life-echo/api/.agents/skills/redis-development/rules/vector-hybrid-search.md

title, impact, impactDescription, tags, description, alwaysApply

title	impact	impactDescription	tags	description	alwaysApply
Use Hybrid Search for Better Results	MEDIUM	Combining vector + filters improves relevance and reduces search space	vector, hybrid, filters, redisvl, search	Use Hybrid Search for Better Results	true

Use Hybrid Search for Better Results

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.

Correct: Apply filters to reduce search space.

from redisvl.query import VectorQuery
from redisvl.query.filter import Num, Tag

filters = (Tag("category") == "technology") & (Num("date") >= 2024) & (Num("date") <= 2025)

query = VectorQuery(
    vector=query_embedding,
    vector_field_name="embedding",
    return_fields=["content", "category", "date"],
    num_results=10,
    filter_expression=filters
)

results = index.query(query)

Incorrect: Searching entire vector space when filters apply.

# Bad: No filter - searches all vectors then filters client-side
results = index.query(VectorQuery(
    vector=query_embedding,
    vector_field_name="embedding",
    num_results=1000
))
# Client-side filtering - wasteful
filtered = [r for r in results if r["category"] == "technology"]

Tips:

• Use TAG fields for category filters
• Use NUMERIC fields for date/price ranges
• Redis auto-selects the filtered vector execution strategy; tune hybrid_policy only when needed
• For true text + vector fusion, use HybridQuery on Redis >= 8.4.0 with redis-py >= 7.1.0; use AggregateHybridQuery on earlier Redis versions

Reference: Redis Vector Search