openai[patch]: release 0.2.8 (#28062 )

community[patch]: release 0.3.7 (#28061 )
core[patch]: release 0.3.17 (#28060 )
2026-02-13 14:21:27 +00:00 · 2024-11-12 14:57:11 -05:00 · 2024-11-12 19:54:58 +00:00 · 2024-11-12 19:38:56 +00:00 · 2024-11-12 14:31:07 -05:00 · 2024-11-12 13:58:13 -05:00
42 changed files with 1286 additions and 1320 deletions
--- a/.github/scripts/check_diff.py
+++ b/.github/scripts/check_diff.py
@@ -37,7 +37,6 @@ IGNORED_PARTNERS = [
 PY_312_MAX_PACKAGES = [
    f"libs/partners/{integration}"
    for integration in [
-        "anthropic",
        "chroma",
        "couchbase",
        "huggingface",
--- a/docs/cassettes/summarize_map_reduce_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
+++ b/docs/cassettes/summarize_map_reduce_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
--- a/docs/cassettes/summarize_map_reduce_27c8fed0-b2d7-4549-a086-f5ee657efc41.msgpack.zlib
+++ b/docs/cassettes/summarize_map_reduce_27c8fed0-b2d7-4549-a086-f5ee657efc41.msgpack.zlib
--- a/docs/cassettes/summarize_map_reduce_4f26c1e3-3d3c-44f7-bb5f-46db9dc40f4b.msgpack.zlib
+++ b/docs/cassettes/summarize_map_reduce_4f26c1e3-3d3c-44f7-bb5f-46db9dc40f4b.msgpack.zlib
--- a/docs/cassettes/summarize_refine_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
+++ b/docs/cassettes/summarize_refine_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
@@ -1 +1 @@
-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
--- a/docs/cassettes/summarize_refine_21711ff5-4e06-4843-9109-e7d89e679449.msgpack.zlib
+++ b/docs/cassettes/summarize_refine_21711ff5-4e06-4843-9109-e7d89e679449.msgpack.zlib
--- a/docs/docs/concepts/chat_models.mdx
+++ b/docs/docs/concepts/chat_models.mdx
@@ -8,7 +8,7 @@ Modern LLMs are typically accessed through a chat model interface that takes a l

 The newest generation of chat models offer additional capabilities:

-* [Tool calling](/docs/concepts/tool_calling): Many popular chat models offer a native [tool calling](/docs/concepts/tool_calling) API. This API allows developers to build rich applications that enable AI to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
+* [Tool calling](/docs/concepts/tool_calling): Many popular chat models offer a native [tool calling](/docs/concepts/tool_calling) API. This API allows developers to build rich applications that enable LLMs to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
 * [Structured output](/docs/concepts/structured_outputs): A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
 * [Multimodality](/docs/concepts/multimodality): The ability to work with data other than text; for example, images, audio, and video.

@@ -19,7 +19,7 @@ LangChain provides a consistent interface for working with chat models from diff
 * Integrations with many chat model providers (e.g., Anthropic, OpenAI, Ollama, Microsoft Azure, Google Vertex, Amazon Bedrock, Hugging Face, Cohere, Groq). Please see [chat model integrations](/docs/integrations/chat/) for an up-to-date list of supported models.
 * Use either LangChain's [messages](/docs/concepts/messages) format or OpenAI format.
 * Standard [tool calling API](/docs/concepts/tool_calling): standard interface for binding tools to models, accessing tool call requests made by models, and sending tool results back to the model.
-* Standard API for structuring outputs (/docs/concepts/structured_outputs) via the `with_structured_output` method.
+* Standard API for [structuring outputs](/docs/concepts/structured_outputs/#structured-output-method) via the `with_structured_output` method.
 * Provides support for [async programming](/docs/concepts/async), [efficient batching](/docs/concepts/runnables/#optimized-parallel-execution-batch), [a rich streaming API](/docs/concepts/streaming).
 * Integration with [LangSmith](https://docs.smith.langchain.com) for monitoring and debugging production-grade applications based on LLMs.
 * Additional features like standardized [token usage](/docs/concepts/messages/#aimessage), [rate limiting](#rate-limiting), [caching](#caching) and more.
@@ -44,7 +44,7 @@ Models that do **not** include the prefix "Chat" in their name or include "LLM"

 ## Interface

-LangChain chat models implement the [BaseChatModel](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface. Because [BaseChatModel] also implements the [Runnable Interface](/docs/concepts/runnables), chat models support a [standard streaming interface](/docs/concepts/streaming), [async programming](/docs/concepts/async), optimized [batching](/docs/concepts/runnables/#optimized-parallel-execution-batch), and more. Please see the [Runnable Interface](/docs/concepts/runnables) for more details.
+LangChain chat models implement the [BaseChatModel](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface. Because `BaseChatModel` also implements the [Runnable Interface](/docs/concepts/runnables), chat models support a [standard streaming interface](/docs/concepts/streaming), [async programming](/docs/concepts/async), optimized [batching](/docs/concepts/runnables/#optimized-parallel-execution-batch), and more. Please see the [Runnable Interface](/docs/concepts/runnables) for more details.

 Many of the key methods of chat models operate on [messages](/docs/concepts/messages) as input and return messages as output.

--- a/docs/docs/concepts/streaming.mdx
+++ b/docs/docs/concepts/streaming.mdx
@@ -77,13 +77,13 @@ When using `stream()` or `astream()` with chat models, the output is streamed as

 [LangGraph](/docs/concepts/architecture#langgraph) compiled graphs are [Runnables](/docs/concepts/runnables) and support the standard streaming APIs.

-When using the *stream* and *astream* methods with LangGraph, you can **one or more** [streaming mode](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamMode) which allow you to control the type of output that is streamed. The available streaming modes are:
+When using the *stream* and *astream* methods with LangGraph, you can choose **one or more** [streaming mode](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamMode) which allow you to control the type of output that is streamed. The available streaming modes are:

 - **"values"**: Emit all values of the [state](https://langchain-ai.github.io/langgraph/concepts/low_level/) for each step.
 - **"updates"**: Emit only the node name(s) and updates that were returned by the node(s) after each step.
 - **"debug"**: Emit debug events for each step.
 - **"messages"**: Emit LLM [messages](/docs/concepts/messages) [token-by-token](/docs/concepts/tokens).
- **"custom"**: Emit custom output witten using [LangGraph's StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter).
+- **"custom"**: Emit custom output written using [LangGraph's StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter).

 For more information, please see:
 * [LangGraph streaming conceptual guide](https://langchain-ai.github.io/langgraph/concepts/streaming/) for more information on how to stream when working with LangGraph.
--- a/docs/docs/contributing/tutorials/docs.mdx
+++ b/docs/docs/contributing/tutorials/docs.mdx
@@ -8,7 +8,7 @@ This tutorial will guide you through making a simple documentation edit, like co

 ---

-## Editing a Documentation Page on GitHub**
+## Editing a Documentation Page on GitHub

 Sometimes you want to make a small change, like fixing a typo, and the easiest way to do this is to use GitHub's editor directly.

--- a/docs/docs/how_to/index.mdx
+++ b/docs/docs/how_to/index.mdx
@@ -74,6 +74,7 @@ These are the core building blocks you can use when building applications.
 ### Chat models

 [Chat Models](/docs/concepts/chat_models) are newer forms of language models that take messages in and output a message.
+See [supported integrations](/docs/integrations/chat/) for details on getting started with chat models from a specific provider.

 - [How to: do function/tool calling](/docs/how_to/tool_calling)
 - [How to: get models to return structured output](/docs/how_to/structured_output)
@@ -153,6 +154,7 @@ What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of languag
 ### Embedding models

 [Embedding Models](/docs/concepts/embedding_models) take a piece of text and create a numerical representation of it.
+See [supported integrations](/docs/integrations/text_embedding/) for details on getting started with embedding models from a specific provider.

 - [How to: embed text data](/docs/how_to/embed_text)
 - [How to: cache embedding results](/docs/how_to/caching_embeddings)
@@ -160,6 +162,7 @@ What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of languag
 ### Vector stores

 [Vector stores](/docs/concepts/vectorstores) are databases that can efficiently store and retrieve embeddings.
+See [supported integrations](/docs/integrations/vectorstores/) for details on getting started with vector stores from a specific provider.

 - [How to: use a vector store to retrieve data](/docs/how_to/vectorstores)

--- a/docs/docs/integrations/llms/azure_openai.ipynb
+++ b/docs/docs/integrations/llms/azure_openai.ipynb
@@ -85,7 +85,7 @@
    "```python\n",
    "import openai\n",
    "\n",
-    "client = AzureOpenAI(\n",
+    "client = openai.AzureOpenAI(\n",
    "    api_version=\"2023-12-01-preview\",\n",
    ")\n",
    "\n",
--- a/docs/docs/integrations/llms/vllm.ipynb
+++ b/docs/docs/integrations/llms/vllm.ipynb
@@ -266,8 +266,18 @@
    "from langchain_community.llms import VLLM\n",
    "from vllm.lora.request import LoRARequest\n",
    "\n",
-    "llm = VLLM(model=\"meta-llama/Llama-2-7b-hf\", enable_lora=True)\n",
-    "\n",
+    "llm = VLLM(\n",
+    "    model=\"meta-llama/Llama-3.2-3B-Instruct\",\n",
+    "    max_new_tokens=300,\n",
+    "    top_k=1,\n",
+    "    top_p=0.90,\n",
+    "    temperature=0.1,\n",
+    "    vllm_kwargs={\n",
+    "        \"gpu_memory_utilization\": 0.5,\n",
+    "        \"enable_lora\": True,\n",
+    "        \"max_model_len\": 350,\n",
+    "    },\n",
+    ")\n",
    "LoRA_ADAPTER_PATH = \"path/to/adapter\"\n",
    "lora_adapter = LoRARequest(\"lora_adapter\", 1, LoRA_ADAPTER_PATH)\n",
    "\n",
--- a/docs/docs/integrations/tools/tavily_search.ipynb
+++ b/docs/docs/integrations/tools/tavily_search.ipynb
@@ -186,7 +186,7 @@
    }
   ],
   "source": [
-    "# This is usually generated by a model, but we'll create a tool call directly for demon purposes.\n",
+    "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
    "model_generated_tool_call = {\n",
    "    \"args\": {\"query\": \"euro 2024 host nation\"},\n",
    "    \"id\": \"1\",\n",
--- a/docs/docs/introduction.mdx
+++ b/docs/docs/introduction.mdx
@@ -29,9 +29,9 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
 Concretely, the framework consists of the following open-source libraries:

 - **`langchain-core`**: Base abstractions and LangChain Expression Language.
- **`langchain-community`**: Third party integrations.
-  - Partner packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Some integrations have been further split into their own lightweight packages that only depend on **`langchain-core`**.
+- Integration packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
+- **`langchain-community`**: Third-party integrations that are community maintained.
 - **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
 - **[LangServe](/docs/langserve)**: Deploy LangChain chains as REST APIs.
 - **[LangSmith](https://docs.smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
@@ -62,7 +62,8 @@ Explore the full list of LangChain tutorials [here](/docs/tutorials), and check

 [Here](/docs/how_to) you’ll find short answers to “How do I….?” types of questions.
 These how-to guides don’t cover topics in depth – you’ll find that material in the [Tutorials](/docs/tutorials) and the [API Reference](https://python.langchain.com/api_reference/).
-However, these guides will help you quickly accomplish common tasks.
+However, these guides will help you quickly accomplish common tasks using [chat models](/docs/how_to/#chat-models),
+[vector stores](/docs/how_to/#vector-stores), and other common LangChain components.

 Check out [LangGraph-specific how-tos here](https://langchain-ai.github.io/langgraph/how-tos/).

@@ -72,6 +73,13 @@ Introductions to all the key parts of LangChain you’ll need to know! [Here](/d

 For a deeper dive into LangGraph concepts, check out [this page](https://langchain-ai.github.io/langgraph/concepts/).

+## [Integrations](integrations/providers/index.mdx)
+
+LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it.
+If you're looking to get up and running quickly with [chat models](/docs/integrations/chat/), [vector stores](/docs/integrations/vectorstores/),
+or other LangChain components from a specific provider, check out our growing list of [integrations](/docs/integrations/providers/).
+
+
 ## [API reference](https://python.langchain.com/api_reference/)
 Head to the reference section for full documentation of all classes and methods in the LangChain Python packages.

@@ -91,8 +99,5 @@ See what changed in v0.3, learn how to migrate legacy code, read up on our versi
 ### [Security](/docs/security)
 Read up on [security](/docs/security) best practices to make sure you're developing safely with LangChain.

-### [Integrations](integrations/providers/index.mdx)
-LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/providers/).
-
 ### [Contributing](contributing/index.mdx)
 Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.
--- a/docs/docs/tutorials/index.mdx
+++ b/docs/docs/tutorials/index.mdx
@@ -4,28 +4,35 @@ sidebar_class_name: hidden
 ---
 # Tutorials

-New to LangChain or LLM app development in general? Read this material to quickly get up and running.
+New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications.
+
+If you're looking to get up and running quickly with [chat models](/docs/integrations/chat/), [vector stores](/docs/integrations/vectorstores/),
+or other LangChain components from a specific provider, check out our supported [integrations](/docs/integrations/providers/).
+
+Refer to the [how-to guides](/docs/how_to) for more detail on using common LangChain components.
+
+See the [conceptual documentation](/docs/concepts) for high level explanations of all LangChain concepts.

 ## Basics
- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain)
- [Build a Chatbot](/docs/tutorials/chatbot)
- [Build vector stores and retrievers](/docs/tutorials/retrievers)
- [Build an Agent](/docs/tutorials/agents)
+- [LLM applications](/docs/tutorials/llm_chain): Build and deploy a simple LLM application.
+- [Chatbots](/docs/tutorials/chatbot): Build a chatbot that incorporates memory.
+- [Vector stores](/docs/tutorials/retrievers): Build vector stores and use them to retrieve data.
+- [Agents](/docs/tutorials/agents): Build an agent that interacts with external tools.

 ## Working with external knowledge
- [Build a Retrieval Augmented Generation (RAG) Application](/docs/tutorials/rag)
- [Build a Conversational RAG Application](/docs/tutorials/qa_chat_history)
- [Build a Question/Answering system over SQL data](/docs/tutorials/sql_qa)
- [Build a Query Analysis System](/docs/tutorials/query_analysis)
- [Build a local RAG application](/docs/tutorials/local_rag)
- [Build a Question Answering application over a Graph Database](/docs/tutorials/graph)
- [Build a PDF ingestion and Question/Answering system](/docs/tutorials/pdf_qa/)
+- [Retrieval Augmented Generation (RAG)](/docs/tutorials/rag): Build an application that uses your own documents to inform its responses.
+- [Conversational RAG](/docs/tutorials/qa_chat_history): Build a RAG application that incorporates a memory of its user interactions.
+- [Question-Answering with SQL](/docs/tutorials/sql_qa): Build a question-answering system that executes SQL queries to inform its responses.
+- [Query Analysis](/docs/tutorials/query_analysis): Build a RAG application that analyzes questions to generate filters and other structured queries.
+- [Local RAG](/docs/tutorials/local_rag): Build a RAG application using LLMs running locally on your machine.
+- [Question-Answering with Graph Databases](/docs/tutorials/graph): Build a question-answering system that queries a graph database to inform its responses.
+- [Question-Answering with PDFs](/docs/tutorials/pdf_qa/): Build a question-answering system that ingests PDFs and uses them to inform its responses.

 ## Specialized tasks
- [Build an Extraction Chain](/docs/tutorials/extraction)
- [Generate synthetic data](/docs/tutorials/data_generation)
- [Classify text into labels](/docs/tutorials/classification)
- [Summarize text](/docs/tutorials/summarization)
+- [Extraction](/docs/tutorials/extraction): Extract structured data from text and other unstructured media.
+- [Synthetic data](/docs/tutorials/data_generation): Generate synthetic data using LLMs.
+- [Classification](/docs/tutorials/classification): Classify text into categories or labels.
+- [Summarization](/docs/tutorials/summarization): Generate summaries of (potentially long) texts.

 ## LangGraph

--- a/docs/docs/tutorials/llm_chain.ipynb
+++ b/docs/docs/tutorials/llm_chain.ipynb
@@ -93,7 +93,7 @@
   "source": [
    "## Using Language Models\n",
    "\n",
-    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably - select the one you want to use below!\n",
+    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably. For details on getting started with a specific model, refer to [supported integrations](/docs/integrations/chat/).\n",
    "\n",
    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
    "\n",
--- a/docs/docs/tutorials/sql_qa.ipynb
+++ b/docs/docs/tutorials/sql_qa.ipynb
@@ -28,7 +28,7 @@
    "\n",
    "At a high-level, the steps of these systems are:\n",
    "\n",
-    "1. **Convert question to DSL query**: Model converts user input to a SQL query.\n",
+    "1. **Convert question to SQL query**: Model converts user input to a SQL query.\n",
    "2. **Execute SQL query**: Execute the query.\n",
    "3. **Answer the question**: Model responds to user input using the query results.\n",
    "\n",
--- a/docs/docs/versions/migrating_memory/index.mdx
+++ b/docs/docs/versions/migrating_memory/index.mdx
@@ -108,9 +108,8 @@ And specific backend implementations of abstractions:
 | `SQLiteEntityStore`       | A specific implementation of `BaseEntityStore` that uses SQLite as the backend.                          |
 | `UpstashRedisEntityStore` | A specific implementation of `BaseEntityStore` that uses Upstash as the backend.                         |

-These abstractions have not received much development since their initial release. The reason
-is that for these abstractions to be useful they typically require a lot of specialization for a particular application, so these
-abstractions are not as widely used as the conversation history management abstractions.
+These abstractions have received limited development since their initial release. This is because they generally require significant customization for a specific application to be effective, making
+them less widely used than the conversation history management abstractions.

 For this reason, there are no migration guides for these abstractions. If you're struggling to migrate an application
 that relies on these abstractions, please:
--- a/libs/community/langchain_community/chat_models/anyscale.py
+++ b/libs/community/langchain_community/chat_models/anyscale.py
@@ -5,10 +5,22 @@ from __future__ import annotations
 import logging
 import os
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Optional, Set
+import warnings
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Optional,
+    Sequence,
+    Set,
+    Type,
+    Union,
+)

 import requests
 from langchain_core.messages import BaseMessage
+from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from pydantic import Field, SecretStr, model_validator

@@ -197,10 +209,20 @@ class ChatAnyscale(ChatOpenAI):
            encoding = tiktoken_.get_encoding(model)
        return model, encoding

-    def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int:
+    def get_num_tokens_from_messages(
+        self,
+        messages: list[BaseMessage],
+        tools: Optional[
+            Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
+        ] = None,
+    ) -> int:
        """Calculate num tokens with tiktoken package.
        Official documentation: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
        """
+        if tools is not None:
+            warnings.warn(
+                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
+            )
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
--- a/libs/community/langchain_community/chat_models/everlyai.py
+++ b/libs/community/langchain_community/chat_models/everlyai.py
@@ -4,9 +4,21 @@ from __future__ import annotations

 import logging
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Optional, Set
+import warnings
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Optional,
+    Sequence,
+    Set,
+    Type,
+    Union,
+)

 from langchain_core.messages import BaseMessage
+from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from pydantic import Field, model_validator

@@ -138,11 +150,21 @@ class ChatEverlyAI(ChatOpenAI):
            encoding = tiktoken_.get_encoding(model)
        return model, encoding

-    def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int:
+    def get_num_tokens_from_messages(
+        self,
+        messages: list[BaseMessage],
+        tools: Optional[
+            Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
+        ] = None,
+    ) -> int:
        """Calculate num tokens with tiktoken package.

        Official documentation: https://github.com/openai/openai-cookbook/blob/
        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
+        if tools is not None:
+            warnings.warn(
+                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
+            )
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
--- a/libs/community/langchain_community/chat_models/openai.py
+++ b/libs/community/langchain_community/chat_models/openai.py
@@ -46,6 +46,7 @@ from langchain_core.messages import (
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
 from langchain_core.utils import (
    get_from_dict_or_env,
    get_pydantic_field_names,
@@ -644,11 +645,21 @@ class ChatOpenAI(BaseChatModel):
        _, encoding_model = self._get_encoding_model()
        return encoding_model.encode(text)

-    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+    def get_num_tokens_from_messages(
+        self,
+        messages: List[BaseMessage],
+        tools: Optional[
+            Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
+        ] = None,
+    ) -> int:
        """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.

        Official documentation: https://github.com/openai/openai-cookbook/blob/
        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
+        if tools is not None:
+            warnings.warn(
+                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
+            )
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
--- a/libs/community/langchain_community/embeddings/baichuan.py
+++ b/libs/community/langchain_community/embeddings/baichuan.py
@@ -15,7 +15,7 @@ from pydantic import (
 from requests import RequestException
 from typing_extensions import Self

-BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
+BAICHUAN_API_URL: str = "https://api.baichuan-ai.com/v1/embeddings"

 # BaichuanTextEmbeddings is an embedding model provided by Baichuan Inc. (https://www.baichuan-ai.com/home).
 # As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB
@@ -25,7 +25,7 @@ BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
 # Official Website: https://platform.baichuan-ai.com/docs/text-Embedding
 # An API-key is required to use this embedding model. You can get one by registering
 # at https://platform.baichuan-ai.com/docs/text-Embedding.
-# BaichuanTextEmbeddings support 512 token window and preduces vectors with
+# BaichuanTextEmbeddings support 512 token window and produces vectors with
 # 1024 dimensions.


--- a/libs/community/langchain_community/embeddings/dashscope.py
+++ b/libs/community/langchain_community/embeddings/dashscope.py
@@ -143,8 +143,6 @@ class DashScopeEmbeddings(BaseModel, Embeddings):

        Args:
            texts: The list of texts to embed.
-            chunk_size: The chunk size of embeddings. If None, will use the chunk size
-                specified by the class.

        Returns:
            List of embeddings, one for each text.
--- a/libs/community/langchain_community/embeddings/fake.py
+++ b/libs/community/langchain_community/embeddings/fake.py
@@ -36,7 +36,8 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
        np.random.seed(seed)
        return list(np.random.normal(size=self.size))

-    def _get_seed(self, text: str) -> int:
+    @staticmethod
+    def _get_seed(text: str) -> int:
        """
        Get a seed for the random generator, using the hash of the text.
        """
--- a/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
+++ b/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
@@ -2,7 +2,7 @@ from __future__ import annotations

 import uuid
 import warnings
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Tuple

 import numpy as np
 from langchain_core.documents import Document
@@ -23,57 +23,18 @@ SCRIPT_SCORING_SEARCH = "script_scoring"
 PAINLESS_SCRIPTING_SEARCH = "painless_scripting"
 MATCH_ALL_QUERY = {"match_all": {}}  # type: Dict

-
-def _import_opensearch() -> Any:
-    """Import OpenSearch if available, otherwise raise error."""
-    try:
-        from opensearchpy import OpenSearch
-    except ImportError:
-        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
-    return OpenSearch
+if TYPE_CHECKING:
+    from opensearchpy import AsyncOpenSearch, OpenSearch


-def _import_async_opensearch() -> Any:
-    """Import AsyncOpenSearch if available, otherwise raise error."""
-    try:
-        from opensearchpy import AsyncOpenSearch
-    except ImportError:
-        raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
-    return AsyncOpenSearch
-
-
-def _import_bulk() -> Any:
-    """Import bulk if available, otherwise raise error."""
-    try:
-        from opensearchpy.helpers import bulk
-    except ImportError:
-        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
-    return bulk
-
-
-def _import_async_bulk() -> Any:
-    """Import async_bulk if available, otherwise raise error."""
-    try:
-        from opensearchpy.helpers import async_bulk
-    except ImportError:
-        raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
-    return async_bulk
-
-
-def _import_not_found_error() -> Any:
-    """Import not found error if available, otherwise raise error."""
-    try:
-        from opensearchpy.exceptions import NotFoundError
-    except ImportError:
-        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
-    return NotFoundError
-
-
-def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
+def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> OpenSearch:
    """Get OpenSearch client from the opensearch_url, otherwise raise error."""
    try:
-        opensearch = _import_opensearch()
-        client = opensearch(opensearch_url, **kwargs)
+        from opensearchpy import OpenSearch
+
+        client = OpenSearch(opensearch_url, **kwargs)
+    except ImportError:
+        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
    except ValueError as e:
        raise ImportError(
            f"OpenSearch client string provided is not in proper format. "
@@ -82,11 +43,14 @@ def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
    return client


-def _get_async_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
+def _get_async_opensearch_client(opensearch_url: str, **kwargs: Any) -> AsyncOpenSearch:
    """Get AsyncOpenSearch client from the opensearch_url, otherwise raise error."""
    try:
-        async_opensearch = _import_async_opensearch()
-        client = async_opensearch(opensearch_url, **kwargs)
+        from opensearchpy import AsyncOpenSearch
+
+        client = AsyncOpenSearch(opensearch_url, **kwargs)
+    except ImportError:
+        raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
    except ValueError as e:
        raise ImportError(
            f"AsyncOpenSearch client string provided is not in proper format. "
@@ -127,7 +91,7 @@ def _is_aoss_enabled(http_auth: Any) -> bool:


 def _bulk_ingest_embeddings(
-    client: Any,
+    client: OpenSearch,
    index_name: str,
    embeddings: List[List[float]],
    texts: Iterable[str],
@@ -142,16 +106,19 @@ def _bulk_ingest_embeddings(
    """Bulk Ingest Embeddings into given index."""
    if not mapping:
        mapping = dict()
+    try:
+        from opensearchpy.exceptions import NotFoundError
+        from opensearchpy.helpers import bulk
+    except ImportError:
+        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)

-    bulk = _import_bulk()
-    not_found_error = _import_not_found_error()
    requests = []
    return_ids = []
    mapping = mapping

    try:
        client.indices.get(index=index_name)
-    except not_found_error:
+    except NotFoundError:
        client.indices.create(index=index_name, body=mapping)

    for i, text in enumerate(texts):
@@ -177,7 +144,7 @@ def _bulk_ingest_embeddings(


 async def _abulk_ingest_embeddings(
-    client: Any,
+    client: AsyncOpenSearch,
    index_name: str,
    embeddings: List[List[float]],
    texts: Iterable[str],
@@ -193,14 +160,18 @@ async def _abulk_ingest_embeddings(
    if not mapping:
        mapping = dict()

-    async_bulk = _import_async_bulk()
-    not_found_error = _import_not_found_error()
+    try:
+        from opensearchpy.exceptions import NotFoundError
+        from opensearchpy.helpers import async_bulk
+    except ImportError:
+        raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
+
    requests = []
    return_ids = []

    try:
        await client.indices.get(index=index_name)
-    except not_found_error:
+    except NotFoundError:
        await client.indices.create(index=index_name, body=mapping)

    for i, text in enumerate(texts):
@@ -230,7 +201,7 @@ async def _abulk_ingest_embeddings(
 def _default_scripting_text_mapping(
    dim: int,
    vector_field: str = "vector_field",
-) -> Dict:
+) -> Dict[str, Any]:
    """For Painless Scripting or Script Scoring,the default mapping to create index."""
    return {
        "mappings": {
@@ -249,7 +220,7 @@ def _default_text_mapping(
    ef_construction: int = 512,
    m: int = 16,
    vector_field: str = "vector_field",
-) -> Dict:
+) -> Dict[str, Any]:
    """For Approximate k-NN Search, this is the default mapping to create index."""
    return {
        "settings": {"index": {"knn": True, "knn.algo_param.ef_search": ef_search}},
@@ -275,7 +246,7 @@ def _default_approximate_search_query(
    k: int = 4,
    vector_field: str = "vector_field",
    score_threshold: Optional[float] = 0.0,
-) -> Dict:
+) -> Dict[str, Any]:
    """For Approximate k-NN Search, this is the default query."""
    return {
        "size": k,
@@ -291,7 +262,7 @@ def _approximate_search_query_with_boolean_filter(
    vector_field: str = "vector_field",
    subquery_clause: str = "must",
    score_threshold: Optional[float] = 0.0,
-) -> Dict:
+) -> Dict[str, Any]:
    """For Approximate k-NN Search, with Boolean Filter."""
    return {
        "size": k,
@@ -313,7 +284,7 @@ def _approximate_search_query_with_efficient_filter(
    k: int = 4,
    vector_field: str = "vector_field",
    score_threshold: Optional[float] = 0.0,
-) -> Dict:
+) -> Dict[str, Any]:
    """For Approximate k-NN Search, with Efficient Filter for Lucene and
    Faiss Engines."""
    search_query = _default_approximate_search_query(
@@ -330,7 +301,7 @@ def _default_script_query(
    pre_filter: Optional[Dict] = None,
    vector_field: str = "vector_field",
    score_threshold: Optional[float] = 0.0,
-) -> Dict:
+) -> Dict[str, Any]:
    """For Script Scoring Search, this is the default query."""

    if not pre_filter:
@@ -376,7 +347,7 @@ def _default_painless_scripting_query(
    pre_filter: Optional[Dict] = None,
    vector_field: str = "vector_field",
    score_threshold: Optional[float] = 0.0,
-) -> Dict:
+) -> Dict[str, Any]:
    """For Painless Scripting Search, this is the default query."""

    if not pre_filter:
@@ -692,7 +663,10 @@ class OpenSearchVectorSearch(VectorStore):
            refresh_indices: Whether to refresh the index
                            after deleting documents. Defaults to True.
        """
-        bulk = _import_bulk()
+        try:
+            from opensearchpy.helpers import bulk
+        except ImportError:
+            raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)

        body = []

--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.

 [[package]]
 name = "aiohappyeyeballs"
@@ -842,37 +842,37 @@ typing-inspect = ">=0.4.0,<1"

 [[package]]
 name = "debugpy"
-version = "1.8.7"
+version = "1.8.8"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
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+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1830,7 +1833,7 @@ files = [

 [[package]]
 name = "langchain"
-version = "0.3.6"
+version = "0.3.7"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1859,7 +1862,7 @@ url = "../langchain"

 [[package]]
 name = "langchain-core"
-version = "0.3.15"
+version = "0.3.17"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1884,7 +1887,7 @@ url = "../core"

 [[package]]
 name = "langchain-standard-tests"
-version = "0.1.1"
+version = "0.3.0"
 description = "Standard tests for LangChain implementations"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1903,7 +1906,7 @@ url = "../standard-tests"

 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.1"
+version = "0.3.2"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1919,13 +1922,13 @@ url = "../text-splitters"

 [[package]]
 name = "langsmith"
-version = "0.1.138"
+version = "0.1.142"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
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 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
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 python-versions = ">=3.9"
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 tests = ["pytest", "simplejson"]

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+    {file = "watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282"},
 ]

 [package.extras]
@@ -4331,19 +4301,15 @@ files = [

 [[package]]
 name = "webcolors"
-version = "24.8.0"
+version = "24.11.1"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
-    {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
+    {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
+    {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
 ]

-[package.extras]
-docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["coverage[toml]"]
-
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -4559,13 +4525,13 @@ propcache = ">=0.2.0"

 [[package]]
 name = "zipp"
-version = "3.20.2"
+version = "3.21.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
 ]

 [package.extras]
@@ -4579,4 +4545,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "0179027f05f5159e95267551c013cac474abd3e9f137399f715835b3070d9c33"
+content-hash = "a02b86c4746a93545fe99f8a30ea9becedead16f881254d7763a052ec50c9648"
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "langchain-community"
-version = "0.3.5"
+version = "0.3.7"
 description = "Community contributed LangChain integrations."
 authors = []
 license = "MIT"
@@ -30,8 +30,8 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy

 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.15"
-langchain = "^0.3.6"
+langchain-core = "^0.3.17"
+langchain = "^0.3.7"
 SQLAlchemy = ">=1.4,<2.0.36"
 requests = "^2"
 PyYAML = ">=5.3"
--- a/libs/core/langchain_core/language_models/base.py
+++ b/libs/core/langchain_core/language_models/base.py
@@ -1,5 +1,6 @@
 from __future__ import annotations

+import warnings
 from abc import ABC, abstractmethod
 from collections.abc import Mapping, Sequence
 from functools import cache
@@ -364,17 +365,31 @@ class BaseLanguageModel(
        """
        return len(self.get_token_ids(text))

-    def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int:
+    def get_num_tokens_from_messages(
+        self,
+        messages: list[BaseMessage],
+        tools: Optional[Sequence] = None,
+    ) -> int:
        """Get the number of tokens in the messages.

        Useful for checking if an input fits in a model's context window.

+        **Note**: the base implementation of get_num_tokens_from_messages ignores
+        tool schemas.
+
        Args:
            messages: The message inputs to tokenize.
+            tools: If provided, sequence of dict, BaseModel, function, or BaseTools
+                to be converted to tool schemas.

        Returns:
            The sum of the number of tokens across the messages.
        """
+        if tools is not None:
+            warnings.warn(
+                "Counting tokens in tool schemas is not yet supported. Ignoring tools.",
+                stacklevel=2,
+            )
        return sum([self.get_num_tokens(get_buffer_string([m])) for m in messages])

    @classmethod
--- a/libs/core/poetry.lock
+++ b/libs/core/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 = "annotated-types"
@@ -1188,7 +1188,7 @@ files = [

 [[package]]
 name = "langchain-standard-tests"
-version = "0.1.1"
+version = "0.3.0"
 description = "Standard tests for LangChain implementations"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1197,7 +1197,7 @@ develop = true

 [package.dependencies]
 httpx = "^0.27.0"
-langchain-core = "^0.3.0"
+langchain-core = "^0.3.15"
 pytest = ">=7,<9"
 syrupy = "^4"

@@ -1207,7 +1207,7 @@ url = "../standard-tests"

 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.0"
+version = "0.3.2"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1215,7 +1215,7 @@ files = []
 develop = true

 [package.dependencies]
-langchain-core = "^0.3.0"
+langchain-core = "^0.3.15"

 [package.source]
 type = "directory"
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "langchain-core"
-version = "0.3.15"
+version = "0.3.17"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
--- a/libs/core/tests/unit_tests/messages/test_utils.py
+++ b/libs/core/tests/unit_tests/messages/test_utils.py
@@ -1,5 +1,8 @@
 import base64
 import json
+import typing
+from collections.abc import Sequence
+from typing import Any, Callable, Optional, Union

 import pytest

@@ -19,6 +22,7 @@ from langchain_core.messages.utils import (
    merge_message_runs,
    trim_messages,
 )
+from langchain_core.tools import BaseTool


@pytest.mark.parametrize("msg_cls", [HumanMessage, AIMessage, SystemMessage])
@@ -431,7 +435,15 @@ def dummy_token_counter(messages: list[BaseMessage]) -> int:


 class FakeTokenCountingModel(FakeChatModel):
-    def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int:
+    def get_num_tokens_from_messages(
+        self,
+        messages: list[BaseMessage],
+        tools: Optional[
+            Sequence[
+                Union[typing.Dict[str, Any], type, Callable, BaseTool]  # noqa: UP006
+            ]
+        ] = None,
+    ) -> int:
        return dummy_token_counter(messages)


--- a/libs/langchain/tests/unit_tests/chat_models/test_base.py
+++ b/libs/langchain/tests/unit_tests/chat_models/test_base.py
@@ -3,7 +3,6 @@ from unittest import mock

 import pytest
 from langchain_core.language_models import BaseChatModel
-from langchain_core.messages import HumanMessage
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import RunnableConfig, RunnableSequence
 from pydantic import SecretStr
@@ -180,9 +179,6 @@ def test_configurable_with_default() -> None:
    )

    assert model_with_config.model == "claude-3-sonnet-20240229"  # type: ignore[attr-defined]
-    # Anthropic defaults to using `transformers` for token counting.
-    with pytest.raises(ImportError):
-        model_with_config.get_num_tokens_from_messages([(HumanMessage("foo"))])  # type: ignore[attr-defined]

    assert model_with_config.model_dump() == {  # type: ignore[attr-defined]
        "name": None,
--- a/libs/partners/anthropic/langchain_anthropic/chat_models.py
+++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py
@@ -21,7 +21,7 @@ from typing import (
 )

 import anthropic
-from langchain_core._api import deprecated
+from langchain_core._api import beta, deprecated
 from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
@@ -1113,6 +1113,41 @@ class ChatAnthropic(BaseChatModel):
        else:
            return llm | output_parser

+    @beta()
+    def get_num_tokens_from_messages(
+        self,
+        messages: List[BaseMessage],
+        tools: Optional[
+            Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
+        ] = None,
+    ) -> int:
+        """Count tokens in a sequence of input messages.
+
+        Args:
+            messages: The message inputs to tokenize.
+            tools: If provided, sequence of dict, BaseModel, function, or BaseTools
+                to be converted to tool schemas.
+
+        .. versionchanged:: 0.3.0
+
+                Uses Anthropic's token counting API to count tokens in messages. See:
+                https://docs.anthropic.com/en/docs/build-with-claude/token-counting
+        """
+        formatted_system, formatted_messages = _format_messages(messages)
+        kwargs: Dict[str, Any] = {}
+        if isinstance(formatted_system, str):
+            kwargs["system"] = formatted_system
+        if tools:
+            kwargs["tools"] = [convert_to_anthropic_tool(tool) for tool in tools]
+
+        response = self._client.beta.messages.count_tokens(
+            betas=["token-counting-2024-11-01"],
+            model=self.model,
+            messages=formatted_messages,  # type: ignore[arg-type]
+            **kwargs,
+        )
+        return response.input_tokens
+

 class AnthropicTool(TypedDict):
    """Anthropic tool definition."""
--- a/libs/partners/anthropic/langchain_anthropic/llms.py
+++ b/libs/partners/anthropic/langchain_anthropic/llms.py
@@ -109,7 +109,6 @@ class _AnthropicCommon(BaseLanguageModel):
        )
        self.HUMAN_PROMPT = anthropic.HUMAN_PROMPT
        self.AI_PROMPT = anthropic.AI_PROMPT
-        self.count_tokens = self.client.count_tokens
        return self

    @property
@@ -375,9 +374,11 @@ class AnthropicLLM(LLM, _AnthropicCommon):

    def get_num_tokens(self, text: str) -> int:
        """Calculate number of tokens."""
-        if not self.count_tokens:
-            raise NameError("Please ensure the anthropic package is loaded")
-        return self.count_tokens(text)
+        raise NotImplementedError(
+            "Anthropic's legacy count_tokens method was removed in anthropic 0.39.0 "
+            "and langchain-anthropic 0.3.0. Please use "
+            "ChatAnthropic.get_num_tokens_from_messages instead."
+        )


@deprecated(since="0.1.0", removal="0.3.0", alternative="AnthropicLLM")
--- a/libs/partners/anthropic/poetry.lock
+++ b/libs/partners/anthropic/poetry.lock
--- a/libs/partners/anthropic/pyproject.toml
+++ b/libs/partners/anthropic/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "langchain-anthropic"
-version = "0.2.4"
+version = "0.3.0"
 description = "An integration package connecting AnthropicMessages and LangChain"
 authors = []
 readme = "README.md"
@@ -20,7 +20,7 @@ disallow_untyped_defs = "True"

 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-anthropic = ">=0.30.0,<1"
+anthropic = ">=0.39.0,<1"
 langchain-core = "^0.3.15"
 pydantic = "^2.7.4"

--- a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py
+++ b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py
@@ -317,7 +317,7 @@ async def test_anthropic_async_streaming_callback() -> None:
 def test_anthropic_multimodal() -> None:
    """Test that multimodal inputs are handled correctly."""
    chat = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
-    messages = [
+    messages: list[BaseMessage] = [
        HumanMessage(
            content=[
                {
@@ -334,6 +334,8 @@ def test_anthropic_multimodal() -> None:
    response = chat.invoke(messages)
    assert isinstance(response, AIMessage)
    assert isinstance(response.content, str)
+    num_tokens = chat.get_num_tokens_from_messages(messages)
+    assert num_tokens > 0


 def test_streaming() -> None:
@@ -505,6 +507,60 @@ def test_with_structured_output() -> None:
    assert response["location"]


+def test_get_num_tokens_from_messages() -> None:
+    llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")  # type: ignore[call-arg]
+
+    # Test simple case
+    messages = [
+        SystemMessage(content="You are a scientist"),
+        HumanMessage(content="Hello, Claude"),
+    ]
+    num_tokens = llm.get_num_tokens_from_messages(messages)
+    assert num_tokens > 0
+
+    # Test tool use
+    @tool(parse_docstring=True)
+    def get_weather(location: str) -> str:
+        """Get the current weather in a given location
+
+        Args:
+            location: The city and state, e.g. San Francisco, CA
+        """
+        return "Sunny"
+
+    messages = [
+        HumanMessage(content="What's the weather like in San Francisco?"),
+    ]
+    num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
+    assert num_tokens > 0
+
+    messages = [
+        HumanMessage(content="What's the weather like in San Francisco?"),
+        AIMessage(
+            content=[
+                {"text": "Let's see.", "type": "text"},
+                {
+                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
+                    "input": {"location": "SF"},
+                    "name": "get_weather",
+                    "type": "tool_use",
+                },
+            ],
+            tool_calls=[
+                {
+                    "name": "get_weather",
+                    "args": {"location": "SF"},
+                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
+                    "type": "tool_call",
+                },
+            ],
+        ),
+        ToolMessage(content="Sunny", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"),
+    ]
+    num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
+    assert num_tokens > 0
+
+
 class GetWeather(BaseModel):
    """Get the current weather in a given location"""

--- a/libs/partners/anthropic/tests/unit_tests/test_chat_models.py
+++ b/libs/partners/anthropic/tests/unit_tests/test_chat_models.py
@@ -331,7 +331,7 @@ def dummy_tool() -> BaseTool:
        arg1: int = Field(..., description="foo")
        arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'")

-    class DummyFunction(BaseTool):
+    class DummyFunction(BaseTool):  # type: ignore[override]
        args_schema: Type[BaseModel] = Schema
        name: str = "dummy_function"
        description: str = "dummy function"
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -632,24 +632,6 @@ class BaseChatOpenAI(BaseChatModel):
        default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
        base_generation_info = {}

-        if "response_format" in payload and is_basemodel_subclass(
-            payload["response_format"]
-        ):
-            # TODO: Add support for streaming with Pydantic response_format.
-            warnings.warn("Streaming with Pydantic response_format not yet supported.")
-            chat_result = self._generate(
-                messages, stop, run_manager=run_manager, **kwargs
-            )
-            msg = chat_result.generations[0].message
-            yield ChatGenerationChunk(
-                message=AIMessageChunk(
-                    **msg.dict(exclude={"type", "additional_kwargs"}),
-                    # preserve the "parsed" Pydantic object without converting to dict
-                    additional_kwargs=msg.additional_kwargs,
-                ),
-                generation_info=chat_result.generations[0].generation_info,
-            )
-            return
        if self.include_response_headers:
            raw_response = self.client.with_raw_response.create(**payload)
            response = raw_response.parse()
@@ -783,24 +765,6 @@ class BaseChatOpenAI(BaseChatModel):
        payload = self._get_request_payload(messages, stop=stop, **kwargs)
        default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
        base_generation_info = {}
-        if "response_format" in payload and is_basemodel_subclass(
-            payload["response_format"]
-        ):
-            # TODO: Add support for streaming with Pydantic response_format.
-            warnings.warn("Streaming with Pydantic response_format not yet supported.")
-            chat_result = await self._agenerate(
-                messages, stop, run_manager=run_manager, **kwargs
-            )
-            msg = chat_result.generations[0].message
-            yield ChatGenerationChunk(
-                message=AIMessageChunk(
-                    **msg.dict(exclude={"type", "additional_kwargs"}),
-                    # preserve the "parsed" Pydantic object without converting to dict
-                    additional_kwargs=msg.additional_kwargs,
-                ),
-                generation_info=chat_result.generations[0].generation_info,
-            )
-            return
        if self.include_response_headers:
            raw_response = await self.async_client.with_raw_response.create(**payload)
            response = raw_response.parse()
@@ -922,8 +886,13 @@ class BaseChatOpenAI(BaseChatModel):
        _, encoding_model = self._get_encoding_model()
        return encoding_model.encode(text)

-    # TODO: Count bound tools as part of input.
-    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+    def get_num_tokens_from_messages(
+        self,
+        messages: List[BaseMessage],
+        tools: Optional[
+            Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
+        ] = None,
+    ) -> int:
        """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.

        **Requirements**: You must have the ``pillow`` installed if you want to count
@@ -933,7 +902,18 @@ class BaseChatOpenAI(BaseChatModel):
        counting.

        OpenAI reference: https://github.com/openai/openai-cookbook/blob/
-        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
+        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
+
+        Args:
+            messages: The message inputs to tokenize.
+            tools: If provided, sequence of dict, BaseModel, function, or BaseTools
+                to be converted to tool schemas.
+        """
+        # TODO: Count bound tools as part of input.
+        if tools is not None:
+            warnings.warn(
+                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
+            )
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
@@ -999,6 +979,28 @@ class BaseChatOpenAI(BaseChatModel):
        num_tokens += 3
        return num_tokens

+    def _should_stream(
+        self,
+        *,
+        async_api: bool,
+        run_manager: Optional[
+            Union[CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun]
+        ] = None,
+        response_format: Optional[Union[dict, type]] = None,
+        **kwargs: Any,
+    ) -> bool:
+        if isinstance(response_format, type) and is_basemodel_subclass(response_format):
+            # TODO: Add support for streaming with Pydantic response_format.
+            warnings.warn("Streaming with Pydantic response_format not yet supported.")
+            return False
+        if self.model_name is not None and self.model_name.startswith("o1"):
+            # TODO: Add support for streaming with o1 once supported.
+            return False
+
+        return super()._should_stream(
+            async_api=async_api, run_manager=run_manager, **kwargs
+        )
+
    @deprecated(
        since="0.2.1",
        alternative="langchain_openai.chat_models.base.ChatOpenAI.bind_tools",
--- a/libs/partners/openai/poetry.lock
+++ b/libs/partners/openai/poetry.lock
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 python-versions = ">=3.9,<4.0"
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 [package.dependencies]
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 [package.extras]
 dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
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 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
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+    {file = "watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a"},
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+    {file = "watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f"},
+    {file = "watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282"},
 ]

 [package.extras]
@@ -1561,4 +1562,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "77af861c052decd1c194936575c15491a606fd763556b427e2c659d5ea7aae72"
+content-hash = "193b209048042991c70a06289c41e67d44cc104c970a70857beacc8a51f0459b"
--- a/libs/partners/openai/pyproject.toml
+++ b/libs/partners/openai/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "langchain-openai"
-version = "0.2.6"
+version = "0.2.8"
 description = "An integration package connecting OpenAI and LangChain"
 authors = []
 readme = "README.md"
@@ -23,7 +23,7 @@ ignore_missing_imports = true

 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.15"
+langchain-core = "^0.3.17"
 openai = "^1.54.0"
 tiktoken = ">=0.7,<1"

--- a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
@@ -1061,3 +1061,27 @@ def test_prediction_tokens() -> None:
    ]
    assert output_token_details["accepted_prediction_tokens"] > 0
    assert output_token_details["rejected_prediction_tokens"] > 0
+
+
+def test_stream_o1() -> None:
+    list(ChatOpenAI(model="o1-mini").stream("how are you"))
+
+
+async def test_astream_o1() -> None:
+    async for _ in ChatOpenAI(model="o1-mini").astream("how are you"):
+        pass
+
+
+class Foo(BaseModel):
+    response: str
+
+
+def test_stream_response_format() -> None:
+    list(ChatOpenAI(model="gpt-4o-mini").stream("how are ya", response_format=Foo))
+
+
+async def test_astream_response_format() -> None:
+    async for _ in ChatOpenAI(model="gpt-4o-mini").astream(
+        "how are ya", response_format=Foo
+    ):
+        pass
Author	SHA1	Message	Date
ccurme	5eaa0e8c45	openai[patch]: release 0.2.8 (#28062 )	2024-11-12 14:57:11 -05:00
ccurme	15b7dd3ad7	community[patch]: release 0.3.7 (#28061 )	2024-11-12 19:54:58 +00:00
ccurme	5460096086	core[patch]: release 0.3.17 (#28060 )	2024-11-12 19:38:56 +00:00
ccurme	1538ee17f9	anthropic[major]: support python 3.13 (#27916 ) Last week Anthropic released version 0.39.0 of its python sdk, which enabled support for Python 3.13. This release deleted a legacy `client.count_tokens` method, which we currently access during init of the `Anthropic` LLM. Anthropic has replaced this functionality with the [client.beta.messages.count_tokens() API](https://github.com/anthropics/anthropic-sdk-python/pull/726). To enable support for `anthropic >= 0.39.0` and Python 3.13, here we drop support for the legacy token counting method, and add support for the new method via `ChatAnthropic.get_num_tokens_from_messages`. To fully support the token counting API, we update the signature of `get_num_tokens_from_message` to accept tools everywhere. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	2024-11-12 14:31:07 -05:00
Syed Hyder Zaidi	759b6ed17a	docs: Fix typo in Tavily Search example (#28034 ) Changed "demon" to "demo" in the code comment for clarity. PR Title docs: Fix typo in Tavily Search example PR Message Description: This PR fixes a typo in the code comment of the Tavily Search documentation. Changed "demon" to "demo" for clarity and to avoid confusion. Issue: No specific issue was mentioned, but this is a minor improvement in documentation. Dependencies: No additional dependencies required.	2024-11-12 13:58:13 -05:00
ZhangShenao	ca7375ac20	Improvement[Community]Improve Embeddings API (#28038 ) - Fix `BaichuanTextEmbeddings` api url - Remove unused params in api doc - Fix word spelling	2024-11-12 13:57:35 -05:00
Aditya Anand	e290736696	Update streaming.mdx (#28055 ) fix: correct grammar in documentation for streaming modes Updated sentence to clarify usage of "choose" in "When using the stream and astream methods with LangGraph, you can choose one or more streaming modes..." for better readability. 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" - [ ] PR message: *Delete this entire checklist* and replace with - Description: a description of the change - Issue: the issue # it fixes, if applicable - Dependencies: any dependencies required for this change - Twitter handle: if your PR gets announced, and you'd like a mention, we'll gladly shout you out! - [ ] Add tests and docs: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [ ] Lint and test: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ 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.	2024-11-12 16:43:12 +00:00
Aditya Anand	f9212c77e7	DOC: Fix typo in documentation for streaming modes, correcting 'witte… (#28052 ) …n' to 'written' in 'Emit custom output written using LangGraph’s StreamWriter.' ### Changes: - Corrected the typo in the phrase 'Emit custom output witten using LangGraph’s StreamWriter.' to 'Emit custom output written using LangGraph’s StreamWriter.' - Enhanced the clarity of the documentation surrounding LangGraph’s streaming modes, specifically around the StreamWriter functionality. - Provided additional context and emphasis on the role of the StreamWriter class in handling custom output. ### Issue Reference: - GitHub issue: https://github.com/langchain-ai/langchain/issues/28051 This update addresses the issue raised regarding the incorrect spelling and aims to improve the clarity of the streaming mode documentation for better user understanding. 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" - [ ] PR message: Description: Fixed a typo in the documentation for streaming modes, changing "witten" to "written" in the phrase "Emit custom output witten using LangGraph’s StreamWriter." Issue: This PR addresses and fixes the typo in the documentation referenced in [#28051](https://github.com/langchain-ai/langchain/issues/28051). Issue: This PR addresses and fixes the typo in the documentation referenced in [#28051](https://github.com/langchain-ai/langchain/issues/28051). - [ ] Add tests and docs: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [ ] Lint and test: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ 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.	2024-11-12 11:42:30 -05:00
Bagatur	139881b108	openai[patch]: fix azure oai stream check (#28048 )	2024-11-12 15:42:06 +00:00
Bagatur	9611f0b55d	openai[patch]: Release 0.2.7 (#28047 )	2024-11-12 15:16:15 +00:00
Bagatur	5c14e1f935	community[patch]: Release 0.3.6 (#28046 )	2024-11-12 15:15:07 +00:00
Bagatur	9ebd7ebed8	core[patch]: Release 0.3.16 (#28045 )	2024-11-12 14:57:15 +00:00
Changyong Um	9484cc0962	community[docs]: modify parameter for the LoRA adapter on the vllm page (#27930 ) Description: This PR modifies the documentation regarding the configuration of the VLLM with the LoRA adapter. The updates aim to provide clear instructions for users on how to set up the LoRA adapter when using the VLLM. - before ```python VLLM(..., enable_lora=True) ``` - after ```python VLLM(..., vllm_kwargs={ "enable_lora": True } ) ``` This change clarifies that users should use the vllm_kwargs to enable the LoRA adapter. Co-authored-by: Um Changyong <changyong.um@sfa.co.kr>	2024-11-11 15:41:56 -05:00
Zapiron	0b85f9035b	docs: Makes the phrasing more smooth and reasoning more clear (#28020 ) Updated the phrasing and reasoning on the "abstraction not receiving much development" part of the documentation --------- Co-authored-by: ccurme <chester.curme@gmail.com>	2024-11-11 17:17:29 +00:00
Zapiron	f695b96484	docs:Fixed missing hyperlink and changed AI to LLMs for clarity (#28006 ) Changed "AI" to "LLM" in a paragraph Fixed missing hyperlink for the structured output point	2024-11-11 12:14:29 -05:00
Choy Fuguan	c0f3777657	docs: removed bolding from header (#28001 ) removed extra ** after heading two	2024-11-11 12:13:02 -05:00
Salman Faroz	44df79cf52	Correcting AzureOpenAI initialization (#28014 )	2024-11-11 12:10:59 -05:00
Hammad Randhawa	57fc62323a	docs : Update sql_qa.ipynb (#28026 ) Text Documentation Bug: Changed DSL query to SQL query.	2024-11-11 12:04:09 -05:00
ccurme	922b6b0e46	docs: update some cassettes (#28010 )	2024-11-09 21:04:18 +00:00
ccurme	8e91c7ceec	docs: add cross-links (#28000 ) Mainly to improve visibility of integration pages.	2024-11-09 08:57:58 -05:00
Bagatur	33dbfba08b	openai[patch]: default to invoke on o1 stream() (#27983 )	2024-11-08 19:12:59 -08:00
Bagatur	503f2487a5	docs: intro nit (#27998 )	2024-11-08 11:51:17 -08:00
ccurme	ff2152b115	docs: update tutorials index and add get started guides (#27996 )	2024-11-08 14:47:32 -05:00
Eric Pinzur	c421997caa	community[patch]: Added type hinting to OpenSearch clients (#27946 ) Description: * When working with OpenSearchVectorSearch to make OpenSearchGraphVectorStore (coming soon), I noticed that there wasn't type hinting for the underlying OpenSearch clients. This fixes that issue. * Confirmed tests are still passing with code changes. Note that there is some additional code duplication now, but I think this approach is cleaner overall.	2024-11-08 11:04:57 -08:00