github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-05-09 09:08:40 +00:00
Code Issues Packages Projects Releases Wiki Activity
erick/neo4j-migrate-to-repo
langchain/docs/docs/how_to/custom_chat_model.ipynb
Eugene Yurtsev 4466caadba
concepts: update llm stub page and re-link (#27567) 
Update text llm stub page and re-link content
2024-10-22 23:03:36 -04:00

570 lines
22 KiB
Plaintext
Raw Permalink Blame History

{
"cells": [
{
"cell_type": "markdown",
"id": "e3da9a3f-f583-4ba6-994e-0e8c1158f5eb",
"metadata": {},
"source": [
"# How to create a custom chat model class\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"\n",
":::\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 [`BaseChatModel`](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) 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](/docs/concepts/messages):\n",
"\n",
"| Message Type | Description |\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",
"| `AIMessageChunk` / `HumanMessageChunk` / ... | Chunk variant of each type of message. |\n",
"\n",
"\n",
":::note\n",
"`ToolMessage` and `FunctionMessage` closely follow OpenAI's `function` and `tool` roles.\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": {
"tags": []
},
"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": {
"tags": []
},
"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": {
"tags": []
},
"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": "bbfebea1",
"metadata": {},
"source": [
"## Base Chat Model\n",
"\n",
"Let's implement a chat model that echoes back the first `n` characters 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:\n",
"\n",
"| Method/Property | Description | Required/Optional |\n",
"|------------------------------------|-------------------------------------------------------------------|--------------------|\n",
"| `_generate` | Use to generate a chat result from a prompt | Required |\n",
"| `_llm_type` (property) | Used to uniquely identify the type of the model. Used for logging.| Required |\n",
"| `_identifying_params` (property) | Represent model parameterization for tracing purposes. | Optional |\n",
"| `_stream` | Use to implement streaming. | Optional |\n",
"| `_agenerate` | Use to implement a native async method. | Optional |\n",
"| `_astream` | Use to implement async version of `_stream`. | Optional |\n",
"\n",
"\n",
":::tip\n",
"The `_astream` implementation uses `run_in_executor` to launch the sync `_stream` in a separate thread if `_stream` is implemented, otherwise it fallsback to use `_agenerate`.\n",
"\n",
"You can use this trick if you want to reuse the `_stream` implementation, but if you're able to implement code that's natively async that's a better solution since that code will run with less overhead.\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": {
"tags": []
},
"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",
" model_name: str\n",
" \"\"\"The name of the model\"\"\"\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",
" # Replace this with actual logic to generate a response from a list\n",
" # of messages.\n",
" last_message = messages[-1]\n",
" tokens = last_message.content[: self.n]\n",
" message = AIMessage(\n",
" content=tokens,\n",
" additional_kwargs={}, # Used to add additional payload (e.g., function calling request)\n",
" response_metadata={ # Use for response metadata\n",
" \"time_in_seconds\": 3,\n",
" },\n",
" )\n",
" ##\n",
"\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",
" # This is optional in newer versions of LangChain\n",
" # The on_llm_new_token will be called automatically\n",
" run_manager.on_llm_new_token(token, chunk=chunk)\n",
"\n",
" yield chunk\n",
"\n",
" # Let's add some other information (e.g., response metadata)\n",
" chunk = ChatGenerationChunk(\n",
" message=AIMessageChunk(content=\"\", response_metadata={\"time_in_sec\": 3})\n",
" )\n",
" if run_manager:\n",
" # This is optional in newer versions of LangChain\n",
" # The on_llm_new_token will be called automatically\n",
" run_manager.on_llm_new_token(token, chunk=chunk)\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",
"\n",
" This information is used by the LangChain callback system, which\n",
" is used for tracing purposes make it possible to monitor LLMs.\n",
" \"\"\"\n",
" return {\n",
" # The model name allows users to specify custom token counting\n",
" # rules in LLM monitoring applications (e.g., in LangSmith users\n",
" # can provide per token pricing for their model and monitor\n",
" # costs for the given LLM.)\n",
" \"model_name\": self.model_name,\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": 6,
"id": "27689f30-dcd2-466b-ba9d-f60b7d434110",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Meo', response_metadata={'time_in_seconds': 3}, id='run-ddb42bd6-4fdd-4bd2-8be5-e11b67d3ac29-0')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = CustomChatModelAdvanced(n=3, model_name=\"my_custom_model\")\n",
"\n",
"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": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='hel', response_metadata={'time_in_seconds': 3}, id='run-4d3cc912-44aa-454b-977b-ca02be06c12e-0')"
]
},
"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": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[AIMessage(content='hel', response_metadata={'time_in_seconds': 3}, id='run-9620e228-1912-4582-8aa1-176813afec49-0'),\n",
" AIMessage(content='goo', response_metadata={'time_in_seconds': 3}, id='run-1ce8cdf8-6f75-448e-82f7-1bb4a121df93-0')]"
]
},
"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": {
"tags": []
},
"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": {
"tags": []
},
"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": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_chat_model_start', 'run_id': '125a2a16-b9cd-40de-aa08-8aa9180b07d0', 'name': 'CustomChatModelAdvanced', 'tags': [], 'metadata': {}, 'data': {'input': 'cat'}}\n",
"{'event': 'on_chat_model_stream', 'run_id': '125a2a16-b9cd-40de-aa08-8aa9180b07d0', 'tags': [], 'metadata': {}, 'name': 'CustomChatModelAdvanced', 'data': {'chunk': AIMessageChunk(content='c', id='run-125a2a16-b9cd-40de-aa08-8aa9180b07d0')}}\n",
"{'event': 'on_chat_model_stream', 'run_id': '125a2a16-b9cd-40de-aa08-8aa9180b07d0', 'tags': [], 'metadata': {}, 'name': 'CustomChatModelAdvanced', 'data': {'chunk': AIMessageChunk(content='a', id='run-125a2a16-b9cd-40de-aa08-8aa9180b07d0')}}\n",
"{'event': 'on_chat_model_stream', 'run_id': '125a2a16-b9cd-40de-aa08-8aa9180b07d0', 'tags': [], 'metadata': {}, 'name': 'CustomChatModelAdvanced', 'data': {'chunk': AIMessageChunk(content='t', id='run-125a2a16-b9cd-40de-aa08-8aa9180b07d0')}}\n",
"{'event': 'on_chat_model_stream', 'run_id': '125a2a16-b9cd-40de-aa08-8aa9180b07d0', 'tags': [], 'metadata': {}, 'name': 'CustomChatModelAdvanced', 'data': {'chunk': AIMessageChunk(content='', response_metadata={'time_in_sec': 3}, id='run-125a2a16-b9cd-40de-aa08-8aa9180b07d0')}}\n",
"{'event': 'on_chat_model_end', 'name': 'CustomChatModelAdvanced', 'run_id': '125a2a16-b9cd-40de-aa08-8aa9180b07d0', 'tags': [], 'metadata': {}, 'data': {'output': AIMessageChunk(content='cat', response_metadata={'time_in_sec': 3}, id='run-125a2a16-b9cd-40de-aa08-8aa9180b07d0')}}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: 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": "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://python.langchain.com/api_reference/langchain/index.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",
"\n",
"Streaming (if you're implementing it):\n",
"\n",
"* [ ] Implement the _stream method to get streaming working\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.\n",
"\n",
"\n",
"Identifying Params:\n",
"\n",
"* [ ] Include a `model_name` in identifying params\n",
"\n",
"\n",
"Optimizations:\n",
"\n",
"Consider providing native async support to reduce the overhead from the model!\n",
" \n",
"* [ ] Provided a native async of `_agenerate` (used by `ainvoke`)\n",
"* [ ] Provided a native async of `_astream` (used by `astream`)\n",
"\n",
"## Next steps\n",
"\n",
"You've now learned how to create your own custom chat models.\n",
"\n",
"Next, check out the other how-to guides chat models in this section, like [how to get a model to return structured output](/docs/how_to/structured_output) or [how to track chat model token usage](/docs/how_to/chat_token_usage_tracking)."
]
}
],
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue View Git Blame Copy Permalink