mirror of
https://github.com/hwchase17/langchain
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135 lines
5.1 KiB
Plaintext
135 lines
5.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9418c7ff",
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"metadata": {},
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"source": [
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"# Async callbacks\n",
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"\n",
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"If you are planning to use the async API, it is recommended to use `AsyncCallbackHandler` to avoid blocking the runloop. \n",
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"\n",
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"**Advanced** if you use a sync `CallbackHandler` while using an async method to run your llm/chain/tool/agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f771eea0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"zzzz....\n",
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"Hi! I just woke up. Your llm is starting\n",
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"Sync handler being called in a `thread_pool_executor`: token: \n",
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"Sync handler being called in a `thread_pool_executor`: token: Why\n",
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"Sync handler being called in a `thread_pool_executor`: token: don\n",
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"Sync handler being called in a `thread_pool_executor`: token: 't\n",
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"Sync handler being called in a `thread_pool_executor`: token: scientists\n",
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"Sync handler being called in a `thread_pool_executor`: token: trust\n",
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"Sync handler being called in a `thread_pool_executor`: token: atoms\n",
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"Sync handler being called in a `thread_pool_executor`: token: ?\n",
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"Sync handler being called in a `thread_pool_executor`: token: \n",
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"\n",
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"\n",
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"Sync handler being called in a `thread_pool_executor`: token: Because\n",
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"Sync handler being called in a `thread_pool_executor`: token: they\n",
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"Sync handler being called in a `thread_pool_executor`: token: make\n",
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"Sync handler being called in a `thread_pool_executor`: token: up\n",
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"Sync handler being called in a `thread_pool_executor`: token: everything\n",
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"Sync handler being called in a `thread_pool_executor`: token: .\n",
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"Sync handler being called in a `thread_pool_executor`: token: \n",
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"zzzz....\n",
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"Hi! I just woke up. Your llm is ending\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[ChatGeneration(text=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", generation_info=None, message=AIMessage(content=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", additional_kwargs={}, example=False))]], llm_output={'token_usage': {}, 'model_name': 'gpt-3.5-turbo'})"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import asyncio\n",
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"from typing import Any, Dict, List\n",
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"\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.schema import LLMResult, HumanMessage\n",
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"from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler\n",
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"\n",
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"\n",
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"class MyCustomSyncHandler(BaseCallbackHandler):\n",
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" def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
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" print(f\"Sync handler being called in a `thread_pool_executor`: token: {token}\")\n",
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"\n",
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"\n",
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"class MyCustomAsyncHandler(AsyncCallbackHandler):\n",
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" \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
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"\n",
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" async def on_llm_start(\n",
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" self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any\n",
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" ) -> None:\n",
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" \"\"\"Run when chain starts running.\"\"\"\n",
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" print(\"zzzz....\")\n",
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" await asyncio.sleep(0.3)\n",
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" class_name = serialized[\"name\"]\n",
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" print(\"Hi! I just woke up. Your llm is starting\")\n",
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"\n",
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" async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n",
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" \"\"\"Run when chain ends running.\"\"\"\n",
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" print(\"zzzz....\")\n",
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" await asyncio.sleep(0.3)\n",
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" print(\"Hi! I just woke up. Your llm is ending\")\n",
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"\n",
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"\n",
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"# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
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"# Additionally, we pass in a list with our custom handler\n",
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"chat = ChatOpenAI(\n",
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" max_tokens=25,\n",
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" streaming=True,\n",
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" callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],\n",
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")\n",
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"\n",
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"await chat.agenerate([[HumanMessage(content=\"Tell me a joke\")]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "01778cac",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"display_name": "venv",
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"language": "python",
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"name": "venv"
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"language_info": {
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"codemirror_mode": {
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"nbformat": 4,
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