Bind the log context for tool continuations

gen_continuation is not wrapped by @log_activity, so the llm_* log lines
from a resumed turn carried no user, agent, conversation or endpoint. Every
OpenAI-compatible /v1 client-tool round landed anonymous in the logs.

Add agent_log_context, which binds the same keys log_activity binds while
keeping any enclosing activity_id, and wrap gen_continuation in it. Both
now derive the keys from one helper.

tests/agents/test_continuation_log_context.py

@@ -0,0 +1,97 @@
+"""A tool continuation stamps its log lines like the turn it resumes."""
+
+from unittest.mock import Mock
+
+import pytest
+
+from docsgpt.core import log_context
+
+
+def _agent(seen):
+    from docsgpt.agents.classic_agent import ClassicAgent
+
+    def _model_call(*_args, **_kwargs):
+        seen.append(dict(log_context.snapshot()))
+        return iter(["Answer"])
+
+    llm = Mock()
+    llm._supports_tools = True
+    llm._supports_structured_output = Mock(return_value=False)
+    llm.__class__.__name__ = "MockLLM"
+    llm.gen_stream = Mock(side_effect=_model_call)
+    llm.gen = Mock(side_effect=_model_call)
+
+    handler = Mock()
+    handler.process_message_flow = Mock(return_value=iter([]))
+    handler.create_tool_message = Mock(return_value={"role": "tool", "tool_call_id": "c1", "content": "r"})
+
+    executor = Mock()
+    executor.tool_calls = []
+    executor.prepare_tools_for_llm = Mock(return_value=[])
+    executor.get_truncated_tool_calls = Mock(return_value=[])
+
+    def _execute(_tools, _call, _llm_class):
+        yield {"type": "tool_call", "data": {"status": "pending"}}
+        return ("result", "c1")
+
+    executor.execute = Mock(side_effect=_execute)
+    return ClassicAgent(
+        endpoint="stream",
+        llm_name="openai",
+        model_id="gpt-4",
+        api_key="test",
+        agent_id="aaaaaaaa-aaaa-aaaa-aaaa-aaaaaaaaaaaa",
+        decoded_token={"sub": "user-cont"},
+        llm=llm,
+        llm_handler=handler,
+        tool_executor=executor,
+    )
+
+
+def _resume(agent):
+    pending = [
+        {
+            "call_id": "c1",
+            "name": "search_0",
+            "tool_name": "search",
+            "tool_id": "0",
+            "action_name": "search",
+            "arguments": {"q": "x"},
+            "pause_type": "requires_client_execution",
+            "thought_signature": None,
+        }
+    ]
+    actions = [{"call_id": "c1", "decision": "approved"}]
+    return list(agent.gen_continuation([{"role": "system", "content": "s"}], {"0": {"name": "search"}}, pending, actions))
+
+
+@pytest.mark.unit
+def test_model_calls_in_a_continuation_carry_the_turns_identity():
+    seen = []
+    _resume(_agent(seen))
+
+    assert seen, "the continuation should hand back to the model"
+    assert seen[0]["user_id"] == "user-cont"
+    assert seen[0]["agent_id"] == "aaaaaaaa-aaaa-aaaa-aaaa-aaaaaaaaaaaa"
+    assert seen[0]["endpoint"] == "stream"
+    assert "activity_id" not in seen[0], "a continuation is not an activity of its own"
+
+
+@pytest.mark.unit
+def test_the_binding_does_not_outlive_the_continuation():
+    _resume(_agent([]))
+
+    assert "user_id" not in log_context.snapshot()
+
+
+@pytest.mark.unit
+def test_an_enclosing_activity_keeps_its_id():
+    seen = []
+    token = log_context.bind(activity_id="act-1")
+    try:
+        _resume(_agent(seen))
+    finally:
+        log_context.reset(token)
+
+    assert seen[0]["activity_id"] == "act-1"
+    assert seen[0]["user_id"] == "user-cont"