Implement SQLite support check for Chroma's trigram tokenizer, enhancing compatibility with cloud environments. Update README for model command syntax and add pysqlite3-binary to requirements for improved SQLite functionality.

tests/test_memory_embedding_policy.py

@@ -0,0 +1,68 @@
+import asyncio
+from pathlib import Path
+
+from src.ai.memory import MemorySystem
+
+
+def test_query_does_not_use_embedding_when_disabled(tmp_path: Path):
+    calls = {"count": 0}
+
+    async def fake_embed(_text: str):
+        calls["count"] += 1
+        return [0.1] * 8
+
+    memory = MemorySystem(
+        storage_path=tmp_path / "memory.json",
+        embed_func=fake_embed,
+        use_vector_db=False,
+        use_query_embedding=False,
+    )
+
+    # 入库路径仍会触发 embedding
+    stored = asyncio.run(
+        memory.add_long_term(
+            user_id="u1",
+            content="我喜欢苹果和香蕉",
+            importance=0.9,
+            metadata={"source": "test"},
+        )
+    )
+    assert stored is not None
+    assert calls["count"] == 1
+
+    # 查询上下文与搜索均不触发 embedding
+    asyncio.run(memory.get_context(user_id="u1", query="苹果"))
+    asyncio.run(memory.search_long_term(user_id="u1", query="苹果", limit=5))
+    assert calls["count"] == 1
+
+    asyncio.run(memory.close())
+
+
+def test_query_uses_embedding_when_enabled(tmp_path: Path):
+    calls = {"count": 0}
+
+    async def fake_embed(_text: str):
+        calls["count"] += 1
+        return [0.1] * 8
+
+    memory = MemorySystem(
+        storage_path=tmp_path / "memory.json",
+        embed_func=fake_embed,
+        use_vector_db=False,
+        use_query_embedding=True,
+    )
+
+    asyncio.run(
+        memory.add_long_term(
+            user_id="u1",
+            content="北京天气不错",
+            importance=0.9,
+            metadata={"source": "test"},
+        )
+    )
+    assert calls["count"] == 1
+
+    asyncio.run(memory.get_context(user_id="u1", query="北京"))
+    assert calls["count"] >= 2
+
+    asyncio.run(memory.close())