_ivf_index( @_handle_exceptions -def drop_table_purge(client: Connection, table_name: str) -> None: +def drop_table_purge(client: Any, table_name: str) -> None: """Drop a table and purge it from the database. Args: @@ -369,9 +398,11 @@ def drop_table_purge(client: Connection, table_name: str) -> None: Raises: RuntimeError: If an error occurs while dropping the table. """ - if _table_exists(client, table_name): - cursor = client.cursor() - with cursor: + connection = _get_connection(client) + if connection is None: + raise ValueError("Failed to acquire a connection.") + if _table_exists(connection, table_name): + with connection.cursor() as cursor: ddl = f"DROP TABLE {table_name} PURGE" cursor.execute(ddl) logger.info("Table dropped successfully...") @@ -381,7 +412,7 @@ def drop_table_purge(client: Connection, table_name: str) -> None: @_handle_exceptions -def drop_index_if_exists(client: Connection, index_name: str) -> None: +def drop_index_if_exists(client: Any, index_name: str) -> None: """Drop an index if it exists. Args: @@ -391,9 +422,12 @@ def drop_index_if_exists(client: Connection, index_name: str) -> None: Raises: RuntimeError: If an error occurs while dropping the index. """ - if _index_exists(client, index_name): + connection = _get_connection(client) + if connection is None: + raise ValueError("Failed to acquire a connection.") + if _index_exists(connection, index_name): drop_query = f"DROP INDEX {index_name}" - with client.cursor() as cursor: + with connection.cursor() as cursor: cursor.execute(drop_query) logger.info(f"Index {index_name} has been dropped.") else: @@ -426,7 +460,7 @@ class OracleVS(VectorStore): def __init__( self, - client: Connection, + client: Any, embedding_function: Union[ Callable[[str], List[float]], Embeddings, @@ -445,8 +479,11 @@ class OracleVS(VectorStore): ) from e self.insert_mode = "array" + connection = _get_connection(client) + if connection is None: + raise ValueError("Failed to acquire a connection.") - if client.thin is True: + if hasattr(connection, "thin") and connection.thin: if oracledb.__version__ == "2.1.0": raise Exception( "Oracle DB python thin client driver version 2.1.0 not supported" @@ -494,8 +531,7 @@ class OracleVS(VectorStore): self.table_name = table_name self.distance_strategy = distance_strategy self.params = params - - _create_table(client, table_name, embedding_dim) + _create_table(connection, table_name, embedding_dim) except oracledb.DatabaseError as db_err: logger.exception(f"Database error occurred while create table: {db_err}") raise RuntimeError( @@ -613,13 +649,16 @@ class OracleVS(VectorStore): ) ] - with self.client.cursor() as cursor: + connection = _get_connection(self.client) + if connection is None: + raise ValueError("Failed to acquire a connection.") + with connection.cursor() as cursor: cursor.executemany( f"INSERT INTO {self.table_name} (id, embedding, metadata, " f"text) VALUES (:1, :2, :3, :4)", docs, ) - self.client.commit() + connection.commit() return processed_ids def similarity_search( @@ -630,6 +669,7 @@ class OracleVS(VectorStore): **kwargs: Any, ) -> List[Document]: """Return docs most similar to query.""" + embedding: List[float] = [] if isinstance(self.embedding_function, Embeddings): embedding = self.embedding_function.embed_query(query) documents = self.similarity_search_by_vector( @@ -657,6 +697,7 @@ class OracleVS(VectorStore): **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return docs most similar to query.""" + embedding: List[float] = [] if isinstance(self.embedding_function, Embeddings): embedding = self.embedding_function.embed_query(query) docs_and_scores = self.similarity_search_by_vector_with_relevance_scores( @@ -707,25 +748,27 @@ class OracleVS(VectorStore): embedding_arr = array.array("f", embedding) query = f""" - SELECT id, - text, - metadata, - vector_distance(embedding, :embedding, - {_get_distance_function(self.distance_strategy)}) as distance - FROM {self.table_name} - ORDER BY distance - FETCH APPROX FIRST {k} ROWS ONLY + SELECT id, + text, + metadata, + vector_distance(embedding, :embedding, + {_get_distance_function(self.distance_strategy)}) as distance + FROM {self.table_name} + ORDER BY distance + FETCH APPROX FIRST {k} ROWS ONLY """ + # Execute the query - with self.client.cursor() as cursor: + connection = _get_connection(self.client) + if connection is None: + raise ValueError("Failed to acquire a connection.") + with connection.cursor() as cursor: cursor.execute(query, embedding=embedding_arr) results = cursor.fetchall() # Filter results if filter is provided for result in results: - metadata = json.loads( - self._get_clob_value(result[2]) if result[2] is not None else "{}" - ) + metadata = dict(result[2]) if isinstance(result[2], dict) else {} # Apply filtering based on the 'filter' dictionary if filter: @@ -761,7 +804,7 @@ class OracleVS(VectorStore): k: int, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, - ) -> List[Tuple[Document, float, np.ndarray]]: + ) -> List[Tuple[Document, float, NDArray[np.float32]]]: embedding_arr: Any if self.insert_mode == "clob": embedding_arr = json.dumps(embedding) @@ -771,27 +814,29 @@ class OracleVS(VectorStore): documents = [] query = f""" - SELECT id, - text, - metadata, - vector_distance(embedding, :embedding, { + SELECT id, + text, + metadata, + vector_distance(embedding, :embedding, { _get_distance_function(self.distance_strategy) }) as distance, - embedding - FROM {self.table_name} - ORDER BY distance - FETCH APPROX FIRST {k} ROWS ONLY + embedding + FROM {self.table_name} + ORDER BY distance + FETCH APPROX FIRST {k} ROWS ONLY """ # Execute the query - with self.client.cursor() as cursor: + connection = _get_connection(self.client) + if connection is None: + raise ValueError("Failed to acquire a connection.") + with connection.cursor() as cursor: cursor.execute(query, embedding=embedding_arr) results = cursor.fetchall() for result in results: page_content_str = self._get_clob_value(result[1]) - metadata_str = self._get_clob_value(result[2]) - metadata = json.loads(metadata_str) + metadata = result[2] if isinstance(result[2], dict) else {} # Apply filter if provided and matches; otherwise, add all # documents @@ -984,9 +1029,12 @@ class OracleVS(VectorStore): f"id{i}": hashed_id for i, hashed_id in enumerate(hashed_ids, start=1) } - with self.client.cursor() as cursor: + connection = _get_connection(self.client) + if connection is None: + raise ValueError("Failed to acquire a connection.") + with connection.cursor() as cursor: cursor.execute(ddl, bind_vars) - self.client.commit() + connection.commit() @classmethod @_handle_exceptions @@ -997,10 +1045,10 @@ class OracleVS(VectorStore): metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> OracleVS: - """Return VectorStore initialized from texts and embeddings.""" - client = kwargs.get("client") + client: Any = kwargs.get("client", None) if client is None: raise ValueError("client parameter is required...") + params = kwargs.get("params", {}) table_name = str(kwargs.get("table_name", "langchain")) From 994c5465e08abfcc67293f9bbccddf17f4daaed7 Mon Sep 17 00:00:00 2001 From: Jawahar S Date: Wed, 12 Feb 2025 05:04:29 +0530 Subject: [PATCH 09/11] feat: add support for IBM WatsonX AI chat models (#29688) **Description:** Updated init_chat_model to support Granite models deployed on IBM WatsonX **Dependencies:** [langchain-ibm](https://github.com/langchain-ai/langchain-ibm) Tagging @baskaryan @efriis for review when you get a chance. --- libs/langchain/langchain/chat_models/base.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/libs/langchain/langchain/chat_models/base.py b/libs/langchain/langchain/chat_models/base.py index 8799f627e0b..1a2ead99043 100644 --- a/libs/langchain/langchain/chat_models/base.py +++ b/libs/langchain/langchain/chat_models/base.py @@ -118,6 +118,7 @@ def init_chat_model( - 'ollama' -> langchain-ollama - 'google_anthropic_vertex' -> langchain-google-vertexai - 'deepseek' -> langchain-deepseek + - 'ibm' -> langchain-ibm - 'nvidia' -> langchain-nvidia-ai-endpoints Will attempt to infer model_provider from model if not specified. The @@ -428,6 +429,11 @@ def _init_chat_model_helper( from langchain_nvidia_ai_endpoints import ChatNVIDIA return ChatNVIDIA(model=model, **kwargs) + elif model_provider == "ibm": + _check_pkg("langchain_ibm") + from langchain_ibm import ChatWatsonx + + return ChatWatsonx(model_id=model, **kwargs) else: supported = ", ".join(_SUPPORTED_PROVIDERS) raise ValueError( @@ -453,6 +459,7 @@ _SUPPORTED_PROVIDERS = { "bedrock_converse", "google_anthropic_vertex", "deepseek", + "ibm", } From 9f3bcee30afb9d29f339878634e7fe44fbe2ca2b Mon Sep 17 00:00:00 2001 From: Mohammad Mohtashim <45242107+keenborder786@users.noreply.github.com> Date: Wed, 12 Feb 2025 04:51:18 +0500 Subject: [PATCH 10/11] (Community): Adding Structured Support for ChatPerplexity (#29361) - **Description:** Adding Structured Support for ChatPerplexity - **Issue:** #29357 - This is implemented as per the Perplexity official docs: https://docs.perplexity.ai/guides/structured-outputs --------- Co-authored-by: ccurme --- .../chat_models/perplexity.py | 118 +++++++++++++++++- 1 file changed, 114 insertions(+), 4 deletions(-) diff --git a/libs/community/langchain_community/chat_models/perplexity.py b/libs/community/langchain_community/chat_models/perplexity.py index d2decb38df9..0e5ea613447 100644 --- a/libs/community/langchain_community/chat_models/perplexity.py +++ b/libs/community/langchain_community/chat_models/perplexity.py @@ -3,19 +3,23 @@ from __future__ import annotations import logging +from operator import itemgetter from typing import ( Any, Dict, Iterator, List, + Literal, Mapping, Optional, Tuple, Type, + TypeVar, Union, ) from langchain_core.callbacks import CallbackManagerForLLMRun +from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import ( BaseChatModel, generate_from_stream, @@ -34,17 +38,27 @@ from langchain_core.messages import ( SystemMessageChunk, ToolMessageChunk, ) +from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.utils import ( - from_env, - get_pydantic_field_names, +from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough +from langchain_core.utils import from_env, get_pydantic_field_names +from langchain_core.utils.pydantic import ( + is_basemodel_subclass, ) -from pydantic import ConfigDict, Field, model_validator +from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator from typing_extensions import Self +_BM = TypeVar("_BM", bound=BaseModel) +_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM], Type] +_DictOrPydantic = Union[Dict, _BM] + logger = logging.getLogger(__name__) +def _is_pydantic_class(obj: Any) -> bool: + return isinstance(obj, type) and is_basemodel_subclass(obj) + + class ChatPerplexity(BaseChatModel): """`Perplexity AI` Chat models API. @@ -282,3 +296,99 @@ class ChatPerplexity(BaseChatModel): def _llm_type(self) -> str: """Return type of chat model.""" return "perplexitychat" + + def with_structured_output( + self, + schema: Optional[_DictOrPydanticClass] = None, + *, + method: Literal["json_schema"] = "json_schema", + include_raw: bool = False, + strict: Optional[bool] = None, + **kwargs: Any, + ) -> Runnable[LanguageModelInput, _DictOrPydantic]: + """Model wrapper that returns outputs formatted to match the given schema for Preplexity. + Currently, Preplexity only supports "json_schema" method for structured output + as per their official documentation: https://docs.perplexity.ai/guides/structured-outputs + + Args: + schema: + The output schema. Can be passed in as: + + - a JSON Schema, + - a TypedDict class, + - or a Pydantic class + + method: The method for steering model generation, currently only support: + + - "json_schema": Use the JSON Schema to parse the model output + + + include_raw: + If False then only the parsed structured output is returned. If + an error occurs during model output parsing it will be raised. If True + then both the raw model response (a BaseMessage) and the parsed model + response will be returned. If an error occurs during output parsing it + will be caught and returned as well. The final output is always a dict + with keys "raw", "parsed", and "parsing_error". + + kwargs: Additional keyword args aren't supported. + + Returns: + A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`. + + | If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict. + + | If ``include_raw`` is True, then Runnable outputs a dict with keys: + + - "raw": BaseMessage + - "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above. + - "parsing_error": Optional[BaseException] + + """ # noqa: E501 + if method == "json_schema": + if schema is None: + raise ValueError( + "schema must be specified when method is not 'json_schema'. " + "Received None." + ) + is_pydantic_schema = _is_pydantic_class(schema) + if is_pydantic_schema and hasattr( + schema, "model_json_schema" + ): # accounting for pydantic v1 and v2 + response_format = schema.model_json_schema() # type: ignore[union-attr] + elif is_pydantic_schema: + response_format = schema.schema() # type: ignore[union-attr] + elif isinstance(schema, dict): + response_format = schema + elif type(schema).__name__ == "_TypedDictMeta": + adapter = TypeAdapter(schema) # if use passes typeddict + response_format = adapter.json_schema() + + llm = self.bind( + response_format={ + "type": "json_schema", + "json_schema": {"schema": response_format}, + } + ) + output_parser = ( + PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type] + if is_pydantic_schema + else JsonOutputParser() + ) + else: + raise ValueError( + f"Unrecognized method argument. Expected 'json_schema' Received:\ + '{method}'" + ) + + if include_raw: + parser_assign = RunnablePassthrough.assign( + parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None + ) + parser_none = RunnablePassthrough.assign(parsed=lambda _: None) + parser_with_fallback = parser_assign.with_fallbacks( + [parser_none], exception_key="parsing_error" + ) + return RunnableMap(raw=llm) | parser_with_fallback + else: + return llm | output_parser From f4e3e86fbb08dcaa00f4e2d1b126a94ca5adfec7 Mon Sep 17 00:00:00 2001 From: Jacob Lee Date: Tue, 11 Feb 2025 16:51:41 -0800 Subject: [PATCH 11/11] feat(langchain): Infer o3 modelstrings passed to init_chat_model as OpenAI (#29743) --- libs/langchain/langchain/chat_models/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langchain/langchain/chat_models/base.py b/libs/langchain/langchain/chat_models/base.py index 1a2ead99043..1b73f153d82 100644 --- a/libs/langchain/langchain/chat_models/base.py +++ b/libs/langchain/langchain/chat_models/base.py @@ -464,7 +464,7 @@ _SUPPORTED_PROVIDERS = { def _attempt_infer_model_provider(model_name: str) -> Optional[str]: - if any(model_name.startswith(pre) for pre in ("gpt-3", "gpt-4", "o1")): + if any(model_name.startswith(pre) for pre in ("gpt-3", "gpt-4", "o1", "o3")): return "openai" elif model_name.startswith("claude"): return "anthropic"