Advanced RAG Pipelines and Evaluation

Advanced RAG pipeline optimization framework using DSPy. Implements modular RAG pipelines with Query-Rewriting, Sub-Query Decomposition, and Hybrid Search via Weaviate. Automates prompt tuning and few-shot selection using MIPRO, COPRO, and BootstrapFewShot optimizers on datasets like FreshQA, HotpotQA, TriviaQA, Wikipedia and PubMedQA.

🚀 Production-ready modular RAG monorepo: Local LLM inference (vLLM) • Hybrid retrieval with Qdrant • Semantic caching • Docling document parsing • Cross-encoder reranking • DeepEval evaluation • Full observability with Langfuse • Open WebUI chat interface • OpenAI-compatible API • Fully Dockerized

Project demonstrating LLM testing using Deepeval with OpenAI and local LLMs as judge

LLM & RAG evaluation framework using DeepEval. Includes 11+ executable tests for metrics like Faithfulness, Hallucination, and Agentic Tool Usage

JUnit-style XML report generation for deepeval test runs

A robust, modular pipeline for automated LLM chatbot evaluation, using DeepEval, GROQ models, and Confident AI dashboard logging. Designed for systematic QA, reliable evaluation, and portfolio-quality results in AI/QA engineering.

Framework for evaluating and improving LLM-generated scientific abstracts using ROUGE metrics, semantic embeddings, and LLM-as-judge techniques.

An example that combines Behave (BDD testing) with DeepEval (LLM evaluation) to create human-readable, stakeholder-friendly tests for AI Agents / chatbots.

[UNDER DEVELOPMENT] Clinical-RAG is a production-grade, citation-backed AI system designed to bridge the "Trust Gap" in medical information retrieval.