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https://github.com/zhayujie/chatgpt-on-wechat.git
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115 lines
3.2 KiB
Python
115 lines
3.2 KiB
Python
"""
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Memory configuration module
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Provides global memory configuration with simplified workspace structure
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"""
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import os
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from dataclasses import dataclass, field
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from typing import Optional, List
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from pathlib import Path
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@dataclass
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class MemoryConfig:
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"""Configuration for memory storage and search"""
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# Storage paths (default: ~/cow)
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workspace_root: str = field(default_factory=lambda: os.path.expanduser("~/cow"))
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# Embedding config
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embedding_provider: str = "openai" # "openai" | "local"
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embedding_model: str = "text-embedding-3-small"
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embedding_dim: int = 1536
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# Chunking config
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chunk_max_tokens: int = 500
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chunk_overlap_tokens: int = 50
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# Search config
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max_results: int = 10
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min_score: float = 0.1
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# Hybrid search weights
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vector_weight: float = 0.7
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keyword_weight: float = 0.3
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# Memory sources
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sources: List[str] = field(default_factory=lambda: ["memory", "session"])
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# Sync config
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enable_auto_sync: bool = True
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sync_on_search: bool = True
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def get_workspace(self) -> Path:
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"""Get workspace root directory"""
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return Path(self.workspace_root)
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def get_memory_dir(self) -> Path:
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"""Get memory files directory"""
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return self.get_workspace() / "memory"
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def get_db_path(self) -> Path:
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"""Get SQLite database path for long-term memory index"""
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index_dir = self.get_memory_dir() / "long-term"
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index_dir.mkdir(parents=True, exist_ok=True)
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return index_dir / "index.db"
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def get_skills_dir(self) -> Path:
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"""Get skills directory"""
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return self.get_workspace() / "skills"
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def get_agent_workspace(self, agent_name: Optional[str] = None) -> Path:
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"""
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Get workspace directory for an agent
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Args:
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agent_name: Optional agent name (not used in current implementation)
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Returns:
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Path to workspace directory
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"""
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workspace = self.get_workspace()
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# Ensure workspace directory exists
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workspace.mkdir(parents=True, exist_ok=True)
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return workspace
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# Global memory configuration
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_global_memory_config: Optional[MemoryConfig] = None
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def get_default_memory_config() -> MemoryConfig:
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"""
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Get the global memory configuration.
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If not set, returns a default configuration.
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Returns:
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MemoryConfig instance
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"""
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global _global_memory_config
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if _global_memory_config is None:
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_global_memory_config = MemoryConfig()
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return _global_memory_config
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def set_global_memory_config(config: MemoryConfig):
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"""
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Set the global memory configuration.
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This should be called before creating any MemoryManager instances.
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Args:
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config: MemoryConfig instance to use globally
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Example:
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>>> from agent.memory import MemoryConfig, set_global_memory_config
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>>> config = MemoryConfig(
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... workspace_root="~/my_agents",
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... embedding_provider="openai",
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... vector_weight=0.8
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... )
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>>> set_global_memory_config(config)
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"""
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global _global_memory_config
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_global_memory_config = config
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