rag-llm-api-pipeline 0.1.0

Multimodal RAG pipeline for low-compute, local, real-world deployment

RAG-LLM-API-Pipeline

A fully local GPU poor, multimodal Retrieval-Augmented Generation (RAG) system powered by open-source local LLMs. This pipeline is designed for operational technology environments to provide AI-assisted access to technical knowledge, manuals, and historical data — securely and offline, at min cost.

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✅ Key Features

• 🔍 Retrieval-Augmented Generation (RAG) using FAISS + SentenceTransformers
• 🧠 Query handling via a local, open-source Large Language Model (LLM)
• 📄 Supports multiple input formats:
  • PDFs
  • Plain text files
  • Images (OCR via Tesseract)
  • Audio files (.wav, .flac, .aiff)
  • Videos (.mp4 with audio extraction)
• 💻 Interfaces:
  • Command Line Interface (CLI)
  • Local REST API (FastAPI)
• 🛠️ Asset definition via YAML configuration
• 🔐 Works in fully local environments after setup

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📂 Project Structure

rag_llm_api_pipeline/
├── api/                # FastAPI application
│   └── server.py
├── cli/                # Command-line interface
│   └── main.py
├── config/
│   └── system.yaml     # Asset and document config
├── data/
│   └── manuals/        # PDF, image, audio, etc.
├── loader.py           # Multimodal file loader
├── retriever.py        # Embedding, FAISS search
├── llm_wrapper.py      # Local LLM text generation
├── requirements.txt    # Python dependencies
├── README.md

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🛠️ Setup Instructions (Windows + Anaconda)

1. Create Python Environment

conda create -n rag_env python=3.10
conda activate rag_env

2. Install Dependencies

Via Conda (system-level tools):

conda install -c conda-forge ffmpeg pytesseract pyaudio

Via Pip (Python packages):

pip install -r requirements.txt

Ensure Tesseract is installed and in your system PATH. You can get it from https://github.com/tesseract-ocr/tesseract.

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🚀 Usage

CLI Example

python cli/main.py --system Pump_A --question "What is the pressure threshold for operation?"

API Server

Start the server:

uvicorn api.server:app --reload

Query with curl or Postman:

curl -X POST http://localhost:8000/query \
     -H "Content-Type: application/json" \
     -d '{"system": "Pump_A", "question": "Explain the restart procedure"}'

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🧱 Configuration

Edit config/system.yaml to define your assets and associated documents:

assets:
  - name: Pump_A
    docs:
      - manuals/pump_manual.pdf
      - manuals/startup_guide.png
      - manuals/technician_note.wav

Documents can be PDFs, plain text, images, or audio/video files.

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🐧 Setup Instructions (Linux)

1. Create Python Environment

python3 -m venv rag_env
source rag_env/bin/activate

Or with conda:

conda create -n rag_env python=3.10
conda activate rag_env

2. Install System Dependencies

sudo apt update
sudo apt install -y ffmpeg tesseract-ocr libpulse-dev portaudio19-dev

Optional: install language packs for OCR (e.g., tesseract-ocr-eng).

3. Install Python Packages

pip install -r requirements.txt

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🔁 Running the Application on Linux

CLI

python cli/main.py --system Pump_A --question "What is the restart sequence for this machine?"

API Server

uvicorn api.server:app --host 0.0.0.0 --port 8000

cURL Query

curl -X POST http://localhost:8000/query \
     -H "Content-Type: application/json" \
     -d '{"system": "Pump_A", "question": "What does error E204 indicate?"}'

📚 How it Works

1. Index Building:

   • Files are parsed using loader.py.
   • Text chunks are embedded with MiniLM.
   • FAISS index stores embeddings for fast similarity search.

2. Query Execution:

   • User provides a natural language question.
   • Relevant text chunks are retrieved from the index.
   • LLM generates an answer based on retrieved context.

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🧠 Model Info

• Default LLM: tiiuae/falcon-rw-1b (run locally via transformers)
• Embedding model: sentence-transformers/all-MiniLM-L6-v2
• All models are open-source and run offline.

You can replace these with any local-compatible Hugging Face model.

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🔐 Security & Offline Use

• No cloud or external dependencies required after initial setup.
• Ideal for OT environments.
• All processing is local: embeddings, LLM inference, and data storage.

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📜 License

MIT License

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📧 Contact

For issues, improvements, or contributions, please open an issue or PR.