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https://gitee.com/wanwujie/deer-flow
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0ea666e0cf
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>
70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
"""Configuration for memory mechanism."""
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from pydantic import BaseModel, Field
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class MemoryConfig(BaseModel):
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"""Configuration for global memory mechanism."""
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enabled: bool = Field(
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default=True,
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description="Whether to enable memory mechanism",
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)
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storage_path: str = Field(
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default=".deer-flow/memory.json",
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description="Path to store memory data (relative to backend directory)",
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)
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debounce_seconds: int = Field(
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default=30,
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ge=1,
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le=300,
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description="Seconds to wait before processing queued updates (debounce)",
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)
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model_name: str | None = Field(
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default=None,
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description="Model name to use for memory updates (None = use default model)",
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)
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max_facts: int = Field(
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default=100,
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ge=10,
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le=500,
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description="Maximum number of facts to store",
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)
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fact_confidence_threshold: float = Field(
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default=0.7,
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ge=0.0,
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le=1.0,
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description="Minimum confidence threshold for storing facts",
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)
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injection_enabled: bool = Field(
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default=True,
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description="Whether to inject memory into system prompt",
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)
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max_injection_tokens: int = Field(
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default=2000,
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ge=100,
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le=8000,
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description="Maximum tokens to use for memory injection",
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)
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# Global configuration instance
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_memory_config: MemoryConfig = MemoryConfig()
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def get_memory_config() -> MemoryConfig:
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"""Get the current memory configuration."""
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return _memory_config
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def set_memory_config(config: MemoryConfig) -> None:
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"""Set the memory configuration."""
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global _memory_config
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_memory_config = config
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def load_memory_config_from_dict(config_dict: dict) -> None:
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"""Load memory configuration from a dictionary."""
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global _memory_config
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_memory_config = MemoryConfig(**config_dict)
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