README.md

---
title: Codey Bryant 3.0
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
app_file: app_hf.py
pinned: true
license: mit
---

# 🤖 Codey Bryant 3.0 - SOTA RAG Coding Assistant

## Advanced RAG Architecture
**Codey Bryant 3.0** implements state-of-the-art Retrieval-Augmented Generation (RAG) with four key innovations:

### 1. **HyDE (Hypothetical Document Embeddings)**
Generates hypothetical answers to improve retrieval relevance for vague queries.

### 2. **Query Rewriting**
Transforms casual/vague questions into specific, searchable queries.

### 3. **Multi-Query Retrieval**
Searches multiple query variations to increase recall.

### 4. **Answer-Space Retrieval**
Retrieves from both question AND answer embeddings for better context.

## Technical Stack
- **LLM**: TinyLlama 1.1B (4-bit quantized)
- **Embeddings**: all-MiniLM-L6-v2
- **Retrieval**: FAISS + BM25 hybrid
- **Datasets**: OPC Educational + Evol-Instruct
- **Framework**: Gradio + Hugging Face Transformers

## Performance Features
- Handles vague queries like "it's not working"
- Streaming responses
- Context-aware generation
- Hybrid dense-sparse retrieval
- Persistent artifact storage

## Getting Started
1. Click **"Initialize Assistant"** (required once)
2. Ask Python coding questions
3. Get intelligent, context-aware responses

## Example Queries
- "How to read a CSV file in Python?"
- "Why am I getting 'list index out of range'?"
- "Make this function faster..."
- "Help, my code isn't working!"
- "Best way to sort a dictionary by value?"


## Why This Architecture?
1. **HyDE**: Addresses the "semantic gap" between queries and documents
2. **Query Rewriting**: Improves retrieval for conversational queries
3. **Multi-Query**: Increases recall for complex questions
4. **Answer-Space**: Provides better context for generation

## 📁 Repository Structure