RAG With MinIO

In this Repo, we will demonstrate how to use MinIO to build a Retrieval Augmented Generation(RAG) based chat application using commodity hardware.

• Use MinIO to store all the documents, processed chunks and the embeddings using the vector database.
• Use MinIO's bucket notification feature to trigger events when adding or removing documents to a bucket
• Webhook that consumes the event and process the documents using Langchain and saves the metadata and chunked documents to a metadata bucket
• Trigger MinIO bucket notification events for newly added or removed chunked documents
• A Webhook that consumes the events and generates embeddings and save it to the Vector Database (LanceDB) that is persisted in MinIO

Architecture

Key Tools Used

• MinIO - Object Store to persist all the Data
• LanceDB - Serverless open-source Vector Database that persists data in object store
• Ollama - To run LLM and embedding model locally (OpenAI API compatible)
• Gradio - Interface through which to interact with RAG application
• FastAPI - Server for the Webhooks that receives bucket notification from MinIO and exposes the Gradio App
• LangChain & Unstructured - To Extract useful text from our documents and Chunk them for Embedding

Models Uses

• LLM - Phi-3-128K (3.8B Parameters)
• Embeddings - Nomic Embed Text v1.5 (Matryoshka Embeddings/ 768 Dim, 8K context)

Setup

Install the required packages using the following command:

pip install -r requirements.txt

Running the Application

You can follow the step by step described in the Notebook to run the application.