mirror of
https://github.com/supermemoryai/supermemory.git
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42f308b224
Port the normalized, priority-ordered (static > dynamic > search) profile deduplication into the Python SDKs, injecting one owned memory block per request that replaces the prior block rather than accumulating. Dedup is request-local (no shared state), so it stays correct under concurrency. Covers OpenAI, Agent Framework (middleware + context provider), Cartesia, and Pipecat. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
129 lines
4 KiB
Python
129 lines
4 KiB
Python
"""Regression tests for pydantic/dict memory helpers (#1266)."""
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from __future__ import annotations
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import sys
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import types
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import unittest
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from types import SimpleNamespace
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def _install_test_stubs() -> None:
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if "loguru" not in sys.modules:
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loguru_module = types.ModuleType("loguru")
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class _Logger:
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def info(self, *_args, **_kwargs):
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return None
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def warning(self, *_args, **_kwargs):
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return None
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def error(self, *_args, **_kwargs):
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return None
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loguru_module.logger = _Logger()
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sys.modules["loguru"] = loguru_module
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if "pydantic" not in sys.modules:
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pydantic_module = types.ModuleType("pydantic")
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class BaseModel:
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def __init__(self, **kwargs):
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for key, value in kwargs.items():
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setattr(self, key, value)
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def Field(*, default=None, **_kwargs):
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return default
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pydantic_module.BaseModel = BaseModel
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pydantic_module.Field = Field
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sys.modules["pydantic"] = pydantic_module
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_install_test_stubs()
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from supermemory_cartesia.utils import deduplicate_memories, format_memories_to_text
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class TestDeduplicateMemories(unittest.TestCase):
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def test_accepts_dict_search_results(self) -> None:
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result = deduplicate_memories(
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static=["User likes Python"],
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dynamic=[],
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search_results=[{"memory": "User prefers async", "updatedAt": "2026-01-01T00:00:00Z"}],
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)
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self.assertEqual(result["static"], ["User likes Python"])
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self.assertEqual(len(result["search_results"]), 1)
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def test_accepts_pydantic_like_search_results(self) -> None:
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# Mirrors supermemory.types.search_memories_response.Result
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model = SimpleNamespace(
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id="mem_1",
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similarity=0.9,
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memory="User prefers async",
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updated_at="2026-01-01T00:00:00Z",
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)
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result = deduplicate_memories(
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static=[],
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dynamic=[],
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search_results=[model],
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)
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self.assertEqual(len(result["search_results"]), 1)
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self.assertIs(result["search_results"][0], model)
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def test_dedupes_model_against_static_string(self) -> None:
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model = SimpleNamespace(memory="User likes Python", updated_at=None)
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result = deduplicate_memories(
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static=["User likes Python"],
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dynamic=[],
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search_results=[model],
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)
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self.assertEqual(result["search_results"], [])
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def test_dedupes_normalized_fact_variants(self) -> None:
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result = deduplicate_memories(
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static=["User likes Python", " user likes python "],
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dynamic=["[2026-08-10] USER LIKES PYTHON"],
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search_results=[],
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)
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self.assertEqual(result["static"], ["User likes Python"])
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self.assertEqual(result["dynamic"], [])
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class TestFormatMemoriesToText(unittest.TestCase):
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def test_formats_pydantic_like_search_results(self) -> None:
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text = format_memories_to_text(
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{
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"static": [],
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"dynamic": [],
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"search_results": [
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SimpleNamespace(
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memory="User prefers async",
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updated_at="2020-01-01T00:00:00Z",
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)
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],
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}
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)
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self.assertIn("User prefers async", text)
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self.assertIn("Relevant Memories", text)
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def test_formats_search_execute_content_field(self) -> None:
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text = format_memories_to_text(
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{
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"static": [],
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"dynamic": [],
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"search_results": [
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SimpleNamespace(
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content="User owns a telescope",
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updated_at="2020-01-01T00:00:00Z",
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memory=None,
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)
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],
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}
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)
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self.assertIn("User owns a telescope", text)
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if __name__ == "__main__":
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unittest.main()
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