FactLite 1.0.0

A lightweight framework for fact-checking AI-generated content

FactLite 🪶

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Give Your LLM a "System 2" Brain with a Single Decorator.

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In the last mile of deploying Generative AI, hallucination is the final boss. Heavy frameworks like LangChain introduce too much boilerplate and complexity, while raw API calls offer no safety net.

FactLite is a production-ready, feather-light Python micro-framework designed to solve this exact problem. It enhances your existing LLM calls with an automated, self-correcting evaluation loop, inspired by the top-tier Agentic "Reflexion" Architecture, without forcing you to refactor your codebase.

🚀 Key Features

• ✨ Zero-Intrusion: Add fact-checking and self-correction to any function with a single @verify decorator. No need to rewrite your existing logic.
• ⚡️ Async-Native & Concurrency Safe: Built from the ground up to support async/await. The evaluation process runs in a separate thread to prevent blocking your main event loop, making it perfect for high-performance web backends like FastAPI.
• 🤖 Agentic Workflow: Implements an automated Generate -> Evaluate -> Reflect loop. Your LLM is forced to critique and iteratively improve its own answers until they meet your quality standards.
• 🧩 Extensible & Pluggable:
  • Bring your own judge! Use the built-in LLMJudge or create your own validation logic (e.g., regex, database lookups, type checks) with CustomJudge.
  • Define your own failure policies. Raise an error, return a safe message, or trigger a webhook with custom FallbackAction.
• 🌐 Framework Agnostic: FactLite doesn't care how you call your LLM. Whether you're using the openai SDK, anthropic's client, or a simple requests.post call to a local model, as long as it's a Python function that returns a string, FactLite can safeguard it.

📦 Installation

pip install factlite

🎯 Quick Start: The "Aha!" Moment

See how easy it is to upgrade your existing code from a simple API call to a self-correcting agent.

Before: A standard, unprotected LLM call.

import openai

client = openai.OpenAI(api_key="your-key")

def ask_ai(question: str):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": question}]
    )
    return response.choices[0].message.content

# This might return a factually incorrect answer, and you'd never know.
print(ask_ai("Was Li Bai an emperor in the Song Dynasty?"))

After: Protected by FactLite with a single line of code.

import openai
from FactLite import verify, rules, action

client = openai.OpenAI(api_key="your-key")

# Configure a powerful judge and your API key
config = verify.config(
    api_key="your-key",
    rule=rules.LLMJudge(model="gpt-4o-mini"),
    max_retries=1
)

@verify(config=config, user_prompt="question") # Just add this decorator!
def ask_ai(question: str):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": question}]
    )
    return response.choices[0].message.content

# Now, the function will automatically correct itself before returning.
print(ask_ai("Was Li Bai an emperor in the Song Dynasty?"))

What you'll see in your console:

10:30:05 - [FactLite] - Generating initial answer...
10:30:08 - [FactLite] - Evaluating answer quality...
10:30:12 - [FactLite] - ❌ Hallucination or error detected: The answer incorrectly states that Li Bai was related to the Song Dynasty. He was a poet from the Tang Dynasty.
10:30:12 - [FactLite] - Triggering reflection and rewrite, attempt 1...
10:30:16 - [FactLite] - Evaluating answer quality...
10:30:19 - [FactLite] - ✅ Correction successful, returning the verified answer!

No, Li Bai was not an emperor in the Song Dynasty. He was a renowned poet who lived during the Tang Dynasty (701-762 AD).

💡 Advanced Usage

Async Support

FactLite automatically detects and supports async functions.

from openai import AsyncOpenAI

async_client = AsyncOpenAI(api_key="your-key")

@verify(config=config, user_prompt="question")
async def ask_ai_async(question: str):
    response = await async_client.chat.completions.create(...)
    return response.choices[0].message.content

# Run it
import asyncio
asyncio.run(ask_ai_async("Tell me about the Tang Dynasty."))

Custom Rules (CustomJudge)

Go beyond LLM-based checks. Enforce any local business logic you can imagine.

def company_policy_judge(prompt, answer):
    # Rule 1: No short answers
    if len(answer) < 50:
        return {"is_pass": False, "feedback": "Answer is too short. Please be more detailed."}
    # Rule 2: Don't mention competitors
    if "Google" in answer:
        return {"is_pass": False, "feedback": "Do not mention competitor names."}
    return {"is_pass": True, "feedback": ""}

@verify(rule=rules.CustomJudge(eval_func=company_policy_judge), user_prompt="prompt")
def ask_support_bot(prompt: str):
    # ... your LLM call
    pass

Custom Failure Actions (FallbackAction)

Decide exactly what happens when an answer fails all retries.

from FactLite import action

@verify(
    ...,
    on_fail=action.ReturnSafeMessage("I'm sorry, I cannot provide a confident answer to that question at the moment.")
)
def ask_sensitive_question(...):
    pass

@verify(..., on_fail=action.RaiseError())
def ask_critical_question(...):
    pass

🛠️ How It Works

FactLite's @verify decorator wraps your function in a simple yet powerful control loop:

1. Generate: Your original function is called to produce an initial draft.
2. Evaluate: The configured rule (e.g., LLMJudge) is invoked to assess the draft.
3. Reflect & Retry:
   • If the evaluation passes, the answer is returned to the user.
   • If it fails, the feedback is combined with the original prompt to create a "reflection prompt," forcing the LLM to correct its mistake. The process repeats from Step 1 until max_retries is reached.
4. Fallback: If all retries fail, the configured on_fail action is executed.

🤝 Contributing

Contributions are welcome! Whether it's a new rule, a new fallback action, or a performance improvement, feel free to open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.