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A beginner-friendly Retrieval-Augmented Generation (RAG) system using LangChain, FAISS, and a local LLM.

This project is a beginner-friendly demonstration of a Retrieval-Augmented Generation (RAG) system built entirely on local, open-source technologies. The application allows a user to upload their resume (PDF, DOCX, or TXT), which is then indexed into a local vector database.

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Beginner-friendly RAG system using FastAPI, ChromaDB, and Ollama for local document Q&A with source grounding.

A beginner oriented fully local Retrieval Augmented Generation (RAG) system built with Ollama. Learn step by step how to split documents, create embeddings, retrieve context and guide a language model without relying on cloud services.

AI-powered semantic search system for SAP FI/CO documents built using a Retrieval-Augmented Generation (RAG) pipeline. The project uses Sentence Transformers for embeddings, cosine-similarity search for retrieval, and a Flask web interface for interacting with the knowledge base. Fully local, lightweight, and beginner-friendly.

A beginner-friendly Python implementation of a RAG (Retrieval-Augmented Generation) system. Ingest PDF, DOCX, and TXT files, then ask natural language questions about them. Uses sentence-transformers for local embeddings, ChromaDB for vector storage, and optionally connects to any OpenAI-compatible LLM (OpenRouter, OpenAI) for answer generation