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a911d1c2ec
* set use_docker to default to true * black formatting * centralize checking and add env variable option * set docker env flag for contrib tests * set docker env flag for contrib tests * better error message and cleanup * disable explicit docker tests * docker is installed so can't check for that in test * pr comments and fix test * rename and fix function descriptions * documentation * update notebooks so that they can be run with change in default * add unit tests for new code * cache and restore env var * skip on windows because docker is running in the CI but there are problems connecting the volume * update documentation * move header * update contrib tests
327 lines
12 KiB
Plaintext
327 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Using Guidance with AutoGen\n",
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"\n",
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"This notebook shows how Guidance can be used to enable structured responses from AutoGen agents. In particular, this notebook focuses on creating agents that always output a valid code block or valid json object.\n"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"import re\n",
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"\n",
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"from guidance import assistant, gen, models, system, user\n",
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"from pydantic import BaseModel\n",
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"\n",
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"from autogen import Agent, AssistantAgent, UserProxyAgent, config_list_from_json\n",
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"\n",
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"llm_config = config_list_from_json(\"OAI_CONFIG_LIST\")[0] # use the first config\n",
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"gpt = models.OpenAI(\"gpt-4\", api_key=llm_config.get(\"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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"metadata": {},
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"source": [
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"The example below uses guidance to create a `guidance_coder` agent that only responds with valid code blocks."
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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": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[33muser\u001b[0m (to guidance_coder):\n",
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"\n",
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"Plot and save a chart of nvidia and tsla stock price change YTD.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mguidance_coder\u001b[0m (to user):\n",
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"\n",
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"```python\n",
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"# filename: stock_price_change.py\n",
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"\n",
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"import pandas as pd\n",
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"import yfinance as yf\n",
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"import matplotlib.pyplot as plt\n",
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"from datetime import datetime\n",
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"\n",
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"# Get today's date\n",
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"today = datetime.today().strftime('%Y-%m-%d')\n",
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"\n",
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"# Download stock data\n",
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"nvda = yf.download('NVDA', start='2022-01-01', end=today)\n",
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"tsla = yf.download('TSLA', start='2022-01-01', end=today)\n",
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"\n",
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"# Calculate percentage change in closing price\n",
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"nvda['Pct Change'] = nvda['Close'].pct_change()\n",
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"tsla['Pct Change'] = tsla['Close'].pct_change()\n",
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"\n",
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"# Plot percentage change\n",
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"plt.figure(figsize=(14,7))\n",
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"plt.plot(nvda['Pct Change'], label='NVDA')\n",
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"plt.plot(tsla['Pct Change'], label='TSLA')\n",
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"plt.title('Nvidia and Tesla Stock Price Change YTD')\n",
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"plt.xlabel('Date')\n",
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"plt.ylabel('Percentage Change')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"\n",
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"# Save the plot as a PNG file\n",
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"plt.savefig('stock_price_change.png')\n",
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"```\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[31m\n",
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">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[33muser\u001b[0m (to guidance_coder):\n",
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"\n",
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"exitcode: 0 (execution succeeded)\n",
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"Code output: \n",
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"\n",
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"[*********************100%%**********************] 1 of 1 completed\n",
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"\n",
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"[*********************100%%**********************] 1 of 1 completed\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mguidance_coder\u001b[0m (to user):\n",
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"\n",
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"Great! The code executed successfully and the chart of Nvidia and Tesla stock price change Year-To-Date (YTD) has been saved as 'stock_price_change.png' in the current directory. You can open this file to view the chart.\n",
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"\n",
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"TERMINATE\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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"source": [
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"def is_valid_code_block(code):\n",
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" pattern = r\"```[\\w\\s]*\\n([\\s\\S]*?)\\n```\"\n",
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" match = re.search(pattern, code)\n",
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" if match:\n",
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" return True\n",
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" else:\n",
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" return False\n",
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"\n",
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"\n",
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"def generate_structured_response(recipient, messages, sender, config):\n",
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" gpt = models.OpenAI(\"gpt-4\", api_key=llm_config.get(\"api_key\"), echo=False)\n",
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"\n",
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" # populate the recipient with the messages from the history\n",
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" with system():\n",
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" lm = gpt + recipient.system_message\n",
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"\n",
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" for message in messages:\n",
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" if message.get(\"role\") == \"user\":\n",
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" with user():\n",
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" lm += message.get(\"content\")\n",
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" else:\n",
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" with assistant():\n",
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" lm += message.get(\"content\")\n",
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"\n",
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" # generate a new response and store it\n",
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" with assistant():\n",
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" lm += gen(name=\"initial_response\")\n",
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" # ask the agent to reflect on the nature of the response and store it\n",
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" with user():\n",
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" lm += \"Does the very last response from you contain code? Respond with yes or no.\"\n",
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" with assistant():\n",
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" lm += gen(name=\"contains_code\")\n",
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" # if the response contains code, ask the agent to generate a proper code block\n",
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" if \"yes\" in lm[\"contains_code\"].lower():\n",
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" with user():\n",
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" lm += \"Respond with a single blocks containing the valid code. Valid code blocks start with ```\"\n",
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" with assistant():\n",
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" lm += \"```\" + gen(name=\"code\")\n",
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" response = \"```\" + lm[\"code\"]\n",
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"\n",
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" is_valid = is_valid_code_block(response)\n",
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" if not is_valid:\n",
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" raise ValueError(f\"Failed to generate a valid code block\\n {response}\")\n",
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"\n",
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" # otherwise, just use the initial response\n",
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" else:\n",
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" response = lm[\"initial_response\"]\n",
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"\n",
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" return True, response\n",
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"\n",
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"\n",
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"guidance_agent = AssistantAgent(\"guidance_coder\", llm_config=llm_config)\n",
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"guidance_agent.register_reply(Agent, generate_structured_response, 1)\n",
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"user_proxy = UserProxyAgent(\n",
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" \"user\",\n",
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" human_input_mode=\"TERMINATE\",\n",
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" code_execution_config={\n",
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" \"work_dir\": \"coding\",\n",
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" \"use_docker\": False,\n",
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" }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
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" is_termination_msg=lambda msg: \"TERMINATE\" in msg.get(\"content\"),\n",
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")\n",
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"user_proxy.initiate_chat(guidance_agent, message=\"Plot and save a chart of nvidia and tsla stock price change YTD.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The example below uses Guidance to enable a `guidance_labeler` agent that only responds with a valid JSON that labels a given comment/joke."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[33muser\u001b[0m (to guidance_labeler):\n",
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"\n",
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"\n",
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"Label the TEXT via the following instructions:\n",
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" \n",
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"The label must be a JSON of the format:\n",
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"{\n",
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" \"label\": str,\n",
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" \"explanation\": str\n",
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"}\n",
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" \n",
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"TEXT: what did the fish say when it bumped into a wall? Dam!\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mguidance_labeler\u001b[0m (to user):\n",
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"\n",
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"{\"label\":\"Joke\",\"explanation\":\"The text is a joke, using a play on words where the fish says 'Dam!' after bumping into a wall, which is a pun on the word 'damn' and a 'dam' which is a barrier that stops or restricts the flow of water, often creating a reservoir, and is something a fish might encounter.\"}\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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"source": [
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"class Response(BaseModel):\n",
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" label: str\n",
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" explanation: str\n",
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"\n",
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"\n",
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"response_prompt_instructions = \"\"\"The label must be a JSON of the format:\n",
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"{\n",
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" \"label\": str,\n",
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" \"explanation\": str\n",
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"}\"\"\"\n",
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"\n",
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"\n",
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"def generate_structured_response(recipient, messages, sender, config):\n",
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" gpt = models.OpenAI(\"gpt-4\", api_key=llm_config.get(\"api_key\"), echo=False)\n",
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"\n",
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" # populate the recipient with the messages from the history\n",
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" with system():\n",
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" lm = gpt + recipient.system_message\n",
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"\n",
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" for message in messages:\n",
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" if message.get(\"role\") == \"user\":\n",
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" with user():\n",
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" lm += message.get(\"content\")\n",
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" else:\n",
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" with assistant():\n",
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" lm += message.get(\"content\")\n",
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"\n",
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" # generate a new response and store it\n",
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" with assistant():\n",
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" lm += gen(name=\"initial_response\")\n",
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" # ask the agent to reflect on the nature of the response and store it\n",
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" with user():\n",
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" lm += \"Does the very last response from you contain JSON object? Respond with yes or no.\"\n",
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" with assistant():\n",
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" lm += gen(name=\"contains_json\")\n",
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" # if the response contains code, ask the agent to generate a proper code block\n",
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" if \"yes\" in lm[\"contains_json\"].lower():\n",
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" with user():\n",
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" lm += (\n",
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" \"What was that JSON object? Only respond with that valid JSON string. A valid JSON string starts with {\"\n",
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" )\n",
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" with assistant():\n",
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" lm += \"{\" + gen(name=\"json\")\n",
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" response = \"{\" + lm[\"json\"]\n",
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" # verify that the response is valid json\n",
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" try:\n",
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" response_obj = Response.model_validate_json(response)\n",
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" response = response_obj.model_dump_json()\n",
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" except Exception as e:\n",
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" response = str(e)\n",
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" # otherwise, just use the initial response\n",
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" else:\n",
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" response = lm[\"initial_response\"]\n",
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"\n",
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" return True, response\n",
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"\n",
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"\n",
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"guidance_agent = AssistantAgent(\"guidance_labeler\", llm_config=llm_config, system_message=\"You are a helpful assistant\")\n",
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"guidance_agent.register_reply(Agent, generate_structured_response, 1)\n",
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"user_proxy = UserProxyAgent(\"user\", human_input_mode=\"ALWAYS\", code_execution_config=False)\n",
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"user_proxy.initiate_chat(\n",
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" guidance_agent,\n",
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" message=f\"\"\"\n",
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"Label the TEXT via the following instructions:\n",
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"\n",
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"{response_prompt_instructions}\n",
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"\n",
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"TEXT: what did the fish say when it bumped into a wall? Dam!\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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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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