Haystack is designed to help developers easily create custom search solutions that leverage the latest advancements in machine learning. With its components like document stores, retrievers, and readers, Haystack can connect to various data sources and effectively process queries. Its modular architecture supports mixed search strategies, including semantic search and traditional keyword-based search, making it a versatile tool for enterprises looking to enhance their search capabilities.

LLM-Powered RAG System is a developer-focused framework for building retrieval-augmented generation (RAG) pipelines. It provides modules for embedding document collections, indexing via FAISS, Pinecone, or Weaviate, and retrieving relevant context at runtime. The system uses LangChain wrappers to orchestrate LLM calls, supports prompt templates, streaming responses, and multi-vector store adapters. It simplifies end-to-end RAG deployment for knowledge bases, allowing customization at each stage—from embedding model configuration to prompt design and result post-processing.