krionis-pipeline 1.0.1

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

Krionis Pipeline 1.0

Krionis Pipeline is a local-first Retrieval-Augmented Generation platform for controlled, auditable AI workflows. It is designed for secure and airgapped environments where teams need API-driven retrieval, human review controls, traceability, and a practical operator experience without depending on cloud infrastructure.

Backward compatibility is preserved:

• import rag_llm_api_pipeline still works
• rag-cli still works
• krionis-cli is available as the branded CLI entry point

What Is New In 1.0

• Mandatory HITL gating for flagged outputs
• Review queue persistence with original and final responses stored separately
• Append-only audit tracing across query, retrieval, generation, and signoff
• SQLite-backed result metadata for Good and Bad feedback plus review outcomes
• API-driven index visibility and manual cache rebuild operations
• Split operator UX for control, telemetry, runtime, configuration, records, and review
• CPU-friendly quantized default model profile for local deployment
• Isolated generation worker to keep the API responsive during model load or failure
• Docker packaging for integrated platform deployment
• Expanded API documentation for custom frontend development

Core Capabilities

🔍 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

---

Quickstart

Required Setup

Before starting the platform, make sure your working directory contains:

• config/system.yaml
• data/manuals/

Install:

pip install krionis-pipeline

Build the retrieval index and start the API:

krionis-cli build-index --system TestSystem
uvicorn rag_llm_api_pipeline.api.server:app --host 127.0.0.1 --port 8000

Open:

• http://127.0.0.1:8000/ for the operator console
• http://127.0.0.1:8000/api/docs for the API reference
• http://127.0.0.1:8000/ui/reviews for the review queue

Installation

pip install krionis-pipeline

---

🛠️ 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.

---

🚀 Usage

Please review the quickstart guide.

---

🐧 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

---

🔁 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

bash 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.

---

## 🧠 Model Info

- All models are open-source and run offline.

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

---

## 🔐 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.

---

## 📜 License

MIT License

---

## 📧 Contact

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

## Documentation

The repository includes a documentation portal scaffold for developers building on top of the Krionis API.

- Interactive API docs: `/api/docs`
- ReDoc reference: `/api/reference`
- Static docs source: `docs/`
- Docs build config: `mkdocs.yml`