Advanced Retrieval Pipeline for RAG (HyDE, Hybrid Search, Reranking) | Build 100% Local Retrieval

2026-01-28 · Source: Venelin Valkov · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, long

Summary

This content details an advanced retrieval pipeline for Retrieval Augmented Generation (RAG) systems, designed for 100% local execution. The pipeline begins with a user query, which is first expanded using Hypothetical Document Embeddings (HyDE) to generate a hypothetical answer that improves embedding quality. This expanded query then feeds into a hybrid search component, leveraging both vector embeddings for semantic similarity and PostgreSQL's full-text search for keyword matching. The results from these two search methods are combined using reciprocal rank fusion. Finally, a reranker, specifically using the FlashRank library with models like MS Marco Mini V2 or Quint 3, refines the list of candidate documents, scoring them for relevance before passing the top-ranked chunks to a Large Language Model (LLM). The system also extends the PostgreSQL schema to include document-level metadata for enhanced filtering.

Key takeaway

For AI Engineers building robust RAG systems, integrating a multi-stage retrieval pipeline is crucial. You should implement HyDE for query expansion, a hybrid search combining vector and full-text capabilities (e.g., with PostgreSQL), and a dedicated reranking step using libraries like FlashRank. This layered approach significantly boosts the precision and recall of retrieved documents, leading to more accurate and contextually relevant LLM outputs.

Key insights

Combining HyDE, hybrid search, and reranking significantly enhances RAG retrieval accuracy and relevance.

Principles

• Vector search captures semantic meaning.
• Keyword search excels at exact matches.
• Reranking improves final document relevance.

Method

The pipeline expands queries with HyDE, performs hybrid search (vector + full-text) in PostgreSQL, fuses results with reciprocal rank fusion, and then reranks candidates using FlashRank before LLM delivery.

In practice

• Use PostgreSQL for hybrid search.
• Employ FlashRank for reranking.
• Extend schema with document metadata.

Topics

• Retrieval-Augmented Generation
• Hybrid Search
• Hypothetical Document Embeddings
• Document Reranking
• PostgreSQL

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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• Build a Hybrid RAG System with FAISS, BM25, LangGraph and Claude Sonnet Model — Hybrid RAG combines semantic and keyword search via Reciprocal Rank Fusion to enhance retrieval accuracy for diverse query types.
• How Retrieval Systems Power Modern RAG Applications — RAG combines external retrieval with LLMs to overcome knowledge cutoffs and hallucinations, providing grounded, up-to-date responses.
• Hybrid Search for RAG: BM25 + Vectors (When Each Wins) — Effective RAG systems require hybrid search, combining lexical and semantic retrieval, to prevent retrieval failures.
• What No RAG Tutorial Tells You Before You Start Building — Effective RAG implementation hinges on robust retrieval strategies, not just LLM capabilities, to prevent confident but incorrect answers.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Venelin Valkov.