Building RAG and Agentic Applications with Haystack 2.0, RAGAS and LangGraph 1.0 published by Packt
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
🔰 A Comprehensive RAG repository covering basic vanilla RAG techniques, advanced retrieval methods, hybrid search fusion approaches, hands-on reranking techniques with code + explanation 📚✨
Multi-agent LangGraph RAG for financial Q&A — 72.7% on FinanceBench under κ=0.932 calibrated judge. RBAC at the vector layer, multi-party HITL on high-stakes answers, self-hosted LLM observability. pip install financebench-rag-agent
Winning solution of Capital One Launchpad Hackathon 2025
Universal Agent Evaluation Framework (UAEF) is a framework-agnostic evaluation system for AI agents. Invoke any agent and score it on multiple metrics spanning tool calling, response quality, safety, performance, and reasoning. Track experiments against baselines, catch regressions automatically, and get LLM-generated insights.
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.
A curated collection of papers, frameworks, tools, and resources on Retrieval-Augmented Generation (RAG). Maintained for students of the Text Mining and Data Visualization course as a starting point for thesis research.
Hybrid retrieval-augmented generation(RAG) pipeline for financial document.
A production-grade RAG system and neural voice assistant for low-resource Kannada literature ('Heli Hogu Kaarana'). It features BM25 + ChromaDB hybrid search with page-level routing, Gemini/Groq model fallbacks, and Sarvam AI TTS for streaming audio. Validated using RAGAS with 0.92 Faithfulness and 0.88 Answer Relevancy.
AI/ML Engineer Portfolio | Japanese RAG Production System (FastAPI + real evaluation) | Credit Card Fraud Detection (XGBoost + SHAP + Docker) | Japanese Sentiment Analysis (BERT) | Open to mid-level AI/ML roles
Autonomous multi-agent due-diligence desk — LangGraph orchestration, agentic hybrid RAG, MCP tools, and a self-improving LLM-Ops loop.
Intelligent document Q&A platform powered by Retrieval-Augmented Generation (RAG), hybrid search, reranking, and LLM-based reasoning.
Minimal RAG pipeline for Indian nutrition Q&A — Chroma + Groq + FastAPI, evaluated with RAGAS, containerized with CI/CD
LangGraph-orchestrated RAG multi-agent pipeline that routes queries to specialized agents. Modular design for ingestion, routing and evaluation.
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.
Production RAG pipeline for F-16 technical manual using Docling, Qdrant, Ollama, and evaluation pipeline
A high-performance Retrieval-Augmented Generation pipeline for technical Q&A workloads. Combines hybrid retrieval (dense + BM25), query expansion, Reciprocal Rank Fusion (RRF), and cross-encoder re-ranking to improve retrieval precision and answer grounding. Evaluated with Ragas, showing measurable gains in context recall and faithfulness.
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