feat: assistant IA — approbation groupee des actions, bloc d'etapes, refresh UI et bouton Stop (BUG-074, BUG-075, BUG-076, BUG-077)

tests/test_bookslm.py

@@ -1065,6 +1065,81 @@ class TestBooksLMAgentEndpoint:
         assert "C'est fait." in resp2.text
         assert "event: message" in resp2.text
 
+    def test_agent_confirmation_batches_actions_and_confirm_all(self, bookslm_client, monkeypatch):
+        """BUG-075: one confirmation lists every mutating call of the turn and
+        ``confirm_all`` authorizes the rest of the run without pausing again."""
+        import re
+
+        import backend.bookslm_routes as routes
+        from backend.ai_chat import LLMResponse, ToolCall
+        from backend.tools import registry
+        from backend.tools.api import ToolRisk
+        from backend.tools.registry import ToolSpec
+        from backend.tools.schemas import ListVaultsInput
+
+        calls = []
+
+        def handler(ctx, params):
+            calls.append(params)
+            return {"ok": True}
+
+        spec = ToolSpec(
+            name="_agent_write_batch",
+            description="write for tests",
+            input_model=ListVaultsInput,
+            handler=handler,
+            risk=ToolRisk.WRITE,
+        )
+        monkeypatch.setitem(registry._REGISTRY, "_agent_write_batch", spec)
+
+        responses = [LLMResponse(tool_calls=[
+            ToolCall(id="c1", name="_agent_write_batch", arguments={}),
+            ToolCall(id="c2", name="_agent_write_batch", arguments={}),
+        ])]
+
+        async def fake_chat_completion(messages, **kwargs):
+            return responses.pop(0)
+
+        monkeypatch.setattr(routes, "chat_completion", fake_chat_completion)
+        monkeypatch.setattr(routes, "_resolve_provider_name", lambda requested: "deepseek")
+
+        token, _ = _login_bookslm(bookslm_client)
+        payload = {"vault": "TestVault", "directory": "", "message": "crée tout", "mode": "directory"}
+        resp = bookslm_client.post(
+            "/api/ai/bookslm/agent",
+            json=payload,
+            headers={"Authorization": f"Bearer {token}"},
+        )
+        assert "event: confirmation" in resp.text
+        match = re.search(r"event: confirmation\ndata: (.*)", resp.text)
+        assert match, resp.text
+        confirmation = json.loads(match.group(1))
+        # Both mutating calls are pending, not one.
+        assert [a["tool"] for a in confirmation["pending"]["actions"]] == [
+            "_agent_write_batch", "_agent_write_batch",
+        ]
+        assert calls == [], "nothing runs before the approval"
+
+        # Resume with a global approval: the two pending calls run, and a third
+        # mutating call of the same run runs without a second confirmation.
+        responses.append(LLMResponse(tool_calls=[ToolCall(id="c3", name="_agent_write_batch", arguments={})]))
+        responses.append(LLMResponse(content="Terminé."))
+        resume_payload = dict(
+            payload,
+            confirm=confirmation["pending"],
+            confirm_messages=confirmation["messages"],
+            confirm_all=True,
+        )
+        resp2 = bookslm_client.post(
+            "/api/ai/bookslm/agent",
+            json=resume_payload,
+            headers={"Authorization": f"Bearer {token}"},
+        )
+        assert resp2.status_code == 200
+        assert "event: confirmation" not in resp2.text, "confirm_all must not pause again"
+        assert len(calls) == 3, "the whole plan ran in one approval"
+        assert "Terminé." in resp2.text
+
     def test_agent_message_reports_effective_model(self, bookslm_client, monkeypatch):
         """The SSE payload carries the model really used, not the raw request.