mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-10 03:37:51 +02:00
docs: add how-to on how to stream tokens from within a tool (#984)
This commit is contained in:
Vadym Barda
@@ -24,6 +24,7 @@ _MANUAL = {
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"streaming-tokens-without-langchain.ipynb",
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"streaming-content.ipynb",
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"streaming-events-from-within-tools.ipynb",
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"streaming-events-from-within-tools-without-langchain.ipynb",
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"streaming-from-final-node.ipynb",
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"persistence.ipynb",
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"memory/manage-conversation-history.ipynb",
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@@ -51,6 +51,7 @@ These guides show how to use different streaming modes.
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- [How to stream arbitrarily nested content](streaming-content.ipynb)
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- [How to configure multiple streaming modes at the same time](stream-multiple.ipynb)
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- [How to stream events from within a tool](streaming-events-from-within-tools.ipynb)
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- [How to stream events from within a tool without LangChain models](streaming-events-from-within-tools-without-langchain.ipynb)
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- [How to stream events from the final node](streaming-from-final-node.ipynb)
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## Other
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@@ -152,6 +152,7 @@ nav:
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- Stream arbitrarily nested content: how-tos/streaming-content.ipynb
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- Configure multiple streaming modes: how-tos/stream-multiple.ipynb
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- Stream events from within tools: how-tos/streaming-events-from-within-tools.ipynb
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- Stream events from within tools without LangChain models: how-tos/streaming-events-from-within-tools-without-langchain.ipynb
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- Stream events from the final node: how-tos/streaming-from-final-node.ipynb
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- Other:
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- Run graph asynchronously: how-tos/async.ipynb
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@@ -0,0 +1,406 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b23ced4e-dc29-43be-9f94-0c36bb181b8a",
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"metadata": {},
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"source": [
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"# How to stream events from within a tool (without LangChain LLMs / tools)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7044eeb8-4074-4f9c-8a62-962488744557",
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"metadata": {},
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"source": [
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"In this example we will stream tokens from within tools that an agent is using. We'll also be using OpenAI client library directly, without using LangChain chat models. We will use a ReAct agent as an example."
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]
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},
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{
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"cell_type": "markdown",
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"id": "a37f60af-43ea-4aa6-847a-df8cc47065f5",
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"metadata": {},
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"source": [
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"## Setup"
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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": 1,
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"id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture --no-stderr\n",
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"%pip install -U langgraph openai"
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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": 2,
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"id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
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"metadata": {},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"OPENAI_API_KEY: ········\n"
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]
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}
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],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"\n",
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"def _set_env(var: str):\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
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"\n",
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"\n",
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"_set_env(\"OPENAI_API_KEY\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e3d02ebb-c2e1-4ef7-b187-810d55139317",
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"metadata": {},
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"source": [
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"## Define model, tools and graph"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3ba684f1-d46b-42e4-95cf-9685209a5992",
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"metadata": {},
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"source": [
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"### Define a node that will call OpenAI API"
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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": 3,
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"id": "d59234f9-173e-469d-a725-c13e0979663e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openai import AsyncOpenAI\n",
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"from langchain_core.language_models.chat_models import ChatGenerationChunk\n",
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"from langchain_core.messages import AIMessageChunk\n",
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"from langchain_core.runnables.config import ensure_config, get_callback_manager_for_config\n",
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"\n",
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"openai_client = AsyncOpenAI()"
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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": "b29a083e-b753-4392-807d-3625ac85f08f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# define tool schema for openai tool calling\n",
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"\n",
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"tool = {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"get_items\",\n",
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" \"description\": \"Use this tool to look up which items are in the given place.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"place\": {\n",
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" \"type\": \"string\"\n",
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" }\n",
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" },\n",
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" \"required\": [\n",
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" \"place\"\n",
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" ]\n",
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" }\n",
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" }\n",
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"}"
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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": 5,
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"id": "7af98437-f0d8-4110-a33b-ae5d1331d509",
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"metadata": {},
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"outputs": [],
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"source": [
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"async def call_model(state, config=None):\n",
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" config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
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" callback_manager = get_callback_manager_for_config(config)\n",
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" messages = state[\"messages\"]\n",
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" \n",
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" llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]\n",
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" response = await openai_client.chat.completions.create(\n",
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" messages=messages,\n",
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" model=\"gpt-3.5-turbo\",\n",
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" tools=[tool],\n",
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" stream=True\n",
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" )\n",
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"\n",
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" response_content = \"\"\n",
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" role = None\n",
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"\n",
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" tool_call_id = None\n",
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" tool_call_function_name = None\n",
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" tool_call_function_arguments = \"\"\n",
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" async for chunk in response:\n",
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" delta = chunk.choices[0].delta\n",
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" if delta.role is not None:\n",
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" role = delta.role\n",
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"\n",
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" if delta.content:\n",
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" response_content += delta.content\n",
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" llm_run_manager.on_llm_new_token(delta.content)\n",
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"\n",
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" if delta.tool_calls:\n",
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" # note: for simplicity we're only handling a single tool call here\n",
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" if delta.tool_calls[0].function.name is not None:\n",
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" tool_call_function_name = delta.tool_calls[0].function.name\n",
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" tool_call_id = delta.tool_calls[0].id\n",
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"\n",
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" # note: we're wrapping the tools calls in ChatGenerationChunk so that the events from .astream_events in the graph can render tool calls correctly\n",
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" tool_call_chunk = ChatGenerationChunk(\n",
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" message=AIMessageChunk(content=\"\", additional_kwargs={\"tool_calls\": [delta.tool_calls[0].dict()]})\n",
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" )\n",
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" llm_run_manager.on_llm_new_token(\"\", chunk=tool_call_chunk)\n",
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" tool_call_function_arguments += delta.tool_calls[0].function.arguments\n",
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"\n",
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" if tool_call_function_name is not None:\n",
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" tool_calls = [\n",
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" {\n",
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" \"id\": tool_call_id,\n",
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" \"function\": {\"name\": tool_call_function_name, \"arguments\": tool_call_function_arguments},\n",
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" \"type\": \"function\"\n",
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" }\n",
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" ]\n",
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" else:\n",
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" tool_calls = None\n",
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"\n",
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" response_message = {\n",
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" \"role\": role,\n",
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" \"content\": response_content,\n",
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" \"tool_calls\": tool_calls\n",
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" }\n",
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" return {\"messages\": [response_message]}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a3877e8-8ace-40d5-ad04-cbf21c6f3250",
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"metadata": {},
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"source": [
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"### Define our tools and a tool-calling node"
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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": 6,
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"id": "b90941d8-afe4-42ec-9262-9c3b87c3b1ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.runnables import RunnableLambda\n",
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"\n",
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"async def get_items(place: str) -> str:\n",
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" \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
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" # NOTE: we need to define a special langchain runnable that we'll be using for logging the streaming outputs from within a tool\n",
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" tool_logger = RunnableLambda(lambda inputs: inputs).with_config({\"tags\": [\"tool_call\"]})\n",
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"\n",
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" # this can be replaced with any actual streaming logic that you might have\n",
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" def stream(place: str):\n",
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" if \"bed\" in place: # For under the bed\n",
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" yield from [\"socks\", \"shoes\", \"dust bunnies\"]\n",
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" elif \"shelf\" in place: # For 'shelf'\n",
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" yield from [\"books\", \"penciles\", \"pictures\"]\n",
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" else: # if the agent decides to ask about a different place\n",
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" yield \"cat snacks\"\n",
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"\n",
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" tokens = []\n",
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" for token in stream(place):\n",
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" tool_logger.invoke(token)\n",
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" tokens.append(token)\n",
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"\n",
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" return \", \".join(tokens)"
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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": 7,
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"id": "746129da-e926-4844-8da7-bd3bac8276c3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# define mapping to look up functions when running tools\n",
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"function_name_to_function = {\n",
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" \"get_items\": get_items\n",
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"}"
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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": 8,
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"id": "6829bd73-8a8e-4726-b73d-393a897b42d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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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": 9,
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"id": "8fc0fbe8-691f-45b1-b506-1222c797d588",
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"metadata": {},
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"outputs": [],
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"source": [
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"async def call_tools(state):\n",
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" messages = state[\"messages\"]\n",
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"\n",
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" tool_call = messages[-1][\"tool_calls\"][0]\n",
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" function_name = tool_call[\"function\"][\"name\"]\n",
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" function_arguments = tool_call[\"function\"][\"arguments\"]\n",
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" arguments = json.loads(function_arguments)\n",
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" \n",
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||||
" function_response = await function_name_to_function[function_name](**arguments) \n",
|
||||
" tool_message = {\n",
|
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" \"tool_call_id\": tool_call[\"id\"],\n",
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" \"role\": \"tool\",\n",
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" \"name\": function_name,\n",
|
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" \"content\": function_response,\n",
|
||||
" }\n",
|
||||
" return {\n",
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||||
" \"messages\": [tool_message]\n",
|
||||
" }"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6685898c-9a1c-4803-a492-bd70574ebe38",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Define our graph"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
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||||
"id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import operator\n",
|
||||
"from typing import Annotated, TypedDict, Literal\n",
|
||||
"\n",
|
||||
"from langgraph.graph import StateGraph, END"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "09a7c707-e088-4814-9d2f-eff68ab88771",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class State(TypedDict):\n",
|
||||
" messages: Annotated[list, operator.add]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "d36f2d38-024c-4be0-a79a-96cfd23f9fe5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def should_continue(state) -> Literal[\"tools\", END]:\n",
|
||||
" messages = state['messages']\n",
|
||||
" last_message = messages[-1]\n",
|
||||
" if last_message[\"tool_calls\"]:\n",
|
||||
" return \"tools\"\n",
|
||||
" return END"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "a53aab90-9206-46ef-be83-81c2de2c007a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"workflow = StateGraph(State)\n",
|
||||
"workflow.set_entry_point(\"model\")\n",
|
||||
"workflow.add_node(\"model\", call_model) # i.e. our \"agent\"\n",
|
||||
"workflow.add_node(\"tools\", call_tools)\n",
|
||||
"workflow.add_conditional_edges(\"model\", should_continue)\n",
|
||||
"workflow.add_edge(\"tools\", \"model\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "5aac9d6a-f182-4ddd-af77-3e26fdf4170b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"graph = workflow.compile()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d046e2ef-f208-4831-ab31-203b2e75a49a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Stream tokens from within the tool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "45c96a79-4147-42e3-89fd-d942b2b49f6c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Tool token socks\n",
|
||||
"Tool token shoes\n",
|
||||
"Tool token dust bunnies\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"async for event in graph.astream_events({\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]}, version=\"v2\"):\n",
|
||||
" tags = event.get(\"tags\", [])\n",
|
||||
" if event[\"event\"] == \"on_chain_end\" and \"tool_call\" in tags:\n",
|
||||
" print(\"Tool token\", event[\"data\"][\"output\"])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "langgraph",
|
||||
"language": "python",
|
||||
"name": "langgraph"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -37,10 +37,18 @@
|
||||
},
|
||||
{
|
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"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"OPENAI_API_KEY: ········\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
@@ -72,7 +80,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 2,
|
||||
"id": "d59234f9-173e-469d-a725-c13e0979663e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -87,7 +95,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 3,
|
||||
"id": "b29a083e-b753-4392-807d-3625ac85f08f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -116,7 +124,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 4,
|
||||
"id": "7af98437-f0d8-4110-a33b-ae5d1331d509",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -155,6 +163,7 @@
|
||||
" tool_call_function_name = delta.tool_calls[0].function.name\n",
|
||||
" tool_call_id = delta.tool_calls[0].id\n",
|
||||
"\n",
|
||||
" # note: we're wrapping the tools calls in ChatGenerationChunk so that the events from .astream_events in the graph can render tool calls correctly\n",
|
||||
" tool_call_chunk = ChatGenerationChunk(\n",
|
||||
" message=AIMessageChunk(content=\"\", additional_kwargs={\"tool_calls\": [delta.tool_calls[0].dict()]})\n",
|
||||
" )\n",
|
||||
@@ -190,7 +199,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 5,
|
||||
"id": "2cb38dd9-74d8-456d-9e39-4655f2bf3f37",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -207,7 +216,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 6,
|
||||
"id": "746129da-e926-4844-8da7-bd3bac8276c3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -220,7 +229,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 7,
|
||||
"id": "6829bd73-8a8e-4726-b73d-393a897b42d1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -230,7 +239,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 8,
|
||||
"id": "8fc0fbe8-691f-45b1-b506-1222c797d588",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -265,7 +274,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 9,
|
||||
"id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -278,7 +287,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 10,
|
||||
"id": "09a7c707-e088-4814-9d2f-eff68ab88771",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -289,7 +298,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 11,
|
||||
"id": "d36f2d38-024c-4be0-a79a-96cfd23f9fe5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -304,7 +313,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 12,
|
||||
"id": "a53aab90-9206-46ef-be83-81c2de2c007a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -319,7 +328,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 13,
|
||||
"id": "5aac9d6a-f182-4ddd-af77-3e26fdf4170b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -337,7 +346,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 14,
|
||||
"id": "45c96a79-4147-42e3-89fd-d942b2b49f6c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -353,103 +362,48 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"Invalid Tool Calls:\n",
|
||||
" get_items (call_3wKFL4ENl2NkqQoByU9QvJhh)\n",
|
||||
" Call ID: call_3wKFL4ENl2NkqQoByU9QvJhh\n",
|
||||
" Args:\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"Tool Calls:\n",
|
||||
" (None)\n",
|
||||
" Call ID: None\n",
|
||||
" Args:\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"Invalid Tool Calls:\n",
|
||||
" None (None)\n",
|
||||
" Call ID: None\n",
|
||||
" Args:\n",
|
||||
" place\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"Invalid Tool Calls:\n",
|
||||
" None (None)\n",
|
||||
" Call ID: None\n",
|
||||
" Args:\n",
|
||||
" \":\"\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"Invalid Tool Calls:\n",
|
||||
" None (None)\n",
|
||||
" Call ID: None\n",
|
||||
" Args:\n",
|
||||
" bed\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"Invalid Tool Calls:\n",
|
||||
" None (None)\n",
|
||||
" Call ID: None\n",
|
||||
" Args:\n",
|
||||
" room\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"Invalid Tool Calls:\n",
|
||||
" None (None)\n",
|
||||
" Call ID: None\n",
|
||||
" Args:\n",
|
||||
" \"}\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
"In\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" the\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" bedroom\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
",\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" there\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" are\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" socks\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
",\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" shoes\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
",\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" and\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" dust\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
" b\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
"unn\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
"ies\n",
|
||||
"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
|
||||
"\n",
|
||||
".\n"
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_xUcx3IPa8GREPOpjHVj5k9Wx', 'function': {'arguments': '', 'name': 'get_items'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': 'get_items', 'args': '', 'id': 'call_xUcx3IPa8GREPOpjHVj5k9Wx', 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': 'get_items', 'args': '', 'id': 'call_xUcx3IPa8GREPOpjHVj5k9Wx', 'index': 0}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '{\"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [{'name': '', 'args': {}, 'id': None}], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '{\"', 'id': None, 'index': 0}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'place', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'place', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'place', 'id': None, 'index': 0}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '\":\"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '\":\"', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '\":\"', 'id': None, 'index': 0}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'bed', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'bed', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'bed', 'id': None, 'index': 0}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'room', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'room', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'room', 'id': None, 'index': 0}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '\"}', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '\"}', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '\"}', 'id': None, 'index': 0}]}\n",
|
||||
"LLM token {'content': 'In', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' the', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' bedroom', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' you', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' have', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' socks', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' shoes', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' and', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' some', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' dust', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' b', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': 'unn', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': 'ies', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': '.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' Is', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' there', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' anything', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' else', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' you', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' would', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' like', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' to', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' know', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': '?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# NOTE: we're first streaming tool call tokens and then \n",
|
||||
"async for event in graph.astream_events({\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]}, version=\"v2\"):\n",
|
||||
" tags = event.get(\"tags\", [])\n",
|
||||
" if event[\"event\"] == \"on_chat_model_stream\" and \"agent_llm\" in tags:\n",
|
||||
" event[\"data\"][\"chunk\"].pretty_print()"
|
||||
" print(\"LLM token\", event[\"data\"][\"chunk\"].dict())"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user