From 865cabff052fe74996bef45faaf00df6f322c215 Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Mon, 19 Feb 2024 22:03:49 -0500
Subject: [PATCH] Docs: Add custom chat model documenation (#17595)

This PR adds documentation about how to implement a custom chat model.
---
 .../model_io/chat/custom_chat_model.ipynb     | 644 ++++++++++++++++++
 docs/docs/modules/model_io/chat/index.mdx     |   9 +-
 .../language_models/chat_models.py            |   2 +-
 3 files changed, 651 insertions(+), 4 deletions(-)
 create mode 100644 docs/docs/modules/model_io/chat/custom_chat_model.ipynb

diff --git a/docs/docs/modules/model_io/chat/custom_chat_model.ipynb b/docs/docs/modules/model_io/chat/custom_chat_model.ipynb
new file mode 100644
index 0000000000000..b91ca4cfd4333
--- /dev/null
+++ b/docs/docs/modules/model_io/chat/custom_chat_model.ipynb
@@ -0,0 +1,644 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e3da9a3f-f583-4ba6-994e-0e8c1158f5eb",
+   "metadata": {},
+   "source": [
+    "# Custom Chat Model\n",
+    "\n",
+    "In this guide, we'll learn how to create a custom chat model using LangChain abstractions.\n",
+    "\n",
+    "Wrapping your LLM with the standard `ChatModel` interface allow you to use your LLM in existing LangChain programs with minimal code modifications!\n",
+    "\n",
+    "As an bonus, your LLM will automatically become a LangChain `Runnable` and will benefit from some optimizations out of the box (e.g., batch via a threadpool), async support, the `astream_events` API, etc.\n",
+    "\n",
+    "## Inputs and outputs\n",
+    "\n",
+    "First, we need to talk about messages which are the inputs and outputs of chat models.\n",
+    "\n",
+    "### Messages\n",
+    "\n",
+    "Chat models take messages as inputs and return a message as output. \n",
+    "\n",
+    "LangChain has a few built-in message types:\n",
+    "\n",
+    "- `SystemMessage`: Used for priming AI behavior, usually passed in as the first of a sequence of input messages.\n",
+    "- `HumanMessage`: Represents a message from a person interacting with the chat model.\n",
+    "- `AIMessage`: Represents a message from the chat model. This can be either text or a request to invoke a tool.\n",
+    "- `FunctionMessage` / `ToolMessage`: Message for passing the results of tool invocation back to the model.\n",
+    "\n",
+    "::: {.callout-note}\n",
+    "`ToolMessage` and `FunctionMessage` closely follow OpenAIs `function` and `tool` arguments.\n",
+    "\n",
+    "This is a rapidly developing field and as more models add function calling capabilities, expect that there will be additions to this schema.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c5046e6a-8b09-4a99-b6e6-7a605aac5738",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import (\n",
+    "    AIMessage,\n",
+    "    BaseMessage,\n",
+    "    FunctionMessage,\n",
+    "    HumanMessage,\n",
+    "    SystemMessage,\n",
+    "    ToolMessage,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53033447-8260-4f53-bd6f-b2f744e04e75",
+   "metadata": {},
+   "source": [
+    "### Streaming Variant\n",
+    "\n",
+    "All the chat messages have a streaming variant that contains `Chunk` in the name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d4656e9d-bfa1-4703-8f79-762fe6421294",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import (\n",
+    "    AIMessageChunk,\n",
+    "    FunctionMessageChunk,\n",
+    "    HumanMessageChunk,\n",
+    "    SystemMessageChunk,\n",
+    "    ToolMessageChunk,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81ebf3f4-c760-4898-b921-fdb469453d4a",
+   "metadata": {},
+   "source": [
+    "These chunks are used when streaming output from chat models, and they all define an additive property!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9c15c299-6f8a-49cf-a072-09924fd44396",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessageChunk(content='Hello World!')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AIMessageChunk(content=\"Hello\") + AIMessageChunk(content=\" World!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e952d64-6d38-4a2b-b996-8812c204a12c",
+   "metadata": {},
+   "source": [
+    "## Simple Chat Model\n",
+    "\n",
+    "Inherting from `SimpleChatModel` is great for prototyping!\n",
+    "\n",
+    "It won't allow you to implement all features that you might want out of a chat model, but it's quick to implement, and if you need more you can transition to `BaseChatModel` shown below.\n",
+    "\n",
+    "Let's implement a chat model that echoes back the last `n` characters of the prompt!\n",
+    "\n",
+    "You need to implement the following:\n",
+    "\n",
+    "* The method `_call` - Use to generate a chat result from a prompt.\n",
+    "\n",
+    "In addition, you have the option to specify the following:\n",
+    "\n",
+    "* The property `_identifying_params` - Represent model parameterization for logging purposes.\n",
+    "\n",
+    "Optional:\n",
+    "\n",
+    "* `_stream` - Use to implement streaming.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbfebea1",
+   "metadata": {},
+   "source": [
+    "## Base Chat Model\n",
+    "\n",
+    "Let's implement a chat model that echoes back the first `n` characetrs of the last message in the prompt!\n",
+    "\n",
+    "To do so, we will inherit from `BaseChatModel` and we'll need to implement the following methods/properties:\n",
+    "\n",
+    "In addition, you have the option to specify the following:\n",
+    "\n",
+    "To do so inherit from `BaseChatModel` which is a lower level class and implement the methods:\n",
+    "\n",
+    "* `_generate` - Use to generate a chat result from a prompt\n",
+    "* The property `_llm_type` - Used to uniquely identify the type of the model. Used for logging.\n",
+    "\n",
+    "Optional:\n",
+    "\n",
+    "* `_stream` - Use to implement streaming.\n",
+    "* `_agenerate` - Use to implement a native async method.\n",
+    "* `_astream` - Use to implement async version of `_stream`.\n",
+    "* The property `_identifying_params` - Represent model parameterization for logging purposes.\n",
+    "\n",
+    "\n",
+    ":::{.callout-caution}\n",
+    "\n",
+    "Currently, to get async streaming to work (via `astream`), you must provide an implementation of `_astream`.\n",
+    "\n",
+    "By default if `_astream` is not provided, then async streaming falls back on `_agenerate` which does not support\n",
+    "token by token streaming.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e7047bd-c235-46f6-85e1-d6d7e0868eb1",
+   "metadata": {},
+   "source": [
+    "### Implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25ba32e5-5a6d-49f4-bb68-911827b84d61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Any, AsyncIterator, Dict, Iterator, List, Optional\n",
+    "\n",
+    "from langchain_core.callbacks import (\n",
+    "    AsyncCallbackManagerForLLMRun,\n",
+    "    CallbackManagerForLLMRun,\n",
+    ")\n",
+    "from langchain_core.language_models import BaseChatModel, SimpleChatModel\n",
+    "from langchain_core.messages import AIMessageChunk, BaseMessage, HumanMessage\n",
+    "from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult\n",
+    "from langchain_core.runnables import run_in_executor\n",
+    "\n",
+    "\n",
+    "class CustomChatModelAdvanced(BaseChatModel):\n",
+    "    \"\"\"A custom chat model that echoes the first `n` characters of the input.\n",
+    "\n",
+    "    When contributing an implementation to LangChain, carefully document\n",
+    "    the model including the initialization parameters, include\n",
+    "    an example of how to initialize the model and include any relevant\n",
+    "    links to the underlying models documentation or API.\n",
+    "\n",
+    "    Example:\n",
+    "\n",
+    "        .. code-block:: python\n",
+    "\n",
+    "            model = CustomChatModel(n=2)\n",
+    "            result = model.invoke([HumanMessage(content=\"hello\")])\n",
+    "            result = model.batch([[HumanMessage(content=\"hello\")],\n",
+    "                                 [HumanMessage(content=\"world\")]])\n",
+    "    \"\"\"\n",
+    "\n",
+    "    n: int\n",
+    "    \"\"\"The number of characters from the last message of the prompt to be echoed.\"\"\"\n",
+    "\n",
+    "    def _generate(\n",
+    "        self,\n",
+    "        messages: List[BaseMessage],\n",
+    "        stop: Optional[List[str]] = None,\n",
+    "        run_manager: Optional[CallbackManagerForLLMRun] = None,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> ChatResult:\n",
+    "        \"\"\"Override the _generate method to implement the chat model logic.\n",
+    "\n",
+    "        This can be a call to an API, a call to a local model, or any other\n",
+    "        implementation that generates a response to the input prompt.\n",
+    "\n",
+    "        Args:\n",
+    "            messages: the prompt composed of a list of messages.\n",
+    "            stop: a list of strings on which the model should stop generating.\n",
+    "                  If generation stops due to a stop token, the stop token itself\n",
+    "                  SHOULD BE INCLUDED as part of the output. This is not enforced\n",
+    "                  across models right now, but it's a good practice to follow since\n",
+    "                  it makes it much easier to parse the output of the model\n",
+    "                  downstream and understand why generation stopped.\n",
+    "            run_manager: A run manager with callbacks for the LLM.\n",
+    "        \"\"\"\n",
+    "        last_message = messages[-1]\n",
+    "        tokens = last_message.content[: self.n]\n",
+    "        message = AIMessage(content=tokens)\n",
+    "        generation = ChatGeneration(message=message)\n",
+    "        return ChatResult(generations=[generation])\n",
+    "\n",
+    "    def _stream(\n",
+    "        self,\n",
+    "        messages: List[BaseMessage],\n",
+    "        stop: Optional[List[str]] = None,\n",
+    "        run_manager: Optional[CallbackManagerForLLMRun] = None,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> Iterator[ChatGenerationChunk]:\n",
+    "        \"\"\"Stream the output of the model.\n",
+    "\n",
+    "        This method should be implemented if the model can generate output\n",
+    "        in a streaming fashion. If the model does not support streaming,\n",
+    "        do not implement it. In that case streaming requests will be automatically\n",
+    "        handled by the _generate method.\n",
+    "\n",
+    "        Args:\n",
+    "            messages: the prompt composed of a list of messages.\n",
+    "            stop: a list of strings on which the model should stop generating.\n",
+    "                  If generation stops due to a stop token, the stop token itself\n",
+    "                  SHOULD BE INCLUDED as part of the output. This is not enforced\n",
+    "                  across models right now, but it's a good practice to follow since\n",
+    "                  it makes it much easier to parse the output of the model\n",
+    "                  downstream and understand why generation stopped.\n",
+    "            run_manager: A run manager with callbacks for the LLM.\n",
+    "        \"\"\"\n",
+    "        last_message = messages[-1]\n",
+    "        tokens = last_message.content[: self.n]\n",
+    "\n",
+    "        for token in tokens:\n",
+    "            chunk = ChatGenerationChunk(message=AIMessageChunk(content=token))\n",
+    "\n",
+    "            if run_manager:\n",
+    "                run_manager.on_llm_new_token(token, chunk=chunk)\n",
+    "\n",
+    "            yield chunk\n",
+    "\n",
+    "    async def _astream(\n",
+    "        self,\n",
+    "        messages: List[BaseMessage],\n",
+    "        stop: Optional[List[str]] = None,\n",
+    "        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> AsyncIterator[ChatGenerationChunk]:\n",
+    "        \"\"\"An async variant of astream.\n",
+    "\n",
+    "        If not provided, the default behavior is to delegate to the _generate method.\n",
+    "\n",
+    "        The implementation below instead will delegate to `_stream` and will\n",
+    "        kick it off in a separate thread.\n",
+    "\n",
+    "        If you're able to natively support async, then by all means do so!\n",
+    "        \"\"\"\n",
+    "        result = await run_in_executor(\n",
+    "            None,\n",
+    "            self._stream,\n",
+    "            messages,\n",
+    "            stop=stop,\n",
+    "            run_manager=run_manager.get_sync() if run_manager else None,\n",
+    "            **kwargs,\n",
+    "        )\n",
+    "        for chunk in result:\n",
+    "            yield chunk\n",
+    "\n",
+    "    @property\n",
+    "    def _llm_type(self) -> str:\n",
+    "        \"\"\"Get the type of language model used by this chat model.\"\"\"\n",
+    "        return \"echoing-chat-model-advanced\"\n",
+    "\n",
+    "    @property\n",
+    "    def _identifying_params(self) -> Dict[str, Any]:\n",
+    "        \"\"\"Return a dictionary of identifying parameters.\"\"\"\n",
+    "        return {\"n\": self.n}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3c3d030-8d8b-4891-962d-a2d39b331883",
+   "metadata": {},
+   "source": [
+    ":::{.callout-tip}\n",
+    "The `_astream` implementation uses `run_in_executor` to launch the sync `_stream` in a separate thread.\n",
+    "\n",
+    "You can use this trick if you want to reuse the `_stream` implementation, but if you're able to implement code\n",
+    "that's natively async that's a better solution since that code will run with less overhead.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e9af284-f2d3-44e2-ac6a-09b73d89ada3",
+   "metadata": {},
+   "source": [
+    "### Let's test it 🧪\n",
+    "\n",
+    "The chat model will implement the standard `Runnable` interface of LangChain which many of the LangChain abstractions support!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "34bf2d48-556a-48be-aee7-496fb02332f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = CustomChatModelAdvanced(n=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "27689f30-dcd2-466b-ba9d-f60b7d434110",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Meo')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.invoke(\n",
+    "    [\n",
+    "        HumanMessage(content=\"hello!\"),\n",
+    "        AIMessage(content=\"Hi there human!\"),\n",
+    "        HumanMessage(content=\"Meow!\"),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "406436df-31bf-466b-9c3d-39db9d6b6407",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='hel')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.invoke(\"hello\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a72ffa46-6004-41ef-bbe4-56fa17a029e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content='hel'), AIMessage(content='goo')]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.batch([\"hello\", \"goodbye\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3633be2c-2ea0-42f9-a72f-3b5240690b55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c|a|t|"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in model.stream(\"cat\"):\n",
+    "    print(chunk.content, end=\"|\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f8a7c42-aec4-4116-adf3-93133d409827",
+   "metadata": {},
+   "source": [
+    "Please see the implementation of `_astream` in the model! If you do not implement it, then no output will stream.!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d73995-eeab-48c6-a7d8-32c98ba29fc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c|a|t|"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in model.astream(\"cat\"):\n",
+    "    print(chunk.content, end=\"|\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80dc55b-d159-4527-9191-407a7c6d6042",
+   "metadata": {},
+   "source": [
+    "Let's try to use the astream events API which will also help double check that all the callbacks were implemented!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "17840eba-8ff4-4e73-8e4f-85f16eb1c9d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'event': 'on_chat_model_start', 'run_id': 'e03c0b21-521f-4cb4-a837-02fed65cf1cf', 'name': 'CustomChatModelAdvanced', 'tags': [], 'metadata': {}, 'data': {'input': 'cat'}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': 'e03c0b21-521f-4cb4-a837-02fed65cf1cf', 'tags': [], 'metadata': {}, 'name': 'CustomChatModelAdvanced', 'data': {'chunk': AIMessageChunk(content='c')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': 'e03c0b21-521f-4cb4-a837-02fed65cf1cf', 'tags': [], 'metadata': {}, 'name': 'CustomChatModelAdvanced', 'data': {'chunk': AIMessageChunk(content='a')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': 'e03c0b21-521f-4cb4-a837-02fed65cf1cf', 'tags': [], 'metadata': {}, 'name': 'CustomChatModelAdvanced', 'data': {'chunk': AIMessageChunk(content='t')}}\n",
+      "{'event': 'on_chat_model_end', 'name': 'CustomChatModelAdvanced', 'run_id': 'e03c0b21-521f-4cb4-a837-02fed65cf1cf', 'tags': [], 'metadata': {}, 'data': {'output': AIMessageChunk(content='cat')}}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n",
+      "  warn_beta(\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for event in model.astream_events(\"cat\", version=\"v1\"):\n",
+    "    print(event)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42f9553f-7d8c-4277-aeb4-d80d77839d90",
+   "metadata": {},
+   "source": [
+    "## Identifying Params\n",
+    "\n",
+    "LangChain has a callback system which allows implementing loggers to monitor the behavior of LLM applications.\n",
+    "\n",
+    "Remember the `_identifying_params` property from earlier? \n",
+    "\n",
+    "It's passed to the callback system and is accessible for user specified loggers.\n",
+    "\n",
+    "Below we'll implement a handler with just a single `on_chat_model_start` event to see where `_identifying_params` appears."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cc7e6b5f-711b-48aa-9ebe-92a13e230c37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---\n",
+      "On chat model start.\n",
+      "{'invocation_params': {'n': 3, '_type': 'echoing-chat-model-advanced', 'stop': ['woof']}, 'options': {'stop': ['woof']}, 'name': None, 'batch_size': 1}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='meo')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Union\n",
+    "from uuid import UUID\n",
+    "\n",
+    "from langchain_core.callbacks import AsyncCallbackHandler\n",
+    "from langchain_core.outputs import (\n",
+    "    ChatGenerationChunk,\n",
+    "    ChatResult,\n",
+    "    GenerationChunk,\n",
+    "    LLMResult,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "class SampleCallbackHandler(AsyncCallbackHandler):\n",
+    "    \"\"\"Async callback handler that handles callbacks from LangChain.\"\"\"\n",
+    "\n",
+    "    async def on_chat_model_start(\n",
+    "        self,\n",
+    "        serialized: Dict[str, Any],\n",
+    "        messages: List[List[BaseMessage]],\n",
+    "        *,\n",
+    "        run_id: UUID,\n",
+    "        parent_run_id: Optional[UUID] = None,\n",
+    "        tags: Optional[List[str]] = None,\n",
+    "        metadata: Optional[Dict[str, Any]] = None,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run when a chat model starts running.\"\"\"\n",
+    "        print(\"---\")\n",
+    "        print(\"On chat model start.\")\n",
+    "        print(kwargs)\n",
+    "\n",
+    "\n",
+    "model.invoke(\"meow\", stop=[\"woof\"], config={\"callbacks\": [SampleCallbackHandler()]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44ee559b-b1da-4851-8c97-420ab394aff9",
+   "metadata": {},
+   "source": [
+    "## Contributing\n",
+    "\n",
+    "We appreciate all chat model integration contributions. \n",
+    "\n",
+    "Here's a checklist to help make sure your contribution gets added to LangChain:\n",
+    "\n",
+    "Documentation:\n",
+    "\n",
+    "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [APIReference](https://api.python.langchain.com/en/stable/langchain_api_reference.html).\n",
+    "* The class doc-string for the model contains a link to the model API if the model is powered by a service.\n",
+    "\n",
+    "Tests:\n",
+    "\n",
+    "* [ ] Add unit or integration tests to the overridden methods. Verify that `invoke`, `ainvoke`, `batch`, `stream` work if you've over-ridden the corresponding code.\n",
+    "\n",
+    "Streaming (if you're implementing it):\n",
+    "\n",
+    "* [ ] Provided an async implementation via `_astream`\n",
+    "* [ ] Make sure to invoke the `on_llm_new_token` callback\n",
+    "* [ ] `on_llm_new_token` is invoked BEFORE yielding the chunk\n",
+    "\n",
+    "Stop Token Behavior:\n",
+    "\n",
+    "* [ ] Stop token should be respected\n",
+    "* [ ] Stop token should be INCLUDED as part of the response\n",
+    "\n",
+    "Secret API Keys:\n",
+    "\n",
+    "* [ ] If your model connects to an API it will likely accept API keys as part of its initialization. Use Pydantic's `SecretStr` type for secrets, so they don't get accidentally printed out when folks print the model."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/modules/model_io/chat/index.mdx b/docs/docs/modules/model_io/chat/index.mdx
index 2ebb19c93d671..058192951d8da 100644
--- a/docs/docs/modules/model_io/chat/index.mdx
+++ b/docs/docs/modules/model_io/chat/index.mdx
@@ -4,11 +4,13 @@ sidebar_position: 3
 
 # Chat Models
 
-ChatModels are a core component of LangChain.
-LangChain does not serve its own ChatModels, but rather provides a standard interface for interacting with many different models. To be specific, this interface is one that takes as input a list of messages and returns a message.
+Chat Models are a core component of LangChain.
 
+A chat model is a language model that uses chat messages as inputs and returns chat messages as outputs (as opposed to using plain text).
 
-There are lots of model providers (OpenAI, Cohere, Hugging Face, etc) - the `ChatModel` class is designed to provide a standard interface for all of them.
+LangChain has integrations with many model providers (OpenAI, Cohere, Hugging Face, etc.) and exposes a standard interface to interact with all of these models.
+
+LangChain allows you to use models in sync, async, batching and streaming modes and provides other features (e.g., caching) and more.
 
 ## [Quick Start](./quick_start)
 
@@ -27,3 +29,4 @@ This includes:
 - [How to use ChatModels that support function calling](./function_calling)
 - [How to stream responses from a ChatModel](./streaming)
 - [How to track token usage in a ChatModel call](./token_usage_tracking)
+- [How to creat a custom ChatModel](./custom_chat_model)
diff --git a/libs/core/langchain_core/language_models/chat_models.py b/libs/core/langchain_core/language_models/chat_models.py
index 6279116093e5f..fe622f61b0295 100644
--- a/libs/core/langchain_core/language_models/chat_models.py
+++ b/libs/core/langchain_core/language_models/chat_models.py
@@ -794,7 +794,7 @@ def dict(self, **kwargs: Any) -> Dict:
 
 
 class SimpleChatModel(BaseChatModel):
-    """Simple Chat Model."""
+    """A simplified implementation for a chat model to inherit from."""
 
     def _generate(
         self,