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Welcome to the DeepChain Refinement Wiki

Welcome to the official wiki for DeepChain Refinement - a sophisticated multi-stage prompt refinement system. This wiki provides detailed information about the project's architecture, components, and usage.

Table of Contents

1. Introduction
2. Architecture Overview
3. Installation Guide
4. Usage Guide
5. Components Deep Dive
6. Troubleshooting
7. Contributing Guidelines

Introduction

DeepChain Refinement is a sophisticated system that uses chain-of-thought reasoning and multi-stage prompting to enhance AI responses. The system is built on Ollama architecture and powered by the Gemma2:9B model.

Key Features

• Chain-of-Thought Processing
• Multi-stage Prompting
• Progressive Refinement
• Response Synthesis
• Integrated Intent Analysis
• Hallucination Mitigation

Architecture Overview

The system operates through three main processing stages:

1. Basic Analysis Stage

• Initial prompt processing
• Basic intent recognition
• Preliminary response generation
• Initial fact validation

2. Detailed Refinement Stage

• Enhanced context analysis
• Deep fact verification
• Response structuring
• Consistency checking

3. Comprehensive Synthesis Stage

• Final response compilation
• Cross-validation
• Content enrichment
• Quality assurance

Installation Guide

Prerequisites

• Python 3.8 or higher
• Ollama installed and running
• gemma2:9b model
• Git

Step-by-Step Installation

1. Clone the repository:

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

2. Install required dependencies:

pip install -r requirements.txt

3. Verify Ollama installation:

ollama --version

4. Ensure gemma2:9b model is available:

ollama list

Usage Guide

Basic Usage

1. Start the application:

python src/main.py

2. Enter your prompt when prompted
3. Wait for the three-stage processing to complete
4. Review the refined and enriched response

Advanced Usage

Custom Configuration

You can modify the default settings by adjusting the parameters in the configuration file:

{
    "model_name": "gemma2:9b",
    "temperature": 0.7,
    "max_tokens": 2000,
    "stages": {
        "basic_analysis": true,
        "detailed_refinement": true,
        "comprehensive_synthesis": true
    }
}

Response Customization

You can customize the response format by adjusting the synthesis parameters:

• Detail level
• Response structure
• Output format

Components Deep Dive

Intent Analysis System

The intent analysis system uses advanced natural language processing to understand:

• Primary user intent
• Secondary objectives
• Context requirements
• Implicit needs

Multi-stage Prompt Generator

The prompt generator creates specialized prompts for each processing stage:

• Initial analysis prompts
• Refinement queries
• Synthesis instructions

Response Processing Engine

The processing engine handles:

• Content verification
• Fact checking
• Context integration
• Response optimization

Final Synthesis Module

The synthesis module combines:

• Verified information
• Contextual insights
• Structured formatting
• Enhanced details

Troubleshooting

Common Issues

1. Connection Issues

Error: Could not connect to Ollama
Solution: Ensure Ollama service is running

2. Model Loading Issues

Error: Model not found
Solution: Run 'ollama pull gemma2:9b'

3. Memory Issues

# Adjust memory limits in config:
config = {
    "max_memory": "8G",
    "batch_size": 1
}

Contributing Guidelines

How to Contribute

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

Code Style

• Follow PEP 8
• Use type hints
• Write documentation
• Include tests

Testing

Run tests before submitting:

pytest tests/

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Support

For support:

1. Check this documentation
2. Search existing issues
3. Create a new issue if needed

License

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