86 lines
2.2 KiB
Markdown
86 lines
2.2 KiB
Markdown
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---
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title: Configure Vector Indexes Properly
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impact: HIGH
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impactDescription: Correct configuration is essential for vector search accuracy
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tags: vector, index, hnsw, flat, embeddings, rqe
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description: Configure Vector Indexes Properly
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alwaysApply: true
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---
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## Configure Vector Indexes Properly
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Set the correct dimensions, algorithm, and distance metric for your embeddings. Vector indexes can be created via CLI, Redis Insight, or any client library.
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**Correct:** Create index via Redis CLI or Insight.
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```
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FT.CREATE idx:docs ON HASH PREFIX 1 doc:
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SCHEMA
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content TEXT
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embedding VECTOR HNSW 6
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TYPE FLOAT32
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DIM 1536
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DISTANCE_METRIC COSINE
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```
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**Correct:** Create index via Python (redis-py).
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```python
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from redis import Redis
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from redis.commands.search.field import TextField, VectorField
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from redis.commands.search.index_definition import IndexDefinition
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r = Redis()
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# Define schema with vector field
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schema = [
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TextField("content"),
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VectorField(
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"embedding",
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algorithm="HNSW",
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attributes={
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"TYPE": "FLOAT32",
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"DIM": 1536, # Must match your embedding model
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"DISTANCE_METRIC": "COSINE"
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}
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)
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]
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r.ft("idx:docs").create_index(schema, definition=IndexDefinition(prefix=["doc:"]))
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```
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**Correct:** Create index via RedisVL.
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```python
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from redisvl.index import SearchIndex
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from redisvl.schema import IndexSchema
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schema = IndexSchema.from_dict({
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"index": {"name": "idx:docs", "prefix": "doc:"},
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"fields": [
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{"name": "content", "type": "text"},
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{"name": "embedding", "type": "vector", "attrs": {
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"dims": 1536,
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"algorithm": "HNSW",
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"datatype": "FLOAT32",
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"distance_metric": "COSINE"
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}}
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]
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})
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index = SearchIndex(schema)
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index.create(overwrite=True)
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```
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**Incorrect:** Mismatched dimensions or wrong distance metric.
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```python
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# Bad: Wrong dimensions for your model
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{"dims": 768} # But your selected embedding model outputs a different size
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# Bad: Wrong metric for normalized embeddings
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{"distance_metric": "L2"} # When embeddings are normalized for COSINE
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```
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Reference: [Redis Vector Search](https://redis.io/docs/latest/develop/ai/search-and-query/vectors/)
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