Hybrid Search

Learn how to implement and use hybrid search with R2R

Introduction

R2R’s hybrid search blends keyword-based full-text search with semantic vector search, delivering results that are both contextually relevant and precise. By unifying these approaches, hybrid search excels at handling complex queries where both exact terms and overall meaning matter.

Understanding Search Modes

R2R supports multiple search modes that can simplify or customize the configuration for you:

  • basic: Primarily semantic search. Suitable for straightforward scenarios where semantic understanding is key, but you don’t need the additional context of keyword matching.
  • advanced: Combines semantic and full-text search by default, effectively enabling hybrid search with well-tuned default parameters. Ideal if you want the benefits of hybrid search without manual configuration.
  • custom: Allows you full control over the search settings, including toggling semantic and full-text search independently. Choose this if you want to fine-tune weights, limits, and other search behaviors.

When using advanced mode, R2R automatically configures hybrid search for you. For custom mode, you can directly set use_hybrid_search=True or enable both use_semantic_search and use_fulltext_search to achieve a hybrid search setup.

How R2R Hybrid Search Works

  1. Full-Text Search:
    Leverages Postgres’s ts_rank_cd and websearch_to_tsquery to find documents containing your keywords.

  2. Semantic Search:
    Uses vector embeddings to locate documents contextually related to your query, even if they don’t share exact keywords.

  3. Reciprocal Rank Fusion (RRF):
    Merges results from both full-text and semantic searches using a formula like:

    COALESCE(1.0 / (rrf_k + full_text.rank_ix), 0.0) * full_text_weight +
    COALESCE(1.0 / (rrf_k + semantic.rank_ix), 0.0) * semantic_weight

    This ensures that documents relevant both semantically and by keyword ranking float to the top.

  4. Result Ranking:
    Orders the final set of results based on the combined RRF score, providing balanced, meaningful search outcomes.

Key Features

Full-Text Search

  • Uses Postgres indexing and querying for quick, exact term matches.
  • Great for retrieving documents where specific terminology is critical.
  • Embeds queries and documents into vector representations.
  • Finds documents related to the query’s meaning, not just its wording.

Hybrid Integration

  • By enabling both use_fulltext_search and use_semantic_search, or choosing the advanced mode, you get the best of both worlds.
  • RRF blends these results, ensuring that documents align with the query’s intent and exact terms where needed.

Configuration

Choosing a Search Mode:

  • basic: Semantic-only.

    1search_mode = "basic"
    2# Semantic search only, no full-text matching

  • advanced: Hybrid by default.

    1search_mode = "advanced"
    2# Hybrid search is automatically enabled with well-tuned defaults

  • custom: Manually configure hybrid search.

    1search_mode = "custom"
    2# Enable both semantic and full-text search and set weights as needed:
    3search_settings = {
    4  "use_semantic_search": True,
    5  "use_fulltext_search": True,
    6  "use_hybrid_search": True,
    7  "hybrid_settings": {
    8    "full_text_weight": 1.0,
    9    "semantic_weight": 5.0,
    10    "full_text_limit": 200,
    11    "rrf_k": 50
    12  }
    13}

For more details on runtime configuration and combining search_mode with custom search_settings, refer to the Search API documentation.

Best Practices

  1. Optimize Database and Embeddings:
    Ensure Postgres indexing and vector store configurations are optimal for performance.

  2. Adjust Weights and Limits:
    Tweak full_text_weight, semantic_weight, and rrf_k values when using custom mode. If you’re using advanced mode, the defaults are already tuned for general use cases.

  3. Regular Updates:
    Keep embeddings and indexes up-to-date to maintain search quality.

  4. Choose Appropriate Embeddings:
    Select an embedding model that fits your content domain for the best semantic results.

Conclusion

R2R’s hybrid search delivers robust, context-aware retrieval by merging semantic and keyword-driven approaches. Whether you pick basic mode for simplicity, advanced mode for out-of-the-box hybrid search, or custom mode for granular control, R2R ensures you can tailor the search experience to your unique needs.