@arizeai/phoenix-mcp

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "SUknhuHKyc-E" }, "source": [ "# <center>OpenAI agent pattern: prompt chaining agent</center>\n", "\n", "A starter guide for building an agent which chains two prompts together to generate an output using the `openai-agents` library.\n", "\n", "In the following example, we'll build a stock portfolio creation system using this pattern:\n", "1. **Search Agent (Generation):** Searches the web for information on particular stock tickers.\n", "2. **Report Agent (Generation):** Creates a portfolio of stocks and ETFs that supports the user's investment strategy." ] }, { "cell_type": "markdown", "metadata": { "id": "n69HR7eJswNt" }, "source": [ "### Install Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Install base libraries for OpenAI\n", "!pip install -q openai openai-agents pydantic\n", "\n", "# Install optional libraries for OpenInference/OpenTelemetry tracing\n", "!pip install -q arize-phoenix-otel openinference-instrumentation-openai-agents openinference-instrumentation-openai" ] }, { "cell_type": "markdown", "metadata": { "id": "jQnyEnJisyn3" }, "source": [ "### Setup Keys\n", "\n", "Add your OpenAI API key to the environment variable `OPENAI_API_KEY`.\n", "\n", "Copy your Phoenix `API_KEY` from your settings page at [app.phoenix.arize.com](https://app.phoenix.arize.com)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from getpass import getpass\n", "\n", "if \"OPENAI_API_KEY\" not in os.environ:\n", " os.environ[\"OPENAI_API_KEY\"] = getpass(\"🔑 Enter your OpenAI API key: \")\n", "\n", "if \"PHOENIX_API_KEY\" not in os.environ:\n", " os.environ[\"PHOENIX_API_KEY\"] = getpass(\"🔑 Enter your Phoenix API key: \")\n", "\n", "if \"PHOENIX_COLLECTOR_ENDPOINT\" not in os.environ:\n", " os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = getpass(\"🔑 Enter your Phoenix Collector Endpoint\")" ] }, { "cell_type": "markdown", "metadata": { "id": "kfid5cE99yN5" }, "source": [ "### Setup Tracing" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from phoenix.otel import register\n", "\n", "tracer_provider = register(\n", " project_name=\"openai-agents\",\n", " auto_instrument=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating the agent\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pprint import pprint\n", "from textwrap import dedent\n", "\n", "from agents import Agent, Runner, TResponseInputItem, WebSearchTool\n", "from agents.model_settings import ModelSettings\n", "from pydantic import BaseModel, Field\n", "\n", "\n", "class PortfolioItem(BaseModel):\n", " ticker: str = Field(description=\"The ticker of the stock or ETF.\")\n", " allocation: float = Field(\n", " description=\"The percentage allocation of the ticker in the portfolio. The sum of all allocations should be 100.\"\n", " )\n", " reason: str = Field(description=\"The reason why this ticker is included in the portfolio.\")\n", "\n", "\n", "class Portfolio(BaseModel):\n", " tickers: list[PortfolioItem] = Field(\n", " description=\"A list of tickers that could support the user's stated investment strategy.\"\n", " )\n", "\n", "\n", "portfolio_agent = Agent(\n", " name=\"Portfolio Agent\",\n", " instructions=dedent(\n", " \"\"\"You are a senior financial analyst. You will be provided with a stock research report. Your task is to create a portfolio of stocks and ETFs that could support the user's stated investment strategy. Include facts and data from the research report in the stated reasons for the portfolio allocation.\"\"\"\n", " ),\n", " model=\"o4-mini\",\n", " output_type=Portfolio,\n", ")\n", "\n", "research_agent = Agent(\n", " name=\"FinancialSearchAgent\",\n", " instructions=dedent(\n", " \"\"\"You are a research assistant specializing in financial topics. Given an investment strategy, use web search to retrieve up‑to‑date context and produce a short summary of stocks that support the investment strategy at most 50 words. Focus on key numbers, events, or quotes that will be useful to a financial analyst.\"\"\"\n", " ),\n", " model=\"gpt-4.1\",\n", " tools=[WebSearchTool()],\n", " model_settings=ModelSettings(tool_choice=\"required\", parallel_tool_calls=True),\n", ")\n", "\n", "user_input = input(\"Enter your investment strategy: \")\n", "input_items: list[TResponseInputItem] = [\n", " {\"content\": f\"My investment strategy: {user_input}\", \"role\": \"user\"}\n", "]\n", "\n", "\n", "research_output = await Runner.run(research_agent, input_items)\n", "pprint(research_output.final_output)\n", "\n", "input_items = research_output.to_input_list()\n", "portfolio_output = await Runner.run(portfolio_agent, input_items)\n", "pprint(portfolio_output.final_output)" ] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }