Merge pull request #3 from DanielMeixner/p4nb

"Add Phase 4 - Efficiency code" - Jupyter Notebook

requirements.txt

@@ -6,3 +6,4 @@ pydantic==2.7.1
 python-dotenv==1.0.1
 quadrant==1.0
 requests==2.32.0
+tiktoken==0.7.0

src-agents/phase3/notebook.ipynb

@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Phase 3 - Function Calling\n",
+    "If not already done run this in the top level folder:\n",
+    "```\n",
+    "pip install -r requirements.txt\n",
+    "```\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import requests\n",
+    "import os\n",
+    "import openai\n",
+    "from enum import Enum\n",
+    "from pydantic import BaseModel\n",
+    "from openai import AzureOpenAI\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "# Load environment variables\n",
+    "if load_dotenv():\n",
+    "    print(\"Found Azure OpenAI API Base Endpoint: \" + os.getenv(\"AZURE_OPENAI_ENDPOINT\"))\n",
+    "else: \n",
+    "    print(\"Azure OpenAI API Base Endpoint not found. Have you configured the .env file?\")\n",
+    "    \n",
+    "API_KEY = os.getenv(\"AZURE_OPENAI_API_KEY\")\n",
+    "API_VERSION = os.getenv(\"OPENAI_API_VERSION\")\n",
+    "RESOURCE_ENDPOINT = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
+    "\n",
+    "\n",
+    "client = AzureOpenAI(\n",
+    "    azure_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\"),\n",
+    "    api_key = os.getenv(\"AZURE_OPENAI_API_KEY\"),\n",
+    "    api_version = os.getenv(\"AZURE_OPENAI_VERSION\")\n",
+    ")\n",
+    "deployment_name = os.getenv(\"AZURE_OPENAI_COMPLETION_DEPLOYMENT_NAME\")\n",
+    "model_name = os.getenv(\"AZURE_OPENAI_COMPLETION_MODEL\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the object model for receiving questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class QuestionType(str, Enum):\n",
+    "    multiple_choice = \"multiple_choice\"\n",
+    "    true_false = \"true_false\"\n",
+    "    popular_choice = \"popular_choice\"\n",
+    "    estimation = \"estimation\"\n",
+    "\n",
+    "class Ask(BaseModel):\n",
+    "    question: str | None = None\n",
+    "    type: QuestionType\n",
+    "    correlationToken: str | None = None\n",
+    "\n",
+    "class Answer(BaseModel):\n",
+    "    answer: str\n",
+    "    correlationToken: str | None = None\n",
+    "    promptTokensUsed: int | None = None\n",
+    "    completionTokensUsed: int | None = None\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample Function "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytz\n",
+    "from datetime import datetime\n",
+    "\n",
+    "def get_current_time(location):\n",
+    "    try:\n",
+    "        # Get the timezone for the city\n",
+    "        timezone = pytz.timezone(location)\n",
+    "\n",
+    "        # Get the current time in the timezone\n",
+    "        now = datetime.now(timezone)\n",
+    "        current_time = now.strftime(\"%I:%M:%S %p\")\n",
+    "\n",
+    "        return current_time\n",
+    "    except:\n",
+    "        return \"Sorry, I couldn't find the timezone for that location.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the model aware of the function\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions = [\n",
+    "        {\n",
+    "            \"type\": \"function\",\n",
+    "            \"function\": {\n",
+    "                \"name\": \"get_current_time\",\n",
+    "                \"description\": \"Get the current time in a given location\",\n",
+    "                \"parameters\": {\n",
+    "                    \"type\": \"object\",\n",
+    "                    \"properties\": {\n",
+    "                        \"location\": {\n",
+    "                            \"type\": \"string\",\n",
+    "                            \"description\": \"The location name. The pytz is used to get the timezone for that location. Location names should be in a format like America/New_York, Asia/Bangkok, Europe/London\",\n",
+    "                        }\n",
+    "                    },\n",
+    "                    \"required\": [\"location\"],\n",
+    "                },\n",
+    "            }\n",
+    "        }\n",
+    "    ]   \n",
+    "available_functions = {\n",
+    "            \"get_current_time\": get_current_time          \n",
+    "        } "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "YOUR Mission: Adjust the function below and reuse it in the main.py file later to deploy to Azure and to update your service\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "async def ask_question(ask: Ask):\n",
+    "    \"\"\"\n",
+    "    Ask a question\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    question = ask.question\n",
+    "    messages= [{\"role\" : \"assistant\", \"content\" : question}\n",
+    "               , { \"role\" : \"system\", \"content\" : \"if asked for time answer in the format HH:MM:SS AM/PM only. Do not answer with a full sentence.\"}\n",
+    "               ]\n",
+    "    first_response = client.chat.completions.create(\n",
+    "        model = deployment_name,\n",
+    "        messages = messages,\n",
+    "        tools = functions,\n",
+    "        tool_choice = \"auto\",\n",
+    "    )\n",
+    "\n",
+    "    print(first_response)\n",
+    "    response_message = first_response.choices[0].message\n",
+    "    tool_calls = response_message.tool_calls\n",
+    "\n",
+    "     # Step 2: check if GPT wanted to call a function\n",
+    "    if tool_calls:\n",
+    "        print(\"Recommended Function call:\")\n",
+    "        print(tool_calls)\n",
+    "        print()\n",
+    "    \n",
+    "        # Step 3: call the function\n",
+    "        messages.append(response_message)\n",
+    "\n",
+    "        for tool_call in tool_calls:\n",
+    "            function_name = tool_call.function.name\n",
+    "            # verify function exists\n",
+    "            if function_name not in available_functions:\n",
+    "                return \"Function \" + function_name + \" does not exist\"\n",
+    "            else:\n",
+    "                print(\"Calling function: \" + function_name)\n",
+    "            function_to_call = available_functions[function_name]\n",
+    "            function_args = json.loads(tool_call.function.arguments)\n",
+    "            print(function_args)\n",
+    "            function_response = function_to_call(**function_args)\n",
+    "            messages.append(\n",
+    "                {\n",
+    "                    \"tool_call_id\": tool_call.id,\n",
+    "                    \"role\": \"tool\",\n",
+    "                    \"name\": function_name,\n",
+    "                    \"content\": function_response,\n",
+    "                }\n",
+    "            ) \n",
+    "            print(\"Addding this message to the next prompt:\") \n",
+    "            print (messages)\n",
+    "            \n",
+    "             # extend conversation with function response\n",
+    "            second_response = client.chat.completions.create(\n",
+    "                model = model_name,\n",
+    "                messages = messages)  # get a new response from the model where it can see the function response\n",
+    "            \n",
+    "            print(\"second_response\")\n",
+    "            \n",
+    "            return second_response.choices[0].message.content\n",
+    "            \n",
+    "\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use this snippet to try your method with several questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "ask = Ask(question=\"What time is it in New York?\", type=QuestionType.estimation)\n",
+    "answer = await ask_question(ask)\n",
+    "print('Answer:', answer )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure you transfer your code changes into main.py (or additional files). Then redeploy your container using this command.\n",
+    "```\n",
+    "bash ./azd-hooks/deploy.sh phase1 $AZURE_ENV_NAME\n",
+    "```\n",
+    "Make sure to provide the URL of your endpoint in the team portal!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}