Hybrid Search

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Introduction

R2R’s hybrid search combines traditional keyword-based searching with modern semantic understanding, providing more accurate and contextually relevant results. This approach is particularly effective for complex queries where both specific terms and overall meaning are crucial.

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How R2R Hybrid Search Works

1. Full-Text Search: Utilizes PostgreSQL’s full-text search with ts_rank_cd and websearch_to_tsquery.
2. Semantic Search: Performs vector similarity search using the query’s embedded representation.
3. Reciprocal Rank Fusion (RRF): Merges results from full-text and semantic searches using the formula:

   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
   

4. Result Ranking: Orders final results based on the combined RRF score.

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Key Features

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Full-Text Search

The full-text search component incorporates:

• PostgreSQL’s tsvector for efficient text searching
• websearch_to_tsquery for parsing user queries
• ts_rank_cd for ranking full-text search results

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Semantic Search

The semantic search component uses:

• Vector embeddings for storing and querying semantic representations
• Cosine similarity for measuring the relevance of documents to the query

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Configuration

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VectorSearchSettings

Key settings for vector search configuration:

class VectorSearchSettings(BaseModel):
    use_hybrid_search: bool
    search_limit: int
    filters: dict[str, Any]
    hybrid_search_settings: Optional[HybridSearchSettings]
    # ... other settings

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HybridSearchSettings

Specific parameters for hybrid search:

class HybridSearchSettings(BaseModel):
    full_text_weight: float
    semantic_weight: float
    full_text_limit: int
    rrf_k: int

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Usage Example

from r2r import R2RClient

client = R2RClient()

vector_settings = {
    "use_hybrid_search": True,
    "search_limit": 20,
    # Can add logical filters, as shown:
    # "filters":{"category": {"$eq": "technology"}},
    "hybrid_search_settings" : {
        "full_text_weight": 1.0,
        "semantic_weight": 5.0,
        "full_text_limit": 200,
        "rrf_k": 50
    }
}

results = client.search(
    query="Who was Aristotle?",
    vector_search_settings=vector_settings
)
print(results)

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Results Comparison

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Basic Vector Search

{
  "results": {
    "vector_search_results": [
      {
        "score": 0.780314067545999,
        "text": "Aristotle[A] (Greek: Ἀριστοτέλης Aristotélēs, pronounced [aristotélɛːs]; 384–322 BC) was an Ancient Greek philosopher and polymath...",
        "metadata": {
          "title": "aristotle.txt",
          "version": "v0",
          "chunk_order": 0
        }
      }, ...
    ]
  }
}

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Hybrid Search with RRF

{
  "results": {
    "vector_search_results": [
      {
        "score": 0.0185185185185185,
        "text": "Aristotle[A] (Greek: Ἀριστοτέλης Aristotélēs, pronounced [aristotélɛːs]; 384–322 BC) was an Ancient Greek philosopher and polymath...",
        "metadata": {
          "title": "aristotle.txt",
          "version": "v0",
          "chunk_order": 0,
          "semantic_rank": 1,
          "full_text_rank": 3
        }, ...
      }
    ]
  }
}

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Best Practices

1. Optimize PostgreSQL indexing for both full-text and vector searches
2. Regularly update search indices
3. Monitor performance and adjust weights as needed
4. Use appropriate vector dimensions and embedding models for your use case

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Conclusion

R2R’s hybrid search offers a powerful solution for complex information retrieval needs, combining the strengths of keyword matching and semantic understanding. Its flexible configuration and use of Reciprocal Rank Fusion make it adaptable to a wide range of use cases, from technical documentation to broad, context-dependent queries.