@sf-bot/rag-indexes

RAG vector index helpers for HNSW and IVFFlat.

Overview

This package provides SQL functions for creating and managing pgvector indexes on RAG embeddings.

Functions

$3

Create an HNSW index for fast approximate nearest neighbor search.

``sql
-- Create HNSW index with defaults (m=16, ef_construction=64, cosine distance)
SELECT rag.create_hnsw_index('collection-uuid');

-- Create with custom parameters
SELECT rag.create_hnsw_index(
'collection-uuid',
'text-embedding-3-small',
32, -- m: connections per layer
128, -- ef_construction: index build quality
'l2' -- distance: cosine, l2, or ip
);
`

Parameters:
- m: Max connections per layer (default: 16). Higher = better recall, more memory
- ef_construction: Build-time quality (default: 64). Higher = better recall, slower build
- distance_op: Distance function - cosine, l2/euclidean, or ip/inner_product

$3

Create an IVFFlat index for fast indexing with good recall.

`sql
-- Create IVFFlat index with defaults (lists=100, cosine distance)
SELECT rag.create_ivfflat_index('collection-uuid');

-- Create with custom parameters
SELECT rag.create_ivfflat_index(
'collection-uuid',
'text-embedding-3-small',
200, -- lists: number of clusters
'cosine' -- distance: cosine, l2, or ip
);
`

Note: IVFFlat requires data to be present before creating the index.

$3

List all vector indexes, optionally filtered by collection.

`sql
SELECT * FROM rag.list_vector_indexes();
SELECT * FROM rag.list_vector_indexes('collection-uuid');
`

$3

Drop a vector index by name.

`sql
SELECT rag.drop_vector_index('idx_rag_embedding_hnsw_...');
`

When to Use Which Index

| Index | Best For | Trade-offs |
|-------|----------|------------|
| HNSW | High recall, easy management | Slower inserts, more memory |
| IVFFlat | Fast indexing, large datasets | Requires pre-populated data |

Dependencies

- @sf-bot/rag-core`

License

SF License