life-echo/api/.agents/skills/redis-development/rules/vector-algorithm-choice.md

title, impact, impactDescription, tags, description, alwaysApply

title	impact	impactDescription	tags	description	alwaysApply
Choose HNSW vs FLAT Based on Requirements	HIGH	HNSW trades accuracy for speed, FLAT provides exact results	vector, hnsw, flat, algorithm, performance	Choose HNSW vs FLAT Based on Requirements	true

Choose HNSW vs FLAT Based on Requirements

Select the right algorithm based on your accuracy requirements and dataset size.

Algorithm	Speed	Accuracy	Memory	Best For
HNSW	Fast (approximate)	~95%+ recall tunable	Higher	Large datasets (>10k vectors)
FLAT	Slower (exact)	100% (exact)	Lower	Small datasets, accuracy-critical

Correct: Use HNSW for large-scale production workloads.

from redisvl.schema import IndexSchema

# HNSW - fast approximate search, tunable accuracy
schema = IndexSchema.from_dict({
    "index": {"name": "idx:docs", "prefix": "doc:"},
    "fields": [
        {"name": "embedding", "type": "vector", "attrs": {
            "dims": 1536,
            "algorithm": "HNSW",
            "distance_metric": "COSINE",
            "datatype": "FLOAT32",
            "m": 16,                      # Higher = more accurate, more memory
            "ef_construction": 200        # Higher = better index quality, slower build
        }}
    ]
})

Correct: Use FLAT when exact results are required.

# FLAT - exact brute-force search, guaranteed accuracy
schema = IndexSchema.from_dict({
    "index": {"name": "idx:small", "prefix": "small:"},
    "fields": [
        {"name": "embedding", "type": "vector", "attrs": {
            "dims": 1536,
            "algorithm": "FLAT",
            "distance_metric": "COSINE"
        }}
    ]
})

Tuning HNSW accuracy vs speed:

• M: Connections per node (16-64). Higher = better recall, more memory
• EF_CONSTRUCTION: Build-time parameter (100-500). Higher = better graph quality
• EF_RUNTIME: Query-time parameter. Higher = better recall, slower queries

Reference: Redis Vector Search