记忆系统，上下文处理+mem0

art-agent/backend/app/memory.py

@@ -0,0 +1,99 @@
+"""
+Mem0 记忆层初始化与健康检查。
+启动时验证 Ollama 可达和模型可用，失败则抛出 RuntimeError 阻止后端启动。
+"""
+
+import logging
+import httpx
+from mem0 import Memory
+
+from app.config import (
+    get_deepseek_api_key,
+    get_ollama_base_url,
+    get_mem0_embedding_model,
+)
+
+logger = logging.getLogger(__name__)
+
+_memory: Memory | None = None
+
+
+def _check_ollama_health() -> None:
+    """检查 Ollama 服务是否可达，以及 embedding 模型是否已安装。"""
+    base_url = get_ollama_base_url()
+    model_name = get_mem0_embedding_model()
+
+    try:
+        resp = httpx.get(f"{base_url}/api/tags", timeout=5)
+        resp.raise_for_status()
+    except (httpx.ConnectError, httpx.TimeoutException, httpx.HTTPStatusError) as e:
+        raise RuntimeError(
+            f"无法连接 Ollama 服务（{base_url}）。"
+            f"请确认 Ollama 已启动：启动方式参见 https://ollama.com\n"
+            f"原始错误：{e}"
+        ) from e
+
+    models = resp.json().get("models", [])
+    installed = [m.get("name", "").split(":")[0] for m in models]
+    if model_name not in installed:
+        raise RuntimeError(
+            f"Ollama 已运行，但未找到 embedding 模型 '{model_name}'。\n"
+            f"已安装的模型：{installed}\n"
+            f"请运行：ollama pull {model_name}"
+        )
+
+    logger.info("Ollama 健康检查通过：%s 模型可用", model_name)
+
+
+def init_memory() -> Memory:
+    """初始化 Mem0 Memory 单例。首次调用时执行健康检查。"""
+    global _memory
+    if _memory is not None:
+        return _memory
+
+    _check_ollama_health()
+
+    deepseek_key = get_deepseek_api_key()
+    if not deepseek_key:
+        raise RuntimeError(
+            "DEEPSEEK_API_KEY 未配置。Mem0 需要该 key 进行事实提取。\n"
+            "请在 .env 中设置 DEEPSEEK_API_KEY"
+        )
+
+    config = {
+        "llm": {
+            "provider": "deepseek",
+            "config": {
+                "model": "deepseek-chat",
+                "temperature": 0.1,
+                "max_tokens": 1500,
+                "api_key": deepseek_key,
+            },
+        },
+        "embedder": {
+            "provider": "ollama",
+            "config": {
+                "model": get_mem0_embedding_model(),
+                "ollama_base_url": get_ollama_base_url(),
+            },
+        },
+        "vector_store": {
+            "provider": "qdrant",
+            "config": {
+                "collection_name": "epeekit_memories",
+                "embedding_model_dims": 768,
+                "path": "./data/qdrant",
+            },
+        },
+    }
+
+    _memory = Memory.from_config(config)
+    logger.info("Mem0 记忆层初始化成功")
+    return _memory
+
+
+def get_memory() -> Memory:
+    """获取已初始化的 Memory 实例。未初始化时自动调用 init_memory()。"""
+    if _memory is None:
+        return init_memory()
+    return _memory