mirror of
https://github.com/supermemoryai/supermemory.git
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8de27afa2c
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>
156 lines
4.8 KiB
Python
156 lines
4.8 KiB
Python
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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from unittest.mock import AsyncMock
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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 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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if "pipecat" not in sys.modules:
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pipecat_module = types.ModuleType("pipecat")
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sys.modules["pipecat"] = pipecat_module
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frames_module = types.ModuleType("pipecat.frames.frames")
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class Frame: # pragma: no cover - import stub
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pass
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class InputAudioRawFrame: # pragma: no cover - import stub
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pass
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class LLMContextFrame: # pragma: no cover - import stub
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pass
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class LLMMessagesFrame: # pragma: no cover - import stub
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pass
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frames_module.Frame = Frame
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frames_module.InputAudioRawFrame = InputAudioRawFrame
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frames_module.LLMContextFrame = LLMContextFrame
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frames_module.LLMMessagesFrame = LLMMessagesFrame
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llm_context_module = types.ModuleType("pipecat.processors.aggregators.llm_context")
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class LLMContext: # pragma: no cover - import stub
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pass
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llm_context_module.LLMContext = LLMContext
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openai_context_module = types.ModuleType(
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"pipecat.processors.aggregators.openai_llm_context"
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)
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class OpenAILLMContextFrame: # pragma: no cover - import stub
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pass
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openai_context_module.OpenAILLMContextFrame = OpenAILLMContextFrame
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frame_processor_module = types.ModuleType("pipecat.processors.frame_processor")
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class FrameDirection: # pragma: no cover - import stub
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pass
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class FrameProcessor:
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def __init__(self, *args, **kwargs):
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return None
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frame_processor_module.FrameDirection = FrameDirection
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frame_processor_module.FrameProcessor = FrameProcessor
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sys.modules["pipecat.frames.frames"] = frames_module
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sys.modules["pipecat.processors.aggregators.llm_context"] = llm_context_module
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sys.modules[
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"pipecat.processors.aggregators.openai_llm_context"
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] = openai_context_module
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sys.modules["pipecat.processors.frame_processor"] = frame_processor_module
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_install_test_stubs()
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from supermemory_pipecat.service import SupermemoryPipecatService
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class _MockSupermemoryClient:
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def __init__(self, response):
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self.profile = AsyncMock(return_value=response)
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class TestSupermemoryPipecatNullProfile(unittest.IsolatedAsyncioTestCase):
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async def test_retrieve_memories_handles_null_profile(self) -> None:
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service = SupermemoryPipecatService(api_key="mock_key", user_id="new_user_123")
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response = SimpleNamespace(profile=None, search_results=None)
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service._supermemory_client = _MockSupermemoryClient(response)
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result = await service._retrieve_memories("Hello world")
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self.assertEqual(
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result,
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{
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"profile": {"static": [], "dynamic": []},
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"search_results": [],
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},
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)
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def test_query_mode_keeps_search_fact_also_present_in_profile(self) -> None:
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fact = "User likes machine learning projects"
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service = SupermemoryPipecatService(
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api_key="mock_key",
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user_id="user-123",
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session_id="conversation-456",
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params=SupermemoryPipecatService.InputParams(mode="query"),
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)
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class Context:
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def __init__(self):
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self.messages = [{"role": "user", "content": "What do I like?"}]
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def get_messages(self):
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return self.messages
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def add_message(self, message):
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self.messages.append(message)
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context = Context()
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service._enhance_context_with_memories(
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context,
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"What do I like?",
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{
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"profile": {"static": [fact], "dynamic": []},
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"search_results": [SimpleNamespace(memory=fact)],
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},
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)
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self.assertTrue(
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any(fact in message.get("content", "") for message in context.messages)
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)
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