RAG using LlamaIndex：Computer Network Q&A System powered by LlamaIndex | 基于 LlamaIndex 框架的计算机网络智能问答系统 - HyDE+混合检索 + vLLM 推理+Ragas评估

🤖 The RAG application retrieves data from Notion

This project integrates LangFlow as a backend API with a Streamlit frontend for a chatbot interface. It also includes RAGAS evaluation for measuring the performance of RAG (Retrieval-Augmented Generation) pipelines.

RAG Evaluation Pipeline (RAGAS)

Advanced RAG system with enhanced retrieval and error-handling capabilities. Implemented totally locally with open-source tools — LangGraph, Qdrant, Llama.cpp server, Qwen3-0.6B-UD-Q8_K_XL.gguf and MLflow server for observability.

An enterprise-grade, full-stack AI travel planner which provides data-driven itineraries for Lucknow, India and showcases production-ready architecture, combining a FastAPI backend with a Streamlit frontend. It leverages an advanced agentic RAG system, context-aware responses by integrating a local knowledge base with live, external APIs.

LangGraph-orchestrated RAG multi-agent pipeline that routes queries to specialized agents. Modular design for ingestion, routing and evaluation.

A realtime Concierge Agent made using Pipecat and LanGraph with COT reasoning.

An enterprise-grade contextual RAG chatbot with ZenML pipelines, CrewAI agents, Ollama models, and OpenWebUI — designed for intelligent, local, and explainable document querying.

Senor 2.0 is an LLM-powered chatbot trained on Indian legal documents, designed to assist Indian citizens in understanding and navigating legal procedures.

Contextual RAG Chatbot with LlamaIndex, Ollama & PGVector

Create syntetic datasets for RAG evaluation