Spaces:
Runtime error
Runtime error
File size: 7,183 Bytes
c82bf42 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Function Calling Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 0. Import OpenAI API Key\n",
"- Firstly, load OpenAI API key from `.env` file\n",
"- Please create a .env file in the root directory of this project\n",
"and add `OPENAI_API_KEY=sk-*******`\n",
"or just replace the value of OPENAI_API_KEY below!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from os import environ\n",
"from dotenv import load_dotenv\n",
"\n",
"\n",
"load_dotenv()\n",
"OPENAI_API_KEY = environ[\"OPENAI_API_KEY\"]\n",
"assert OPENAI_API_KEY is not None, \"Please set OPENAI_API_KEY in .env file\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Load function call from FunctionCalls class\n",
"\n",
"- `FunctionCalls` class is a collection of functions.\n",
"- It contains many pre-defined functions.\n",
"- The functions will be parsed into `FunctionCall` objects only once when Python starts.\n",
"- You can get `FunctionCall` object by calling `FunctionCalls.get_function_call` method.\n",
"- The argument of `FunctionCalls.get_function_call` method is a function to parse."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from typing import Literal\n",
"from app.models.function_calling.base import FunctionCall\n",
"from app.models.function_calling.functions import FunctionCalls\n",
"\n",
"web_search_function: FunctionCall = FunctionCalls.get_function_call(\n",
" FunctionCalls.web_search\n",
")\n",
"\n",
"# `functions` contains a list of all the functions that can be called\n",
"functions: list[FunctionCall] = [web_search_function]\n",
"\n",
"# \"auto\" will automatically select a function based on the prompt\n",
"function_call: FunctionCall | Literal[\"auto\", \"none\"] = \"auto\"\n",
"\n",
"# \"none\" will not call any function\n",
"function_call = \"none\"\n",
"\n",
"# A specific function is forcibly called by passing the function call object.\n",
"# NOTE: `function_call` must be in the `functions`,\n",
"# if you define `function_call` as a `FunctionCall` object.\n",
"function_call = web_search_function"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"- function name: web_search\n",
"- function arguments: {'query_to_search': 'current price of AAPL stock'}\n"
]
}
],
"source": [
"from app.models.completion_models import FunctionCallParsed\n",
"from app.utils.function_calling.request import request_function_call\n",
"\n",
"# `messages` contains a list of messages to send\n",
"messages: list[dict[str, str]] = [\n",
" {\"role\": \"user\", \"content\": \"What is price of AAPL now?\"}\n",
"]\n",
"\n",
"# `function_call_parsed` is a dictionary containing `name` and `arguments` of the function call\n",
"# NOTE:\n",
"function_call_parsed: FunctionCallParsed = await request_function_call(\n",
" messages=messages,\n",
" functions=functions,\n",
" function_call=function_call,\n",
" model=\"gpt-3.5-turbo\",\n",
" api_key=OPENAI_API_KEY,\n",
")\n",
"\n",
"print(f\"- function name: {function_call_parsed['name']}\")\n",
"print(f\"- function arguments: {function_call_parsed.get('arguments')}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from typing import Optional\n",
"from app.models.completion_models import FunctionCallParsed\n",
"from app.utils.function_calling.request import request_function_call\n",
"\n",
"# `messages` contains a list of messages to send\n",
"messages: list[dict[str, str]] = [\n",
" {\"role\": \"user\", \"content\": \"What is price of AAPL now?\"}\n",
"]\n",
"\n",
"# `function_call_parsed` is a dictionary containing `name` and `arguments` of the function call\n",
"# NOTE: if `function_call` is \"none\", an error will be raised when requesting a function call.\n",
"is_error_raised: bool = False\n",
"try:\n",
" function_call_parsed: Optional[\n",
" FunctionCallParsed\n",
" ] = await request_function_call(\n",
" messages=messages,\n",
" functions=functions,\n",
" function_call=\"none\",\n",
" model=\"gpt-3.5-turbo\",\n",
" api_key=OPENAI_API_KEY,\n",
" )\n",
"except ValueError:\n",
" is_error_raised = True\n",
"\n",
"assert is_error_raised"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Define a custom function"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"- function name: weather\n",
"- function arguments: {'location': 'Boston', 'unit': 'celsius'}\n"
]
}
],
"source": [
"from typing import Annotated\n",
"from app.utils.function_calling.parser import parse_function_call_from_function\n",
"from app.utils.function_calling.request import request_function_call\n",
"\n",
"\n",
"def weather(\n",
" location: Annotated[\n",
" str,\n",
" \"The location to get the weather for.\",\n",
" ],\n",
" unit: Annotated[\n",
" str,\n",
" [\"celsius\", \"fahrenheit\"],\n",
" \"The unit of temperature.\",\n",
" ],\n",
"):\n",
" \"\"\"Get the weather for a location.\"\"\"\n",
" pass\n",
"\n",
"\n",
"messages: list[dict[str, str]] = [\n",
" {\"role\": \"user\", \"content\": \"What’s the weather like in Boston?\"}\n",
"]\n",
"function_call: FunctionCall = parse_function_call_from_function(weather)\n",
"functions: list[FunctionCall] = [function_call]\n",
"function_call_parsed: FunctionCallParsed = await request_function_call(\n",
" messages=messages,\n",
" functions=functions,\n",
" function_call=function_call,\n",
" model=\"gpt-3.5-turbo\",\n",
" api_key=OPENAI_API_KEY,\n",
")\n",
"print(f\"- function name: {function_call_parsed['name']}\")\n",
"print(f\"- function arguments: {function_call_parsed.get('arguments')}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.11.4"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|