Building RAG and Agentic Applications with Haystack 2.0, RAGAS and LangGraph 1.0 published by Packt

🤖 The RAG application retrieves data from Notion

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.

🔰 A Comprehensive RAG repository covering basic vanilla RAG techniques, advanced retrieval methods, hybrid search fusion approaches, hands-on reranking techniques with code + explanation 📚✨

RAG Evaluation Pipeline (RAGAS)

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.

A LangChain-based Retrieval-Augmented Generation (RAG) chatbot for medical data. Integrates with Gemini/Grok AI to deliver accurate, context-aware answers in healthcare and biomedical domains.

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

Retrieval-Augmented Generation (RAG) system for extracting information from legal documents such as NDAs, contracts, and privacy policies. Includes preprocessing, EDA, vector search using ChromaDB, and evaluation with ROUGE, BLEU, and RAGAS metrics.

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

Contextual RAG Chatbot with LlamaIndex, Ollama & PGVector

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

A practical guide for building and evaluating an end-to-end Retrieval Augmented Generation (RAG) system with memory and more!

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.

Create syntetic datasets for RAG evaluation

Streamlit, LangChain, OpenAI, FAISS, Ollama, ChromaDB, Llama 3.1 for PDF RAG Chat Interaction

Implements a Retrieval-Augmented Generation (RAG) system.

Finetuning LLMs using Unsloth on text-to-sql tasks with minimal compute