feat: ruvector-postgres v0.3.0 — 43 new SQL functions (ADR-044)

.github/workflows/postgres-extension-ci.yml

@@ -89,7 +89,7 @@ jobs:
             ${{ runner.os }}-cargo-build-target-${{ matrix.pg_version }}-
 
       - name: Install cargo-pgrx
-        run: cargo install cargo-pgrx --version 0.12.0 --locked
+        run: cargo install cargo-pgrx --version 0.12.9 --locked
 
       - name: Initialize pgrx (Ubuntu)
         if: runner.os == 'Linux'
@@ -114,7 +114,7 @@ jobs:
         working-directory: crates/ruvector-postgres
 
       - name: Run tests
-        run: cargo pgrx test pg${{ matrix.pg_version }} --no-default-features
+        run: cargo pgrx test pg${{ matrix.pg_version }} --no-default-features --features pg${{ matrix.pg_version }}
         working-directory: crates/ruvector-postgres
 
   # Test with all features enabled
@@ -133,11 +133,13 @@ jobs:
 
       - name: Install PostgreSQL
         run: |
+          sudo sh -c 'echo "deb http://apt.postgresql.org/pub/repos/apt $(lsb_release -cs)-pgdg main" > /etc/apt/sources.list.d/pgdg.list'
+          wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | sudo apt-key add -
           sudo apt-get update
           sudo apt-get install -y postgresql-17 postgresql-server-dev-17
 
       - name: Install cargo-pgrx
-        run: cargo install cargo-pgrx --version 0.12.0 --locked
+        run: cargo install cargo-pgrx --version 0.12.9 --locked
 
       - name: Initialize pgrx
         run: cargo pgrx init --pg17=/usr/lib/postgresql/17/bin/pg_config
@@ -150,7 +152,7 @@ jobs:
 
       - name: Test with all features
         run: |
-          cargo pgrx test pg17 --no-default-features --features index-all,quant-all
+          cargo pgrx test pg17 --no-default-features --features pg17,index-all,quant-all
         working-directory: crates/ruvector-postgres
 
   # Benchmark on pull requests
@@ -170,11 +172,13 @@ jobs:
 
       - name: Install PostgreSQL
         run: |
+          sudo sh -c 'echo "deb http://apt.postgresql.org/pub/repos/apt $(lsb_release -cs)-pgdg main" > /etc/apt/sources.list.d/pgdg.list'
+          wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | sudo apt-key add -
           sudo apt-get update
           sudo apt-get install -y postgresql-17 postgresql-server-dev-17
 
       - name: Install cargo-pgrx
-        run: cargo install cargo-pgrx --version 0.12.0 --locked
+        run: cargo install cargo-pgrx --version 0.12.9 --locked
 
       - name: Initialize pgrx
         run: cargo pgrx init --pg17=/usr/lib/postgresql/17/bin/pg_config
@@ -237,7 +241,7 @@ jobs:
           sudo apt-get install -y postgresql-${{ matrix.pg_version }} postgresql-server-dev-${{ matrix.pg_version }}
 
       - name: Install cargo-pgrx
-        run: cargo install cargo-pgrx --version 0.12.0 --locked
+        run: cargo install cargo-pgrx --version 0.12.9 --locked
 
       - name: Initialize pgrx
         run: cargo pgrx init --pg${{ matrix.pg_version }}=/usr/lib/postgresql/${{ matrix.pg_version }}/bin/pg_config

.github/workflows/ruvector-postgres-ci.yml

@@ -36,7 +36,7 @@ on:
 env:
   CARGO_TERM_COLOR: always
   RUST_BACKTRACE: 1
-  PGRX_VERSION: '0.12.6'
+  PGRX_VERSION: '0.12.9'
   RUST_VERSION: 'stable'
 
 # Concurrency control - cancel in-progress runs for same PR

README.md

@@ -32,10 +32,10 @@ Most vector databases are static — they store embeddings and search them. That
 | 🌿 **Git-like branching** | ❌ | ✅ Branch your data like code — only changes are copied |
 | ⚡ **Sublinear Solvers** | ❌ | ✅ O(log n) sparse linear systems, PageRank, spectral methods |
 
-**One package. Everything included:** vector search, graph queries, GNN learning, distributed clustering, local LLMs, 40+ attention mechanisms, cognitive containers ([RVF](./crates/rvf/README.md) — self-booting `.rvf` files with eBPF, witness chains, and COW branching), and WASM support.
+**One package. Everything included:** vector search, graph queries, GNN learning, distributed clustering, local LLMs, 46 attention mechanisms, cognitive containers ([RVF](./crates/rvf/README.md) — self-booting `.rvf` files with eBPF, witness chains, and COW branching), and WASM support.
 
 <details>
-<summary>📋 See Full Capabilities (43 features)</summary>
+<summary>📋 See Full Capabilities (49 features)</summary>
 
 **Core Vector Database**
 | # | Capability | What It Does |
@@ -60,57 +60,63 @@ Most vector databases are static — they store embeddings and search them. That
 | 10 | **Run LLMs locally** | ruvllm with GGUF, Metal/CUDA/ANE acceleration |
 | 11 | **RuvLTRA models** | Pre-trained GGUF for routing & embeddings (<10ms) → [HuggingFace](https://huggingface.co/ruv/ruvltra) |
 | 12 | **SONA learning** | Self-Optimizing Neural Architecture with LoRA, EWC++ |
-| 13 | **40+ attention mechanisms** | Flash, linear, graph, hyperbolic, mincut-gated (50% compute) |
+| 13 | **46 attention mechanisms** | Flash, linear, graph, hyperbolic, mincut-gated (50% compute) |
 | 14 | **Spiking neural networks** | Event-driven neuromorphic computing |
 | 15 | **Mincut-gated transformer** | Dynamic attention via graph min-cut optimization |
 | 16 | **Route AI requests** | Semantic routing + FastGRNN for LLM optimization |
+| 17 | **Sublinear Solvers in SQL** | PageRank, CG, Laplacian solver — O(log n) to O(√n) via PostgreSQL |
+| 18 | **Math Distances in SQL** | Wasserstein, Sinkhorn OT, KL divergence, spectral clustering |
+| 19 | **Topological Data Analysis** | Persistent homology, Betti numbers, embedding drift detection |
+| 20 | **Sona Learning in SQL** | Micro-LoRA trajectory learning with EWC++ forgetting prevention |
+| 21 | **Domain Expansion** | Cross-domain transfer learning with contextual bandits |
+| 22 | **Extended Attention** | O(n) linear, MoE, hyperbolic, sliding window attention in SQL |
 
 **Cognitive Containers ([RVF](./crates/rvf/README.md))**
 | # | Capability | What It Does |
 |---|------------|--------------|
-| 17 | **Self-boot as a microservice** | A `.rvf` file contains a real Linux kernel — drop it on a VM and it boots in 125 ms |
-| 18 | **eBPF acceleration** | Hot vectors served in kernel data path via XDP, socket filter, and TC programs |
-| 19 | **5.5 KB WASM runtime** | Same file runs queries in a browser tab with zero backend |
-| 20 | **COW branching** | Git-like copy-on-write — 1M-vector parent, 100 edits = ~2.5 MB child |
-| 21 | **Witness chains** | Tamper-evident hash-linked audit trail for every operation |
-| 22 | **Post-quantum signatures** | ML-DSA-65 and SLH-DSA-128s alongside Ed25519 |
-| 23 | **DNA-style lineage** | Track parent/child derivation chains with cryptographic hashes |
-| 24 | **24 segment types** | VEC, INDEX, KERNEL, EBPF, WASM, COW_MAP, WITNESS, CRYPTO, and 16 more |
+| 23 | **Self-boot as a microservice** | A `.rvf` file contains a real Linux kernel — drop it on a VM and it boots in 125 ms |
+| 24 | **eBPF acceleration** | Hot vectors served in kernel data path via XDP, socket filter, and TC programs |
+| 25 | **5.5 KB WASM runtime** | Same file runs queries in a browser tab with zero backend |
+| 26 | **COW branching** | Git-like copy-on-write — 1M-vector parent, 100 edits = ~2.5 MB child |
+| 27 | **Witness chains** | Tamper-evident hash-linked audit trail for every operation |
+| 28 | **Post-quantum signatures** | ML-DSA-65 and SLH-DSA-128s alongside Ed25519 |
+| 29 | **DNA-style lineage** | Track parent/child derivation chains with cryptographic hashes |
+| 30 | **24 segment types** | VEC, INDEX, KERNEL, EBPF, WASM, COW_MAP, WITNESS, CRYPTO, and 16 more |
 
 **Specialized Processing**
 | # | Capability | What It Does |
 |---|------------|--------------|
-| 25 | **SciPix OCR** | LaTeX/MathML extraction from scientific documents |
-| 26 | **DAG workflows** | Self-learning directed acyclic graph execution |
-| 27 | **Cognitum Gate** | Cognitive AI gateway with TileZero acceleration |
-| 28 | **FPGA transformer** | Hardware-accelerated transformer inference |
-| 29 | **Quantum coherence** | ruQu for quantum error correction via dynamic min-cut |
-| 30 | **Sublinear Solvers** | 8 algorithms: Neumann, CG, Forward Push, TRUE, BMSSP — O(log n) to O(√n) |
+| 31 | **SciPix OCR** | LaTeX/MathML extraction from scientific documents |
+| 32 | **DAG workflows** | Self-learning directed acyclic graph execution |
+| 33 | **Cognitum Gate** | Cognitive AI gateway with TileZero acceleration |
+| 34 | **FPGA transformer** | Hardware-accelerated transformer inference |
+| 35 | **Quantum coherence** | ruQu for quantum error correction via dynamic min-cut |
+| 36 | **Sublinear Solvers** | 8 algorithms: Neumann, CG, Forward Push, TRUE, BMSSP — O(log n) to O(√n) |
 
 **Genomics & Health**
 | # | Capability | What It Does |
 |---|------------|--------------|
-| 31 | **rvDNA genomic analysis** | Variant calling, protein translation, HNSW k-mer search in 12 ms |
-| 32 | **`.rvdna` file format** | AI-native binary with pre-computed vectors, tensors, and embeddings |
-| 33 | **Instant diagnostics** | Sickle cell, cancer mutations, drug dosing — runs on any device |
-| 34 | **Privacy-first WASM** | Browser-based genomics, data never leaves the device |
+| 37 | **rvDNA genomic analysis** | Variant calling, protein translation, HNSW k-mer search in 12 ms |
+| 38 | **`.rvdna` file format** | AI-native binary with pre-computed vectors, tensors, and embeddings |
+| 39 | **Instant diagnostics** | Sickle cell, cancer mutations, drug dosing — runs on any device |
+| 40 | **Privacy-first WASM** | Browser-based genomics, data never leaves the device |
 
 **Platform & Integration**
 | # | Capability | What It Does |
 |---|------------|--------------|
-| 35 | **Run anywhere** | Node.js, browser (WASM), edge (rvLite), HTTP server, Rust, bare metal |
-| 36 | **Drop into Postgres** | pgvector-compatible extension with SIMD acceleration |
-| 37 | **MCP integration** | Model Context Protocol server for AI assistant tools |
-| 38 | **Cloud deployment** | One-click deploy to Cloud Run, Kubernetes |
-| 39 | **13 Rust crates + 4 npm packages** | [RVF SDK](./crates/rvf/README.md) published on [crates.io](https://crates.io/crates/rvf-runtime) and [npm](https://www.npmjs.com/package/@ruvector/rvf) |
+| 41 | **Run anywhere** | Node.js, browser (WASM), edge (rvLite), HTTP server, Rust, bare metal |
+| 42 | **Drop into Postgres** | pgvector-compatible extension with SIMD acceleration |
+| 43 | **MCP integration** | Model Context Protocol server for AI assistant tools |
+| 44 | **Cloud deployment** | One-click deploy to Cloud Run, Kubernetes |
+| 45 | **13 Rust crates + 4 npm packages** | [RVF SDK](./crates/rvf/README.md) published on [crates.io](https://crates.io/crates/rvf-runtime) and [npm](https://www.npmjs.com/package/@ruvector/rvf) |
 
 **Self-Learning & Adaptation**
 | # | Capability | What It Does |
 |---|------------|--------------|
-| 40 | **Self-learning hooks** | Q-learning, neural patterns, HNSW memory |
-| 41 | **ReasoningBank** | Trajectory learning with verdict judgment |
-| 42 | **Economy system** | Tokenomics, CRDT-based distributed state |
-| 43 | **Agentic synthesis** | Multi-agent workflow composition |
+| 46 | **Self-learning hooks** | Q-learning, neural patterns, HNSW memory |
+| 47 | **ReasoningBank** | Trajectory learning with verdict judgment |
+| 48 | **Economy system** | Tokenomics, CRDT-based distributed state |
+| 49 | **Agentic synthesis** | Multi-agent workflow composition |
 
 </details>
 
@@ -3217,7 +3223,7 @@ let distances = batch_distances(&query, &database);  // 8-54x speedup
 [![Docker Hub](https://img.shields.io/docker/pulls/ruvnet/ruvector-postgres?label=docker%20pulls)](https://hub.docker.com/r/ruvnet/ruvector-postgres)
 [![Docker](https://img.shields.io/docker/v/ruvnet/ruvector-postgres?label=docker)](https://hub.docker.com/r/ruvnet/ruvector-postgres)
 
-**The most advanced PostgreSQL vector extension** — a drop-in pgvector replacement with 230+ SQL functions, hardware-accelerated SIMD operations, and built-in AI capabilities. Transform your existing PostgreSQL database into a full-featured vector search engine with GNN layers, attention mechanisms, and self-learning capabilities.
+**The most advanced PostgreSQL vector extension** — a drop-in pgvector replacement with 143 SQL functions, hardware-accelerated SIMD operations, and built-in AI capabilities. Transform your existing PostgreSQL database into a full-featured vector search engine with GNN layers, attention mechanisms, and self-learning capabilities.
 
 ```bash
 # Quick Install from Docker Hub
@@ -3235,17 +3241,17 @@ CREATE EXTENSION ruvector;
 
 **Why RuVector Postgres?**
 - **Zero Migration** — Works with existing pgvector code, just swap the extension
-- **10x More Functions** — 230+ SQL functions vs pgvector's ~20
+- **10x More Functions** — 143 SQL functions vs pgvector's ~20
 - **2x Faster** — AVX-512/AVX2/NEON SIMD acceleration
-- **AI-Native** — GNN layers, 40+ attention mechanisms, local embeddings
+- **AI-Native** — GNN layers, 46 attention mechanisms, local embeddings
 - **Self-Learning** — Improves search quality over time with ReasoningBank
 
 | Feature | pgvector | RuVector Postgres |
 |---------|----------|-------------------|
-| SQL Functions | ~20 | **230+** |
+| SQL Functions | ~20 | **143** |
 | SIMD Acceleration | Basic | AVX-512/AVX2/NEON (~2x faster) |
 | Index Types | HNSW, IVFFlat | HNSW, IVFFlat + Hyperbolic |
-| Attention Mechanisms | ❌ | 39 types (Flash, Linear, Graph) |
+| Attention Mechanisms | ❌ | 46 types (Flash, Linear, Graph) |
 | GNN Layers | ❌ | GCN, GraphSAGE, GAT, GIN |
 | Sparse Vectors | ❌ | BM25, TF-IDF, SPLADE |
 | Self-Learning | ❌ | ReasoningBank, trajectory learning |
@@ -3298,8 +3304,9 @@ volumes:
 ```
 
 **Available Tags:**
-- `ruvnet/ruvector-postgres:latest` - PostgreSQL + RuVector 2.0
-- `ruvnet/ruvector-postgres:2.0.0` - Specific version
+- `ruvnet/ruvector-postgres:latest` - PostgreSQL + RuVector 0.3.0
+- `ruvnet/ruvector-postgres:0.3.0` - Current release (143 SQL functions)
+- `ruvnet/ruvector-postgres:2.0.0` - Previous release
 
 </details>
 
@@ -3401,15 +3408,21 @@ pg15 = ["ruvector-postgres/pg15"]
 # AI features (opt-in)
 ai-complete = ["ruvector-postgres/ai-complete"]  # All AI features
 learning = ["ruvector-postgres/learning"]         # Self-learning
-attention = ["ruvector-postgres/attention"]       # 40+ attention mechanisms
+attention = ["ruvector-postgres/attention"]       # 46 attention mechanisms
 gnn = ["ruvector-postgres/gnn"]                   # Graph neural networks
 hyperbolic = ["ruvector-postgres/hyperbolic"]     # Hyperbolic embeddings
 embeddings = ["ruvector-postgres/embeddings"]     # Local embedding generation
+solver = ["ruvector-postgres/solver"]                   # Sublinear solvers
+math-distances = ["ruvector-postgres/math-distances"]   # Math distances & spectral
+tda = ["ruvector-postgres/tda"]                         # Topological data analysis
+sona-learning = ["ruvector-postgres/sona-learning"]     # Sona learning
+domain-expansion = ["ruvector-postgres/domain-expansion"] # Domain expansion
+analytics-complete = ["solver", "math-distances", "tda"] # All analytics
 ```
 
 **Build with all features:**
 ```bash
-cargo pgrx install --release --features "ai-complete,embeddings"
+cargo pgrx install --release --features "ai-complete,embeddings,analytics-complete,attention-extended,sona-learning,domain-expansion"
 ```
 
 </details>
@@ -3466,7 +3479,7 @@ SELECT ruvector_flash_attention(query, key, value);
 
 </details>
 
-See [ruvector-postgres README](./crates/ruvector-postgres/README.md) for full SQL API reference (230+ functions).
+See [ruvector-postgres README](./crates/ruvector-postgres/README.md) for full SQL API reference (143 functions).
 
 </details>
 

crates/ruqu-algorithms/src/lib.rs

@@ -39,7 +39,7 @@ pub mod qaoa;
 pub mod surface_code;
 pub mod vqe;
 
-pub use grover::{GroverConfig, GroverResult, run_grover};
-pub use qaoa::{Graph, QaoaConfig, QaoaResult, run_qaoa};
-pub use surface_code::{SurfaceCodeConfig, SurfaceCodeResult, run_surface_code};
-pub use vqe::{VqeConfig, VqeResult, run_vqe};
+pub use grover::{run_grover, GroverConfig, GroverResult};
+pub use qaoa::{run_qaoa, Graph, QaoaConfig, QaoaResult};
+pub use surface_code::{run_surface_code, SurfaceCodeConfig, SurfaceCodeResult};
+pub use vqe::{run_vqe, VqeConfig, VqeResult};

crates/ruqu-algorithms/src/qaoa.rs

@@ -123,7 +123,11 @@ pub struct QaoaResult {
 ///
 /// `gammas` and `betas` must each have length `p`.
 pub fn build_qaoa_circuit(graph: &Graph, gammas: &[f64], betas: &[f64]) -> QuantumCircuit {
-    assert_eq!(gammas.len(), betas.len(), "gammas and betas must have equal length");
+    assert_eq!(
+        gammas.len(),
+        betas.len(),
+        "gammas and betas must have equal length"
+    );
     let n = graph.num_nodes;
     let p = gammas.len();
     let mut circuit = QuantumCircuit::new(n);

crates/ruqu-algorithms/src/surface_code.rs

@@ -320,9 +320,7 @@ fn most_common_data_qubit(
 /// # Errors
 ///
 /// Returns a [`ruqu_core::error::QuantumError`] on simulator failures.
-pub fn run_surface_code(
-    config: &SurfaceCodeConfig,
-) -> ruqu_core::error::Result<SurfaceCodeResult> {
+pub fn run_surface_code(config: &SurfaceCodeConfig) -> ruqu_core::error::Result<SurfaceCodeResult> {
     assert_eq!(
         config.distance, 3,
         "Only distance-3 surface codes are currently supported"
@@ -370,11 +368,9 @@ pub fn run_surface_code(
         //    row has odd parity -> logical error.
         let mut row_parity = 1.0_f64;
         for &q in &logical_row {
-            let z_exp = state.expectation_value(
-                &ruqu_core::types::PauliString {
-                    ops: vec![(q, ruqu_core::types::PauliOp::Z)],
-                },
-            );
+            let z_exp = state.expectation_value(&ruqu_core::types::PauliString {
+                ops: vec![(q, ruqu_core::types::PauliOp::Z)],
+            });
             // Each Z expectation is in [-1, 1]. For a computational basis
             // state, it is exactly +1 (|0>) or -1 (|1>). For superpositions
             // we approximate: sign of the product captures parity.
@@ -428,7 +424,11 @@ mod tests {
         }
         // All 9 data qubits should be covered by X stabilizers.
         for q in 0..9u32 {
-            assert!(covered.contains(&q), "data qubit {} not covered by X stabilizers", q);
+            assert!(
+                covered.contains(&q),
+                "data qubit {} not covered by X stabilizers",
+                q
+            );
         }
     }
 
@@ -446,7 +446,10 @@ mod tests {
         let layout = SurfaceCodeLayout::distance_3();
         let syndrome = vec![false; 8];
         let corrections = decode_syndrome(&syndrome, &layout);
-        assert!(corrections.is_empty(), "no corrections when syndrome is trivial");
+        assert!(
+            corrections.is_empty(),
+            "no corrections when syndrome is trivial"
+        );
     }
 
     #[test]
@@ -471,10 +474,7 @@ mod tests {
 
     #[test]
     fn test_most_common_data_qubit() {
-        let stabilizers = vec![
-            vec![0, 1, 3, 4],
-            vec![1, 2, 4, 5],
-        ];
+        let stabilizers = vec![vec![0, 1, 3, 4], vec![1, 2, 4, 5]];
         // Both stabilizers 0 and 1 triggered: qubit 1 and 4 appear in both.
         let result = most_common_data_qubit(&stabilizers, &[0, 1]);
         assert!(result == Some(1) || result == Some(4));

crates/ruqu-algorithms/src/vqe.rs

@@ -119,10 +119,7 @@ pub fn build_ansatz(num_qubits: u32, depth: u32, params: &[f64]) -> QuantumCircu
 /// ansatz parameters.
 ///
 /// Builds the ansatz, simulates it, and returns `<psi|H|psi>`.
-pub fn evaluate_energy(
-    config: &VqeConfig,
-    params: &[f64],
-) -> ruqu_core::error::Result<f64> {
+pub fn evaluate_energy(config: &VqeConfig, params: &[f64]) -> ruqu_core::error::Result<f64> {
     let circuit = build_ansatz(config.num_qubits, config.ansatz_depth, params);
     let sim_config = SimConfig {
         seed: config.seed,