alvinfeiandClaude Opus 5.5
dc762df78b
MEMORY_PIPELINE_LOG_ENABLED (default true) turns off the pipeline JSONL log. With the DB trace also off, store_pipeline_log becomes a no-op that does no timing, redaction, background work or database write. The init line states the real on/off state. Lines that carried conversation or memory text at INFO/WARNING now log ids, counts and lengths; the text goes to DEBUG: [add] input, search queries and rewrites, coding tasks and fact statements/subjects, reconcile op detail, extracted info, embedding inputs and unparsable model output. Bump hy-memory to 2.0.8 (not yet published). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
106 lines
3.9 KiB
Python
106 lines
3.9 KiB
Python
# -*- coding: utf-8 -*-
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"""EmbedService INFO 日志：含 request_id / provider / model / api / 入参 / 出参(dims)。"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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from unittest.mock import AsyncMock, MagicMock, patch
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from hy_memory.config import MemoryConfig, EmbedderConfig
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from hy_memory.core.embed_service import EmbedService, _embeddings_api_url
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from hy_memory.utils.log_setup import set_request_id
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def run(coro):
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loop = asyncio.new_event_loop()
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try:
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return loop.run_until_complete(coro)
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finally:
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loop.close()
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class TestEmbeddingsApiUrl:
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def test_join_v1(self):
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assert _embeddings_api_url("https://host/v1") == "https://host/v1/embeddings"
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def test_strip_trailing_slash(self):
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assert _embeddings_api_url("https://host/v1/") == "https://host/v1/embeddings"
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def test_empty(self):
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assert _embeddings_api_url("") == "/embeddings"
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class TestEmbedInfoLog:
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def test_success_logs_info_with_dims_not_vector(self, caplog):
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async def _test():
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cfg = MemoryConfig(
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embedder=EmbedderConfig(
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provider="openai",
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model="text-embedding-test",
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api_key="sk-test",
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base_url="https://example.com/v1",
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embedding_dims=8,
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max_retries=0,
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)
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)
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svc = EmbedService(cfg)
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set_request_id("rid-embed-001")
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fake_usage = MagicMock()
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fake_usage.model_dump.return_value = {"prompt_tokens": 3, "total_tokens": 3}
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fake_item = MagicMock()
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fake_item.embedding = [0.123456789] * 8
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fake_resp = MagicMock()
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fake_resp.model = "text-embedding-test"
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fake_resp.usage = fake_usage
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fake_resp.data = [fake_item]
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fake_raw = MagicMock()
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fake_raw.status_code = 200
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fake_raw.headers = {"content-type": "application/json", "authorization": "secret"}
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fake_raw.parse.return_value = fake_resp
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create = AsyncMock(return_value=fake_raw)
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fake_client = MagicMock()
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fake_client.embeddings.with_raw_response.create = create
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with patch(
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"openai.AsyncOpenAI",
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return_value=fake_client,
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), caplog.at_level(logging.INFO, logger="hy_memory.core.embed_service"):
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vectors, meta = await svc._embed_openai(["hello world"])
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assert len(vectors) == 1
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assert len(vectors[0]) == 8
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assert meta["http_status"] == 200
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info_msgs = [
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r.getMessage()
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for r in caplog.records
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if r.name == "hy_memory.core.embed_service"
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and r.levelno == logging.INFO
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and r.getMessage().startswith("[embed] {")
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]
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assert len(info_msgs) == 1
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payload = json.loads(info_msgs[0][len("[embed] "):])
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assert payload["request_id"] == "rid-embed-001"
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assert payload["status"] == "ok"
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assert payload["provider"] == "openai"
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assert payload["model"] == "text-embedding-test"
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assert payload["api"] == "https://example.com/v1/embeddings"
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# 待向量化的原文不进 INFO，只记每条字符数
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assert "input" not in payload["request"]
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assert payload["request"]["input_chars"] == [11]
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assert "hello world" not in info_msgs[0]
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assert payload["request"]["dimensions"] == 8
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assert payload["response"]["vector_dims"] == [8]
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assert payload["response"]["vector_count"] == 1
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# 不得把向量数值打进日志（用哨兵值检测，避免与耗时等数字误撞）
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blob = json.dumps(payload)
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assert "0.123456789" not in blob
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assert "embedding" not in payload["response"]
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run(_test())
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