feat(plugins): consolidate official plugins with independent releases (LANG-180) (#2797)

Co-authored-by: multica-agent <[email protected]>
2026-11-04 06:42:10 +08:00 · 2026-10-28 17:32:24 +08:00
parent d099033d79
commit 3a77306c3c
1144 changed files with 142049 additions and 130 deletions
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+import pytest
+
+from components.knowledge_engine.memory_engine import LongTermMemoryEngine
+from langbot_plugin.api.entities.builtin.rag.context import RetrievalContext
+from store.memory_store import MemoryStore
+
+
+class FakePlugin:
+    def __init__(self, *, fail_hybrid: bool = False):
+        self.memory_store = MemoryStore(self)
+        self.fail_hybrid = fail_hybrid
+        self.search_calls: list[dict] = []
+        self.records = [
+            {
+                "id": "active-1",
+                "metadata": {
+                    "content": "Active memory",
+                    "importance": "2",
+                    "timestamp": "2026-01-01T00:00:00Z",
+                    "user_key": "bot-1:group_1",
+                    "status": "active",
+                },
+                "distance": 0.1,
+            },
+            {
+                "id": "superseded-1",
+                "metadata": {
+                    "content": "Superseded memory",
+                    "importance": "5",
+                    "timestamp": "2026-01-02T00:00:00Z",
+                    "user_key": "bot-1:group_1",
+                    "status": "superseded",
+                },
+                "distance": 0.0,
+            },
+        ]
+
+    async def invoke_embedding(self, _embedding_model_uuid: str, texts: list[str]) -> list[list[float]]:
+        return [[1.0] for _ in texts]
+
+    async def vector_search(self, collection_id, query_vector, top_k=5, filters=None, **_kwargs):
+        self.search_calls.append(_kwargs)
+        if _kwargs.get("search_type") == "hybrid" and self.fail_hybrid:
+            raise RuntimeError("hybrid unavailable")
+        return self.records[:top_k]
+
+
+@pytest.mark.asyncio
+async def test_engine_retrieve_excludes_non_active_episodes_by_default():
+    engine = LongTermMemoryEngine()
+    engine.plugin = FakePlugin()
+
+    response = await engine.retrieve(
+        RetrievalContext(
+            query="memory",
+            knowledge_base_id="kb-1",
+            creation_settings={"embedding_model_uuid": "emb-1", "isolation": "session"},
+            retrieval_settings={
+                "top_k": 5,
+                "session_name": "group_1",
+                "bot_uuid": "bot-1",
+            },
+        )
+    )
+
+    assert [entry.id for entry in response.results] == ["active-1"]
+    assert "Active memory" in (response.results[0].content[0].text or "")
+
+
+@pytest.mark.asyncio
+async def test_engine_retrieve_uses_hybrid_and_falls_back_to_vector():
+    engine = LongTermMemoryEngine()
+    plugin = FakePlugin(fail_hybrid=True)
+    engine.plugin = plugin
+
+    response = await engine.retrieve(
+        RetrievalContext(
+            query="memory",
+            knowledge_base_id="kb-1",
+            creation_settings={
+                "embedding_model_uuid": "emb-1",
+                "isolation": "session",
+                "retrieval_strategy": "auto",
+                "vector_weight": 0.6,
+            },
+            retrieval_settings={
+                "top_k": 5,
+                "session_name": "group_1",
+                "bot_uuid": "bot-1",
+            },
+        )
+    )
+
+    assert [entry.id for entry in response.results] == ["active-1"]
+    assert plugin.search_calls[0] == {
+        "search_type": "hybrid",
+        "query_text": "memory",
+        "vector_weight": 0.6,
+    }
+    assert plugin.search_calls[1] == {}