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(docs) parallel function calling with Azure OpenAI
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ishaan-jaff
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1 changed files with 219 additions and 2 deletions
221
cookbook/Parallel_function_calling.ipynb
vendored
221
cookbook/Parallel_function_calling.ipynb
vendored
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@ -34,6 +34,17 @@
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"!pip install litellm"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"This tutorial walks through the steps doing parallel function calling using\n",
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" - OpenAI\n",
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" - Azure OpenAI"
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],
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"metadata": {
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"id": "sG5ANaazjU0g"
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}
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},
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{
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"cell_type": "code",
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"source": [
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@ -50,6 +61,8 @@
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{
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"cell_type": "markdown",
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"source": [
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"\n",
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"# OpenAI gpt-3.5-turbo-1106\n",
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"## Step 1: send the conversation and available functions to the model"
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],
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"metadata": {
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@ -248,14 +261,218 @@
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Using Azure OpenAI"
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],
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"metadata": {
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"id": "1cIIFEvXjofp"
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}
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},
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{
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"cell_type": "code",
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"source": [],
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"source": [
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"# set Azure env variables\n",
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"import os\n",
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"os.environ['AZURE_API_KEY'] = \"\" # litellm reads AZURE_API_KEY from .env and sends the request\n",
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"os.environ['AZURE_API_BASE'] = \"https://openai-gpt-4-test-v-1.openai.azure.com/\"\n",
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"os.environ['AZURE_API_VERSION'] = \"2023-07-01-preview\""
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],
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"metadata": {
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"id": "lG9mUnModeeE"
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},
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"execution_count": null,
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"execution_count": 32,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Step 1"
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],
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"metadata": {
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"id": "17S-Ysksj-E_"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import litellm\n",
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"import json\n",
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"# Example dummy function hard coded to return the same weather\n",
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"# In production, this could be your backend API or an external API\n",
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"def get_current_weather(location, unit=\"fahrenheit\"):\n",
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" \"\"\"Get the current weather in a given location\"\"\"\n",
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" if \"tokyo\" in location.lower():\n",
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" return json.dumps({\"location\": \"Tokyo\", \"temperature\": \"10\", \"unit\": \"celsius\"})\n",
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" elif \"san francisco\" in location.lower():\n",
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" return json.dumps({\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"})\n",
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" elif \"paris\" in location.lower():\n",
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" return json.dumps({\"location\": \"Paris\", \"temperature\": \"22\", \"unit\": \"celsius\"})\n",
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" else:\n",
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" return json.dumps({\"location\": location, \"temperature\": \"unknown\"})\n",
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"\n",
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"messages = [{\"role\": \"user\", \"content\": \"What's the weather like in San Francisco, Tokyo, and Paris?\"}]\n",
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"tools = [\n",
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" {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"get_current_weather\",\n",
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" \"description\": \"Get the current weather in a given location\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"location\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
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" },\n",
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" \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
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" },\n",
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" \"required\": [\"location\"],\n",
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" },\n",
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" },\n",
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" }\n",
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"]\n",
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"\n",
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"response = litellm.completion(\n",
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" model=\"azure/chatgpt-functioncalling\", # model = azure/<your-azure-deployment-name>\n",
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" messages=messages,\n",
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" tools=tools,\n",
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" tool_choice=\"auto\", # auto is default, but we'll be explicit\n",
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")\n",
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"print(\"\\nLLM Response1:\\n\", response)\n",
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"response_message = response.choices[0].message\n",
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"tool_calls = response.choices[0].message.tool_calls\n",
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"print(\"\\nTool Choice:\\n\", tool_calls)\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "boAIHLEXj80m",
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"outputId": "00afcf09-5b6b-4805-c374-ba089cc6eb43"
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},
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"execution_count": 33,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"LLM Response1:\n",
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" ModelResponse(id='chatcmpl-8MOBPvEnqG7qitkmVqZmCrzSGEmDj', choices=[Choices(finish_reason='tool_calls', index=0, message=Message(content=None, role='assistant', tool_calls=[ChatCompletionMessageToolCall(id='call_7gZ0PkmmmgzTOxfF01ATp0U5', function=Function(arguments='{\\n \"location\": \"San Francisco, CA\"\\n}', name='get_current_weather'), type='function')]))], created=1700346867, model='gpt-35-turbo', object='chat.completion', system_fingerprint=None, usage={'completion_tokens': 19, 'prompt_tokens': 88, 'total_tokens': 107}, _response_ms=833.4319999999999)\n",
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"\n",
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"Tool Choice:\n",
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" [ChatCompletionMessageToolCall(id='call_7gZ0PkmmmgzTOxfF01ATp0U5', function=Function(arguments='{\\n \"location\": \"San Francisco, CA\"\\n}', name='get_current_weather'), type='function')]\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Step 2"
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],
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"metadata": {
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"id": "hqh1y1IMkmGO"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Check if the model wants to call a function\n",
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"if tool_calls:\n",
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" # Execute the functions and prepare responses\n",
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" available_functions = {\n",
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" \"get_current_weather\": get_current_weather,\n",
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" }\n",
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"\n",
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" messages.append(response_message) # Extend conversation with assistant's reply\n",
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"\n",
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" for tool_call in tool_calls:\n",
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" print(f\"\\nExecuting tool call\\n{tool_call}\")\n",
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" function_name = tool_call.function.name\n",
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" function_to_call = available_functions[function_name]\n",
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" function_args = json.loads(tool_call.function.arguments)\n",
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" # calling the get_current_weather() function\n",
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" function_response = function_to_call(\n",
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" location=function_args.get(\"location\"),\n",
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" unit=function_args.get(\"unit\"),\n",
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" )\n",
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" print(f\"Result from tool call\\n{function_response}\\n\")\n",
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"\n",
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" # Extend conversation with function response\n",
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" messages.append(\n",
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" {\n",
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" \"tool_call_id\": tool_call.id,\n",
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" \"role\": \"tool\",\n",
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" \"name\": function_name,\n",
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" \"content\": function_response,\n",
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" }\n",
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" )\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "FGu7DY7PkOiG",
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"outputId": "96d39ae7-7fc8-4dd8-c82f-5ee9a486724c"
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},
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"execution_count": 34,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"Executing tool call\n",
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"ChatCompletionMessageToolCall(id='call_7gZ0PkmmmgzTOxfF01ATp0U5', function=Function(arguments='{\\n \"location\": \"San Francisco, CA\"\\n}', name='get_current_weather'), type='function')\n",
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"Result from tool call\n",
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"{\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"}\n",
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"\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Step 3"
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],
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"metadata": {
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"id": "4MjYyeajkpBl"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"second_response = litellm.completion(\n",
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" model=\"azure/chatgpt-functioncalling\",\n",
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" messages=messages,\n",
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")\n",
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"print(\"Second Response\\n\", second_response)\n",
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"print(\"Second Response Message\\n\", second_response.choices[0].message.content)\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "qHgXyZq1kqGn",
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"outputId": "61a30470-d7f5-484d-c42b-681c9b60b34a"
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},
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"execution_count": 36,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Second Response\n",
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" ModelResponse(id='chatcmpl-8MOC90vwZ2LHX0DE796XYtsOxdGcc', choices=[Choices(finish_reason='stop', index=0, message=Message(content='The current weather in San Francisco is 72°F.', role='assistant'))], created=1700346913, model='gpt-35-turbo', object='chat.completion', system_fingerprint=None, usage={'completion_tokens': 11, 'prompt_tokens': 69, 'total_tokens': 80}, _response_ms=824.882)\n",
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"Second Response Message\n",
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" The current weather in San Francisco is 72°F.\n"
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]
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}
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]
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}
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]
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}
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