Gorilla Openfunctions V0

🚀 Gorilla OpenFunctions

Gorilla OpenFunctions extends the Large Language Model (LLM) Chat Completion feature. It can formulate executable API calls based on natural - language instructions and API context.

🚀 Try it out on Colab
📣 Read more in our OpenFunctions blog release

🚀 Quick Start

Gorilla OpenFunctions extends Large Language Model(LLM) Chat Completion feature to formulate executable APIs call given natural language instructions and API context.

✨ Features

Available Models

💻 Usage Examples

Hosted Usage

1. OpenFunctions is compatible with OpenAI Functions

!pip install openai==0.28.1

2. Point to Gorilla hosted servers

import openai

def get_gorilla_response(prompt="Call me an Uber ride type \"Plus\" in Berkeley at zipcode 94704 in 10 minutes", model="gorilla-openfunctions-v0", functions=[]):
  openai.api_key = "EMPTY"
  openai.api_base = "http://luigi.millennium.berkeley.edu:8000/v1"
  try:
    completion = openai.ChatCompletion.create(
      model="gorilla-openfunctions-v0",
      temperature=0.0,
      messages=[{"role": "user", "content": prompt}],
      functions=functions,
    )
    return completion.choices[0].message.content
  except Exception as e:
    print(e, model, prompt)

3. Pass the user argument and set of functions, Gorilla OpenFunctions returns a fully formatted json

query = "Call me an Uber ride type \"Plus\" in Berkeley at zipcode 94704 in 10 minutes"
functions = [
    {
        "name": "Uber Carpool",
        "api_name": "uber.ride",
        "description": "Find suitable ride for customers given the location, type of ride, and the amount of time the customer is willing to wait as parameters",
        "parameters":  [{"name": "loc", "description": "location of the starting place of the uber ride"}, {"name":"type", "enum": ["plus", "comfort", "black"], "description": "types of uber ride user is ordering"}, {"name": "time", "description": "the amount of time in minutes the customer is willing to wait"}]
    }
]
get_gorilla_response(query, functions=functions)

4. Expected output

uber.ride(loc="berkeley", type="plus", time=10)

Local Usage

import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

def get_prompt(user_query: str, functions: list = []) -> str:
    """
    Generates a conversation prompt based on the user's query and a list of functions.

    Parameters:
    - user_query (str): The user's query.
    - functions (list): A list of functions to include in the prompt.

    Returns:
    - str: The formatted conversation prompt.
    """
    if len(functions) == 0:
        return f"USER: <<question>> {user_query}\nASSISTANT: "
    functions_string = json.dumps(functions)
    return f"USER: <<question>> {user_query} <<function>> {functions_string}\nASSISTANT: "

# Device setup
device : str = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Model and tokenizer setup
model_id : str = "gorilla-llm/gorilla-openfunctions-v0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True)

# Move model to device
model.to(device)

# Pipeline setup
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=128,
    batch_size=16,
    torch_dtype=torch_dtype,
    device=device,
)

# Example usage
query: str = "Call me an Uber ride type \"Plus\" in Berkeley at zipcode 94704 in 10 minutes"
functions = [
    {
        "name": "Uber Carpool",
        "api_name": "uber.ride",
        "description": "Find suitable ride for customers given the location, type of ride, and the amount of time the customer is willing to wait as parameters",
        "parameters":  [
            {"name": "loc", "description": "Location of the starting place of the Uber ride"},
            {"name": "type", "enum": ["plus", "comfort", "black"], "description": "Types of Uber ride user is ordering"},
            {"name": "time", "description": "The amount of time in minutes the customer is willing to wait"}
        ]
    }
]

# Generate prompt and obtain model output
prompt = get_prompt(query, functions=functions)
output = pipe(prompt)

print(output)

📄 License

All the models, and data used to train the models is released under Apache 2.0. Gorilla is an open source effort from UC Berkeley and we welcome contributors. Please email us your comments, criticism, and questions. More information about the project can be found at https://gorilla.cs.berkeley.edu/