RAG Chatbot with Confluence

Create RAG chatbot using Confluence knowledge base

Query your Confluence documentation with this self-hosted RAG chatbot. Features source-cited answers, a modern UI, and runs entirely on your local machine with Ollama, ensuring your data never leaves your control.

Agentic AI chatbot for Slack with autonomous tool-calling loop. Connects to Jira, Confluence, and GitHub. Features RAG knowledge retrieval (PostgreSQL + pgvector), reaction-based approval gates, and multi-provider LLM support (OpenAI, Bedrock, Vertex).

RAG Chatbot with Confluence as DB

AI-powered RAG chatbot with Confluence integration, semantic search, vector embeddings, and FastAPI + React frontend

AI-powered Internal Assistant built for AI Agent Hackathon – Product Space. Uses RAG with vector embeddings + chatbot to fetch answers from scattered docs (Notion, Google Docs, Confluence). Includes GUI & sample PDF dataset. Live demo link available.

A Slack-integrated chatbot built with Retrieval-Augmented Generation (RAG) to make organizational knowledge more accessible. The bot ingests internal documents (wikis, FAQs, Confluence pages, etc.), indexes them in a vector database (Pinecone), and retrieves relevant context for natural language queries.

RAG Chatbot is an AI-powered Q&A system that ingests documents, URLs, and Confluence pages to deliver context-aware answers with citations. Built with Flask, FAISS, and OpenAI, it supports chat history, multi-LLM switching, and a modern web UI for managing and querying knowledge

An intelligent RAG-based chatbot that leverages LangChain and FAISS to answer queries related to internal platform APIs by extracting knowledge from Confluence pages or fallback Excel documents. Built with FastAPI (backend) and Angular (frontend), this assistant enables developers and stakeholders to access API service information effortlessly.