RagBase - Private Chat with Your Documents

Completely local RAG with chat UI

Demo

Check out the RagBase on Streamlit Cloud. Runs with Groq API.

Installation

Clone the repo:

git clone git@github.com:curiousily/ragbase.git
cd ragbase

Install the Python development headers for your python version:

sudo apt-get install python3.12-dev

Install or update the dependencies (requires Poetry):

poetry install

or

poetry update

Install/restart ollama:

sudo snap install ollama

or

sudo snap restart ollama

Fetch your LLM (gemma2:9b by default):

ollama pull gemma2:9b

Run the Ollama server

ollama serve

Start RagBase:

poetry run streamlit run app.py

Architecture

Ingestor

Extracts text from PDF documents and creates chunks (using semantic and character splitter) that are stored in a vector databse

Retriever

Given a query, searches for similar documents, reranks the result and applies LLM chain filter before returning the response.

QA Chain

Combines the LLM with the retriever to answer a given user question

Tech Stack

• Ollama - run local LLM
• Groq API - fast inference for mutliple LLMs
• LangChain - build LLM-powered apps
• Qdrant - vector search/database
• FlashRank - fast reranking
• FastEmbed - lightweight and fast embedding generation
• Streamlit - build UI for data apps
• PDFium - PDF processing and text extraction
• PDFium - PDF processing and text extraction

Add Groq API Key (Optional)

You can also use the Groq API to replace the local LLM, for that you'll need a .env file with Groq API key:

GROQ_API_KEY=YOUR API KEY