Step 8

Hybrid Retrieval

Combines keyword precision with vector recall.

What It Does

Hybrid retrieval uses a combination of retrieval methods – typically dense vector search and traditional keyword (sparse) search – to find relevant documents. Instead of relying on just one approach, it runs both methods and then merges the results, leveraging the strengths of each system.

Why It Matters

Hybrid retrieval aims to get the best of both worlds. Sparse (term-based) search excels at exact matches – if the user's query uses the same terminology as a document, a keyword search will precisely find that document. Dense search, on the other hand, excels at finding conceptually related info (synonyms, paraphrases) but might also return something contextually similar yet not actually relevant. By combining them, the pipeline can catch relevant documents that one method alone might miss.

Common Challenges

  • Implementing and maintaining two search systems instead of one
  • Normalizing scores between different retrieval methods for fair comparison
  • Balancing the weight given to each retrieval method
  • Handling increased latency from running multiple searches
  • Managing duplicated results across retrieval methods
  • Optimizing the fusion algorithm for your specific use case

Interactive Demo

hybridRetrieval.tryHybridRetrieval

hybridRetrieval.searchThroughSampleCollection

How do vector embeddings work?
What is BM25 algorithm?
Hybrid search systems benefits
Query processing for search
Limitations of keyword search

hybridRetrieval.searchConfiguration

hybridRetrieval.aboutHybridRetrieval

hybridRetrieval.aboutDescription

Skip the Complexity

Building a robust Hybrid Retrieval solution is challenging. Respeak's Enterprise RAG Platform handles this complexity for you.