mirror of
https://gitee.com/wanwujie/deer-flow
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1c542ab7f1
* feat(内存存储): 添加可配置的内存存储提供者支持 实现内存存储的抽象基类 MemoryStorage 和文件存储实现 FileMemoryStorage 重构内存数据加载和保存逻辑到存储提供者中 添加 storage_class 配置项以支持自定义存储提供者 * refactor(memory): 重构内存存储模块并更新相关测试 将内存存储逻辑从updater模块移动到独立的storage模块 使用存储接口模式替代直接文件操作 更新所有相关测试以使用新的存储接口 * Update backend/packages/harness/deerflow/agents/memory/storage.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update backend/packages/harness/deerflow/agents/memory/storage.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * fix(内存存储): 添加线程安全锁并增加测试用例 添加线程锁确保内存存储单例初始化的线程安全 增加对无效代理名称的验证测试 补充单例线程安全性和异常处理的测试用例 * Update backend/tests/test_memory_storage.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * fix(agents): 使用统一模式验证代理名称 修改代理名称验证逻辑以使用仓库中定义的AGENT_NAME_PATTERN模式，确保代码库一致性并防止路径遍历等安全问题。同时更新测试用例以覆盖更多无效名称情况。 --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
289 lines
11 KiB
Python
289 lines
11 KiB
Python
from unittest.mock import MagicMock, patch
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from deerflow.agents.memory.prompt import format_conversation_for_update
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from deerflow.agents.memory.updater import MemoryUpdater, _extract_text
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from deerflow.config.memory_config import MemoryConfig
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def _make_memory(facts: list[dict[str, object]] | None = None) -> dict[str, object]:
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return {
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"version": "1.0",
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"lastUpdated": "",
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"user": {
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"workContext": {"summary": "", "updatedAt": ""},
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"personalContext": {"summary": "", "updatedAt": ""},
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"topOfMind": {"summary": "", "updatedAt": ""},
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},
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"history": {
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"recentMonths": {"summary": "", "updatedAt": ""},
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"earlierContext": {"summary": "", "updatedAt": ""},
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"longTermBackground": {"summary": "", "updatedAt": ""},
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},
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"facts": facts or [],
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}
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def _memory_config(**overrides: object) -> MemoryConfig:
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config = MemoryConfig()
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for key, value in overrides.items():
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setattr(config, key, value)
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return config
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def test_apply_updates_skips_existing_duplicate_and_preserves_removals() -> None:
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updater = MemoryUpdater()
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current_memory = _make_memory(
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facts=[
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{
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"id": "fact_existing",
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"content": "User likes Python",
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"category": "preference",
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"confidence": 0.9,
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"createdAt": "2026-03-18T00:00:00Z",
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"source": "thread-a",
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},
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{
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"id": "fact_remove",
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"content": "Old context to remove",
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"category": "context",
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"confidence": 0.8,
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"createdAt": "2026-03-18T00:00:00Z",
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"source": "thread-a",
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},
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]
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)
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update_data = {
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"factsToRemove": ["fact_remove"],
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"newFacts": [
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{"content": "User likes Python", "category": "preference", "confidence": 0.95},
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],
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}
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with patch(
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"deerflow.agents.memory.updater.get_memory_config",
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return_value=_memory_config(max_facts=100, fact_confidence_threshold=0.7),
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):
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result = updater._apply_updates(current_memory, update_data, thread_id="thread-b")
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assert [fact["content"] for fact in result["facts"]] == ["User likes Python"]
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assert all(fact["id"] != "fact_remove" for fact in result["facts"])
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def test_apply_updates_skips_same_batch_duplicates_and_keeps_source_metadata() -> None:
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updater = MemoryUpdater()
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current_memory = _make_memory()
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update_data = {
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"newFacts": [
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{"content": "User prefers dark mode", "category": "preference", "confidence": 0.91},
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{"content": "User prefers dark mode", "category": "preference", "confidence": 0.92},
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{"content": "User works on DeerFlow", "category": "context", "confidence": 0.87},
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],
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}
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with patch(
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"deerflow.agents.memory.updater.get_memory_config",
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return_value=_memory_config(max_facts=100, fact_confidence_threshold=0.7),
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):
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result = updater._apply_updates(current_memory, update_data, thread_id="thread-42")
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assert [fact["content"] for fact in result["facts"]] == [
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"User prefers dark mode",
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"User works on DeerFlow",
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]
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assert all(fact["id"].startswith("fact_") for fact in result["facts"])
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assert all(fact["source"] == "thread-42" for fact in result["facts"])
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def test_apply_updates_preserves_threshold_and_max_facts_trimming() -> None:
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updater = MemoryUpdater()
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current_memory = _make_memory(
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facts=[
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{
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"id": "fact_python",
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"content": "User likes Python",
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"category": "preference",
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"confidence": 0.95,
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"createdAt": "2026-03-18T00:00:00Z",
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"source": "thread-a",
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},
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{
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"id": "fact_dark_mode",
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"content": "User prefers dark mode",
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"category": "preference",
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"confidence": 0.8,
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"createdAt": "2026-03-18T00:00:00Z",
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"source": "thread-a",
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},
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]
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)
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update_data = {
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"newFacts": [
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{"content": "User prefers dark mode", "category": "preference", "confidence": 0.9},
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{"content": "User uses uv", "category": "context", "confidence": 0.85},
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{"content": "User likes noisy logs", "category": "behavior", "confidence": 0.6},
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],
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}
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with patch(
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"deerflow.agents.memory.updater.get_memory_config",
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return_value=_memory_config(max_facts=2, fact_confidence_threshold=0.7),
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):
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result = updater._apply_updates(current_memory, update_data, thread_id="thread-9")
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assert [fact["content"] for fact in result["facts"]] == [
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"User likes Python",
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"User uses uv",
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]
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assert all(fact["content"] != "User likes noisy logs" for fact in result["facts"])
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assert result["facts"][1]["source"] == "thread-9"
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# ---------------------------------------------------------------------------
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# _extract_text — LLM response content normalization
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# ---------------------------------------------------------------------------
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class TestExtractText:
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"""_extract_text should normalize all content shapes to plain text."""
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def test_string_passthrough(self):
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assert _extract_text("hello world") == "hello world"
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def test_list_single_text_block(self):
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assert _extract_text([{"type": "text", "text": "hello"}]) == "hello"
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def test_list_multiple_text_blocks_joined(self):
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content = [
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{"type": "text", "text": "part one"},
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{"type": "text", "text": "part two"},
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]
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assert _extract_text(content) == "part one\npart two"
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def test_list_plain_strings(self):
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assert _extract_text(["raw string"]) == "raw string"
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def test_list_string_chunks_join_without_separator(self):
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content = ['{"user"', ': "alice"}']
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assert _extract_text(content) == '{"user": "alice"}'
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def test_list_mixed_strings_and_blocks(self):
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content = [
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"raw text",
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{"type": "text", "text": "block text"},
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]
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assert _extract_text(content) == "raw text\nblock text"
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def test_list_adjacent_string_chunks_then_block(self):
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content = [
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"prefix",
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"-continued",
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{"type": "text", "text": "block text"},
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]
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assert _extract_text(content) == "prefix-continued\nblock text"
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def test_list_skips_non_text_blocks(self):
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content = [
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{"type": "image_url", "image_url": {"url": "http://img.png"}},
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{"type": "text", "text": "actual text"},
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]
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assert _extract_text(content) == "actual text"
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def test_empty_list(self):
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assert _extract_text([]) == ""
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def test_list_no_text_blocks(self):
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assert _extract_text([{"type": "image_url", "image_url": {}}]) == ""
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def test_non_str_non_list(self):
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assert _extract_text(42) == "42"
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# ---------------------------------------------------------------------------
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# format_conversation_for_update — handles mixed list content
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# ---------------------------------------------------------------------------
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class TestFormatConversationForUpdate:
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def test_plain_string_messages(self):
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human_msg = MagicMock()
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human_msg.type = "human"
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human_msg.content = "What is Python?"
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ai_msg = MagicMock()
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ai_msg.type = "ai"
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ai_msg.content = "Python is a programming language."
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result = format_conversation_for_update([human_msg, ai_msg])
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assert "User: What is Python?" in result
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assert "Assistant: Python is a programming language." in result
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def test_list_content_with_plain_strings(self):
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"""Plain strings in list content should not be lost."""
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msg = MagicMock()
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msg.type = "human"
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msg.content = ["raw user text", {"type": "text", "text": "structured text"}]
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result = format_conversation_for_update([msg])
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assert "raw user text" in result
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assert "structured text" in result
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# ---------------------------------------------------------------------------
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# update_memory — structured LLM response handling
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# ---------------------------------------------------------------------------
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class TestUpdateMemoryStructuredResponse:
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"""update_memory should handle LLM responses returned as list content blocks."""
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def _make_mock_model(self, content):
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model = MagicMock()
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response = MagicMock()
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response.content = content
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model.invoke.return_value = response
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return model
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def test_string_response_parses(self):
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updater = MemoryUpdater()
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valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
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with (
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patch.object(updater, "_get_model", return_value=self._make_mock_model(valid_json)),
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patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
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patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
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patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
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):
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msg = MagicMock()
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msg.type = "human"
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msg.content = "Hello"
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ai_msg = MagicMock()
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ai_msg.type = "ai"
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ai_msg.content = "Hi there"
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ai_msg.tool_calls = []
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result = updater.update_memory([msg, ai_msg])
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assert result is True
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def test_list_content_response_parses(self):
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"""LLM response as list-of-blocks should be extracted, not repr'd."""
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updater = MemoryUpdater()
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valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
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list_content = [{"type": "text", "text": valid_json}]
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with (
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patch.object(updater, "_get_model", return_value=self._make_mock_model(list_content)),
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patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
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patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
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patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
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):
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msg = MagicMock()
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msg.type = "human"
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msg.content = "Hello"
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ai_msg = MagicMock()
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ai_msg.type = "ai"
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ai_msg.content = "Hi"
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ai_msg.tool_calls = []
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result = updater.update_memory([msg, ai_msg])
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assert result is True
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