# -*- coding: utf-8 -*- """ coding 写入链路 pipeline log 打点测试。 验证 judge / extractor / reconciler 分阶段实时落 CODING_JUDGE / CODING_EXTRACT / CODING_RECONCILE 三个 step（对标 chat 链路），且： - prompt / response 完整写入（不截断） - crash 不丢：store 入库崩溃时，前面已完成阶段的 log 仍在库 用真实 SqliteCache（含 observability 包装）+ mock LLM/embed，避免外部依赖。 注意：全部资源在单个 test 协程内构建（一个 event loop），避免 pytest-asyncio auto 模式下 async fixture 与 test 分处不同 loop 导致 owner_loop 跨 loop 派发。 """ import asyncio import json import os import shutil import tempfile import uuid import pytest from hy_memory.coding.extractor import CodingMemoryExtractor from hy_memory.coding.judge import classify_messages_is_coding from hy_memory.coding.reconciler import CodingMemoryReconciler from hy_memory.coding.store import CodingMemoryStore from hy_memory.coding.writer import CodingWriter from hy_memory.pipelines.base import ChatMessage, ToolCall from tests.coding._helpers import MockLLMProvider, MockEmbedService def _classify_resp(is_coding, reason="reason"): return json.dumps({"is_coding": is_coding, "reason": reason}) def _extract_resp(memories): return json.dumps({"memories": memories}) def _reconcile_resp(ops): return json.dumps({"ops": ops}) def _coding_messages(): return [ ChatMessage(role="user", content="帮我打包发布到私有包索引"), ChatMessage(role="assistant", content="", tool_calls=[ ToolCall(id="1", name="Read", arguments={"path": "scripts/publish_package.sh"}), ]), ChatMessage(role="tool", content="200 lines", tool_call_id="1", tool_name="Read"), ChatMessage(role="assistant", content="", tool_calls=[ ToolCall(id="2", name="Bash", arguments={"command": "twine upload"}), ]), ChatMessage(role="tool", content="OK uploaded", tool_call_id="2", tool_name="Bash"), ] _DRAFT = { "task": "打包发布到私有包索引", "search_keys": ["twine 上传", "私有索引发布"], "solution": "运行 scripts/publish_package.sh，用 twine upload 上传到私有索引", "boundary_envs": "twine>=4.0", "boundary_scope": "project", "confidence": 0.9, } async def _make_cache_and_store(d): """在当前（test）event loop 上建 cache + store，保证 owner_loop 一致。""" os.environ["MEMORY_PIPELINE_TRACE_ENABLED"] = "true" os.environ["MEMORY_LOG_LLM_CONTENT"] = "true" os.environ["MEMORY_LOG_DIR"] = os.path.join(d, "logs") from hy_memory.config import MemoryConfig from hy_memory.data.cache_sqlite import SqliteCache from hy_memory.utils.pipeline_observability import install_pipeline_observability cfg = MemoryConfig.from_env() cfg.cache.db_path = os.path.join(d, "cache.db") cfg.coding.db_path = os.path.join(d, "coding_memory.db") cfg.vector_store.provider = "chroma" cfg.vector_store.persist_directory = os.path.join(d, "chroma") cfg.vector_store.collection_name = f"t_{uuid.uuid4().hex[:8]}" cfg.vector_store.embedding_dims = 1536 cache = SqliteCache(cfg) await cache.initialize() install_pipeline_observability(cache, log_label="test") store = CodingMemoryStore( cfg, embed_service=MockEmbedService(), db_path=cfg.coding.db_path, ) await store.initialize() return cache, store def _build_writer(cache, store, llm): embed = MockEmbedService() extractor = CodingMemoryExtractor(llm, cache=cache) reconciler = CodingMemoryReconciler( store=store, embed_service=embed, llm_provider=llm, cache=cache, ) return CodingWriter( store=store, extractor=extractor, reconciler=reconciler, llm_provider=llm, embed_service=embed, cache=cache, ) @pytest.mark.asyncio class TestCodingPipelineLog: async def test_judge_emits_coding_judge(self): d = tempfile.mkdtemp(prefix="coding_plog_") try: cache, store = await _make_cache_and_store(d) from hy_memory.utils.pipeline_observability import drain_pipeline_log_tasks rid = uuid.uuid4().hex llm = MockLLMProvider(_classify_resp(True, "concrete code task")) result = await classify_messages_is_coding( _coding_messages(), llm, cache=cache, request_id=rid, user_id="u1", agent_id="codebuddy", ) assert result is True await drain_pipeline_log_tasks() logs = await cache.get_pipeline_logs(request_id=rid) judge = next(l for l in logs if l["step"] == "CODING_JUDGE") assert "single-label scene classifier" in judge["prompt"] assert "is_coding" in judge["response"] await cache.close(); await store.close() finally: shutil.rmtree(d, ignore_errors=True) async def test_judge_logs_even_when_chat(self): """判为 chat 也留一条 CODING_JUDGE（记录判过、结论 chat）。""" d = tempfile.mkdtemp(prefix="coding_plog_") try: cache, store = await _make_cache_and_store(d) from hy_memory.utils.pipeline_observability import drain_pipeline_log_tasks rid = uuid.uuid4().hex llm = MockLLMProvider(_classify_resp(False, "casual")) result = await classify_messages_is_coding( _coding_messages(), llm, cache=cache, request_id=rid, user_id="u1", agent_id="codebuddy", ) assert result is False await drain_pipeline_log_tasks() steps = [l["step"] for l in await cache.get_pipeline_logs(request_id=rid)] assert "CODING_JUDGE" in steps await cache.close(); await store.close() finally: shutil.rmtree(d, ignore_errors=True) async def test_extract_and_reconcile_steps(self): """writer 端到端：CODING_EXTRACT + CODING_RECONCILE 落库，prompt/response 完整。""" d = tempfile.mkdtemp(prefix="coding_plog_") try: cache, store = await _make_cache_and_store(d) from hy_memory.utils.pipeline_observability import drain_pipeline_log_tasks rid = uuid.uuid4().hex llm = MockLLMProvider([ _extract_resp([_DRAFT]), _reconcile_resp([{"draft_idx": 0, "action": "ADD"}]), ]) writer = _build_writer(cache, store, llm) resp = await writer.write( _coding_messages(), user_id="u1", agent_id="codebuddy", workspace_id="github.com/example/project", request_id=rid, ) assert resp["success"] is True assert resp["experience_ids"] await drain_pipeline_log_tasks() logs = await cache.get_pipeline_logs(request_id=rid) steps = [l["step"] for l in logs] assert "CODING_EXTRACT" in steps assert "CODING_RECONCILE" in steps ext = next(l for l in logs if l["step"] == "CODING_EXTRACT") rec = next(l for l in logs if l["step"] == "CODING_RECONCILE") assert "可长期保留的 WORKING 记忆" in ext["prompt"] # response 是 LLM 原始输出（json.dumps 默认 ensure_ascii → task 以转义形式完整保留） assert "task" in ext["response"] and "twine" in ext["response"] assert rec["prompt"] and rec["response"] await cache.close(); await store.close() finally: shutil.rmtree(d, ignore_errors=True) async def test_reconcile_log_survives_store_crash(self): """crash 不丢：store.insert 抛异常时，CODING_EXTRACT + CODING_RECONCILE （在 execute 入库之前打点）仍已落库。""" d = tempfile.mkdtemp(prefix="coding_plog_") try: cache, store = await _make_cache_and_store(d) from hy_memory.utils.pipeline_observability import drain_pipeline_log_tasks rid = uuid.uuid4().hex llm = MockLLMProvider([ _extract_resp([_DRAFT]), _reconcile_resp([{"draft_idx": 0, "action": "ADD"}]), ]) async def _boom(*a, **k): raise RuntimeError("mock store crash") store.insert = _boom writer = _build_writer(cache, store, llm) # write 不应抛（reconciler execute 内部 try/except 吞掉 ADD 失败） await writer.write( _coding_messages(), user_id="u1", agent_id="codebuddy", workspace_id="github.com/example/project", request_id=rid, ) await drain_pipeline_log_tasks() steps = [l["step"] for l in await cache.get_pipeline_logs(request_id=rid)] assert "CODING_EXTRACT" in steps assert "CODING_RECONCILE" in steps await cache.close(); await store.close() finally: shutil.rmtree(d, ignore_errors=True)