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agentframe/config.py

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+"""AgentFrame 配置系统: 环境变量 + JSON 配置文件 + 默认值"""
+import json
+import os
+from dataclasses import dataclass, field, asdict
+
+
+@dataclass
+class LLMConfig:
+    """LLM Provider 配置"""
+    provider: str = "deepseek"              # deepseek | openai | mock
+    api_key: str = ""                       # 从环境变量 AGENTFRAME_API_KEY 或 DEEPSEEK_API_KEY
+    base_url: str = "https://api.deepseek.com/v1"
+    model: str = "deepseek-v4-pro"          # 主模型
+    fast_model: str = "deepseek-v4-flash"   # 快速模型 (检测/摘要)
+    max_tokens: int = 2000
+    temperature: float = 0.8
+    timeout: int = 120
+
+
+@dataclass
+class MemoryConfig:
+    """上下文保持核心配置"""
+    n_layers: int = 27                      # 模型层数 (DeepSeek-V2 27 层)
+    quant_bits: int = 4                     # 量化位宽 (4 = INT4, 35.6x)
+    top_k: int = 32                         # 路由检索 top-k
+    vram_limit_mb: int = 10240              # 显存层上限
+    ram_limit_mb: int = 32768               # 内存层上限
+    seed: int = 42
+    reversible: bool = False                # 可逆量化开关
+
+
+@dataclass
+class APIConfig:
+    """API 服务配置"""
+    host: str = "0.0.0.0"
+    port: int = 8090
+    debug: bool = False
+    max_history_turns: int = 12             # 对话历史保留轮数
+
+
+@dataclass
+class AgentFrameConfig:
+    """总配置"""
+    llm: LLMConfig = field(default_factory=LLMConfig)
+    memory: MemoryConfig = field(default_factory=MemoryConfig)
+    api: APIConfig = field(default_factory=APIConfig)
+
+    @classmethod
+    def from_env(cls) -> "AgentFrameConfig":
+        """从环境变量加载 (最高优先级)"""
+        cfg = cls()
+        cfg.llm.api_key = (
+            os.environ.get("AGENTFRAME_API_KEY")
+            or os.environ.get("DEEPSEEK_API_KEY")
+            or cfg.llm.api_key
+        )
+        cfg.llm.base_url = os.environ.get("AGENTFRAME_BASE_URL", cfg.llm.base_url)
+        cfg.llm.model = os.environ.get("AGENTFRAME_MODEL", cfg.llm.model)
+        cfg.llm.fast_model = os.environ.get("AGENTFRAME_FAST_MODEL", cfg.llm.fast_model)
+        cfg.api.port = int(os.environ.get("AGENTFRAME_PORT", cfg.api.port))
+        return cfg
+
+    @classmethod
+    def from_file(cls, path: str) -> "AgentFrameConfig":
+        """从 JSON 配置文件加载"""
+        with open(path) as f:
+            data = json.load(f)
+        cfg = cls.from_env()
+        # 覆盖: 文件 < 环境变量
+        if "llm" in data:
+            for k, v in data["llm"].items():
+                setattr(cfg.llm, k, v)
+        if "memory" in data:
+            for k, v in data["memory"].items():
+                setattr(cfg.memory, k, v)
+        if "api" in data:
+            for k, v in data["api"].items():
+                setattr(cfg.api, k, v)
+        return cfg
+
+    def to_dict(self) -> dict:
+        """导出为 dict (序列化用)"""
+        return {
+            "llm": asdict(self.llm),
+            "memory": asdict(self.memory),
+            "api": asdict(self.api),
+        }
+
+    def save(self, path: str):
+        """保存配置到 JSON"""
+        with open(path, "w") as f:
+            json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)