A sophisticated RAG (Retrieval-Augmented Generation) system that enables intelligent conversations with documents, images, and audio files through a clean ChatGPT-style interface.

# Start the application
streamlit run chatbot_app.py

• Python 3.8+ - Primary language
• Streamlit - Web interface and UI framework
• SQLite3 - File metadata storage and management

• Ollama - Local LLM hosting (Llama 3.1 8B model)
• PyTorch - Deep learning framework
• Transformers - Hugging Face model library
• OpenAI Whisper - Speech-to-text conversion (base model)
• BLIP - Image captioning (Salesforce/blip-image-captioning-base)

• ChromaDB - Vector storage and similarity search
• Nomic Embed Text - Text embeddings via Ollama (768-dim vectors)
• CLIP - Visual embeddings for images (openai/clip-vit-base-patch32)
• FAISS - Alternative vector search (Facebook AI)

• PyPDF2 - PDF text extraction
• python-docx - Word document processing
• pdfplumber - Advanced PDF parsing
• python-pptx - PowerPoint file support

• Pillow (PIL) - Image manipulation
• OpenCV - Computer vision operations
• Tesseract OCR - Text extraction from images
• pytesseract - Python wrapper for Tesseract

• PyDub - Audio file manipulation
• librosa - Audio analysis and processing
• Whisper - Audio transcription

• NumPy - Numerical computations
• PyYAML - Configuration management
• tqdm - Progress bars
• requests - HTTP client

smartrag/
├── chatbot_app.py              # Main Streamlit application
├── config.yaml                 # System configuration
├── requirements.txt            # Python dependencies
├── multimodal_rag/            # Core RAG system
│   ├── system.py              # Main RAG orchestrator
│   ├── base.py                # Base classes and interfaces
│   ├── processors/            # File processors
│   │   ├── document_processor.py  # PDF, DOCX, TXT
│   │   ├── image_processor.py     # Images with OCR
│   │   └── audio_processor.py     # Audio transcription
│   └── vector_stores/         # Vector database implementations
│       ├── chroma_store.py    # ChromaDB integration
│       └── faiss_store.py     # FAISS integration
├── file_storage.db            # SQLite database
├── vector_db/                 # ChromaDB persistence
└── user_data/                 # User session data

• Documents: PDF, DOCX, DOC, TXT, MD, RTF
• Images: JPG, PNG, BMP, TIFF, WEBP (with OCR)
• Audio: MP3, WAV, M4A, OGG, FLAC, AAC

• Local LLM inference with Ollama
• Semantic search with vector embeddings
• Image understanding and captioning
• Speech-to-text transcription
• Context-aware document retrieval

• ChatGPT-style conversation interface
• File upload and management
• Real-time processing feedback
• Document viewer for stored files
• Recent uploads tracking

SmartRAG uses a single source of truth configuration system with Pydantic validation.

The system uses config.yaml with priority chain:

CLI Overrides > Environment Variables > config.yaml > Defaults

Example config.yaml:

system:
  name: "SmartRAG System"
  debug: false
  log_level: "INFO"

models:
  llm_model: "llama3.1:8b" # Ollama Llama 3.1 8B
  embedding_model: "nomic-embed-text" # 768-dim embeddings
  vision_model: "Salesforce/blip-image-captioning-base"
  whisper_model: "base"

vector_store:
  type: "chromadb"
  embedding_dimension: 768 # Must match embedding model

processing:
  chunk_size: 1000
  chunk_overlap: 200
  ocr_enabled: true # Tesseract OCR

export SMARTRAG_LLM_MODEL=llama2:7b
export SMARTRAG_TEMPERATURE=0.5
export SMARTRAG_DEBUG=true

from config_schema import load_config

config = load_config(
    "config.yaml",
    models__llm_model="llama2:7b",
    generation__temperature=0.5
)

📖 See CONFIG.md for comprehensive configuration documentation

• Python: 3.8 or higher
• Ollama: For local LLM inference
• Tesseract OCR: For image text extraction
• FFmpeg: For audio processing (optional)

# Clone the repository
git clone https://github.com/itanishqshelar/SmartRAG.git
cd SmartRAG

# Start with Docker Compose
cd docker
docker-compose up -d

# Access at http://localhost:8501

See docker/README.md for detailed Docker deployment instructions.

1. Install dependencies:

   pip install -r requirements.txt

2. Install Ollama and pull models:

   # Install Ollama (see ollama.ai)
   ollama pull llama3.1:8b
   ollama pull nomic-embed-text

3. Install Tesseract OCR:

   • Windows: Download from GitHub releases
   • macOS: brew install tesseract
   • Linux: sudo apt-get install tesseract-ocr

4. Run the application:

   streamlit run chatbot_app.py

[User Input] → [Streamlit UI] → [RAG System] → [File Processors]
                    ↓                              ↓
                                        [Document: PyPDF2/python-docx]
                                        [Image: Tesseract OCR + BLIP]
                                        [Audio: Whisper Transcription]
                    ↓                              ↓
[SQLite DB] ← [Text Chunks] → [Nomic Embed Text (Ollama)] → [ChromaDB]
                                                                ↓
[Vector Search] → [Context Retrieval] → [Llama 3.1 8B (Ollama)] → [Response]

• Text Documents: Extracted with PyPDF2/python-docx → Chunked → Embedded with Nomic Embed Text • Images: OCR with Tesseract + Captioning with BLIP → Combined text → Embedded with Nomic Embed Text
• Audio: Transcribed with Whisper → Chunked → Embedded with Nomic Embed Text • Storage: All embeddings stored in ChromaDB (768-dim vectors) for semantic search • Generation: Retrieved context fed to Llama 3.1 8B via Ollama for response generation

1. Upload Files: Drag & drop or browse files in the sidebar
2. Chat: Ask questions about your uploaded content
3. View Files: Use the eye icon to preview stored documents
4. Manage Data: Clear chat history or uploaded files as needed

• Fully Offline: All processing happens locally
• No Data Sent: No external API calls for LLM inference
• Local Storage: Files and embeddings stored on your machine

system.ingest_file("presentation.pdf") # Slides system.ingest_file("screenshot.png") # Image with text system.ingest_file("meeting_recording.mp3") # Audio transcript

response = system.query("What was discussed about the Q4 budget?")

### Batch Processing

```python
# Process entire directories
results = system.ingest_directory("./company_docs/", recursive=True)

# Get processing summary
successful = sum(1 for r in results.values() if r.success)
total_chunks = sum(len(r.chunks) for r in results.values() if r.success)
print(f"Processed {successful} files, created {total_chunks} chunks")

Run the test suite to verify installation:

# Run all tests
python -m pytest tests/

# Run specific test file
python tests/test_system.py

# Run with coverage
pip install coverage
coverage run tests/test_system.py
coverage report

ChromaDB (Default - Recommended)

vector_store:
  type: "chromadb"
  persist_directory: "./vector_db"
  collection_name: "documents"
  embedding_dimension: 768 # For nomic-embed-text

FAISS (Alternative - High performance)

vector_store:
  type: "faiss"
  persist_directory: "./faiss_db"
  embedding_dimension: 768 # Must match nomic-embed-text

python cli.py interactive

FROM python:3.10-slim

WORKDIR /app
COPY . .

RUN pip install -r requirements.txt
CMD ["python", "cli.py", "interactive"]

from fastapi import FastAPI
from multimodal_rag.system import MultimodalRAGSystem

app = FastAPI()
system = MultimodalRAGSystem()

@app.post("/query")
async def query_endpoint(query: str):
    response = system.query(query)
    return {"answer": response.answer, "sources": len(response.sources)}

# Install development dependencies
pip install -r requirements.txt
pip install pytest black flake8

# Run tests
python -m pytest

# Format code
black multimodal_rag/ tests/ examples/

# Lint code
flake8 multimodal_rag/ tests/ examples/

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

• ChromaDB for vector storage
• Sentence Transformers for embeddings
• Hugging Face Transformers for language models
• OpenAI Whisper for speech recognition
• Tesseract OCR for text extraction

SmartRAG - Intelligent multimodal document understanding for the modern age.