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DocsGPT/tests/worker/test_agent_workers.py
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Alex b4c4ab68f0 feat: durability and idempotency keys (#2450)
* feat: durability and idempotency keys

* feat: more durable frontend

* fix: tests

* fix: mini issues

* fix: better json validation

* fix: tests
2026-05-04 23:25:41 +01:00

176 lines
6.1 KiB
Python

"""Smoke tests for ``agent_webhook_worker`` and ``run_agent_logic``.
Neither task writes to Postgres directly — they only *read* the agent
row (and, in ``run_agent_logic``, the referenced source row). The
concrete PG side-effect we assert is therefore a read: the task has
to resolve the row from the ephemeral DB to proceed at all; if the
lookup returned ``None`` the task would short-circuit with a
"not found" error.
The LLM, retriever, and agent factory are all stubbed — we only care
that the PG read path wires up correctly.
"""
from __future__ import annotations
from unittest.mock import MagicMock
import pytest
from application.storage.db.repositories.agents import AgentsRepository
@pytest.mark.unit
class TestAgentWebhookWorker:
def test_resolves_agent_by_uuid_and_runs_logic(
self, pg_conn, patch_worker_db, task_self, monkeypatch
):
from application import worker
agent = AgentsRepository(pg_conn).create(
user_id="alice",
name="hook-agent",
status="active",
agent_type="classic",
retriever="classic",
chunks=2,
key="sk-test-123",
)
agent_id = str(agent["id"])
# Capture the resolved agent_config + input; return a fake result.
captured: dict = {}
def _fake_run_agent_logic(agent_config, input_data):
captured["config"] = agent_config
captured["input"] = input_data
return {"answer": "ok", "sources": [], "tool_calls": [], "thought": ""}
monkeypatch.setattr(worker, "run_agent_logic", _fake_run_agent_logic)
result = worker.agent_webhook_worker(
task_self, agent_id, {"event": "ping"}
)
assert result == {
"status": "success",
"result": {"answer": "ok", "sources": [], "tool_calls": [], "thought": ""},
}
# The row pulled from PG is the one we seeded.
assert captured["config"]["name"] == "hook-agent"
assert str(captured["config"]["id"]) == agent_id
assert captured["input"] == '{"event": "ping"}'
def test_missing_agent_raises(
self, pg_conn, patch_worker_db, task_self, monkeypatch
):
from application import worker
monkeypatch.setattr(worker, "run_agent_logic", lambda *a, **k: {})
with pytest.raises(ValueError, match="not found"):
worker.agent_webhook_worker(task_self, "no-such-agent", {})
def test_run_agent_logic_failure_propagates(
self, pg_conn, patch_worker_db, task_self, monkeypatch
):
from application import worker
from application.storage.db.repositories.agents import AgentsRepository
agent = AgentsRepository(pg_conn).create(
user_id="alice", name="hook-agent", status="active",
agent_type="classic", retriever="classic", chunks=2, key="sk-test-123",
)
agent_id = str(agent["id"])
def _boom(*a, **k):
raise RuntimeError("LLM exploded")
monkeypatch.setattr(worker, "run_agent_logic", _boom)
with pytest.raises(RuntimeError, match="LLM exploded"):
worker.agent_webhook_worker(task_self, agent_id, {"event": "ping"})
@pytest.mark.unit
class TestRunAgentLogic:
def test_reads_source_row_from_pg(
self, pg_conn, patch_worker_db, monkeypatch
):
"""``run_agent_logic`` looks up the agent's ``source_id`` in PG to
pick up the source's ``retriever`` override. Proving the read
wires up end-to-end is enough for a smoke test."""
from application import worker
from application.storage.db.repositories.sources import SourcesRepository
src = SourcesRepository(pg_conn).create(
"src",
user_id="alice",
type="local",
retriever="hybrid",
)
source_id = str(src["id"])
# Silence model/provider resolution so we don't need a real key.
monkeypatch.setattr(
"application.core.model_utils.get_default_model_id", lambda: "gpt-4"
)
monkeypatch.setattr(
"application.core.model_utils.validate_model_id", lambda m, **_kwargs: True
)
monkeypatch.setattr(
"application.core.model_utils.get_provider_from_model_id",
lambda m, **_kwargs: "openai",
)
monkeypatch.setattr(
"application.core.model_utils.get_api_key_for_provider",
lambda p: "sk-test",
)
monkeypatch.setattr(
"application.utils.calculate_doc_token_budget",
lambda model_id=None, **_kwargs: 1000,
)
monkeypatch.setattr(
"application.api.answer.services.stream_processor.get_prompt",
lambda prompt_id: "prompt text",
)
# Retriever search returns no docs; agent gen yields a single answer
# line so the aggregation loop runs through.
captured_source: dict = {}
class _FakeRetriever:
def __init__(self, *args, **kwargs):
captured_source.update(kwargs.get("source", {}))
def search(self, query):
return []
monkeypatch.setattr(
"application.retriever.retriever_creator.RetrieverCreator.create_retriever",
lambda *a, **kw: _FakeRetriever(**kw),
)
fake_agent = MagicMock(name="agent")
fake_agent.gen.return_value = iter([{"answer": "done"}])
monkeypatch.setattr(
"application.agents.agent_creator.AgentCreator.create_agent",
lambda *a, **kw: fake_agent,
)
agent_config = {
"id": "agent-uuid",
"source_id": source_id,
"user_id": "alice",
"key": "sk-user",
"agent_type": "classic",
"chunks": 2,
"prompt_id": "default",
}
result = worker.run_agent_logic(agent_config, "hello")
assert result["answer"] == "done"
# Proves the PG read hit the seeded source and its id flowed into
# the retriever's ``source={"active_docs": ...}`` param.
assert captured_source.get("active_docs") == source_id