mirror of
https://github.com/bytedance/deer-flow.git
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3182ecb1e4
* feat(subagents): preserve RAG evidence in durable batch exports * docs(subagents): clarify evidence omissions and migration head * docs(persistence): update the migration chain head * test(persistence): track the new batch evidence migration head * test(persistence): verify batch evidence migration on PostgreSQL * docs(subagents): retain evidence migration compatibility guidance * fix(subagents): propagate cited evidence forwarding omissions * fix(tests): adapt batch evidence PostgreSQL CI connection options * fix(tests): preserve TLS policy in batch evidence migration fixture
450 lines
22 KiB
Python
450 lines
22 KiB
Python
"""RAG producer -> durable worker -> reopened SQL -> owner JSONL evidence."""
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import asyncio
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import importlib.util
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import json
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import os
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import sys
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import uuid
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from copy import deepcopy
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from datetime import UTC, datetime, timedelta
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from enum import Enum
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from pathlib import Path
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from types import SimpleNamespace
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from unittest.mock import AsyncMock
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import pytest
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import pytest_asyncio
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from langchain_core.messages import ToolMessage
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from support.postgres import asyncpg_test_url
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from deerflow.community.ragflow.formatting import format_retrieval_sources
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from deerflow.config.database_config import DatabaseConfig
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from deerflow.config.subagent_batches_config import SubagentBatchesConfig
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from deerflow.config.subagent_runtime_config import SubagentRuntimeConfig
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from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
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from deerflow.persistence.subagent_batches import SubagentBatchRepository
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from deerflow.subagents import batch_service
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from deerflow.subagents.batch_runtime import BatchSubmitRequest
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from deerflow.subagents.step_events import capture_step_message
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class Status(Enum):
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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@property
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def is_terminal(self):
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return self is not Status.RUNNING
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def retrieval(*, text="Original evidence", name="knowledge_search"):
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content, artifact = format_retrieval_sources(
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{"chunks": [{"id": "chunk", "dataset_id": "kb", "document_id": "doc", "document_keyword": "Manual.pdf", "content": text}]},
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dataset_names_by_id={"kb": "Engineering"},
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max_chars_per_chunk=20_000,
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max_total_chars=30_000,
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)
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captured = []
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capture_step_message(ToolMessage(content=content, artifact=artifact, name=name, tool_call_id="search", id="message"), captured, set())
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return content, captured
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@pytest_asyncio.fixture(params=["sqlite", "postgres"])
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async def env(monkeypatch, tmp_path, request):
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schema = None
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if request.param == "postgres":
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uri = os.environ.get("TEST_POSTGRES_URI")
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if not uri:
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pytest.skip("requires TEST_POSTGRES_URI (real Postgres batch evidence chain)")
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schema = f"batch_evidence_{uuid.uuid4().hex}"
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db = DatabaseConfig(backend="postgres", postgres_url=asyncpg_test_url(uri), postgres_schema=schema)
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else:
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db = DatabaseConfig(backend="sqlite", sqlite_dir=str(tmp_path / "db"))
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service = None
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try:
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await init_engine_from_config(db)
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if schema:
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from sqlalchemy import text
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async with get_session_factory()() as session:
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assert (await session.execute(text("SELECT current_schema()"))).scalar_one() == schema
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repo = SubagentBatchRepository(get_session_factory())
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state = SimpleNamespace(repo=repo, db=db, result=SimpleNamespace(status=Status.COMPLETED, result="plain report", ai_messages=[], error=None, stop_reason=None, token_usage_records=[]))
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class Executor:
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def __init__(self, **kwargs):
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state.owner = kwargs["user_id"]
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state.scope = kwargs["knowledge_scope"]
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def execute_async(self, prompt, task_id=None):
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return task_id
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monkeypatch.setattr(batch_service, "SubagentExecutor", Executor)
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monkeypatch.setattr(batch_service, "SubagentStatus", Status)
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monkeypatch.setattr(batch_service, "get_background_task_result", lambda _: state.result)
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monkeypatch.setattr(batch_service, "cleanup_background_task", lambda _: None)
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monkeypatch.setattr(batch_service, "resolve_subagent_model_name", lambda *args, **kwargs: "offline-model")
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monkeypatch.setattr("deerflow.tools.get_available_tools", lambda **kwargs: [])
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state.service = batch_service.SubagentBatchService(repository=repo, config=SubagentBatchesConfig(), runtime_config=SubagentRuntimeConfig(), app_config=SimpleNamespace())
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service = state.service
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yield state
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finally:
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try:
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if service is not None:
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await service.stop()
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finally:
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try:
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await close_engine()
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finally:
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if schema:
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import sqlalchemy as sa
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from sqlalchemy.ext.asyncio import create_async_engine
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engine = create_async_engine(db.app_sqlalchemy_url)
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try:
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async with engine.begin() as conn:
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await conn.execute(sa.text(f'DROP SCHEMA IF EXISTS "{schema}" CASCADE'))
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finally:
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await engine.dispose()
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async def execute(env):
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batch = await env.service.submit(
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BatchSubmitRequest(
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user_id="user-1",
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thread_id="thread-1",
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run_id="run-1",
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tool_call_id="call-1",
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submission_key="submission",
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title="Research",
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subagent_type="general-purpose",
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items=[{"key": "one", "prompt": "Research the selected knowledge base"}],
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max_live_items=None,
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max_running_items=None,
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execution_spec={"subagent_config": {"name": "general-purpose", "description": "Researcher", "system_prompt": "Research"}, "knowledge_scope": {"version": 1, "mode": "selected", "dataset_ids": ["kb"]}},
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)
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)
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await env.service.run_once(now=datetime.now(UTC))
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await asyncio.gather(*list(env.service._executions.values()))
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return batch
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@pytest.mark.asyncio
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@pytest.mark.parametrize("tool_name", ["knowledge_search", "task"])
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async def test_cited_evidence_survives_worker_database_reopen_and_export(env, monkeypatch, tool_name):
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from app.gateway.routers import subagent_batches as router
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env.result.result, env.result.ai_messages = retrieval(name=tool_name)
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source = env.result.ai_messages[0]["artifact"]["knowledge_sources"]["sources"][0]
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expected_source = deepcopy(source)
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batch = await execute(env)
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assert env.owner == "user-1" and env.scope["dataset_ids"] == ["kb"]
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# Actual engine teardown/reopen; neither the worker nor provider can serve evidence.
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await env.service.stop()
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await close_engine()
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await init_engine_from_config(env.db)
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repo = SubagentBatchRepository(get_session_factory())
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source["text"] = "Provider content changed after retrieval"
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env.result.ai_messages = []
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assert "result_artifact" not in (await repo.list_items(batch["id"], user_id="user-1"))[0]
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assert await repo.list_items(batch["id"], user_id="other", include_result=True) is None
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monkeypatch.setattr(router, "get_current_user", AsyncMock(return_value="user-1"))
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request = SimpleNamespace(app=SimpleNamespace(state=SimpleNamespace(subagent_batch_repo=repo, subagent_batches_available=False)))
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response = await router.export_batch_results.__wrapped__(thread_id="thread-1", batch_id=batch["id"], request=request)
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rows = [json.loads(line) async for line in response.body_iterator]
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row = rows[0]
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assert row["result"] == env.result.result
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assert row["result_artifact"]["knowledge_sources"]["sources"] == [expected_source]
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assert "Original evidence" in row["result_artifact"]["knowledge_sources"]["sources"][0]["text"]
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assert "execution_spec" not in row and "prompt" not in row
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@pytest.mark.asyncio
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async def test_real_subagent_graph_captures_artifact_for_durable_export(env, monkeypatch, tmp_path):
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from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
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from langchain_core.messages import AIMessage
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from langchain_core.tools import StructuredTool
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from deerflow.config.app_config import AppConfig
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from deerflow.subagents.config import SubagentConfig
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# conftest stubs executor to break a circular import. Load the production
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# executor, keeping its actual graph, middleware, ToolNode and step capture.
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path = Path(__file__).parents[1] / "packages/harness/deerflow/subagents/executor.py"
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spec = importlib.util.spec_from_file_location("_batch_evidence_executor", path)
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module = importlib.util.module_from_spec(spec)
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monkeypatch.setitem(sys.modules, spec.name, module)
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spec.loader.exec_module(module)
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monkeypatch.setenv("DEER_FLOW_HOME", str(tmp_path / "graph"))
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content, messages = retrieval()
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artifact = messages[0]["artifact"]
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class Model(GenericFakeChatModel):
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def bind_tools(self, tools, **kwargs):
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return self
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model = Model(messages=iter([AIMessage(content="Searching", tool_calls=[{"name": "knowledge_search", "args": {}, "id": "search"}])]))
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monkeypatch.setattr(module, "create_chat_model", lambda **kwargs: model)
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tool = StructuredTool.from_function(lambda: (content, artifact), name="knowledge_search", description="Offline captured evidence", response_format="content_and_artifact", return_direct=True)
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config = AppConfig.model_validate({"models": [{"name": "offline", "use": "langchain_openai:ChatOpenAI", "model": "offline"}], "sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider"}, "summarization": {"enabled": False}})
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executor = module.SubagentExecutor(config=SubagentConfig(name="researcher", description="Research", skills=[]), tools=[tool], parent_model="offline", app_config=config, thread_id="thread-1", user_id="user-1")
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try:
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env.result = await executor._aexecute("Return evidence")
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assert env.result.status is module.SubagentStatus.COMPLETED, env.result.error
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assert env.result.result == content
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captured = [m for m in env.result.ai_messages if m["type"] == "tool"]
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assert captured[0]["artifact"] == artifact
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monkeypatch.setattr(batch_service, "SubagentStatus", module.SubagentStatus)
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batch = await execute(env)
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row = (await env.repo.list_items(batch["id"], user_id="user-1", include_result=True))[0]
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assert row["status"] == "succeeded"
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assert row["result_artifact"]["knowledge_sources"]["sources"] == artifact["knowledge_sources"]["sources"]
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finally:
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module._shutdown_isolated_subagent_loop()
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@pytest.mark.parametrize("change", ["uncited", "unsupported-tool", "future-version", "invalid-id", "invalid-provider", "invalid-text", "invalid-locator", "overlong-name"])
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def test_unrelated_or_invalid_evidence_is_not_persisted(change):
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from deerflow.community.ragflow.sources import durable_source_artifact
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content, messages = retrieval()
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source = messages[0]["artifact"]["knowledge_sources"]["sources"][0]
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if change == "uncited":
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content = "A report with no citations"
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elif change == "unsupported-tool":
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messages[0]["name"] = "arbitrary_tool"
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elif change == "future-version":
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messages[0]["artifact"]["knowledge_sources"]["version"] = 2
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elif change == "invalid-id":
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content = content.replace(source["id"], "invalid")
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source["id"] = "invalid"
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elif change == "invalid-provider":
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source["provider"] = "other"
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elif change == "invalid-text":
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source["text"] = None
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elif change == "invalid-locator":
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source["document_id"] = ""
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else:
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source["document_name"] = "x" * 513
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result = durable_source_artifact(messages, content, max_chars=1000)
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assert result is None or result["knowledge_sources"]["sources"] == []
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@pytest.mark.parametrize("max_chars", [1, 1000, 1800, 10_000])
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def test_evidence_budget_keeps_whole_records_and_reports_omissions(max_chars):
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from deerflow.community.ragflow.sources import durable_source_artifact
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content, messages = retrieval(text='Quoted "evidence" and 中文 ' * 200)
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original = deepcopy(messages)
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small_content, small_messages = retrieval(text="A small source fits")
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# Both citations occur in the report; a large early source cannot starve a small later one.
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result = durable_source_artifact(messages + small_messages, content + small_content, max_chars=max_chars)
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assert messages == original
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if result is None:
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assert max_chars == 1
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return
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assert len(json.dumps(result, ensure_ascii=False)) <= max_chars
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payload = result["knowledge_sources"]
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assert len(payload["sources"]) + payload["omitted_count"] == 2
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originals = [m["artifact"]["knowledge_sources"]["sources"][0] for m in messages + small_messages]
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assert all(source in originals for source in payload["sources"])
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if max_chars in (1000, 1800):
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assert payload["sources"] == [originals[1]]
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assert payload["omitted_count"] == 1
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def test_forwarding_limit_counts_duplicate_omissions_once():
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from deerflow.community.ragflow.sources import cited_source_artifact, durable_source_artifact
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entries = [retrieval(text=f"Evidence {index}") for index in range(102)]
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content = "\n".join(entry[0] for entry in entries)
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messages = [message for entry in entries for message in entry[1]]
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result = durable_source_artifact(messages + messages, content, max_chars=100_000)
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assert len(result["knowledge_sources"]["sources"]) == 100
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assert result["knowledge_sources"]["omitted_count"] == 2
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# Ordinary task forwarding retains its existing artifact shape.
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assert "omitted_count" not in cited_source_artifact(messages, content)["knowledge_sources"]
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def forwarded_task(messages, content):
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from deerflow.tools.builtins.task_tool import _task_result_command
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command = _task_result_command(tool_call_id="child", status="completed", result=content, source_messages=messages)
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captured = []
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capture_step_message(command.update["messages"][0], captured, set())
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return captured
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@pytest.mark.asyncio
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async def test_nested_task_omissions_survive_worker_reopen_and_export(env, monkeypatch):
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from app.gateway.routers import subagent_batches as router
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entries = [retrieval(text=f"Evidence {index}") for index in range(102)]
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report = "\n".join(entry[0] for entry in entries)
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messages = [message for entry in entries for message in entry[1]]
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forwarded = forwarded_task(messages, report)
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# Repeated child messages and a second delegation must not double-count.
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env.result.result = report
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env.result.ai_messages = forwarded_task(forwarded + forwarded, report)
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snapshot = deepcopy(env.result.ai_messages)
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batch = await execute(env)
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await env.service.stop()
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await close_engine()
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await init_engine_from_config(env.db)
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repo = SubagentBatchRepository(get_session_factory())
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monkeypatch.setattr(router, "get_current_user", AsyncMock(return_value="user-1"))
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request = SimpleNamespace(app=SimpleNamespace(state=SimpleNamespace(subagent_batch_repo=repo)))
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response = await router.export_batch_results.__wrapped__(thread_id="thread-1", batch_id=batch["id"], request=request)
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row = json.loads([line async for line in response.body_iterator][0])
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payload = row["result_artifact"]["knowledge_sources"]
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assert len(payload["sources"]) == 100
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assert payload["omitted_count"] == 2
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assert "omitted_source_ids" not in payload
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assert env.result.ai_messages == snapshot
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@pytest.mark.parametrize("selection", ["retained", "omitted", "unknown", "partial"])
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def test_forwarded_omissions_follow_only_current_complete_references(selection):
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from deerflow.community.ragflow.sources import durable_source_artifact
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entries = [retrieval(text=f"Evidence {index}") for index in range(101)]
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report = "\n".join(entry[0] for entry in entries)
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messages = [message for entry in entries for message in entry[1]]
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forwarded = forwarded_task(messages, report)
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selected = {"retained": entries[0][0], "omitted": entries[-1][0], "unknown": "[citation:1](#knowledge-" + "f" * 32 + "-1)", "partial": entries[-1][0].split(")")[0]}[selection]
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result = durable_source_artifact(forwarded, selected, max_chars=1000)
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if selection in {"unknown", "partial"}:
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assert result is None
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else:
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assert result["knowledge_sources"]["omitted_count"] == (selection == "omitted")
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assert len(result["knowledge_sources"]["sources"]) == (selection == "retained")
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@pytest.mark.parametrize("reverse", [False, True])
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def test_recaptured_source_supersedes_forwarded_omission(reverse):
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from deerflow.community.ragflow.sources import durable_source_artifact
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content, messages = retrieval()
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source_id = messages[0]["artifact"]["knowledge_sources"]["sources"][0]["id"]
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omitted = {"type": "tool", "name": "task", "artifact": {"knowledge_sources": {"version": 1, "sources": [], "omitted_source_ids": [source_id, source_id, None, "invalid"], "omitted_count": 999}}}
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inputs = [omitted, *messages] if not reverse else [*messages, omitted]
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result = durable_source_artifact(inputs, content, max_chars=1000)
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assert result["knowledge_sources"]["sources"] == messages[0]["artifact"]["knowledge_sources"]["sources"]
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assert result["knowledge_sources"]["omitted_count"] == 0
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@pytest.mark.asyncio
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async def test_report_truncation_selects_only_complete_stored_citations(env):
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content, messages = retrieval()
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env.service._config = SubagentBatchesConfig(max_result_chars=1000, result_preview_max_chars=64)
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env.result.result = "x" * 1100 + content
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env.result.ai_messages = messages
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batch = await execute(env)
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row = (await env.repo.list_items(batch["id"], user_id="user-1", include_result=True))[0]
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assert row["result"] == "x" * 1000 and row["result_truncated"] is True
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assert row["result_artifact"] is None
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@pytest.mark.asyncio
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async def test_plain_results_keep_nullable_evidence(env):
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batch = await execute(env)
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row = (await env.repo.list_items(batch["id"], user_id="user-1", include_result=True))[0]
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assert row["result"] == "plain report" and row["result_artifact"] is None
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@pytest.mark.asyncio
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@pytest.mark.parametrize("action", ["cancel", "cancel-running", "stale-lease", "failure", "retry"])
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async def test_fenced_or_failed_attempts_cannot_publish_evidence(env, action):
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content, messages = retrieval()
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from deerflow.community.ragflow.sources import durable_source_artifact
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artifact = durable_source_artifact(messages, content, max_chars=1000)
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batch = await env.repo.create_batch(
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batch_id="batch",
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user_id="user-1",
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thread_id="thread-1",
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run_id=None,
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tool_call_id=None,
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submission_key="key",
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title="R",
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subagent_type="general-purpose",
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items=[{"key": "item", "prompt": "Research"}],
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max_live_items=1,
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max_running_items=1,
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max_attempts=2 if action == "stale-lease" else 1,
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execution_spec={},
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)
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now = datetime.now(UTC)
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item = (await env.repo.claim_items(now=now, lease_owner="worker", lease_seconds=60, limit=1))[0]
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if action == "cancel-running":
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assert await env.repo.mark_item_running(item["id"], lease_owner="worker", now=now)
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if action in {"cancel", "cancel-running"}:
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await env.repo.cancel_batch(batch["id"], user_id="user-1")
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if action == "stale-lease":
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reclaimed = await env.repo.claim_items(now=now + timedelta(seconds=61), lease_owner="replacement", lease_seconds=60, limit=1)
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assert len(reclaimed) == 1 and reclaimed[0]["attempt"] == 2
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accepted = await env.repo.finalize_item(
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|
item["id"],
|
|
lease_owner="worker",
|
|
succeeded=action not in {"failure", "retry"},
|
|
result=content,
|
|
result_preview=content[:64],
|
|
result_truncated=False,
|
|
result_artifact=artifact,
|
|
error="failed",
|
|
stop_reason=None,
|
|
token_usage=None,
|
|
model_name=None,
|
|
completed_at=now,
|
|
)
|
|
assert accepted is (action not in {"cancel", "cancel-running", "stale-lease"})
|
|
if action == "retry":
|
|
# A legacy/external failure row may have old evidence; explicit retry clears it.
|
|
from deerflow.persistence.subagent_batches.model import SubagentBatchItemRow
|
|
|
|
async with get_session_factory()() as session:
|
|
stored = await session.get(SubagentBatchItemRow, item["id"])
|
|
stored.result_artifact = artifact
|
|
await session.commit()
|
|
assert await env.repo.retry_item(batch["id"], item["id"], user_id="other") is None
|
|
await env.repo.retry_item(batch["id"], item["id"], user_id="user-1")
|
|
row = (await env.repo.list_items(batch["id"], user_id="user-1", include_result=True))[0]
|
|
assert row["result_artifact"] is None
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.parametrize("user,permission,thread,status", [("user-1", True, "thread-1", 200), ("user-1", False, "thread-1", 403), ("other", True, "thread-1", 404), ("user-1", True, "other-thread", 404), (None, True, "thread-1", 401)])
|
|
async def test_http_export_checks_permission_thread_and_batch_owner(env, monkeypatch, user, permission, thread, status):
|
|
import httpx
|
|
from fastapi import FastAPI
|
|
|
|
from app.gateway import deps
|
|
from app.gateway.authz import AuthContext
|
|
from app.gateway.routers import subagent_batches as router
|
|
|
|
env.result.result, env.result.ai_messages = retrieval()
|
|
batch = await execute(env)
|
|
app = FastAPI()
|
|
app.include_router(router.router)
|
|
app.state.subagent_batch_repo = env.repo
|
|
|
|
@app.middleware("http")
|
|
async def auth_boundary(request, call_next):
|
|
request.state.auth = AuthContext(user=SimpleNamespace(id=user) if user else None, permissions=["threads:read"] if permission else [])
|
|
return await call_next(request)
|
|
|
|
# Authentication and the thread store are controlled; the actual permission decorator,
|
|
# route, SQL batch ownership, streaming and serialization remain production code.
|
|
monkeypatch.setattr(deps, "get_thread_store", lambda request: SimpleNamespace(check_access=AsyncMock(return_value=thread == "thread-1")))
|
|
monkeypatch.setattr(router, "get_current_user", AsyncMock(return_value=user))
|
|
async with httpx.AsyncClient(transport=httpx.ASGITransport(app=app), base_url="http://test") as client:
|
|
response = await client.get(f"/api/threads/{thread}/subagent-batches/{batch['id']}/results.jsonl")
|
|
assert response.status_code == status
|
|
if status == 200:
|
|
row = json.loads(response.text)
|
|
assert row["result_artifact"]["knowledge_sources"]["sources"][0]["text"] == "Original evidence"
|
|
else:
|
|
assert "Original evidence" not in response.text
|