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Open Source Attribution Notice

Using open source software without proper attribution or in violation of license terms is not only ethically problematic but may also constitute a legal violation. I believe in supporting the open source community that makes projects like this possible.

If you're using code or tools from this repository or GitHub, please ensure you maintain all attribution notices and comply with all applicable licenses.

The license above is a modified MIT LICENSE for the purpose of this project 👆

(The project is ongoing and updates will be made going into 2025 )

pRISM

Procedural Intelligence & RAG-based Semantic Multi-Model

pRISM combines RAG (Retrieval-Augmented Generation) with multi-model consensus to create more reliable AI outputs, specifically designed for legal document processing. By integrating multiple language models and incorporating human expert feedback, we aim to make AI-generated legal content more transparent and trustworthy.

Note: This is a research project - please don't use it for actual legal work without expert review!

What Makes pRISM Different?

  • Multi-Model Consensus: Instead of relying on a single LLM, we aggregate outputs from multiple models using techniques like weighted majority voting. In our testing, this has significantly reduced hallucination rates (full benchmarks coming soon).

  • Domain-Specific Fine-Tuning: We use LoRA to adapt models for legal tasks without breaking the bank on compute costs. Our initial tests show promising improvements in legal terminology accuracy.

  • Human-in-the-Loop: Legal experts review outputs and provide feedback, which gets incorporated back into the system to improve future results.

Demos

Quick Start

  1. Clone and install:
git clone https://github.com/your-repo/pRISM.git
cd pRISM
pip install -r requirements.txt

Core Components

RAG Pipeline (rag_pipeline.py)

The heart of pRISM. Handles document retrieval and generation using FAISS for vector search and LangChain for orchestration.

Real-Time API (real_time_inference_api.py)

FastAPI service for running inferences. Currently supports:

  • Basic document queries
  • Multi-model consensus outputs
  • Confidence scoring
  • Explanation generation

Evaluation Tools

  • performance_benchmarking.py: Measures latency and accuracy
  • evaluation_report_generator.py: Generates detailed PDF reports
  • automated_feedback_loops.py: Incorporates user feedback for continuous improvement

Contributing

This is a research project and we'd love your input! Feel free to open issues or PRs, especially if you have experience with:

  • Legal document processing
  • RAG implementations
  • Model fine-tuning
  • Evaluation methodologies

License

MIT License - see LICENSE.md

Built with 💡 for exploring better ways to handle legal AI