mini-openclaw/backend/test_risk_core.py

import tempfile
import unittest
from pathlib import Path
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
from graph.agent import AgentManager
from graph.risk_task import invoke_risk_task
from tools.risk_task_tools import create_risk_tools
from tools.skills_scanner import discover_skills, install_skill, scan_skills


class ToolModel(BaseChatModel):
    """No network: still exercises the real LangChain agent/tool execution loop."""
    skill_id: str = "risk/SKILL.md"
    card_count: int = 1
    @property
    def _llm_type(self): return "test-tool-model"
    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):
            calls=[("list_data_cards",{}), *[("read_data_card",{"index":i}) for i in range(self.card_count)],("read_checks_and_rules",{}),("read_selected_skill",{"skill_id":self.skill_id})]
            message=AIMessage(content="",tool_calls=[{"name":name,"args":args,"id":str(i),"type":"tool_call"} for i,(name,args) in enumerate(calls)])
        else:
            message=AIMessage(content="请核查财务负债；依据财务卡 v1 的 liabilities 字段。")
        return ChatResult(generations=[ChatGeneration(message=message)])


class CoreTests(unittest.TestCase):
    def context(self):
        return {"peer_datasets":[{"card":"财务卡","version":"v1","records":[{"values":{"liabilities":20}}]}],"scalar_values":{"metrics.debt_ratio":.8},"human_rules":[],"rule_findings":[]}
    def test_real_core_tool_cycle_and_isolation(self):
        manager=AgentManager()
        tools=create_risk_tools(self.context(),{}, {"risk/SKILL.md":"核实负债"})
        output=manager.invoke_scoped("进行研判",model=ToolModel(),tools=tools,system_prompt="读取工具并给建议")
        self.assertEqual(output["engine"],"mini-openclaw/AgentManager")
        self.assertEqual(len([m for m in output["messages"] if m["type"]=="tool"]),4)
        self.assertIsNone(manager._llm)
        self.assertEqual(manager._tools,[])
        self.assertNotIn("terminal",[t.name for t in tools])
    def test_native_scanner_and_adapter(self):
        with tempfile.TemporaryDirectory() as root:
            base=Path(root);file=base/"skills"/"risk"/"SKILL.md";file.parent.mkdir(parents=True)
            file.write_text("---\nname: risk\ndescription: Check financial evidence\n---\nOnly suggest checks.",encoding="utf-8")
            self.assertEqual(discover_skills(base)[0]["id"],"risk/SKILL.md")
            self.assertIn("risk/SKILL.md",scan_skills(base))
            with patch("graph.risk_task.ChatOpenAI",return_value=ToolModel()):
                output=invoke_risk_task({"native_skills":["risk/SKILL.md"]},self.context(),{},"http://127.0.0.1:11434/v1",base)
            self.assertIn("Only suggest",output["native_skills"][0]["content"])
            self.assertTrue(output["messages"])
    def test_archived_native_skills_are_hidden_and_folder_import_is_managed(self):
        with tempfile.TemporaryDirectory() as root:
            base=Path(root)
            archived=base/"skills"/"old"/"SKILL.md";archived.parent.mkdir(parents=True)
            archived.write_text("---\nname: old-skill\ndescription: Old unrelated method\nstatus: archived\n---\nOld",encoding="utf-8")
            content="---\nname: leasing-check\ndisplay_name: 金租核查\ndescription: Review leasing evidence\ninputs: [lease.total_value]\n---\nRead evidence first."
            installed=install_skill(base,content)
            self.assertEqual(installed["id"],"imported/leasing-check/SKILL.md")
            self.assertEqual([s["name"] for s in discover_skills(base)],["leasing-check"])
            with self.assertRaises(FileExistsError):install_skill(base,content)
    def test_tools_cannot_read_unselected_resources(self):
        tools={t.name:t for t in create_risk_tools(self.context(),{}, {})}
        self.assertIn("授权",tools["read_data_card"].invoke({"index":99}))
        self.assertIn("未被",tools["read_selected_skill"].invoke({"skill_id":"../../config.json"}))
    def test_snapshot_immutable_after_tool_creation(self):
        context=self.context();tools={t.name:t for t in create_risk_tools(context,{}, {})}
        context["peer_datasets"].clear()
        self.assertIn("财务卡",tools["read_data_card"].invoke({"index":0}))
    def test_api_to_core_and_persisted_trace(self):
        from fastapi import FastAPI
        from fastapi.testclient import TestClient
        from api import risk_agent_config as api
        with tempfile.TemporaryDirectory() as root, patch.object(api,"DB_PATH",Path(root)/"test.sqlite"), patch.dict("os.environ",{"XINGBANG_LOCAL_MODEL_URL":"http://127.0.0.1:11434/v1","XINGBANG_AUTH_USERS":"","XINGBANG_REQUIRE_AUTH":"0"}):
            app=FastAPI();app.include_router(api.router)
            with TestClient(app) as client, patch("graph.risk_task.ChatOpenAI",return_value=ToolModel(skill_id="risk_evidence/SKILL.md",card_count=3)):
                response=client.post("/api/xingbang/risk-agent/plans/plan-default/run",json={"project_id":"XB-2026-001"})
                self.assertEqual(response.status_code,200,response.text)
                result=response.json()
                self.assertEqual(result["engine"],"mini-openclaw/AgentManager")
                trace=result["agent_traces"][0]
                self.assertEqual(len([m for m in trace["messages"] if m["type"]=="tool"]),6)
                saved=client.get("/api/xingbang/risk-agent/runs/history").json()[0]
                self.assertEqual(saved["agent_traces"],result["agent_traces"])

if __name__=="__main__":unittest.main()