How OmniRAG AI Works

Explore the advanced Retrieval-Augmented Generation pipeline that powers our intelligent research assistant.

Document Upload

Secure ingestion of PDF and text documents with high-fidelity extraction.

OCR Support

PDF Parsing

Text Cleaning

Semantic Chunking

Breaking documents into meaningful segments while preserving context.

Recursive Splitting

Semantic Boundaries

Overlap Management

Vector Embeddings

Converting text into mathematical vectors using state-of-the-art models.

HuggingFace Models

Multi-lingual Support

High-dim Vectors

Vector Storage

Storing embeddings in ChromaDB for ultra-fast semantic retrieval.

Persistent Storage

Metadata Filtering

Scalable Indexing

Hybrid Retrieval

Finding the most relevant context using semantic and keyword search.

Vector Similarity

BM25 Scoring

Re-ranking Engine

LLM Generation

Generating precise answers using context-aware Llama models via Groq.

Conversational Memory

Source Attribution

Streaming Output

System Architecture

A robust and scalable infrastructure designed for modern AI workloads.

Frontend

Next.js & Framer Motion

Backend

FastAPI & LangChain

Vector DB

ChromaDB

LLM Engine

Groq Llama 3

Memory

Persistent Chat History

The Technology Stack

Next.js

React Framework

FastAPI

Python API

LangChain

AI Orchestration

ChromaDB

Vector Database

Groq

Inference Engine

HuggingFace

Embedding Models

TailwindCSS

Modern Styling

Framer Motion

Fluid Animations

Ready to start?

Experience the power of our RAG pipeline in the interactive workspace.

Launch Workspace