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feat(arkitect): responses
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‎arkitect/core/component/memory/__init__.py‎

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# Copyright 2025 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .base_memory_service import BaseMemoryService
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from .in_memory_memory_service import (
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InMemoryMemoryService,
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InMemoryMemoryServiceSingleton,
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)
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from .mem0_memory_service import Mem0MemoryService, Mem0MemoryServiceSingleton
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__all__ = [
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"BaseMemoryService",
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"InMemoryMemoryService",
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"InMemoryMemoryServiceSingleton",
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"Mem0MemoryService",
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"Mem0MemoryServiceSingleton",
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]

‎arkitect/core/component/memory/base_memory_service.py‎

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# Copyright 2025 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from abc import ABC, abstractmethod
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from typing import Any
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from openai.types.responses import Response
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from pydantic import BaseModel
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from volcenginesdkarkruntime.types.chat.chat_completion_message import (
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ChatCompletionMessage,
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)
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from arkitect.types.llm.model import ArkMessage
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class Memory(BaseModel):
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memory_content: str
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reference: Any | None = None
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metadata: Any | None = None
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class SearchMemoryResponse(BaseModel):
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memories: list[Memory]
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@property
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def content(self) -> str:
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return "\n".join([m.memory_content for m in self.memories])
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class BaseMemoryService(ABC):
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@abstractmethod
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async def update_memory(
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self,
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user_id: str,
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new_messages: list[ArkMessage | dict | Response | ChatCompletionMessage],
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**kwargs: Any,
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) -> None:
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pass
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@abstractmethod
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async def search_memory(
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self,
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user_id: str,
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query: str,
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**kwargs: Any,
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) -> SearchMemoryResponse:
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pass

‎arkitect/core/component/memory/in_memory_memory_service.py‎

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# Copyright 2025 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any
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from openai.types.responses import Response
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from typing_extensions import override
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from volcenginesdkarkruntime import AsyncArk
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from volcenginesdkarkruntime.types.chat.chat_completion_message import (
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ChatCompletionMessage,
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)
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from arkitect.core.component.memory.base_memory_service import (
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BaseMemoryService,
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Memory,
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SearchMemoryResponse,
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)
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from arkitect.core.component.memory.utils import format_message_as_string
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from arkitect.types.llm.model import ArkMessage
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from arkitect.utils.common import Singleton
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DEFAULT_SEARCH_MEM_PROMPT = """
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You have obtained a series of interactions between a user and an AI assistant.
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Please identify the user’s profile and other key information from
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past interactions to help answer the user’s new question.
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"""
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DEFAULT_SEARCH_LLM_MODEL = "doubao-1-5-pro-32k-250115"
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class InMemoryMemoryService(BaseMemoryService):
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def __init__(
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self,
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default_search_model: str = DEFAULT_SEARCH_LLM_MODEL,
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default_search_prompt: str = DEFAULT_SEARCH_MEM_PROMPT,
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) -> None:
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self.default_search_model = default_search_model
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self.default_search_prompt = default_search_prompt
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self.memory: dict = {}
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self._cached_query: dict = {}
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self._llm = AsyncArk()
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@override
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async def update_memory(
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self,
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user_id: str,
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new_messages: list[ArkMessage | dict | Response | ChatCompletionMessage],
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**kwargs: Any,
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) -> None:
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if user_id not in self.memory:
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self.memory[user_id] = []
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self.memory[user_id].extend(new_messages)
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# invalidate cache
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self._cached_query[user_id] = {}
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@override
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async def search_memory(
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self,
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user_id: str,
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query: str,
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**kwargs: Any,
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) -> SearchMemoryResponse:
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if user_id not in self.memory:
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return SearchMemoryResponse(
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memories=[
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Memory(
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memory_content="no memory found for this user",
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reference=None,
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)
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]
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)
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if self._cached_query.get(user_id, {}).get(query, None) is not None:
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return self._cached_query[user_id][query]
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memories = self.memory[user_id]
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results = "用户过去的交互记录\n\n"
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for memory in memories:
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content = format_message_as_string(memory)
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results += content
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summary = await self._llm.chat.completions.create(
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model=self.default_search_model,
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messages=[
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{
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"role": "system",
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"content": self.default_search_prompt,
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},
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{
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"role": "user",
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"content": results,
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},
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],
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stream=False,
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)
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memory_response = SearchMemoryResponse(
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memories=[
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Memory(
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memory_content=summary.choices[0].message.content,
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reference=None,
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)
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]
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)
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if user_id not in self._cached_query:
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self._cached_query[user_id] = {}
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self._cached_query[user_id][query] = memory_response
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return memory_response
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@override
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async def delete_user(self, user_id: str) -> None:
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if user_id in self.memory:
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del self.memory[user_id]
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class InMemoryMemoryServiceSingleton(InMemoryMemoryService, Singleton):
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pass

‎arkitect/core/component/memory/mem0_memory_service.py‎

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# Copyright 2025 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import asyncio
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import os
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from typing import Any
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from mem0 import AsyncMemory as Mem0Memory
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from mem0.configs.base import MemoryConfig as Mem0Config
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from mem0.embeddings.configs import EmbedderConfig
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from mem0.llms.configs import LlmConfig
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from mem0.vector_stores.configs import VectorStoreConfig
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from openai.types.responses import Response
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from pydantic import BaseModel
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from typing_extensions import override
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from volcenginesdkarkruntime import AsyncArk
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from volcenginesdkarkruntime.types.chat.chat_completion_message import (
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ChatCompletionMessage,
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)
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from arkitect.core.component.memory.base_memory_service import (
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BaseMemoryService,
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Memory,
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SearchMemoryResponse,
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)
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from arkitect.core.component.memory.utils import format_message_as_dict
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from arkitect.telemetry.logger import ERROR, INFO
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from arkitect.types.llm.model import ArkMessage
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from arkitect.utils.common import Singleton
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DEFAULT_EMBEDDING_MODEL = "doubao-embedding-text-240715"
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DEFAULT_LLM_MODEL = "doubao-1-5-vision-pro-32k-250115"
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DEFAULT_BASE_URL = "https://ark.cn-beijing.volces.com/api/v3"
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default_ark_config = Mem0Config(
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embedder=EmbedderConfig(
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provider="openai",
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config={
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"model": DEFAULT_EMBEDDING_MODEL,
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"openai_base_url": DEFAULT_BASE_URL,
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"api_key": os.getenv("ARK_API_KEY"),
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"embedding_dims": 2560,
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},
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),
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llm=LlmConfig(
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provider="openai",
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config={
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"model": DEFAULT_LLM_MODEL,
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"openai_base_url": DEFAULT_BASE_URL,
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"api_key": os.getenv("ARK_API_KEY"),
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},
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),
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vector_store=VectorStoreConfig(config={"embedding_model_dims": 2560}),
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)
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class Mem0MemoryService(BaseMemoryService):
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def __init__(self, config: Mem0Config = default_ark_config) -> None:
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self.mem0_config = config if config else Mem0Config()
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self._llm = AsyncArk()
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self.memory = Mem0Memory(config=self.mem0_config)
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self._task_queue: asyncio.Queue = asyncio.Queue()
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@override
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async def update_memory(
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self,
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user_id: str,
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new_messages: list[ArkMessage | dict | Response | ChatCompletionMessage],
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blocking: bool = False,
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**kwargs: Any,
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) -> None:
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conversation = []
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for item in new_messages:
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conversation.append(format_message_as_dict(item))
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if blocking:
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await self._add_memory(conversation, user_id)
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else:
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await self._task_queue.put(
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asyncio.create_task(self._add_memory(conversation, user_id))
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)
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INFO("Memory update submitted")
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async def _add_memory(self, conversation, user_id):
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await self.memory.add(conversation, user_id=user_id)
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INFO("Memory update completed")
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async def _background_processor(self) -> None:
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while True:
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task = await self._task_queue.get()
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try:
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await task
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except Exception as e:
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ERROR(f"Memory update failed: {e}")
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self._task_queue.task_done()
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@override
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async def search_memory(
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self,
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user_id: str,
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query: str,
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**kwargs: Any,
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) -> SearchMemoryResponse:
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relevant_memories = await self.memory.search(
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query=query, user_id=user_id, limit=3
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)
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fetched_results = relevant_memories.get("results", [])
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memeory_string = ""
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for element in fetched_results:
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memeory_string += element.get("memory", "") + "\n"
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return SearchMemoryResponse(
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memories=[
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Memory(
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memory_content=memeory_string,
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reference=None,
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metadata=relevant_memories,
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)
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]
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)
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async def delete_user(self, user_id: str) -> None:
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await self.memory.delete_all(user_id=user_id)
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class Mem0MemoryServiceSingleton(Mem0MemoryService, Singleton):
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pass

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