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{
"cells": [
{
"cell_type": "markdown",
"id": "eec74b22",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"# Function Calling with Hugging Face Inference Providers\n",
"\n",
"This notebook demonstrates how to use function calling with both OpenAI-compatible and Hugging Face native clients using Hugging Face Inference Providers.\n",
"\n",
"## Overview\n",
"- **OpenAI-Compatible**: Use familiar OpenAI API syntax with HF Inference Providers\n",
"- **Hugging Face Native**: Use HF's native InferenceClient with function calling\n",
"- **Shared Functions**: Reusable function definitions and schemas across both approaches\n",
"\n",
"## Installation\n",
"\n",
"First, install the required dependencies:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f23485bd",
"metadata": {},
"outputs": [],
"source": [
"%pip install openai huggingface-hub python-dotenv\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e39a23ae",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ben/code/inference-providers-mcp/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import json\n",
"import os\n",
"from typing import Dict, Any, Optional\n",
"from openai import OpenAI\n",
"from huggingface_hub import InferenceClient\n",
"from dotenv import load_dotenv\n",
"\n",
"# Load environment variables\n",
"load_dotenv()\n",
"\n",
"# Create a shared configuration\n",
"HF_TOKEN = os.getenv(\"HF_TOKEN\")\n"
]
},
{
"cell_type": "markdown",
"id": "0b45612f",
"metadata": {},
"source": [
"# Define some functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cd13326",
"metadata": {},
"outputs": [],
"source": [
"# Shared function definitions (mock weather API)\n",
"def get_current_weather(location: str) -> Dict[str, Any]:\n",
" \"\"\"Get current weather information for a location.\"\"\"\n",
" return {\n",
" \"location\": location,\n",
" \"temperature\": \"22°C\",\n",
" \"condition\": \"Sunny\",\n",
" \"humidity\": \"65%\",\n",
" \"wind_speed\": \"5 km/h\",\n",
" }\n",
"\n",
"\n",
"def get_weather_forecast(location: str, date: str) -> Dict[str, Any]:\n",
" \"\"\"Get weather forecast for a location on a specific date.\"\"\"\n",
" return {\n",
" \"location\": location,\n",
" \"date\": date,\n",
" \"forecast\": \"Sunny with a chance of rain\",\n",
" \"temperature\": \"20°C\",\n",
" \"humidity\": \"70%\",\n",
" }\n",
"\n",
"\n",
"# Available functions registry\n",
"AVAILABLE_FUNCTIONS = {\n",
" \"get_current_weather\": get_current_weather,\n",
" \"get_weather_forecast\": get_weather_forecast,\n",
"}\n",
"\n",
"# Shared tool schemas (compatible with both OpenAI and HF)\n",
"TOOL_SCHEMAS = [\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_current_weather\",\n",
" \"description\": \"Get current weather information for a location\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"location\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"City and country (e.g., 'Paris, France')\",\n",
" }\n",
" },\n",
" \"required\": [\"location\"],\n",
" },\n",
" },\n",
" },\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_weather_forecast\",\n",
" \"description\": \"Get weather forecast for a location on a specific date\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"location\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"City and country (e.g., 'London, UK')\",\n",
" },\n",
" \"date\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Date in YYYY-MM-DD format\",\n",
" },\n",
" },\n",
" \"required\": [\"location\", \"date\"],\n",
" },\n",
" },\n",
" },\n",
"]\n"
]
},
{
"cell_type": "markdown",
"id": "f48298c3",
"metadata": {},
"source": [
"# Implement a Function Calling app"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7c4b21dc",
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_PROMPT = \"\"\"\n",
"You are a helpful assistant that can answer questions and help with tasks.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "775ae07e",
"metadata": {},
"outputs": [],
"source": [
"def process_function_calls(response_message, messages):\n",
" \"\"\"Process function calls and return updated messages.\"\"\"\n",
" if not response_message.tool_calls:\n",
" return messages, False\n",
"\n",
" # Add assistant's response to messages\n",
" messages.append(response_message)\n",
"\n",
" # Process each tool call\n",
" for tool_call in response_message.tool_calls:\n",
" function_name = tool_call.function.name\n",
" function_args = json.loads(tool_call.function.arguments)\n",
"\n",
" print(f\"🔧 Calling: {function_name}\")\n",
" print(f\"📝 Args: {function_args}\")\n",
"\n",
" # Call the function\n",
" if function_name in AVAILABLE_FUNCTIONS:\n",
" func = AVAILABLE_FUNCTIONS[function_name]\n",
" result = func(**function_args)\n",
" print(f\"✅ Result: {result}\")\n",
"\n",
" # Add function result to messages\n",
" messages.append(\n",
" {\n",
" \"tool_call_id\": tool_call.id,\n",
" \"role\": \"tool\",\n",
" \"name\": function_name,\n",
" \"content\": json.dumps(result),\n",
" }\n",
" )\n",
" else:\n",
" print(f\"❌ Function {function_name} not found\")\n",
"\n",
" return messages, True\n",
"\n",
"\n",
"def chat_with_functions(user_message, client, model) -> str:\n",
" \"\"\"Unified function calling handler for both OpenAI and HF clients.\"\"\"\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": user_message},\n",
" ]\n",
"\n",
" # Initial API call\n",
" response = client.chat.completions.create(\n",
" model=model,\n",
" messages=messages,\n",
" tools=TOOL_SCHEMAS,\n",
" tool_choice=\"auto\",\n",
" )\n",
"\n",
" response_message = response.choices[0].message\n",
"\n",
" # Process function calls if any\n",
" messages, had_tool_calls = process_function_calls(response_message, messages)\n",
"\n",
" if had_tool_calls:\n",
" # Get final response after function calls\n",
" final_response = client.chat.completions.create(\n",
" model=model,\n",
" messages=messages,\n",
" tools=TOOL_SCHEMAS,\n",
" tool_choice=\"auto\",\n",
" )\n",
" final_content = final_response.choices[0].message.content\n",
" else:\n",
" final_content = response_message.content\n",
"\n",
" return final_content\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8b26419b",
"metadata": {},
"outputs": [],
"source": [
"client = OpenAI(\n",
" api_key=HF_TOKEN,\n",
" base_url=\"https://router.huggingface.co/groq/openai/v1\",\n",
")\n",
"\n",
"if False:\n",
" # Initialize HF client with inference provider\n",
" client = InferenceClient(provider=\"groq\")"
]
},
{
"cell_type": "markdown",
"id": "c410bafc",
"metadata": {},
"source": [
"# Demo!"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "32ee9713",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔧 Calling: get_current_weather\n",
"📝 Args: {'location': 'Berlin, Germany'}\n",
"✅ Result: {'location': 'Berlin, Germany', 'temperature': '22°C', 'condition': 'Sunny', 'humidity': '65%', 'wind_speed': '5 km/h'}\n"
]
}
],
"source": [
"query = \"What's the current weather in Berlin?\"\n",
"\n",
"response = chat_with_functions(\n",
" user_message=query,\n",
" client=client,\n",
" model=\"moonshotai/kimi-k2-instruct\",\n",
")"
]
}
],
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"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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