life-echo/api/.agents/skills/redis-development/rules/vector-index-creation.md

title, impact, impactDescription, tags, description, alwaysApply

title	impact	impactDescription	tags	description	alwaysApply
Configure Vector Indexes Properly	HIGH	Correct configuration is essential for vector search accuracy	vector, index, hnsw, flat, embeddings, rqe	Configure Vector Indexes Properly	true

Configure Vector Indexes Properly

Set the correct dimensions, algorithm, and distance metric for your embeddings. Vector indexes can be created via CLI, Redis Insight, or any client library.

Correct: Create index via Redis CLI or Insight.

FT.CREATE idx:docs ON HASH PREFIX 1 doc:
    SCHEMA
        content TEXT
        embedding VECTOR HNSW 6
            TYPE FLOAT32
            DIM 1536
            DISTANCE_METRIC COSINE

Correct: Create index via Python (redis-py).

from redis import Redis
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.index_definition import IndexDefinition

r = Redis()

# Define schema with vector field
schema = [
    TextField("content"),
    VectorField(
        "embedding",
        algorithm="HNSW",
        attributes={
            "TYPE": "FLOAT32",
            "DIM": 1536,  # Must match your embedding model
            "DISTANCE_METRIC": "COSINE"
        }
    )
]

r.ft("idx:docs").create_index(schema, definition=IndexDefinition(prefix=["doc:"]))

Correct: Create index via RedisVL.

from redisvl.index import SearchIndex
from redisvl.schema import IndexSchema

schema = IndexSchema.from_dict({
    "index": {"name": "idx:docs", "prefix": "doc:"},
    "fields": [
        {"name": "content", "type": "text"},
        {"name": "embedding", "type": "vector", "attrs": {
            "dims": 1536,
            "algorithm": "HNSW",
            "datatype": "FLOAT32",
            "distance_metric": "COSINE"
        }}
    ]
})

index = SearchIndex(schema)
index.create(overwrite=True)

Incorrect: Mismatched dimensions or wrong distance metric.

# Bad: Wrong dimensions for your model
{"dims": 768}  # But your selected embedding model outputs a different size

# Bad: Wrong metric for normalized embeddings
{"distance_metric": "L2"}  # When embeddings are normalized for COSINE

Reference: Redis Vector Search