Build a Pure‑Python Hybrid RAG Engine with BM25, HNSW, and Cross‑Encoder Reranking
Learn to stitch together BM25, HNSW, and a miniature GPT‑2 model in pure Python for a standout RAG project.
Cross-Encoder
Leveraging Cross‑Encoder Reranking and Long‑Context Windows for High‑Fidelity Retrieval‑Augmented Generation Pipelines
Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto architecture for building knowledge‑intensive language systems. By coupling a retriever—typically a dense vector search over a large corpus—with a generator that conditions on the retrieved passages, RAG can produce answers that are both fluent and grounded in external data. However, two practical bottlenecks often limit the fidelity of such pipelines: Noisy or sub‑optimal retrieval results – the initial retrieval step (e.g., using a bi‑encoder) may return passages that are only loosely related to the query, leading the generator to hallucinate or produce vague answers. Limited context windows in the generator – even when the retrieved set is perfect, many modern LLMs can only ingest a few hundred to a few thousand tokens, forcing developers to truncate or rank‑order passages heuristically. Two complementary techniques have emerged to address these pain points: ...