import json import unittest from copy import deepcopy from unittest.mock import patch from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.messages import AIMessage, ToolMessage from langchain_core.outputs import ChatResult, ChatGeneration import test_project_assistant as fixtures from services.assistant_store import get, put, listing from services.component_registry import draft, release, ordered, normalize from services.component_evaluation import evaluate from services.assistant_model import ModelResult, configuration class StructuredToolModel(BaseChatModel): invalid: bool = False propose: bool = False cite_document: bool = False @property def _llm_type(self): return "isolated-structured-test" def bind_tools(self, tools, **kwargs): return self def _generate(self, messages, stop=None, run_manager=None, **kwargs): if not any(isinstance(m, ToolMessage) for m in messages): task = json.loads(next(m.content for m in messages if m.type == "human")) calls = [("read_project_snapshot", {}), ("read_checks", {})] + [("read_method", {"skill_id": skill_id}) for skill_id in task.get("methods", ["risk_evidence/SKILL.md"])] if self.cite_document: calls.append(("read_document", {"document_id": task["documents"][0]["id"]})) message = AIMessage(content="", tool_calls=[{"name": name, "args": args, "id": str(i), "type": "tool_call"} for i, (name, args) in enumerate(calls)]) else: snapshots = [json.loads(m.content) for m in messages if isinstance(m, ToolMessage) and m.name == "read_project_snapshot"] data = snapshots[0] evidence_id = next(iter(data["sources"].values()))[0]["id"] if self.cite_document: document = json.loads(next(m.content for m in messages if isinstance(m, ToolMessage) and m.name == "read_document")) evidence_id = document["chunks"][0]["evidence_id"] checks = [{"code": "source.read", "title": "本项目来源已读取", "result": "pass", "evidence_ids": ["other-project-id" if self.invalid else evidence_id], "suggested_action": "按材料继续核实", "fields": []}] payload = {"findings": checks, "requirement_response": "已按本次要求读取 " + data["project_id"] + " 的冻结快照，必查项保留。"} if self.propose: payload["experience_proposal"] = {"title": "回款凭证检查", "trigger": "依赖特定回款", "action": "补充到账依据", "exceptions": "已有完整凭证", "conditions": {"collection.dependency": True}, "required_field": "collection.evidence", "exception_field": "collection.evidence"} message = AIMessage(content=json.dumps(payload, ensure_ascii=False)) return ChatResult(generations=[ChatGeneration(message=message)]) class ComponentTests(fixtures.ProjectAssistantTests): # Reuse only the fixture helpers, not inherited test execution (filtered below). def test_model_endpoint_policy_allows_only_private_or_official_deepseek(self): with patch.dict("os.environ", {"XINGBANG_LOCAL_MODEL_URL": "https://api.deepseek.com", "XINGBANG_LOCAL_MODEL_NAME": "deepseek-chat"}): config = configuration() self.assertTrue(config["available"]) self.assertEqual(config["name"], "deepseek-chat") with patch.dict("os.environ", {"XINGBANG_LOCAL_MODEL_URL": "http://api.deepseek.com"}): with self.assertRaises(ValueError): configuration() def test_requirement_response_object_is_normalized_to_readable_text(self): result = ModelResult.model_validate({ "findings": [], "requirement_response": {"数据读取": "完成", "待核实": "现金流缺失"}, }) self.assertEqual(result.requirement_response, "数据读取：完成；待核实：现金流缺失") with patch.dict("os.environ", {"XINGBANG_LOCAL_MODEL_URL": "https://example.com/v1"}): with self.assertRaises(ValueError): configuration() def test_real_agent_manager_and_invalid_evidence(self): with patch.dict("os.environ", {"XINGBANG_LOCAL_MODEL_URL": "http://127.0.0.1:11434/v1"}), patch("services.assistant_model.ChatOpenAI", return_value=StructuredToolModel()): a = self.run_project("DEMO-A", "跳过现金流检查") b = self.run_project("DEMO-B", "重点看现金流") self.assertEqual(a["status"], "completed") self.assertEqual(a["summary"]["open"], 0) model = next(s for s in b["steps"] if s["step_id"] == "corporate.analysis") self.assertEqual(model["trace"]["engine"], "mini-openclaw/AgentManager") self.assertIn("DEMO-B", model["outputs"]["requirement_response"]) self.assertTrue(any(f["code"] == "financial.cashflow.missing" for f in b["findings"])) self.assertFalse(set(e["id"] for e in a["evidence"]) & set(e["id"] for e in b["evidence"])) with patch.dict("os.environ", {"XINGBANG_LOCAL_MODEL_URL": "http://127.0.0.1:11434/v1"}), patch("services.assistant_model.ChatOpenAI", return_value=StructuredToolModel(invalid=True)): bad = self.run_project("DEMO-A") self.assertEqual(bad["status"], "partial") self.assertIn("未授权证据", next(s for s in bad["steps"] if s["step_id"] == "corporate.analysis")["errors"][0]["message"]) def test_feedback_proposal_uses_agent_and_stays_private_draft(self): first = self.run_project() feedback = self.post("/projects/DEMO-B/feedback", {"category": "rule_omission", "target_kind": "run", "target_id": first["id"], "note": "回款依赖项目需要凭证检查"}).json() with patch.dict("os.environ", {"XINGBANG_LOCAL_MODEL_URL": "http://127.0.0.1:11434/v1"}), patch("services.assistant_model.ChatOpenAI", return_value=StructuredToolModel(propose=True)): queued = self.post("/projects/DEMO-B/runs", {"feedback_id": feedback["id"], "requirement": "提炼经验草稿"}).json() self.worker.process_next() run = self.service.resource("run", queued["id"]) self.assertIn("proposed_experience_id", run) draft = self.service.resource("experience", run["proposed_experience_id"]) self.assertEqual(draft["status"], "draft") self.assertEqual(draft["scope"], "project") self.assertEqual(draft["feedback_id"], feedback["id"]) self.assertEqual(self.project("DEMO-C")["experiences"], []) def test_document_chunk_citation_is_persisted_with_true_locator(self): with patch.dict("os.environ", {"XINGBANG_LOCAL_MODEL_URL": "http://127.0.0.1:11434/v1"}), patch("services.assistant_model.ChatOpenAI", return_value=StructuredToolModel(cite_document=True)): run = self.run_project("DEMO-A") self.assertEqual(run["status"], "completed") refs = [e for e in run["evidence"] if e["field"] == "document.text"] self.assertEqual(len(refs), 1) self.assertEqual(refs[0]["locator"], "第1行") self.assertTrue(refs[0]["source"].endswith(".csv")) def test_component_publish_gate_and_frozen_queue(self): queued = self.post("/projects/DEMO-B/runs").json() with self.service.connect() as db: original = get(db, "component_version", "corporate.coverage@1.0.0") body = {k: v for k, v in original.items() if k not in ("revision", "component_id")} body.update(id="corporate.coverage", version="1.0.1", name="新版覆盖测算") candidate = draft(db, body, "author") with self.assertRaises(ValueError): release(db, candidate["id"], "reviewer") evaluation = evaluate(db, candidate["id"], "author") self.assertTrue(evaluation["passed"], evaluation) release(db, candidate["id"], "reviewer") self.worker.process_next() old = self.service.resource("run", queued["id"]) self.assertEqual(next(s for s in old["components"]["skills"] if s["id"] == "corporate.coverage")["version"], "1.0.0") newer = self.run_project() self.assertEqual(next(s for s in newer["component_versions"] if s["id"] == "corporate.coverage")["version"], "1.0.1") with self.service.connect() as db: body.update(version="1.0.2", denominator="lease.total_value") bad = draft(db, body, "author") self.assertFalse(evaluate(db, bad["id"], "author")["passed"]) with self.assertRaises(ValueError): release(db, bad["id"], "reviewer") def test_cycles_missing_dependencies_and_output_conflicts(self): for items in ([{"id": "a", "depends_on": ["b"]}], [{"id": "a", "depends_on": ["b"]}, {"id": "b", "depends_on": ["a"]}], [{"id": "a", "engine": "ratio", "output": "same"}, {"id": "b", "engine": "ratio", "output": "same"}]): with self.assertRaises(ValueError): ordered(items) # unittest's loader would otherwise repeat all integration tests through inheritance. for name in list(fixtures.ProjectAssistantTests.__dict__): if name.startswith("test_"): setattr(ComponentTests, name, None) if __name__ == "__main__": unittest.main()