Magector

Semantic code search engine for Magento 2 and Adobe Commerce, powered by ONNX embeddings and HNSW vector search.

Magector indexes an entire Magento 2 or Adobe Commerce codebase and lets you search it with natural language. Instead of grepping for keywords, ask questions like "how are checkout totals calculated?" or "where is the product price determined?" and get ranked, relevant results in under 50ms.

Why Magector

Magento 2 and Adobe Commerce have 18,000+ source files across hundreds of modules. Finding the right code is slow:

Magector understands that a query about "payment capture" should return Sales/Model/Order/Payment/Operations/CaptureOperation.php, not just files containing the word "capture".

Magector vs Built-in AI Search

Claude Code and Cursor both have built-in code search -- but they rely on keyword matching (grep/ripgrep) and file-tree heuristics. On a Magento 2 / Adobe Commerce codebase with 18,000+ files, that approach breaks down fast.

What this means in practice

Without Magector, asking Claude Code or Cursor "how are checkout totals calculated?" triggers multiple grep searches, reads dozens of files, and still may miss the right ones. With Magector, the AI calls magento_search("checkout totals calculation") and gets the exact files ranked by relevance in one step -- saving tokens and time.

Magector doesn't replace your AI tool -- it gives it a better search engine.

Features

• Semantic search -- find code by meaning, not exact keywords
• 99.2% accuracy -- validated with 101 E2E test queries across 16 tool categories, plus 557 Rust-level test cases
• Hybrid search -- combines semantic vector similarity with keyword re-ranking for best-of-both-worlds results
• Structured JSON output -- results include file path, class name, methods list, role badges, and content snippets for minimal round-trips
• Persistent serve mode -- keeps ONNX model and HNSW index resident in memory, eliminating cold-start latency
• Incremental re-indexing -- background file watcher detects changes and updates the index without restart (tombstone + compact strategy)
• ONNX embeddings -- native 384-dim transformer embeddings via ONNX Runtime
• 36K+ vectors -- indexes the complete Magento 2 / Adobe Commerce codebase including framework internals
• Magento-aware -- understands controllers, plugins, observers, blocks, resolvers, repositories, and 20+ Magento patterns
• Adobe Commerce compatible -- works with both Magento Open Source and Adobe Commerce (B2B, Staging, and all Commerce-specific modules)
• AST-powered -- tree-sitter parsing for PHP and JavaScript extracts classes, methods, namespaces, and inheritance
• Cross-tool discovery -- tool descriptions include keywords and "See also" references so AI clients find the right tool on the first try
• SONA feedback learning -- self-adjusting search that learns from MCP tool call patterns (e.g., search → find_plugin refines future rankings for similar queries)
• SONA v2 with MicroLoRA + EWC++ -- rank-2 low-rank adapter (1536 params, ~6KB) adjusts query embeddings based on learned patterns; Elastic Weight Consolidation prevents catastrophic forgetting during online learning
• Diff analysis -- risk scoring and change classification for git commits and staged changes
• Complexity analysis -- cyclomatic complexity, function count, and hotspot detection across modules
• Fast -- 10-45ms queries via persistent serve process, batched ONNX embedding with adaptive thread scaling
• MCP server -- 20 tools integrating with Claude Code, Cursor, and any MCP-compatible AI tool
• Clean architecture -- Rust core handles all indexing/search, Node.js MCP server delegates to it

Architecture

flowchart TD
  subgraph rust ["Rust Core"]
    A["AST Parser · PHP + JS"]
    B["Pattern Detection · 20+"]
    C["ONNX Embedder · 384d"]
    D["HNSW + Reranking"]
    A --> B --> C --> D
  end
  subgraph node ["Node.js Layer"]
    E["MCP Server · 20 tools"]
    F["Persistent Serve"]
    G["CLI · init/index/search"]
    E --> F
    G --> F
  end
  node -->|stdin/stdout JSON| rust

  style rust fill:#f4a460,color:#000
  style node fill:#68b684,color:#000

Indexing Pipeline

flowchart TD
  A[Source File] --> B[AST Parser]
  B --> C[Pattern Detection]
  C --> D[Text Enrichment]
  D --> E[ONNX Embedding]
  E --> F[(HNSW Index)]
  A --> G[Metadata]
  G --> F

Search Pipeline

flowchart TD
  Q[Query] --> E1[Synonym Enrichment]
  E1 --> E2[ONNX Embedding]
  E2 --> H[HNSW Search]
  H --> R[Hybrid Reranking]
  R --> SA[SONA Adjustment + MicroLoRA]
  SA --> J[Structured JSON]

Components

Quick Start

Prerequisites

• Node.js 18+

1. Initialize in Your Project

cd /path/to/your/magento2  # or Adobe Commerce project
npx magector init

This single command handles the entire setup:

flowchart TD
  A["npx magector init"] --> B[Verify Project]
  B --> C[Download Model]
  C --> D[Index Codebase]
  D --> E[Detect IDE]
  E --> F[Write Config]
  F --> G[Update .gitignore]

2. Search

npx magector search "product price calculation"
npx magector search "checkout totals collector" -l 20

3. Re-index After Changes

npx magector index

4. IDE Setup Only (Skip Indexing)

npx magector setup

CLI Reference

Rust Core CLI

magector-core <COMMAND>

Commands:
  index       Index a Magento codebase
  search      Search the index semantically
  serve       Start persistent server mode (stdin/stdout JSON protocol)
  validate    Run validation suite (downloads Magento if needed)
  download    Download Magento 2 Open Source
  stats       Show index statistics
  embed       Generate embedding for text

index

magector-core index [OPTIONS]

Options:
  -m, --magento-root <PATH>   Path to Magento root directory
  -d, --database <PATH>       Index database path [default: ./magector.db]
  -c, --model-cache <PATH>    Model cache directory [default: ./models]
  -v, --verbose               Enable verbose output

search

magector-core search <QUERY> [OPTIONS]

Options:
  -d, --database <PATH>   Index database path [default: ./magector.db]
  -l, --limit <N>         Number of results [default: 10]
  -f, --format <FORMAT>   Output format: text, json [default: text]

serve

magector-core serve [OPTIONS]

Options:
  -d, --database <PATH>       Index database path [default: ./magector.db]
  -c, --model-cache <PATH>    Model cache directory [default: ./models]
  -m, --magento-root <PATH>   Magento root (enables file watcher)
      --watch-interval <SECS> File watcher poll interval [default: 60]

Starts a persistent process that reads JSON queries from stdin and writes JSON responses to stdout. Keeps the ONNX model and HNSW index resident in memory for fast repeated queries.

When --magento-root is provided, a background file watcher polls for changed files every --watch-interval seconds and incrementally re-indexes them without restart. Modified and deleted files are soft-deleted (tombstoned) in the HNSW index; new vectors are appended. When tombstoned entries exceed 20% of total vectors, the index is automatically compacted by rebuilding the HNSW graph.

Protocol (one JSON object per line):

// Request:
{"command":"search","query":"product price","limit":10}

// Response:
{"ok":true,"data":[{"id":123,"score":0.85,"metadata":{...}}]}

// Stats request:
{"command":"stats"}

// Watcher status:
{"command":"watcher_status"}
// Response:
{"ok":true,"data":{"running":true,"tracked_files":18234,"last_scan_changes":3,"interval_secs":60}}

// SONA feedback:
{"command":"feedback","signals":[{"type":"refinement_to_plugin","query":"checkout totals","timestamp":1700000000000}]}
// Response:
{"ok":true,"data":{"learned":1}}

// SONA status:
{"command":"sona_status"}
// Response:
{"ok":true,"data":{"learned_patterns":5,"total_observations":12}}

Node.js CLI

npx magector init [path]        # Full setup: index + IDE config
npx magector index [path]       # Index (or re-index) Magento codebase
npx magector search <query>     # Search indexed code
npx magector stats              # Show indexer statistics
npx magector setup [path]       # IDE setup only (no indexing)
npx magector mcp                # Start MCP server
npx magector help               # Show help

Environment Variables

MCP Server Tools

The MCP server exposes 20 tools for AI-assisted Magento 2 and Adobe Commerce development. All search tools return structured JSON with file paths, class names, methods, role badges, and content snippets -- enabling AI clients to parse results programmatically and minimize file-read round-trips.

Output Format

All search tools return structured JSON:

{
  "results": [
    {
      "rank": 1,
      "score": 0.892,
      "path": "vendor/magento/module-catalog/Model/ProductRepository.php",
      "module": "Magento_Catalog",
      "className": "ProductRepository",
      "namespace": "Magento\\Catalog\\Model",
      "methods": ["save", "getById", "getList", "delete", "deleteById"],
      "magentoType": "repository",
      "fileType": "php",
      "badges": ["repository"],
      "snippet": "class ProductRepository implements ProductRepositoryInterface..."
    }
  ],
  "count": 1
}

Key fields:

• methods -- list of method names in the class (avoids needing to read the file)
• badges -- role indicators: plugin, controller, observer, repository, graphql-resolver, model, block
• snippet -- first 300 characters of indexed content for quick assessment

Search Tools

Magento-Specific Finders

Flow Tracing

Auto-detects entry type from pattern (/V1/... → API, snake_case → event, camelCase → GraphQL, path/segments → route), or override with entryType. Use depth: "shallow" (entry + config + plugins) or depth: "deep" (adds observers, layout, templates, DI preferences).

Analysis Tools

Utility Tools

Tool Cross-References

Each tool description includes "See also" hints to help AI clients chain tools effectively:

graph TD
  cls["find_class"] --> plg["find_plugin"]
  cls --> prf["find_preference"]
  cls --> mtd["find_method"]
  cfg["find_config"] --> obs["find_observer"]
  cfg --> prf
  cfg --> api["find_api"]
  plg --> cls
  plg --> mtd
  tpl["find_template"] --> blk["find_block"]
  blk --> tpl
  blk --> cfg
  dbs["find_db_schema"] --> cls
  gql["find_graphql"] --> cls
  gql --> mtd
  ctl["find_controller"] --> cfg
  trc["trace_flow"] -.-> ctl
  trc -.-> plg
  trc -.-> obs
  trc -.-> tpl
  trc -.-> api
  trc -.-> gql

  style cls fill:#4a90d9,color:#fff
  style mtd fill:#4a90d9,color:#fff
  style cfg fill:#e8a838,color:#000
  style plg fill:#d94a4a,color:#fff
  style obs fill:#d94a4a,color:#fff
  style prf fill:#e8a838,color:#000
  style api fill:#e8a838,color:#000
  style tpl fill:#68b684,color:#000
  style blk fill:#68b684,color:#000
  style dbs fill:#9b59b6,color:#fff
  style gql fill:#9b59b6,color:#fff
  style ctl fill:#4a90d9,color:#fff
  style trc fill:#2ecc71,color:#000

Query Examples

magento_search("how are checkout totals calculated")
magento_search("product price with tier pricing and catalog rules")
magento_find_class("ProductRepositoryInterface")
magento_find_method("getById")
magento_find_config("di.xml plugin for ProductRepository")
magento_find_plugin({ targetClass: "Topmenu" })
magento_find_observer("sales_order_place_after")
magento_find_preference("StoreManagerInterface")
magento_find_api("/V1/orders")
magento_find_controller("catalog/product/view")
magento_find_graphql("placeOrder")
magento_find_db_schema("sales_order")
magento_find_cron("indexer")
magento_find_block("cart totals")
magento_find_template("minicart")
magento_analyze_diff({ commitHash: "abc123" })
magento_complexity({ module: "Magento_Catalog", threshold: 10 })
magento_trace_flow({ entryPoint: "checkout/cart/add", depth: "deep" })
magento_trace_flow({ entryPoint: "/V1/products" })
magento_trace_flow({ entryPoint: "placeOrder", entryType: "graphql" })
magento_trace_flow({ entryPoint: "sales_order_place_after" })

Supported Platforms

Pre-built binaries are provided for the following platforms:

Note: macOS Intel (x86_64) is not supported as a pre-built binary. Intel Mac users can build from source.

Validation

Magector is validated at two levels:

1. E2E MCP accuracy tests -- 101 queries across 16 tool categories via stdio JSON-RPC
2. Rust-level validation -- 557 test cases across 50+ categories against Magento 2.4.7

E2E Accuracy (MCP Tools)

---
config:
  themeVariables:
    pie1: "#4caf50"
    pie2: "#f44336"
---
pie title Test Pass Rate (101 queries)
  "Passed (101)" : 101
  "Failed (0)" : 0

Integration Tests

64 integration tests covering MCP protocol compliance, tool schemas, tool calls, analysis tools, and stdout JSON integrity.

Running Tests

# E2E accuracy tests (101 queries, requires indexed codebase)
npm run test:accuracy
npm run test:accuracy:verbose

# Integration tests (64 tests)
npm test

# SONA/MicroLoRA benefit evaluation (180 queries, baseline vs post-training)
npm run test:sona-eval
npm run test:sona-eval:verbose

# Rust validation (557 test cases)
cd rust-core && cargo run --release -- validate -m ./magento2 --skip-index

Project Structure

magector/
├── src/                          # Node.js source
│   ├── cli.js                    # CLI entry point (npx magector <command>)
│   ├── mcp-server.js             # MCP server (20 tools, structured JSON output)
│   ├── binary.js                 # Platform binary resolver
│   ├── model.js                  # ONNX model resolver/downloader
│   ├── init.js                   # Full init command (index + IDE config)
│   ├── magento-patterns.js       # Magento pattern detection (JS)
│   ├── templates/                # IDE rules templates
│   │   ├── cursorrules.js        # .cursorrules content
│   │   └── claude-md.js          # CLAUDE.md content
│   └── validation/               # JS validation suite
│       ├── validator.js
│       ├── benchmark.js
│       ├── test-queries.js
│       ├── test-data-generator.js
│       └── accuracy-calculator.js
├── tests/                        # Automated tests
│   ├── mcp-server.test.js        # Integration tests (64 tests)
│   ├── mcp-accuracy.test.js      # E2E accuracy tests (101 queries)
│   ├── mcp-sona.test.js          # SONA feedback integration tests (8 tests)
│   ├── mcp-sona-eval.test.js     # SONA/MicroLoRA benefit evaluation (180 queries)
│   └── results/                  # Test result artifacts
│       ├── accuracy-report.json
│       └── sona-eval-report.json
├── platforms/                    # Platform-specific binary packages
│   ├── darwin-arm64/             # macOS ARM (Apple Silicon)
│   ├── linux-x64/                # Linux x64
│   ├── linux-arm64/              # Linux ARM64
│   └── win32-x64/                # Windows x64
├── rust-core/                    # Rust high-performance core
│   ├── Cargo.toml
│   ├── src/
│   │   ├── main.rs               # Rust CLI (index, search, serve, validate)
│   │   ├── lib.rs                # Library exports
│   │   ├── indexer.rs             # Core indexing with progress output
│   │   ├── embedder.rs            # ONNX embedding (MiniLM-L6-v2)
│   │   ├── vectordb.rs            # HNSW vector database + hybrid search + tombstones
│   │   ├── watcher.rs             # File watcher for incremental re-indexing
│   │   ├── ast.rs                 # Tree-sitter AST (PHP + JS)
│   │   ├── magento.rs             # Magento pattern detection (Rust)
│   │   ├── sona.rs                # SONA feedback learning + MicroLoRA + EWC++
│   │   └── validation.rs          # 557 test cases, validation framework
│   └── models/                   # ONNX model files (auto-downloaded)
│       ├── all-MiniLM-L6-v2.onnx
│       └── tokenizer.json
├── .github/
│   └── workflows/
│       └── release.yml           # Cross-compile + publish CI
├── scripts/
│   └── setup.sh                  # Claude Code MCP setup script
├── config/
│   └── mcp-config.json           # MCP server configuration template
├── package.json
├── .gitignore
├── LICENSE
└── README.md

How It Works

1. Indexing

Magector scans every .php, .js, .xml, .phtml, and .graphqls file in a Magento 2 or Adobe Commerce codebase:

1. AST parsing -- Tree-sitter extracts class names, namespaces, methods, inheritance, and interface implementations from PHP and JavaScript files
2. Pattern detection -- Identifies Magento-specific patterns: controllers, models, repositories, plugins, observers, blocks, GraphQL resolvers, admin grids, cron jobs, and more
3. Search text enrichment -- Combines AST metadata with Magento pattern keywords to create semantically rich text representations
4. Embedding -- ONNX Runtime generates 384-dimensional vectors using all-MiniLM-L6-v2
5. Indexing -- Vectors are stored in an HNSW index for sub-millisecond approximate nearest neighbor search

2. Searching

1. Query text is enriched with pattern synonyms (e.g., "controller" adds "action execute http request dispatch")
2. The enriched query is embedded into the same 384-dimensional vector space
3. HNSW finds the nearest neighbors by cosine similarity
4. Hybrid reranking boosts results with keyword matches in path and search text
5. SONA adjustment -- MicroLoRA adapts the query embedding based on learned patterns; EWC++ prevents forgetting earlier learning
6. Results are returned as structured JSON with file path, class name, methods, role badges, and content snippet

3. Persistent Serve Mode

The MCP server spawns a persistent Rust process (magector-core serve) that keeps the ONNX model and HNSW index loaded in memory. Queries are sent as JSON over stdin and responses returned via stdout -- eliminating the ~2.6s cold-start overhead of loading the model per query. Falls back to single-shot execFileSync if the serve process is unavailable.

flowchart TD
  subgraph startup ["Startup (once)"]
    S1[Load Model] --> S2[Load Index]
    S2 --> S3[Ready Signal]
  end
  subgraph query ["Per Query (10-45ms)"]
    Q1[stdin JSON] --> Q2[Embed]
    Q2 --> Q3[HNSW Search]
    Q3 --> Q4[Rerank]
    Q4 --> Q5[stdout JSON]
  end
  startup --> query
  subgraph fallback ["Fallback"]
    F1[execFileSync ~2.6s]
  end

  style startup fill:#e8f4e8,color:#000
  style query fill:#e8e8f4,color:#000
  style fallback fill:#f4e8e8,color:#000

4. File Watcher (Incremental Re-indexing)

When the serve process is started with --magento-root, a background thread polls the filesystem for changes every 60 seconds (configurable via --watch-interval). Changed files are incrementally re-indexed without restarting the server.

Since hnsw_rs does not support point deletion, Magector uses a tombstone strategy: old vectors for modified/deleted files are marked as tombstoned and filtered out of search results. New vectors are appended. When tombstoned entries exceed 20% of total vectors, the HNSW graph is automatically rebuilt (compacted) to reclaim memory and restore search performance.

flowchart TD
  W1[Sleep 60s] --> W2[Scan Filesystem]
  W2 --> W3{Changes?}
  W3 -->|No| W1
  W3 -->|Yes| W4[Tombstone Old Vectors]
  W4 --> W5[Parse + Embed New Files]
  W5 --> W6[Append to HNSW]
  W6 --> W7{Tombstone > 20%?}
  W7 -->|Yes| W8[Compact / Rebuild HNSW]
  W7 -->|No| W9[Save to Disk]
  W8 --> W9
  W9 --> W1

  style W4 fill:#f4e8e8,color:#000
  style W5 fill:#e8f4e8,color:#000
  style W8 fill:#e8e8f4,color:#000

5. MCP Integration

The MCP server delegates all search/index operations to the Rust core binary. Analysis tools (diff, complexity) use ruvector JS modules directly.

sequenceDiagram
  participant Dev
  participant AI
  participant MCP
  participant Rust
  participant HNSW

  Dev->>AI: "checkout totals?"
  AI->>MCP: magento_search(...)
  MCP->>Rust: JSON query
  Rust->>HNSW: embed + search
  HNSW-->>Rust: candidates
  Rust-->>MCP: JSON results
  MCP-->>AI: paths, methods, badges
  AI-->>Dev: TotalsCollector.php

6. SONA Feedback Learning

The MCP server tracks sequences of tool calls and sends feedback signals to the Rust process. Over time, this adjusts search result rankings based on observed usage patterns.

How it works: The Node.js SessionTracker watches for follow-up tool calls after magento_search. If a user searches and then immediately calls magento_find_plugin, SONA learns that similar queries should boost plugin results. The learned weights are persisted to a .sona file alongside the index.

Characteristics:

• Score adjustments are capped at ±0.15 to avoid overwhelming semantic similarity
• Learning rate decays with repeated observations (diminishing returns)
• Learned weights are keyed by normalized, order-independent query term hashes
• Always active -- no feature flags or build-time opt-in required
• Persisted via bincode to <db_path>.sona

SONA v2: MicroLoRA + EWC++

SONA v2 adds embedding-level adaptation via a MicroLoRA adapter and Elastic Weight Consolidation:

• adjust_query_embedding() applies the LoRA transform + L2 normalization before vector search; cosine similarity guard (≥0.90) skips destructive adjustments
• learn_with_embeddings() updates LoRA weights from feedback signals with EWC regularization (λ=2000) and decaying learning rate
• 3-tier scoring with negative learning: positive signals boost the followed feature type, mild negative learning (0.1×) demotes unrelated types
• V1→V2 persistence format is backward-compatible (auto-upgrades on load)

cd rust-core && cargo build --release

Magento Patterns Detected

mindmap
  root((Patterns))
    PHP
      Controller
      Model
      Repository
      Block
      Helper
      ViewModel
    Interception
      Plugin
      Observer
      Preference
    XML
      di.xml
      events.xml
      webapi.xml
      routes.xml
      crontab.xml
      db_schema.xml
    Frontend
      Template
      JavaScript
      GraphQL

Magector understands these Magento 2 architectural patterns:

Configuration

Cursor IDE Rules

Copy .cursorrules to your Magento project root for optimized AI-assisted development. The rules instruct the AI to:

1. Use Magector MCP tools before reading files manually
2. Write effective semantic queries
3. Follow Magento development patterns
4. Interpret search results correctly

Model Configuration

The ONNX model (all-MiniLM-L6-v2) is automatically downloaded on first run to ~/.magector/models/. To use a different location:

magector-core index -m /path/to/magento -c /custom/model/path

Development

Building from Source

git clone https://github.com/krejcif/magector.git
cd magector

# Install Node.js dependencies
npm install

# Build the Rust core
cd rust-core
cargo build --release
cd ..

# The CLI will automatically find the dev binary at rust-core/target/release/magector-core
node src/cli.js help

Building

# Rust core
cd rust-core
cargo build --release

# Run unit tests
cargo test

# Run validation
cargo run --release -- validate

Testing

# Integration tests (64 tests, requires indexed codebase)
npm test

# E2E accuracy tests (101 queries)
npm run test:accuracy
npm run test:accuracy:verbose

# Run without index (unit + schema tests only)
npm run test:no-index

# Rust unit tests (33 tests including SONA)
cd rust-core && cargo test

# SONA integration tests (8 tests)
node tests/mcp-sona.test.js

# SONA/MicroLoRA benefit evaluation (180 queries)
npm run test:sona-eval

# Rust validation (557 test cases)
cd rust-core && cargo run --release -- validate -m ./magento2 --skip-index

Adding New Magento Patterns

1. Add pattern detection in rust-core/src/magento.rs
2. Add search text enrichment in rust-core/src/indexer.rs
3. Add validation test cases in rust-core/src/validation.rs
4. Add E2E accuracy test cases in tests/mcp-accuracy.test.js
5. Rebuild and run validation to verify:

cargo build --release
./target/release/magector-core validate -m ./magento2 --skip-index
npm run test:accuracy

Adding MCP Tools

1. Define the tool schema in src/mcp-server.js (ListToolsRequestSchema handler)
2. Include keyword-rich descriptions and cross-tool "See also" references
3. Implement the handler in the CallToolRequestSchema handler
4. Return structured JSON via formatSearchResults()
5. Add E2E test cases in tests/mcp-accuracy.test.js
6. Test with Claude Code or the MCP inspector

Technical Details

Embedding Model

• Model: all-MiniLM-L6-v2
• Dimensions: 384
• Pooling: Mean pooling with attention mask
• Normalization: L2 normalized
• Runtime: ONNX Runtime (via ort crate)

Vector Index

• Algorithm: HNSW (Hierarchical Navigable Small World)
• Library: hnsw_rs
• Parameters: M=32, max_layers=16, ef_construction=200
• Distance metric: Cosine similarity
• Hybrid search: Semantic nearest-neighbor + keyword reranking in path and search text + SONA/MicroLoRA feedback adjustments
• Incremental updates: Tombstone soft-delete + periodic HNSW rebuild (compact)
• Persistence: Bincode V2 binary serialization (backward-compatible with V1)

Index Structure

Each indexed file produces a vector entry with metadata:

struct IndexMetadata {
    path: String,
    file_type: String,          // php, xml, js, template, graphql
    magento_type: String,       // controller, model, block, plugin, ...
    class_name: Option<String>,
    namespace: Option<String>,
    methods: Vec<String>,       // extracted method names
    search_text: String,        // enriched searchable text
    is_controller: bool,
    is_plugin: bool,
    is_observer: bool,
    is_model: bool,
    is_block: bool,
    is_repository: bool,
    is_resolver: bool,
    // ... 20+ pattern flags
}

Performance Characteristics

Performance Optimizations

• Persistent serve mode -- Rust process keeps ONNX model + HNSW index in memory via stdin/stdout JSON protocol
• Query cache -- LRU cache (200 entries) avoids re-embedding identical queries
• Hybrid reranking -- combines semantic similarity with keyword matching for better precision
• Batched ONNX embedding -- 32 texts per inference call (vs. 1-at-a-time), 3-5x faster embedding
• Dynamic thread scaling -- ONNX intra-op threads scale to CPU core count
• Thread-local AST parsers -- each rayon thread gets its own tree-sitter parser (no mutex contention)
• Bincode persistence -- binary serialization replaces JSON (3-5x faster save/load, ~5x smaller files)
• Adaptive HNSW capacity -- pre-sized to actual vector count
• Parallel HNSW insert -- batch insert uses hnsw_rs parallel insertion on load and index
• Tuned ef_search -- optimized search parameters for 36K vector index (ef_search=50 for search, 64 for hybrid)
• SONA feedback learning -- learns from MCP tool call patterns to adjust search rankings; MicroLoRA adapts query embeddings, EWC++ prevents forgetting

Roadmap

gantt
  title Roadmap
  dateFormat YYYY-MM
  axisFormat %b
  section Done
    Hybrid search       :done, 2025-01, 30d
    Serve mode          :done, 2025-02, 30d
    JSON output         :done, 2025-03, 15d
    Cross-tool hints    :done, 2025-03, 15d
    E2E tests           :done, 2025-03, 15d
    Adobe Commerce      :done, 2025-03, 15d
  section Next
    SONA feedback       :done, 2025-04, 30d
    Incremental index   :done, 2025-04, 30d
    SONA v2 MicroLoRA   :done, 2025-05, 15d
    Method chunking     :active, 2025-06, 30d
    Intent detection    :2025-07, 30d
    Type filtering      :2025-08, 30d
  section Future
    VSCode extension    :2025-09, 60d
    Web UI              :2025-11, 60d

• Hybrid search (semantic + keyword re-ranking)
• Persistent serve mode (eliminates cold-start latency)
• Structured JSON output (methods, badges, snippets)
• Cross-tool discovery hints for AI clients
• E2E accuracy test suite (101 queries)
• Adobe Commerce support (B2B, Staging, and all Commerce-specific modules)
• SONA feedback learning (search rankings adapt to MCP tool call patterns)
• SONA v2 with MicroLoRA + EWC++ (embedding-level adaptation, prevents catastrophic forgetting)
• Method-level chunking (per-method vectors for direct method search)
• Query intent classification (auto-detect "give me XML" vs "give me PHP")
• Filtered search by file type at the vector level
• Incremental indexing (background file watcher with tombstone + compact strategy)
• VSCode extension
• Web UI for browsing results

License

MIT License. See LICENSE for details.

Contributing

Contributions are welcome. Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Add tests for new functionality
4. Run validation to ensure accuracy doesn't regress: npm run test:accuracy
5. Submit a pull request

Built with Rust and Node.js for the Magento and Adobe Commerce community.