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examples/function_calling/fireworks_function_calling_demo.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "1bfdf168", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import openai\n", | ||
| "import json" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "eb849e97", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "{\n", | ||
| " \"content\": null,\n", | ||
| " \"role\": \"assistant\",\n", | ||
| " \"function_call\": null,\n", | ||
| " \"tool_calls\": [\n", | ||
| " {\n", | ||
| " \"id\": \"call_GKEtwoiSicLsGwT60Pkd4Xpz\",\n", | ||
| " \"function\": {\n", | ||
| " \"arguments\": \"{\\\"metric\\\": \\\"net_income\\\", \\\"financial_year\\\": 2022, \\\"company\\\": \\\"Nike\\\"}\",\n", | ||
| " \"name\": \"get_financial_data\"\n", | ||
| " },\n", | ||
| " \"type\": \"function\",\n", | ||
| " \"index\": 0\n", | ||
| " }\n", | ||
| " ]\n", | ||
| "}\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "client = openai.OpenAI(\n", | ||
| " base_url = \"https://api.fireworks.ai/inference/v1\",\n", | ||
| " api_key = \"<YOUR_FIREWORKS_API_KEY>\"\n", | ||
| ")\n", | ||
| "\n", | ||
| "messages = [\n", | ||
| " {\"role\": \"system\", \"content\": f\"You are a helpful assistant with access to functions. Use them if required.\"},\n", | ||
| " {\"role\": \"user\", \"content\": \"What are Nike's net income in 2022?\"}\n", | ||
| "]\n", | ||
| "\n", | ||
| "# Describe the functions available to the agent in great detail in JSON Schema\n", | ||
| "tools = [\n", | ||
| " {\n", | ||
| " \"type\": \"function\",\n", | ||
| " \"function\": {\n", | ||
| " \"name\": \"get_financial_data\",\n", | ||
| " \"description\": \"Get financial data for a company given the metric and year.\",\n", | ||
| " \"parameters\": {\n", | ||
| " \"type\": \"object\",\n", | ||
| " \"properties\": {\n", | ||
| " \"metric\": {\n", | ||
| " \"type\": \"enum\",\n", | ||
| " \"choices\": [\"net_income\", \"revenue\", \"ebdita\"],\n", | ||
| " },\n", | ||
| " \"financial_year\": {\"type\": \"int\", \n", | ||
| " \"description\": \"Year for which we want to get financial data.\"\n", | ||
| " },\n", | ||
| " \"company\": {\n", | ||
| " \"type\": \"string\",\n", | ||
| " \"description\": \"Name of the company for which we want to get financial data.\"\n", | ||
| " }\n", | ||
| " },\n", | ||
| " \"required\": [\"metric\", \"financial_year\", \"company\"],\n", | ||
| " },\n", | ||
| " },\n", | ||
| " }\n", | ||
| "]\n", | ||
| "\n", | ||
| "chat_completion = client.chat.completions.create(\n", | ||
| " model=\"accounts/fireworks/models/fw-function-call-34b-v0\",\n", | ||
| " messages=messages,\n", | ||
| " tools=tools,\n", | ||
| " temperature=0.1\n", | ||
| ")\n", | ||
| "print(chat_completion.choices[0].message.model_dump_json(indent=4))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "3b8f1629", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "metric='net_income' financial_year=2022 company='Nike'\n", | ||
| "{'net_income': 6046000000}\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Locally available tool implementation. Can be replaced by any remote API\n", | ||
| "def get_financial_data(metric: str, financial_year: int, company: str):\n", | ||
| " print(f\"{metric=} {financial_year=} {company=}\")\n", | ||
| " if metric == \"net_income\" and financial_year == 2022 and company == \"Nike\":\n", | ||
| " return {\"net_income\": 6_046_000_000}\n", | ||
| " else:\n", | ||
| " raise NotImplementedError()\n", | ||
| "\n", | ||
| "function_call = chat_completion.choices[0].message.tool_calls[0].function\n", | ||
| "tool_response = locals()[function_call.name](**json.loads(function_call.arguments))\n", | ||
| "print(tool_response)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "id": "2db51424", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Nike's net income for the year 2022 was $6,046,000,000. \n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "agent_response = chat_completion.choices[0].message\n", | ||
| "\n", | ||
| "# Append the response from the agent\n", | ||
| "messages.append(\n", | ||
| " {\n", | ||
| " \"role\": agent_response.role, \n", | ||
| " \"content\": \"\",\n", | ||
| " \"tool_calls\": [\n", | ||
| " tool_call.model_dump()\n", | ||
| " for tool_call in chat_completion.choices[0].message.tool_calls\n", | ||
| " ]\n", | ||
| " }\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Append the response from the tool \n", | ||
| "messages.append(\n", | ||
| " {\n", | ||
| " \"role\": \"function\",\n", | ||
| " \"content\": json.dumps(tool_response)\n", | ||
| " }\n", | ||
| ")\n", | ||
| "\n", | ||
| "next_chat_completion = client.chat.completions.create(\n", | ||
| " model=\"accounts/fireworks/models/fw-function-call-34b-v0\",\n", | ||
| " messages=messages,\n", | ||
| " tools=tools,\n", | ||
| " temperature=0.1\n", | ||
| ")\n", | ||
| "\n", | ||
| "print(next_chat_completion.choices[0].message.content)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "81a6b85b", | ||
| "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": "3.10.12" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |