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https://gitee.com/wanwujie/deer-flow
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Implement a memory system that stores user context and conversation history in memory.json, uses LLM to summarize conversations, and injects relevant context into system prompts for personalized responses. Key components: - MemoryConfig for configuration management - MemoryUpdateQueue with debounce for batch processing - MemoryUpdater for LLM-based memory extraction - MemoryMiddleware to queue conversations after agent execution - Memory injection into lead agent system prompt Note: Add memory section to config.yaml to enable (see config.example.yaml) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
"""Memory module for DeerFlow.
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This module provides a global memory mechanism that:
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- Stores user context and conversation history in memory.json
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- Uses LLM to summarize and extract facts from conversations
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- Injects relevant memory into system prompts for personalized responses
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"""
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from src.agents.memory.prompt import (
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FACT_EXTRACTION_PROMPT,
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MEMORY_UPDATE_PROMPT,
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format_conversation_for_update,
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format_memory_for_injection,
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)
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from src.agents.memory.queue import (
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ConversationContext,
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MemoryUpdateQueue,
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get_memory_queue,
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reset_memory_queue,
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)
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from src.agents.memory.updater import (
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MemoryUpdater,
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get_memory_data,
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reload_memory_data,
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update_memory_from_conversation,
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)
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__all__ = [
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# Prompt utilities
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"MEMORY_UPDATE_PROMPT",
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"FACT_EXTRACTION_PROMPT",
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"format_memory_for_injection",
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"format_conversation_for_update",
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# Queue
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"ConversationContext",
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"MemoryUpdateQueue",
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"get_memory_queue",
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"reset_memory_queue",
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# Updater
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"MemoryUpdater",
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"get_memory_data",
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"reload_memory_data",
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"update_memory_from_conversation",
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]
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