Merge pull request #228 from flacout/community-contribution-branch

contribute to week 2 examples with a tool: python interpreter

week2/community-contributions/week2_code_interpreter_tool.ipynb

@@ -0,0 +1,225 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
+   "metadata": {},
+   "source": [
+    "# A tool to evaluate a mathematical expression\n",
+    "\n",
+    "This week the tool used in FlightAI was a database lookup function.\n",
+    "\n",
+    "Here I implement a python code interpreter function as tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b0e8691-71f9-486c-859d-ea371401dfa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e2792ae-ff53-4b83-b2c3-866533ba2b29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "# Print the key prefixes to help with any debugging\n",
+    "\n",
+    "load_dotenv()\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
+    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
+    "\n",
+    "if openai_api_key:\n",
+    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"OpenAI API Key not set\")\n",
+    "    \n",
+    "if anthropic_api_key:\n",
+    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
+    "else:\n",
+    "    print(\"Anthropic API Key not set\")\n",
+    "\n",
+    "if google_api_key:\n",
+    "    print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"Google API Key not set\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79e44ee9-af02-448c-a747-17780ee55791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai = OpenAI()\n",
+    "MODEL = \"gpt-4o-mini\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ec55b1-0eff-43f1-9346-28145fa2fc47",
+   "metadata": {},
+   "source": [
+    "# Defining the tool function\n",
+    "\n",
+    "Add print statements to make sure the function is used instead of the native gpt interpreter capability.\n",
+    "\n",
+    "I used multi shot in the system prompt to make sure gpt generate the code in the format that the tool accept."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94e0e171-4975-457b-88cb-c0d90f51ca65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_math_expression(my_code):\n",
+    "    print(f\"EXECUTING FUNCTION WITH CODE: {my_code}\")\n",
+    "    exec(my_code)\n",
+    "    r = locals()['interpreter_result'] \n",
+    "    return r\n",
+    "\n",
+    "\n",
+    "math_function = {\n",
+    "    \"name\": \"evaluate_math_expression\",\n",
+    "    \"description\": \"Give the result of a math expression. \\\n",
+    "    Call this whenever you need to know the result of a mathematical expression. \\\n",
+    "    Generate python code ALWAYS with the final result assigned to a variable called 'interpreter_result'. \\\n",
+    "    For example when a user asks 'What is 2+2' generate 'interpreter_result = 2+2', and pass this code to the tool. \\\n",
+    "    Another example if a user ask 'What is log(5)' generate 'import math; interpreter_result = math.log(5)' and pass this code to the tool.\",\n",
+    "    \n",
+    "    \"parameters\": {\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "            \"my_code\": {\n",
+    "                \"type\": \"string\",\n",
+    "                \"description\": \"The python math expression to evaluate\",\n",
+    "            },\n",
+    "        },\n",
+    "        \"required\": [\"my_code\"],\n",
+    "        \"additionalProperties\": False\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "tools = [{\"type\": \"function\", \"function\": math_function}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c85c01cc-776e-4a9d-b506-ea0d68fc072d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluate_math_expression(\"import math; interpreter_result = math.log(5)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "858c5848-5835-4dff-9dc0-68babd367e11",
+   "metadata": {},
+   "source": [
+    "# Using the tool in a UI program\n",
+    "\n",
+    "You can ask messages like:\n",
+    "- \"What is 2+2?\"\n",
+    "- \"What is 3 power 2?\"\n",
+    "- \"I have 25 apples. I buy 10 apples. How manny apples do I have?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c119b48b-d4b4-41ae-aa2f-2ec2f09af2f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_message = \"You are a math assistant. \\\n",
+    "Generate python code to give result of a math expression, always name the result 'interpreter_result'. \\\n",
+    "For example when a user asks 'What is 2+2', generate 'interpreter_result = 2+2' and pass this code to the tool. \\\n",
+    "Another example: if a user ask 'What is log(5)' generate 'import math; interpreter_result = math.log(5)'\"\n",
+    "\n",
+    "def chat(message, history):\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
+    "    response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
+    "\n",
+    "    if response.choices[0].finish_reason==\"tool_calls\":\n",
+    "        message = response.choices[0].message\n",
+    "        print(message)\n",
+    "        response = handle_tool_call(message)\n",
+    "        print(response)\n",
+    "        messages.append(message)\n",
+    "        messages.append(response)\n",
+    "        response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
+    "    \n",
+    "    return response.choices[0].message.content\n",
+    "\n",
+    "\n",
+    "def handle_tool_call(message):\n",
+    "    tool_call = message.tool_calls[0]\n",
+    "    arguments = json.loads(tool_call.function.arguments)\n",
+    "    my_code = arguments.get('my_code')\n",
+    "    interpreter_result = evaluate_math_expression(my_code)\n",
+    "    response = {\n",
+    "        \"role\": \"tool\",\n",
+    "        \"content\": json.dumps({\"my_code\": my_code,\"interpreter_result\": interpreter_result}),\n",
+    "        \"tool_call_id\": tool_call.id\n",
+    "    }\n",
+    "    return response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3e50093-d7b6-4972-a8ba-6964f22218d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75c81d73-d2d6-4e6b-8511-94d4a725f595",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}