feat(embed): static (model2vec) embeddings + extra-languages gating — v0.19.0

Cargo.toml

@@ -60,7 +60,7 @@ members = [
 ]
 
 [workspace.package]
-version = "0.18.6"
+version = "0.19.0"
 edition = "2021"
 license = "Apache-2.0"
 repository = "https://github.com/codegraph-ai/codegraph"

README.md

@@ -77,13 +77,29 @@ one tool and exits without the MCP stdio handshake — ideal for scripting.
 |------|---------|-------------|
 | `--workspace <path>` | current dir | Directories to index (repeatable for multi-project) |
 | `--exclude <dir>` | — | Directories to skip (repeatable) |
-| `--embedding-model <model>` | `bge-small` | `bge-small` (384d, fast), `jina-code-v2` (768d, 6× slower), or `granite-97m` (384d, 32K ctx, ~3× slower) |
+| `--embedding-model <model>` | `bge-small` | `bge-small` (384d, fast), `jina-code-v2` (768d, 6× slower), `granite-97m` (384d, 32K ctx, ~3× slower), or `static` (model2vec, 256d — ~100× faster indexing, no ONNX; needs a local model dir, see below) |
 | `--full-body-embedding` | `true` | Embed full function body (~50 lines) for better semantic search and duplicate detection |
 | `--max-files <n>` | 5000 | Maximum files to index |
 | `--profile <name>` | `all` | Filter the exposed MCP tool surface to a named subset (see below) |
 | `--graph-only` | off | Skip embedding generation — build the graph and serve structural tools only. No ONNX model load, 10-50× faster indexing. Semantic search unavailable. For CI / one-shot graph queries. |
 | `--run-tool <name>` | — | One-shot mode: index, run a single tool, print its result, exit. No MCP handshake. Pair with `--tool-args '<json>'`. |
 
+#### `--embedding-model static` — model2vec fast indexing
+
+Static (model2vec) embeddings replace the ONNX transformer with a token→vector
+lookup table: indexing is **~100× faster** (this repo's 5,873 symbols embed in
+~1 s vs ~3.4 min with BGE) and there's **no ONNX runtime or 1.5 GB RAM gate**.
+Retrieval stays **hybrid (BM25 + semantic)**, so end-to-end quality is **~90% of
+BGE**. The VS Code extension ships the model bundled, so `static` works there
+with no setup. For the CLI/MCP server it needs a local model directory
+(`config.json` + `tokenizer.json` + `model.safetensors`):
+
+- Point at it with `CODEGRAPH_STATIC_MODEL=/path/to/model` (or the VS Code
+  `codegraph.staticModelPath` setting to override the bundled model). Default:
+  `~/.codegraph/static_models/jina-code-static-256`.
+- Distill one from any sentence-transformer (Apache-2.0 Jina-Code by default) in
+  ~30 s on CPU: `python scripts/distill_static_model.py`.
+
 #### `--profile` — narrow the MCP tool surface
 
 The full 32-tool surface is convenient but inflates the agent's prompt-context cost. A profile exposes only the slice you need (also settable via the `CODEGRAPH_TOOL_PROFILE` env var):
@@ -103,7 +119,8 @@ The full 32-tool surface is convenient but inflates the agent's prompt-context c
   "codegraph.indexOnStartup": true,
   "codegraph.indexPaths": ["/path/to/project-a", "/path/to/project-b"],
   "codegraph.excludePatterns": ["**/cmake-build-debug/**", "**/generated/**"],
-  "codegraph.embeddingModel": "bge-small",
+  "codegraph.embeddingModel": "bge-small",        // or "static" for ~100× faster indexing
+  "codegraph.staticModelPath": "",                // model2vec model dir when embeddingModel is "static"
   "codegraph.maxFileSizeKB": 1024,
   "codegraph.debug": false
 }
@@ -313,6 +330,12 @@ Additional tools available in [CodeGraph Pro](https://codegraph.astudioplus.com/
 
 HTTP handler detection: Python (FastAPI/Flask/Django), TypeScript (NestJS), Java (Spring/JAX-RS), Go (stdlib/Gin/Echo/Fiber), C# (ASP.NET), Ruby (Rails), PHP (Laravel/Symfony).
 
+> **Community vs full builds:** COBOL, Fortran, Perl, Dart, Zig, and R are
+> compiled only with `--features extra-languages`. The default community binary
+> omits them — they had zero usage in telemetry and their tree-sitter grammars
+> add ~25 MB (COBOL's parse tables alone are 30 MB). The other 32 languages are
+> always available.
+
 ---
 
 ## Architecture

crates/codegraph-memory/Cargo.toml

@@ -46,6 +46,12 @@ anyhow = "1.0"
 # Logging
 log = "0.4"
 
+# Static (lookup-table) embeddings: HuggingFace tokenizer + a safetensors
+# token->vector matrix, mean-pooled. No ONNX — the fast indexing path.
+tokenizers = "0.21"
+safetensors = "0.4"
+half = "2"
+
 # Embeddings - fastembed with BGE-Small-EN-v1.5
 # macOS/Linux: static link ONNX Runtime (ort-download-binaries)
 # Windows: load onnxruntime DLL at runtime (ort-load-dynamic, avoids CRT /MT vs /MD mismatch)