chatterer 0.1.3

The highest-level interface for various LLM APIs.

Chatterer

Simplified, Structured AI Assistant Framework

chatterer is a Python library designed as a type-safe LangChain wrapper for interacting with various language models (OpenAI, Anthropic, Gemini, Ollama, etc.). It supports structured outputs via Pydantic models, plain text responses, and asynchronous calls.

The structured reasoning in chatterer is inspired by the Atom-of-Thought pipeline.

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Quick Install

pip install chatterer

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Quickstart Example

Generate text quickly using OpenAI:

from chatterer import Chatterer

chat = Chatterer.openai("gpt-4o-mini")
response = chat.generate("What is the meaning of life?")
print(response)

Messages can be input as plain strings or structured lists:

response = chat.generate([{ "role": "user", "content": "What's 2+2?" }])
print(response)

Structured Output with Pydantic

from pydantic import BaseModel

class AnswerModel(BaseModel):
    question: str
    answer: str

response = chat.generate_pydantic(AnswerModel, "What's the capital of France?")
print(response.question, response.answer)

Async Example

import asyncio

async def main():
    response = await chat.agenerate("Explain async in Python briefly.")
    print(response)

asyncio.run(main())

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Atom-of-Thought Pipeline (AoT)

AoTPipeline provides structured reasoning by:

• Detecting question domains (general, math, coding, philosophy, multihop).
• Decomposing questions recursively.
• Generating direct, decomposition-based, and simplified answers.
• Combining answers via ensemble.

AoT Usage Example

from chatterer import Chatterer
from chatterer.strategies import AoTStrategy, AoTPipeline

pipeline = AoTPipeline(chatterer=Chatterer.openai(), max_depth=2)
strategy = AoTStrategy(pipeline=pipeline)

question = "What would Newton discover if hit by an apple falling from 100 meters?"
answer = strategy.invoke(question)
print(answer)

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Supported Models

• OpenAI
• Anthropic
• Google Gemini
• Ollama (local models)

Initialize models easily:

openai_chat = Chatterer.openai("gpt-4o-mini")
anthropic_chat = Chatterer.anthropic("claude-3-7-sonnet-20250219")
gemini_chat = Chatterer.google("gemini-2.0-flash")
ollama_chat = Chatterer.ollama("deepseek-r1:1.5b")

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Advanced Features

• Streaming responses
• Async/Await support
• Structured outputs with Pydantic models

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Logging

Built-in logging for easy debugging:

import logging
logging.basicConfig(level=logging.DEBUG)

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Contributing

Feel free to open an issue or pull request.

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License

MIT License