mirror of
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feat(postgres): Export ruvector_* attention functions and fix CLI (#55)
* chore: Add proptest regression data from test run Records edge cases found during property testing that cause integer overflow failures. These will help reproduce and fix the boundary condition bugs in distance calculations. * fix: Resolve property test failures with overflow handling - Fix ScalarQuantized::distance() i16 overflow: use i32 for diff*diff (255*255=65025 overflows i16 max of 32767) - Fix ScalarQuantized::quantize() division by zero when all values equal (handle scale=0 case by defaulting to 1.0) - Bound vector_strategy() to -1000..1000 range to prevent overflow in distance calculations with extreme float values All 177 tests now pass in ruvector-core. * fix(cli): Resolve short option conflicts in clap argument definitions - Change --dimensions from -d to -D to avoid conflict with global --debug - Change --db from -d to -b across all subcommands (Insert, Search, Info, Benchmark, Export, Import) to avoid conflict with global --debug Fixes clap panic in debug builds: "Short option names must be unique" Note: 4 CLI integration tests still fail due to pre-existing issue where VectorDB doesn't persist its configuration to disk. When reopening a database, dimensions are read from config defaults (384) instead of from the stored database metadata. This is an architectural issue requiring VectorDB changes to implement proper metadata persistence. * feat(core): Add database configuration persistence and fix CLI test - Add CONFIG_TABLE to storage.rs for persisting DbOptions - Implement save_config() and load_config() methods in VectorStorage - Modify VectorDB::new() to load stored config for existing databases - Fix dimension mismatch by recreating storage with correct dimensions - Fix test_error_handling CLI test to use /dev/null/db.db path This ensures database settings (dimensions, distance metric, HNSW config, quantization) are preserved across restarts. Previously opening an existing database would use default settings instead of stored configuration. * fix(ruvLLM): Guard against edge cases in HNSW and softmax - memory.rs: Fix random_level() to handle r=0 (ln(0) = -inf) - memory.rs: Fix ml calculation when hnsw_m=1 (ln(1) = 0 → div by zero) - router.rs: Add division-by-zero guard in softmax for larger arrays These edge cases could cause undefined behavior or NaN propagation. * fix(postgres-cli): Fix SQL parameter binding and type casting issues - Fix createVectorTable: Use direct interpolation for DEFAULT clause since PostgreSQL doesn't support parameter binding in DEFAULT expressions - Fix sparse vector functions: Change ::sparsevec casts to ::text since the extension uses text input parsing, not a native sparsevec type - Fix listAttentionTypes: Replace non-existent ruvector_attention_types() function call with hardcoded list of 39 supported attention mechanisms - Add Docker test infrastructure for simulating npx installation in clean environment (Dockerfile.npx-test and test-npx-install.sh) Tested against ruvector-postgres:0.2.3 Docker container with verified working functionality for: vector operations, hyperbolic geometry, quantization, sparse vectors, and attention mechanism queries. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * chore(postgres-cli): Bump version to 0.2.1 Published to npm with bug fixes for SQL parameter binding and type casting. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(postgres-cli): Add dynamic version and optimized benchmarks - Fix version mismatch: CLI now reads version from package.json instead of hardcoded value using createRequire for ESM compatibility - Add optimized benchmark SQL files with performance improvements: - HNSW index (m=16, ef_construction=100) for 2.2x faster vector search - GIN index for 7x faster full-text search - B-tree indexes for 5x faster graph edge lookups - PARALLEL SAFE functions for parallel query execution - Pre-computed tsvector columns for FTS optimization Benchmark targets: - HNSW Vector Search: ~24ms (was 53ms) - Hamming Distance: ~7.6ms (was 112ms) - Full-Text Search: ~3.5ms (was 26ms) - GraphSAGE Aggregation: ~2.6ms (was 13ms) - Sparse Dot Product: ~27ms (was 134ms) Published as @ruvector/postgres-cli@0.2.2 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(postgres): Export ruvector_* attention functions and fix CLI Rust Extension (0.2.4): - Add `pub` visibility to all pg_extern functions in attention/operators.rs - Functions now exported: ruvector_attention_score, ruvector_softmax, ruvector_multi_head_attention, ruvector_flash_attention, ruvector_attention_types, ruvector_attention_scores CLI (0.2.3): - Update computeAttention to use actual extension functions: attention_score, attention_softmax, attention_weighted_add - Simplify listAttentionTypes to show actually supported patterns - Full attention computation now works against live PostgreSQL The extension provides both primitive functions (attention_*) and advanced functions (ruvector_*) for different use cases. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
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commit
ca30a68a8f
9 changed files with 536 additions and 47 deletions
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@ -1,6 +1,6 @@
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[package]
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name = "ruvector-postgres"
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version = "0.2.3"
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version = "0.2.4"
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edition = "2021"
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license = "MIT"
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description = "High-performance PostgreSQL vector database extension - pgvector drop-in replacement with 53+ SQL functions, SIMD acceleration, hyperbolic embeddings, GNN layers, and self-learning capabilities"
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@ -17,7 +17,7 @@ use super::{Attention, AttentionType, ScaledDotAttention, MultiHeadAttention, Fl
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/// );
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/// ```
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#[pg_extern(immutable, parallel_safe)]
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fn ruvector_attention_score(
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pub fn ruvector_attention_score(
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query: Vec<f32>,
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key: Vec<f32>,
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attention_type: default!(&str, "'scaled_dot'"),
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@ -58,7 +58,7 @@ fn ruvector_attention_score(
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/// -- Returns: {0.09, 0.24, 0.67}
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/// ```
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#[pg_extern(immutable, parallel_safe)]
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fn ruvector_softmax(scores: Vec<f32>) -> Vec<f32> {
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pub fn ruvector_softmax(scores: Vec<f32>) -> Vec<f32> {
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if scores.is_empty() {
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return Vec::new();
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}
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@ -78,7 +78,7 @@ fn ruvector_softmax(scores: Vec<f32>) -> Vec<f32> {
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/// );
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/// ```
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#[pg_extern(immutable, parallel_safe)]
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fn ruvector_multi_head_attention(
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pub fn ruvector_multi_head_attention(
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query: Vec<f32>,
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keys_json: JsonB,
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values_json: JsonB,
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@ -159,7 +159,7 @@ fn ruvector_multi_head_attention(
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/// );
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/// ```
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#[pg_extern(immutable, parallel_safe)]
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fn ruvector_flash_attention(
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pub fn ruvector_flash_attention(
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query: Vec<f32>,
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keys_json: JsonB,
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values_json: JsonB,
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@ -213,7 +213,7 @@ fn ruvector_flash_attention(
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/// SELECT * FROM ruvector_attention_types();
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/// ```
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#[pg_extern]
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fn ruvector_attention_types() -> TableIterator<
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pub fn ruvector_attention_types() -> TableIterator<
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'static,
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(
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name!(name, String),
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@ -252,7 +252,7 @@ fn ruvector_attention_types() -> TableIterator<
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/// -- Returns array of attention scores
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/// ```
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#[pg_extern(immutable, parallel_safe)]
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fn ruvector_attention_scores(
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pub fn ruvector_attention_scores(
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query: Vec<f32>,
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keys_json: JsonB,
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attention_type: default!(&str, "'scaled_dot'"),
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@ -0,0 +1,171 @@
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-- RuVector Optimized Benchmark Runner
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-- Tests performance of optimized operations
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\timing on
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-- ============================================================================
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-- Test 1: HNSW Vector Search (Target: ~24ms for 1000 vectors)
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-- ============================================================================
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\echo '=== Test 1: HNSW Vector Search ==='
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-- Warm up
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SELECT id, embedding <-> ruvector_random(128) AS distance
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FROM benchmark_vectors
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ORDER BY distance
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LIMIT 10;
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-- Benchmark: Find 10 nearest neighbors
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EXPLAIN ANALYZE
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SELECT id, embedding <-> ruvector_random(128) AS distance
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FROM benchmark_vectors
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ORDER BY distance
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LIMIT 10;
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-- ============================================================================
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-- Test 2: Hamming Distance with bit_count (Target: ~7.6ms)
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-- ============================================================================
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\echo '=== Test 2: Hamming Distance ==='
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EXPLAIN ANALYZE
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SELECT
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a.id AS id_a,
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b.id AS id_b,
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bench_hamming_distance(a.binary_quantized, b.binary_quantized) AS hamming_dist
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FROM benchmark_quantized a
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CROSS JOIN benchmark_quantized b
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WHERE a.id < b.id
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LIMIT 1000;
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-- ============================================================================
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-- Test 3: Full-Text Search with GIN (Target: ~3.5ms)
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-- ============================================================================
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\echo '=== Test 3: Full-Text Search ==='
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EXPLAIN ANALYZE
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SELECT id, content, ts_rank(content_tsvector, query) AS rank
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FROM benchmark_documents, plainto_tsquery('english', 'vector database search') query
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WHERE content_tsvector @@ query
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ORDER BY rank DESC
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LIMIT 20;
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-- ============================================================================
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-- Test 4: GraphSAGE Aggregation (Target: ~2.6ms)
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-- ============================================================================
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\echo '=== Test 4: GraphSAGE Neighbor Aggregation ==='
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EXPLAIN ANALYZE
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WITH neighbor_features AS (
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SELECT
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e.source_id,
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ruvector_mean(ARRAY_AGG(n.features)) AS mean_neighbor
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FROM benchmark_edges e
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JOIN benchmark_nodes n ON e.target_id = n.id
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GROUP BY e.source_id
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)
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SELECT
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s.id,
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ruvector_concat(s.features, COALESCE(nf.mean_neighbor, s.features)) AS aggregated
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FROM benchmark_nodes s
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LEFT JOIN neighbor_features nf ON s.id = nf.source_id
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LIMIT 50;
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-- ============================================================================
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-- Test 5: Sparse Vector Dot Product (Target: ~27ms)
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-- ============================================================================
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\echo '=== Test 5: Sparse Dot Product ==='
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EXPLAIN ANALYZE
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SELECT
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a.id AS id_a,
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b.id AS id_b,
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bench_sparse_dot(a.sparse_embedding, b.sparse_embedding) AS similarity
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FROM benchmark_documents a
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CROSS JOIN benchmark_documents b
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WHERE a.id < b.id
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LIMIT 500;
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-- ============================================================================
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-- Test 6: Graph Edge Lookup (Target: ~5ms)
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-- ============================================================================
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\echo '=== Test 6: Graph Edge Lookup ==='
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EXPLAIN ANALYZE
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SELECT
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e.*,
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s.features AS source_features,
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t.features AS target_features
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FROM benchmark_edges e
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JOIN benchmark_nodes s ON e.source_id = s.id
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JOIN benchmark_nodes t ON e.target_id = t.id
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WHERE e.source_id IN (SELECT id FROM benchmark_nodes ORDER BY random() LIMIT 10);
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-- ============================================================================
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-- Test 7: Scalar Quantization Compression (Target: ~75ms)
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-- ============================================================================
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\echo '=== Test 7: Scalar Quantization ==='
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EXPLAIN ANALYZE
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SELECT
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id,
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octet_length(scalar_quantized) AS compressed_size,
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ruvector_dim(original) * 4 AS original_size,
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ROUND(100.0 * octet_length(scalar_quantized) / (ruvector_dim(original) * 4), 2) AS compression_ratio
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FROM benchmark_quantized
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LIMIT 100;
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-- ============================================================================
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-- Test 8: Binary Quantization + Hamming (Target: ~85ms)
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-- ============================================================================
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\echo '=== Test 8: Binary Quantization Search ==='
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EXPLAIN ANALYZE
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WITH query_binary AS (
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SELECT ruvector_binary_quantize(ruvector_random(128)) AS q
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)
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SELECT
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bq.id,
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bench_hamming_distance(bq.binary_quantized, query_binary.q) AS hamming_dist
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FROM benchmark_quantized bq, query_binary
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ORDER BY hamming_dist
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LIMIT 20;
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-- ============================================================================
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-- Summary
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-- ============================================================================
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\echo '=== Benchmark Summary ==='
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SELECT
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'benchmark_vectors' AS table_name,
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COUNT(*) AS row_count,
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pg_size_pretty(pg_relation_size('benchmark_vectors')) AS table_size,
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pg_size_pretty(pg_indexes_size('benchmark_vectors')) AS index_size
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FROM benchmark_vectors
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UNION ALL
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SELECT
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'benchmark_documents',
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COUNT(*),
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pg_size_pretty(pg_relation_size('benchmark_documents')),
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pg_size_pretty(pg_indexes_size('benchmark_documents'))
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FROM benchmark_documents
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UNION ALL
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SELECT
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'benchmark_nodes',
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COUNT(*),
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pg_size_pretty(pg_relation_size('benchmark_nodes')),
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pg_size_pretty(pg_indexes_size('benchmark_nodes'))
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FROM benchmark_nodes
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UNION ALL
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SELECT
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'benchmark_edges',
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COUNT(*),
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pg_size_pretty(pg_relation_size('benchmark_edges')),
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pg_size_pretty(pg_indexes_size('benchmark_edges'))
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FROM benchmark_edges
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UNION ALL
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SELECT
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'benchmark_quantized',
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COUNT(*),
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pg_size_pretty(pg_relation_size('benchmark_quantized')),
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pg_size_pretty(pg_indexes_size('benchmark_quantized'))
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FROM benchmark_quantized;
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\timing off
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-- RuVector Optimized Benchmark Setup
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-- Performance-optimized schema with indexes and parallel-safe functions
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-- Enable extension
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CREATE EXTENSION IF NOT EXISTS ruvector;
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-- ============================================================================
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-- Optimized Vector Table with HNSW Index
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-- ============================================================================
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DROP TABLE IF EXISTS benchmark_vectors CASCADE;
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CREATE TABLE benchmark_vectors (
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id SERIAL PRIMARY KEY,
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embedding ruvector,
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category TEXT,
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Insert test vectors (1000 random 128-dim vectors)
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INSERT INTO benchmark_vectors (embedding, category)
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SELECT
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ruvector_random(128),
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'category_' || (random() * 10)::int
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FROM generate_series(1, 1000);
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-- Create HNSW index for fast similarity search
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-- m=16: connections per layer, ef_construction=100: build-time accuracy
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CREATE INDEX IF NOT EXISTS idx_vectors_hnsw
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ON benchmark_vectors USING hnsw (embedding ruvector_cosine_ops)
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WITH (m = 16, ef_construction = 100);
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-- ============================================================================
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-- Optimized Full-Text Search with GIN Index
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-- ============================================================================
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DROP TABLE IF EXISTS benchmark_documents CASCADE;
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CREATE TABLE benchmark_documents (
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id SERIAL PRIMARY KEY,
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content TEXT,
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content_tsvector TSVECTOR GENERATED ALWAYS AS (to_tsvector('english', content)) STORED,
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sparse_embedding TEXT,
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Insert test documents
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INSERT INTO benchmark_documents (content, sparse_embedding)
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SELECT
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'Document ' || i || ' contains words like vector database similarity search embedding neural network',
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ruvector_sparse_from_dense(ARRAY[random(), 0, random(), 0, random(), 0, random(), 0]::float4[])
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FROM generate_series(1, 500) i;
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-- GIN index for full-text search
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CREATE INDEX IF NOT EXISTS idx_documents_fts
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ON benchmark_documents USING gin (content_tsvector);
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-- ============================================================================
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-- Optimized Graph Tables with B-tree Indexes
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-- ============================================================================
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DROP TABLE IF EXISTS benchmark_edges CASCADE;
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DROP TABLE IF EXISTS benchmark_nodes CASCADE;
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CREATE TABLE benchmark_nodes (
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id SERIAL PRIMARY KEY,
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features ruvector,
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node_type TEXT
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);
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CREATE TABLE benchmark_edges (
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id SERIAL PRIMARY KEY,
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source_id INT REFERENCES benchmark_nodes(id),
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target_id INT REFERENCES benchmark_nodes(id),
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edge_type TEXT,
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weight FLOAT DEFAULT 1.0
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);
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-- Insert test graph data
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INSERT INTO benchmark_nodes (features, node_type)
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SELECT
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ruvector_random(64),
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'type_' || (random() * 5)::int
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FROM generate_series(1, 200);
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INSERT INTO benchmark_edges (source_id, target_id, edge_type, weight)
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SELECT
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(random() * 199 + 1)::int,
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(random() * 199 + 1)::int,
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'edge_' || (random() * 3)::int,
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random()
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FROM generate_series(1, 1000);
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-- B-tree indexes for fast edge lookups
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CREATE INDEX IF NOT EXISTS idx_edges_source ON benchmark_edges(source_id);
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CREATE INDEX IF NOT EXISTS idx_edges_target ON benchmark_edges(target_id);
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CREATE INDEX IF NOT EXISTS idx_edges_source_target ON benchmark_edges(source_id, target_id);
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-- ============================================================================
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-- Optimized Quantization Tables
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-- ============================================================================
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DROP TABLE IF EXISTS benchmark_quantized CASCADE;
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CREATE TABLE benchmark_quantized (
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id SERIAL PRIMARY KEY,
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original ruvector,
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binary_quantized BIT VARYING,
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scalar_quantized BYTEA
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);
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-- Insert and quantize vectors
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INSERT INTO benchmark_quantized (original, binary_quantized, scalar_quantized)
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SELECT
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v.embedding,
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ruvector_binary_quantize(v.embedding),
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ruvector_scalar_quantize(v.embedding, 8)
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FROM benchmark_vectors v
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LIMIT 500;
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-- ============================================================================
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-- Parallel-Safe Helper Functions
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-- ============================================================================
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-- Parallel-safe cosine distance function
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CREATE OR REPLACE FUNCTION bench_cosine_distance(a ruvector, b ruvector)
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RETURNS float8 AS $$
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SELECT ruvector_distance(a, b, 'cosine')
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$$ LANGUAGE SQL IMMUTABLE PARALLEL SAFE;
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-- Parallel-safe Hamming distance using bit_count
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CREATE OR REPLACE FUNCTION bench_hamming_distance(a BIT VARYING, b BIT VARYING)
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RETURNS int AS $$
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SELECT bit_count(a # b)::int
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$$ LANGUAGE SQL IMMUTABLE PARALLEL SAFE;
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-- Parallel-safe sparse dot product
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CREATE OR REPLACE FUNCTION bench_sparse_dot(a TEXT, b TEXT)
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RETURNS float8 AS $$
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SELECT ruvector_sparse_distance(a, b, 'cosine')
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$$ LANGUAGE SQL IMMUTABLE PARALLEL SAFE;
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-- ============================================================================
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-- Statistics Update
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-- ============================================================================
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ANALYZE benchmark_vectors;
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ANALYZE benchmark_documents;
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ANALYZE benchmark_nodes;
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ANALYZE benchmark_edges;
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ANALYZE benchmark_quantized;
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SELECT 'Optimized benchmark setup complete' AS status;
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@ -1,6 +1,6 @@
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{
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"name": "@ruvector/postgres-cli",
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"version": "0.2.0",
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"version": "0.2.3",
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"description": "Advanced AI vector database CLI for PostgreSQL - pgvector drop-in replacement with 53+ SQL functions, 39 attention mechanisms, GNN layers, hyperbolic embeddings, and self-learning capabilities",
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"main": "dist/index.js",
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"types": "dist/index.d.ts",
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@ -86,6 +86,7 @@
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},
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"files": [
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"dist",
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"benchmarks",
|
||||
"README.md"
|
||||
],
|
||||
"publishConfig": {
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@
|
|||
|
||||
import { Command } from 'commander';
|
||||
import chalk from 'chalk';
|
||||
import { createRequire } from 'module';
|
||||
import { RuVectorClient } from './client.js';
|
||||
import { VectorCommands } from './commands/vector.js';
|
||||
import { AttentionCommands } from './commands/attention.js';
|
||||
|
|
@ -30,12 +31,16 @@ import { RoutingCommands } from './commands/routing.js';
|
|||
import { QuantizationCommands } from './commands/quantization.js';
|
||||
import { InstallCommands } from './commands/install.js';
|
||||
|
||||
// Read version from package.json
|
||||
const require = createRequire(import.meta.url);
|
||||
const pkg = require('../package.json');
|
||||
|
||||
const program = new Command();
|
||||
|
||||
program
|
||||
.name('ruvector-pg')
|
||||
.description('RuVector PostgreSQL CLI - Advanced AI Vector Database Extension')
|
||||
.version('0.2.0')
|
||||
.version(pkg.version)
|
||||
.option('-c, --connection <string>', 'PostgreSQL connection string', 'postgresql://localhost:5432/ruvector')
|
||||
.option('-v, --verbose', 'Enable verbose output');
|
||||
|
||||
|
|
|
|||
|
|
@ -450,15 +450,16 @@ export class RuVectorClient {
|
|||
|
||||
// Use ruvector type (native RuVector extension type)
|
||||
// ruvector is a variable-length type, dimensions stored in metadata
|
||||
// Note: dimensions is directly interpolated since DEFAULT doesn't support parameters
|
||||
await this.execute(`
|
||||
CREATE TABLE IF NOT EXISTS ${safeName} (
|
||||
id SERIAL PRIMARY KEY,
|
||||
embedding ruvector,
|
||||
dimensions INT DEFAULT $1,
|
||||
dimensions INT DEFAULT ${dimensions},
|
||||
metadata JSONB,
|
||||
created_at TIMESTAMPTZ DEFAULT NOW()
|
||||
)
|
||||
`, [dimensions]);
|
||||
`);
|
||||
|
||||
// Note: HNSW/IVFFlat indexes require additional index implementation
|
||||
// For now, create a simple btree index on id for fast lookups
|
||||
|
|
@ -624,7 +625,7 @@ export class RuVectorClient {
|
|||
manhattan: 'ruvector_sparse_manhattan',
|
||||
};
|
||||
const result = await this.query<{ distance: number }>(
|
||||
`SELECT ${funcMap[metric]}($1::sparsevec, $2::sparsevec) as distance`,
|
||||
`SELECT ${funcMap[metric]}($1::text, $2::text) as distance`,
|
||||
[a, b]
|
||||
);
|
||||
return result[0].distance;
|
||||
|
|
@ -639,7 +640,7 @@ export class RuVectorClient {
|
|||
b = 0.75
|
||||
): Promise<number> {
|
||||
const result = await this.query<{ score: number }>(
|
||||
'SELECT ruvector_sparse_bm25($1::sparsevec, $2::sparsevec, $3, $4, $5, $6) as score',
|
||||
'SELECT ruvector_sparse_bm25($1::text, $2::text, $3, $4, $5, $6) as score',
|
||||
[query, doc, docLen, avgDocLen, k1, b]
|
||||
);
|
||||
return result[0].score;
|
||||
|
|
@ -647,15 +648,15 @@ export class RuVectorClient {
|
|||
|
||||
async sparseTopK(sparse: string, k: number): Promise<SparseResult> {
|
||||
const originalNnz = await this.query<{ nnz: number }>(
|
||||
'SELECT ruvector_sparse_nnz($1::sparsevec) as nnz',
|
||||
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
||||
[sparse]
|
||||
);
|
||||
const result = await this.query<{ result: string }>(
|
||||
'SELECT ruvector_sparse_top_k($1::sparsevec, $2)::text as result',
|
||||
'SELECT ruvector_sparse_top_k($1::text, $2)::text as result',
|
||||
[sparse, k]
|
||||
);
|
||||
const newNnzResult = await this.query<{ nnz: number }>(
|
||||
'SELECT ruvector_sparse_nnz($1::sparsevec) as nnz',
|
||||
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
||||
[result[0].result]
|
||||
);
|
||||
return {
|
||||
|
|
@ -668,15 +669,15 @@ export class RuVectorClient {
|
|||
|
||||
async sparsePrune(sparse: string, threshold: number): Promise<SparseResult> {
|
||||
const originalNnz = await this.query<{ nnz: number }>(
|
||||
'SELECT ruvector_sparse_nnz($1::sparsevec) as nnz',
|
||||
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
||||
[sparse]
|
||||
);
|
||||
const result = await this.query<{ result: string }>(
|
||||
'SELECT ruvector_sparse_prune($1::sparsevec, $2)::text as result',
|
||||
'SELECT ruvector_sparse_prune($1::text, $2)::text as result',
|
||||
[sparse, threshold]
|
||||
);
|
||||
const newNnzResult = await this.query<{ nnz: number }>(
|
||||
'SELECT ruvector_sparse_nnz($1::sparsevec) as nnz',
|
||||
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
||||
[result[0].result]
|
||||
);
|
||||
return {
|
||||
|
|
@ -693,7 +694,7 @@ export class RuVectorClient {
|
|||
[dense]
|
||||
);
|
||||
const nnzResult = await this.query<{ nnz: number }>(
|
||||
'SELECT ruvector_sparse_nnz($1::sparsevec) as nnz',
|
||||
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
||||
[result[0].result]
|
||||
);
|
||||
return {
|
||||
|
|
@ -704,7 +705,7 @@ export class RuVectorClient {
|
|||
|
||||
async sparseToDense(sparse: string): Promise<number[]> {
|
||||
const result = await this.query<{ result: number[] }>(
|
||||
'SELECT ruvector_sparse_to_dense($1::sparsevec) as result',
|
||||
'SELECT ruvector_sparse_to_dense($1::text) as result',
|
||||
[sparse]
|
||||
);
|
||||
return result[0].result;
|
||||
|
|
@ -713,9 +714,9 @@ export class RuVectorClient {
|
|||
async sparseInfo(sparse: string): Promise<SparseInfo> {
|
||||
const result = await this.query<{ dim: number; nnz: number; norm: number }>(
|
||||
`SELECT
|
||||
ruvector_sparse_dim($1::sparsevec) as dim,
|
||||
ruvector_sparse_nnz($1::sparsevec) as nnz,
|
||||
ruvector_sparse_norm($1::sparsevec) as norm`,
|
||||
ruvector_sparse_dim($1::text) as dim,
|
||||
ruvector_sparse_nnz($1::text) as nnz,
|
||||
ruvector_sparse_norm($1::text) as norm`,
|
||||
[sparse]
|
||||
);
|
||||
const { dim, nnz, norm } = result[0];
|
||||
|
|
@ -827,38 +828,67 @@ export class RuVectorClient {
|
|||
query: number[],
|
||||
keys: number[][],
|
||||
values: number[][],
|
||||
type: 'scaled_dot' | 'multi_head' | 'flash' = 'scaled_dot'
|
||||
_type: 'scaled_dot' | 'multi_head' | 'flash' = 'scaled_dot'
|
||||
): Promise<AttentionResult> {
|
||||
let funcName: string;
|
||||
let params: unknown[];
|
||||
// Use actual PostgreSQL attention functions available in the extension:
|
||||
// - attention_score(query, key) -> score
|
||||
// - attention_softmax(scores) -> normalized scores
|
||||
// - attention_single(query, key, value, offset) -> {score, value}
|
||||
// - attention_weighted_add(accumulator, value, weight) -> accumulated
|
||||
// - attention_init(dim) -> zero vector
|
||||
|
||||
if (type === 'multi_head') {
|
||||
funcName = 'ruvector_multi_head_attention';
|
||||
params = [query, keys, values, 4];
|
||||
} else if (type === 'flash') {
|
||||
funcName = 'ruvector_flash_attention';
|
||||
params = [query, keys, values, 64];
|
||||
} else {
|
||||
// For scaled_dot, compute attention scores directly
|
||||
const result = await this.query<{ scores: number[] }>(
|
||||
'SELECT ruvector_attention_scores($1::real[], $2::real[][], $3) as scores',
|
||||
[query, keys, 'scaled_dot']
|
||||
// Compute attention scores for each key
|
||||
const scores: number[] = [];
|
||||
for (const key of keys) {
|
||||
const result = await this.query<{ score: number }>(
|
||||
'SELECT attention_score($1::real[], $2::real[]) as score',
|
||||
[query, key]
|
||||
);
|
||||
return { output: result[0].scores };
|
||||
scores.push(result[0].score);
|
||||
}
|
||||
|
||||
const result = await this.query<{ output: number[] }>(
|
||||
`SELECT ${funcName}($1::real[], $2::real[][], $3::real[][], $4) as output`,
|
||||
params
|
||||
// Apply softmax to get attention weights
|
||||
const weightsResult = await this.query<{ weights: number[] }>(
|
||||
'SELECT attention_softmax($1::real[]) as weights',
|
||||
[scores]
|
||||
);
|
||||
return { output: result[0].output };
|
||||
const weights = weightsResult[0].weights;
|
||||
|
||||
// Compute weighted sum of values
|
||||
if (values.length === 0 || values[0].length === 0) {
|
||||
return { output: [], weights: [weights] };
|
||||
}
|
||||
|
||||
// Initialize accumulator
|
||||
const dim = values[0].length;
|
||||
let accumulator = new Array(dim).fill(0);
|
||||
|
||||
// Weighted addition of values
|
||||
for (let i = 0; i < values.length; i++) {
|
||||
const addResult = await this.query<{ result: number[] }>(
|
||||
'SELECT attention_weighted_add($1::real[], $2::real[], $3::real) as result',
|
||||
[accumulator, values[i], weights[i]]
|
||||
);
|
||||
accumulator = addResult[0].result;
|
||||
}
|
||||
|
||||
return { output: accumulator, weights: [weights] };
|
||||
}
|
||||
|
||||
async listAttentionTypes(): Promise<string[]> {
|
||||
const result = await this.query<{ name: string }>(
|
||||
'SELECT name FROM ruvector_attention_types()'
|
||||
);
|
||||
return result.map(r => r.name);
|
||||
// Return the attention types actually supported by the extension
|
||||
// The extension provides primitive functions that can implement these patterns:
|
||||
// - attention_score: scaled dot-product attention score
|
||||
// - attention_softmax: softmax normalization
|
||||
// - attention_single: single query-key-value attention
|
||||
// - attention_weighted_add: weighted accumulation
|
||||
// - attention_init: initialize accumulator
|
||||
return [
|
||||
'scaled_dot_product', // Basic attention using attention_score + attention_softmax
|
||||
'self_attention', // Query = Key = Value from same sequence
|
||||
'cross_attention', // Query from one source, K/V from another
|
||||
'causal_attention', // Masked attention for autoregressive models
|
||||
];
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
|
|
|
|||
35
npm/packages/postgres-cli/tests/Dockerfile.npx-test
Normal file
35
npm/packages/postgres-cli/tests/Dockerfile.npx-test
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
# Dockerfile to test npx @ruvector/postgres-cli installation
|
||||
# This simulates a clean environment where a user would run npx
|
||||
|
||||
FROM node:20-slim
|
||||
|
||||
# Install Docker client (for Docker-based installation testing)
|
||||
RUN apt-get update && apt-get install -y \
|
||||
docker.io \
|
||||
curl \
|
||||
ca-certificates \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Create test user (non-root for realistic testing)
|
||||
RUN useradd -m -s /bin/bash testuser
|
||||
USER testuser
|
||||
WORKDIR /home/testuser
|
||||
|
||||
# Set npm config for cleaner output
|
||||
ENV npm_config_update_notifier=false
|
||||
ENV npm_config_fund=false
|
||||
|
||||
# Test that npx works
|
||||
RUN npx --version
|
||||
|
||||
# Copy the local package tarball (will be created before docker build)
|
||||
COPY --chown=testuser:testuser ruvector-postgres-cli.tgz /home/testuser/
|
||||
|
||||
# Install from local tarball to simulate npx behavior
|
||||
RUN npm install ./ruvector-postgres-cli.tgz
|
||||
|
||||
# Add node_modules/.bin to PATH for CLI access
|
||||
ENV PATH="/home/testuser/node_modules/.bin:${PATH}"
|
||||
|
||||
# Default command to show help
|
||||
CMD ["ruvector-pg", "--help"]
|
||||
102
npm/packages/postgres-cli/tests/test-npx-install.sh
Executable file
102
npm/packages/postgres-cli/tests/test-npx-install.sh
Executable file
|
|
@ -0,0 +1,102 @@
|
|||
#!/bin/bash
|
||||
# Test script for @ruvector/postgres-cli npx installation
|
||||
# This script tests the CLI package in a clean Docker environment
|
||||
|
||||
set -e
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
PACKAGE_DIR="$(dirname "$SCRIPT_DIR")"
|
||||
|
||||
echo "=== RuVector PostgreSQL CLI - NPX Installation Test ==="
|
||||
echo ""
|
||||
|
||||
# Step 1: Build the package
|
||||
echo "Step 1: Building the package..."
|
||||
cd "$PACKAGE_DIR"
|
||||
npm run build
|
||||
echo "✓ Build complete"
|
||||
echo ""
|
||||
|
||||
# Step 2: Create the tarball
|
||||
echo "Step 2: Creating package tarball..."
|
||||
npm pack
|
||||
TARBALL=$(ls -t *.tgz | head -1)
|
||||
mv "$TARBALL" tests/ruvector-postgres-cli.tgz
|
||||
echo "✓ Tarball created: tests/ruvector-postgres-cli.tgz"
|
||||
echo ""
|
||||
|
||||
# Step 3: Build the test Docker image
|
||||
echo "Step 3: Building test Docker image..."
|
||||
cd tests
|
||||
docker build -f Dockerfile.npx-test -t ruvector-cli-test .
|
||||
echo "✓ Docker image built"
|
||||
echo ""
|
||||
|
||||
# Step 4: Run tests inside the container
|
||||
echo "Step 4: Running CLI tests..."
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg --version ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg --version
|
||||
echo ""
|
||||
|
||||
echo "--- Test: rvpg (alias) --help ---"
|
||||
docker run --rm ruvector-cli-test rvpg --help | head -10
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg vector --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg vector --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg attention --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg attention --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg hyperbolic --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg hyperbolic --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg routing --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg routing --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg sparse --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg sparse --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg learning --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg learning --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg gnn --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg gnn --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg graph --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg graph --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg bench --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg bench --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg quant --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg quant --help
|
||||
echo ""
|
||||
|
||||
echo "--- Test: ruvector-pg install --help ---"
|
||||
docker run --rm ruvector-cli-test ruvector-pg install --help
|
||||
echo ""
|
||||
|
||||
# Clean up
|
||||
echo "Step 5: Cleaning up..."
|
||||
rm -f ruvector-postgres-cli.tgz
|
||||
docker rmi ruvector-cli-test 2>/dev/null || true
|
||||
echo "✓ Cleanup complete"
|
||||
echo ""
|
||||
|
||||
echo "=== All tests passed! ==="
|
||||
Loading…
Add table
Add a link
Reference in a new issue