feat(retrieval): add optional Qwen semantic layer

shared/retrieval/requirements.txt

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+mcp>=1.9,<2
+sentence-transformers>=4.1,<5

shared/retrieval/server.py

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+#!/usr/bin/env python3
+"""Local MCP semantic search backed by Qwen3-Embedding-0.6B."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from pathlib import Path
+import sqlite3
+
+
+MODEL = "Qwen/Qwen3-Embedding-0.6B"
+TEXT_EXTENSIONS = {".c", ".cpp", ".cs", ".go", ".java", ".js", ".json", ".md", ".py", ".ps1", ".rs", ".sh", ".toml", ".ts", ".tsx", ".txt", ".yaml", ".yml"}
+
+
+def chunks(path: Path):
+    text = path.read_text(encoding="utf-8", errors="ignore")
+    for start in range(0, len(text), 1050):
+        value = text[start:start + 1200].strip()
+        if value:
+            yield value
+
+
+class Index:
+    def __init__(self, root: Path, data: Path):
+        self.root = root.resolve()
+        self.data = data.resolve()
+        self.data.mkdir(parents=True, exist_ok=True)
+        self.db = sqlite3.connect(self.data / "index.sqlite3")
+        self.db.execute("create table if not exists chunks (path text, text text, vector blob)")
+        self.db.commit()
+        self.model = None
+
+    def files(self):
+        ignored = {".git", ".venv", "generated", "node_modules", ".my-ai-configuration"}
+        result = []
+        for path in self.root.rglob("*"):
+            if len(result) >= 2000 or not path.is_file() or path.suffix.lower() not in TEXT_EXTENSIONS or path.stat().st_size > 524288:
+                continue
+            if ignored.intersection(path.relative_to(self.root).parts):
+                continue
+            result.append(path)
+        return result
+
+    def encoder(self):
+        if self.model is None:
+            from sentence_transformers import SentenceTransformer
+            self.model = SentenceTransformer(MODEL)
+        return self.model
+
+    def rebuild(self):
+        encoder = self.encoder()
+        values = []
+        for path in self.files():
+            for value in chunks(path):
+                values.append((str(path.relative_to(self.root)), value))
+        self.db.execute("delete from chunks")
+        if values:
+            vectors = encoder.encode([value for _, value in values], normalize_embeddings=True)
+            self.db.executemany("insert into chunks values (?, ?, ?)", ((path, value, vector.tobytes()) for (path, value), vector in zip(values, vectors)))
+        self.db.commit()
+        return len(values)
+
+    def search(self, query: str, top_k: int):
+        if not query.strip():
+            raise ValueError("query must not be empty")
+        if self.db.execute("select count(*) from chunks").fetchone()[0] == 0:
+            self.rebuild()
+        vector = self.encoder().encode([query], normalize_embeddings=True)[0]
+        rows = []
+        for path, text, blob in self.db.execute("select path, text, vector from chunks"):
+            import numpy as np
+            score = float(np.dot(vector, np.frombuffer(blob, dtype=np.float32)))
+            rows.append({"path": path, "score": round(score, 6), "text": text})
+        return sorted(rows, key=lambda item: item["score"], reverse=True)[:max(1, min(top_k, 20))]
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--root", default=os.getcwd(), type=Path)
+    parser.add_argument("--data-dir", required=True, type=Path)
+    parser.add_argument("--model-cache", required=True, type=Path)
+    args = parser.parse_args()
+    args.model_cache.mkdir(parents=True, exist_ok=True)
+    os.environ.setdefault("HF_HOME", str(args.model_cache.resolve()))
+    from mcp.server.fastmcp import FastMCP
+
+    index = Index(args.root, args.data_dir)
+    mcp = FastMCP("my-ai-qwen3-retrieval")
+
+    @mcp.tool()
+    def semantic_search(query: str, top_k: int = 5) -> list[dict]:
+        """Find repository passages by meaning with Qwen3-Embedding-0.6B."""
+        return index.search(query, top_k)
+
+    @mcp.tool()
+    def rebuild_index() -> str:
+        """Rebuild the local repository embedding index."""
+        return json.dumps({"chunks": index.rebuild()})
+
+    mcp.run()
+
+
+if __name__ == "__main__":
+    main()