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Function Calling

Checking if a model supports function calling

Use litellm.supports_function_calling(model="") -> returns True if model supports Function calling, False if not

assert litellm.supports_function_calling(model="gpt-3.5-turbo") == True
assert litellm.supports_function_calling(model="azure/gpt-4-1106-preview") == True
assert litellm.supports_function_calling(model="palm/chat-bison") == False
assert litellm.supports_function_calling(model="ollama/llama2") == False

Checking if a model supports parallel function calling

Use litellm.supports_parallel_function_calling(model="") -> returns True if model supports parallel function calling, False if not

assert litellm.supports_parallel_function_calling(model="gpt-4-turbo-preview") == True
assert litellm.supports_parallel_function_calling(model="gpt-4") == False

Parallel Function calling

Parallel function calling is the model's ability to perform multiple function calls together, allowing the effects and results of these function calls to be resolved in parallel

Quick Start - gpt-3.5-turbo-1106

Open In Colab

In this example we define a single function get_current_weather.

  • Step 1: Send the model the get_current_weather with the user question
  • Step 2: Parse the output from the model response - Execute the get_current_weather with the model provided args
  • Step 3: Send the model the output from running the get_current_weather function

Full Code - Parallel function calling with gpt-3.5-turbo-1106

import litellm
import json
# set openai api key
import os
os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request

# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
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"})


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("\nFirst LLM Response:\n", response)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls

print("\nLength of tool calls", len(tool_calls))

# 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

# 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
second_response = litellm.completion(
model="gpt-3.5-turbo-1106",
messages=messages,
) # get a new response from the model where it can see the function response
print("\nSecond LLM response:\n", second_response)
return second_response
except Exception as e:
print(f"Error occurred: {e}")

test_parallel_function_call()

Explanation - Parallel function calling

Below is an explanation of what is happening in the code snippet above for Parallel function calling with gpt-3.5-turbo-1106

Step1: litellm.completion() with tools set to get_current_weather

import litellm
import json
# set openai api key
import os
os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
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"})

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("\nLLM Response1:\n", response)
response_message = response.choices[0].message
tool_calls = response.choices[0].message.tool_calls
Expected output

In the output you can see the model calls the function multiple times - for San Francisco, Tokyo, Paris

ModelResponse(
id='chatcmpl-8MHBKZ9t6bXuhBvUMzoKsfmmlv7xq',
choices=[
Choices(finish_reason='tool_calls',
index=0,
message=Message(content=None, role='assistant',
tool_calls=[
ChatCompletionMessageToolCall(id='call_DN6IiLULWZw7sobV6puCji1O', function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), type='function'),

ChatCompletionMessageToolCall(id='call_ERm1JfYO9AFo2oEWRmWUd40c', function=Function(arguments='{"location": "Tokyo", "unit": "celsius"}', name='get_current_weather'), type='function'),

ChatCompletionMessageToolCall(id='call_2lvUVB1y4wKunSxTenR0zClP', function=Function(arguments='{"location": "Paris", "unit": "celsius"}', name='get_current_weather'), type='function')
]))
],
created=1700319953,
model='gpt-3.5-turbo-1106',
object='chat.completion',
system_fingerprint='fp_eeff13170a',
usage={'completion_tokens': 77, 'prompt_tokens': 88, 'total_tokens': 165},
_response_ms=1177.372
)

Step 2 - Parse the Model Response and Execute Functions

After sending the initial request, parse the model response to identify the function calls it wants to make. In this example, we expect three tool calls, each corresponding to a location (San Francisco, Tokyo, and Paris).

# Check if the model wants to call a function
if tool_calls:
# Execute the functions and prepare responses
available_functions = {
"get_current_weather": get_current_weather,
}

messages.append(response_message) # Extend conversation with assistant's reply

for tool_call in tool_calls:
print(f"\nExecuting tool call\n{tool_call}")
function_name = tool_call.function.name
function_to_call = available_functions[function_name]
function_args = json.loads(tool_call.function.arguments)
# calling the get_current_weather() function
function_response = function_to_call(
location=function_args.get("location"),
unit=function_args.get("unit"),
)
print(f"Result from tool call\n{function_response}\n")

# Extend conversation with function response
messages.append(
{
"tool_call_id": tool_call.id,
"role": "tool",
"name": function_name,
"content": function_response,
}
)

Step 3 - Second litellm.completion() call

Once the functions are executed, send the model the information for each function call and its response. This allows the model to generate a new response considering the effects of the function calls.

second_response = litellm.completion(
model="gpt-3.5-turbo-1106",
messages=messages,
)
print("Second Response\n", second_response)

Expected output

ModelResponse(
id='chatcmpl-8MHBLh1ldADBP71OrifKap6YfAd4w',
choices=[
Choices(finish_reason='stop', index=0,
message=Message(content="The current weather in San Francisco is 72°F, in Tokyo it's 10°C, and in Paris it's 22°C.", role='assistant'))
],
created=1700319955,
model='gpt-3.5-turbo-1106',
object='chat.completion',
system_fingerprint='fp_eeff13170a',
usage={'completion_tokens': 28, 'prompt_tokens': 169, 'total_tokens': 197},
_response_ms=1032.431
)

Parallel Function Calling - Azure OpenAI

# set Azure env variables
import os
os.environ['AZURE_API_KEY'] = "" # litellm reads AZURE_API_KEY from .env and sends the request
os.environ['AZURE_API_BASE'] = "https://openai-gpt-4-test-v-1.openai.azure.com/"
os.environ['AZURE_API_VERSION'] = "2023-07-01-preview"

import litellm
import json
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
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"})

## 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="azure/chatgpt-functioncalling", # model = azure/<your-azure-deployment-name>
messages=messages,
tools=tools,
tool_choice="auto", # auto is default, but we'll be explicit
)
print("\nLLM Response1:\n", response)
response_message = response.choices[0].message
tool_calls = response.choices[0].message.tool_calls
print("\nTool Choice:\n", tool_calls)

## Step 2 - Parse the Model Response and Execute Functions
# Check if the model wants to call a function
if tool_calls:
# Execute the functions and prepare responses
available_functions = {
"get_current_weather": get_current_weather,
}

messages.append(response_message) # Extend conversation with assistant's reply

for tool_call in tool_calls:
print(f"\nExecuting tool call\n{tool_call}")
function_name = tool_call.function.name
function_to_call = available_functions[function_name]
function_args = json.loads(tool_call.function.arguments)
# calling the get_current_weather() function
function_response = function_to_call(
location=function_args.get("location"),
unit=function_args.get("unit"),
)
print(f"Result from tool call\n{function_response}\n")

# Extend conversation with function response
messages.append(
{
"tool_call_id": tool_call.id,
"role": "tool",
"name": function_name,
"content": function_response,
}
)

## Step 3 - Second litellm.completion() call
second_response = litellm.completion(
model="azure/chatgpt-functioncalling",
messages=messages,
)
print("Second Response\n", second_response)
print("Second Response Message\n", second_response.choices[0].message.content)

Deprecated - Function Calling with completion(functions=functions)

import os, litellm
from litellm import completion

os.environ['OPENAI_API_KEY'] = ""

messages = [
{"role": "user", "content": "What is the weather like in Boston?"}
]

# python function that will get executed
def get_current_weather(location):
if location == "Boston, MA":
return "The weather is 12F"

# JSON Schema to pass to OpenAI
functions = [
{
"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 = completion(model="gpt-3.5-turbo-0613", messages=messages, functions=functions)
print(response)

litellm.function_to_dict - Convert Functions to dictionary for OpenAI function calling

function_to_dict allows you to pass a function docstring and produce a dictionary usable for OpenAI function calling

Using function_to_dict

  1. Define your function get_current_weather
  2. Add a docstring to your function get_current_weather
  3. Pass the function to litellm.utils.function_to_dict to get the dictionary for OpenAI function calling
# function with docstring
def get_current_weather(location: str, unit: str):
"""Get the current weather in a given location

Parameters
----------
location : str
The city and state, e.g. San Francisco, CA
unit : {'celsius', 'fahrenheit'}
Temperature unit

Returns
-------
str
a sentence indicating the weather
"""
if location == "Boston, MA":
return "The weather is 12F"

# use litellm.utils.function_to_dict to convert function to dict
function_json = litellm.utils.function_to_dict(get_current_weather)
print(function_json)

Output from function_to_dict

{
'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', 'description': 'Temperature unit', 'enum': "['fahrenheit', 'celsius']"}
},
'required': ['location', 'unit']
}
}

Using function_to_dict with Function calling

import os, litellm
from litellm import completion

os.environ['OPENAI_API_KEY'] = ""

messages = [
{"role": "user", "content": "What is the weather like in Boston?"}
]

def get_current_weather(location: str, unit: str):
"""Get the current weather in a given location

Parameters
----------
location : str
The city and state, e.g. San Francisco, CA
unit : str {'celsius', 'fahrenheit'}
Temperature unit

Returns
-------
str
a sentence indicating the weather
"""
if location == "Boston, MA":
return "The weather is 12F"

functions = [litellm.utils.function_to_dict(get_current_weather)]

response = completion(model="gpt-3.5-turbo-0613", messages=messages, functions=functions)
print(response)

Function calling for Non-OpenAI LLMs

Adding Function to prompt

For Non OpenAI LLMs LiteLLM allows you to add the function to the prompt set: litellm.add_function_to_prompt = True

Usage

import os, litellm
from litellm import completion

# IMPORTANT - Set this to TRUE to add the function to the prompt for Non OpenAI LLMs
litellm.add_function_to_prompt = True # set add_function_to_prompt for Non OpenAI LLMs

os.environ['ANTHROPIC_API_KEY'] = ""

messages = [
{"role": "user", "content": "What is the weather like in Boston?"}
]

def get_current_weather(location):
if location == "Boston, MA":
return "The weather is 12F"

functions = [
{
"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 = completion(model="claude-2", messages=messages, functions=functions)
print(response)