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| import sys, os | |
| import traceback | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| import os, io | |
| sys.path.insert( | |
| 0, os.path.abspath("../..") | |
| ) # Adds the parent directory to the system path | |
| import pytest | |
| import litellm | |
| from litellm import embedding, completion, completion_cost, Timeout | |
| from litellm import RateLimitError | |
| import pytest | |
| litellm.num_retries = 0 | |
| litellm.cache = None | |
| # litellm.set_verbose=True | |
| import json | |
| # litellm.success_callback = ["langfuse"] | |
| def get_current_weather(location, unit="fahrenheit"): | |
| """Get the current weather in a given location""" | |
| if "tokyo" in location.lower(): | |
| return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) | |
| elif "san francisco" in location.lower(): | |
| return json.dumps( | |
| {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"} | |
| ) | |
| elif "paris" in location.lower(): | |
| return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) | |
| else: | |
| return json.dumps({"location": location, "temperature": "unknown"}) | |
| # Example dummy function hard coded to return the same weather | |
| # In production, this could be your backend API or an external API | |
| def test_parallel_function_call(): | |
| try: | |
| # Step 1: send the conversation and available functions to the model | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": "What's the weather like in San Francisco, Tokyo, and Paris?", | |
| } | |
| ] | |
| tools = [ | |
| { | |
| "type": "function", | |
| "function": { | |
| "name": "get_current_weather", | |
| "description": "Get the current weather in a given location", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "location": { | |
| "type": "string", | |
| "description": "The city and state, e.g. San Francisco, CA", | |
| }, | |
| "unit": { | |
| "type": "string", | |
| "enum": ["celsius", "fahrenheit"], | |
| }, | |
| }, | |
| "required": ["location"], | |
| }, | |
| }, | |
| } | |
| ] | |
| response = litellm.completion( | |
| model="gpt-3.5-turbo-1106", | |
| messages=messages, | |
| tools=tools, | |
| tool_choice="auto", # auto is default, but we'll be explicit | |
| ) | |
| print("Response\n", response) | |
| response_message = response.choices[0].message | |
| tool_calls = response_message.tool_calls | |
| print("length of tool calls", len(tool_calls)) | |
| print("Expecting there to be 3 tool calls") | |
| assert ( | |
| len(tool_calls) > 1 | |
| ) # this has to call the function for SF, Tokyo and parise | |
| # Step 2: check if the model wanted to call a function | |
| if tool_calls: | |
| # Step 3: call the function | |
| # Note: the JSON response may not always be valid; be sure to handle errors | |
| available_functions = { | |
| "get_current_weather": get_current_weather, | |
| } # only one function in this example, but you can have multiple | |
| messages.append( | |
| response_message | |
| ) # extend conversation with assistant's reply | |
| print("Response message\n", response_message) | |
| # Step 4: send the info for each function call and function response to the model | |
| for tool_call in tool_calls: | |
| function_name = tool_call.function.name | |
| function_to_call = available_functions[function_name] | |
| function_args = json.loads(tool_call.function.arguments) | |
| function_response = function_to_call( | |
| location=function_args.get("location"), | |
| unit=function_args.get("unit"), | |
| ) | |
| messages.append( | |
| { | |
| "tool_call_id": tool_call.id, | |
| "role": "tool", | |
| "name": function_name, | |
| "content": function_response, | |
| } | |
| ) # extend conversation with function response | |
| print(f"messages: {messages}") | |
| second_response = litellm.completion( | |
| model="gpt-3.5-turbo-1106", messages=messages, temperature=0.2, seed=22 | |
| ) # get a new response from the model where it can see the function response | |
| print("second response\n", second_response) | |
| return second_response | |
| except Exception as e: | |
| pytest.fail(f"Error occurred: {e}") | |
| test_parallel_function_call() | |
| def test_parallel_function_call_stream(): | |
| try: | |
| # Step 1: send the conversation and available functions to the model | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": "What's the weather like in San Francisco, Tokyo, and Paris?", | |
| } | |
| ] | |
| tools = [ | |
| { | |
| "type": "function", | |
| "function": { | |
| "name": "get_current_weather", | |
| "description": "Get the current weather in a given location", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "location": { | |
| "type": "string", | |
| "description": "The city and state, e.g. San Francisco, CA", | |
| }, | |
| "unit": { | |
| "type": "string", | |
| "enum": ["celsius", "fahrenheit"], | |
| }, | |
| }, | |
| "required": ["location"], | |
| }, | |
| }, | |
| } | |
| ] | |
| response = litellm.completion( | |
| model="gpt-3.5-turbo-1106", | |
| messages=messages, | |
| tools=tools, | |
| stream=True, | |
| tool_choice="auto", # auto is default, but we'll be explicit | |
| complete_response=True, | |
| ) | |
| print("Response\n", response) | |
| # for chunk in response: | |
| # print(chunk) | |
| response_message = response.choices[0].message | |
| tool_calls = response_message.tool_calls | |
| print("length of tool calls", len(tool_calls)) | |
| print("Expecting there to be 3 tool calls") | |
| assert ( | |
| len(tool_calls) > 1 | |
| ) # this has to call the function for SF, Tokyo and parise | |
| # Step 2: check if the model wanted to call a function | |
| if tool_calls: | |
| # Step 3: call the function | |
| # Note: the JSON response may not always be valid; be sure to handle errors | |
| available_functions = { | |
| "get_current_weather": get_current_weather, | |
| } # only one function in this example, but you can have multiple | |
| messages.append( | |
| response_message | |
| ) # extend conversation with assistant's reply | |
| print("Response message\n", response_message) | |
| # Step 4: send the info for each function call and function response to the model | |
| for tool_call in tool_calls: | |
| function_name = tool_call.function.name | |
| function_to_call = available_functions[function_name] | |
| function_args = json.loads(tool_call.function.arguments) | |
| function_response = function_to_call( | |
| location=function_args.get("location"), | |
| unit=function_args.get("unit"), | |
| ) | |
| messages.append( | |
| { | |
| "tool_call_id": tool_call.id, | |
| "role": "tool", | |
| "name": function_name, | |
| "content": function_response, | |
| } | |
| ) # extend conversation with function response | |
| print(f"messages: {messages}") | |
| second_response = litellm.completion( | |
| model="gpt-3.5-turbo-1106", messages=messages, temperature=0.2, seed=22 | |
| ) # get a new response from the model where it can see the function response | |
| print("second response\n", second_response) | |
| return second_response | |
| except Exception as e: | |
| pytest.fail(f"Error occurred: {e}") | |
| test_parallel_function_call_stream() | |