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langgraph/examples/streaming-tokens-without-langchain.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "b23ced4e-dc29-43be-9f94-0c36bb181b8a",
"metadata": {},
"source": [
"# How to stream LLM tokens (without LangChain LLMs)"
]
},
{
"cell_type": "markdown",
"id": "7044eeb8-4074-4f9c-8a62-962488744557",
"metadata": {},
"source": [
"In this example we will stream tokens from the language model powering an agent. We'll be using OpenAI client library directly, without using LangChain chat models. We will also use a ReAct agent as an example."
]
},
{
"cell_type": "markdown",
"id": "a37f60af-43ea-4aa6-847a-df8cc47065f5",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U langgraph openai"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OPENAI_API_KEY: ········\n"
]
}
],
"source": [
"import getpass\n",
"import os\n",
"\n",
"\n",
"def _set_env(var: str):\n",
" if not os.environ.get(var):\n",
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
"\n",
"\n",
"_set_env(\"OPENAI_API_KEY\")"
]
},
{
"cell_type": "markdown",
"id": "e3d02ebb-c2e1-4ef7-b187-810d55139317",
"metadata": {},
"source": [
"## Define model, tools and graph"
]
},
{
"cell_type": "markdown",
"id": "3ba684f1-d46b-42e4-95cf-9685209a5992",
"metadata": {},
"source": [
"### Define a node that will call OpenAI API"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d59234f9-173e-469d-a725-c13e0979663e",
"metadata": {},
"outputs": [],
"source": [
"from openai import AsyncOpenAI\n",
"from langchain_core.language_models.chat_models import ChatGenerationChunk\n",
"from langchain_core.messages import AIMessageChunk\n",
"from langchain_core.runnables.config import ensure_config, get_callback_manager_for_config\n",
"\n",
"openai_client = AsyncOpenAI()\n",
"# define tool schema for openai tool calling\n",
"\n",
"tool = {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_items\",\n",
" \"description\": \"Use this tool to look up which items are in the given place.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"place\": {\n",
" \"type\": \"string\"\n",
" }\n",
" },\n",
" \"required\": [\n",
" \"place\"\n",
" ]\n",
" }\n",
" }\n",
"}\n",
"async def call_model(state, config=None):\n",
" config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
" callback_manager = get_callback_manager_for_config(config)\n",
" messages = state[\"messages\"]\n",
" \n",
" llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]\n",
" response = await openai_client.chat.completions.create(\n",
" messages=messages,\n",
" model=\"gpt-3.5-turbo\",\n",
" tools=[tool],\n",
" stream=True\n",
" )\n",
"\n",
" response_content = \"\"\n",
" role = None\n",
"\n",
" tool_call_id = None\n",
" tool_call_function_name = None\n",
" tool_call_function_arguments = \"\"\n",
" async for chunk in response:\n",
" delta = chunk.choices[0].delta\n",
" if delta.role is not None:\n",
" role = delta.role\n",
"\n",
" if delta.content:\n",
" response_content += delta.content\n",
" llm_run_manager.on_llm_new_token(delta.content)\n",
"\n",
" if delta.tool_calls:\n",
" # note: for simplicity we're only handling a single tool call here\n",
" if delta.tool_calls[0].function.name is not None:\n",
" 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",
" llm_run_manager.on_llm_new_token(\"\", chunk=tool_call_chunk)\n",
" tool_call_function_arguments += delta.tool_calls[0].function.arguments\n",
"\n",
" if tool_call_function_name is not None:\n",
" tool_calls = [\n",
" {\n",
" \"id\": tool_call_id,\n",
" \"function\": {\"name\": tool_call_function_name, \"arguments\": tool_call_function_arguments},\n",
" \"type\": \"function\"\n",
" }\n",
" ]\n",
" else:\n",
" tool_calls = None\n",
"\n",
" response_message = {\n",
" \"role\": role,\n",
" \"content\": response_content,\n",
" \"tool_calls\": tool_calls\n",
" }\n",
" return {\"messages\": [response_message]}"
]
},
{
"cell_type": "markdown",
"id": "3a3877e8-8ace-40d5-ad04-cbf21c6f3250",
"metadata": {},
"source": [
"### Define our tools and a tool-calling node"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b756ea32",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"async def get_items(place: str) -> str:\n",
" \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
" if \"bed\" in place: # For under the bed\n",
" return \"socks, shoes and dust bunnies\"\n",
" if \"shelf\" in place: # For 'shelf'\n",
" return \"books, penciles and pictures\"\n",
" else: # if the agent decides to ask about a different place\n",
" return \"cat snacks\"\n",
"\n",
"# define mapping to look up functions when running tools\n",
"function_name_to_function = {\n",
" \"get_items\": get_items\n",
"}\n",
"\n",
"async def call_tools(state):\n",
" messages = state[\"messages\"]\n",
"\n",
" tool_call = messages[-1][\"tool_calls\"][0]\n",
" function_name = tool_call[\"function\"][\"name\"]\n",
" function_arguments = tool_call[\"function\"][\"arguments\"]\n",
" arguments = json.loads(function_arguments)\n",
" \n",
" function_response = await function_name_to_function[function_name](**arguments) \n",
" tool_message = {\n",
" \"tool_call_id\": tool_call[\"id\"],\n",
" \"role\": \"tool\",\n",
" \"name\": function_name,\n",
" \"content\": function_response,\n",
" }\n",
" return {\n",
" \"messages\": [tool_message]\n",
" }"
]
},
{
"cell_type": "markdown",
"id": "6685898c-9a1c-4803-a492-bd70574ebe38",
"metadata": {},
"source": [
"### Define our graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"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\n",
"\n",
"class State(TypedDict):\n",
" messages: Annotated[list, operator.add]\n",
"\n",
"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\n",
"\n",
"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\")\n",
"graph = workflow.compile()"
]
},
{
"cell_type": "markdown",
"id": "d046e2ef-f208-4831-ab31-203b2e75a49a",
"metadata": {},
"source": [
"## Stream tokens"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "45c96a79-4147-42e3-89fd-d942b2b49f6c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vadymbarda/.virtualenvs/langgraph/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.\n",
" warn_beta(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"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": [
"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",
" print(\"LLM token\", event[\"data\"][\"chunk\"].dict())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "adb0f7bc-6e51-478e-bd32-8f72df072d6c",
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}
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