krionis-pipeline 0.8.1

Krionis Pipeline - multimodal RAG pipeline for low-compute, local, real-world deployment

Krionis Pipeline (formerly RAG-LLM-API-Pipeline)

A fully local, GPU-poor, multimodal Retrieval-Augmented Generation (RAG) system powered by open-source local LLMs.
Designed for secure technology environments, (such as OT, secure, airgapped applications, edge) it provides AI-assisted access to technical knowledge, manuals, and historical data — securely, offline, and at minimal cost.

⚠️ Backward compatibility:

• Existing imports (import rag_llm_api_pipeline) and CLI (rag-cli) still work.

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✅ Key Features

🔍 Retrieval-Augmented Generation (RAG)

• FAISS/HNSW vector indices
• SentenceTransformers embeddings

🧠 Flexible LLM Integration

• HuggingFace open-source models (Qwen, Mistral, LLaMA, etc.)
• Mixed precision: fp32, fp16, bfloat16
• Dynamic model/device/precision switching via YAML

🔧 1-line YAML Configuration

• System-specific documents
• Embedding & generation model selection
• CPU/GPU inference toggle
• Index rebuilding, token limits, chunking

📂 Multimodal Input Support

• PDFs
• Plain text
• Images (OCR via Tesseract)
• Audio (.wav)
• Video (.mp4)

💻 Multiple Interfaces

• CLI (rag-cli / krionis-cli) for single-line querying
• FastAPI-powered REST API for local serving
• Lightweight HTML Web UI for interactive search

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🚀 Quickstart

Install:

pip install krionis-pipeline


###🛠️ Per-system configuration via system.yaml for flexible deployments
###🔐 Fully local operation — no cloud dependencies required

###✅ Quickstart guide and prebuilt example included
###✅ Runs on CPU or GPU with smart memory management
###✅ Web UI + CLI + API, all in one package

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## 📦 Installation

```bash
pip install krionis-pipeline

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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

Please review the quickstart guide.

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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 TestSystem --question "What is the restart sequence for this machine?"

API Server

uvicorn rag_llm_api_pipeline.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": "TestSystem", "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

• All models are open-source and run offline.

You can replace 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.