mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-04-25 11:18:22 +00:00
7eb3a150b5
* Add MVP memory management actions * Fix memory settings locale coverage * Polish memory management interactions * Add memory search and type filters * Refine memory settings review feedback * docs: simplify memory settings review setup * fix: restore memory updater compatibility helpers * fix: address memory settings review feedback * docs: soften memory sample review wording --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: JeffJiang <for-eleven@hotmail.com>
345 lines
12 KiB
Python
345 lines
12 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 (
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MemoryUpdater,
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_extract_text,
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clear_memory_data,
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delete_memory_fact,
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)
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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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def test_clear_memory_data_resets_all_sections() -> None:
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with patch("deerflow.agents.memory.updater._save_memory_to_file", return_value=True):
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result = clear_memory_data()
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assert result["version"] == "1.0"
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assert result["facts"] == []
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assert result["user"]["workContext"]["summary"] == ""
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assert result["history"]["recentMonths"]["summary"] == ""
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def test_delete_memory_fact_removes_only_matching_fact() -> None:
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current_memory = _make_memory(
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facts=[
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{
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"id": "fact_keep",
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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_delete",
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"content": "User prefers tabs",
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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-b",
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},
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]
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)
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with (
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patch("deerflow.agents.memory.updater.get_memory_data", return_value=current_memory),
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patch("deerflow.agents.memory.updater._save_memory_to_file", return_value=True),
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):
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result = delete_memory_fact("fact_delete")
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assert [fact["id"] for fact in result["facts"]] == ["fact_keep"]
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def test_delete_memory_fact_raises_for_unknown_id() -> None:
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with patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()):
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try:
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delete_memory_fact("fact_missing")
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except KeyError as exc:
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assert exc.args == ("fact_missing",)
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else:
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raise AssertionError("Expected KeyError for missing fact id")
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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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