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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,230 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\"\"\" \n", | ||
| "the input will be a string and based on this string i need to call a function \n", | ||
| "\n", | ||
| "option i am considering:\n", | ||
| "\n", | ||
| "- use openai function calling\n", | ||
| "- create my own function calling framework\n", | ||
| "\n", | ||
| "really good blog https://docs.mistral.ai/capabilities/function_calling/\n", | ||
| "\"\"\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#tools \n", | ||
| "def go_to_place_in_map(place):\n", | ||
| " print(f\"going to {place}\")\n", | ||
| " #thats it for now - later i will add more functionalit" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "tools = [\n", | ||
| " {\n", | ||
| " \"type\": \"function\",\n", | ||
| " \"function\": {\n", | ||
| " \"name\": \"go_to_place_in_map\",\n", | ||
| " \"description\": \"Go to a place in the map\",\n", | ||
| " \"parameters\": {\n", | ||
| " \"type\": \"object\",\n", | ||
| " \"properties\": {\n", | ||
| " \"place\": {\n", | ||
| " \"type\": \"string\",\n", | ||
| " \"description\": \"The name of the place to navigate to.\",\n", | ||
| " }\n", | ||
| " },\n", | ||
| " \"required\": [\"place\"],\n", | ||
| " },\n", | ||
| " },\n", | ||
| " }\n", | ||
| "]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import functools\n", | ||
| "\n", | ||
| "names_to_functions = {\n", | ||
| " 'go_to_place_in_map': functools.partial(go_to_place_in_map)\n", | ||
| "}" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#this cell figures out what function to call\n", | ||
| "from openai import OpenAI\n", | ||
| "import os \n", | ||
| "\n", | ||
| "openai_api_key = os.environ.get(\"OPENAI_API_KEY\")\n", | ||
| "client = OpenAI(api_key=openai_api_key)\n", | ||
| "\n", | ||
| "\n", | ||
| "system=f\"\"\"\n", | ||
| "You are a chatbot on top of a map. Your job is to help the user navigate the map. You can use the available functions to help you with this task.\n", | ||
| "\"\"\"\n", | ||
| "user_query=\"Go to berlin.\"\n", | ||
| "user_content=f\"\"\"\n", | ||
| "You are giving a chat conversation and based on this conversation you need to decide if an function from the available functions needs to be called. the available functions are:\n", | ||
| "\n", | ||
| "---\n", | ||
| "{tools}\n", | ||
| "---\n", | ||
| "\n", | ||
| "in the case a function can be called return the function name and the arguments that need to be passed to the function in JSON. Its important that the name of the key for the function name is `function_name` and the name of the key for the arguments is `arguments`. In the case a function cannot be called return an empty JSON object. \n", | ||
| "\n", | ||
| "Following the chat conversation:\n", | ||
| "\n", | ||
| "---\n", | ||
| "{user_query}\n", | ||
| "---\n", | ||
| "\"\"\"\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "completion = client.chat.completions.create(\n", | ||
| " model=\"gpt-3.5-turbo\",\n", | ||
| " response_format={ \"type\": \"json_object\" },\n", | ||
| " temperature=0.0,\n", | ||
| " messages=[\n", | ||
| " {\"role\": \"system\", \"content\": system},\n", | ||
| " {\"role\": \"user\", \"content\": user_content}\n", | ||
| " ]\n", | ||
| ")\n", | ||
| "response_content = completion.choices[0].message.content" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "{\n", | ||
| " \"function_name\": \"go_to_place_in_map\",\n", | ||
| " \"arguments\": {\n", | ||
| " \"place\": \"berlin\"\n", | ||
| " }\n", | ||
| "}\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "print(response_content)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "go_to_place_in_map {'place': 'berlin'}\n", | ||
| "going to berlin\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#this cell is executing a function if there is one to execute\n", | ||
| "import json\n", | ||
| "\n", | ||
| "response_json = json.loads(response_content)\n", | ||
| "\n", | ||
| "function_name = response_json[\"function_name\"]\n", | ||
| "function_params = response_json[\"arguments\"]\n", | ||
| "print(function_name, function_params)\n", | ||
| "\n", | ||
| "function_result = names_to_functions[function_name](**function_params)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 11, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "If you need any more information or further assistance while you're in Berlin, feel free to ask. Enjoy your time in the city!\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#generate final answer\n", | ||
| "prompt_for_final_response=\"Provide a final response to the user\"\n", | ||
| "\n", | ||
| "completion = client.chat.completions.create(\n", | ||
| " model=\"gpt-4-turbo\",\n", | ||
| " temperature=0.0,\n", | ||
| " messages = [\n", | ||
| " {\"role\": \"system\", \"content\": system},\n", | ||
| " {\"role\": \"user\", \"content\": user_query},\n", | ||
| " {\"role\": \"assistant\", \"content\": response_content},\n", | ||
| " {\"role\": \"user\", \"content\": prompt_for_final_response}\n", | ||
| "]\n", | ||
| ")\n", | ||
| "response_content = completion.choices[0].message.content\n", | ||
| "print(response_content)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "base", | ||
| "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": "3.11.7" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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