DeepChain is a Python-based pipeline that applies a three-stage refinement process to LLM outputs (using gemma2:9b via Ollama). By combining chain-of-thought prompting, context-aware improvements, and fact‐checking, it produces more accurate and in-depth answers from a compact model.

Key Features

• Three-Stage Refinement Pipeline

  1. Basic Analysis: Generates an initial (“naive”) response to your prompt.
  2. Contextual Refinement: Adds context, performs intent analysis, and fact-checks the initial output.
  3. Final Synthesis: Integrates and cross-validates information from earlier stages into a polished, comprehensive answer.

• Hallucination Reduction
  Intermediate checks and cross-validation between stages reduce misinformation and incorrect facts.

• Easy Setup & Lightweight
  Just Python 3.8+ + Ollama + gemma2:9b. No heavyweight frameworks are required.

• Simple, Clear Code
  All core logic lives in a single src/main.py, making it easy to understand and extend.

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Demo / Usage Example

In this example, the user sends the ambiguous query "How many disks does Madonna have?". Through DeepChain's refinement process, the system demonstrates how a small Gemma2:9B model is enhanced with multi-stage reasoning capabilities. The system showcases improved analytical performance by providing a comprehensive answer that categorizes Madonna's discography into studio albums, live recordings, and compilations. The answer includes key information about her most significant releases, offering the appropriate context for a complete answer to the query - all achieved by applying refinement techniques to a relatively compact 9B parameter model that wouldn't typically exhibit such sophisticated reasoning on its own.

1. Clone and Install

   git clone https://github.com/KazKozDev/deepchain-refinement.git
   cd deepchain-refinement
   pip install -r requirements.txt

2. Run the Pipeline

   python src/main.py --prompt "How many discs does Madonna have?"

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Installation

1. Ensure Python 3.8+ is installed.
2. Install Ollama and confirm it is running locally.
3. Clone this repository:

   git clone https://github.com/KazKozDev/deepchain-refinement.git
   cd deepchain-refinement

4. Install Python dependencies:

   pip install -r requirements.txt

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Project Structure

deepchain-refinement/
├── src/
│   └── main.py        # Core implementation with three refinement stages
├── requirements.txt   # Python dependencies
├── LICENSE            # MIT license text
└── README.md          # This file

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If you like this project, please give it a star ⭐

For questions, feedback, or support, reach out to:

Artem KK | MIT LICENSE