krionis-pipeline 0.9.0
Krionis Pipeline - multimodal RAG pipeline for low-compute, local, real-world deployment
Requires Python: >=3.8

# 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.  

---

## ✅ 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  

---

## 🚀 Quickstart

### ⚙️ Required Setup

Before starting the orchestrator, always make sure your working directory contains:

- **`config\system.yaml`** – the main configuration file used by both the orchestrator and the pipeline.  
- **`data\manual\`** – a directory with manually curated data (shared by both the pipeline and orchestrator).

These must be present in the directory where you launch the CLI (`pwd` on Linux/macOS, current folder in Windows).

Install:

```bash
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

---

## 📦 Installation

```bash
pip install krionis-pipeline

```

---

## 🛠️ Setup Instructions (Windows + Anaconda)

### 1. Create Python Environment
```bash
conda create -n rag_env python=3.10
conda activate rag_env
```

### 2. Install Dependencies
#### Via Conda (system-level tools):
```bash
conda install -c conda-forge ffmpeg pytesseract pyaudio
```

#### Via Pip (Python packages):
```bash
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
```bash
python3 -m venv rag_env
source rag_env/bin/activate
```

Or with `conda`:
```bash
conda create -n rag_env python=3.10
conda activate rag_env
```

### 2. Install System Dependencies
```bash
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
```bash
pip install -r requirements.txt
```

---

## 🔁 Running the Application on Linux

### CLI
```bash
python cli/main.py --system TestSystem --question "What is the restart sequence for this machine?"
```

### API Server
```bash
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.