EVA - AI Assistant with RAG & Voice

A modular AI assistant with multi-provider LLM support, session-based RAG, voice control, and intelligent conversation memory.

Features

• 🤖 Multi-Provider LLM - Ollama, OpenAI, Anthropic, Groq, HuggingFace
• 📄 Document Ingestion - PDF, DOCX, images (OCR), and 20+ formats
• 🧠 3-Memory Architecture - Session RAG + Conversation Summaries + LLM Context
• 🔍 Hybrid Retrieval - Dense (ChromaDB) + Sparse (BM25) + Reranking
• 🎯 Intent Classification - Auto-routes between RAG and normal chat
• 🎤 Voice Mode - Hands-free with Whisper STT + Piper TTS
• 💻 OS Control - Open apps, search files, browse web via voice/text

Quick Start

# Clone & setup
git clone https://github.com/Het2239/Eva-AI.git
cd eva_rag

# Install dependencies
pip install -r requirements.txt

# Setup voice (optional)
./setup_voice.sh

# Add API key
echo "GROQ_API_KEY=gsk_..." > .env

# Start chat
eva chat

Usage

Chat Mode

eva chat

Commands: /ingest <file>, /listen, /status, /end, /quit

You: /ingest document.pdf
✓ Added 12 chunks to session

You: What does the document say about X?
EVA: Based on the document... [RAG: document.pdf]

You: /listen
🎤 Listening... (speak your command)

Voice Mode

eva voice

Hands-free mode with continuous listening and spoken responses.

OS & Web Commands

Works in both chat and voice mode:

• "Open VS Code"
• "Play Shape of You on YouTube Music"
• "Find physics quiz PDF in Downloads"
• "Open GitHub"

Project Structure

eva_rag/
├── models.py              # LLM configuration
├── setup_voice.sh         # Voice setup script
├── eva/
│   ├── speech.py          # STT (Whisper) + TTS (Piper)
│   ├── voice_agent.py     # Voice control loop
│   └── os_tools.py        # OS/Web actions
└── rag/
    ├── document_loader.py # Docling + Unstructured
    ├── semantic_splitter.py
    ├── chunk_processor.py
    ├── vector_store.py    # ChromaDB + BM25
    ├── retriever.py       # Hybrid + Reranking
    ├── intent_classifier.py
    └── agent.py           # EVAAgent

Documentation

Document	Description
ARCHITECTURE.md	System overview, memory model, API reference
TECHNICAL_GUIDE.md	Pipeline flowcharts, code locations, tunable parameters

Configuration

LLM (models.py)

CHAT_MODEL = ModelConfig(
    provider=Provider.GROQ,
    model_name="llama-3.1-70b-versatile",
    temperature=0.7,
)

Voice (eva/speech.py)

Edit SpeechConfig to change:

• Whisper model: base / small / medium / large
• Piper voice: Download from Piper Voices

Roadmap

• Multi-provider LLM support
• Document parsing (Docling + Unstructured)
• Semantic chunking (BGE embeddings)
• Hybrid retrieval (ChromaDB + BM25)
• Cross-encoder reranking
• Session-based agent
• Voice mode (Whisper + Piper)
• OS control & web actions
• Web UI
• Plugin system

License

MIT

Author

Het Patel (@Het2239)