Functionary is a language model that can interpret and execute functions/plugins.
The model determines when to execute functions, whether in parallel or serially, and can understand their outputs. It only triggers functions as needed. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls.
Documentation is available at https://functionary.meetkai.com/.
CodeLabs-style Guides and Tutorials using Functionary are available at https://functionary.meetkai.com/guides.
Make sure you have PyTorch installed. Then to install the required dependencies, run:
pip install -r requirements.txtNow you can start a blazing fast vLLM server:
python3 server_vllm.py --model "meetkai/functionary-small-v2.2" --host 0.0.0.0from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="functionary")
client.chat.completions.create(
model="meetkai/functionary-small-v2.2",
messages=[{"role": "user",
"content": "What is the weather for Istanbul?"}
],
tools=[{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}],
tool_choice="auto"
)Details (click to expand)
import requests
data = {
'model': 'meetkai/functionary-small-v2.2', # model name here is the value of argument "--model" in deploying: server_vllm.py or server.py
'messages': [
{
"role": "user",
"content": "What is the weather for Istanbul?"
}
],
'tools':[ # For functionary-7b-v2 we use "tools"; for functionary-7b-v1.4 we use "functions" = [{"name": "get_current_weather", "description":..., "parameters": ....}]
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
}
response = requests.post("http://127.0.0.1:8000/v1/chat/completions", json=data, headers={
"Content-Type": "application/json",
"Authorization": "Bearer xxxx"
})
# Print the response text
print(response.text)If you're having trouble with dependencies, and you have nvidia-container-toolkit, you can start your environment like this:
sudo docker run --gpus all -it --shm-size=8g --name functionary -v ${PWD}/functionary_workspace:/workspace -p 8000:8000 nvcr.io/nvidia/pytorch:22.12-py3| Model | Description | Compute Requirements (for FP16 HF model weights) |
|---|---|---|
| functionary-small-v2.2 / GGUF | 8k context | Any GPU with 24GB VRAM |
| functionary-medium-v2.2 / GGUF | 8k context, better accuracy | 2 x A100-80GB or equivalent |
| functionary-7b-v2.1 / GGUF | 8k context | Any GPU with 24GB VRAM |
| functionary-7b-v2 / GGUF | Parallel function call support. | Any GPU with 24GB VRAM |
| functionary-7b-v1.4 / GGUF | 4k context, better accuracy (deprecated) | Any GPU with 24GB VRAM |
| functionary-7b-v1.1 | 4k context (deprecated) | Any GPU with 24GB VRAM |
| functionary-7b-v0.1 | 2k context (deprecated) Not recommended, use 2.1 onwards | Any GPU with 24GB VRAM |
- v1 models are compatible with both OpenAI-python v0 and v1.
- v2 models are designed for compatibility with OpenAI-python v1.
The difference between OpenAI-python v0 and v1 you may refer to the official documentation here
| Feature/Project | Functionary | NexusRaven | Gorilla | Glaive | GPT-4-1106-preview |
|---|---|---|---|---|---|
| Single Function Call | âś… | âś… | âś… | âś… | âś… |
| Parallel Function Calls | ✅ | ✅ | ✅ | ❌ | ✅ |
| Nested Function Calls | ❌ | ✅ | ❌ | ❌ | ❌ |
| Following Up on Missing Function Arguments | ✅ | ❌ | ❌ | ❌ | ✅ |
| Multi-turn | ✅ | ❌ | ❌ | ✅ | ✅ |
| Generate Model Responses Grounded in Tools Execution Results | ✅ | ❌ | ❌ | ❌ | ✅ |
| Chit-Chat | ✅ | ❌ | ✅ | ✅ | ✅ |
You can find more details of the features in here
Details (click to expand)
Make sure that llama-cpp-python is successully installed in your system. The following is the sample code:
from llama_cpp import Llama
from functionary.prompt_template import get_prompt_template_from_tokenizer
from transformers import AutoTokenizer
tools = [ # For functionary-7b-v2 we use "tools"; for functionary-7b-v1.4 we use "functions" = [{"name": "get_current_weather", "description":..., "parameters": ....}]
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g., San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
# You can download gguf files from https://huggingface.co/meetkai/functionary-7b-v2-GGUF/tree/main
llm = Llama(model_path="PATH_TO_GGUF_FILE", n_ctx=4096, n_gpu_layers=-1)
messages = [
{"role": "user", "content": "what's the weather like in Hanoi?"}
]
# Create tokenizer from HF.
# We found that the tokenizer from llama_cpp is not compatible with tokenizer from HF that we trained
# The reason might be we added new tokens to the original tokenizer
# So we will use tokenizer from HuggingFace
tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-7b-v2", legacy=True)
# prompt_template will be used for creating the prompt
prompt_template = get_prompt_template_from_tokenizer(tokenizer)
# Before inference, we need to add an empty assistant (message without content or function_call)
messages.append({"role": "assistant"})
# Create the prompt to use for inference
prompt_str = prompt_template.get_prompt_from_messages(messages, tools)
token_ids = tokenizer.encode(prompt_str)
gen_tokens = []
# Get list of stop_tokens
stop_token_ids = [tokenizer.encode(token)[-1] for token in prompt_template.get_stop_tokens_for_generation()]
print("stop_token_ids: ", stop_token_ids)
# We use function generate (instead of __call__) so we can pass in list of token_ids
for token_id in llm.generate(token_ids, temp=0):
if token_id in stop_token_ids:
break
gen_tokens.append(token_id)
llm_output = tokenizer.decode(gen_tokens)
# parse the message from llm_output
result = prompt_template.parse_assistant_response(llm_output)
print(result)The output would be:
{'role': 'assistant', 'content': None, 'tool_calls': [{'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{\n "location": "Hanoi"\n}'}}]}Note: we should use the tokenizer from Huggingface to convert prompt into token_ids instead of using the tokenizer from LLama_cpp because we found that tokenizer from LLama_cpp doesn't give the same result as that from Huggingface. The reason might be in the training, we added new tokens to the tokenizer and LLama_Cpp doesn't handle this succesfully
To call the real python function, get the result and extract the result to respond, you can use chatlab. The following example uses chatlab==0.16.0:
Please note that Chatlab currently doesn't support Parallel Function calls. This sample code is compatible only with Functionary Version 1.4 and may not work correctly with Functionary Version 2.0.
from chatlab import Conversation
import openai
import os
openai.api_key = "functionary" # We just need to set this something other than None
os.environ['OPENAI_API_KEY'] = "functionary" # chatlab requires us to set this too
openai.api_base = "http://localhost:8000/v1"
# now provide the function with description
def get_car_price(car_name: str):
"""this function is used to get the price of the car given the name
:param car_name: name of the car to get the price
"""
car_price = {
"tang": {"price": "$20000"},
"song": {"price": "$25000"}
}
for key in car_price:
if key in car_name.lower():
return {"price": car_price[key]}
return {"price": "unknown"}
chat = Conversation(model="meetkai/functionary-7b-v2")
chat.register(get_car_price) # register this function
chat.submit("what is the price of the car named Tang?") # submit user prompt
# print the flow
for message in chat.messages:
role = message["role"].upper()
if "function_call" in message:
func_name = message["function_call"]["name"]
func_param = message["function_call"]["arguments"]
print(f"{role}: call function: {func_name}, arguments:{func_param}")
else:
content = message["content"]
print(f"{role}: {content}")The output will look like this:
USER: what is the price of the car named Tang?
ASSISTANT: call function: get_car_price, arguments:{
"car_name": "Tang"
}
FUNCTION: {'price': {'price': '$20000'}}
ASSISTANT: The price of the car named Tang is $20,000.
Here are a few examples of how you can use this function calling system:
The function plan_trip(destination: string, duration: int, interests: list) can take user input such as "I want to plan a 7-day trip to Paris with a focus on art and culture" and generate an itinerary accordingly.
Details (click to expand)
client.chat.completions.create((
model="meetkai/functionary-7b-v2",
messages=[
{"role": "user", "content": 'I want to plan a 7-day trip to Paris with a focus on art and culture'},
],
tools=[
{
"type": "function",
"function": {
"name": "plan_trip",
"description": "Plan a trip based on user's interests",
"parameters": {
"type": "object",
"properties": {
"destination": {
"type": "string",
"description": "The destination of the trip",
},
"duration": {
"type": "integer",
"description": "The duration of the trip in days",
},
"interests": {
"type": "array",
"items": {"type": "string"},
"description": "The interests based on which the trip will be planned",
},
},
"required": ["destination", "duration", "interests"],
}
}
}
]
)Response will have:
{"role": "assistant", "content": null, "tool_calls": [{"type": "function", "function": {"name": "plan_trip", "arguments": '{\n "destination": "Paris",\n "duration": 7,\n "interests": ["art", "culture"]\n}'}}]}Then you need to call plan_trip function with provided arguments.
If you would like a commentary from the model, then you'll call the model again with the response from the function, the model will write necessary commentary.
A function like estimate_property_value(property_details: dict) could allow users to input details about a property (such as location, size, number of rooms, etc.) and receive an estimated market value.
Details (click to expand)
client.chat.completions.create(
model="meetkai/functionary-7b-v2",
messages=[
{
"role": "user",
"content": 'What is the estimated value of a 3-bedroom house in San Francisco with 2000 sq ft area?'
},
{
"role": "assistant",
"content": None,
"tool_calls": [
{
"type": "function",
"function": {
"name": "estimate_property_value",
"arguments": '{\n "property_details": {"location": "San Francisco", "size": 2000, "rooms": 3}\n}'
}
}
]
}
],
tools=[
{
"type": "function",
"function": {
"name": "estimate_property_value",
"description": "Estimate the market value of a property",
"parameters": {
"type": "object",
"properties": {
"property_details": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The location of the property"
},
"size": {
"type": "integer",
"description": "The size of the property in square feet"
},
"rooms": {
"type": "integer",
"description": "The number of rooms in the property"
}
},
"required": ["location", "size", "rooms"]
}
},
"required": ["property_details"]
}
}
}
],
tool_choice="auto"
)Response will have:
{"role": "assistant", "content": null, "tool_calls": [{"type": "function", "function": {"name": "plan_trip", "arguments": '{\n "destination": "Paris",\n "duration": 7,\n "interests": ["art", "culture"]\n}'}}]}Then you need to call plan_trip function with provided arguments.
If you would like a commentary from the model, then you'll call the model again with the response from the function, the model will write necessary commentary.
A function parse_customer_complaint(complaint: {issue: string, frequency: string, duration: string}) could help in extracting structured information from a complex, narrative customer complaint, identifying the core issue and potential solutions. The complaint object could include properties such as issue (the main problem), frequency (how often the issue occurs), and duration (how long the issue has been occurring).
Details (click to expand)
client.chat.completions.create(
model="meetkai/functionary-7b-v2",
messages=[
{"role": "user", "content": 'My internet has been disconnecting frequently for the past week'},
],
tools=[
{
"type": "function",
"function": {
"name": "parse_customer_complaint",
"description": "Parse a customer complaint and identify the core issue",
"parameters": {
"type": "object",
"properties": {
"complaint": {
"type": "object",
"properties": {
"issue": {
"type": "string",
"description": "The main problem",
},
"frequency": {
"type": "string",
"description": "How often the issue occurs",
},
"duration": {
"type": "string",
"description": "How long the issue has been occurring",
},
},
"required": ["issue", "frequency", "duration"],
},
},
"required": ["complaint"],
}
}
}
],
tool_choice="auto"
)Response will have:
{"role": "assistant", "content": null, "tool_calls": [{"type": "function", "function": {"name": "parse_customer_complaint", "arguments": '{\n "complaint": {"issue": "internet disconnecting", "frequency": "frequently", "duration": "past week"}\n}'}}]}Then you need to call parse_customer_complaint function with provided arguments. If you would like a commentary from the model, then you'll call the model again with the response from the function, the model will write necessary commentary.
We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.
The prompt example can be found here: V1 and V2
We don't change the logit probabilities to conform to a certain schema, but the model itself knows how to conform. This allows us to use existing tools and caching systems with ease.
Evaluation function call prediction in our in-house dataset. The accuracy metric measures the overall correctness of predicted function calls, including function name prediction and arguments extraction.
| Dataset | Model Name | Function Calling Accuracy (Name & Arguments) |
|---|---|---|
| In-house data | MeetKai-functionary-small-v2.2 | 0.546 |
| In-house data | MeetKai-functionary-medium-v2.2 | 0.664 |
| In-house data | OpenAI-gpt-3.5-turbo-1106 | 0.531 |
| In-house data | OpenAI-gpt-4-1106-preview | 0.737 |
See training README
- OpenAPI specification based plugin support.
- Fast inference server
- vLLM
- text-generation-inference ? See: License Issue
- Streaming Support
- function_call parameter to server
- Grammar Sampling to ensure 100% accuracy for function and parameter names
- Parallel function calling support
- Python function calling support (Automatic detection of type annotations and calling them automatically)
- Real world usage examples, such as creating agents.
- Train Mixtral based model
- Code interpreter support
- Please consider opening a PR for future requests