Running local LLMs with Ollama to perform RAG for answering questions based on sample PDFs

A lightweight local web chatbot for chatting with PDF files (focused on scientific papers), to get context-powered answers. Uses RAG (Retrieval-Augmented Generation) to combine vector search and generate LLM-based answers that cite sources.

RAG (Retrieval-Augmented Generation) chatbot using LangChain, ChromaDB, and Ollama for local document querying. Features custom knowledge base from PDFs, local LLM with mistral, vector storage, and modular architecture.

The RAG-based Local LLM for PDFs project uses a Retrieval-Augmented Generation model to enable intelligent querying and summarization of PDF content locally, ensuring data privacy. It extracts and indexes document content, combining retrieval and generative capabilities for contextual answers, enhancing efficiency in document automation workflows.

A Django-based RAG chatbot that answers questions using only the content from uploaded PDFs. Built with LangChain, Ollama, and Chroma for local LLM and embedding support.

A Python-based Retrieval-Augmented Generation (RAG) application for querying PDF documents. This project demonstrates advanced RAG techniques, including local LLM integration, vector database updates, and response quality testing.

Smart RAG Chatbot is a role-based PDF question-answering assistant powered by a local LLM (gemma:2b via Ollama). It uses LangChain and FAISS for efficient retrieval and provides intelligent, context-aware responses from your PDFs, all through a simple Streamlit interface. Designed for privacy and offline use with cutting-edge RAG architecture.

An offline Retrieval-Augmented Generation (RAG) system that analyzes CSV data and PDF documents to generate evidence-based business insights. Includes FAISS vector search, SentenceTransformer embeddings, and optional local LLM synthesis for strategic decision-making.

A powerful RAG-based application for analyzing scientific papers and documents using local LLM models. SciAgent enables users to upload PDF documents, ask questions, generate summaries, and perform semantic search - all running locally without requiring cloud API keys.

Offline Multimodal RAG System: Ingest, index, and query DOC, PDF, images, and audio using a local LLM. Provides semantic search and context-aware responses entirely offline, supporting OCR, speech-to-text, and vector-based retrieval for versatile, private data access.

A multi-provider RAG application with a Tkinter GUI. It builds a local vector knowledge base from PDFs using FAISS and sentence-transformers. The app queries this data and sends prompts to various LLM APIs like Google, OpenAI, and Anthropic. A build script bundles the app and embedding model into a single executable for distribution.

RAG-based PDF chatbot using LangChain, FAISS, and local LLMs for context-aware question answering.

Production-ready RAG system with FastAPI, FAISS, and local LLM (Ollama) for PDF-based question answering.

A Streamlit-based RAG chatbot powered by local LLMs from Ollama and HuggingFace embeddings for offline PDF question answering.

FastAPI-based RAG backend for PDF ingestion, semantic retrieval, and local LLM-powered question answering using ChromaDB and Ollama.

A local LLM chatbot utilizing RAG to process PDFs. Provides prompt-based responses directly from document content. Ideal for private, secure, and context-aware AI interactions with your local files.

A local AI-powered study assistant that converts PDFs and notes into searchable content using Retrieval-Augmented Generation (RAG), FAISS-based vector search, and a lightweight local LLM for grounded question answering and summarization.

A Streamlit-based PDF question answering system using Retrieval-Augmented Generation (RAG), FAISS vector search, local HuggingFace embeddings, and a Groq-powered LLM for context-aware responses.

📚 RAG-based chatbot using FastAPI + Streamlit. Upload PDFs/texts, auto-chunk them, and query with context-aware answers via FAISS vector search. Supports HuggingFace embeddings (free/local) and optional Gemini LLM for enhanced responses.

Local Company Policy Chatbot – A local RAG-based chatbot that answers questions from PDFs, DOCX, or TXT company policy files. Uses Ollama LLM, LangChain, and Chroma DB for embeddings, with multi-document support, progress bar, and chat history. Fully runs locally for data privacy.

A fully local Retrieval-Augmented Generation system for analyzing long financial reports. Supports PDF ingestion, chunking, embedding search, and LLM-based financial QA. Built to demonstrate practical NLP & RAG engineering capabilities.

# RAG-Based AI System A Retrieval-Augmented Generation (RAG) system that answers questions from: - Connected databases - Uploaded documents (PDF, DOCX, TXT) ## Features - Database-aware question answering - Document ingestion & retrieval - Ollama local LLM support - GLM-4 integration for scalability

Retrieval-Augmented Generation (RAG) with AI using LangChain, Python, Ollama, and ChromaDB for intelligent PDF reading and analysis. This project enables semantic search and natural language question answering based on PDF documents, combining vector databases with local LLMs.

A basic implementation of a Retrieval-Augmented Generation (RAG) chatbot based on OpenAI's models. This project allows you to enrich chatbot responses with contextual data from local files (PDFs and TXT). The modular design makes it easy to customize for other LLMs or advanced RAG techniques.

RAG-based chatbot that lets users upload company PDFs and ask questions in natural language. It uses FAISS for semantic search and a local LLM (Ollama) to answer only from the uploaded documents, with a FastAPI backend and React frontend.

pdf-chat-engine is a local RAG based chat engine that leverages Large Language Models (LLMs) for interactive document management. Users can upload PDF documents and engage in chat, ensuring privacy and quick analysis without relying on public chat engines.

RAG-based chatbot that lets users upload company PDFs and ask questions in natural language. It uses FAISS for semantic search and a local LLM (Ollama) to answer only from the uploaded documents, with a FastAPI backend and React frontend.

North Oaks Contract AI is a specialized RAG-based platform designed for health system staff to interact with complex legal documents. It transforms static PDFs into searchable, chat-ready assets using local LLMs, high-precision OCR, and vector similarity search

Entry for the Precision FDA Democratizing and Demystifying AI - GenAI Community Challenge. The tool is a rag based local llm setup capable of accurately answering questions using FDA data from the Cosmetic Guidance PDF

A Docker-based service that runs a small local LLM with API endpoints for questions/prompts and a web UI for uploading context documents (PDF, DOC, DOCX) to create a vector database for RAG (Retrieval-Augmented Generation).