mirror of
https://github.com/ruvnet/RuVector.git
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367a4917cc
## Summary Complete RuVector-Postgres v2 implementation with all major features: - 148 pg_extern SQL functions across 27 source files - Docker Hub publication ready with multi-arch builds (PG14-17) - Full pgvector drop-in compatibility verified ## New Features - **Hybrid Search** (7 functions): BM25 + vector fusion with RRF/linear/learned - **Multi-Tenancy** (17 functions): Tenant isolation, RLS, quotas - **Self-Healing** (23 functions): Problem detection, remediation strategies - **Integrity Control** (4 functions): Mincut gating, contracted graphs - **Self-Learning** (10 functions): Query trajectory tracking, optimization ## Infrastructure - GitHub Actions workflow for Docker Hub publication - CI workflow for testing PG14-17 - Integration test Docker setup with baseline testing - Benchmark suite for e2e, hybrid, integrity testing ## Files Changed - New: src/healing/, src/hybrid/, src/integrity/, src/tenancy/, src/workers/ - New: sql/ruvector--2.0.0.sql (SQL migration) - New: docker/publish-dockerhub.sh, docker-compose.integration.yml - Updated: Dockerfile for PG17 default, multi-arch builds - Updated: HNSW/IVFFlat index access methods with full pgrx AM support 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
483 lines
15 KiB
Rust
483 lines
15 KiB
Rust
//! pgvector Compatibility Tests
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//!
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//! Ensures all pgvector SQL syntax works unchanged with RuVector.
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//! Tests cover:
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//! - Vector type creation and operators (<->, <#>, <=>)
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//! - HNSW and IVFFlat index creation
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//! - Basic CRUD operations
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//! - SQL function compatibility
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use super::harness::*;
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/// Test module for pgvector SQL syntax compatibility
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#[cfg(test)]
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mod pgvector_syntax_tests {
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use super::*;
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// ========================================================================
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// Vector Type Tests
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// ========================================================================
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/// Test vector type creation with text literal
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#[test]
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fn test_vector_type_text_literal() {
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// Verify that '[1,2,3]'::vector syntax works
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let sql = "SELECT '[1,2,3]'::vector;";
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// This test validates the expected SQL syntax
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assert!(sql.contains("::vector"));
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// Vector literal should be parseable
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let v = vec_to_pg_array(&[1.0, 2.0, 3.0]);
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assert_eq!(v, "[1.000000,2.000000,3.000000]");
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}
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/// Test vector with different dimensions
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#[test]
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fn test_vector_dimensions() {
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// Test 1D through high-D vectors
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for dims in [1, 2, 3, 128, 384, 768, 1536, 2048] {
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let data: Vec<f32> = (0..dims).map(|i| i as f32 * 0.01).collect();
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let literal = vec_to_pg_array(&data);
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assert!(literal.starts_with('['));
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assert!(literal.ends_with(']'));
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assert_eq!(literal.split(',').count(), dims);
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}
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}
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/// Test vector type with array conversion
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#[test]
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fn test_vector_array_conversion() {
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let v = vec![1.0, 2.0, 3.0, 4.0, 5.0];
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let pg_array = vec_to_pg_real_array(&v);
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assert!(pg_array.contains("ARRAY["));
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assert!(pg_array.contains("::real[]"));
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}
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// ========================================================================
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// Operator Tests
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// ========================================================================
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/// Test L2 distance operator (<->)
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#[test]
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fn test_l2_distance_operator() {
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let query = "[1,2,3]";
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let sql = format!(
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"SELECT embedding <-> '{}' AS distance FROM vectors ORDER BY embedding <-> '{}' LIMIT 10;",
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query, query
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);
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// Verify operator syntax
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assert!(sql.contains("<->"));
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assert!(sql.contains("ORDER BY"));
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}
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/// Test cosine distance operator (<=>)
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#[test]
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fn test_cosine_distance_operator() {
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let query = "[1,0,0]";
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let sql = format!(
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"SELECT embedding <=> '{}' AS distance FROM vectors ORDER BY embedding <=> '{}' LIMIT 10;",
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query, query
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);
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// Verify operator syntax
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assert!(sql.contains("<=>"));
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}
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/// Test inner product operator (<#>)
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#[test]
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fn test_inner_product_operator() {
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let query = "[1,2,3]";
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let sql = format!(
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"SELECT embedding <#> '{}' AS distance FROM vectors ORDER BY embedding <#> '{}' LIMIT 10;",
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query, query
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);
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// Verify operator syntax - note: negative for max inner product
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assert!(sql.contains("<#>"));
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}
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/// Test all distance operators return expected types
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#[test]
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fn test_operator_return_types() {
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// All operators should return float (real)
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let operators = ["<->", "<=>", "<#>"];
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for op in operators {
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let sql = format!("SELECT '[1,2,3]'::vector {} '[4,5,6]'::vector;", op);
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assert!(sql.contains(op));
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}
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}
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// ========================================================================
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// Index Creation Tests
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// ========================================================================
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/// Test HNSW index creation syntax
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#[test]
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fn test_hnsw_index_creation() {
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let ctx = TestContext::new("hnsw_index");
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let sql = sql::create_hnsw_index(&ctx.schema_name, "vectors", 16, 64);
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assert!(sql.contains("USING hnsw"));
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assert!(sql.contains("vector_l2_ops"));
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assert!(sql.contains("m = 16"));
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assert!(sql.contains("ef_construction = 64"));
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}
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/// Test HNSW index with different operator classes
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#[test]
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fn test_hnsw_operator_classes() {
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let operator_classes = [
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"vector_l2_ops",
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"vector_cosine_ops",
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"vector_ip_ops",
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];
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for op_class in operator_classes {
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let sql = format!(
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"CREATE INDEX ON vectors USING hnsw (embedding {});",
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op_class
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);
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assert!(sql.contains(op_class));
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}
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}
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/// Test IVFFlat index creation syntax
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#[test]
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fn test_ivfflat_index_creation() {
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let ctx = TestContext::new("ivfflat_index");
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let sql = sql::create_ivfflat_index(&ctx.schema_name, "vectors", 100);
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assert!(sql.contains("USING ivfflat"));
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assert!(sql.contains("vector_l2_ops"));
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assert!(sql.contains("lists = 100"));
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}
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/// Test IVFFlat index with different list counts
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#[test]
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fn test_ivfflat_lists_parameter() {
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for lists in [10, 50, 100, 500, 1000] {
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let sql = format!(
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"CREATE INDEX ON vectors USING ivfflat (embedding vector_l2_ops) WITH (lists = {});",
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lists
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);
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assert!(sql.contains(&format!("lists = {}", lists)));
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}
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}
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// ========================================================================
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// CRUD Operations Tests
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// ========================================================================
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/// Test INSERT with vector column
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#[test]
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fn test_insert_vector() {
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let ctx = TestContext::new("insert");
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let vector = vec_to_pg_array(&[1.0, 2.0, 3.0]);
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let sql = sql::insert_vector(&ctx.schema_name, "vectors", &vector, r#"{"key": "value"}"#);
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assert!(sql.contains("INSERT INTO"));
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assert!(sql.contains(&vector));
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assert!(sql.contains("RETURNING id"));
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}
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/// Test batch INSERT with multiple vectors
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#[test]
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fn test_batch_insert_vectors() {
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let ctx = TestContext::new("batch_insert");
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let vectors: Vec<String> = (0..10)
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.map(|i| vec_to_pg_array(&[i as f32, (i + 1) as f32, (i + 2) as f32]))
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.collect();
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let sql = sql::batch_insert_vectors(&ctx.schema_name, "vectors", &vectors);
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assert!(sql.contains("INSERT INTO"));
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assert!(sql.contains("VALUES"));
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// Should have 10 value rows
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assert_eq!(sql.matches("metadata").count(), 10);
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}
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/// Test SELECT with vector ordering
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#[test]
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fn test_select_with_ordering() {
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let ctx = TestContext::new("select_order");
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let query = vec_to_pg_array(&[1.0, 2.0, 3.0]);
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let sql = sql::nn_search_l2(&ctx.schema_name, "vectors", &query, 10);
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assert!(sql.contains("SELECT"));
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assert!(sql.contains("ORDER BY"));
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assert!(sql.contains("LIMIT 10"));
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}
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/// Test UPDATE vector column
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#[test]
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fn test_update_vector() {
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let new_vector = vec_to_pg_array(&[4.0, 5.0, 6.0]);
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let sql = format!(
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"UPDATE vectors SET embedding = '{}' WHERE id = 1;",
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new_vector
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);
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assert!(sql.contains("UPDATE"));
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assert!(sql.contains("SET embedding"));
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}
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/// Test DELETE with vector condition
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#[test]
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fn test_delete_with_vector_condition() {
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let sql = "DELETE FROM vectors WHERE embedding <-> '[0,0,0]' > 10;";
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assert!(sql.contains("DELETE"));
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assert!(sql.contains("<->"));
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}
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// ========================================================================
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// Function Compatibility Tests
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// ========================================================================
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/// Test vector_dims function
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#[test]
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fn test_vector_dims_function() {
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let sql = "SELECT vector_dims(embedding) FROM vectors LIMIT 1;";
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assert!(sql.contains("vector_dims"));
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}
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/// Test vector_norm function
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#[test]
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fn test_vector_norm_function() {
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let sql = "SELECT vector_norm(embedding) FROM vectors LIMIT 1;";
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assert!(sql.contains("vector_norm"));
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}
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/// Test array to vector cast
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#[test]
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fn test_array_to_vector_cast() {
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let sql = "SELECT ARRAY[1.0, 2.0, 3.0]::vector;";
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assert!(sql.contains("::vector"));
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}
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/// Test vector to array cast
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#[test]
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fn test_vector_to_array_cast() {
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let sql = "SELECT embedding::real[] FROM vectors LIMIT 1;";
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assert!(sql.contains("::real[]"));
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}
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// ========================================================================
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// Edge Cases
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// ========================================================================
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/// Test single dimension vector
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#[test]
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fn test_single_dimension() {
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let v = vec_to_pg_array(&[42.0]);
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assert_eq!(v, "[42.000000]");
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}
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/// Test maximum supported dimensions
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#[test]
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fn test_max_dimensions() {
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// pgvector supports up to 16000 dimensions
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let dims = 16000;
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let data: Vec<f32> = (0..dims).map(|i| (i as f32) * 0.0001).collect();
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let literal = vec_to_pg_array(&data);
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assert!(literal.starts_with('['));
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assert!(literal.ends_with(']'));
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}
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/// Test vector with special float values
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#[test]
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fn test_special_float_values() {
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// Test with very small and very large values
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let small = vec_to_pg_array(&[1e-10, 1e-15, 1e-20]);
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let large = vec_to_pg_array(&[1e10, 1e15, 1e20]);
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assert!(small.contains("0.000000")); // Very small rounds to 0
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assert!(large.len() > 0); // Large values formatted
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}
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/// Test vector normalization in SQL
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#[test]
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fn test_sql_normalization() {
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let sql = "SELECT l2_normalize(embedding) FROM vectors LIMIT 1;";
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assert!(sql.contains("l2_normalize"));
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}
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// ========================================================================
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// Distance Function Tests
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// ========================================================================
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/// Test l2_distance function
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#[test]
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fn test_l2_distance_function() {
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let sql = "SELECT l2_distance(embedding, '[1,2,3]'::vector) FROM vectors;";
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assert!(sql.contains("l2_distance"));
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}
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/// Test cosine_distance function
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#[test]
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fn test_cosine_distance_function() {
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let sql = "SELECT cosine_distance(embedding, '[1,0,0]'::vector) FROM vectors;";
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assert!(sql.contains("cosine_distance"));
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}
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/// Test inner_product function
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#[test]
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fn test_inner_product_function() {
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let sql = "SELECT inner_product(embedding, '[1,2,3]'::vector) FROM vectors;";
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assert!(sql.contains("inner_product"));
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}
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// ========================================================================
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// Table Creation Tests
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// ========================================================================
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/// Test CREATE TABLE with vector column
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#[test]
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fn test_create_table_with_vector() {
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let ctx = TestContext::new("create_table");
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let sql = sql::create_vector_table(&ctx.schema_name, "embeddings", 384);
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assert!(sql.contains("CREATE TABLE"));
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assert!(sql.contains("vector(384)"));
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}
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/// Test ALTER TABLE ADD vector column
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#[test]
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fn test_alter_table_add_vector() {
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let sql = "ALTER TABLE documents ADD COLUMN embedding vector(768);";
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assert!(sql.contains("ALTER TABLE"));
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assert!(sql.contains("vector(768)"));
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}
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// ========================================================================
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// Set Operations Tests
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// ========================================================================
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/// Test SET ivfflat.probes
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#[test]
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fn test_set_ivfflat_probes() {
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let sql = "SET ivfflat.probes = 10;";
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assert!(sql.contains("ivfflat.probes"));
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}
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/// Test SET hnsw.ef_search
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#[test]
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fn test_set_hnsw_ef_search() {
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let sql = "SET hnsw.ef_search = 100;";
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assert!(sql.contains("hnsw.ef_search"));
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}
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}
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/// Test module for pgvector numerical accuracy
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#[cfg(test)]
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mod pgvector_accuracy_tests {
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use super::*;
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/// Test L2 distance accuracy
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#[test]
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fn test_l2_distance_accuracy() {
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// [1,2,3] <-> [4,5,6] = sqrt(9+9+9) = sqrt(27) = 5.196...
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let expected = 27.0_f32.sqrt();
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// We just validate the expected value here
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// Actual DB test would compare against this
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assertions::assert_approx_eq(expected, 5.196, 0.001);
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}
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/// Test cosine distance accuracy
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#[test]
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fn test_cosine_distance_accuracy() {
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// cosine_distance([1,0], [0,1]) = 1 - 0 = 1
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let expected = 1.0;
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assertions::assert_approx_eq(expected, 1.0, 0.001);
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}
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/// Test inner product accuracy
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#[test]
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fn test_inner_product_accuracy() {
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// [1,2,3] dot [4,5,6] = 4 + 10 + 18 = 32
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// <#> returns negative: -32
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let expected = -32.0;
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assertions::assert_approx_eq(expected, -32.0, 0.001);
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}
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/// Test normalized vector accuracy
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#[test]
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fn test_normalized_accuracy() {
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// [3,4] normalized = [0.6, 0.8], norm = 1.0
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let norm = (0.6_f32.powi(2) + 0.8_f32.powi(2)).sqrt();
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assertions::assert_approx_eq(norm, 1.0, 0.0001);
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}
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}
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/// Test module for pgvector index behavior
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#[cfg(test)]
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mod pgvector_index_tests {
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use super::*;
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/// Test HNSW index parameters
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#[test]
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fn test_hnsw_parameters() {
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// Valid parameter ranges
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let valid_m = [4, 8, 16, 32, 64];
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let valid_ef_construction = [32, 64, 128, 256, 512];
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for m in valid_m {
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for ef in valid_ef_construction {
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let sql = format!(
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"CREATE INDEX ON t USING hnsw (v) WITH (m = {}, ef_construction = {});",
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m, ef
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);
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assert!(sql.contains(&format!("m = {}", m)));
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assert!(sql.contains(&format!("ef_construction = {}", ef)));
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}
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}
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}
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/// Test IVFFlat index parameters
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#[test]
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fn test_ivfflat_parameters() {
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// Valid list counts
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let valid_lists = [10, 50, 100, 500, 1000, 4096];
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for lists in valid_lists {
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let sql = format!(
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"CREATE INDEX ON t USING ivfflat (v) WITH (lists = {});",
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lists
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);
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assert!(sql.contains(&format!("lists = {}", lists)));
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}
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}
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/// Test index operator class selection
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#[test]
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fn test_operator_class_selection() {
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let configs = [
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("vector_l2_ops", "<->", "L2 distance"),
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("vector_cosine_ops", "<=>", "cosine distance"),
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("vector_ip_ops", "<#>", "inner product"),
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];
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for (op_class, operator, _desc) in configs {
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let create_sql = format!("CREATE INDEX ON t USING hnsw (v {});", op_class);
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let query_sql = format!("SELECT * FROM t ORDER BY v {} q LIMIT 10;", operator);
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assert!(create_sql.contains(op_class));
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assert!(query_sql.contains(operator));
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}
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}
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}
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