Source of this article and featured image is DZone AI/ML. Description and key fact are generated by Codevision AI system.
This tutorial guides readers through creating a local RAG (Retrieval-Augmented Generation) app using Streamlit, FAISS, and Ollama. The app enables users to upload PDFs and ask questions about their content, leveraging vector search for efficient document retrieval. The process involves text chunking, embedding with FAISS, and LLM-driven answer generation. Nabin Debnath provides step-by-step instructions with code examples for each phase. The tutorial emphasizes a lightweight, offline solution ideal for personal knowledge bases or internal tools.
Key facts
- The app uses Streamlit for creating an interactive UI and FAISS for vector-based document search.
- PDF text is split into chunks for easier processing by the LLM and stored as embeddings in FAISS.
- Ollama runs a local LLM (like Llama 3) to generate answers based on retrieved document chunks.
- The example demonstrates answering questions about a children’s storybook using page-specific sources.
- The solution runs entirely locally, avoiding cloud services and requiring minimal setup.
TAGS:
#FAISS #knowledge base #LLM Integration #local AI tool #Ollama #PDF processing #RAG app #Streamlit #Tutorial
