53 lines
1.8 KiB
Markdown
53 lines
1.8 KiB
Markdown
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---
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title: Use Hybrid Search for Better Results
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impact: MEDIUM
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impactDescription: Combining vector + filters improves relevance and reduces search space
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tags: vector, hybrid, filters, redisvl, search
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description: Use Hybrid Search for Better Results
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alwaysApply: true
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---
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## Use Hybrid Search for Better Results
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Combine vector similarity with attribute filtering for more relevant results. In this rule, "hybrid" means filtered vector search. Redis and RedisVL also use "hybrid search" for text + vector fusion via `FT.HYBRID` / `HybridQuery`.
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**Correct:** Apply filters to reduce search space.
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```python
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from redisvl.query import VectorQuery
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from redisvl.query.filter import Num, Tag
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filters = (Tag("category") == "technology") & (Num("date") >= 2024) & (Num("date") <= 2025)
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query = VectorQuery(
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vector=query_embedding,
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vector_field_name="embedding",
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return_fields=["content", "category", "date"],
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num_results=10,
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filter_expression=filters
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)
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results = index.query(query)
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```
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**Incorrect:** Searching entire vector space when filters apply.
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```python
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# Bad: No filter - searches all vectors then filters client-side
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results = index.query(VectorQuery(
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vector=query_embedding,
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vector_field_name="embedding",
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num_results=1000
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))
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# Client-side filtering - wasteful
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filtered = [r for r in results if r["category"] == "technology"]
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```
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**Tips:**
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- Use TAG fields for category filters
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- Use NUMERIC fields for date/price ranges
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- Redis auto-selects the filtered vector execution strategy; tune `hybrid_policy` only when needed
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- For true text + vector fusion, use `HybridQuery` on Redis >= 8.4.0 with redis-py >= 7.1.0; use `AggregateHybridQuery` on earlier Redis versions
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Reference: [Redis Vector Search](https://redis.io/docs/latest/develop/ai/search-and-query/vectors/)
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