RAG Pipeline Rebuild with Haystack

"In the world of ever-evolving AI stacks, sometimes going old-school has its own charm..." While everyone is busy exploring cutting-edge frameworks like LangGraph, LangChain, and Google ADK, I decided to revisit the underrated but reliable 🔹 Haystack by deepset. 💡 Why? To rebuild a full RAG pipeline—not just to test it, but to truly revise and solidify the foundational concepts of retrieval-augmented generation. 🧩 What I liked: Modular structure – makes it easy to plug in retrievers, readers, and generators. Very beginner-friendly with YouTube tutorials and minimal boilerplate. Great for small to mid-scale projects where simplicity and speed matter. ⚠️ But it’s not perfect: The ecosystem isn’t as vast as LangChain—limited support for newer LLMs and agent-based workflows. Lacks integration variety for modern tools like vector DBs, memory modules, or multi-agent orchestrators. Yet, the ease of getting up and running with RAG pipelines in Haystack is unmatched—especially if you're learning, prototyping, or just want to build something that works without over-engineering. 🔗 Whether you're just getting into RAG or trying to refresh your understanding, Haystack might just be the right place to start. Would love to hear your thoughts—what frameworks are you using for RAG today? #RAG #Haystack #LangChain #AIEngineering #LLM #FastAPI #Pinecone #Mistral #RetrievalAugmentedGeneration #opensource #deeplearning #aiagent #ragpipeline #buildinpublic