修改引用逻辑，修改长期记忆bug

app/__init__.py

@@ -2,7 +2,7 @@
 AI Agent 应用模块
 """
 
-from .agent import AIAgentService
+from app.agent import AIAgentService
-from .graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
+from app.graph.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
 
 __all__ = ["AIAgentService", "AVAILABLE_TOOLS", "TOOLS_BY_NAME"]

app/agent/__init__.py

@@ -0,0 +1,7 @@
+"""
+Agent 子模块
+"""
+
+from app.agent.service import AIAgentService
+
+__all__ = ["AIAgentService"]

app/agent/history.py

@@ -3,11 +3,9 @@
 利用 LangGraph 的 checkpointer 获取对话历史和摘要
 """
 
-from typing import List, Dict, Any, Optional
+from typing import List, Dict, Any
-import logging
 from app.logger import error # 保持兼容，或者替换为 logger
 
-
 class ThreadHistoryService:
     """线程历史查询服务"""
     

app/agent/prompts.py

@@ -1,6 +1,5 @@
 # app/prompts.py
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.tools import BaseTool
 
 def create_system_prompt(tools: list = None) -> ChatPromptTemplate:
     """
@@ -11,7 +10,7 @@ def create_system_prompt(tools: list = None) -> ChatPromptTemplate:
         tool_descs = []
         for tool in tools:
             # 提取工具名称和描述的第一行
-            name = getattr(tool, 'name', tool.__name__)
+            name = getattr(tool, 'name', None) or getattr(tool, '__name__', 'unknown_tool')
             desc = (tool.description or "").split('\n')[0]
             tool_descs.append(f"- {name}: {desc}")
         tools_section = "\n".join(tool_descs)

app/agent/service.py

@@ -3,27 +3,17 @@ AI Agent 服务类 - 支持多模型动态切换
 接收外部传入的 checkpointer，不负责管理连接生命周期
 """
 
-import os
 import json
 from dotenv import load_dotenv
 
-from langchain_community.chat_models import ChatZhipuAI
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-from pydantic import SecretStr
-from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
-
 # 本地模块
-from app.graph_builder import GraphBuilder, GraphContext
+from app.graph.graph_builder import GraphBuilder, GraphContext
-from app.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
+from app.graph.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
-from app.rag import RAGPipeline
+from app.agent.llm_factory import LLMFactory
-from app.rag.tools import create_rag_tool_sync
+from app.agent.rag_initializer import init_rag_tool
-from rag_core import create_parent_retriever
+from app.logger import info, warning
-from app.llm_factory import LLMFactory
-from app.rag_initializer import init_rag_tool
-from app.logger import debug, info, warning, error
 load_dotenv()
 
-
 class AIAgentService:
     def __init__(self, checkpointer):
         self.checkpointer = checkpointer

app/backend.py

@@ -16,7 +16,7 @@ from pydantic import BaseModel
 from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
 from app.agent import AIAgentService
 from app.history import ThreadHistoryService
-from app.logger import debug, info, warning, error
+from app.logger import info, error
 
 # 加载 .env 文件
 load_dotenv()
@@ -28,7 +28,6 @@ DB_URI = os.getenv(
     "postgresql://postgres:huang1998@ai-postgres:5432/langgraph_db?sslmode=disable"
 )
 
-
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     """应用生命周期管理：创建并注入全局服务"""
@@ -53,7 +52,6 @@ async def lifespan(app: FastAPI):
         # 5. 关闭时自动清理数据库连接（async with 负责）
         info("🛑 应用关闭，数据库连接池已释放")
 
-
 app = FastAPI(lifespan=lifespan)
 
 # CORS 中间件（允许前端跨域）
@@ -65,14 +63,12 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-
 # ========== 健康检查端点 ==========
 @app.get("/health")
 async def health_check():
     """健康检查端点，用于 Docker 和 CI/CD 监控"""
     return {"status": "ok", "service": "ai-agent-backend"}
 
-
 # ========== Pydantic 模型 ==========
 class ChatRequest(BaseModel):
     message: str
@@ -80,7 +76,6 @@ class ChatRequest(BaseModel):
     model: str = "zhipu"
     user_id: str = "default_user"
 
-
 class ChatResponse(BaseModel):
     reply: str
     thread_id: str
@@ -90,18 +85,15 @@ class ChatResponse(BaseModel):
     total_tokens: int = 0
     elapsed_time: float = 0.0
 
-
 # ========== 依赖注入函数 ==========
 def get_agent_service(request: Request) -> AIAgentService:
     """从 app.state 中获取全局 AIAgentService 实例"""
     return request.app.state.agent_service
 
-
 def get_history_service(request: Request) -> ThreadHistoryService:
     """从 app.state 中获取全局 ThreadHistoryService 实例"""
     return request.app.state.history_service
 
-
 # ========== HTTP 端点 ==========
 @app.post("/chat", response_model=ChatResponse)
 async def chat_endpoint(
@@ -135,7 +127,6 @@ async def chat_endpoint(
         elapsed_time=elapsed_time
     )
 
-
 # ========== 历史查询接口 ==========
 @app.get("/threads")
 async def list_threads(
@@ -147,7 +138,6 @@ async def list_threads(
     threads = await history_service.get_user_threads(user_id, limit)
     return {"threads": threads}
 
-
 @app.get("/thread/{thread_id}/messages")
 async def get_thread_messages(
     thread_id: str,
@@ -158,7 +148,6 @@ async def get_thread_messages(
     messages = await history_service.get_thread_messages(thread_id)
     return {"messages": messages}
 
-
 @app.get("/thread/{thread_id}/summary")
 async def get_thread_summary(
     thread_id: str,
@@ -169,7 +158,6 @@ async def get_thread_summary(
     summary = await history_service.get_thread_summary(thread_id)
     return summary
 
-
 # ========== 流式对话接口 ==========
 @app.post("/chat/stream")
 async def chat_stream_endpoint(
@@ -204,7 +192,6 @@ async def chat_stream_endpoint(
         }
     )
 
-
 # ========== WebSocket 端点（可选） ==========
 @app.websocket("/ws")
 async def websocket_endpoint(
@@ -228,7 +215,6 @@ async def websocket_endpoint(
     except WebSocketDisconnect:
         pass
 
-
 if __name__ == "__main__":
     import uvicorn
     # 使用环境变量或默认端口 8079（避免与 llama.cpp 的 8081 端口冲突）

app/config.py

@@ -18,6 +18,7 @@ MEMORY_SUMMARIZE_INTERVAL = int(os.getenv("MEMORY_SUMMARIZE_INTERVAL", "10"))
 # Qdrant 向量数据库地址
 QDRANT_URL = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
 QDRANT_COLLECTION_NAME = os.getenv("QDRANT_COLLECTION_NAME", "mem0_user_memories")
+QDRANT_API_KEY = os.getenv("QDRANT_API_KEY", "your-qdrant-api-key")
 
 # llama.cpp Embedding 服务地址 (用于 Mem0 的向量化)
 LLAMACPP_EMBEDDING_URL = os.getenv("LLAMACPP_EMBEDDING_URL", "http://127.0.0.1:8082/v1")

app/graph/__init__.py

@@ -0,0 +1,8 @@
+"""
+Graph 子模块
+"""
+
+from app.graph.graph_builder import GraphBuilder
+from app.graph.state import MessagesState, GraphContext
+
+__all__ = ["GraphBuilder", "MessagesState", "GraphContext"]

app/graph/graph_builder.py

@@ -5,18 +5,17 @@ LangGraph 状态图构建模块 - 精简版，仅负责组装图
 
 from langchain_core.language_models import BaseLLM
 from langgraph.graph import StateGraph, START, END
-
-# 本地模块
 from app.graph.state import MessagesState, GraphContext
 from app.nodes import (
+    should_continue,
     create_llm_call_node,
     create_tool_call_node,
     create_retrieve_memory_node,
     create_summarize_node,
-    should_continue
+    finalize_node,
 )
+from app.nodes.memory_trigger import memory_trigger_node, set_mem0_client
 from app.memory import Mem0Client
-from app.nodes.finalize import finalize_node
 
 
 class GraphBuilder:
@@ -45,6 +44,9 @@ class GraphBuilder:
         Returns:
             StateGraph 实例
         """
+        # 注入全局客户端
+        set_mem0_client(self.mem0_client)
+        
         builder = StateGraph(MessagesState, context_schema=GraphContext)
         
         # ⭐ 通过工厂函数创建节点（依赖注入）
@@ -55,6 +57,7 @@ class GraphBuilder:
         
         # 添加节点
         builder.add_node("retrieve_memory", retrieve_memory_node)
+        builder.add_node("memory_trigger", memory_trigger_node)
         builder.add_node("llm_call", llm_call_node)
         builder.add_node("tool_node", tool_call_node)
         builder.add_node("summarize", summarize_node)
@@ -62,7 +65,8 @@ class GraphBuilder:
 
         # 添加边
         builder.add_edge(START, "retrieve_memory")
-        builder.add_edge("retrieve_memory", "llm_call")
+        builder.add_edge("retrieve_memory", "memory_trigger")
+        builder.add_edge("memory_trigger", "llm_call")
         builder.add_conditional_edges(
             "llm_call",
             should_continue,

app/graph/graph_tools.py

@@ -3,7 +3,6 @@
 """
 
 # 标准库
-import os
 from pathlib import Path
 
 # 第三方库
@@ -13,7 +12,6 @@ import requests
 from bs4 import BeautifulSoup
 from langchain_core.tools import tool
 
-
 def _file_allow_check(filename: str) -> Path:
     """检查用户文件名是否位于允许目录 './user_docs' 下，防止路径遍历攻击。"""
     allowed_dir = Path("./user_docs").resolve()
@@ -28,13 +26,11 @@ def _file_allow_check(filename: str) -> Path:
 
     return file_path
 
-
 @tool
 def get_current_temperature(location: str) -> str:
     """获取指定地点的当前温度。"""
     return f'当前{location}的温度为25℃'
 
-
 @tool
 def read_local_file(filename: str) -> str:
     """读取用户指定名称的本地文本文件内容并返回摘要。"""
@@ -46,7 +42,6 @@ def read_local_file(filename: str) -> str:
     except Exception as e:
         return f"读取文件时出错：{str(e)}"
 
-
 @tool
 def read_pdf_summary(filename: str) -> str:
     """读取PDF文件并返回内容文本摘要。"""
@@ -61,7 +56,6 @@ def read_pdf_summary(filename: str) -> str:
     except Exception as e:
         return f"读取PDF出错：{e}"
 
-
 @tool
 def read_excel_as_markdown(filename: str) -> str:
     """读取Excel文件，并将其主要数据转换为Markdown表格格式。"""
@@ -73,7 +67,6 @@ def read_excel_as_markdown(filename: str) -> str:
     except Exception as e:
         return f"读取Excel出错：{e}"
 
-
 @tool
 def fetch_webpage_content(url: str) -> str:
     """抓取给定URL的网页正文内容，并返回清晰的纯文本。"""
@@ -91,7 +84,6 @@ def fetch_webpage_content(url: str) -> str:
     except Exception as e:
         return f"抓取网页时出错：{str(e)}"
 
-
 # 工具列表和映射（全局常量）
 AVAILABLE_TOOLS = [
     get_current_temperature,

app/graph/retrieve_memory.py

@@ -4,15 +4,13 @@
 """
 
 from typing import Any, Dict
-from langgraph.runtime import Runtime
 
 # 本地模块
-from app.graph.state import MessagesState, GraphContext
+from app.graph.state import MessagesState
 from app.memory.mem0_client import Mem0Client
 from app.utils.logging import log_state_change
 from app.logger import debug
 
-
 def create_retrieve_memory_node(mem0_client: Mem0Client):
     """
     工厂函数：创建记忆检索节点

app/graph/state.py

@@ -4,12 +4,11 @@ LangGraph 状态定义模块
 """
 
 import operator
-from typing import Annotated, Any
+from typing import Annotated
 from typing_extensions import TypedDict
 from dataclasses import dataclass
 from langchain_core.messages import AnyMessage
 
-
 class MessagesState(TypedDict):
     """对话状态类型定义"""
     messages: Annotated[list[AnyMessage], operator.add]
@@ -19,7 +18,6 @@ class MessagesState(TypedDict):
     last_elapsed_time: float  # 本次调用耗时（秒）
     turns_since_last_summary: int  # 距离上次生成摘要的轮数
 
-
 @dataclass
 class GraphContext:
     """图执行上下文"""

app/graph_builder.py

@@ -1,79 +0,0 @@
-"""
-LangGraph 状态图构建模块 - 精简版，仅负责组装图
-所有节点逻辑已拆分到独立模块
-"""
-
-from langchain_core.language_models import BaseLLM
-from langgraph.graph import StateGraph, START, END
-
-# 本地模块
-from app.graph.state import MessagesState, GraphContext
-from app.nodes import (
-    create_llm_call_node,
-    create_tool_call_node,
-    create_retrieve_memory_node,
-    create_summarize_node,
-    should_continue
-)
-from app.memory import Mem0Client
-from app.nodes.finalize import finalize_node
-
-
-class GraphBuilder:
-    """LangGraph 状态图构建器 - 仅负责组装图"""
-
-    def __init__(self, llm: BaseLLM, tools: list, tools_by_name: dict):
-        """
-        初始化构建器
-        
-        Args:
-            llm: 大语言模型实例
-            tools: 工具列表
-            tools_by_name: 名称到工具函数的映射
-        """
-        self.llm = llm
-        self.tools = tools
-        self.tools_by_name = tools_by_name
-        
-        # ⭐ 创建 Mem0 客户端（懒加载，首次使用时初始化）
-        self.mem0_client = Mem0Client(llm)
-
-    def build(self) -> StateGraph:
-        """
-        构建未编译的状态图
-        
-        Returns:
-            StateGraph 实例
-        """
-        builder = StateGraph(MessagesState, context_schema=GraphContext)
-        
-        # ⭐ 通过工厂函数创建节点（依赖注入）
-        retrieve_memory_node = create_retrieve_memory_node(self.mem0_client)
-        llm_call_node = create_llm_call_node(self.llm, self.tools)
-        tool_call_node = create_tool_call_node(self.tools_by_name)
-        summarize_node = create_summarize_node(self.mem0_client)
-        
-        # 添加节点
-        builder.add_node("retrieve_memory", retrieve_memory_node)
-        builder.add_node("llm_call", llm_call_node)
-        builder.add_node("tool_node", tool_call_node)
-        builder.add_node("summarize", summarize_node)
-        builder.add_node("finalize", finalize_node)
-
-        # 添加边
-        builder.add_edge(START, "retrieve_memory")
-        builder.add_edge("retrieve_memory", "llm_call")
-        builder.add_conditional_edges(
-            "llm_call",
-            should_continue,
-            {
-                "tool_node": "tool_node",
-                "summarize": "summarize",
-                "finalize": "finalize"
-            }
-        )
-        builder.add_edge("tool_node", "llm_call")
-        builder.add_edge("summarize", "finalize")
-        builder.add_edge("finalize", END)
-        
-        return builder

app/memory/mem0_client.py

@@ -4,13 +4,12 @@ Mem0 记忆层客户端封装模块
 """
 
 import asyncio
-from typing import Optional, List, Dict, Any
+from typing import Optional, List, Dict
 from mem0 import AsyncMemory
 
-from app.config import QDRANT_URL, QDRANT_COLLECTION_NAME, LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY
+from app.config import QDRANT_URL, QDRANT_COLLECTION_NAME, QDRANT_API_KEY, LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY
 from app.logger import info, warning, error
 
-
 class Mem0Client:
     """Mem0 异步客户端封装类"""
 
@@ -37,8 +36,9 @@ class Mem0Client:
                     "provider": "qdrant",
                     "config": {
                         "url": QDRANT_URL,                     # 直接使用完整 URL
+                        "api_key": QDRANT_API_KEY,
                         "collection_name": QDRANT_COLLECTION_NAME,
-                        "embedding_model_dims": 768,
+                        "embedding_model_dims": 1024,
                     }
                 },
                 "llm": {
@@ -50,7 +50,7 @@ class Mem0Client:
                 "embedder": {
                     "provider": "openai",
                     "config": {
-                        "model": "embeddinggemma-300M-Q8_0",
+                        "model": "Qwen3-Embedding-0.6B-Q8_0",
                         "api_key": LLAMACPP_API_KEY,
                         "openai_base_url": LLAMACPP_EMBEDDING_URL,
                     },

app/nodes/finalize.py

@@ -4,15 +4,13 @@
 """
 
 from typing import Any, Dict
-from langgraph.runtime import Runtime
 from langgraph.config import get_stream_writer
 
 # 本地模块
-from app.graph.state import MessagesState, GraphContext
+from app.graph.state import MessagesState
 from app.utils.logging import log_state_change
 from app.logger import info, error
 
-
 from langchain_core.runnables.config import RunnableConfig
 
 async def finalize_node(state: MessagesState, config: RunnableConfig) -> Dict[str, Any]:

app/nodes/llm_call.py

@@ -3,21 +3,17 @@ LLM 调用节点模块
 负责调用大语言模型并处理响应
 """
 
-import asyncio
 import time
 from typing import Any, Dict
 from langchain_core.language_models import BaseLLM
 from langchain_core.messages import AIMessage
-from langchain_core.runnables import RunnableLambda
-from langgraph.runtime import Runtime
 
 # 本地模块
-from app.graph.state import MessagesState, GraphContext
+from app.graph.state import MessagesState
-from app.prompts import create_system_prompt
+from app.agent.prompts import create_system_prompt
-from app.utils.logging import log_state_change, print_llm_input
+from app.utils.logging import log_state_change
 from app.logger import debug, info, error
 
-
 def create_llm_call_node(llm: BaseLLM, tools: list):
     """
     工厂函数：创建 LLM 调用节点

app/nodes/memory_trigger.py

@@ -0,0 +1,38 @@
+from typing import Any, Dict
+from langchain_core.runnables.config import RunnableConfig
+from app.graph.state import MessagesState
+from app.memory.mem0_client import Mem0Client
+from app.logger import info
+
+# 全局变量，在 GraphBuilder 中注入
+_mem0_client: Mem0Client = None
+
+def set_mem0_client(client: Mem0Client):
+    global _mem0_client
+    _mem0_client = client
+
+async def memory_trigger_node(state: MessagesState, config: RunnableConfig) -> Dict[str, Any]:
+    """检测用户消息中的记忆指令，若命中则主动调用 Mem0 存储"""
+    if _mem0_client is None:
+        return {}
+
+    messages = state.get("messages", [])
+    if not messages:
+        return {}
+
+    last_msg = messages[-1]
+    content = last_msg.content if hasattr(last_msg, 'content') else str(last_msg)
+
+    # 触发词（可自行扩展）
+    trigger_words = ["记住", "记下", "保存", "备忘", "记录下", "别忘了"]
+    if any(word in content for word in trigger_words):
+        user_id = config.get("metadata", {}).get("user_id", "default_user")
+        # 确保 Mem0 已初始化
+        if not _mem0_client._initialized:
+            await _mem0_client.initialize()
+        # 将用户消息作为事实来源提交给 Mem0
+        mem0_messages = [{"role": "user", "content": content}]
+        await _mem0_client.add_memories(mem0_messages, user_id=user_id)
+        info(f"📌 检测到记忆指令，已主动触发 Mem0 存储")
+
+    return {}  # 不修改状态

app/nodes/summarize.py

@@ -4,15 +4,13 @@
 """
 
 from typing import Any, Dict
-from langgraph.runtime import Runtime
 
 # 本地模块
-from app.graph.state import MessagesState, GraphContext
+from app.graph.state import MessagesState
 from app.memory.mem0_client import Mem0Client
 from app.utils.logging import log_state_change
 from app.logger import debug, info, error, warning
 
-
 def create_summarize_node(mem0_client: Mem0Client):
     """
     工厂函数：创建记忆存储节点

app/nodes/tool_call.py

@@ -6,15 +6,13 @@
 import asyncio
 from typing import Any, Dict
 from langchain_core.messages import AIMessage, ToolMessage
-from langgraph.runtime import Runtime
 from langgraph.config import get_stream_writer
 
 # 本地模块
-from app.graph.state import MessagesState, GraphContext
+from app.graph.state import MessagesState
 from app.utils.logging import log_state_change
 from app.logger import debug, info
 
-
 def create_tool_call_node(tools_by_name: Dict[str, Any]):
     """
     工厂函数：创建工具执行节点

app/rag/__init__.py

@@ -13,7 +13,7 @@ RAG 检索与生成模块
     用户查询 → 多路改写 → 并行检索 → RRF 融合 → 重排序 → 返回父文档
 
 示例用法：
-    >>> from app.rag import RAGPipeline, create_rag_tool
+    >>> from app.rag.rag import RAGPipeline, create_rag_tool
     >>> from rag_indexer.builder import IndexBuilder, IndexBuilderConfig
     >>> from langchain_openai import ChatOpenAI
     >>>
@@ -34,16 +34,16 @@ RAG 检索与生成模块
     >>> rag_tool = create_rag_tool(retriever=retriever, llm=llm)
 """
 
-from .retriever import (
+from app.rag.retriever import (
     create_base_retriever,
     create_hybrid_retriever,
     create_qdrant_client,
 )
-from .reranker import LLaMaCPPReranker
+from app.rag.reranker import LLaMaCPPReranker
-from .query_transform import MultiQueryGenerator
+from app.rag.query_transform import MultiQueryGenerator
-from .fusion import reciprocal_rank_fusion
+from app.rag.fusion import reciprocal_rank_fusion
-from .pipeline import RAGPipeline
+from app.rag.pipeline import RAGPipeline
-from .tools import create_rag_tool, create_rag_tool_sync
+from app.rag.tools import create_rag_tool_sync
 
 
 __all__ = [
@@ -65,6 +65,5 @@ __all__ = [
     "RAGPipeline",
     
     # 工具创建（供 Agent 使用）
-    "create_rag_tool",
     "create_rag_tool_sync",
 ]

app/rag/fusion.py

@@ -1,6 +1,6 @@
 # rag/fusion.py
 
-from typing import List, Dict, Tuple
+from typing import List, Dict
 from langchain_core.documents import Document
 
 def reciprocal_rank_fusion(

app/rag/pipeline.py

@@ -2,15 +2,13 @@
 
 import asyncio
 import os
-from typing import List, Optional
+from typing import List
 from langchain_core.documents import Document
 from langchain_core.language_models import BaseLanguageModel
 
-from .retriever import create_qdrant_client  # 可能不需要直接使用
+from app.rag.reranker import LLaMaCPPReranker
-from .reranker import LLaMaCPPReranker
+from app.rag.query_transform import MultiQueryGenerator
-from .query_transform import MultiQueryGenerator
+from app.rag.fusion import reciprocal_rank_fusion
-from .fusion import reciprocal_rank_fusion
-
 
 class RAGPipeline:
     """

app/rag/reranker.py

@@ -2,9 +2,8 @@
 重排序器模块 (适配版)
 使用远程 llama.cpp 服务 (兼容 OpenAI Rerank API) 替代本地 Cross-Encoder
 """
-import os
 import requests
-from typing import List, Optional
+from typing import List
 from langchain_core.documents import Document
 
 class LLaMaCPPReranker:

app/rag/test.py

@@ -11,7 +11,6 @@ RAG 系统使用示例（重构版）
 import asyncio
 import sys
 import os
-from pathlib import Path
 
 from dotenv import load_dotenv
 
@@ -19,12 +18,12 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # 添加项目根目录到路径
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../.."))
 
-from rag_indexer.index_builder import IndexBuilder, IndexBuilderConfig
+from rag_indexer.index_builder import IndexBuilderConfig
 from rag_indexer.splitters import SplitterType
-from rag.pipeline import RAGPipeline
+from app.rag.pipeline import RAGPipeline
-from rag.tools import create_rag_tool
+from app.rag.tools import create_rag_tool_sync
 from pydantic import SecretStr
 # 使用本地 LLM（通过 OpenAI 兼容接口）
 from langchain_openai import ChatOpenAI
@@ -32,7 +31,6 @@ from rag_core.retriever_factory import create_parent_retriever
 
 load_dotenv()
 
-
 def create_llm():
         """创建本地 vLLM 服务 LLM"""
         vllm_base_url = os.getenv(
@@ -60,8 +58,7 @@ async def demonstrate_full_pipeline():
     print("演示：固定流程 RAG 检索（多路改写 + RRF + 重排序 + 父文档）")
     print("=" * 60)
 
-
+    retriever = create_parent_retriever(collection_name="rag_documents", search_k=5)
-    retriever = retriever = create_parent_retriever(collection_name="my_docs", search_k=5)
 
     if retriever is None:
         print("错误：检索器未初始化，请确保索引已构建。")
@@ -103,7 +100,6 @@ async def demonstrate_full_pipeline():
         import traceback
         traceback.print_exc()
 
-
 async def demonstrate_tool_creation():
     """
     演示创建 RAG 工具（供 Agent 使用）
@@ -119,12 +115,11 @@ async def demonstrate_tool_creation():
     )
     retriever = retriever = create_parent_retriever(collection_name="rag_documents", search_k=5)
 
-
     # 2. 创建 LLM
     llm = create_llm()
 
     # 3. 创建工具
-    rag_tool = create_rag_tool(
+    rag_tool = create_rag_tool_sync(
         retriever=retriever,
         llm=llm,
         num_queries=3,
@@ -136,18 +131,16 @@ async def demonstrate_tool_creation():
     print(f"工具描述: {rag_tool.description[:100]}...")
 
     # 4. 模拟 Agent 调用工具
-    query = "请告诉我 RAG 系统的核心组件有哪些？"
+    query = "请告诉我 打虎英雄是谁？"
     print(f"\n模拟调用: {query}")
     print("-" * 40)
 
     result = await rag_tool.ainvoke({"query": query})
     print(result[:800] + "..." if len(result) > 800 else result)
 
-
 async def main():
     await demonstrate_full_pipeline()
     await demonstrate_tool_creation()
 
-
 if __name__ == "__main__":
     asyncio.run(main())

app/rag/tools.py

@@ -4,11 +4,11 @@ RAG 工具模块
 将检索功能封装为 LangChain Tool，供 Agent 调用。
 采用固定流水线：多路改写 → 并行检索 → RRF 融合 → 重排序 → 返回父文档。
 """
-from typing import Optional, Callable
+from typing import Callable
 from langchain_core.tools import tool
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.retrievers import BaseRetriever
-from .pipeline import RAGPipeline
+from app.rag.pipeline import RAGPipeline
 
 def create_rag_tool_sync(
     retriever: BaseRetriever,

app/test_backend.py

@@ -0,0 +1,307 @@
+#!/usr/bin/env python3
+"""
+完整后端测试 - 验证 Agent 所有功能
+包括：短期记忆、长期记忆、工具调用、流式对话、历史查询
+"""
+
+import asyncio
+import os
+import sys
+import uuid
+from dotenv import load_dotenv
+
+# 添加项目根目录到 Python 路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+load_dotenv()
+
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+from app.agent import AIAgentService
+from app.agent.history import ThreadHistoryService
+from app.logger import info, warning, error
+
+# PostgreSQL 连接字符串
+DB_URI = os.getenv(
+    "DB_URI",
+    "postgresql://postgres:***@ai-postgres:5432/langgraph_db?sslmode=disable"
+)
+
+async def print_section(title):
+    """打印测试区块标题"""
+    print("\n" + "=" * 70)
+    print(f"  {title}")
+    print("=" * 70)
+
+async def test_short_term_memory(agent_service):
+    """测试短期记忆（同一 thread_id 继续对话）"""
+    await print_section("测试 1: 短期记忆（Short-term Memory）")
+    
+    thread_id = str(uuid.uuid4())
+    user_id = "test_user_memory"
+    
+    print(f"\n使用 thread_id: {thread_id[:8]}...")
+    print(f"使用 user_id: {user_id}")
+    
+    # 第一轮对话
+    print("\n[第一轮] 发送消息: '我叫张三，今年28岁'")
+    result1 = await agent_service.process_message(
+        "我叫张三，今年28岁", thread_id, "local", user_id
+    )
+    print(f"回复: {result1['reply'][:100]}...")
+    
+    # 第二轮对话 - 测试记忆
+    print("\n[第二轮] 发送消息: '我叫什么名字？今年多大？'")
+    result2 = await agent_service.process_message(
+        "我叫什么名字？今年多大？", thread_id, "local", user_id
+    )
+    print(f"回复: {result2['reply']}")
+    
+    # 验证记忆是否存在
+    if "张三" in result2['reply'] or "28" in result2['reply']:
+        print("\n✅ 短期记忆测试通过！")
+        return True
+    else:
+        print("\n❌ 短期记忆测试失败！")
+        return False
+
+async def test_tool_calling(agent_service):
+    """测试工具调用（RAG 搜索）"""
+    await print_section("测试 2: 工具调用（Tool Calling）")
+    
+    thread_id = str(uuid.uuid4())
+    user_id = "test_user_tools"
+    
+    print(f"\n使用 thread_id: {thread_id[:8]}...")
+    print(f"使用 user_id: {user_id}")
+    
+    # 发送需要 RAG 搜索的问题
+    print("\n发送消息: '请告诉我，打虎英雄是谁？'")
+    result = await agent_service.process_message(
+        "请告诉我，打虎英雄是谁？", thread_id, "local", user_id
+    )
+    print(f"回复: {result['reply'][:200]}...")
+    
+    # 检查是否调用了 RAG 工具（回复中会有水浒传相关内容）
+    if "武松" in result['reply'] or "李忠" in result['reply'] or "水浒传" in result['reply']:
+        print("\n✅ 工具调用测试通过！")
+        return True
+    else:
+        print("\n⚠️  工具调用测试结果不确定，需要手动验证")
+        return None
+
+async def test_streaming(agent_service):
+    """测试流式对话"""
+    await print_section("测试 3: 流式对话（Streaming）")
+    
+    thread_id = str(uuid.uuid4())
+    user_id = "test_user_stream"
+    
+    print(f"\n使用 thread_id: {thread_id[:8]}...")
+    print(f"使用 user_id: {user_id}")
+    
+    print("\n发送消息: '用100字介绍一下AI人工智能' (流式)...")
+    print("流式输出: ", end="", flush=True)
+    
+    full_reply = ""
+    chunk_count = 0
+    
+    try:
+        async for chunk in agent_service.process_message_stream(
+            "用100字介绍一下AI人工智能", thread_id, "local", user_id
+        ):
+            chunk_count += 1
+            if chunk.get("type") == "llm_token":
+                token = chunk.get("token", "")
+                print(token, end="", flush=True)
+                full_reply += token
+            elif chunk.get("type") == "state_update":
+                pass  # 状态更新不显示
+        
+        print(f"\n\n共收到 {chunk_count} 个 chunk")
+        print(f"完整回复长度: {len(full_reply)} 字")
+        
+        if chunk_count > 0 and len(full_reply) > 10:
+            print("\n✅ 流式对话测试通过！")
+            return True
+        else:
+            print("\n❌ 流式对话测试失败！")
+            return False
+            
+    except Exception as e:
+        print(f"\n❌ 流式对话异常: {e}")
+        return False
+
+async def test_history_service(agent_service, history_service):
+    """测试历史查询服务"""
+    await print_section("测试 4: 历史查询服务（History Service）")
+    
+    user_id = "test_user_history"
+    
+    # 先创建几个对话
+    print(f"\n为 user_id={user_id} 创建测试对话...")
+    
+    thread_ids = []
+    for i in range(3):
+        thread_id = str(uuid.uuid4())
+        thread_ids.append(thread_id)
+        
+        await agent_service.process_message(
+            f"这是第 {i+1} 个测试对话", thread_id, "local", user_id
+        )
+        print(f"  创建线程 {i+1}: {thread_id[:8]}...")
+    
+    # 1. 测试获取用户线程列表
+    print("\n[4.1] 测试获取用户线程列表...")
+    threads = await history_service.get_user_threads(user_id, limit=10)
+    print(f"  找到 {len(threads)} 个线程")
+    
+    if len(threads) >= 3:
+        print("  ✅ 线程列表查询通过")
+    else:
+        print("  ⚠️  线程数量少于预期")
+    
+    # 2. 测试获取单个线程的消息历史
+    if thread_ids:
+        test_thread_id = thread_ids[0]
+        print(f"\n[4.2] 测试获取线程消息历史 (thread_id={test_thread_id[:8]}...)")
+        messages = await history_service.get_thread_messages(test_thread_id)
+        print(f"  找到 {len(messages)} 条消息")
+        
+        if len(messages) >= 2:  # 至少有一问一答
+            print("  ✅ 消息历史查询通过")
+        else:
+            print("  ⚠️  消息数量少于预期")
+        
+        # 3. 测试获取线程摘要
+        print(f"\n[4.3] 测试获取线程摘要...")
+        summary = await history_service.get_thread_summary(test_thread_id)
+        print(f"  摘要: {summary.get('summary', '')[:50]}...")
+        print(f"  消息数: {summary.get('message_count', 0)}")
+        
+        if summary.get('message_count', 0) > 0:
+            print("  ✅ 线程摘要查询通过")
+        else:
+            print("  ⚠️  摘要查询结果不确定")
+    
+    return len(threads) >= 3
+
+async def test_long_term_memory(agent_service):
+    """测试长期记忆（mem0）"""
+    await print_section("测试 5: 长期记忆（Long-term Memory - mem0）")
+    
+    thread_id1 = str(uuid.uuid4())
+    thread_id2 = str(uuid.uuid4())  # 不同的线程
+    user_id = "test_user_longterm"
+    
+    print(f"\n使用 user_id: {user_id}")
+    print(f"线程 1: {thread_id1[:8]}...")
+    print(f"线程 2: {thread_id2[:8]}...")
+    
+    # 在第一个线程中保存信息
+    print("\n[线程 1] 发送消息: '记住，我的宠物名字叫小白，是一只猫'")
+    result1 = await agent_service.process_message(
+        "记住，我的宠物名字叫小白，是一只猫", thread_id1, "local", user_id
+    )
+    print(f"回复: {result1['reply'][:100]}...")
+    
+    # 等待一下，让 mem0 保存
+    await asyncio.sleep(1)
+    
+    # 在第二个线程中询问（不同的 thread_id）
+    print("\n[线程 2] 发送消息: '我的宠物叫什么名字？是什么动物？'")
+    result2 = await agent_service.process_message(
+        "我的宠物叫什么名字？是什么动物？", thread_id2, "local", user_id
+    )
+    print(f"回复: {result2['reply']}")
+    
+    # 验证长期记忆
+    if "小白" in result2['reply'] or "猫" in result2['reply']:
+        print("\n✅ 长期记忆测试通过！")
+        return True
+    else:
+        print("\n⚠️  长期记忆可能未启用，或需要手动验证")
+        return None
+
+async def main():
+    """主测试函数"""
+    print("\n" + "=" * 70)
+    print("  后端完整功能测试")
+    print("=" * 70)
+    
+    results = {}
+    
+    try:
+        # 创建数据库连接和服务
+        print("\n正在初始化数据库连接...")
+        async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
+            await checkpointer.setup()
+            print("✅ 数据库连接成功")
+            
+            # 创建服务实例
+            print("\n正在初始化 Agent 服务...")
+            agent_service = AIAgentService(checkpointer)
+            await agent_service.initialize()
+            print("✅ Agent 服务初始化成功")
+            
+            history_service = ThreadHistoryService(checkpointer)
+            print("✅ 历史服务初始化成功")
+            
+            print(f"\n可用模型: {list(agent_service.graphs.keys())}")
+            
+            # 运行测试
+            results["短期记忆"] = await test_short_term_memory(agent_service)
+            await asyncio.sleep(1)
+            
+            results["工具调用"] = await test_tool_calling(agent_service)
+            await asyncio.sleep(1)
+            
+            results["流式对话"] = await test_streaming(agent_service)
+            await asyncio.sleep(1)
+            
+            results["历史查询"] = await test_history_service(agent_service, history_service)
+            await asyncio.sleep(1)
+            
+            results["长期记忆"] = await test_long_term_memory(agent_service)
+            await asyncio.sleep(1)
+            
+            # 打印总结
+            await print_section("测试总结")
+            print("\n测试结果:")
+            print("-" * 40)
+            
+            pass_count = 0
+            fail_count = 0
+            skip_count = 0
+            
+            for test_name, result in results.items():
+                if result is True:
+                    status = "✅ 通过"
+                    pass_count += 1
+                elif result is False:
+                    status = "❌ 失败"
+                    fail_count += 1
+                else:
+                    status = "⚠️  待验证"
+                    skip_count += 1
+                print(f"  {test_name:12s}: {status}")
+            
+            print("-" * 40)
+            print(f"总计: {len(results)} 个测试")
+            print(f"通过: {pass_count}, 失败: {fail_count}, 待验证: {skip_count}")
+            
+            if fail_count == 0:
+                print("\n🎉 所有核心测试通过！")
+            else:
+                print(f"\n⚠️  有 {fail_count} 个测试失败")
+            
+    except Exception as e:
+        error(f"\n❌ 测试运行异常: {e}")
+        import traceback
+        traceback.print_exc()
+        return 1
+    
+    return 0 if fail_count == 0 else 1
+
+if __name__ == "__main__":
+    exit_code = asyncio.run(main())
+    sys.exit(exit_code)

frontend/__init__.py

@@ -3,7 +3,7 @@ AI Agent 前端模块
 采用分层架构设计，包含配置、状态、API客户端和UI组件
 """
 
-from .logger import debug, info, warning, error
+from frontend.logger import debug, info, warning, error
 
 __version__ = "2.0.0"
 __all__ = ["debug", "info", "warning", "error"]

frontend/components/sidebar.py

@@ -9,8 +9,6 @@ from datetime import datetime
 # 使用绝对导入
 from frontend.state import AppState
 from frontend.api_client import api_client
-from frontend.config import config
-
 
 def render_sidebar():
     """渲染左侧栏"""
@@ -25,7 +23,6 @@ def render_sidebar():
     st.divider()
     _render_user_section()
 
-
 def _render_user_section():
     """渲染用户登录区域"""
     # st.header("👤 用户") # 移除显眼的标题，改用更柔和的 caption
@@ -36,7 +33,6 @@ def _render_user_section():
     else:
         _render_user_info()
 
-
 def _render_login_form():
     """渲染登录表单"""
     username = st.text_input(
@@ -54,7 +50,6 @@ def _render_login_form():
     
     # st.info("💡 建议登录以隔离对话历史") # 移除多余色块
 
-
 def _render_user_info():
     """渲染用户信息"""
     st.markdown(f"**当前用户**: `{AppState.get_user_id()}`")
@@ -64,7 +59,6 @@ def _render_user_info():
         _refresh_threads()
         st.rerun()
 
-
 def _render_history_section():
     """渲染历史对话列表"""
     col1, col2 = st.columns([3, 1])
@@ -76,7 +70,6 @@ def _render_history_section():
     
     _render_thread_list()
 
-
 def _render_history_actions():
     """渲染历史操作按钮"""
     # 移除了 type="primary"，让它变成普通的线框按钮，不再是大红块
@@ -84,7 +77,6 @@ def _render_history_actions():
         AppState.start_new_thread()
         st.rerun()
 
-
 def _render_thread_list():
     """渲染线程列表"""
     # 仅在初次加载时拉取，或由外部主动调用 _refresh_threads() 更新
@@ -101,7 +93,6 @@ def _render_thread_list():
     for thread in threads:
         _render_thread_item(thread)
 
-
 def _render_thread_item(thread: dict):
     """
     渲染单个线程项
@@ -130,7 +121,6 @@ def _render_thread_item(thread: dict):
     ):
         _load_thread(thread_id)
 
-
 def _format_time(time_str: str) -> str:
     """
     格式化时间字符串
@@ -150,13 +140,11 @@ def _format_time(time_str: str) -> str:
     except Exception:
         return time_str[:10]
 
-
 def _refresh_threads():
     """刷新历史线程列表"""
     threads = api_client.get_user_threads(AppState.get_user_id())
     AppState.set_threads(threads)
 
-
 def _load_thread(thread_id: str):
     """
     加载指定线程的消息历史

frontend/state.py

@@ -7,7 +7,7 @@ import uuid
 from typing import List, Dict, Any
 import streamlit as st
 
-from .config import config
+from frontend.config import config
 
 
 class AppState:

rag_core/__init__.py

@@ -4,10 +4,10 @@ RAG Core - 公共 RAG 组件包
 提供嵌入模型、向量存储和文档存储的公共功能，被 rag_indexer 和 app/rag 共用。
 """
 
-from .embedders import LlamaCppEmbedder
+from rag_core.embedders import LlamaCppEmbedder
-from .vector_store import QdrantVectorStore, QDRANT_URL, QDRANT_API_KEY
+from rag_core.vector_store import QdrantVectorStore, QDRANT_URL, QDRANT_API_KEY
-from .store import PostgresDocStore, create_docstore
+from rag_core.store import PostgresDocStore, create_docstore
-from .retriever_factory import create_parent_retriever
+from rag_core.retriever_factory import create_parent_retriever
 
 
 __all__ = [

rag_core/embedders.py

@@ -5,11 +5,9 @@
 import os
 import httpx
 from typing import List, Optional
-from urllib.parse import urljoin
 
 from langchain_core.embeddings import Embeddings
 
-
 class LlamaCppEmbedder:
     """通过 OpenAI 兼容 API 封装 llama.cpp 嵌入服务。"""
 
@@ -17,7 +15,7 @@ class LlamaCppEmbedder:
         self,
         base_url: Optional[str] = None,
         api_key: Optional[str] = None,
-        model: str = "embeddinggemma-300M-Q8_0",
+        model: str = "Qwen3-Embedding-0.6B-Q8_0",
     ):
         self.base_url = base_url or os.getenv("LLAMACPP_EMBEDDING_URL", "http://127.0.0.1:8082")
         self.api_key = api_key or os.getenv("LLAMACPP_API_KEY", "")
@@ -71,7 +69,6 @@ class LlamaCppEmbedder:
             else:
                 raise ValueError(f"未知的嵌入 API 响应格式: {data}")
 
-
 class _LlamaCppLangchainAdapter(Embeddings):
     """将 LlamaCppEmbedder 适配为 LangChain Embeddings 接口。"""
 

rag_core/retriever_factory.py

@@ -2,14 +2,7 @@
 from langchain_core.embeddings import Embeddings
 from langchain_classic.retrievers import ParentDocumentRetriever
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from rag_indexer.splitters import SplitterType, get_splitter
+from typing import Optional
-import asyncio
-import logging
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import List, Union, Optional, Any, Dict, Tuple
-from httpx import RemoteProtocolError
-from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.stores import BaseStore
 from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
@@ -17,7 +10,6 @@ from langchain_classic.retrievers import ParentDocumentRetriever
 
 from rag_core import LlamaCppEmbedder, QdrantVectorStore, create_docstore
 
-
 def create_parent_retriever(
     collection_name: str = "rag_documents",
     embeddings: Optional[Embeddings] = None,

rag_core/store/__init__.py

@@ -15,8 +15,8 @@
 """
 
 
-from .postgres import PostgresDocStore
+from rag_core.store.postgres import PostgresDocStore
-from .factory import create_docstore, get_docstore_uri, DEFAULT_DB_URI
+from rag_core.store.factory import create_docstore, get_docstore_uri, DEFAULT_DB_URI
 
 __version__ = "2.0.0"
 

rag_core/store/factory.py

@@ -9,7 +9,7 @@ import logging
 from typing import Optional, Tuple
 
 from langchain_core.stores import BaseStore
-from .postgres import PostgresDocStore
+from rag_core.store.postgres import PostgresDocStore
 
 logger = logging.getLogger(__name__)
 

rag_core/store/postgres.py

@@ -4,12 +4,10 @@
 使用 asyncpg 实现真正的异步 PostgreSQL 文档存储，支持高并发访问。
 """
 
-from __future__ import annotations
-
 import asyncio
 import json
 import logging
-from typing import List, Dict, Any, Optional, Iterator, Tuple, Sequence, cast
+from typing import List, Dict, Any, Optional, Iterator, Tuple, Sequence
 
 from langchain_core.documents import Document
 from langchain_core.stores import BaseStore
@@ -18,7 +16,6 @@ import asyncpg
 
 logger = logging.getLogger(__name__)
 
-
 class PostgresDocStore(BaseStore[str, Any]):
     """
     异步 PostgreSQL 文档存储实现。

rag_core/vector_store.py

@@ -13,7 +13,7 @@ from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams
 from httpx import RemoteProtocolError
 from qdrant_client.http.exceptions import ResponseHandlingException
-from .client import create_qdrant_client
+from rag_core.client import create_qdrant_client
 
 logger = logging.getLogger(__name__)
 
@@ -35,7 +35,7 @@ class QdrantVectorStore:
         self._last_connection_time: Optional[float] = None
 
         if embeddings is None:
-            from .embedders import LlamaCppEmbedder
+            from rag_core.embedders import LlamaCppEmbedder
             embedder = LlamaCppEmbedder()
             self.embeddings = embedder.as_langchain_embeddings()
         else:
@@ -96,7 +96,7 @@ class QdrantVectorStore:
     def create_collection(self, vector_size: Optional[int] = None, force_recreate: bool = False):
         """创建集合，设置合适的向量维度。"""
         if vector_size is None:
-            from .embedders import LlamaCppEmbedder
+            from rag_core.embedders import LlamaCppEmbedder
             embedder = LlamaCppEmbedder()
             vector_size = embedder.get_embedding_dimension()
 

rag_indexer/__init__.py

@@ -23,9 +23,9 @@ Offline RAG Indexer module.
     >>> await builder.build_from_file("document.pdf")
 """
 
-from .index_builder import IndexBuilder, IndexBuilderConfig, DocstoreConfig
+from rag_indexer.index_builder import IndexBuilder, IndexBuilderConfig, DocstoreConfig
-from .loaders import DocumentLoader
+from rag_indexer.loaders import DocumentLoader
-from .splitters import SplitterType, get_splitter
+from rag_indexer.splitters import SplitterType, get_splitter
 
 # 从 rag_core 重新导出常用组件
 from rag_core import (

rag_indexer/index_builder.py

@@ -8,24 +8,21 @@ import asyncio
 import logging
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import List, Union, Optional, Any, Dict, Tuple
+from typing import List, Union, Optional, Any, Dict
 
 from httpx import RemoteProtocolError
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.stores import BaseStore
 from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
-from langchain_classic.retrievers import ParentDocumentRetriever
 from qdrant_client.http.exceptions import ResponseHandlingException
 
-from .loaders import DocumentLoader
+from rag_indexer.loaders import DocumentLoader
-from .splitters import SplitterType, get_splitter, SemanticChunkerAdapter
+from rag_indexer.splitters import SplitterType, get_splitter
 from rag_core import LlamaCppEmbedder, QdrantVectorStore, create_docstore, create_parent_retriever
 
-
 logger = logging.getLogger(__name__)
 
-
 # ---------- 配置数据类 ----------
 @dataclass
 class DocstoreConfig:
@@ -36,7 +33,6 @@ class DocstoreConfig:
     # 若要从外部注入已创建好的 docstore，可直接设置此字段
     instance: Optional[BaseStore] = None
 
-
 @dataclass
 class IndexBuilderConfig:
     """索引构建器配置。"""
@@ -60,7 +56,6 @@ class IndexBuilderConfig:
     # 其他切分器参数（当 splitter_type 非父子块时使用）
     extra_splitter_kwargs: Dict[str, Any] = field(default_factory=dict)
 
-
 # ---------- 索引构建器 ----------
 class IndexBuilder:
     """RAG 索引构建主流水线，支持单块切分与父子块切分。"""

scripts/start.sh

@@ -250,7 +250,7 @@ start_embedding() {
     echo -e "${BLUE}🚀 启动 llama.cpp Embedding 容器...${NC}"
     
     # 检查模型文件
-    if [ ! -f "/home/huang/Study/AIModel/GGUF/embeddinggemma-300M-Q8_0.gguf" ]; then
+    if [ ! -f "/home/huang/Study/AIModel/GGUF/Qwen3-Embedding-0.6B-Q8_0.gguf" ]; then
         echo -e "${RED}✗ 错误：Embedding 模型文件不存在${NC}"
         exit 1
     fi
@@ -263,13 +263,16 @@ start_embedding() {
         --device=/dev/dri \
         -v /home/huang/Study/AIModel/GGUF:/models \
         -p 8082:8080 \
+        -e LLAMA_ARG_CTX_SIZE=16384 \
+        -e LLAMA_ARG_N_PARALLEL=1 \
+        -e LLAMA_ARG_BATCH=512 \
+        -e LLAMA_ARG_N_GPU_LAYERS=99 \
+        -e LLAMA_ARG_API_KEY=huang1998 \
         ghcr.io/ggml-org/llama.cpp:server-rocm \
-        -m /models/embeddinggemma-300M-Q8_0.gguf \
+        -m /models/Qwen3-Embedding-0.6B-Q8_0.gguf \
         --host 0.0.0.0 \
         --port 8080 \
-        -ngl 99 \
+        --embeddings
-        --embeddings \
-        -c 512
     
     echo -e "${GREEN}✓ llama.cpp Embedding 容器已启动 (端口 8082)${NC}"
     sleep 5