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506 lines
14 KiB
Plaintext
506 lines
14 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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"# Function Calling with OpenAI APIs"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import openai\n",
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"import json\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv()\n",
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"\n",
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"# set openai api key\n",
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"openai.api_key = os.environ['OPENAI_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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"### Define a Get Completion Function"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def get_completion(messages, model=\"gpt-3.5-turbo-1106\", temperature=0, max_tokens=300, tools=None, tool_choice=None):\n",
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" response = openai.chat.completions.create(\n",
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" model=model,\n",
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" messages=messages,\n",
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" temperature=temperature,\n",
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" max_tokens=max_tokens,\n",
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" tools=tools,\n",
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" tool_choice=tool_choice\n",
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" )\n",
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" return response.choices[0].message"
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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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"### Define Dummy Function"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Defines a dummy function to get the current weather\n",
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"def get_current_weather(location, unit=\"fahrenheit\"):\n",
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" \"\"\"Get the current weather in a given location\"\"\"\n",
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" weather = {\n",
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" \"location\": location,\n",
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" \"temperature\": \"50\",\n",
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" \"unit\": unit,\n",
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" }\n",
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"\n",
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" return json.dumps(weather)"
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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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"### Define Functions\n",
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"\n",
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"As demonstrated in the OpenAI documentation, here is a simple example of how to define the functions that are going to be part of the request. \n",
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"\n",
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"The descriptions are important because these are passed directly to the LLM and the LLM will use the description to determine whether to use the functions or how to use/call."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# define a function as tools\n",
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"tools = [\n",
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" {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"get_current_weather\",\n",
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" \"description\": \"Get the current weather in a given location\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"location\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
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" },\n",
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" \"unit\": {\n",
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" \"type\": \"string\", \n",
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" \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
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" },\n",
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" \"required\": [\"location\"],\n",
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" },\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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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# define a list of messages\n",
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"\n",
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"messages = [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"What is the weather like in London?\"\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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"cell_type": "code",
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"execution_count": 6,
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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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"ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_GYg3yhSh1bbMabne9brwTqmA', function=Function(arguments='{\"location\":\"London\",\"unit\":\"celsius\"}', name='get_current_weather'), type='function')])\n"
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]
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}
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],
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"source": [
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"response = get_completion(messages, tools=tools)\n",
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"print(response)"
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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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"response.tool_calls[0].function.arguments"
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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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"We can now capture the arguments:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"args = json.loads(response.tool_calls[0].function.arguments)"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'{\"location\": \"London\", \"temperature\": \"50\", \"unit\": \"celsius\"}'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_current_weather(**args)"
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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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"### Controlling Function Calling Behavior"
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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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"Let's say we were interested in designing this `function_calling` functionality in the context of an LLM-powered conversational agent. Your solution should then know what function to call or if it needs to be called at all. Let's try a simple example of a greeting message:"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"messages = [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"Hello! How are you?\",\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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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ChatCompletionMessage(content=\"Hello! I'm here and ready to assist you. How can I help you today?\", role='assistant', function_call=None, tool_calls=None)"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_completion(messages, tools=tools)"
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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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"You can specify the behavior you want from function calling, which is desired to control the behavior of your system. By default, the model decide on its own whether to call a function and which function to call. This is achieved by setting `tool_choice: \"auto\"` which is the default setting. "
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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": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ChatCompletionMessage(content=\"Hello! I'm here and ready to assist you. How can I help you today?\", role='assistant', function_call=None, tool_calls=None)"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_completion(messages, tools=tools, tool_choice=\"auto\")"
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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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"Setting `tool_choice: \"none\"` forces the model to not use any of the functions provided. "
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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": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ChatCompletionMessage(content=\"Hello! I'm here and ready to assist you. How can I help you today?\", role='assistant', function_call=None, tool_calls=None)"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_completion(messages, tools=tools, tool_choice=\"none\")"
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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": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ChatCompletionMessage(content='I will check the current weather in London for you.', role='assistant', function_call=None, tool_calls=None)"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"What's the weather like in London?\",\n",
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" }\n",
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"]\n",
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"get_completion(messages, tools=tools, tool_choice=\"none\")"
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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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"You can also force the model to choose a function if that's the behavior you want in your application. Example:"
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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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{
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"data": {
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"text/plain": [
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"ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_ctUCZtwxZYhfF3sirByNY2qC', function=Function(arguments='{\"location\":\"London\",\"unit\":\"celsius\"}', name='get_current_weather'), type='function')])"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"What's the weather like in London?\",\n",
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" }\n",
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"]\n",
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"get_completion(messages, tools=tools, tool_choice={\"type\": \"function\", \"function\": {\"name\": \"get_current_weather\"}})"
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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 OpenAI APIs also support parallel function calling that can call multiple functions in one turn. "
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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": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_GhyrfrY9QWkrJs9vKqxpGW22', function=Function(arguments='{\"location\": \"London\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function'), ChatCompletionMessageToolCall(id='call_Cl9fqGh1AZgi4yD8n5Lrc6NE', function=Function(arguments='{\"location\": \"Belmopan\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function')])"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"What's the weather like in London and Belmopan in the coming days?\",\n",
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" }\n",
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"]\n",
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"get_completion(messages, tools=tools)"
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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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"You can see in the response above that the response contains information from the function calls for the two locations queried. "
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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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"### Function Calling Response for Model Feedback\n",
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"\n",
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"You might also be interested in developing an agent that passes back the result obtained after calling your APIs with the inputs generated from function calling. Let's look at an example next:\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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"messages = []\n",
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"messages.append({\"role\": \"user\", \"content\": \"What's the weather like in Boston!\"})\n",
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"assistant_message = get_completion(messages, tools=tools, tool_choice=\"auto\")\n",
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"assistant_message = json.loads(assistant_message.model_dump_json())\n",
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"assistant_message[\"content\"] = str(assistant_message[\"tool_calls\"][0][\"function\"])\n",
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"\n",
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"#a temporary patch but this should be handled differently\n",
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"# remove \"function_call\" from assistant message\n",
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"del assistant_message[\"function_call\"]"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"messages.append(assistant_message)"
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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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"We then append the results of the `get_current_weather` function and pass it back to the model using a `tool` role."
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get the weather information to pass back to the model\n",
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"weather = get_current_weather(messages[1][\"tool_calls\"][0][\"function\"][\"arguments\"])\n",
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"\n",
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"messages.append({\"role\": \"tool\",\n",
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" \"tool_call_id\": assistant_message[\"tool_calls\"][0][\"id\"],\n",
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" \"name\": assistant_message[\"tool_calls\"][0][\"function\"][\"name\"],\n",
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" \"content\": weather})"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"final_response = get_completion(messages, tools=tools)"
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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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"data": {
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"text/plain": [
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"ChatCompletionMessage(content='The current temperature in Boston, MA is 50°F.', role='assistant', function_call=None, tool_calls=None)"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"final_response"
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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": "papersql",
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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",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.0"
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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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|
}
|