from types import SimpleNamespace from app.services.pmflow_ai_context_memory_job_service import build_context_memory_compaction_job_payload from app.services.pmflow_ai_context_assembly_service import sanitized_context_assembly_payload from app.services.pmflow_ai_context_evidence_service import append_context_evidence_to_workspace, build_context_evidence_packet from app.services.pmflow_ai_context_grounding_verifier import ( revise_answer_with_context_boundary, verify_answer_context_grounding, ) from app.services.pmflow_ai_context_pack_service import PMFlowAIContextPackBuilder, context_pack_used_context_packet from app.services.pmflow_ai_context_replay_runner import PMFlowAIContextReplayRunner from app.schemas.pmflow_ai_agent_runtime import PMFlowAIAgentWorkspace def test_context_memory_compaction_job_payload_is_sanitized_and_anchor_based(): thread = SimpleNamespace(id=11, public_thread_id="thread_abc", message_count=4) user = SimpleNamespace(id=7) payload = build_context_memory_compaction_job_payload( thread=thread, current_user=user, user_message="请记住：首版先做执行包导出。", assistant_answer="已记录，首版优先执行包导出。", context_pack={ "thread_context": {"recent_messages": [{"content": "首版先做执行包导出。"}]}, "trace": {"internal": "should_not_leak"}, "prompt_key": "should_not_leak", }, tenant_project_context={"current_project_id": 101, "raw_internal": {"table": "hidden"}}, run_public_id="run_123", user_message_index=3, assistant_message_index=4, ) assert payload["thread"]["public_thread_id"] == "thread_abc" assert payload["user_id"] == 7 assert payload["run_public_id"] == "run_123" assert "执行包导出" in payload["user_message"] assert "thread_context" in payload["context_pack"] assert "trace" not in payload["context_pack"] assert "prompt_key" not in payload["context_pack"] def test_used_context_packet_reports_thread_memory_and_dropped_risks(): pack = PMFlowAIContextPackBuilder(budget_chars=9000).build( user_question="你还记得上面说的约束吗？", thread_context_pack={ "recent_messages": [ { "message_id": "msg_1", "role": "user", "message_index": 1, "content": "首版必须能导出给外部开发工具的执行包。", } ], "relevant_messages": [], "rolling_summary": {}, }, conversation_context={}, tenant_project_context={}, memory_context_pack={ "memories": [ { "memory_id": "ctxmem_1", "memory_scope": "thread", "scope_ref": "thread:thread_abc", "summary": "用户要求首版支持执行包导出。", "confidence_score": 0.9, } ] }, ) packet = context_pack_used_context_packet(pack) assert packet["packet_version"] == "pmflow_ai_used_context_packet.v1" assert packet["usage_status"] == "provided_to_answer_runtime" assert packet["thread_message_refs"][0]["message_id"] == "msg_1" assert packet["memory_refs"][0]["memory_id"] == "ctxmem_1" assert "执行包" in packet["thread_message_refs"][0]["content_preview"] def test_context_replay_runner_default_cases_pass(): result = PMFlowAIContextReplayRunner().run_cases() assert result["case_count"] >= 4 assert result["failed_count"] == 0 assert all(row["passed"] for row in result["cases"]) def test_context_assembly_payload_keeps_sanitized_refs_not_raw_trace(): pack = PMFlowAIContextPackBuilder(budget_chars=9000).build( user_question="继续上面的执行包约束", thread_context_pack={ "recent_messages": [{"message_id": "m1", "role": "user", "message_index": 1, "content": "执行包必须能给外部工具使用。"}], "relevant_messages": [], "rolling_summary": {}, }, conversation_context={"trace": {"internal": "hidden"}}, tenant_project_context={}, memory_context_pack={"memories": [{"memory_id": "ctxmem_1", "memory_scope": "thread", "summary": "执行包需要外部工具可用。"}]}, ) assembly = sanitized_context_assembly_payload({**pack, "trace": {"internal": "hidden"}}) assert assembly["packet_version"] == "pmflow_ai_context_assembly.v1" assert assembly["thread_refs"][0]["message_id"] == "m1" assert assembly["memory_refs"][0]["memory_id"] == "ctxmem_1" assert "trace" not in assembly assert "scope_ref" not in assembly["memory_refs"][0] def test_context_grounding_verifier_blocks_answer_that_ignores_selected_context(): used_context = { "packet_version": "pmflow_ai_used_context_packet.v1", "thread_message_refs": [ { "message_id": "m1", "role": "user", "message_index": 1, "content_preview": "首版必须能导出给外部开发工具的执行包。", } ], "memory_refs": [], "dropped_risks": [], } failed = verify_answer_context_grounding(answer="可以继续推进产品规划。", used_context_packet=used_context) assert failed["grounding_passed"] is False assert failed["missing_context_count"] == 1 revised = revise_answer_with_context_boundary(answer="可以继续推进产品规划。", grounding_packet=failed) assert "外部开发工具的执行包" in revised passed = verify_answer_context_grounding(answer="下一步要围绕外部开发工具的执行包继续推进。", used_context_packet=used_context) assert passed["grounding_passed"] is True def test_context_evidence_absorber_promotes_prior_thread_plan_without_business_keyword_rules(): pack = PMFlowAIContextPackBuilder(budget_chars=12000).build( user_question="那个方向继续按之前方案出一版新的最小闭环", thread_context_pack={ "recent_messages": [ { "message_id": "m1", "role": "assistant", "message_index": 2, "content": "上一版方案确认先验证数据接入质量、智能助手建议和嵌入现有流程三个最小闭环。", }, { "message_id": "m2", "role": "user", "message_index": 3, "content": "我希望这个方向先用低成本方式验证。", }, ], "relevant_messages": [], "rolling_summary": {}, }, conversation_context={}, tenant_project_context={ "surface_context": { "visible_objects": [ {"object_ref": "project:unrelated", "title": "另一个当前可见项目"}, ] } }, memory_context_pack={ "memories": [ { "memory_id": "ctxmem_prior", "memory_scope": "thread", "summary": "该主题的持续方向是把早期想法收敛成可验证的产品蓝图。", "confidence_score": 0.86, } ] }, ) evidence = build_context_evidence_packet( context_pack=pack, user_question="那个方向继续按之前方案出一版新的最小闭环", ) assert evidence["claims"] assert evidence["continuation_obligations"] assert any(item["semantic_role"] == "assistant_prior_proposal" for item in evidence["claims"]) assert evidence["topic_workspace"]["topic_signature"].startswith("topic_") workspace = PMFlowAIAgentWorkspace(current_goal="继续之前方案") count = append_context_evidence_to_workspace(workspace=workspace, evidence_packet=evidence) assert count >= 2 assert any(claim.source_tool == "context_evidence_absorber" for claim in workspace.claim_ledger) assert not any("同一对话历史第" in claim.claim_text for claim in workspace.claim_ledger) def test_grounding_verifier_requires_context_evidence_obligations(): used_context = { "packet_version": "pmflow_ai_used_context_packet.v1", "thread_message_refs": [], "memory_refs": [], "dropped_risks": [], "context_evidence": { "claims": [ { "claim_id": "ctxclaim_1", "semantic_role": "assistant_prior_proposal", "claim_preview": "上一版方案确认先验证数据接入质量、智能助手建议和嵌入现有流程三个最小闭环。", "required_for_answer": True, } ], "continuation_obligations": [{"claim_id": "ctxclaim_1"}], }, } failed = verify_answer_context_grounding(answer="可以重新规划一个方向。", used_context_packet=used_context) assert failed["grounding_passed"] is False assert any(item["finding_type"] == "context_obligation_underused" for item in failed["findings"]) passed = verify_answer_context_grounding( answer="这版继续沿用数据接入质量、智能助手建议和嵌入现有流程这三个最小闭环。", used_context_packet=used_context, ) assert passed["grounding_passed"] is True