rag-pipelines

What problem does it solve?

Design and implement effective Retrieval-Augmented Generation (RAG) pipelines by applying robust chunking, embedding, and retrieval strategies to ensure accurate, scalable answers over large document collections.

Core Features & Use Cases

• Chunking Guidance: Employ semantic or structure-aware chunking to maximize context retention and retrieval performance.
• Embeddings & Models: Select appropriate embedding models and manage constraints for scalable similarity search.
• Retrieval & Ranking: Implement hybrid search (vector + keyword) with Reciprocal Rank Fusion and a cross-encoder reranker to boost precision.
• Context Integration: Provide clear guidance on injecting retrieved chunks into prompts and ensuring proper citation and traceability.

Quick Start

Draft a RAG pipeline design that applies semantic chunking, vector search, and cross-encoder reranking.