Hybrid Search Implementation FAQs

Question 1

How does hybrid search improve RAG systems?

Accepted Answer

It significantly increases recall by ensuring that specific terms, such as product names or unique identifiers, are captured even if the vector embedding doesn't prioritize them based on semantic similarity alone.

Question 2

When should I use Reciprocal Rank Fusion (RRF)?

Accepted Answer

RRF is best used when you need to combine rankings from different search algorithms (like vector and lexical) without needing to manually normalize their underlying scores to the same scale.

Question 3

Can I add a reranking step?

Accepted Answer

Yes, the skill provides templates for implementing a cross-encoder reranking stage to re-evaluate top candidates for the highest possible precision before passing them to the LLM.

Question 4

Does this skill support PostgreSQL?

Accepted Answer

Yes, it includes specific patterns for implementing hybrid search using the pgvector extension for embeddings and built-in tsvector capabilities for full-text search.

Question 5

What is hybrid search?

Accepted Answer

Hybrid search combines semantic vector search, which understands the context and meaning of a query, with traditional keyword search (BM25) to provide more accurate and comprehensive retrieval results.

Hybrid Search Implementation

主要功能

使用场景

Hybrid Search Implementation

主要功能

使用场景