fix(qwen): isolate and refresh indexes with code-search prompts

scripts/test-retrieval-server.py

@@ -1,6 +1,13 @@
 import importlib.util
+from array import array
+from concurrent.futures import ThreadPoolExecutor
+import os
 from pathlib import Path
+from tempfile import TemporaryDirectory
+from types import SimpleNamespace
+import time
 import unittest
+from unittest.mock import patch
 
 
 ROOT = Path(__file__).resolve().parent.parent
@@ -10,6 +17,112 @@ SPEC.loader.exec_module(MODULE)
 
 
 class RetrievalServerTests(unittest.TestCase):
+    @unittest.skipUnless(os.environ.get('QWEN_TEST_MODEL_CACHE'), 'Set QWEN_TEST_MODEL_CACHE to run the real-model check.')
+    def test_real_models(self):
+        os.environ['HF_HOME'] = os.environ['QWEN_TEST_MODEL_CACHE']
+        with TemporaryDirectory() as temporary:
+            root = Path(temporary)
+            (root / 'authentication.md').write_text('Password reset tokens are created by create_reset_token. The reset_password endpoint sends the user a password reset email.', encoding='utf-8')
+            (root / 'weather.md').write_text('The weather service fetches temperature and rainfall forecasts for a city.', encoding='utf-8')
+            index = MODULE.Index(root, root / 'data')
+            try:
+                for query, expected in [('Where are password reset tokens created?', 'authentication.md'),
+                                        ('Which service provides rainfall forecasts?', 'weather.md')]:
+                    started = time.monotonic()
+                    results = index.search(query, 2)
+                    self.assertEqual(results[0]['path'], expected)
+                    self.assertEqual(len(results), 2)
+                    self.assertGreater(results[0]['score'], results[1]['score'])
+                    print(f'Qwen real search: {expected}, {time.monotonic() - started:.2f}s, embedding device {index.encoder().device}, reranker device {index.reranker().device}', flush=True)
+            finally:
+                index.db.close()
+
+    def test_isolation_freshness_and_incremental_encoding(self):
+        with TemporaryDirectory() as temporary:
+            root = Path(temporary)
+            first, second = root / 'first', root / 'second'
+            first.mkdir()
+            second.mkdir()
+            file = first / 'a.md'
+            file.write_text('alpha', encoding='utf-8')
+            (second / 'a.md').write_text('other', encoding='utf-8')
+            indexes = [MODULE.Index(repository, root / 'data') for repository in (first, second)]
+            encoded = []
+
+            def encode(values, **kwargs):
+                self.assertEqual(kwargs.get('prompt_name'), 'query' if values == ['query'] else None)
+                encoded.extend(values)
+                return [array('f', [1.0]) for _ in values]
+
+            for index in indexes:
+                index.encoder = lambda: SimpleNamespace(encode=encode)
+                index.rerank = lambda query, rows, limit: rows[:limit]
+            numpy = SimpleNamespace(dot=lambda a, b: sum(x * y for x, y in zip(a, b)),
+                                    frombuffer=lambda blob, **kwargs: array('f', blob), float32=None)
+            try:
+                with patch.dict('sys.modules', numpy=numpy):
+                    self.assertEqual(indexes[0].search('query', 5)[0]['text'], 'alpha')
+                    self.assertEqual(indexes[1].search('query', 5)[0]['text'], 'other')
+                    encoded.clear()
+                    with ThreadPoolExecutor(max_workers=1) as executor:
+                        self.assertEqual(executor.submit(indexes[0].search, 'query', 5).result()[0]['text'], 'alpha')
+                    self.assertEqual(encoded, ['query'])
+                    timestamp = file.stat()
+                    file.write_text('bravo', encoding='utf-8')
+                    os.utime(file, ns=(timestamp.st_atime_ns, timestamp.st_mtime_ns))
+                    self.assertEqual(indexes[0].search('query', 5)[0]['text'], 'bravo')
+                    file.rename(first / 'renamed.md')
+                    self.assertEqual(indexes[0].search('query', 5)[0]['path'], 'renamed.md')
+                    (first / 'renamed.md').unlink()
+                    encoded.clear()
+                    self.assertEqual(indexes[0].search('query', 5), [])
+                    self.assertEqual(indexes[0].search('query', 5), [])
+                    self.assertEqual(encoded, [])
+                    self.assertEqual(indexes[1].search('query', 5)[0]['text'], 'other')
+            finally:
+                for index in indexes:
+                    index.db.close()
+
+    def test_failed_refresh_preserves_previous_index(self):
+        with TemporaryDirectory() as temporary:
+            root = Path(temporary)
+            (root / 'a.md').write_text('before', encoding='utf-8')
+            index = MODULE.Index(root, root / 'data')
+            index.encoder = lambda: SimpleNamespace(encode=lambda values, **kwargs: [array('f', [1.0]) for _ in values])
+            try:
+                index.rebuild()
+                (root / 'a.md').write_text('after', encoding='utf-8')
+                (root / 'b.md').write_text('fail', encoding='utf-8')
+
+                def encode(values, **kwargs):
+                    if values == ['fail']:
+                        raise RuntimeError('encoder failed')
+                    return [array('f', [1.0]) for _ in values]
+
+                index.encoder = lambda: SimpleNamespace(encode=encode)
+                with self.assertRaisesRegex(RuntimeError, 'encoder failed'):
+                    index.rebuild()
+                self.assertEqual(index.db.execute('select path, text from chunks').fetchall(), [('a.md', 'before')])
+            finally:
+                index.db.close()
+
+    def test_scan_prunes_ignored_directories_and_stops_at_limit(self):
+        with TemporaryDirectory() as temporary:
+            root = Path(temporary)
+            index = MODULE.Index(root, root / 'data')
+            (root / 'visible.md').write_text('visible', encoding='utf-8')
+            for name in ('node_modules', '.git', 'generated', 'data'):
+                directory = root / name
+                directory.mkdir(exist_ok=True)
+                (directory / 'hidden.md').write_text('hidden', encoding='utf-8')
+            try:
+                self.assertEqual(index.files(), [(root / 'visible.md').resolve()])
+                filenames = ['visible.md'] * 2001
+                with patch.object(MODULE.os, 'walk', return_value=iter([(str(root), [], filenames)])):
+                    self.assertEqual(len(index.files()), 2000)
+            finally:
+                index.db.close()
+
     def test_rerank_uses_cross_encoder_scores(self):
         index = MODULE.Index.__new__(MODULE.Index)
 

shared/retrieval/server.py

@@ -4,19 +4,22 @@
 from __future__ import annotations
 
 import argparse
+import hashlib
+import heapq
 import json
 import os
 from pathlib import Path
 import sqlite3
+import threading
 
 
 MODEL = "Qwen/Qwen3-Embedding-0.6B"
 RERANKER_MODEL = "Qwen/Qwen3-Reranker-0.6B"
+RETRIEVAL_INSTRUCTION = "Given a codebase question, retrieve relevant code and documentation that answer the question."
 TEXT_EXTENSIONS = {".c", ".cpp", ".cs", ".go", ".java", ".js", ".json", ".md", ".py", ".ps1", ".rs", ".sh", ".toml", ".ts", ".tsx", ".txt", ".yaml", ".yml"}
 
 
-def chunks(path: Path):
+def chunks(text: str):
-    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:
@@ -28,33 +31,44 @@ class Index:
         self.root = root.resolve()
         self.data = data.resolve()
         self.data.mkdir(parents=True, exist_ok=True)
-        self.db = sqlite3.connect(self.data / "index.sqlite3")
+        identity = os.path.normcase(str(self.root)) + "\n" + MODEL
+        database = hashlib.sha256(identity.encode()).hexdigest() + ".sqlite3"
+        self.db = sqlite3.connect(self.data / database, check_same_thread=False, timeout=30)
         self.db.execute("create table if not exists chunks (path text, text text, vector blob)")
+        self.db.execute("create index if not exists chunks_path on chunks (path)")
+        self.db.execute("create table if not exists files (path text primary key, digest text)")
         self.db.commit()
+        self.lock = threading.RLock()
         self.model = None
         self.reranker_model = None
 
     def files(self):
         ignored = {".git", ".venv", "generated", "node_modules", ".my-ai-configuration"}
         result = []
-        for path in self.root.rglob("*"):
+        for directory, directories, filenames in os.walk(self.root):
-            if len(result) >= 2000 or not path.is_file() or path.suffix.lower() not in TEXT_EXTENSIONS or path.stat().st_size > 524288:
+            directories[:] = sorted(name for name in directories if name not in ignored
-                continue
+                                    and not (Path(directory) / name).is_symlink()
-            if ignored.intersection(path.relative_to(self.root).parts):
+                                    and (Path(directory) / name).resolve().is_relative_to(self.root)
+                                    and (Path(directory) / name).resolve() != self.data)
+            for name in sorted(filenames):
+                path = Path(directory) / name
+                if path.is_symlink() or path.suffix.lower() not in TEXT_EXTENSIONS or not path.is_file() or path.stat().st_size > 524288:
                     continue
                 result.append(path)
+                if len(result) == 2000:
+                    return result
         return result
 
     def encoder(self):
         if self.model is None:
             from sentence_transformers import SentenceTransformer
-            self.model = SentenceTransformer(MODEL)
+            self.model = SentenceTransformer(MODEL, prompts={"query": f"Instruct: {RETRIEVAL_INSTRUCTION}\nQuery: "})
         return self.model
 
     def reranker(self):
         if self.reranker_model is None:
             from sentence_transformers import CrossEncoder
-            self.reranker_model = CrossEncoder(RERANKER_MODEL)
+            self.reranker_model = CrossEncoder(RERANKER_MODEL, prompts={"query": RETRIEVAL_INSTRUCTION}, default_prompt_name="query")
         return self.reranker_model
 
     def rerank(self, query: str, rows: list[dict], limit: int):
@@ -65,31 +79,43 @@ class Index:
         return sorted(candidates, key=lambda item: item["score"], reverse=True)[:limit]
 
     def rebuild(self):
-        encoder = self.encoder()
+        with self.lock:
-        values = []
+            self.refresh(force=True)
+            return self.db.execute("select count(*) from chunks").fetchone()[0]
+
+    def refresh(self, force=False):
+        previous = dict(self.db.execute("select path, digest from files"))
+        with self.db:
             for path in self.files():
-            for value in chunks(path):
+                relative = path.relative_to(self.root).as_posix()
-                values.append((str(path.relative_to(self.root)), value))
+                content = path.read_bytes()
-        self.db.execute("delete from chunks")
+                digest = hashlib.sha256(content).hexdigest()
-        if values:
+                old_digest = previous.pop(relative, None)
-            vectors = encoder.encode([value for _, value in values], normalize_embeddings=True)
+                if not force and digest == old_digest:
-            self.db.executemany("insert into chunks values (?, ?, ?)", ((path, value, vector.tobytes()) for (path, value), vector in zip(values, vectors)))
+                    continue
-        self.db.commit()
+                values = list(chunks(content.decode("utf-8", errors="ignore")))
-        return len(values)
+                vectors = self.encoder().encode(values, normalize_embeddings=True) if values else []
+                self.db.execute("delete from chunks where path = ?", (relative,))
+                self.db.executemany("insert into chunks values (?, ?, ?)",
+                                    ((relative, value, vector.tobytes()) for value, vector in zip(values, vectors)))
+                self.db.execute("insert or replace into files values (?, ?)", (relative, digest))
+            for relative in previous:
+                self.db.execute("delete from chunks where path = ?", (relative,))
+                self.db.execute("delete from files where path = ?", (relative,))
 
     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:
+        with self.lock:
-            self.rebuild()
+            self.refresh()
-        vector = self.encoder().encode([query], normalize_embeddings=True)[0]
+            if not self.db.execute("select 1 from chunks limit 1").fetchone():
-        rows = []
+                return []
-        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)))
+            vector = self.encoder().encode([query], prompt_name="query", normalize_embeddings=True)[0]
-            rows.append({"path": path, "score": round(score, 6), "text": text})
+            rows = ({"path": path, "score": float(np.dot(vector, np.frombuffer(blob, dtype=np.float32))), "text": text}
+                    for path, text, blob in self.db.execute("select path, text, vector from chunks"))
             limit = max(1, min(top_k, 20))
-        return self.rerank(query, sorted(rows, key=lambda item: item["score"], reverse=True), limit)
+            return self.rerank(query, heapq.nlargest(20, rows, key=lambda item: item["score"]), limit)
 
 
 def main():
@@ -107,7 +133,7 @@ def main():
 
     @mcp.tool()
     def semantic_search(query: str, top_k: int = 5) -> list[dict]:
-        """Find repository passages with Qwen3 embedding and reranking."""
+        """Search repository concepts, behavior, and implementation patterns first with Qwen3 embedding and reranking. Verify returned passages against current files; use direct reads for known paths."""
         return index.search(query, top_k)
 
     @mcp.tool()