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William Fu-Hinthorn
@@ -454,10 +454,10 @@ We also have a lot of examples highlighting how to slightly modify the base chat
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### Advanced + Multi-agent Examples
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- [Multi-agent collaboration](examples/advanced_agents/multi-agent/multi-agent-collaboration.ipynb): how to create two agents that work together to accomplish a task
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- [Multi-agent with supervisor](examples/advanced_agents/multi-agent/agent_supervisor.ipynb): how to orchestrate individual agents by using an LLM as a "supervisor" to distribute work
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- [Hierarchical agent teams](examples/advanced_agents/multi-agent/hierarchical_agent_teams.ipynb): how to orchestrate "teams" of agents as nested graphs that can collaborate to solve a problem
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- [Chat bot evaluation as multi-agent simulation](examples/advanced_agents/multi-agent/agent-simulation-evaluation.ipynb): How to simulate a dialogue between a "virtual user" and your chat bot
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- [Multi-agent collaboration](examples/multi_agent/multi-agent-collaboration.ipynb): how to create two agents that work together to accomplish a task
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- [Multi-agent with supervisor](examples/multi_agent/agent_supervisor.ipynb): how to orchestrate individual agents by using an LLM as a "supervisor" to distribute work
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- [Hierarchical agent teams](examples/multi_agent/hierarchical_agent_teams.ipynb): how to orchestrate "teams" of agents as nested graphs that can collaborate to solve a problem
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- [Chat bot evaluation as multi-agent simulation](examples/multi_agent/agent-simulation-evaluation.ipynb): How to simulate a dialogue between a "virtual user" and your chat bot
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### Async
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@@ -1,412 +0,0 @@
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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": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276",
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"metadata": {},
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"source": [
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"# Chat Bot Evaluation as Multi-agent Simulation\n",
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"\n",
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"Manually evaluating a chat bot on every code change is time-consuming. One way to automate some of the work is to simulate a \"virtual user\". Then you can focus on reviewing samples of the interactions.\n",
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"\n",
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"In thos notebook, you will use LangGraph to create a dialogue simulation between a virtual user and your chat bot. The overall simulation looks something like this:\n",
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"\n",
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"\n",
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"\n",
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"The main steps are:\n",
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"1. Defining the virtual user\n",
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"2. Connecting your chat bot\n",
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"3. Constructing the dialogue simulation graph\n",
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"4. Running!\n",
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"\n",
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"First, we'll set up our environment."
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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": "0d30b6f7-3bec-4d9f-af50-43dfdc81ae6c",
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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 langchain langchain_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": "30c2f3de-c730-4aec-85a6-af2c2f058803",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"import uuid\n",
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"\n",
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"\n",
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"def _set_if_undefined(var: str):\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass(f\"Please provide your {var}\")\n",
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"\n",
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"\n",
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"_set_if_undefined(\"OPENAI_API_KEY\")\n",
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"_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
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"\n",
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"# Optional, add tracing in LangSmith.\n",
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"# This will help you visualize and debug the control flow\n",
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"os.environ[\"LANGCHAIN_PROJECT\"] = \"Agent Simulation Evaluation\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "6b69031e-7bf8-4401-941e-4dccd59ea870",
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"metadata": {},
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"source": [
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"## 1. Define the virtual user\n",
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"\n",
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"The virtual user needs an LLM to reason and instructions for how it's supposed to behave (or what it's trying to accomplish).\n",
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"\n",
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"Below, create an agent and instruct it to role-play a 'simulated' user. By including the `{system_prompt}` placeholder in the prompt and a state variable with the same name `Environment`, you can customize the user behavior each time you simulate a dialogue."
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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": "0f4c63d8-15ef-4ee6-8bb6-654465baae04",
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"metadata": {},
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"outputs": [],
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"source": [
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"import operator\n",
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"from typing import Annotated, Callable, Dict, List, TypedDict\n",
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"\n",
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"from langchain.adapters.openai import convert_message_to_dict\n",
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"from langchain_core.messages import AIMessage, BaseMessage, HumanMessage\n",
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"from langchain_core.runnables import chain\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"from langgraph.graph import END, StateGraph\n",
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"\n",
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"SIMULATED_USER_NAME = \"simulated\"\n",
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"\n",
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"\n",
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"# This is the input to every node in the simulation graph\n",
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"# It tracks the graph state over time. Our only \"state\"\n",
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"# is the conversation messages, while the user config\n",
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"# is provided to make the virtual user more unique or realistic\n",
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"class Environment(TypedDict):\n",
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" messages: Annotated[List[BaseMessage], operator.add]\n",
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" # The system prompt will be fed into the prompt template below\n",
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" # For more control, try adding different parameters to provide the\n",
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" # prompt template below\n",
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" system_prompt: str\n",
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"\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are role-playing a human character: '{name}'. \"\n",
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" \"You are not an AI assistant and you are not supposed to help or assist.\"\n",
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" \" You must behave as this human would throughout the conversation below.\\n\\n\"\n",
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" \"Your messages will bear the name 'simulated', but DO NOT under any circumstances\"\n",
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" \"say that you are 'simulated'. You will be evaluated based on how realistic your\"\n",
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" \"impersonation of this character is. This must feel real! Here are the details for your character:\"\n",
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" \"\\n\"\n",
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" # This system_prompt is specified in the Environment above\n",
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" \"{system_prompt}\"\n",
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" # The stopping criteria of FINISHED is used in the function hould_continue in a later\n",
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" # section. This tells the graph to stop the simulation.\n",
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" '\\n\\nWhen you are finished with the conversation, respond with a single word \"FINISHED\"',\n",
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" ),\n",
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" MessagesPlaceholder(variable_name=\"messages\"),\n",
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" ]\n",
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").partial(name=SIMULATED_USER_NAME)\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
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"\n",
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"\n",
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"def rename_message(message: AIMessage):\n",
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" # If we use an AIMessage, the simulated user may forget to continue role playing.\n",
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" # It will also confuse YOUR chat bot, since IT is supposed to be the AI in this scenario.\n",
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" # We instead convert them to 'Human' messages with the 'simulated' name\n",
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" # Your chat bot will then receive all the user's messages and think they\n",
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" # are human ones\n",
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" return {\n",
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" \"messages\": [HumanMessage(content=message.content, name=SIMULATED_USER_NAME)]\n",
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" }"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d8d5a3a7-72b6-4063-89e6-3ecf2bce3340",
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"metadata": {},
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"source": [
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"Now we can compose these pieces using LCEL. The `|` syntax pipelines the data flow."
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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": "b486511b-57f3-4a7c-be0d-691a7d1006da",
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"metadata": {},
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"outputs": [],
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"source": [
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"virtual_user = prompt | llm | rename_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": "6ef4528d-6b2a-47c7-98b5-50f14984a304",
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"metadata": {},
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"source": [
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"## 2. Define your chat bot\n",
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"\n",
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"Next, define the chat bot. For this notebook, we assume the bot's API accepts a list of messages and responds with a message. If you want to update this, you can change this section and the \"get_messages_for_agent\" function in the simulator below (as well as the environment state if it requires additional inputs).\n",
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"\n",
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"The actual implementation within `my_chat_bot` is configurable and can even be run on another system (e.g., if your system isn't running in python)."
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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": "828479af-cf9c-4888-a365-599643a96b55",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import List\n",
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"\n",
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"import openai\n",
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"\n",
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"\n",
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"# This is flexible, but you can define your agent here, or call your agent API here.\n",
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"def my_chat_bot(messages: List[dict], model=\"gpt-3.5-turbo\") -> dict:\n",
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" completion = openai.chat.completions.create(\n",
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" messages=messages, model=\"gpt-3.5-turbo\"\n",
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" )\n",
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" return completion.choices[0].message.model_dump()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c7669a7e-1602-4af7-a730-84fa19f363f2",
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"metadata": {},
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"source": [
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"Every node in the simulation is pased a state object of the `Environment` type above.\n",
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"\n",
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"The two functions below define the API between the `Environment` state and your chat bot."
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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": "4d495ccc-0f5f-4194-89e5-15dccbbb7412",
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"metadata": {},
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"outputs": [],
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"source": [
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"@chain\n",
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"def get_messages_for_agent(state: Environment):\n",
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" \"\"\"Convert the simulation state to the input\n",
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"\n",
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" for your agent you want to evaluate.\"\"\"\n",
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" messages = []\n",
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" for message in state[\"messages\"]:\n",
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" messages.append(convert_message_to_dict(message))\n",
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" if getattr(message, \"name\", None) != SIMULATED_USER_NAME:\n",
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" # Ensure YOUR chat bot still sees its messages\n",
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" # as assistant messages\n",
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" messages[-1][\"role\"] = \"assistant\"\n",
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" return messages\n",
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"\n",
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"\n",
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"def get_response_message_from_agent(agent_output):\n",
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" \"\"\"Get the response from the agent you are evaluting,\n",
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" and use it to update the simulation state.\"\"\"\n",
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" # If we directly return an AI message from your chat bot, our\n",
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" # virtual user will likely forget it's role playing. To cover this up\n",
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" # we will convert it to a Human message.\n",
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" return {\"messages\": [HumanMessage(content=agent_output[\"content\"])]}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "321312b4-a1f0-4454-a481-fdac4e37cb7d",
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"metadata": {},
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"source": [
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"## 3. Define simulation graph\n",
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"\n",
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"The dialogue simulation is almost ready. It's time to put everything together!\n",
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"\n",
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"Below, create a graph using the `Environment` state defined above. Wire together the\n",
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"virtual user and your chat bot, including a conditional `should_continue` edge to\n",
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"handle the stopping behavior."
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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": "6d4caf50-4108-4a68-9c0a-775fa97fccd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"graph_builder = StateGraph(Environment)\n",
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"graph_builder.add_node(\"user\", virtual_user)\n",
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"graph_builder.add_node(\n",
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" \"chat_bot\",\n",
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" # The \"|\" syntax composes these steps in the pipeline to map between\n",
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" # the simulation state and your chat bot's API\n",
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" get_messages_for_agent | my_chat_bot | get_response_message_from_agent,\n",
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")\n",
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"# Every response from your chat bot will automatically go to the\n",
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"# simulated user\n",
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"graph_builder.add_edge(\"chat_bot\", \"user\")\n",
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"\n",
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"\n",
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"# Recall that we instructed the simulated user to respond \"FINISHED\" when\n",
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"# it is done with the conversation. This function\n",
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"# parses that output and tells the graph to cease execution.\n",
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"# You can add other heuristics here for more control.\n",
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"def should_continue(state: Environment):\n",
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" \"\"\"Determine if the simulation should continue.\"\"\"\n",
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" if state[\"messages\"][-1].content.strip().endswith(\"FINISHED\"):\n",
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" return \"end\"\n",
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" return \"continue\"\n",
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"\n",
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"\n",
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"graph_builder.add_conditional_edges(\n",
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" # Every time the \"user\" node completes ...\n",
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" \"user\",\n",
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" # Call this function ...\n",
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" should_continue,\n",
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" # And based on the outputs of should_continue ...\n",
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" {\n",
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" # End the simulation OR\n",
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" \"end\": END,\n",
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" # continue to the chat_bot node\n",
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" \"continue\": \"chat_bot\",\n",
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" },\n",
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")\n",
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"# The input will first go to your chat bot\n",
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"graph_builder.set_entry_point(\"chat_bot\")\n",
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"simulation = graph_builder.compile()"
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]
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},
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{
|
||||
"cell_type": "markdown",
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||||
"id": "2e0bd26e-8c1d-471d-9fef-d95dc0163491",
|
||||
"metadata": {},
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"source": [
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||||
"## 4. Run Simulation\n",
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"\n",
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"Now we can evaluate our chat bot! We will provide information about the simulated user (as a system prompt)\n",
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"as well as the initial input message from that simulated user to the chat bot."
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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": "9a7cc134-4fbf-4240-8976-7605a6263578",
|
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"metadata": {},
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"outputs": [],
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"source": [
|
||||
"result = simulation.invoke(\n",
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" {\n",
|
||||
" \"system_prompt\": \"You are on a budget. Your family is hard to please.\"\n",
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" \" They all like the beach, except for Aunt Lily, who prefers the mountains.\",\n",
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" \"messages\": [\n",
|
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" HumanMessage(\n",
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" content=\"help me plan my family vacation\", name=SIMULATED_USER_NAME\n",
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" )\n",
|
||||
" ],\n",
|
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" }\n",
|
||||
")"
|
||||
]
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},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
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||||
"id": "0de1664f-1625-42e7-9dad-eb2c061b88d4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
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"data": {
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"text/plain": [
|
||||
"[HumanMessage(content='help me plan my family vacation', name='simulated'),\n",
|
||||
" HumanMessage(content=\"Of course! I'd be happy to help you plan your family vacation. To assist you better, could you please provide some information about your preferences and interests? Include details like the duration of the trip, budget, destination preferences, and any specific activities or landmarks you'd like to include in your itinerary.\"),\n",
|
||||
" HumanMessage(content=\"Oh, planning a family vacation can be quite the task, right? Especially when everyone has different preferences. Well, my family is pretty similar. We're always on a budget, and it can be tough to please everyone. Usually, we end up somewhere by the beach because most of the family loves it, but then there's Aunt Lily who's always campaigning for the mountains.\\n\\nI've found that the trick is to compromise and maybe find a place that has a bit of both. You know, like a coastal area that has access to some nature trails or a scenic mountain spot not too far from the shore. This way, everyone gets a bit of what they like. \\n\\nHave you considered somewhere like that? Maybe a place where you can have a beach day but also take a day trip to the mountains? It might not be the perfect solution for Aunt Lily, but at least it's something. And staying on budget means looking for deals, maybe even renting a house instead of booking multiple hotel rooms. It can save a lot and feels more personal, you know?\", name='simulated'),\n",
|
||||
" HumanMessage(content=\"Absolutely, compromising and finding a destination that offers a mix of beach and mountain activities can be a great solution! Here's a step-by-step approach to planning your family vacation:\\n\\n1. Determine your budget: Set a realistic budget for your vacation, including accommodation, transportation, food, and activities. This will help you make informed decisions throughout the planning process.\\n\\n2. Choose a destination: Look for places that offer both beach and mountain activities. Some popular destinations that might suit your needs could include places like California, Oregon, Hawaii (Big Island or Maui), or the Caribbean islands like Puerto Rico or the Dominican Republic.\\n\\n3. Research accommodation options: Look for vacation rentals or holiday homes that can accommodate your entire family. Websites like Airbnb, Vrbo, or Booking.com can provide a range of options to suit your budget and preferences.\\n\\n4. Plan activities: Research activities and attractions in the chosen destination that cater to both beach and mountain interests. For beach lovers, plan beach days, water sports like snorkeling, surfing or kayaking, and beachside relaxation. For mountain enthusiasts, look for hiking trails, scenic drives, or even a day trip to a nearby national park or mountain range.\\n\\n5. Create a flexible itinerary: Start by planning a rough itinerary that outlines which days will be devoted to beach activities and which will be dedicated to mountain adventures. Allow for some flexibility and downtime for relaxing and enjoying each other's company.\\n\\n6. Make transportation arrangements: Decide on the most convenient and cost-effective transportation option for your family. Depending on the distance, consider driving, flying, or a combination of both.\\n\\n7. Pack accordingly: Ensure everyone brings appropriate clothing and gear for both beach and mountain activities. Pack essentials such as sunscreen, hats, swimsuits, hiking shoes, and layers for changing weather conditions.\\n\\n8. Keep everyone involved: Involve family members in the planning process and gather their input on activities and attractions. This will make everyone feel invested and excited about the trip.\\n\\nRemember, the key is to find a balance and cater to everyone's interests as much as possible. By planning ahead and considering these factors, you can help ensure a memorable and enjoyable family vacation!\"),\n",
|
||||
" HumanMessage(content=\"Oh, that's a solid plan you've got there! It really does cover all the bases, and I like how you've broken it down into manageable steps. That's a good reminder that I should probably start by setting the budget. That's usually the tricky part for us. Once we have that locked down, we can start looking into destinations that won't break the bank.\\n\\nCalifornia and Oregon sound appealing with their combo of beaches and mountains, but I think they might be a bit on the pricey side for us. Hawaii would be a dream come true, but again, I'm not sure it's in the cards budget-wise.\\n\\nI've heard that Puerto Rico and the Dominican Republic can be more affordable and still offer that mix of beach and mountain activities. Maybe I'll dig a little deeper into those options. Plus, the idea of a vacation rental instead of a hotel could really help us save some cash.\\n\\nI've got to make sure to find those activities you mentioned. The family would love things like snorkeling and hiking, and it's a bonus if we can find some free or low-cost activities too. A flexible itinerary is key, especially with my crew. Someone's always changing their mind about what they want to do.\\n\\nThanks for the tips on transportation and packing as well. I always tend to overpack, so it's a good reminder to just stick to the essentials.\\n\\nInvolving the family is... well, it's a process. Everyone has their own opinion, but it's important that they all get a say. Aunt Lily might need some extra convincing, but I'm sure we'll find a compromise.\\n\\nThis has been super helpful. I appreciate the guidance. I guess it's time to get down to the nitty-gritty and start making some decisions. Wish me luck!\", name='simulated'),\n",
|
||||
" HumanMessage(content=\"You're very welcome! I'm glad you found the plan helpful, and it seems like you have a great approach in mind. Setting a budget and researching destinations that fit within that budget is a smart starting point.\\n\\nPuerto Rico and the Dominican Republic are indeed more affordable destinations compared to some others, and they offer a fantastic mix of beach and mountain activities. Exploring more about their attractions, availability of vacation rentals, and affordable activities will help you make an informed decision.\\n\\nRemember that planning low-cost or free activities can be a great way to save money while still enjoying the vacation. Look for local hiking trails, public beaches, parks, or cultural experiences that don't come with hefty price tags.\\n\\nKeeping the itinerary flexible will definitely help accommodate any changing preferences within your family. It's all about finding that balance and ensuring everyone gets to do at least a few things they enjoy.\\n\\nAs you involve your family in the planning process, consider creating a checklist of their preferences and priorities. This way, you can work together to find compromises and make decisions that make everyone happy.\\n\\nI'm glad I could be of assistance, and I wish you the best of luck with your planning! I hope you and your family have a fantastic vacation filled with memorable experiences. Enjoy your trip!\"),\n",
|
||||
" HumanMessage(content='FINISHED', name='simulated')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# These are the message from the final simulation state\n",
|
||||
"result[\"messages\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "73ff30e4-1992-4bc9-834d-f4c08b281d20",
|
||||
"metadata": {
|
||||
"jp-MarkdownHeadingCollapsed": true
|
||||
},
|
||||
"source": [
|
||||
"## (Optional) Review Results\n",
|
||||
"\n",
|
||||
"If you've traced the run, you can see the full simulation trace in the LangSmith UI by going to the `Agent Simulation Evaluation` project.\n",
|
||||
"\n",
|
||||
"Select the last 'ChatOpenAI' call in the trace to see the full conversation in a single view.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"From this run, you can manually annotate it to score its quality. This feedback can be used to compare the quality of different versions of your chat bot.\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "23db8891-5db3-4a98-a283-53d45cd28c60",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Conclusion\n",
|
||||
"\n",
|
||||
"In this notebook, you set up a multi-agent simulation to review how your chat bot behaves with simulated users.\n",
|
||||
"\n",
|
||||
"To implement this for your chat bot, you can create a dataset of user profiles and questions your chat bot should handle and run periodically. You can use an LLM-as-judge to give the bot an initial score and then manually review to spot check. \n",
|
||||
"\n",
|
||||
"LangGraph gives you full control over the simulation so you can manually change the simulated user and the conversation dynamics."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user