microchain
function calling-based LLM agents
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Microchain is a function calling-based LLM agents tool with no bloat. It allows users to define LLM and templates, use various functions like Sum and Product, and create LLM agents for specific tasks. The tool provides a simple and efficient way to interact with OpenAI models and create conversational agents for various applications.
README:
function calling-based LLM agents. Just that, no bloat.
pip install microchain-python
from microchain import OpenAITextGenerator, HFChatTemplate, LLM
generator = OpenAITextGenerator(
model=MODEL_NAME,
api_key=API_KEY,
api_base=API_BASE,
temperature=0.7
)
template = HFChatTemplate(CHAT_TEMPLATE)
llm = LLM(generator=generator, templates=[template])Use HFChatTemplate(template) to use tokenizer.apply_chat_template from huggingface.
You can also use VicunaTemplate() for a classic vicuna-style prompt.
To use ChatGPT APIs you don't need to apply a template:
from microchain import OpenAIChatGenerator, LLM
generator = OpenAIChatGenerator(
model="gpt-3.5-turbo",
api_key=API_KEY,
api_base="https://api.openai.com/v1",
temperature=0.7
)
llm = LLM(generator=generator)Define LLM callable functions as plain Python objects. Use type annotations to instruct the LLM to use the correct types.
from microchain import Function
class Sum(Function):
@property
def description(self):
return "Use this function to compute the sum of two numbers"
@property
def example_args(self):
return [2, 2]
def __call__(self, a: float, b: float):
return a + b
class Product(Function):
@property
def description(self):
return "Use this function to compute the product of two numbers"
@property
def example_args(self):
return [2, 2]
def __call__(self, a: float, b: float):
return a * b
print(Sum().help)
'''
Sum(a: float, b: float)
This function sums two numbers.
Example: Sum(a=2, b=2)
'''
print(Product().help)
'''
Product(a: float, b: float)
Use this function to compute the product of two numbers.
Example: Product(a=2, b=2)
'''Register your functions with an Engine() using the register() function.
Create an Agent() using the llm and the execution engine.
Define a prompt for the LLM and include the functions documentation using engine.help().
It's always a good idea to bootstrap the LLM with examples of function calls. Do this by setting engine.bootstrap = [...] with a list of function calls to run and prepend their results to the chat history.
from microchain import Agent, Engine
from microchain.functions import Reasoning, Stop
engine = Engine()
engine.register(Reasoning())
engine.register(Stop())
engine.register(Sum())
engine.register(Product())
agent = Agent(llm=llm, engine=engine)
agent.prompt = f"""Act as a calculator. You can use the following functions:
{engine.help}
Only output valid Python function calls.
How much is (2*4 + 3)*5?
"""
agent.bootstrap = [
'Reasoning("I need to reason step-by-step")',
]
agent.run()Running it will output something like:
prompt:
Act as a calculator. You can use the following functions:
Reasoning(reasoning: str)
Use this function for your internal reasoning.
Example: Reasoning(reasoning=The next step to take is...)
Stop()
Use this function to stop the program.
Example: Stop()
Sum(a: float, b: float)
Use this function to compute the sum of two numbers.
Example: Sum(a=2, b=2)
Product(a: float, b: float)
Use this function to compute the product of two numbers.
Example: Product(a=2, b=2)
Only output valid Python function calls.
How much is (2*4 + 3)*5?
Running 10 iterations
>> Reasoning("I need to reason step-by-step")
The reasoning has been recorded
>> Reasoning("First, calculate the product of 2 and 4")
The reasoning has been recorded
>> Product(a=2, b=4)
8
>> Reasoning("Then, add 3 to the product of 2 and 4")
The reasoning has been recorded
>> Sum(a=8, b=3)
11
>> Reasoning("Lastly, multiply the sum by 5")
The reasoning has been recorded
>> Product(a=11, b=5)
55
>> Reasoning("So, the result of (2*4 + 3)*5 is 55")
The reasoning has been recorded
>> Stop()
The program has been stopped
You can find more examples here
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