Simple jupyter notebook showing how to do some tool calling in GPT.

tool_calling_chat_gpt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "import json\n",
    "\n",
    "from langchain.tools.render import format_tool_to_openai_function, format_tool_to_openai_tool\n",
    "from langchain.agents import tool\n",
    "\n",
    "openai.api_key = os.getenv(\"OPENAI_API_KEY\")\n",
    "GPT_MODEL_NAME = \"gpt-3.5-turbo-16k-0613\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "workflow = [\n",
    "\t\"Please add 1 and 5\",\n",
    "\t\"please multiply 5 by: \",\n",
    "\t\"please divide the following number up by 15: \",\n",
    "\t\"Say duck this many times please: \"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1 + 5 is equal to 6.', '5 multiplied by 1 is equal to 5.', 'To divide a number by 15, I need to know the number you want to divide. Please provide the number you want to divide.', \"I'm sorry, but I cannot fulfill your request.\"]\n"
     ]
    }
   ],
   "source": [
    "outputs = []\n",
    "output = \"\"\n",
    "for instruction in workflow:\n",
    "    messages = [{\"role\": \"system\", \"content\": \"\"},\n",
    "                {\"role\": \"user\", \"content\": instruction + f\" {output}\"}]\n",
    "    output = openai.chat.completions.create(model=GPT_MODEL_NAME, messages=messages)\n",
    "    outputs.append(output.choices[0].message.content)\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_two_numbers(a: float, b: float) -> float:\n",
    "    \"\"\"This function will add a and b together and return the result.\"\"\"\n",
    "    return a + b\n",
    "\n",
    "def multiply_two_numbers(a: float, b: float) -> float:\n",
    "    \"\"\"This function will multiply a by be and return the result.\"\"\"\n",
    "    return a * b\n",
    "\n",
    "def divide_two_numbers(a: float, b: float) -> float:\n",
    "    \"\"\"This function will divide a by b and return the result.\"\"\"\n",
    "    return a / b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'function',\n",
       " 'function': {'name': 'add_two_numbers',\n",
       "  'description': 'add_two_numbers(a: float, b: float) -> float - This function will add a and b together and return the result.',\n",
       "  'parameters': {'title': 'add_two_numbersSchemaSchema',\n",
       "   'type': 'object',\n",
       "   'properties': {'a': {'title': 'A', 'type': 'number'},\n",
       "    'b': {'title': 'B', 'type': 'number'}},\n",
       "   'required': ['a', 'b']}}}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_tool_to_openai_tool(tool(add_two_numbers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': 'AddTwoNumbers',\n",
       " 'type': 'object',\n",
       " 'properties': {'a': {'title': 'A', 'type': 'number'},\n",
       "  'b': {'title': 'B', 'type': 'number'}},\n",
       " 'required': ['a', 'b']}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pydantic import BaseModel\n",
    "\n",
    "class AddTwoNumbers(BaseModel):\n",
    "    a: float\n",
    "    b: float\n",
    "\n",
    "AddTwoNumbers.schema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "add_two_numbers\n",
      "{\n",
      "  \"a\": 1,\n",
      "  \"b\": 5\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "messages = [{\"role\": \"system\", \"content\": \"\"},\n",
    "            {\"role\": \"user\", \"content\": \"Please add 1 and 5\"}]\n",
    "function = format_tool_to_openai_tool(tool(add_two_numbers))\n",
    "result = openai.chat.completions.create(model=GPT_MODEL_NAME, messages=messages, tools=[function])\n",
    "print(result.choices[0].message.tool_calls[0].function.name)\n",
    "print(result.choices[0].message.tool_calls[0].function.arguments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'chatcmpl-8SGersCGYmrdnIu67Or81Jhg2iNAS',\n",
       " 'choices': [{'finish_reason': 'tool_calls',\n",
       "   'index': 0,\n",
       "   'message': {'content': None,\n",
       "    'role': 'assistant',\n",
       "    'function_call': None,\n",
       "    'tool_calls': [{'id': 'call_bxEckzbIygLcQtixYHScBfCo',\n",
       "      'function': {'arguments': '{\\n  \"a\": 1,\\n  \"b\": 5\\n}',\n",
       "       'name': 'add_two_numbers'},\n",
       "      'type': 'function'}]}}],\n",
       " 'created': 1701747909,\n",
       " 'model': 'gpt-3.5-turbo-16k-0613',\n",
       " 'object': 'chat.completion',\n",
       " 'system_fingerprint': None,\n",
       " 'usage': {'completion_tokens': 23, 'prompt_tokens': 78, 'total_tokens': 101}}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.loads(result.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "f = globals()[result.choices[0].message.tool_calls[0].function.name]\n",
    "print(f(**json.loads(result.choices[0].message.tool_calls[0].function.arguments)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[None, None, None, None]\n"
     ]
    }
   ],
   "source": [
    "# Use all of the functions\n",
    "funcs = [\"add_two_numbers\", \"multiply_two_numbers\", \"divide_two_numbers\"]\n",
    "functions = [format_tool_to_openai_tool(tool(globals()[t])) for t in funcs]\n",
    "\n",
    "outputs = []\n",
    "output = \"\"\n",
    "for instruction in workflow:\n",
    "    messages = [{\"role\": \"system\", \"content\": \"\"},\n",
    "                {\"role\": \"user\", \"content\": instruction + f\" {output}\"}]\n",
    "    output = openai.chat.completions.create(model=GPT_MODEL_NAME, messages=messages, tools=functions)\n",
    "    outputs.append(output.choices[0].message.content)\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'chatcmpl-8SGki2ypSTmdwMo0WtYjKrM9AYsb7',\n",
       " 'choices': [{'finish_reason': 'tool_calls',\n",
       "   'index': 0,\n",
       "   'message': {'content': None,\n",
       "    'role': 'assistant',\n",
       "    'function_call': None,\n",
       "    'tool_calls': [{'id': 'call_V5kcyVWTeIQMHxzUGsoYS8hy',\n",
       "      'function': {'arguments': '{\\n  \"a\": 5,\\n  \"b\": 15\\n}',\n",
       "       'name': 'multiply_two_numbers'},\n",
       "      'type': 'function'}]}}],\n",
       " 'created': 1701748272,\n",
       " 'model': 'gpt-3.5-turbo-16k-0613',\n",
       " 'object': 'chat.completion',\n",
       " 'system_fingerprint': None,\n",
       " 'usage': {'completion_tokens': 23, 'prompt_tokens': 352, 'total_tokens': 375}}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.loads(output.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gpt_process_function_calling(gpt_response):\n",
    "    # Check to see if the call terminated on a function call.\n",
    "    finish_reason = gpt_response.choices[0].finish_reason\n",
    "    # We check if we finished for an explicit function call or if we finished because of a long query\n",
    "    # and gpt suggests a function call\n",
    "    if finish_reason == \"tool_calls\":\n",
    "        function_name = gpt_response.choices[0].message.tool_calls[0].function.name\n",
    "        arguments = json.loads(gpt_response.choices[0].message.tool_calls[0].function.arguments)\n",
    "        func = globals()[function_name]\n",
    "        return func(**arguments)\n",
    "    else:\n",
    "        # if not just pass the response through.\n",
    "        return gpt_response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'Please add 1 and 5 '}]\n",
      "ChatCompletion(id='chatcmpl-8SGCqx9X6pS41KV7MerP4wzTCddJT', choices=[Choice(finish_reason='tool_calls', index=0, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_I5WrEI8EvSgqct7zDHm538Ug', function=Function(arguments='{\\n  \"a\": 1,\\n  \"b\": 5\\n}', name='add_two_numbers'), type='function')]))], created=1701746172, model='gpt-3.5-turbo-16k-0613', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=23, prompt_tokens=173, total_tokens=196))\n",
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'please multiply 5 by the result 6'}]\n",
      "ChatCompletion(id='chatcmpl-8SGCsXdA1Jwhmz1c1AmCGj3Lsa3pc', choices=[Choice(finish_reason='tool_calls', index=0, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_f6sK2Yj9XtA6S33f1Pkx7mLr', function=Function(arguments='{\\n  \"a\": 5,\\n  \"b\": 6\\n}', name='multiply_two_numbers'), type='function')]))], created=1701746174, model='gpt-3.5-turbo-16k-0613', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=23, prompt_tokens=174, total_tokens=197))\n",
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'please divide the result by 15 30'}]\n",
      "ChatCompletion(id='chatcmpl-8SGCtxKcvbc0dDPVOZUREiqVdRgMg', choices=[Choice(finish_reason='tool_calls', index=0, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_1RyQxpVtHxG8wWwayuiCS5a3', function=Function(arguments='{\\n  \"a\": 30,\\n  \"b\": 15\\n}', name='divide_two_numbers'), type='function')]))], created=1701746175, model='gpt-3.5-turbo-16k-0613', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=23, prompt_tokens=174, total_tokens=197))\n",
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'Say duck this many times please:  2.0'}]\n",
      "ChatCompletion(id='chatcmpl-8SGCuWXi8iSTYAB0tLMANEkWfQPcQ', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='duck duck', role='assistant', function_call=None, tool_calls=None))], created=1701746176, model='gpt-3.5-turbo-16k-0613', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=3, prompt_tokens=177, total_tokens=180))\n",
      "[6, 30, 2.0, 'duck duck']\n",
      "[6, 30, 2.0, 'duck duck']\n"
     ]
    }
   ],
   "source": [
    "# Use all of the functions\n",
    "funcs = [\"add_two_numbers\", \"multiply_two_numbers\", \"divide_two_numbers\"]\n",
    "functions = [format_tool_to_openai_tool(tool(globals()[t])) for t in funcs]\n",
    "\n",
    "outputs = []\n",
    "output = \"\"\n",
    "for instruction in workflow:\n",
    "    messages = [{\"role\": \"system\", \"content\": \"\"},\n",
    "                {\"role\": \"user\", \"content\": instruction + f\" {output}\"}]\n",
    "    print(messages)\n",
    "    output = openai.chat.completions.create(model=GPT_MODEL_NAME, messages=messages, tools=functions)\n",
    "    print(output)\n",
    "    output = gpt_process_function_calling(output)\n",
    "    outputs.append(output)\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer, util\n",
    "import inspect\n",
    "import types\n",
    "import torch\n",
    "\n",
    "# Assign the closest two tools for each step in the workflow\n",
    "embedder = SentenceTransformer('all-MiniLM-L6-v2')\n",
    "\n",
    "# Corpus with example sentences\n",
    "function_registry = [\"add_two_numbers\", \"multiply_two_numbers\", \"divide_two_numbers\"]\n",
    "function_descriptions = [inspect.getsource(globals()[func]) for func in function_registry]\n",
    "function_embeddings = embedder.encode(function_descriptions, convert_to_tensor=True)\n",
    "\n",
    "top_k = min(1, len(function_descriptions))\n",
    "workflow_functions = []\n",
    "for query in workflow:\n",
    "    query_embedding = embedder.encode(query, convert_to_tensor=True)\n",
    "\n",
    "    # We use cosine-similarity and torch.topk to find the highest 5 scores\n",
    "    cos_scores = util.cos_sim(query_embedding, function_embeddings)[0]\n",
    "    top_results = torch.topk(cos_scores, k=top_k)\n",
    "    if max(cos_scores) > .2:\n",
    "        workflow_functions.append([function_registry[i] for i in top_results.indices.tolist()])\n",
    "    else:\n",
    "        workflow_functions.append([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['add_two_numbers'], ['multiply_two_numbers'], ['divide_two_numbers'], []]\n"
     ]
    }
   ],
   "source": [
    "print(workflow_functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'Please add 1 and 5 '}]\n",
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'please multiply 5 by:  6'}]\n",
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'please divide the following number up by 15:  30'}]\n",
      "[{'role': 'system', 'content': ''}, {'role': 'user', 'content': 'Say duck this many times please:  2.0'}]\n"
     ]
    }
   ],
   "source": [
    "outputs = []\n",
    "output = \"\"\n",
    "for instruction, functions in zip(workflow, workflow_functions):\n",
    "    kwargs = {}\n",
    "    if len(functions) > 0:\n",
    "        functions = [format_tool_to_openai_tool(tool(globals()[t])) for t in functions]\n",
    "        kwargs = {\"tools\": functions}\n",
    "    messages = [{\"role\": \"system\", \"content\": \"\"},\n",
    "                {\"role\": \"user\", \"content\": instruction + f\" {output}\"}]\n",
    "    print(messages)\n",
    "    output = openai.chat.completions.create(model=GPT_MODEL_NAME,\n",
    "                                            messages=messages,\n",
    "                                            **kwargs)\n",
    "    output = gpt_process_function_calling(output)\n",
    "    outputs.append(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 30, 2.0, 'Duck duck.']\n"
     ]
    }
   ],
   "source": [
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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