ailine/backend/app/graph/rag_nodes.py

"""
RAG 节点模块 - 真正利用已有 RAG 代码
包含：
- rag_retrieve_node: RAG 检索节点（带超时重试）
- rag_re_retrieve_node: 重新检索节点
- 集成 backend/app/rag/tools.py 和 rag_initializer.py
"""

import time
import asyncio
from typing import Dict, Any, Optional
from datetime import datetime

from .state import MainGraphState, ErrorRecord, ErrorSeverity
from .retry_utils import (
    RetryConfig,
    RAG_RETRY_CONFIG,
    create_retry_wrapper_for_node
)

# 真正导入和利用已有 RAG 代码
from ..rag.tools import create_rag_tool_sync
from ..rag.pipeline import RAGPipeline


# ========== 全局 RAG 工具实例（延迟初始化）==========
_GLOBAL_RAG_TOOL: Optional[Any] = None
_GLOBAL_RAG_PIPELINE: Optional[RAGPipeline] = None


def get_global_rag_tool() -> Optional[Any]:
    """
    获取全局 RAG 工具（单例模式）
    
    Returns:
        RAG 工具实例或 None
    """
    return _GLOBAL_RAG_TOOL


def set_global_rag_tool(tool: Any) -> None:
    """
    设置全局 RAG 工具（通常在应用启动时调用）
    
    Args:
        tool: RAG 工具实例
    """
    global _GLOBAL_RAG_TOOL
    _GLOBAL_RAG_TOOL = tool


def set_global_rag_pipeline(pipeline: RAGPipeline) -> None:
    """
    设置全局 RAG Pipeline
    
    Args:
        pipeline: RAGPipeline 实例
    """
    global _GLOBAL_RAG_PIPELINE
    _GLOBAL_RAG_PIPELINE = pipeline


# ========== 从状态获取 RAG 工具 ==========
def get_rag_tool_from_state(state: MainGraphState) -> Optional[Any]:
    """
    从状态中获取 RAG 工具（如果有）
    
    Args:
        state: 主图状态
    
    Returns:
        RAG 工具实例或 None
    """
    # 优先从状态获取
    if "rag_tool" in state.debug_info:
        return state.debug_info["rag_tool"]
    # 其次从全局获取
    return get_global_rag_tool()


# ========== 工具：将 RAG 工具注入到状态 ==========
def inject_rag_tool_to_state(state: MainGraphState, rag_tool: Any) -> MainGraphState:
    """
    将 RAG 工具注入到状态中，供后续节点使用
    
    Args:
        state: 主图状态
        rag_tool: RAG 工具实例
    
    Returns:
        更新后的状态
    """
    state.debug_info["rag_tool"] = rag_tool
    state.debug_info["rag_tool_injected"] = datetime.now().isoformat()
    return state


# ========== RAG 检索核心逻辑（真正利用已有代码）==========
def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
    """
    RAG 检索核心逻辑（真正利用 rag/tools.py）
    
    Args:
        state: 主图状态
    
    Returns:
        更新后的状态
    """
    # 获取检索查询（优先使用推理结果中的优化查询）
    retrieval_query = state.user_query
    if "reasoning_result" in state.debug_info:
        reasoning_result = state.debug_info["reasoning_result"]
        if hasattr(reasoning_result, "retrieval_config"):
            cfg = reasoning_result.retrieval_config
            if cfg and cfg.retrieval_query:
                retrieval_query = cfg.retrieval_query
    
    # 尝试获取 RAG 工具（多种方式）
    rag_tool = get_rag_tool_from_state(state)
    
    if rag_tool:
        # 使用真正的 RAG 工具（来自 rag/tools.py）
        try:
            # 调用 LangChain Tool 的 invoke 方法
            rag_context = rag_tool.invoke(retrieval_query)
            state.rag_context = rag_context
            state.rag_docs = [
                {"source": "rag_retrieval", "content": rag_context}
            ]
            state.rag_retrieved = True
            state.success = True
            state.debug_info["rag_source"] = "rag_tool"
            return state
        except Exception as e:
            raise RuntimeError(f"RAG 工具调用失败: {str(e)}") from e
    elif _GLOBAL_RAG_PIPELINE:
        # 使用 RAG Pipeline 直接检索
        try:
            documents = _GLOBAL_RAG_PIPELINE.retrieve(retrieval_query)
            if documents:
                rag_context = _GLOBAL_RAG_PIPELINE.format_context(documents)
                state.rag_context = rag_context
                state.rag_docs = [
                    {"source": doc.metadata.get("source", "unknown"), "content": doc.page_content}
                    for doc in documents
                ]
            else:
                state.rag_context = f"未找到与 '{retrieval_query}' 相关的知识库信息。"
                state.rag_docs = []
            state.rag_retrieved = True
            state.success = True
            state.debug_info["rag_source"] = "rag_pipeline"
            return state
        except Exception as e:
            raise RuntimeError(f"RAG Pipeline 调用失败: {str(e)}") from e
    else:
        # 没有可用的 RAG 工具/Pipeline
        raise RuntimeError("RAG 工具未初始化，请先调用 set_global_rag_tool() 或 set_global_rag_pipeline()")


# ========== RAG 检索节点（带超时和重试）==========
def rag_retrieve_node(state: MainGraphState) -> MainGraphState:
    """
    RAG 检索节点：带超时和重试，真正利用已有 RAG 代码
    
    Args:
        state: 主图状态
    
    Returns:
        更新后的状态
    """
    state.current_phase = "rag_retrieving"
    
    start_time = time.time()
    last_error = None
    
    for attempt in range(RAG_RETRY_CONFIG.max_retries + 1):
        try:
            # 执行核心逻辑
            result = _rag_retrieve_core(state)
            
            # 成功
            state.debug_info["rag_retrieval"] = {
                "attempt": attempt + 1,
                "success": True,
                "time": time.time() - start_time
            }
            return result
            
        except Exception as e:
            last_error = e
            
            if attempt >= RAG_RETRY_CONFIG.max_retries:
                break
            
            # 指数退避等待
            delay = RAG_RETRY_CONFIG.base_delay * (2 ** attempt)
            time.sleep(min(delay, RAG_RETRY_CONFIG.max_delay))
    
    # 所有重试都失败，记录结构化错误
    error_record = ErrorRecord(
        error_type="RAGRetrievalError",
        error_message=str(last_error) if last_error else "RAG 检索超时",
        severity=ErrorSeverity.WARNING,
        source="rag_retrieve_node",
        timestamp=datetime.now().isoformat(),
        retry_count=RAG_RETRY_CONFIG.max_retries,
        max_retries=RAG_RETRY_CONFIG.max_retries,
        context={
            "query": state.user_query,
            "total_time": time.time() - start_time,
            "timeout": RAG_RETRY_CONFIG.timeout,
            "has_rag_tool": get_global_rag_tool() is not None,
            "has_rag_pipeline": _GLOBAL_RAG_PIPELINE is not None
        }
    )
    
    state.errors.append(error_record)
    state.current_error = error_record
    state.current_phase = "error_handling"
    
    return state


# ========== 重新检索节点 ==========
def rag_re_retrieve_node(state: MainGraphState) -> MainGraphState:
    """
    重新检索节点：用于第二次检索（不同的参数）
    
    Args:
        state: 主图状态
    
    Returns:
        更新后的状态
    """
    state.current_phase = "rag_re_retrieving"
    
    # 记录原始检索信息
    state.debug_info["rag_re_retrieve"] = {
        "original_retrieved": state.rag_retrieved,
        "original_docs_count": len(state.rag_docs)
    }
    
    # 可以在这里修改检索参数（例如：调整查询、增加 k 值）
    # 暂时复用同一个检索逻辑
    return rag_retrieve_node(state)


# ========== 便捷函数：从 rag_initializer 初始化 ==========
async def initialize_rag_from_initializer() -> None:
    """
    从 rag_initializer 初始化 RAG（便捷函数）
    
    注意：这是示例代码，实际使用时需要提供 local_llm_creator
    """
    try:
        from ..agent.rag_initializer import init_rag_tool
        
        # 注意：这里需要传入 local_llm_creator
        # 示例：
        # def my_llm_creator():
        #     from ..model_services import get_llm
        #     return get_llm()
        #
        # rag_tool = await init_rag_tool(my_llm_creator)
        # set_global_rag_tool(rag_tool)
        
        print("⚠️  initialize_rag_from_initializer 需要传入 local_llm_creator")
        print("⚠️  请在应用启动时调用 init_rag_tool() 并设置全局 RAG 工具")
        
    except ImportError as e:
        print(f"⚠️  无法导入 rag_initializer: {e}")
    except Exception as e:
        print(f"⚠️  RAG 初始化失败: {e}")


# ========== 导出 ==========
__all__ = [
    # 节点函数
    "rag_retrieve_node",
    "rag_re_retrieve_node",
    
    # 工具函数
    "inject_rag_tool_to_state",
    "get_rag_tool_from_state",
    
    # 全局 RAG 管理
    "get_global_rag_tool",
    "set_global_rag_tool",
    "set_global_rag_pipeline",
    
    # 初始化
    "initialize_rag_from_initializer"
]