Merge branch 'refs/heads/develop' into fix/memory_bug_fix
This commit is contained in:
lixinyue
@@ -3,6 +3,12 @@ Celery Worker 入口点
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用于启动 Celery Worker: celery -A app.celery_worker worker --loglevel=info
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"""
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from app.celery_app import celery_app
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from app.core.logging_config import LoggingConfig, get_logger
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# Initialize logging system for Celery worker
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LoggingConfig.setup_logging()
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logger = get_logger(__name__)
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logger.info("Celery worker logging initialized")
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# 导入任务模块以注册任务
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import app.tasks
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@@ -267,7 +267,7 @@ async def generate_emotion_suggestions(
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"""生成个性化情绪建议（调用LLM并缓存）
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Args:
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request: 包含 group_id、可选的 config_id 和 force_refresh
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request: 包含 end_user_id
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db: 数据库会话
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current_user: 当前用户
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@@ -275,47 +275,22 @@ async def generate_emotion_suggestions(
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新生成的个性化情绪建议响应
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"""
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try:
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# 验证 config_id（如果提供）
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# 获取终端用户关联的配置
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config_id = request.config_id
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if config_id is None:
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# 如果没有提供 config_id，尝试获取用户关联的配置
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try:
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from app.services.memory_agent_service import (
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get_end_user_connected_config,
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)
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connected_config = get_end_user_connected_config(request.group_id, db)
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config_id = connected_config.get("memory_config_id")
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except ValueError as e:
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return fail(BizCode.INVALID_PARAMETER, "无法获取用户关联的配置", str(e))
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else:
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# 如果提供了 config_id，验证其有效性
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from app.services.memory_config_service import MemoryConfigService
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try:
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config_service = MemoryConfigService(db)
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config = config_service.get_config_by_id(config_id)
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if not config:
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return fail(BizCode.INVALID_PARAMETER, "配置ID无效", f"配置 {config_id} 不存在")
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except Exception as e:
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return fail(BizCode.INVALID_PARAMETER, "配置ID验证失败", str(e))
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api_logger.info(
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f"用户 {current_user.username} 请求生成个性化情绪建议",
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extra={
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"group_id": request.group_id,
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"config_id": config_id
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"end_user_id": request.end_user_id
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}
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)
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# 调用服务层生成建议
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data = await emotion_service.generate_emotion_suggestions(
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end_user_id=request.group_id,
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end_user_id=request.end_user_id,
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db=db
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)
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# 保存到缓存
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await emotion_service.save_suggestions_cache(
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end_user_id=request.group_id,
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end_user_id=request.end_user_id,
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suggestions_data=data,
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db=db,
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expires_hours=24
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@@ -324,7 +299,7 @@ async def generate_emotion_suggestions(
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api_logger.info(
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"个性化建议生成成功",
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extra={
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"group_id": request.group_id,
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"end_user_id": request.end_user_id,
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"suggestions_count": len(data.get("suggestions", []))
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}
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)
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@@ -334,7 +309,7 @@ async def generate_emotion_suggestions(
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except Exception as e:
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api_logger.error(
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f"生成个性化建议失败: {str(e)}",
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extra={"group_id": request.group_id},
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extra={"end_user_id": request.end_user_id},
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exc_info=True
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)
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raise HTTPException(
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@@ -147,6 +147,7 @@ class Settings:
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# Celery configuration (internal)
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CELERY_BROKER: int = int(os.getenv("CELERY_BROKER", "1"))
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CELERY_BACKEND: int = int(os.getenv("CELERY_BACKEND", "2"))
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REFLECTION_INTERVAL_SECONDS: float = float(os.getenv("REFLECTION_INTERVAL_SECONDS", "300"))
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HEALTH_CHECK_SECONDS: float = float(os.getenv("HEALTH_CHECK_SECONDS", "600"))
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MEMORY_INCREMENT_INTERVAL_HOURS: float = float(os.getenv("MEMORY_INCREMENT_INTERVAL_HOURS", "24"))
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@@ -1,16 +0,0 @@
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"""
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LangGraph Graph package for memory agent.
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This package provides the LangGraph workflow orchestrator with modular
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node implementations, routing logic, and state management.
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Package structure:
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- read_graph: Main graph factory for read operations
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- write_graph: Main graph factory for write operations
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- nodes: LangGraph node implementations
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- routing: State routing logic
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- state: State management utilities
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"""
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from app.core.memory.agent.langgraph_graph.read_graph import make_read_graph
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__all__ = ['make_read_graph']
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@@ -4,7 +4,7 @@ LangGraph node implementations.
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This module contains custom node implementations for the LangGraph workflow.
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"""
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from app.core.memory.agent.langgraph_graph.nodes.tool_node import ToolExecutionNode
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from app.core.memory.agent.langgraph_graph.nodes.input_node import create_input_message
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__all__ = ["ToolExecutionNode", "create_input_message"]
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# from app.core.memory.agent.langgraph_graph.nodes.tool_node import ToolExecutionNode
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# from app.core.memory.agent.langgraph_graph.nodes.input_node import create_input_message
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#
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# __all__ = ["ToolExecutionNode", "create_input_message"]
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@@ -0,0 +1,16 @@
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from app.core.memory.agent.utils.llm_tools import ReadState, WriteState
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def content_input_node(state: ReadState) -> ReadState:
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"""开始节点 - 提取内容并保持状态信息"""
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content = state['messages'][0].content if state.get('messages') else ''
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# 返回内容并保持所有状态信息
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return {"data": content}
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def content_input_write(state: WriteState) -> WriteState:
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"""开始节点 - 提取内容并保持状态信息"""
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content = state['messages'][0].content if state.get('messages') else ''
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# 返回内容并保持所有状态信息
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return {"data": content}
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@@ -1,150 +0,0 @@
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"""
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Input node for LangGraph workflow entry point.
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This module provides the create_input_message function which processes initial
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user input with multimodal support and creates the first tool call message.
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"""
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import logging
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import re
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import uuid
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from datetime import datetime
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from typing import Any, Dict
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from app.core.memory.agent.utils.multimodal import MultimodalProcessor
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from app.schemas.memory_config_schema import MemoryConfig
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from langchain_core.messages import AIMessage
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logger = logging.getLogger(__name__)
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async def create_input_message(
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state: Dict[str, Any],
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tool_name: str,
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session_id: str,
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search_switch: str,
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apply_id: str,
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group_id: str,
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multimodal_processor: MultimodalProcessor,
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memory_config: MemoryConfig,
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) -> Dict[str, Any]:
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"""
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Create initial tool call message from user input.
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This function:
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1. Extracts the last message content from state
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2. Processes multimodal inputs (images/audio) using the multimodal processor
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3. Generates a unique message ID
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4. Extracts namespace from session_id
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5. Handles verified_data extraction for backward compatibility
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6. Returns AIMessage with complete tool_calls structure
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Args:
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state: LangGraph state dictionary containing messages
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tool_name: Name of the tool to invoke (typically "Split_The_Problem")
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session_id: Session identifier (format: "call_id_{namespace}")
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search_switch: Search routing parameter
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apply_id: Application identifier
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group_id: Group identifier
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multimodal_processor: Processor for handling image/audio inputs
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memory_config: MemoryConfig object containing all configuration
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Returns:
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State update with AIMessage containing tool_call
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Examples:
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>>> state = {"messages": [HumanMessage(content="What is AI?")]}
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>>> result = await create_input_message(
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... state, "Split_The_Problem", "call_id_user123", "0", "app1", "group1", processor, config
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... )
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>>> result["messages"][0].tool_calls[0]["name"]
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'Split_The_Problem'
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"""
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messages = state.get("messages", [])
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# Extract last message content
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if messages:
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last_message = messages[-1].content if hasattr(messages[-1], 'content') else str(messages[-1])
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else:
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logger.warning("[create_input_message] No messages in state, using empty string")
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last_message = ""
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||||
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||||
logger.debug(f"[create_input_message] Original input: {last_message[:100]}...")
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# Process multimodal input (images/audio)
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try:
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processed_content = await multimodal_processor.process_input(last_message)
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if processed_content != last_message:
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logger.info(
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f"[create_input_message] Multimodal processing converted input "
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f"from {len(last_message)} to {len(processed_content)} chars"
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||||
)
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last_message = processed_content
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||||
except Exception as e:
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logger.error(
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f"[create_input_message] Multimodal processing failed: {e}",
|
||||
exc_info=True
|
||||
)
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||||
# Continue with original content
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# Generate unique message ID
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uuid_str = uuid.uuid4()
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||||
time_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
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||||
# Extract namespace from session_id
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# Expected format: "call_id_{namespace}" or similar
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||||
try:
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||||
namespace = str(session_id).split('_id_')[1]
|
||||
except (IndexError, AttributeError):
|
||||
logger.warning(
|
||||
f"[create_input_message] Could not extract namespace from session_id: {session_id}"
|
||||
)
|
||||
namespace = "unknown"
|
||||
|
||||
# Handle verified_data extraction (backward compatibility)
|
||||
# This regex-based extraction is kept for compatibility with existing data formats
|
||||
if 'verified_data' in str(last_message):
|
||||
try:
|
||||
messages_last = str(last_message).replace('\\n', '').replace('\\', '')
|
||||
query_match = re.findall(r'"query": "(.*?)",', messages_last)
|
||||
if query_match:
|
||||
last_message = query_match[0]
|
||||
logger.debug(
|
||||
f"[create_input_message] Extracted query from verified_data: {last_message}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"[create_input_message] Failed to extract query from verified_data: {e}"
|
||||
)
|
||||
|
||||
# Construct tool call message
|
||||
tool_call_id = f"{session_id}_{uuid_str}"
|
||||
|
||||
logger.info(
|
||||
f"[create_input_message] Creating tool call for '{tool_name}' "
|
||||
f"with ID: {tool_call_id}"
|
||||
)
|
||||
|
||||
# Build tool arguments
|
||||
tool_args = {
|
||||
"sentence": last_message,
|
||||
"sessionid": session_id,
|
||||
"messages_id": str(uuid_str),
|
||||
"search_switch": search_switch,
|
||||
"apply_id": apply_id,
|
||||
"group_id": group_id,
|
||||
"memory_config": memory_config,
|
||||
}
|
||||
|
||||
return {
|
||||
"messages": [
|
||||
AIMessage(
|
||||
content="",
|
||||
tool_calls=[{
|
||||
"name": tool_name,
|
||||
"args": tool_args,
|
||||
"id": tool_call_id
|
||||
}]
|
||||
)
|
||||
]
|
||||
}
|
||||
237
api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py
Normal file
237
api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py
Normal file
@@ -0,0 +1,237 @@
|
||||
import json
|
||||
import time
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.db import get_db
|
||||
|
||||
from app.core.memory.agent.models.problem_models import ProblemExtensionResponse
|
||||
from app.core.memory.agent.utils.llm_tools import (
|
||||
PROJECT_ROOT_,
|
||||
ReadState,
|
||||
)
|
||||
from app.core.memory.agent.utils.redis_tool import store
|
||||
from app.core.memory.agent.utils.session_tools import SessionService
|
||||
from app.core.memory.agent.utils.template_tools import TemplateService
|
||||
from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
|
||||
|
||||
template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
|
||||
db_session = next(get_db())
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
class ProblemNodeService(LLMServiceMixin):
|
||||
"""问题处理节点服务类"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.template_service = TemplateService(template_root)
|
||||
|
||||
# 创建全局服务实例
|
||||
problem_service = ProblemNodeService()
|
||||
|
||||
async def Split_The_Problem(state: ReadState) -> ReadState:
|
||||
"""问题分解节点"""
|
||||
# 从状态中获取数据
|
||||
content = state.get('data', '')
|
||||
group_id = state.get('group_id', '')
|
||||
memory_config = state.get('memory_config', None)
|
||||
|
||||
history = await SessionService(store).get_history(group_id, group_id, group_id)
|
||||
system_prompt = await problem_service.template_service.render_template(
|
||||
template_name='problem_breakdown_prompt.jinja2',
|
||||
operation_name='split_the_problem',
|
||||
history=history,
|
||||
sentence=content
|
||||
)
|
||||
|
||||
try:
|
||||
# 使用优化的LLM服务
|
||||
structured = await problem_service.call_llm_structured(
|
||||
state=state,
|
||||
db_session=db_session,
|
||||
system_prompt=system_prompt,
|
||||
response_model=ProblemExtensionResponse,
|
||||
fallback_value=[]
|
||||
)
|
||||
|
||||
# 添加更详细的日志记录
|
||||
logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
|
||||
|
||||
# 验证结构化响应
|
||||
if not structured or not hasattr(structured, 'root'):
|
||||
logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
|
||||
split_result = json.dumps([], ensure_ascii=False)
|
||||
elif not structured.root:
|
||||
logger.warning("Split_The_Problem: 结构化响应的root为空")
|
||||
split_result = json.dumps([], ensure_ascii=False)
|
||||
else:
|
||||
split_result = json.dumps(
|
||||
[item.model_dump() for item in structured.root],
|
||||
ensure_ascii=False
|
||||
)
|
||||
|
||||
split_result_dict = []
|
||||
for index, item in enumerate(json.loads(split_result)):
|
||||
split_data = {
|
||||
"id": f"Q{index+1}",
|
||||
"question": item['extended_question'],
|
||||
"type": item['type'],
|
||||
"reason": item['reason']
|
||||
}
|
||||
split_result_dict.append(split_data)
|
||||
|
||||
logger.info(f"Split_The_Problem: 成功生成 {len(structured.root) if structured.root else 0} 个分解项")
|
||||
|
||||
result = {
|
||||
"context": split_result,
|
||||
"original": content,
|
||||
"_intermediate": {
|
||||
"type": "problem_split",
|
||||
"title": "问题拆分",
|
||||
"data": split_result_dict,
|
||||
"original_query": content
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Split_The_Problem failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
|
||||
# 提供更详细的错误信息
|
||||
error_details = {
|
||||
"error_type": type(e).__name__,
|
||||
"error_message": str(e),
|
||||
"content_length": len(content),
|
||||
"llm_model_id": memory_config.llm_model_id if memory_config else None
|
||||
}
|
||||
|
||||
logger.error(f"Split_The_Problem error details: {error_details}")
|
||||
|
||||
# 创建默认的空结果
|
||||
result = {
|
||||
"context": json.dumps([], ensure_ascii=False),
|
||||
"original": content,
|
||||
"error": str(e),
|
||||
"_intermediate": {
|
||||
"type": "problem_split",
|
||||
"title": "问题拆分",
|
||||
"data": [],
|
||||
"original_query": content,
|
||||
"error": error_details
|
||||
}
|
||||
}
|
||||
|
||||
# 返回更新后的状态，包含spit_context字段
|
||||
return {"spit_data": result}
|
||||
|
||||
async def Problem_Extension(state: ReadState) -> ReadState:
|
||||
"""问题扩展节点"""
|
||||
# 获取原始数据和分解结果
|
||||
start = time.time()
|
||||
content = state.get('data', '')
|
||||
data = state.get('spit_data', '')['context']
|
||||
group_id = state.get('group_id', '')
|
||||
storage_type = state.get('storage_type', '')
|
||||
user_rag_memory_id = state.get('user_rag_memory_id', '')
|
||||
memory_config = state.get('memory_config', None)
|
||||
|
||||
databasets = {}
|
||||
try:
|
||||
data = json.loads(data)
|
||||
for i in data:
|
||||
databasets[i['extended_question']] = i['type']
|
||||
except (json.JSONDecodeError, KeyError, TypeError) as e:
|
||||
logger.error(f"Problem_Extension: 数据解析失败: {e}")
|
||||
# 使用空字典作为fallback
|
||||
databasets = {}
|
||||
data = []
|
||||
|
||||
history = await SessionService(store).get_history(group_id, group_id, group_id)
|
||||
system_prompt = await problem_service.template_service.render_template(
|
||||
template_name='Problem_Extension_prompt.jinja2',
|
||||
operation_name='problem_extension',
|
||||
history=history,
|
||||
questions=databasets
|
||||
)
|
||||
|
||||
try:
|
||||
# 使用优化的LLM服务
|
||||
response_content = await problem_service.call_llm_structured(
|
||||
state=state,
|
||||
db_session=db_session,
|
||||
system_prompt=system_prompt,
|
||||
response_model=ProblemExtensionResponse,
|
||||
fallback_value=[]
|
||||
)
|
||||
|
||||
logger.info(f"Problem_Extension: 开始处理问题扩展，问题数量: {len(databasets)}")
|
||||
|
||||
# 验证结构化响应
|
||||
if not response_content or not hasattr(response_content, 'root'):
|
||||
logger.warning("Problem_Extension: 结构化响应为空或格式不正确")
|
||||
aggregated_dict = {}
|
||||
elif not response_content.root:
|
||||
logger.warning("Problem_Extension: 结构化响应的root为空")
|
||||
aggregated_dict = {}
|
||||
else:
|
||||
# Aggregate results by original question
|
||||
aggregated_dict = {}
|
||||
for item in response_content.root:
|
||||
try:
|
||||
key = getattr(item, "original_question", None) or (
|
||||
item.get("original_question") if isinstance(item, dict) else None
|
||||
)
|
||||
value = getattr(item, "extended_question", None) or (
|
||||
item.get("extended_question") if isinstance(item, dict) else None
|
||||
)
|
||||
if not key or not value:
|
||||
logger.warning(f"Problem_Extension: 跳过无效项: key={key}, value={value}")
|
||||
continue
|
||||
aggregated_dict.setdefault(key, []).append(value)
|
||||
except Exception as item_error:
|
||||
logger.warning(f"Problem_Extension: 处理项目时出错: {item_error}")
|
||||
continue
|
||||
|
||||
logger.info(f"Problem_Extension: 成功生成 {len(aggregated_dict)} 个扩展问题组")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"LLM call failed for Problem_Extension: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
|
||||
# 提供更详细的错误信息
|
||||
error_details = {
|
||||
"error_type": type(e).__name__,
|
||||
"error_message": str(e),
|
||||
"questions_count": len(databasets),
|
||||
"llm_model_id": memory_config.llm_model_id if memory_config else None
|
||||
}
|
||||
|
||||
logger.error(f"Problem_Extension error details: {error_details}")
|
||||
aggregated_dict = {}
|
||||
|
||||
logger.info("Problem extension")
|
||||
logger.info(f"Problem extension result: {aggregated_dict}")
|
||||
|
||||
# Emit intermediate output for frontend
|
||||
print(time.time() - start)
|
||||
result = {
|
||||
"context": aggregated_dict,
|
||||
"original": data,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "problem_extension",
|
||||
"title": "问题扩展",
|
||||
"data": aggregated_dict,
|
||||
"original_query": content,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
|
||||
return {"problem_extension": result}
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,417 @@
|
||||
# ===== 标准库 =====
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
|
||||
# ===== 第三方库 =====
|
||||
from langchain.agents import create_agent
|
||||
from langchain_openai import ChatOpenAI
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.db import get_db, get_db_context
|
||||
|
||||
from app.schemas import model_schema
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
from app.services.model_service import ModelConfigService
|
||||
|
||||
from app.core.memory.agent.services.search_service import SearchService
|
||||
from app.core.memory.agent.utils.llm_tools import (
|
||||
COUNTState,
|
||||
ReadState,
|
||||
deduplicate_entries,
|
||||
merge_to_key_value_pairs,
|
||||
)
|
||||
from app.core.memory.agent.langgraph_graph.tools.tool import (
|
||||
create_hybrid_retrieval_tool_sync,
|
||||
create_time_retrieval_tool,
|
||||
extract_tool_message_content,
|
||||
)
|
||||
|
||||
from app.core.rag.nlp.search import knowledge_retrieval
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
db = next(get_db())
|
||||
|
||||
|
||||
|
||||
async def rag_config(state):
|
||||
user_rag_memory_id = state.get('user_rag_memory_id', '')
|
||||
kb_config = {
|
||||
"knowledge_bases": [
|
||||
{
|
||||
"kb_id": user_rag_memory_id,
|
||||
"similarity_threshold": 0.7,
|
||||
"vector_similarity_weight": 0.5,
|
||||
"top_k": 10,
|
||||
"retrieve_type": "participle"
|
||||
}
|
||||
],
|
||||
"merge_strategy": "weight",
|
||||
"reranker_id": os.getenv('reranker_id'),
|
||||
"reranker_top_k": 10
|
||||
}
|
||||
return kb_config
|
||||
async def rag_knowledge(state,question):
|
||||
kb_config = await rag_config(state)
|
||||
group_id = state.get('group_id', '')
|
||||
user_rag_memory_id=state.get("user_rag_memory_id",'')
|
||||
retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(group_id)])
|
||||
try:
|
||||
retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
|
||||
clean_content = '\n\n'.join(retrieval_knowledge)
|
||||
cleaned_query = question
|
||||
raw_results = clean_content
|
||||
logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
|
||||
except Exception :
|
||||
retrieval_knowledge=[]
|
||||
clean_content = ''
|
||||
raw_results = ''
|
||||
cleaned_query = question
|
||||
logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
|
||||
return retrieval_knowledge,clean_content,cleaned_query,raw_results
|
||||
|
||||
|
||||
async def llm_infomation(state: ReadState) -> ReadState:
|
||||
memory_config = state.get('memory_config', None)
|
||||
model_id = memory_config.llm_model_id
|
||||
tenant_id = memory_config.tenant_id
|
||||
|
||||
# 使用现有的 memory_config 而不是重新查询数据库
|
||||
# 或者使用线程安全的数据库访问
|
||||
with get_db_context() as db:
|
||||
result_orm = ModelConfigService.get_model_by_id(db=db, model_id=model_id, tenant_id=tenant_id)
|
||||
result_pydantic = model_schema.ModelConfig.model_validate(result_orm)
|
||||
return result_pydantic
|
||||
|
||||
|
||||
async def clean_databases(data) -> str:
|
||||
"""
|
||||
简化的数据库搜索结果清理函数
|
||||
|
||||
Args:
|
||||
data: 搜索结果数据
|
||||
|
||||
Returns:
|
||||
清理后的内容字符串
|
||||
"""
|
||||
try:
|
||||
# 解析JSON字符串
|
||||
if isinstance(data, str):
|
||||
try:
|
||||
data = json.loads(data)
|
||||
except json.JSONDecodeError:
|
||||
return data
|
||||
|
||||
if not isinstance(data, dict):
|
||||
return str(data)
|
||||
|
||||
# 获取结果数据
|
||||
# with open("搜索结果.json","w",encoding='utf-8') as f:
|
||||
# f.write(json.dumps(data, indent=4, ensure_ascii=False))
|
||||
results = data.get('results', data)
|
||||
if not isinstance(results, dict):
|
||||
return str(results)
|
||||
|
||||
# 收集所有内容
|
||||
content_list = []
|
||||
|
||||
# 处理重排序结果
|
||||
reranked = results.get('reranked_results', {})
|
||||
if reranked:
|
||||
for category in ['summaries', 'statements', 'chunks', 'entities']:
|
||||
items = reranked.get(category, [])
|
||||
if isinstance(items, list):
|
||||
content_list.extend(items)
|
||||
# 处理时间搜索结果
|
||||
time_search = results.get('time_search', {})
|
||||
if time_search:
|
||||
if isinstance(time_search, dict):
|
||||
statements = time_search.get('statements', time_search.get('time_search', []))
|
||||
if isinstance(statements, list):
|
||||
content_list.extend(statements)
|
||||
elif isinstance(time_search, list):
|
||||
content_list.extend(time_search)
|
||||
|
||||
# 提取文本内容
|
||||
text_parts = []
|
||||
for item in content_list:
|
||||
if isinstance(item, dict):
|
||||
text = item.get('statement') or item.get('content', '')
|
||||
if text:
|
||||
text_parts.append(text)
|
||||
elif isinstance(item, str):
|
||||
text_parts.append(item)
|
||||
|
||||
|
||||
return '\n'.join(text_parts).strip()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"clean_databases failed: {e}", exc_info=True)
|
||||
return str(data)
|
||||
|
||||
|
||||
async def retrieve_nodes(state: ReadState) -> ReadState:
|
||||
|
||||
'''
|
||||
|
||||
模型信息
|
||||
'''
|
||||
|
||||
problem_extension=state.get('problem_extension', '')['context']
|
||||
storage_type=state.get('storage_type', '')
|
||||
user_rag_memory_id=state.get('user_rag_memory_id', '')
|
||||
group_id=state.get('group_id', '')
|
||||
memory_config = state.get('memory_config', None)
|
||||
original=state.get('data', '')
|
||||
problem_list=[]
|
||||
for key,values in problem_extension.items():
|
||||
for data in values:
|
||||
problem_list.append(data)
|
||||
logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
|
||||
# 创建异步任务处理单个问题
|
||||
async def process_question_nodes(idx, question):
|
||||
try:
|
||||
# Prepare search parameters based on storage type
|
||||
search_params = {
|
||||
"group_id": group_id,
|
||||
"question": question,
|
||||
"return_raw_results": True
|
||||
}
|
||||
if storage_type == "rag" and user_rag_memory_id:
|
||||
retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state, question)
|
||||
else:
|
||||
clean_content, cleaned_query, raw_results = await SearchService().execute_hybrid_search(
|
||||
**search_params, memory_config=memory_config
|
||||
)
|
||||
|
||||
return {
|
||||
"Query_small": cleaned_query,
|
||||
"Result_small": clean_content,
|
||||
"_intermediate": {
|
||||
"type": "search_result",
|
||||
"query": cleaned_query,
|
||||
"raw_results": raw_results,
|
||||
"index": idx + 1,
|
||||
"total": len(problem_list)
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve: hybrid_search failed for question '{question}': {e}",
|
||||
exc_info=True
|
||||
)
|
||||
# Return empty result for this question
|
||||
return {
|
||||
"Query_small": question,
|
||||
"Result_small": "",
|
||||
"_intermediate": {
|
||||
"type": "search_result",
|
||||
"query": question,
|
||||
"raw_results": [],
|
||||
"index": idx + 1,
|
||||
"total": len(problem_list)
|
||||
}
|
||||
}
|
||||
|
||||
# 并发处理所有问题
|
||||
tasks = [process_question_nodes(idx, question) for idx, question in enumerate(problem_list)]
|
||||
databases_anser = await asyncio.gather(*tasks)
|
||||
databases_data = {
|
||||
"Query": original,
|
||||
"Expansion_issue": databases_anser
|
||||
}
|
||||
|
||||
# Collect intermediate outputs before deduplication
|
||||
intermediate_outputs = []
|
||||
for item in databases_anser:
|
||||
if '_intermediate' in item:
|
||||
intermediate_outputs.append(item['_intermediate'])
|
||||
|
||||
# Deduplicate and merge results
|
||||
deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
|
||||
deduplicated_data_merged = merge_to_key_value_pairs(
|
||||
deduplicated_data,
|
||||
'Query_small',
|
||||
'Result_small'
|
||||
)
|
||||
|
||||
# Restructure for Verify/Retrieve_Summary compatibility
|
||||
keys, val = [], []
|
||||
for item in deduplicated_data_merged:
|
||||
for items_key, items_value in item.items():
|
||||
keys.append(items_key)
|
||||
val.append(items_value)
|
||||
|
||||
send_verify = []
|
||||
for i, j in zip(keys, val, strict=False):
|
||||
if j!=['']:
|
||||
send_verify.append({
|
||||
"Query_small": i,
|
||||
"Answer_Small": j
|
||||
})
|
||||
|
||||
dup_databases = {
|
||||
"Query": original,
|
||||
"Expansion_issue": send_verify,
|
||||
"_intermediate_outputs": intermediate_outputs # Preserve intermediate outputs
|
||||
}
|
||||
|
||||
logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
|
||||
return {'retrieve':dup_databases}
|
||||
|
||||
|
||||
|
||||
|
||||
async def retrieve(state: ReadState) -> ReadState:
|
||||
# 从state中获取group_id
|
||||
import time
|
||||
start=time.time()
|
||||
problem_extension = state.get('problem_extension', '')['context']
|
||||
storage_type = state.get('storage_type', '')
|
||||
user_rag_memory_id = state.get('user_rag_memory_id', '')
|
||||
group_id = state.get('group_id', '')
|
||||
memory_config = state.get('memory_config', None)
|
||||
original = state.get('data', '')
|
||||
problem_list = []
|
||||
for key, values in problem_extension.items():
|
||||
for data in values:
|
||||
problem_list.append(data)
|
||||
logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
|
||||
databases_anser = []
|
||||
|
||||
async def get_llm_info():
|
||||
with get_db_context() as db: # 使用同步数据库上下文管理器
|
||||
config_service = MemoryConfigService(db)
|
||||
return await llm_infomation(state)
|
||||
llm_config = await get_llm_info()
|
||||
api_key_obj = llm_config.api_keys[0]
|
||||
api_key = api_key_obj.api_key
|
||||
api_base = api_key_obj.api_base
|
||||
model_name = api_key_obj.model_name
|
||||
llm = ChatOpenAI(
|
||||
model=model_name,
|
||||
api_key=api_key,
|
||||
base_url=api_base,
|
||||
temperature=0.2,
|
||||
)
|
||||
|
||||
time_retrieval_tool = create_time_retrieval_tool(group_id)
|
||||
search_params = { "group_id": group_id, "return_raw_results": True }
|
||||
hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params)
|
||||
agent = create_agent(
|
||||
llm,
|
||||
tools=[time_retrieval_tool,hybrid_retrieval],
|
||||
system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的group_id是: {group_id}"
|
||||
)
|
||||
|
||||
# 创建异步任务处理单个问题
|
||||
import asyncio
|
||||
|
||||
# 在模块级别定义信号量，限制最大并发数
|
||||
SEMAPHORE = asyncio.Semaphore(5) # 限制最多5个并发数据库操作
|
||||
|
||||
async def process_question(idx, question):
|
||||
async with SEMAPHORE: # 限制并发
|
||||
try:
|
||||
if storage_type == "rag" and user_rag_memory_id:
|
||||
retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state, question)
|
||||
else:
|
||||
cleaned_query = question
|
||||
# 使用 asyncio 在线程池中运行同步的 agent.invoke
|
||||
import asyncio
|
||||
response = await asyncio.get_event_loop().run_in_executor(
|
||||
None,
|
||||
lambda: agent.invoke({"messages": question})
|
||||
)
|
||||
tool_results = extract_tool_message_content(response)
|
||||
if tool_results == None:
|
||||
raw_results = []
|
||||
clean_content = ''
|
||||
else:
|
||||
raw_results = tool_results['content']
|
||||
clean_content = await clean_databases(raw_results)
|
||||
|
||||
try:
|
||||
raw_results = raw_results['results']
|
||||
except Exception:
|
||||
raw_results = []
|
||||
|
||||
return {
|
||||
"Query_small": cleaned_query,
|
||||
"Result_small": clean_content,
|
||||
"_intermediate": {
|
||||
"type": "search_result",
|
||||
"query": cleaned_query,
|
||||
"raw_results": raw_results,
|
||||
"index": idx + 1,
|
||||
"total": len(problem_list)
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve: hybrid_search failed for question '{question}': {e}",
|
||||
exc_info=True
|
||||
)
|
||||
# Return empty result for this question
|
||||
return {
|
||||
"Query_small": question,
|
||||
"Result_small": "",
|
||||
"_intermediate": {
|
||||
"type": "search_result",
|
||||
"query": question,
|
||||
"raw_results": [],
|
||||
"index": idx + 1,
|
||||
"total": len(problem_list)
|
||||
}
|
||||
}
|
||||
|
||||
# 并发处理所有问题
|
||||
import asyncio
|
||||
tasks = [process_question(idx, question) for idx, question in enumerate(problem_list)]
|
||||
databases_anser = await asyncio.gather(*tasks)
|
||||
databases_data = {
|
||||
"Query": original,
|
||||
"Expansion_issue": databases_anser
|
||||
}
|
||||
|
||||
# Collect intermediate outputs before deduplication
|
||||
intermediate_outputs = []
|
||||
for item in databases_anser:
|
||||
if '_intermediate' in item:
|
||||
intermediate_outputs.append(item['_intermediate'])
|
||||
|
||||
# Deduplicate and merge results
|
||||
deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
|
||||
deduplicated_data_merged = merge_to_key_value_pairs(
|
||||
deduplicated_data,
|
||||
'Query_small',
|
||||
'Result_small'
|
||||
)
|
||||
|
||||
# Restructure for Verify/Retrieve_Summary compatibility
|
||||
keys, val = [], []
|
||||
for item in deduplicated_data_merged:
|
||||
for items_key, items_value in item.items():
|
||||
keys.append(items_key)
|
||||
val.append(items_value)
|
||||
|
||||
send_verify = []
|
||||
for i, j in zip(keys, val, strict=False):
|
||||
if j != ['']:
|
||||
send_verify.append({
|
||||
"Query_small": i,
|
||||
"Answer_Small": j
|
||||
})
|
||||
|
||||
dup_databases = {
|
||||
"Query": original,
|
||||
"Expansion_issue": send_verify,
|
||||
"_intermediate_outputs": intermediate_outputs # Preserve intermediate outputs
|
||||
}
|
||||
# with open('retrieve_text.json', 'w') as f:
|
||||
# json.dump(dup_databases, f, indent=4)
|
||||
logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
|
||||
return {'retrieve': dup_databases}
|
||||
|
||||
|
||||
303
api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py
Normal file
303
api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py
Normal file
@@ -0,0 +1,303 @@
|
||||
|
||||
|
||||
import time
|
||||
|
||||
from app.core.logging_config import get_agent_logger, log_time
|
||||
from app.db import get_db
|
||||
|
||||
from app.core.memory.agent.models.summary_models import (
|
||||
RetrieveSummaryResponse,
|
||||
SummaryResponse,
|
||||
)
|
||||
from app.core.memory.agent.services.search_service import SearchService
|
||||
from app.core.memory.agent.utils.llm_tools import (
|
||||
PROJECT_ROOT_,
|
||||
ReadState,
|
||||
)
|
||||
from app.core.memory.agent.utils.redis_tool import store
|
||||
from app.core.memory.agent.utils.session_tools import SessionService
|
||||
from app.core.memory.agent.utils.template_tools import TemplateService
|
||||
from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
|
||||
|
||||
template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
|
||||
logger = get_agent_logger(__name__)
|
||||
db_session = next(get_db())
|
||||
|
||||
class SummaryNodeService(LLMServiceMixin):
|
||||
"""总结节点服务类"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.template_service = TemplateService(template_root)
|
||||
|
||||
# 创建全局服务实例
|
||||
summary_service = SummaryNodeService()
|
||||
|
||||
async def summary_history(state: ReadState) -> ReadState:
|
||||
group_id = state.get("group_id", '')
|
||||
history = await SessionService(store).get_history(group_id, group_id, group_id)
|
||||
return history
|
||||
|
||||
async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,search_mode) -> str:
|
||||
"""
|
||||
增强的summary_llm函数，包含更好的错误处理和数据验证
|
||||
"""
|
||||
data = state.get("data", '')
|
||||
|
||||
# 构建系统提示词
|
||||
if str(search_mode) == "0":
|
||||
system_prompt = await summary_service.template_service.render_template(
|
||||
template_name=template_name,
|
||||
operation_name=operation_name,
|
||||
data=retrieve_info,
|
||||
query=data
|
||||
)
|
||||
else:
|
||||
system_prompt = await summary_service.template_service.render_template(
|
||||
template_name=template_name,
|
||||
operation_name=operation_name,
|
||||
query=data,
|
||||
history=history,
|
||||
retrieve_info=retrieve_info
|
||||
)
|
||||
try:
|
||||
# 使用优化的LLM服务进行结构化输出
|
||||
structured = await summary_service.call_llm_structured(
|
||||
state=state,
|
||||
db_session=db_session,
|
||||
system_prompt=system_prompt,
|
||||
response_model=response_model,
|
||||
fallback_value=None
|
||||
)
|
||||
# 验证结构化响应
|
||||
if structured is None:
|
||||
logger.warning(f"LLM返回None，使用默认回答")
|
||||
return "信息不足，无法回答"
|
||||
|
||||
# 根据操作类型提取答案
|
||||
if operation_name == "summary":
|
||||
aimessages = getattr(structured, 'query_answer', None) or "信息不足，无法回答"
|
||||
else:
|
||||
# 处理RetrieveSummaryResponse
|
||||
if hasattr(structured, 'data') and structured.data:
|
||||
aimessages = getattr(structured.data, 'query_answer', None) or "信息不足，无法回答"
|
||||
else:
|
||||
logger.warning(f"结构化响应缺少data字段")
|
||||
aimessages = "信息不足，无法回答"
|
||||
|
||||
# 验证答案不为空
|
||||
if not aimessages or aimessages.strip() == "":
|
||||
aimessages = "信息不足，无法回答"
|
||||
|
||||
return aimessages
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"结构化输出失败: {e}", exc_info=True)
|
||||
|
||||
# 尝试非结构化输出作为fallback
|
||||
try:
|
||||
logger.info("尝试非结构化输出作为fallback")
|
||||
response = await summary_service.call_llm_simple(
|
||||
state=state,
|
||||
db_session=db_session,
|
||||
system_prompt=system_prompt,
|
||||
fallback_message="信息不足，无法回答"
|
||||
)
|
||||
|
||||
if response and response.strip():
|
||||
# 简单清理响应
|
||||
cleaned_response = response.strip()
|
||||
# 移除可能的JSON标记
|
||||
if cleaned_response.startswith('```'):
|
||||
lines = cleaned_response.split('\n')
|
||||
cleaned_response = '\n'.join(lines[1:-1])
|
||||
|
||||
return cleaned_response
|
||||
else:
|
||||
return "信息不足，无法回答"
|
||||
|
||||
except Exception as fallback_error:
|
||||
logger.error(f"Fallback也失败: {fallback_error}")
|
||||
return "信息不足，无法回答"
|
||||
|
||||
async def summary_redis_save(state: ReadState,aimessages) -> ReadState:
|
||||
data = state.get("data", '')
|
||||
group_id = state.get("group_id", '')
|
||||
await SessionService(store).save_session(
|
||||
user_id=group_id,
|
||||
query=data,
|
||||
apply_id=group_id,
|
||||
group_id=group_id,
|
||||
ai_response=aimessages
|
||||
)
|
||||
await SessionService(store).cleanup_duplicates()
|
||||
logger.info(f"sessionid: {aimessages} 写入成功")
|
||||
async def summary_prompt(state: ReadState,aimessages,raw_results) -> ReadState:
|
||||
storage_type=state.get("storage_type",'')
|
||||
user_rag_memory_id=state.get("user_rag_memory_id",'')
|
||||
data=state.get("data", '')
|
||||
input_summary = {
|
||||
"status": "success",
|
||||
"summary_result": aimessages,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "input_summary",
|
||||
"title": "快速答案",
|
||||
"summary": aimessages,
|
||||
"query": data,
|
||||
"raw_results": raw_results,
|
||||
"search_mode": "quick_search",
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
retrieve={
|
||||
"status": "success",
|
||||
"summary_result": aimessages,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "retrieval_summary",
|
||||
"title":"快速检索",
|
||||
"summary": aimessages,
|
||||
"query": data,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
|
||||
return input_summary,retrieve
|
||||
|
||||
async def Input_Summary(state: ReadState) -> ReadState:
|
||||
start=time.time()
|
||||
storage_type=state.get("storage_type",'')
|
||||
memory_config = state.get('memory_config', None)
|
||||
user_rag_memory_id=state.get("user_rag_memory_id",'')
|
||||
data=state.get("data", '')
|
||||
group_id=state.get("group_id", '')
|
||||
logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
|
||||
history = await summary_history( state)
|
||||
search_params = {
|
||||
"group_id": group_id,
|
||||
"question": data,
|
||||
"return_raw_results": True
|
||||
}
|
||||
|
||||
try:
|
||||
retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(**search_params, memory_config=memory_config)
|
||||
except Exception as e:
|
||||
logger.error( f"Input_Summary: hybrid_search failed, using empty results: {e}", exc_info=True )
|
||||
retrieve_info, question, raw_results = "", data, []
|
||||
|
||||
|
||||
try:
|
||||
# aimessages=await summary_llm(state,history,retrieve_info,'Retrieve_Summary_prompt.jinja2',
|
||||
# 'input_summary',RetrieveSummaryResponse)
|
||||
# logger.info(f"快速答案总结==>>:{storage_type}--{user_rag_memory_id}--{aimessages}")
|
||||
summary_result = await summary_prompt(state, retrieve_info, retrieve_info)
|
||||
summary = summary_result[0]
|
||||
except Exception as e:
|
||||
logger.error( f"Input_Summary failed: {e}", exc_info=True )
|
||||
summary= {
|
||||
"status": "fail",
|
||||
"summary_result": "信息不足，无法回答",
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"error": str(e)
|
||||
}
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('检索', duration)
|
||||
return {"summary":summary}
|
||||
|
||||
async def Retrieve_Summary(state: ReadState)-> ReadState:
|
||||
retrieve=state.get("retrieve", '')
|
||||
history = await summary_history( state)
|
||||
import json
|
||||
with open("检索.json","w",encoding='utf-8') as f:
|
||||
f.write(json.dumps(retrieve, indent=4, ensure_ascii=False))
|
||||
retrieve=retrieve.get("Expansion_issue", [])
|
||||
start=time.time()
|
||||
retrieve_info_str=[]
|
||||
for data in retrieve:
|
||||
if data=='':
|
||||
retrieve_info_str=''
|
||||
else:
|
||||
for key, value in data.items():
|
||||
if key=='Answer_Small':
|
||||
for i in value:
|
||||
retrieve_info_str.append(i)
|
||||
retrieve_info_str=list(set(retrieve_info_str))
|
||||
retrieve_info_str='\n'.join(retrieve_info_str)
|
||||
|
||||
aimessages=await summary_llm(state,history,retrieve_info_str,
|
||||
'Retrieve_Summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1")
|
||||
if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
|
||||
await summary_redis_save(state, aimessages)
|
||||
if aimessages == '':
|
||||
aimessages = '信息不足，无法回答'
|
||||
logger.info(f"Summary after retrieval: {aimessages}")
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Retrieval summary', duration)
|
||||
|
||||
# 修复协程调用 - 先await，然后访问返回值
|
||||
summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
|
||||
summary = summary_result[1]
|
||||
return {"summary":summary}
|
||||
|
||||
|
||||
async def Summary(state: ReadState)-> ReadState:
|
||||
start=time.time()
|
||||
query = state.get("data", '')
|
||||
verify=state.get("verify", '')
|
||||
verify_expansion_issue=verify.get("verified_data", '')
|
||||
retrieve_info_str=''
|
||||
for data in verify_expansion_issue:
|
||||
for key, value in data.items():
|
||||
if key=='answer_small':
|
||||
for i in value:
|
||||
retrieve_info_str+=i+'\n'
|
||||
history=await summary_history(state)
|
||||
|
||||
data = {
|
||||
"query": query,
|
||||
"history": history,
|
||||
"retrieve_info": retrieve_info_str
|
||||
}
|
||||
aimessages=await summary_llm(state,history,data,
|
||||
'summary_prompt.jinja2','summary',SummaryResponse,0)
|
||||
|
||||
|
||||
if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
|
||||
await summary_redis_save(state, aimessages)
|
||||
if aimessages == '':
|
||||
aimessages = '信息不足，无法回答'
|
||||
try:
|
||||
duration = time.time() - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Retrieval summary', duration)
|
||||
|
||||
# 修复协程调用 - 先await，然后访问返回值
|
||||
summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
|
||||
summary = summary_result[1]
|
||||
return {"summary":summary}
|
||||
|
||||
async def Summary_fails(state: ReadState)-> ReadState:
|
||||
storage_type=state.get("storage_type", '')
|
||||
user_rag_memory_id=state.get("user_rag_memory_id", '')
|
||||
result= {
|
||||
"status": "success",
|
||||
"summary_result": "没有相关数据",
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
return {"summary":result}
|
||||
@@ -1,234 +0,0 @@
|
||||
"""
|
||||
Tool execution node for LangGraph workflow.
|
||||
|
||||
This module provides the ToolExecutionNode class which wraps tool execution
|
||||
with parameter transformation logic using the ParameterBuilder service.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import time
|
||||
from typing import Any, Callable, Dict
|
||||
|
||||
from app.core.memory.agent.langgraph_graph.state.extractors import (
|
||||
extract_content_payload,
|
||||
extract_tool_call_id,
|
||||
)
|
||||
from app.core.memory.agent.mcp_server.services.parameter_builder import ParameterBuilder
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from langchain_core.messages import AIMessage
|
||||
from langgraph.prebuilt import ToolNode
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ToolExecutionNode:
|
||||
"""
|
||||
Custom LangGraph node that wraps tool execution with parameter transformation.
|
||||
|
||||
This node extracts content from previous tool results, transforms parameters
|
||||
based on tool type using ParameterBuilder, and invokes the tool with the
|
||||
correct argument structure.
|
||||
|
||||
Attributes:
|
||||
tool_node: LangGraph ToolNode wrapping the actual tool
|
||||
id: Node identifier for message IDs
|
||||
tool_name: Name of the tool being executed
|
||||
namespace: Namespace for session management
|
||||
search_switch: Search routing parameter
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
parameter_builder: Service for building tool-specific arguments
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
tool: Callable,
|
||||
node_id: str,
|
||||
namespace: str,
|
||||
search_switch: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
parameter_builder: ParameterBuilder,
|
||||
storage_type: str,
|
||||
user_rag_memory_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
):
|
||||
"""
|
||||
Initialize the tool execution node.
|
||||
|
||||
Args:
|
||||
tool: The tool function to execute
|
||||
node_id: Identifier for this node (used in message IDs)
|
||||
namespace: Namespace for session management
|
||||
search_switch: Search routing parameter
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
parameter_builder: Service for building tool-specific arguments
|
||||
storage_type: Storage type for the workspace
|
||||
user_rag_memory_id: User RAG memory identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
"""
|
||||
self.tool_node = ToolNode([tool])
|
||||
self.id = node_id
|
||||
self.tool_name = tool.name if hasattr(tool, 'name') else str(tool)
|
||||
self.namespace = namespace
|
||||
self.search_switch = search_switch
|
||||
self.apply_id = apply_id
|
||||
self.group_id = group_id
|
||||
self.parameter_builder = parameter_builder
|
||||
self.storage_type = storage_type
|
||||
self.user_rag_memory_id = user_rag_memory_id
|
||||
self.memory_config = memory_config
|
||||
|
||||
logger.info(
|
||||
f"[ToolExecutionNode] Initialized node '{self.id}' for tool '{self.tool_name}'"
|
||||
)
|
||||
|
||||
async def __call__(self, state: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Execute the tool with transformed parameters.
|
||||
|
||||
This method:
|
||||
1. Extracts the last message from state
|
||||
2. Extracts tool call ID using state extractors
|
||||
3. Extracts content payload using state extractors
|
||||
4. Builds tool arguments using parameter builder
|
||||
5. Constructs AIMessage with tool_calls
|
||||
6. Invokes the tool and returns the result
|
||||
|
||||
Args:
|
||||
state: LangGraph state dictionary
|
||||
|
||||
Returns:
|
||||
Updated state with tool result in messages
|
||||
"""
|
||||
messages = state.get("messages", [])
|
||||
logger.debug( self.tool_name)
|
||||
|
||||
if not messages:
|
||||
logger.warning(f"[ToolExecutionNode] {self.id} - No messages in state")
|
||||
return {"messages": [AIMessage(content="Error: No messages in state")]}
|
||||
|
||||
last_message = messages[-1]
|
||||
logger.debug(
|
||||
f"[ToolExecutionNode] {self.id} - Processing message at {time.time()}"
|
||||
)
|
||||
|
||||
try:
|
||||
# Extract tool call ID using state extractors
|
||||
tool_call_id = extract_tool_call_id(last_message)
|
||||
logger.debug(f"[ToolExecutionNode] {self.id} - Extracted tool_call_id: {tool_call_id}")
|
||||
|
||||
except ValueError as e:
|
||||
logger.error(
|
||||
f"[ToolExecutionNode] {self.id} - Failed to extract tool call ID: {e}"
|
||||
)
|
||||
return {"messages": [AIMessage(content=f"Error: {str(e)}")]}
|
||||
|
||||
try:
|
||||
# Extract content payload using state extractors
|
||||
content = extract_content_payload(last_message)
|
||||
logger.debug(
|
||||
f"[ToolExecutionNode] {self.id} - Extracted content type: {type(content)}, content_keys: {list(content.keys()) if isinstance(content, dict) else 'N/A'}"
|
||||
)
|
||||
# Log raw message content for debugging
|
||||
if hasattr(last_message, 'content'):
|
||||
raw = last_message.content
|
||||
logger.debug(f"[ToolExecutionNode] {self.id} - Raw message content (first 500 chars): {str(raw)[:500]}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"[ToolExecutionNode] {self.id} - Failed to extract content: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
content = {}
|
||||
|
||||
try:
|
||||
# Build tool arguments using parameter builder
|
||||
tool_args = self.parameter_builder.build_tool_args(
|
||||
tool_name=self.tool_name,
|
||||
content=content,
|
||||
tool_call_id=tool_call_id,
|
||||
search_switch=self.search_switch,
|
||||
apply_id=self.apply_id,
|
||||
group_id=self.group_id,
|
||||
memory_config=self.memory_config,
|
||||
storage_type=self.storage_type,
|
||||
user_rag_memory_id=self.user_rag_memory_id,
|
||||
)
|
||||
logger.debug(
|
||||
f"[ToolExecutionNode] {self.id} - Built tool args with keys: {list(tool_args.keys())}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"[ToolExecutionNode] {self.id} - Failed to build tool args: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {"messages": [AIMessage(content=f"Error building arguments: {str(e)}")]}
|
||||
|
||||
# Construct tool input message
|
||||
tool_input = {
|
||||
"messages": [
|
||||
AIMessage(
|
||||
content="",
|
||||
tool_calls=[{
|
||||
"name": self.tool_name,
|
||||
"args": tool_args,
|
||||
"id": f"{self.id}_{tool_call_id}",
|
||||
}]
|
||||
)
|
||||
]
|
||||
}
|
||||
|
||||
try:
|
||||
# Invoke the tool
|
||||
result = await self.tool_node.ainvoke(tool_input)
|
||||
|
||||
logger.debug(
|
||||
f"[ToolExecutionNode] {self.id} - Tool execution completed"
|
||||
)
|
||||
|
||||
# Check for error in tool response
|
||||
error_entry = None
|
||||
if result and "messages" in result:
|
||||
for msg in result["messages"]:
|
||||
if hasattr(msg, 'content'):
|
||||
try:
|
||||
import json
|
||||
content = msg.content
|
||||
if isinstance(content, str):
|
||||
parsed = json.loads(content)
|
||||
if isinstance(parsed, dict) and "error" in parsed:
|
||||
error_msg = parsed["error"]
|
||||
logger.warning(
|
||||
f"[ToolExecutionNode] {self.id} - Tool returned error: {error_msg}"
|
||||
)
|
||||
error_entry = {"tool": self.tool_name, "error": error_msg, "node_id": self.id}
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
# Return result with error tracking if error was found
|
||||
if error_entry:
|
||||
result["errors"] = [error_entry]
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"[ToolExecutionNode] {self.id} - Tool execution failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
# Track error in state and return error message
|
||||
from langchain_core.messages import ToolMessage
|
||||
error_entry = {"tool": self.tool_name, "error": str(e), "node_id": self.id}
|
||||
return {
|
||||
"messages": [
|
||||
ToolMessage(
|
||||
content=f"Error executing tool: {str(e)}",
|
||||
tool_call_id=f"{self.id}_{tool_call_id}"
|
||||
)
|
||||
],
|
||||
"errors": [error_entry]
|
||||
}
|
||||
@@ -0,0 +1,85 @@
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.db import get_db
|
||||
|
||||
from app.core.memory.agent.models.verification_models import VerificationResult
|
||||
from app.core.memory.agent.utils.llm_tools import (
|
||||
PROJECT_ROOT_,
|
||||
ReadState,
|
||||
)
|
||||
from app.core.memory.agent.utils.redis_tool import store
|
||||
from app.core.memory.agent.utils.session_tools import SessionService
|
||||
from app.core.memory.agent.utils.template_tools import TemplateService
|
||||
from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
|
||||
|
||||
template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
|
||||
db_session = next(get_db())
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
class VerificationNodeService(LLMServiceMixin):
|
||||
"""验证节点服务类"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.template_service = TemplateService(template_root)
|
||||
|
||||
# 创建全局服务实例
|
||||
verification_service = VerificationNodeService()
|
||||
|
||||
async def Verify_prompt(state: ReadState,messages_deal):
|
||||
storage_type = state.get('storage_type', '')
|
||||
user_rag_memory_id = state.get('user_rag_memory_id', '')
|
||||
data = state.get('data', '')
|
||||
Verify_result = {
|
||||
"status": messages_deal.split_result,
|
||||
"verified_data": messages_deal.expansion_issue,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "verification",
|
||||
"title": "Data Verification",
|
||||
"result": messages_deal.split_result,
|
||||
"reason": messages_deal.reason,
|
||||
"query": data,
|
||||
"verified_count": len(messages_deal.expansion_issue),
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
return Verify_result
|
||||
async def Verify(state: ReadState):
|
||||
content = state.get('data', '')
|
||||
group_id = state.get('group_id', '')
|
||||
memory_config = state.get('memory_config', None)
|
||||
|
||||
history = await SessionService(store).get_history(group_id, group_id, group_id)
|
||||
|
||||
retrieve = state.get("retrieve", '')
|
||||
retrieve = retrieve.get("Expansion_issue", [])
|
||||
messages = {
|
||||
"Query": content,
|
||||
"Expansion_issue": retrieve
|
||||
}
|
||||
|
||||
system_prompt = await verification_service.template_service.render_template(
|
||||
template_name='split_verify_prompt.jinja2',
|
||||
operation_name='split_verify_prompt',
|
||||
history=history,
|
||||
sentence=messages
|
||||
)
|
||||
|
||||
# 使用优化的LLM服务
|
||||
structured = await verification_service.call_llm_structured(
|
||||
state=state,
|
||||
db_session=db_session,
|
||||
system_prompt=system_prompt,
|
||||
response_model=VerificationResult,
|
||||
fallback_value={
|
||||
"split_result": "fail",
|
||||
"expansion_issue": [],
|
||||
"reason": "验证失败"
|
||||
}
|
||||
)
|
||||
|
||||
result = await Verify_prompt(state, structured)
|
||||
return {"verify": result}
|
||||
@@ -0,0 +1,50 @@
|
||||
|
||||
from app.core.memory.agent.utils.llm_tools import WriteState
|
||||
from app.core.memory.agent.utils.write_tools import write
|
||||
from app.core.logging_config import get_agent_logger
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
async def write_node(state: WriteState) -> WriteState:
|
||||
"""
|
||||
Write data to the database/file system.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
content: Data content to write
|
||||
user_id: User identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
|
||||
Returns:
|
||||
dict: Contains 'status', 'saved_to', and 'data' fields
|
||||
"""
|
||||
content=state.get('data','')
|
||||
group_id=state.get('group_id','')
|
||||
memory_config=state.get('memory_config', '')
|
||||
try:
|
||||
result=await write(
|
||||
content=content,
|
||||
user_id=group_id,
|
||||
apply_id=group_id,
|
||||
group_id=group_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
|
||||
|
||||
write_result= {
|
||||
"status": "success",
|
||||
"data": content,
|
||||
"config_id": memory_config.config_id,
|
||||
"config_name": memory_config.config_name,
|
||||
}
|
||||
return {"write_result":write_result}
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Data_write failed: {e}", exc_info=True)
|
||||
write_result= {
|
||||
"status": "error",
|
||||
"message": str(e),
|
||||
}
|
||||
return {"write_result": write_result}
|
||||
@@ -1,469 +1,177 @@
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
import warnings
|
||||
#!/usr/bin/env python3
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import Literal
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.langgraph_graph.nodes import (
|
||||
ToolExecutionNode,
|
||||
create_input_message,
|
||||
)
|
||||
from app.core.memory.agent.mcp_server.services.parameter_builder import ParameterBuilder
|
||||
from app.core.memory.agent.utils.llm_tools import COUNTState, ReadState
|
||||
from app.core.memory.agent.utils.multimodal import MultimodalProcessor
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from dotenv import load_dotenv
|
||||
from langchain_core.messages import AIMessage
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
from langgraph.constants import END, START
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langgraph.constants import START, END
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.prebuilt import ToolNode
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
warnings.filterwarnings("ignore", category=RuntimeWarning)
|
||||
load_dotenv()
|
||||
redishost=os.getenv("REDISHOST")
|
||||
redisport=os.getenv('REDISPORT')
|
||||
redisdb=os.getenv('REDISDB')
|
||||
redispassword=os.getenv('REDISPASSWORD')
|
||||
counter = COUNTState(limit=3)
|
||||
|
||||
# Update loop count in workflow
|
||||
async def update_loop_count(state):
|
||||
"""Update loop counter"""
|
||||
current_count = state.get("loop_count", 0)
|
||||
return {"loop_count": current_count + 1}
|
||||
|
||||
|
||||
def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
|
||||
messages = state["messages"]
|
||||
from app.db import get_db
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
|
||||
# Add boundary check
|
||||
if not messages:
|
||||
return END
|
||||
counter.add(1) # Increment by 1
|
||||
from app.core.memory.agent.utils.llm_tools import ReadState
|
||||
from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_node
|
||||
from app.core.memory.agent.langgraph_graph.nodes.problem_nodes import (
|
||||
Split_The_Problem,
|
||||
Problem_Extension,
|
||||
)
|
||||
from app.core.memory.agent.langgraph_graph.nodes.retrieve_nodes import (
|
||||
retrieve,
|
||||
)
|
||||
from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import (
|
||||
Input_Summary,
|
||||
Retrieve_Summary,
|
||||
Summary_fails,
|
||||
Summary,
|
||||
)
|
||||
from app.core.memory.agent.langgraph_graph.nodes.verification_nodes import Verify
|
||||
from app.core.memory.agent.langgraph_graph.routing.routers import (
|
||||
Split_continue,
|
||||
Retrieve_continue,
|
||||
Verify_continue,
|
||||
)
|
||||
|
||||
loop_count = counter.get_total()
|
||||
logger.debug(f"[should_continue] Current loop count: {loop_count}")
|
||||
|
||||
last_message = messages[-1]
|
||||
last_message_str = str(last_message).replace('\\', '')
|
||||
status_tools = re.findall(r'"split_result": "(.*?)"', last_message_str)
|
||||
logger.debug(f"Status tools: {status_tools}")
|
||||
|
||||
if "success" in status_tools:
|
||||
counter.reset()
|
||||
return "Summary"
|
||||
elif "failed" in status_tools:
|
||||
if loop_count < 2: # Maximum loop count is 3
|
||||
return "content_input"
|
||||
else:
|
||||
counter.reset()
|
||||
return "Summary_fails"
|
||||
else:
|
||||
# Add default return value to avoid returning None
|
||||
counter.reset()
|
||||
return "Summary" # Default based on business requirements
|
||||
|
||||
|
||||
def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
|
||||
"""
|
||||
Determine routing based on search_switch value.
|
||||
|
||||
Args:
|
||||
state: State dictionary containing search_switch
|
||||
|
||||
Returns:
|
||||
Next node to execute
|
||||
"""
|
||||
# Direct dictionary access instead of regex parsing
|
||||
search_switch = state.get("search_switch")
|
||||
|
||||
# Handle case where search_switch might be in messages
|
||||
if search_switch is None and "messages" in state:
|
||||
messages = state.get("messages", [])
|
||||
if messages:
|
||||
last_message = messages[-1]
|
||||
# Try to extract from tool_calls args
|
||||
if hasattr(last_message, "tool_calls") and last_message.tool_calls:
|
||||
for tool_call in last_message.tool_calls:
|
||||
if isinstance(tool_call, dict) and "args" in tool_call:
|
||||
search_switch = tool_call["args"].get("search_switch")
|
||||
break
|
||||
|
||||
# Convert to string for comparison if needed
|
||||
if search_switch is not None:
|
||||
search_switch = str(search_switch)
|
||||
if search_switch == '0':
|
||||
return 'Verify'
|
||||
elif search_switch == '1':
|
||||
return 'Retrieve_Summary'
|
||||
|
||||
# Add default return value to avoid returning None
|
||||
return 'Retrieve_Summary' # Default based on business logic
|
||||
|
||||
|
||||
def Split_continue(state) -> Literal["Split_The_Problem", "Input_Summary"]:
|
||||
"""
|
||||
Determine routing based on search_switch value.
|
||||
|
||||
Args:
|
||||
state: State dictionary containing search_switch
|
||||
|
||||
Returns:
|
||||
Next node to execute
|
||||
"""
|
||||
logger.debug(f"Split_continue state: {state}")
|
||||
|
||||
# Direct dictionary access instead of regex parsing
|
||||
search_switch = state.get("search_switch")
|
||||
|
||||
# Handle case where search_switch might be in messages
|
||||
if search_switch is None and "messages" in state:
|
||||
messages = state.get("messages", [])
|
||||
if messages:
|
||||
last_message = messages[-1]
|
||||
# Try to extract from tool_calls args
|
||||
if hasattr(last_message, "tool_calls") and last_message.tool_calls:
|
||||
for tool_call in last_message.tool_calls:
|
||||
if isinstance(tool_call, dict) and "args" in tool_call:
|
||||
search_switch = tool_call["args"].get("search_switch")
|
||||
break
|
||||
|
||||
# Convert to string for comparison if needed
|
||||
if search_switch is not None:
|
||||
search_switch = str(search_switch)
|
||||
if search_switch == '2':
|
||||
return 'Input_Summary'
|
||||
return 'Split_The_Problem' # Default case
|
||||
|
||||
|
||||
class ProblemExtensionNode:
|
||||
def __init__(self, tool, id, namespace, search_switch, apply_id, group_id, storage_type="", user_rag_memory_id=""):
|
||||
self.tool_node = ToolNode([tool])
|
||||
self.id = id
|
||||
self.tool_name = tool.name if hasattr(tool, 'name') else str(tool)
|
||||
self.namespace = namespace
|
||||
self.search_switch = search_switch
|
||||
self.apply_id = apply_id
|
||||
self.group_id = group_id
|
||||
self.storage_type = storage_type
|
||||
self.user_rag_memory_id = user_rag_memory_id
|
||||
|
||||
async def __call__(self, state):
|
||||
messages = state["messages"]
|
||||
last_message = messages[-1] if messages else ""
|
||||
logger.debug(f"ProblemExtensionNode {self.id} - Current time: {time.time()} - Message: {last_message}")
|
||||
if self.tool_name == 'Input_Summary':
|
||||
tool_call = re.findall("'id': '(.*?)'", str(last_message))[0]
|
||||
else:
|
||||
tool_call = str(re.findall(r"tool_call_id=.*?'(.*?)'", str(last_message))[0]).replace('\\', '').split('_id')[1]
|
||||
|
||||
# Try to extract actual content payload from previous tool result
|
||||
raw_msg = last_message.content if hasattr(last_message, 'content') else str(last_message)
|
||||
extracted_payload = None
|
||||
# Capture ToolMessage content field (supports single/double quotes), avoid greedy matching
|
||||
m = re.search(r"content=(?:\"|\')(.*?)(?:\"|\'),\s*name=", raw_msg, flags=re.S)
|
||||
if m:
|
||||
extracted_payload = m.group(1)
|
||||
else:
|
||||
# Fallback: use raw string directly
|
||||
extracted_payload = raw_msg
|
||||
|
||||
# Try to parse content as JSON first
|
||||
try:
|
||||
content = json.loads(extracted_payload)
|
||||
except Exception:
|
||||
# Try to extract JSON fragment from text and parse
|
||||
parsed = None
|
||||
candidates = re.findall(r"[\[{].*[\]}]", extracted_payload, flags=re.S)
|
||||
for cand in candidates:
|
||||
try:
|
||||
parsed = json.loads(cand)
|
||||
break
|
||||
except Exception:
|
||||
continue
|
||||
# If still fails, use raw string as content
|
||||
content = parsed if parsed is not None else extracted_payload
|
||||
|
||||
# Build correct parameters based on tool name
|
||||
tool_args = {}
|
||||
|
||||
if self.tool_name == "Verify":
|
||||
# Verify tool requires context and usermessages parameters
|
||||
if isinstance(content, dict):
|
||||
tool_args["context"] = content
|
||||
else:
|
||||
tool_args["context"] = {"content": content}
|
||||
tool_args["usermessages"] = str(tool_call)
|
||||
tool_args["apply_id"] = str(self.apply_id)
|
||||
tool_args["group_id"] = str(self.group_id)
|
||||
elif self.tool_name == "Retrieve":
|
||||
# Retrieve tool requires context and usermessages parameters
|
||||
if isinstance(content, dict):
|
||||
tool_args["context"] = content
|
||||
else:
|
||||
tool_args["context"] = {"content": content}
|
||||
tool_args["usermessages"] = str(tool_call)
|
||||
tool_args["search_switch"] = str(self.search_switch)
|
||||
tool_args["apply_id"] = str(self.apply_id)
|
||||
tool_args["group_id"] = str(self.group_id)
|
||||
elif self.tool_name == "Summary":
|
||||
# Summary tool requires string type context parameter
|
||||
if isinstance(content, dict):
|
||||
# Convert dict to JSON string
|
||||
tool_args["context"] = json.dumps(content, ensure_ascii=False)
|
||||
else:
|
||||
tool_args["context"] = str(content)
|
||||
tool_args["usermessages"] = str(tool_call)
|
||||
tool_args["apply_id"] = str(self.apply_id)
|
||||
tool_args["group_id"] = str(self.group_id)
|
||||
elif self.tool_name == "Summary_fails":
|
||||
# Summary_fails tool requires string type context parameter
|
||||
if isinstance(content, dict):
|
||||
# Convert dict to JSON string
|
||||
tool_args["context"] = json.dumps(content, ensure_ascii=False)
|
||||
else:
|
||||
tool_args["context"] = str(content)
|
||||
tool_args["usermessages"] = str(tool_call)
|
||||
tool_args["apply_id"] = str(self.apply_id)
|
||||
tool_args["group_id"] = str(self.group_id)
|
||||
elif self.tool_name == 'Input_Summary':
|
||||
tool_args["context"] = str(last_message)
|
||||
tool_args["usermessages"] = str(tool_call)
|
||||
tool_args["search_switch"] = str(self.search_switch)
|
||||
tool_args["apply_id"] = str(self.apply_id)
|
||||
tool_args["group_id"] = str(self.group_id)
|
||||
tool_args["storage_type"] = getattr(self, 'storage_type', "")
|
||||
tool_args["user_rag_memory_id"] = getattr(self, 'user_rag_memory_id', "")
|
||||
elif self.tool_name == 'Retrieve_Summary':
|
||||
# Retrieve_Summary expects dict directly, not JSON string
|
||||
# content might be a JSON string, try to parse it
|
||||
if isinstance(content, str):
|
||||
try:
|
||||
parsed_content = json.loads(content)
|
||||
# Check if it has a "context" key
|
||||
if isinstance(parsed_content, dict) and "context" in parsed_content:
|
||||
tool_args["context"] = parsed_content["context"]
|
||||
else:
|
||||
tool_args["context"] = parsed_content
|
||||
except json.JSONDecodeError:
|
||||
# If parsing fails, wrap the string
|
||||
tool_args["context"] = {"content": content}
|
||||
elif isinstance(content, dict):
|
||||
# Check if content has a "context" key that needs unwrapping
|
||||
if "context" in content:
|
||||
tool_args["context"] = content["context"]
|
||||
else:
|
||||
tool_args["context"] = content
|
||||
else:
|
||||
tool_args["context"] = {"content": str(content)}
|
||||
|
||||
tool_args["usermessages"] = str(tool_call)
|
||||
tool_args["apply_id"] = str(self.apply_id)
|
||||
tool_args["group_id"] = str(self.group_id)
|
||||
else:
|
||||
# Other tools use context parameter
|
||||
if isinstance(content, dict):
|
||||
tool_args["context"] = content
|
||||
else:
|
||||
tool_args["context"] = {"content": content}
|
||||
tool_args["usermessages"] = str(tool_call)
|
||||
tool_args["apply_id"] = str(self.apply_id)
|
||||
tool_args["group_id"] = str(self.group_id)
|
||||
|
||||
|
||||
tool_input = {
|
||||
"messages": [
|
||||
AIMessage(
|
||||
content="",
|
||||
tool_calls=[{
|
||||
"name": self.tool_name,
|
||||
"args": tool_args,
|
||||
"id": self.id + f"{tool_call}",
|
||||
}]
|
||||
)
|
||||
]
|
||||
}
|
||||
result = await self.tool_node.ainvoke(tool_input)
|
||||
result_text = str(result)
|
||||
|
||||
return {"messages": [AIMessage(content=result_text)]}
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def make_read_graph(namespace, tools, search_switch, apply_id, group_id, memory_config: MemoryConfig, storage_type=None, user_rag_memory_id=None):
|
||||
"""
|
||||
Create a read graph workflow for memory operations.
|
||||
|
||||
Args:
|
||||
namespace: Namespace identifier
|
||||
tools: MCP tools loaded from session
|
||||
search_switch: Search mode switch ("0", "1", or "2")
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type (optional)
|
||||
user_rag_memory_id: User RAG memory ID (optional)
|
||||
"""
|
||||
memory = InMemorySaver()
|
||||
tool = [i.name for i in tools]
|
||||
logger.info(f"Initializing read graph with tools: {tool}")
|
||||
logger.info(f"Using memory_config: {memory_config.config_name} (id={memory_config.config_id})")
|
||||
|
||||
# Extract tool functions
|
||||
Split_The_Problem_ = next((t for t in tools if t.name == "Split_The_Problem"), None)
|
||||
Problem_Extension_ = next((t for t in tools if t.name == "Problem_Extension"), None)
|
||||
Retrieve_ = next((t for t in tools if t.name == "Retrieve"), None)
|
||||
Verify_ = next((t for t in tools if t.name == "Verify"), None)
|
||||
Summary_ = next((t for t in tools if t.name == "Summary"), None)
|
||||
Summary_fails_ = next((t for t in tools if t.name == "Summary_fails"), None)
|
||||
Retrieve_Summary_ = next((t for t in tools if t.name == "Retrieve_Summary"), None)
|
||||
Input_Summary_ = next((t for t in tools if t.name == "Input_Summary"), None)
|
||||
|
||||
# Instantiate services
|
||||
parameter_builder = ParameterBuilder()
|
||||
multimodal_processor = MultimodalProcessor()
|
||||
|
||||
# Create nodes using new modular components
|
||||
Split_The_Problem_node = ToolNode([Split_The_Problem_])
|
||||
|
||||
Problem_Extension_node = ToolExecutionNode(
|
||||
tool=Problem_Extension_,
|
||||
node_id="Problem_Extension_id",
|
||||
namespace=namespace,
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
parameter_builder=parameter_builder,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id,
|
||||
memory_config=memory_config,
|
||||
async def make_read_graph():
|
||||
"""创建并返回 LangGraph 工作流"""
|
||||
try:
|
||||
# Build workflow graph
|
||||
workflow = StateGraph(ReadState)
|
||||
workflow.add_node("content_input", content_input_node)
|
||||
workflow.add_node("Split_The_Problem", Split_The_Problem)
|
||||
workflow.add_node("Problem_Extension", Problem_Extension)
|
||||
workflow.add_node("Input_Summary", Input_Summary)
|
||||
# workflow.add_node("Retrieve", retrieve_nodes)
|
||||
workflow.add_node("Retrieve", retrieve)
|
||||
workflow.add_node("Verify", Verify)
|
||||
workflow.add_node("Retrieve_Summary", Retrieve_Summary)
|
||||
workflow.add_node("Summary", Summary)
|
||||
workflow.add_node("Summary_fails", Summary_fails)
|
||||
|
||||
# 添加边
|
||||
workflow.add_edge(START, "content_input")
|
||||
workflow.add_conditional_edges("content_input", Split_continue)
|
||||
workflow.add_edge("Input_Summary", END)
|
||||
workflow.add_edge("Split_The_Problem", "Problem_Extension")
|
||||
workflow.add_edge("Problem_Extension", "Retrieve")
|
||||
workflow.add_conditional_edges("Retrieve", Retrieve_continue)
|
||||
workflow.add_edge("Retrieve_Summary", END)
|
||||
workflow.add_conditional_edges("Verify", Verify_continue)
|
||||
workflow.add_edge("Summary_fails", END)
|
||||
workflow.add_edge("Summary", END)
|
||||
|
||||
|
||||
'''-----'''
|
||||
# workflow.add_edge("Retrieve", END)
|
||||
|
||||
# 编译工作流
|
||||
graph = workflow.compile()
|
||||
yield graph
|
||||
|
||||
except Exception as e:
|
||||
print(f"创建工作流失败: {e}")
|
||||
raise
|
||||
finally:
|
||||
print("工作流创建完成")
|
||||
|
||||
async def main():
|
||||
"""主函数 - 运行工作流"""
|
||||
message = "昨天有什么好看的电影"
|
||||
group_id = '88a459f5_text09' # 组ID
|
||||
storage_type = 'neo4j' # 存储类型
|
||||
search_switch = '1' # 搜索开关
|
||||
user_rag_memory_id = 'wwwwwwww' # 用户RAG记忆ID
|
||||
|
||||
# 获取数据库会话
|
||||
db_session = next(get_db())
|
||||
config_service = MemoryConfigService(db_session)
|
||||
memory_config = config_service.load_memory_config(
|
||||
config_id=17, # 改为整数
|
||||
service_name="MemoryAgentService"
|
||||
)
|
||||
import time
|
||||
start=time.time()
|
||||
try:
|
||||
async with make_read_graph() as graph:
|
||||
config = {"configurable": {"thread_id": group_id}}
|
||||
# 初始状态 - 包含所有必要字段
|
||||
initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"group_id":group_id
|
||||
,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config}
|
||||
# 获取节点更新信息
|
||||
_intermediate_outputs = []
|
||||
summary = ''
|
||||
|
||||
async for update_event in graph.astream(
|
||||
initial_state,
|
||||
stream_mode="updates",
|
||||
config=config
|
||||
):
|
||||
for node_name, node_data in update_event.items():
|
||||
print(f"处理节点: {node_name}")
|
||||
|
||||
# 处理不同Summary节点的返回结构
|
||||
if 'Summary' in node_name:
|
||||
if 'InputSummary' in node_data and 'summary_result' in node_data['InputSummary']:
|
||||
summary = node_data['InputSummary']['summary_result']
|
||||
elif 'RetrieveSummary' in node_data and 'summary_result' in node_data['RetrieveSummary']:
|
||||
summary = node_data['RetrieveSummary']['summary_result']
|
||||
elif 'summary' in node_data and 'summary_result' in node_data['summary']:
|
||||
summary = node_data['summary']['summary_result']
|
||||
elif 'SummaryFails' in node_data and 'summary_result' in node_data['SummaryFails']:
|
||||
summary = node_data['SummaryFails']['summary_result']
|
||||
|
||||
Retrieve_node = ToolExecutionNode(
|
||||
tool=Retrieve_,
|
||||
node_id="Retrieve_id",
|
||||
namespace=namespace,
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
parameter_builder=parameter_builder,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
spit_data = node_data.get('spit_data', {}).get('_intermediate', None)
|
||||
if spit_data and spit_data != [] and spit_data != {}:
|
||||
_intermediate_outputs.append(spit_data)
|
||||
|
||||
# Problem_Extension 节点
|
||||
problem_extension = node_data.get('problem_extension', {}).get('_intermediate', None)
|
||||
if problem_extension and problem_extension != [] and problem_extension != {}:
|
||||
_intermediate_outputs.append(problem_extension)
|
||||
|
||||
# Retrieve 节点
|
||||
retrieve_node = node_data.get('retrieve', {}).get('_intermediate_outputs', None)
|
||||
if retrieve_node and retrieve_node != [] and retrieve_node != {}:
|
||||
_intermediate_outputs.extend(retrieve_node)
|
||||
|
||||
# Verify 节点
|
||||
verify_n = node_data.get('verify', {}).get('_intermediate', None)
|
||||
if verify_n and verify_n != [] and verify_n != {}:
|
||||
_intermediate_outputs.append(verify_n)
|
||||
|
||||
Verify_node = ToolExecutionNode(
|
||||
tool=Verify_,
|
||||
node_id="Verify_id",
|
||||
namespace=namespace,
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
parameter_builder=parameter_builder,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
|
||||
Summary_node = ToolExecutionNode(
|
||||
tool=Summary_,
|
||||
node_id="Summary_id",
|
||||
namespace=namespace,
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
parameter_builder=parameter_builder,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
|
||||
# Summary 节点
|
||||
summary_n = node_data.get('summary', {}).get('_intermediate', None)
|
||||
if summary_n and summary_n != [] and summary_n != {}:
|
||||
_intermediate_outputs.append(summary_n)
|
||||
|
||||
Summary_fails_node = ToolExecutionNode(
|
||||
tool=Summary_fails_,
|
||||
node_id="Summary_fails_id",
|
||||
namespace=namespace,
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
parameter_builder=parameter_builder,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
# # 过滤掉空值
|
||||
# _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
|
||||
#
|
||||
# # 优化搜索结果
|
||||
# print("=== 开始优化搜索结果 ===")
|
||||
# optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
|
||||
# result=reorder_output_results(optimized_outputs)
|
||||
# # 保存优化后的结果到文件
|
||||
# with open('_intermediate_outputs_optimized.json', 'w', encoding='utf-8') as f:
|
||||
# import json
|
||||
# f.write(json.dumps(result, indent=4, ensure_ascii=False))
|
||||
#
|
||||
print(f"=== 最终摘要 ===")
|
||||
print(summary)
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
Retrieve_Summary_node = ToolExecutionNode(
|
||||
tool=Retrieve_Summary_,
|
||||
node_id="Retrieve_Summary_id",
|
||||
namespace=namespace,
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
parameter_builder=parameter_builder,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
end=time.time()
|
||||
print(100*'y')
|
||||
print(f"总耗时: {end-start}s")
|
||||
print(100*'y')
|
||||
|
||||
Input_Summary_node = ToolExecutionNode(
|
||||
tool=Input_Summary_,
|
||||
node_id="Input_Summary_id",
|
||||
namespace=namespace,
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
parameter_builder=parameter_builder,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
|
||||
async def content_input_node(state):
|
||||
state_search_switch = state.get("search_switch", search_switch)
|
||||
|
||||
tool_name = "Input_Summary" if state_search_switch == '2' else "Split_The_Problem"
|
||||
session_prefix = "input_summary_call_id" if state_search_switch == '2' else "split_call_id"
|
||||
|
||||
return await create_input_message(
|
||||
state=state,
|
||||
tool_name=tool_name,
|
||||
session_id=f"{session_prefix}_{namespace}",
|
||||
search_switch=search_switch,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
multimodal_processor=multimodal_processor,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
|
||||
|
||||
# Build workflow graph
|
||||
workflow = StateGraph(ReadState)
|
||||
workflow.add_node("content_input", content_input_node)
|
||||
workflow.add_node("Split_The_Problem", Split_The_Problem_node)
|
||||
workflow.add_node("Problem_Extension", Problem_Extension_node)
|
||||
workflow.add_node("Retrieve", Retrieve_node)
|
||||
workflow.add_node("Verify", Verify_node)
|
||||
workflow.add_node("Summary", Summary_node)
|
||||
workflow.add_node("Summary_fails", Summary_fails_node)
|
||||
workflow.add_node("Retrieve_Summary", Retrieve_Summary_node)
|
||||
workflow.add_node("Input_Summary", Input_Summary_node)
|
||||
|
||||
# Add edges using imported routers
|
||||
workflow.add_edge(START, "content_input")
|
||||
workflow.add_conditional_edges("content_input", Split_continue)
|
||||
workflow.add_edge("Input_Summary", END)
|
||||
workflow.add_edge("Split_The_Problem", "Problem_Extension")
|
||||
workflow.add_edge("Problem_Extension", "Retrieve")
|
||||
workflow.add_conditional_edges("Retrieve", Retrieve_continue)
|
||||
workflow.add_edge("Retrieve_Summary", END)
|
||||
workflow.add_conditional_edges("Verify", Verify_continue)
|
||||
workflow.add_edge("Summary_fails", END)
|
||||
workflow.add_edge("Summary", END)
|
||||
|
||||
graph = workflow.compile(checkpointer=memory)
|
||||
yield graph
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
"""LangGraph routing logic."""
|
||||
|
||||
from app.core.memory.agent.langgraph_graph.routing.routers import (
|
||||
Verify_continue,
|
||||
Retrieve_continue,
|
||||
Split_continue,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"Verify_continue",
|
||||
"Retrieve_continue",
|
||||
"Split_continue",
|
||||
]
|
||||
@@ -1,123 +1,62 @@
|
||||
"""
|
||||
Routing functions for LangGraph conditional edges.
|
||||
|
||||
This module provides routing functions that determine the next node to execute
|
||||
based on state values. All functions return Literal types for type safety.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import re
|
||||
from typing import Literal
|
||||
|
||||
from app.core.memory.agent.langgraph_graph.state.extractors import extract_search_switch
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.utils.llm_tools import ReadState, COUNTState
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Global counter for Verify routing
|
||||
logger = get_agent_logger(__name__)
|
||||
counter = COUNTState(limit=3)
|
||||
|
||||
|
||||
def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
|
||||
def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summary"]:
|
||||
"""
|
||||
Determine routing after Verify node based on verification result.
|
||||
|
||||
This function checks the verification result in the last message and routes to:
|
||||
- Summary: if verification succeeded
|
||||
- content_input: if verification failed and retry limit not reached
|
||||
- Summary_fails: if verification failed and retry limit reached
|
||||
|
||||
Determine routing based on search_switch value.
|
||||
|
||||
Args:
|
||||
state: LangGraph state containing messages
|
||||
|
||||
state: State dictionary containing search_switch
|
||||
|
||||
Returns:
|
||||
Next node name as Literal type
|
||||
Next node to execute
|
||||
"""
|
||||
messages = state.get("messages", [])
|
||||
|
||||
# Boundary check
|
||||
if not messages:
|
||||
logger.warning("[Verify_continue] No messages in state, defaulting to Summary")
|
||||
counter.reset()
|
||||
return "Summary"
|
||||
|
||||
# Increment counter
|
||||
counter.add(1)
|
||||
logger.debug(f"Split_continue state: {state}")
|
||||
search_switch = state.get('search_switch', '')
|
||||
if search_switch is not None:
|
||||
search_switch = str(search_switch)
|
||||
if search_switch == '2':
|
||||
return 'Input_Summary'
|
||||
return 'Split_The_Problem' # 默认情况
|
||||
|
||||
def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
|
||||
"""
|
||||
Determine routing based on search_switch value.
|
||||
|
||||
Args:
|
||||
state: State dictionary containing search_switch
|
||||
|
||||
Returns:
|
||||
Next node to execute
|
||||
"""
|
||||
search_switch = state.get('search_switch', '')
|
||||
if search_switch is not None:
|
||||
search_switch = str(search_switch)
|
||||
if search_switch == '0':
|
||||
return 'Verify'
|
||||
elif search_switch == '1':
|
||||
return 'Retrieve_Summary'
|
||||
return 'Retrieve_Summary' # Default based on business logic
|
||||
def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
|
||||
status=state.get('verify', '')['status']
|
||||
loop_count = counter.get_total()
|
||||
logger.debug(f"[Verify_continue] Current loop count: {loop_count}")
|
||||
|
||||
# Extract verification result from last message
|
||||
last_message = messages[-1]
|
||||
last_message_str = str(last_message).replace('\\', '')
|
||||
status_tools = re.findall(r'"split_result": "(.*?)"', last_message_str)
|
||||
logger.debug(f"[Verify_continue] Status tools: {status_tools}")
|
||||
|
||||
# Route based on verification result
|
||||
if "success" in status_tools:
|
||||
print(status)
|
||||
if "success" in status:
|
||||
counter.reset()
|
||||
return "Summary"
|
||||
elif "failed" in status_tools:
|
||||
if loop_count < 2: # Max retry count is 2
|
||||
elif "failed" in status:
|
||||
if loop_count < 2: # Maximum loop count is 3
|
||||
return "content_input"
|
||||
else:
|
||||
counter.reset()
|
||||
return "Summary_fails"
|
||||
else:
|
||||
# Default to Summary if status is unclear
|
||||
counter.reset()
|
||||
return "Summary"
|
||||
|
||||
|
||||
def Retrieve_continue(state: dict) -> Literal["Verify", "Retrieve_Summary"]:
|
||||
"""
|
||||
Determine routing after Retrieve node based on search_switch value.
|
||||
|
||||
This function routes based on the search_switch parameter:
|
||||
- search_switch == '0': Route to Verify (verification needed)
|
||||
- search_switch == '1': Route to Retrieve_Summary (direct summary)
|
||||
|
||||
Args:
|
||||
state: LangGraph state dictionary
|
||||
|
||||
Returns:
|
||||
Next node name as Literal type
|
||||
"""
|
||||
search_switch = extract_search_switch(state)
|
||||
|
||||
logger.debug(f"[Retrieve_continue] search_switch: {search_switch}")
|
||||
|
||||
if search_switch == '0':
|
||||
return 'Verify'
|
||||
elif search_switch == '1':
|
||||
return 'Retrieve_Summary'
|
||||
|
||||
# Default to Retrieve_Summary
|
||||
logger.debug("[Retrieve_continue] No valid search_switch, defaulting to Retrieve_Summary")
|
||||
return 'Retrieve_Summary'
|
||||
|
||||
|
||||
def Split_continue(state: dict) -> Literal["Split_The_Problem", "Input_Summary"]:
|
||||
"""
|
||||
Determine routing after content_input node based on search_switch value.
|
||||
|
||||
This function routes based on the search_switch parameter:
|
||||
- search_switch == '2': Route to Input_Summary (direct input summary)
|
||||
- Otherwise: Route to Split_The_Problem (problem decomposition)
|
||||
|
||||
Args:
|
||||
state: LangGraph state dictionary
|
||||
|
||||
Returns:
|
||||
Next node name as Literal type
|
||||
"""
|
||||
logger.debug(f"[Split_continue] state keys: {state.keys()}")
|
||||
|
||||
search_switch = extract_search_switch(state)
|
||||
|
||||
logger.debug(f"[Split_continue] search_switch: {search_switch}")
|
||||
|
||||
if search_switch == '2':
|
||||
return 'Input_Summary'
|
||||
|
||||
# Default to Split_The_Problem
|
||||
return 'Split_The_Problem'
|
||||
# else:
|
||||
# # Add default return value to avoid returning None
|
||||
# counter.reset()
|
||||
# return "Summary" # Default based on business requirements
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
"""LangGraph state management utilities."""
|
||||
|
||||
from app.core.memory.agent.langgraph_graph.state.extractors import (
|
||||
extract_search_switch,
|
||||
extract_tool_call_id,
|
||||
extract_content_payload,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"extract_search_switch",
|
||||
"extract_tool_call_id",
|
||||
"extract_content_payload",
|
||||
]
|
||||
@@ -1,179 +0,0 @@
|
||||
"""
|
||||
State extraction utilities for type-safe access to LangGraph state values.
|
||||
|
||||
This module provides utility functions for extracting values from LangGraph state
|
||||
dictionaries with proper error handling and sensible defaults.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def extract_search_switch(state: dict) -> Optional[str]:
|
||||
"""
|
||||
Extract search_switch from state or messages.
|
||||
"""
|
||||
|
||||
search_switch = state.get("search_switch")
|
||||
|
||||
if search_switch is not None:
|
||||
return str(search_switch)
|
||||
|
||||
# Try to extract from messages
|
||||
messages = state.get("messages", [])
|
||||
if not messages:
|
||||
return None
|
||||
|
||||
# 从最新的消息开始查找
|
||||
for message in reversed(messages):
|
||||
# 尝试从 tool_calls 中提取
|
||||
if hasattr(message, "tool_calls") and message.tool_calls:
|
||||
for tool_call in message.tool_calls:
|
||||
if isinstance(tool_call, dict):
|
||||
# 从 tool_call 的 args 中提取
|
||||
if "args" in tool_call and isinstance(tool_call["args"], dict):
|
||||
search_switch = tool_call["args"].get("search_switch")
|
||||
if search_switch is not None:
|
||||
return str(search_switch)
|
||||
# 直接从 tool_call 中提取
|
||||
search_switch = tool_call.get("search_switch")
|
||||
if search_switch is not None:
|
||||
return str(search_switch)
|
||||
|
||||
# 尝试从 content 中提取（如果是 JSON 格式）
|
||||
if hasattr(message, "content"):
|
||||
try:
|
||||
import json
|
||||
if isinstance(message.content, str):
|
||||
content_data = json.loads(message.content)
|
||||
if isinstance(content_data, dict):
|
||||
search_switch = content_data.get("search_switch")
|
||||
if search_switch is not None:
|
||||
return str(search_switch)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def extract_tool_call_id(message: Any) -> str:
|
||||
"""
|
||||
Extract tool call ID from message using structured attributes.
|
||||
|
||||
This function extracts the tool call ID from a message object, handling both
|
||||
direct attribute access and tool_calls list structures.
|
||||
|
||||
Args:
|
||||
message: Message object (typically ToolMessage or AIMessage)
|
||||
|
||||
Returns:
|
||||
Tool call ID as string
|
||||
|
||||
Raises:
|
||||
ValueError: If tool call ID cannot be extracted
|
||||
|
||||
Examples:
|
||||
>>> message = ToolMessage(content="...", tool_call_id="call_123")
|
||||
>>> extract_tool_call_id(message)
|
||||
'call_123'
|
||||
"""
|
||||
# Try direct attribute access for ToolMessage
|
||||
if hasattr(message, "tool_call_id"):
|
||||
tool_call_id = message.tool_call_id
|
||||
if tool_call_id:
|
||||
return str(tool_call_id)
|
||||
|
||||
# Try extracting from tool_calls list for AIMessage
|
||||
if hasattr(message, "tool_calls") and message.tool_calls:
|
||||
tool_call = message.tool_calls[0]
|
||||
if isinstance(tool_call, dict) and "id" in tool_call:
|
||||
return str(tool_call["id"])
|
||||
|
||||
# Try extracting from id attribute
|
||||
if hasattr(message, "id"):
|
||||
message_id = message.id
|
||||
if message_id:
|
||||
return str(message_id)
|
||||
|
||||
# If all else fails, raise an error
|
||||
raise ValueError(f"Could not extract tool call ID from message: {type(message)}")
|
||||
|
||||
|
||||
def extract_content_payload(message: Any) -> Any:
|
||||
"""
|
||||
Extract content payload from ToolMessage, parsing JSON if needed.
|
||||
|
||||
This function extracts the content from a message and attempts to parse it as JSON
|
||||
if it appears to be a JSON string. It handles various message formats and provides
|
||||
sensible fallbacks.
|
||||
|
||||
Args:
|
||||
message: Message object (typically ToolMessage)
|
||||
|
||||
Returns:
|
||||
Parsed content (dict, list, or str)
|
||||
|
||||
Examples:
|
||||
>>> message = ToolMessage(content='{"key": "value"}')
|
||||
>>> extract_content_payload(message)
|
||||
{'key': 'value'}
|
||||
|
||||
>>> message = ToolMessage(content='plain text')
|
||||
>>> extract_content_payload(message)
|
||||
'plain text'
|
||||
"""
|
||||
# Extract raw content
|
||||
# For ToolMessages (responses from tools), extract from content
|
||||
if hasattr(message, "content"):
|
||||
raw_content = message.content
|
||||
logger.info(f"extract_content_payload: raw_content type={type(raw_content)}, value={str(raw_content)[:500]}")
|
||||
|
||||
# Handle MCP content format: [{'type': 'text', 'text': '...'}]
|
||||
if isinstance(raw_content, list):
|
||||
for block in raw_content:
|
||||
if isinstance(block, dict) and block.get('type') == 'text':
|
||||
raw_content = block.get('text', '')
|
||||
logger.info(f"extract_content_payload: extracted text from MCP format: {str(raw_content)[:300]}")
|
||||
break
|
||||
|
||||
# If content is empty and this is an AIMessage with tool_calls,
|
||||
# extract from args (this handles the initial tool call from content_input)
|
||||
if not raw_content and hasattr(message, "tool_calls") and message.tool_calls:
|
||||
tool_call = message.tool_calls[0]
|
||||
if isinstance(tool_call, dict) and "args" in tool_call:
|
||||
return tool_call["args"]
|
||||
else:
|
||||
raw_content = str(message)
|
||||
|
||||
# If content is already a dict or list, return it directly
|
||||
if isinstance(raw_content, (dict, list)):
|
||||
logger.info(f"extract_content_payload: returning raw dict/list with keys={list(raw_content.keys()) if isinstance(raw_content, dict) else 'list'}")
|
||||
return raw_content
|
||||
|
||||
# Try to parse as JSON
|
||||
if isinstance(raw_content, str):
|
||||
# First, try direct JSON parsing
|
||||
try:
|
||||
parsed = json.loads(raw_content)
|
||||
logger.info(f"extract_content_payload: parsed JSON, keys={list(parsed.keys()) if isinstance(parsed, dict) else 'list'}")
|
||||
return parsed
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
pass
|
||||
|
||||
# If that fails, try to extract JSON from the string
|
||||
# This handles cases where the content is embedded in a larger string
|
||||
import re
|
||||
json_candidates = re.findall(r'[\[{].*[\]}]', raw_content, flags=re.DOTALL)
|
||||
for candidate in json_candidates:
|
||||
try:
|
||||
parsed = json.loads(candidate)
|
||||
logger.info(f"extract_content_payload: parsed JSON from candidate, keys={list(parsed.keys()) if isinstance(parsed, dict) else 'list'}")
|
||||
return parsed
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
continue
|
||||
|
||||
# If all parsing attempts fail, return the raw content
|
||||
logger.info(f"extract_content_payload: returning raw content (parsing failed)")
|
||||
return raw_content
|
||||
320
api/app/core/memory/agent/langgraph_graph/tools/tool.py
Normal file
320
api/app/core/memory/agent/langgraph_graph/tools/tool.py
Normal file
@@ -0,0 +1,320 @@
|
||||
import asyncio
|
||||
import json
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
|
||||
from langchain.tools import tool
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
from app.core.memory.src.search import (
|
||||
search_by_temporal,
|
||||
search_by_keyword_temporal,
|
||||
)
|
||||
|
||||
def extract_tool_message_content(response):
|
||||
"""从agent响应中提取ToolMessage内容和工具名称"""
|
||||
messages = response.get('messages', [])
|
||||
|
||||
for message in messages:
|
||||
if hasattr(message, 'tool_call_id') and hasattr(message, 'content'):
|
||||
# 这是一个ToolMessage
|
||||
tool_content = message.content
|
||||
tool_name = None
|
||||
|
||||
# 尝试获取工具名称
|
||||
if hasattr(message, 'name'):
|
||||
tool_name = message.name
|
||||
elif hasattr(message, 'tool_name'):
|
||||
tool_name = message.tool_name
|
||||
|
||||
try:
|
||||
# 解析JSON内容
|
||||
parsed_content = json.loads(tool_content)
|
||||
return {
|
||||
'tool_name': tool_name,
|
||||
'content': parsed_content
|
||||
}
|
||||
except json.JSONDecodeError:
|
||||
# 如果不是JSON格式，直接返回内容
|
||||
return {
|
||||
'tool_name': tool_name,
|
||||
'content': tool_content
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
|
||||
class TimeRetrievalInput(BaseModel):
|
||||
"""时间检索工具的输入模式"""
|
||||
context: str = Field(description="用户输入的查询内容")
|
||||
group_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果")
|
||||
|
||||
def create_time_retrieval_tool(group_id: str):
|
||||
"""
|
||||
创建一个带有特定group_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements)
|
||||
"""
|
||||
|
||||
def clean_temporal_result_fields(data):
|
||||
"""
|
||||
清理时间搜索结果中不需要的字段，并修改结构
|
||||
|
||||
Args:
|
||||
data: 要清理的数据
|
||||
|
||||
Returns:
|
||||
清理后的数据
|
||||
"""
|
||||
# 需要过滤的字段列表
|
||||
fields_to_remove = {
|
||||
'id', 'apply_id', 'user_id', 'chunk_id', 'created_at',
|
||||
'valid_at', 'invalid_at', 'statement_ids'
|
||||
}
|
||||
|
||||
if isinstance(data, dict):
|
||||
cleaned = {}
|
||||
for key, value in data.items():
|
||||
if key == 'statements' and isinstance(value, dict) and 'statements' in value:
|
||||
# 将 statements: {"statements": [...]} 改为 time_search: {"statements": [...]}
|
||||
cleaned_value = clean_temporal_result_fields(value)
|
||||
# 进一步将内部的 statements 改为 time_search
|
||||
if 'statements' in cleaned_value:
|
||||
cleaned['results'] = {
|
||||
'time_search': cleaned_value['statements']
|
||||
}
|
||||
else:
|
||||
cleaned['results'] = cleaned_value
|
||||
elif key not in fields_to_remove:
|
||||
cleaned[key] = clean_temporal_result_fields(value)
|
||||
return cleaned
|
||||
elif isinstance(data, list):
|
||||
return [clean_temporal_result_fields(item) for item in data]
|
||||
else:
|
||||
return data
|
||||
|
||||
@tool
|
||||
def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, group_id_param: str = None, clean_output: bool = True) -> str:
|
||||
"""
|
||||
优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段
|
||||
显式接收参数：
|
||||
- context: 查询上下文内容
|
||||
- start_date: 开始时间（可选，格式：YYYY-MM-DD）
|
||||
- end_date: 结束时间（可选，格式：YYYY-MM-DD）
|
||||
- group_id_param: 组ID（可选，用于覆盖默认组ID）
|
||||
- clean_output: 是否清理输出中的元数据字段
|
||||
-end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
|
||||
"""
|
||||
async def _async_search():
|
||||
# 使用传入的参数或默认值
|
||||
actual_group_id = group_id_param or group_id
|
||||
actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
|
||||
actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
|
||||
# 基本时间搜索
|
||||
results = await search_by_temporal(
|
||||
group_id=actual_group_id,
|
||||
start_date=actual_start_date,
|
||||
end_date=actual_end_date,
|
||||
limit=10
|
||||
)
|
||||
|
||||
# 清理结果中不需要的字段
|
||||
if clean_output:
|
||||
cleaned_results = clean_temporal_result_fields(results)
|
||||
else:
|
||||
cleaned_results = results
|
||||
|
||||
return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
|
||||
|
||||
return asyncio.run(_async_search())
|
||||
|
||||
@tool
|
||||
def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None, clean_output: bool = True) -> str:
|
||||
"""
|
||||
优化的关键词时间检索工具，结合关键词和时间范围搜索（同步版本），自动过滤不需要的元数据字段
|
||||
显式接收参数：
|
||||
- context: 查询内容
|
||||
- days_back: 向前搜索的天数，默认7天
|
||||
- start_date: 开始时间（可选，格式：YYYY-MM-DD）
|
||||
- end_date: 结束时间（可选，格式：YYYY-MM-DD）
|
||||
- clean_output: 是否清理输出中的元数据字段
|
||||
- end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
|
||||
"""
|
||||
async def _async_search():
|
||||
actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
|
||||
actual_start_date = start_date or (datetime.now() - timedelta(days=days_back)).strftime("%Y-%m-%d")
|
||||
|
||||
# 关键词时间搜索
|
||||
results = await search_by_keyword_temporal(
|
||||
query_text=context,
|
||||
group_id=group_id,
|
||||
start_date=actual_start_date,
|
||||
end_date=actual_end_date,
|
||||
limit=15
|
||||
)
|
||||
|
||||
# 清理结果中不需要的字段
|
||||
if clean_output:
|
||||
cleaned_results = clean_temporal_result_fields(results)
|
||||
else:
|
||||
cleaned_results = results
|
||||
|
||||
return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
|
||||
|
||||
return asyncio.run(_async_search())
|
||||
|
||||
return TimeRetrievalWithGroupId
|
||||
|
||||
|
||||
def create_hybrid_retrieval_tool_async(memory_config, **search_params):
|
||||
"""
|
||||
创建混合检索工具，使用run_hybrid_search进行混合检索，优化输出格式并过滤不需要的字段
|
||||
|
||||
Args:
|
||||
memory_config: 内存配置对象
|
||||
**search_params: 搜索参数，包含group_id, limit, include等
|
||||
"""
|
||||
|
||||
def clean_result_fields(data):
|
||||
"""
|
||||
递归清理结果中不需要的字段
|
||||
|
||||
Args:
|
||||
data: 要清理的数据（可能是字典、列表或其他类型）
|
||||
|
||||
Returns:
|
||||
清理后的数据
|
||||
"""
|
||||
# 需要过滤的字段列表
|
||||
fields_to_remove = {
|
||||
'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids',
|
||||
'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id',
|
||||
'user_id', 'statement_ids', 'updated_at',"chunk_ids","fact_summary"
|
||||
}
|
||||
|
||||
if isinstance(data, dict):
|
||||
# 对字典进行清理
|
||||
cleaned = {}
|
||||
for key, value in data.items():
|
||||
if key not in fields_to_remove:
|
||||
cleaned[key] = clean_result_fields(value) # 递归清理嵌套数据
|
||||
return cleaned
|
||||
elif isinstance(data, list):
|
||||
# 对列表中的每个元素进行清理
|
||||
return [clean_result_fields(item) for item in data]
|
||||
else:
|
||||
# 其他类型直接返回
|
||||
return data
|
||||
|
||||
@tool
|
||||
async def HybridSearch(
|
||||
context: str,
|
||||
search_type: str = "hybrid",
|
||||
limit: int = 10,
|
||||
group_id: str = None,
|
||||
rerank_alpha: float = 0.6,
|
||||
use_forgetting_rerank: bool = False,
|
||||
use_llm_rerank: bool = False,
|
||||
clean_output: bool = True # 新增：是否清理输出字段
|
||||
) -> str:
|
||||
"""
|
||||
优化的混合检索工具，支持关键词、向量和混合搜索，自动过滤不需要的元数据字段
|
||||
|
||||
Args:
|
||||
context: 查询内容
|
||||
search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
|
||||
limit: 结果数量限制
|
||||
group_id: 组ID，用于过滤搜索结果
|
||||
rerank_alpha: 重排序权重参数
|
||||
use_forgetting_rerank: 是否使用遗忘重排序
|
||||
use_llm_rerank: 是否使用LLM重排序
|
||||
clean_output: 是否清理输出中的元数据字段
|
||||
"""
|
||||
try:
|
||||
# 导入run_hybrid_search函数
|
||||
from app.core.memory.src.search import run_hybrid_search
|
||||
|
||||
# 合并参数，优先使用传入的参数
|
||||
final_params = {
|
||||
"query_text": context,
|
||||
"search_type": search_type,
|
||||
"group_id": group_id or search_params.get("group_id"),
|
||||
"limit": limit or search_params.get("limit", 10),
|
||||
"include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]),
|
||||
"output_path": None, # 不保存到文件
|
||||
"memory_config": memory_config,
|
||||
"rerank_alpha": rerank_alpha,
|
||||
"use_forgetting_rerank": use_forgetting_rerank,
|
||||
"use_llm_rerank": use_llm_rerank
|
||||
}
|
||||
|
||||
# 执行混合检索
|
||||
raw_results = await run_hybrid_search(**final_params)
|
||||
|
||||
# 清理结果中不需要的字段
|
||||
if clean_output:
|
||||
cleaned_results = clean_result_fields(raw_results)
|
||||
else:
|
||||
cleaned_results = raw_results
|
||||
|
||||
# 格式化返回结果
|
||||
formatted_results = {
|
||||
"search_query": context,
|
||||
"search_type": search_type,
|
||||
"results": cleaned_results
|
||||
}
|
||||
|
||||
return json.dumps(formatted_results, ensure_ascii=False, indent=2, default=str)
|
||||
|
||||
except Exception as e:
|
||||
error_result = {
|
||||
"error": f"混合检索失败: {str(e)}",
|
||||
"search_query": context,
|
||||
"search_type": search_type,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
return json.dumps(error_result, ensure_ascii=False, indent=2)
|
||||
|
||||
return HybridSearch
|
||||
|
||||
|
||||
def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
|
||||
"""
|
||||
创建同步版本的混合检索工具，优化输出格式并过滤不需要的字段
|
||||
|
||||
Args:
|
||||
memory_config: 内存配置对象
|
||||
**search_params: 搜索参数
|
||||
"""
|
||||
@tool
|
||||
def HybridSearchSync(
|
||||
context: str,
|
||||
search_type: str = "hybrid",
|
||||
limit: int = 10,
|
||||
group_id: str = None,
|
||||
clean_output: bool = True
|
||||
) -> str:
|
||||
"""
|
||||
优化的混合检索工具（同步版本），自动过滤不需要的元数据字段
|
||||
|
||||
Args:
|
||||
context: 查询内容
|
||||
search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
|
||||
limit: 结果数量限制
|
||||
group_id: 组ID，用于过滤搜索结果
|
||||
clean_output: 是否清理输出中的元数据字段
|
||||
"""
|
||||
async def _async_search():
|
||||
# 创建异步工具并执行
|
||||
async_tool = create_hybrid_retrieval_tool_async(memory_config, **search_params)
|
||||
return await async_tool.ainvoke({
|
||||
"context": context,
|
||||
"search_type": search_type,
|
||||
"limit": limit,
|
||||
"group_id": group_id,
|
||||
"clean_output": clean_output
|
||||
})
|
||||
|
||||
return asyncio.run(_async_search())
|
||||
|
||||
return HybridSearchSync
|
||||
@@ -1,30 +1,32 @@
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import sys
|
||||
import warnings
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.utils.llm_tools import WriteState
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from langchain_core.messages import AIMessage
|
||||
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langgraph.constants import END, START
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.prebuilt import ToolNode
|
||||
|
||||
|
||||
from app.db import get_db
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.utils.llm_tools import WriteState
|
||||
from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node
|
||||
from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_write
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
|
||||
warnings.filterwarnings("ignore", category=RuntimeWarning)
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
if sys.platform.startswith("win"):
|
||||
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def make_write_graph(user_id, tools, apply_id, group_id, memory_config: MemoryConfig):
|
||||
async def make_write_graph():
|
||||
"""
|
||||
Create a write graph workflow for memory operations.
|
||||
|
||||
|
||||
Args:
|
||||
user_id: User identifier
|
||||
tools: MCP tools loaded from session
|
||||
@@ -32,43 +34,8 @@ async def make_write_graph(user_id, tools, apply_id, group_id, memory_config: Me
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
"""
|
||||
logger.info("Loading MCP tools: %s", [t.name for t in tools])
|
||||
logger.info(f"Using memory_config: {memory_config.config_name} (id={memory_config.config_id})")
|
||||
|
||||
data_write_tool = next((t for t in tools if t.name == "Data_write"), None)
|
||||
|
||||
if not data_write_tool:
|
||||
logger.error("Data_write tool not found", exc_info=True)
|
||||
raise ValueError("Data_write tool not found")
|
||||
|
||||
write_node = ToolNode([data_write_tool])
|
||||
|
||||
async def call_model(state):
|
||||
messages = state["messages"]
|
||||
last_message = messages[-1]
|
||||
content = last_message[1] if isinstance(last_message, tuple) else last_message.content
|
||||
|
||||
# Call Data_write directly with memory_config
|
||||
write_params = {
|
||||
"content": content,
|
||||
"apply_id": apply_id,
|
||||
"group_id": group_id,
|
||||
"user_id": user_id,
|
||||
"memory_config": memory_config,
|
||||
}
|
||||
logger.debug(f"Passing memory_config to Data_write: {memory_config.config_id}")
|
||||
|
||||
write_result = await data_write_tool.ainvoke(write_params)
|
||||
|
||||
if isinstance(write_result, dict):
|
||||
result_content = write_result.get("data", str(write_result))
|
||||
else:
|
||||
result_content = str(write_result)
|
||||
logger.info("Write content: %s", result_content)
|
||||
return {"messages": [AIMessage(content=result_content)]}
|
||||
|
||||
workflow = StateGraph(WriteState)
|
||||
workflow.add_node("content_input", call_model)
|
||||
workflow.add_node("content_input", content_input_write)
|
||||
workflow.add_node("save_neo4j", write_node)
|
||||
workflow.add_edge(START, "content_input")
|
||||
workflow.add_edge("content_input", "save_neo4j")
|
||||
@@ -76,5 +43,45 @@ async def make_write_graph(user_id, tools, apply_id, group_id, memory_config: Me
|
||||
|
||||
graph = workflow.compile()
|
||||
|
||||
|
||||
yield graph
|
||||
|
||||
|
||||
async def main():
|
||||
"""主函数 - 运行工作流"""
|
||||
message = "今天周一"
|
||||
group_id = 'new_2025test1103' # 组ID
|
||||
|
||||
|
||||
# 获取数据库会话
|
||||
db_session = next(get_db())
|
||||
config_service = MemoryConfigService(db_session)
|
||||
memory_config = config_service.load_memory_config(
|
||||
config_id=17, # 改为整数
|
||||
service_name="MemoryAgentService"
|
||||
)
|
||||
try:
|
||||
async with make_write_graph() as graph:
|
||||
config = {"configurable": {"thread_id": group_id}}
|
||||
# 初始状态 - 包含所有必要字段
|
||||
initial_state = {"messages": [HumanMessage(content=message)], "group_id": group_id, "memory_config": memory_config}
|
||||
|
||||
# 获取节点更新信息
|
||||
async for update_event in graph.astream(
|
||||
initial_state,
|
||||
stream_mode="updates",
|
||||
config=config
|
||||
):
|
||||
for node_name, node_data in update_event.items():
|
||||
if 'save_neo4j'==node_name:
|
||||
massages=node_data
|
||||
massages=massages.get('write_result')['status']
|
||||
print(massages) # | 更新数据: {node_data}
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
asyncio.run(main())
|
||||
@@ -1,28 +0,0 @@
|
||||
"""
|
||||
MCP Server package for memory agent.
|
||||
|
||||
This package provides the FastMCP server implementation with context-based
|
||||
dependency injection for tool functions.
|
||||
|
||||
Package structure:
|
||||
- server: FastMCP server initialization and context setup
|
||||
- tools: MCP tool implementations
|
||||
- models: Pydantic response models
|
||||
- services: Business logic services
|
||||
"""
|
||||
# from app.core.memory.agent.mcp_server.server import (
|
||||
# mcp,
|
||||
# initialize_context,
|
||||
# main,
|
||||
# get_context_resource
|
||||
# )
|
||||
|
||||
# # Import tools to register them (but don't export them)
|
||||
# from app.core.memory.agent.mcp_server import tools
|
||||
|
||||
# __all__ = [
|
||||
# 'mcp',
|
||||
# 'initialize_context',
|
||||
# 'main',
|
||||
# 'get_context_resource',
|
||||
# ]
|
||||
@@ -1,11 +0,0 @@
|
||||
"""
|
||||
MCP Server Instance
|
||||
|
||||
This module contains the FastMCP server instance that is shared across all modules.
|
||||
It's in a separate file to avoid circular import issues.
|
||||
"""
|
||||
from mcp.server.fastmcp import FastMCP
|
||||
|
||||
# Initialize FastMCP server instance
|
||||
# This instance is shared across all tool modules
|
||||
mcp = FastMCP('data_flow')
|
||||
@@ -1,159 +0,0 @@
|
||||
"""
|
||||
MCP Server initialization with FastMCP context setup.
|
||||
|
||||
This module initializes the FastMCP server and registers shared resources
|
||||
in the context for dependency injection into tool functions.
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
|
||||
from app.core.config import settings
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.mcp_server.mcp_instance import mcp
|
||||
from app.core.memory.agent.mcp_server.services.search_service import SearchService
|
||||
from app.core.memory.agent.mcp_server.services.session_service import SessionService
|
||||
from app.core.memory.agent.mcp_server.services.template_service import TemplateService
|
||||
from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
|
||||
from app.core.memory.agent.utils.redis_tool import store
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
def get_context_resource(ctx, resource_name: str):
|
||||
"""
|
||||
Helper function to retrieve a resource from the FastMCP context.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP Context object (passed to tool functions)
|
||||
resource_name: Name of the resource to retrieve
|
||||
|
||||
Returns:
|
||||
The requested resource
|
||||
|
||||
Raises:
|
||||
AttributeError: If the resource doesn't exist
|
||||
|
||||
Example:
|
||||
@mcp.tool()
|
||||
async def my_tool(ctx: Context):
|
||||
template_service = get_context_resource(ctx, 'template_service')
|
||||
llm_client = get_context_resource(ctx, 'llm_client')
|
||||
"""
|
||||
if not hasattr(ctx, 'fastmcp') or ctx.fastmcp is None:
|
||||
raise RuntimeError("Context does not have fastmcp attribute")
|
||||
|
||||
if not hasattr(ctx.fastmcp, resource_name):
|
||||
raise AttributeError(
|
||||
f"Resource '{resource_name}' not found in context. "
|
||||
f"Available resources: {[k for k in dir(ctx.fastmcp) if not k.startswith('_')]}"
|
||||
)
|
||||
|
||||
return getattr(ctx.fastmcp, resource_name)
|
||||
|
||||
|
||||
def initialize_context():
|
||||
"""
|
||||
Initialize and register shared resources in FastMCP context.
|
||||
|
||||
This function sets up all shared resources that will be available
|
||||
to tool functions via dependency injection through the context parameter.
|
||||
|
||||
Resources are stored as attributes on the FastMCP instance and can be
|
||||
accessed via ctx.fastmcp in tool functions.
|
||||
|
||||
Resources registered:
|
||||
- session_store: RedisSessionStore for session management
|
||||
- llm_client: LLM client for structured API calls
|
||||
- app_settings: Application settings (renamed to avoid conflict with FastMCP settings)
|
||||
- template_service: Service for template rendering
|
||||
- search_service: Service for hybrid search
|
||||
- session_service: Service for session operations
|
||||
"""
|
||||
try:
|
||||
# Register Redis session store
|
||||
logger.info("Registering session_store in context")
|
||||
mcp.session_store = store
|
||||
|
||||
# Note: LLM client is NOT loaded at server startup
|
||||
# It should be loaded dynamically when needed, with config_id passed explicitly
|
||||
# to make_write_graph or make_read_graph functions
|
||||
logger.info("LLM client will be loaded dynamically with config_id when needed")
|
||||
mcp.llm_client = None # Placeholder - actual client loaded per-request with config_id
|
||||
|
||||
# Register application settings (renamed to avoid conflict with FastMCP's settings)
|
||||
logger.info("Registering app_settings in context")
|
||||
mcp.app_settings = settings
|
||||
|
||||
# Register template service
|
||||
template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
|
||||
# logger.info(f"Registering template_service in context with root: {template_root}")
|
||||
template_service = TemplateService(template_root)
|
||||
mcp.template_service = template_service
|
||||
|
||||
# Register search service
|
||||
# logger.info("Registering search_service in context")
|
||||
search_service = SearchService()
|
||||
mcp.search_service = search_service
|
||||
|
||||
# Register session service
|
||||
# logger.info("Registering session_service in context")
|
||||
session_service = SessionService(store)
|
||||
mcp.session_service = session_service
|
||||
|
||||
# logger.info("All context resources registered successfully")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to initialize context: {e}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Main entry point for the MCP server.
|
||||
|
||||
Initializes context and starts the server with SSE transport.
|
||||
"""
|
||||
try:
|
||||
logger.info("Starting MCP server initialization")
|
||||
# Initialize context resources
|
||||
initialize_context()
|
||||
|
||||
# Import and register tools (imports trigger tool registration)
|
||||
from app.core.memory.agent.mcp_server.tools import ( # noqa: F401
|
||||
data_tools,
|
||||
problem_tools,
|
||||
retrieval_tools,
|
||||
summary_tools,
|
||||
verification_tools,
|
||||
)
|
||||
|
||||
# Tools are registered via imports above
|
||||
|
||||
# Get MCP port from environment (default: 8081)
|
||||
mcp_port = int(os.getenv("MCP_PORT", "8081"))
|
||||
logger.info(f"Starting MCP server on {settings.SERVER_IP}:{mcp_port} with SSE transport")
|
||||
|
||||
# Configure DNS rebinding protection for Docker container compatibility
|
||||
from mcp.server.fastmcp.server import TransportSecuritySettings
|
||||
|
||||
# Disable DNS rebinding protection to allow Docker container hostnames
|
||||
# This allows containers to connect using service names like 'mcp-server'
|
||||
mcp.settings.transport_security = TransportSecuritySettings(
|
||||
enable_dns_rebinding_protection=False,
|
||||
)
|
||||
logger.info("DNS rebinding protection: disabled for Docker container compatibility")
|
||||
|
||||
# logger.info(f"Starting MCP server on {settings.SERVER_IP}:{mcp_port} with SSE transport")
|
||||
|
||||
# Run the server with SSE transport for HTTP connections
|
||||
import uvicorn
|
||||
app = mcp.sse_app()
|
||||
uvicorn.run(app, host=settings.SERVER_IP, port=mcp_port, log_level="info")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to start MCP server: {e}", exc_info=True)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,27 +0,0 @@
|
||||
"""
|
||||
MCP Tools module.
|
||||
|
||||
This module contains all MCP tool implementations organized by functionality.
|
||||
|
||||
Tools are organized into the following modules:
|
||||
- problem_tools: Question segmentation and extension
|
||||
- retrieval_tools: Database and context retrieval
|
||||
- verification_tools: Data verification
|
||||
- summary_tools: Summarization and summary retrieval
|
||||
- data_tools: Data type differentiation and writing
|
||||
"""
|
||||
|
||||
# Import all tool modules to register them with the MCP server
|
||||
from . import problem_tools
|
||||
from . import retrieval_tools
|
||||
from . import verification_tools
|
||||
from . import summary_tools
|
||||
from . import data_tools
|
||||
|
||||
__all__ = [
|
||||
'problem_tools',
|
||||
'retrieval_tools',
|
||||
'verification_tools',
|
||||
'summary_tools',
|
||||
'data_tools',
|
||||
]
|
||||
@@ -1,155 +0,0 @@
|
||||
"""
|
||||
Data Tools for data type differentiation and writing.
|
||||
|
||||
This module contains MCP tools for distinguishing data types and writing data.
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.mcp_server.mcp_instance import mcp
|
||||
from app.core.memory.agent.mcp_server.models.retrieval_models import (
|
||||
DistinguishTypeResponse,
|
||||
)
|
||||
from app.core.memory.agent.mcp_server.server import get_context_resource
|
||||
from app.core.memory.agent.utils.write_tools import write
|
||||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||||
from app.db import get_db_context
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from mcp.server.fastmcp import Context
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Data_type_differentiation(
|
||||
ctx: Context,
|
||||
context: str,
|
||||
memory_config: MemoryConfig,
|
||||
) -> dict:
|
||||
"""
|
||||
Distinguish the type of data (read or write).
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: Text to analyze for type differentiation
|
||||
memory_config: MemoryConfig object containing LLM configuration
|
||||
|
||||
Returns:
|
||||
dict: Contains 'context' with the original text and 'type' field
|
||||
"""
|
||||
try:
|
||||
# Extract services from context
|
||||
template_service = get_context_resource(ctx, 'template_service')
|
||||
|
||||
# Get LLM client from memory_config using factory pattern
|
||||
with get_db_context() as db:
|
||||
factory = MemoryClientFactory(db)
|
||||
llm_client = factory.get_llm_client_from_config(memory_config)
|
||||
|
||||
# Render template
|
||||
try:
|
||||
system_prompt = await template_service.render_template(
|
||||
template_name='distinguish_types_prompt.jinja2',
|
||||
operation_name='status_typle',
|
||||
user_query=context
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Template rendering failed for Data_type_differentiation: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"type": "error",
|
||||
"message": f"Prompt rendering failed: {str(e)}"
|
||||
}
|
||||
|
||||
# Call LLM with structured response
|
||||
try:
|
||||
structured = await llm_client.response_structured(
|
||||
messages=[{"role": "system", "content": system_prompt}],
|
||||
response_model=DistinguishTypeResponse
|
||||
)
|
||||
|
||||
result = structured.model_dump()
|
||||
|
||||
# Add context to result
|
||||
result["context"] = context
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"LLM call failed for Data_type_differentiation: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"context": context,
|
||||
"type": "error",
|
||||
"message": f"LLM call failed: {str(e)}"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Data_type_differentiation failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"context": context,
|
||||
"type": "error",
|
||||
"message": str(e)
|
||||
}
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Data_write(
|
||||
ctx: Context,
|
||||
content: str,
|
||||
user_id: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
) -> dict:
|
||||
"""
|
||||
Write data to the database/file system.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
content: Data content to write
|
||||
user_id: User identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
|
||||
Returns:
|
||||
dict: Contains 'status', 'saved_to', and 'data' fields
|
||||
"""
|
||||
try:
|
||||
# Ensure output directory exists
|
||||
os.makedirs("data_output", exist_ok=True)
|
||||
file_path = os.path.join("data_output", "user_data.csv")
|
||||
|
||||
# Write data - clients are constructed inside write() from memory_config
|
||||
await write(
|
||||
content=content,
|
||||
user_id=user_id,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
memory_config=memory_config,
|
||||
)
|
||||
logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"saved_to": file_path,
|
||||
"data": content,
|
||||
"config_id": memory_config.config_id,
|
||||
"config_name": memory_config.config_name,
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Data_write failed: {e}", exc_info=True)
|
||||
return {
|
||||
"status": "error",
|
||||
"message": str(e),
|
||||
}
|
||||
@@ -1,304 +0,0 @@
|
||||
"""
|
||||
Problem Tools for question segmentation and extension.
|
||||
|
||||
This module contains MCP tools for breaking down and extending user questions.
|
||||
LLM clients are constructed from MemoryConfig when needed.
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
|
||||
from app.core.logging_config import get_agent_logger, log_time
|
||||
from app.core.memory.agent.mcp_server.mcp_instance import mcp
|
||||
from app.core.memory.agent.mcp_server.models.problem_models import (
|
||||
ProblemBreakdownResponse,
|
||||
ProblemExtensionResponse,
|
||||
)
|
||||
from app.core.memory.agent.mcp_server.server import get_context_resource
|
||||
from app.core.memory.agent.utils.messages_tool import Problem_Extension_messages_deal
|
||||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||||
from app.db import get_db_context
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from mcp.server.fastmcp import Context
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Split_The_Problem(
|
||||
ctx: Context,
|
||||
sentence: str,
|
||||
sessionid: str,
|
||||
messages_id: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
) -> dict:
|
||||
"""
|
||||
Segment the dialogue or sentence into sub-problems.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
sentence: Original sentence to split
|
||||
sessionid: Session identifier
|
||||
messages_id: Message identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
|
||||
Returns:
|
||||
dict: Contains 'context' (JSON string of split results) and 'original' sentence
|
||||
"""
|
||||
start = time.time()
|
||||
|
||||
try:
|
||||
# Extract services from context
|
||||
template_service = get_context_resource(ctx, "template_service")
|
||||
session_service = get_context_resource(ctx, "session_service")
|
||||
|
||||
# Get LLM client from memory_config
|
||||
with get_db_context() as db:
|
||||
factory = MemoryClientFactory(db)
|
||||
llm_client = factory.get_llm_client_from_config(memory_config)
|
||||
|
||||
# Extract user ID from session
|
||||
user_id = session_service.resolve_user_id(sessionid)
|
||||
|
||||
# Get conversation history
|
||||
history = await session_service.get_history(user_id, apply_id, group_id)
|
||||
# Override with empty list for now (as in original)
|
||||
history = []
|
||||
|
||||
# Render template
|
||||
try:
|
||||
system_prompt = await template_service.render_template(
|
||||
template_name='problem_breakdown_prompt.jinja2',
|
||||
operation_name='split_the_problem',
|
||||
history=history,
|
||||
sentence=sentence
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Template rendering failed for Split_The_Problem: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"context": json.dumps([], ensure_ascii=False),
|
||||
"original": sentence,
|
||||
"error": f"Prompt rendering failed: {str(e)}"
|
||||
}
|
||||
|
||||
# Call LLM with structured response
|
||||
try:
|
||||
structured = await llm_client.response_structured(
|
||||
messages=[{"role": "system", "content": system_prompt}],
|
||||
response_model=ProblemBreakdownResponse
|
||||
)
|
||||
|
||||
# Handle RootModel response with .root attribute access
|
||||
if structured is None:
|
||||
# LLM returned None, use empty list as fallback
|
||||
split_result = json.dumps([], ensure_ascii=False)
|
||||
elif hasattr(structured, 'root') and structured.root is not None:
|
||||
split_result = json.dumps(
|
||||
[item.model_dump() for item in structured.root],
|
||||
ensure_ascii=False
|
||||
)
|
||||
elif isinstance(structured, list):
|
||||
# Fallback: treat structured itself as the list
|
||||
split_result = json.dumps(
|
||||
[item.model_dump() for item in structured],
|
||||
ensure_ascii=False
|
||||
)
|
||||
else:
|
||||
# Last resort: use empty list
|
||||
split_result = json.dumps([], ensure_ascii=False)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"LLM call failed for Split_The_Problem: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
split_result = json.dumps([], ensure_ascii=False)
|
||||
|
||||
logger.info("Problem splitting")
|
||||
logger.info(f"Problem split result: {split_result}")
|
||||
|
||||
# Emit intermediate output for frontend
|
||||
result = {
|
||||
"context": split_result,
|
||||
"original": sentence,
|
||||
"_intermediate": {
|
||||
"type": "problem_split",
|
||||
"data": json.loads(split_result) if split_result else [],
|
||||
"original_query": sentence
|
||||
}
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Split_The_Problem failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"context": json.dumps([], ensure_ascii=False),
|
||||
"original": sentence,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
finally:
|
||||
# Log execution time
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Problem splitting', duration)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Problem_Extension(
|
||||
ctx: Context,
|
||||
context: dict,
|
||||
usermessages: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
storage_type: str = "",
|
||||
user_rag_memory_id: str = "",
|
||||
) -> dict:
|
||||
"""
|
||||
Extend the problem with additional sub-questions.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: Dictionary containing split problem results
|
||||
usermessages: User messages identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type for the workspace (optional)
|
||||
user_rag_memory_id: User RAG memory identifier (optional)
|
||||
|
||||
Returns:
|
||||
dict: Contains 'context' (aggregated questions) and 'original' question
|
||||
"""
|
||||
start = time.time()
|
||||
|
||||
try:
|
||||
# Extract services from context
|
||||
template_service = get_context_resource(ctx, "template_service")
|
||||
session_service = get_context_resource(ctx, "session_service")
|
||||
|
||||
# Get LLM client from memory_config
|
||||
with get_db_context() as db:
|
||||
factory = MemoryClientFactory(db)
|
||||
llm_client = factory.get_llm_client_from_config(memory_config)
|
||||
|
||||
# Resolve session ID from usermessages
|
||||
from app.core.memory.agent.utils.messages_tool import Resolve_username
|
||||
sessionid = Resolve_username(usermessages)
|
||||
|
||||
# Get conversation history
|
||||
history = await session_service.get_history(sessionid, apply_id, group_id)
|
||||
# Override with empty list for now (as in original)
|
||||
history = []
|
||||
|
||||
# Process context to extract questions
|
||||
extent_quest, original = await Problem_Extension_messages_deal(context)
|
||||
|
||||
# Format questions for template rendering
|
||||
questions_formatted = []
|
||||
for msg in extent_quest:
|
||||
if msg.get("role") == "user":
|
||||
questions_formatted.append(msg.get("content", ""))
|
||||
|
||||
# Render template
|
||||
try:
|
||||
system_prompt = await template_service.render_template(
|
||||
template_name='Problem_Extension_prompt.jinja2',
|
||||
operation_name='problem_extension',
|
||||
history=history,
|
||||
questions=questions_formatted
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Template rendering failed for Problem_Extension: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"context": {},
|
||||
"original": original,
|
||||
"error": f"Prompt rendering failed: {str(e)}"
|
||||
}
|
||||
|
||||
# Call LLM with structured response
|
||||
try:
|
||||
response_content = await llm_client.response_structured(
|
||||
messages=[{"role": "system", "content": system_prompt}],
|
||||
response_model=ProblemExtensionResponse
|
||||
)
|
||||
|
||||
# Aggregate results by original question
|
||||
aggregated_dict = {}
|
||||
for item in response_content.root:
|
||||
key = getattr(item, "original_question", None) or (
|
||||
item.get("original_question") if isinstance(item, dict) else None
|
||||
)
|
||||
value = getattr(item, "extended_question", None) or (
|
||||
item.get("extended_question") if isinstance(item, dict) else None
|
||||
)
|
||||
if not key or not value:
|
||||
continue
|
||||
aggregated_dict.setdefault(key, []).append(value)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"LLM call failed for Problem_Extension: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
aggregated_dict = {}
|
||||
|
||||
logger.info("Problem extension")
|
||||
logger.info(f"Problem extension result: {aggregated_dict}")
|
||||
|
||||
# Emit intermediate output for frontend
|
||||
result = {
|
||||
"context": aggregated_dict,
|
||||
"original": original,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "problem_extension",
|
||||
"data": aggregated_dict,
|
||||
"original_query": original,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Problem_Extension failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"context": {},
|
||||
"original": context.get("original", ""),
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
finally:
|
||||
# Log execution time
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Problem extension', duration)
|
||||
@@ -1,294 +0,0 @@
|
||||
"""
|
||||
Retrieval Tools for database and context retrieval.
|
||||
|
||||
This module contains MCP tools for retrieving data using hybrid search.
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
|
||||
from app.core.logging_config import get_agent_logger, log_time
|
||||
from app.core.memory.agent.mcp_server.mcp_instance import mcp
|
||||
from app.core.memory.agent.mcp_server.server import get_context_resource
|
||||
from app.core.memory.agent.utils.llm_tools import (
|
||||
deduplicate_entries,
|
||||
merge_to_key_value_pairs,
|
||||
)
|
||||
from app.core.memory.agent.utils.messages_tool import Retriev_messages_deal
|
||||
from app.core.rag.nlp.search import knowledge_retrieval
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from dotenv import load_dotenv
|
||||
from mcp.server.fastmcp import Context
|
||||
|
||||
load_dotenv()
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Retrieve(
|
||||
ctx: Context,
|
||||
context,
|
||||
usermessages: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
storage_type: str = "",
|
||||
user_rag_memory_id: str = "",
|
||||
) -> dict:
|
||||
"""
|
||||
Retrieve data from the database using hybrid search.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: Dictionary or string containing query information
|
||||
usermessages: User messages identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type for the workspace (e.g., 'rag', 'vector')
|
||||
user_rag_memory_id: User RAG memory identifier
|
||||
|
||||
Returns:
|
||||
dict: Contains 'context' with Query and Expansion_issue results
|
||||
"""
|
||||
kb_config = {
|
||||
"knowledge_bases": [
|
||||
{
|
||||
"kb_id": user_rag_memory_id,
|
||||
"similarity_threshold": 0.7,
|
||||
"vector_similarity_weight": 0.5,
|
||||
"top_k": 10,
|
||||
"retrieve_type": "participle"
|
||||
}
|
||||
],
|
||||
"merge_strategy": "weight",
|
||||
"reranker_id": os.getenv('reranker_id'),
|
||||
"reranker_top_k": 10
|
||||
}
|
||||
start = time.time()
|
||||
logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
|
||||
logger.info(f"Retrieve: context type={type(context)}, context={str(context)[:500]}")
|
||||
|
||||
try:
|
||||
# Extract services from context
|
||||
search_service = get_context_resource(ctx, 'search_service')
|
||||
|
||||
databases_anser = []
|
||||
|
||||
# Handle both dict and string context
|
||||
if isinstance(context, dict):
|
||||
# Process dict context with extended questions
|
||||
all_items = []
|
||||
logger.info(f"Retrieve: context keys={list(context.keys())}")
|
||||
content, original = await Retriev_messages_deal(context)
|
||||
logger.info(f"Retrieve: after Retriev_messages_deal - content_type={type(content)}, content={str(content)[:300]}")
|
||||
logger.info(f"Retrieve: original='{original[:100] if original else 'EMPTY'}'")
|
||||
|
||||
if not original:
|
||||
logger.warning(f"Retrieve: original query is empty! context={context}")
|
||||
|
||||
# Extract all query items from content
|
||||
# content is like {original_question: [extended_questions...], ...}
|
||||
for key, values in content.items():
|
||||
if isinstance(values, list):
|
||||
all_items.extend(values)
|
||||
elif isinstance(values, str):
|
||||
all_items.append(values)
|
||||
elif values is not None:
|
||||
# Fallback: convert non-empty non-list values to string
|
||||
all_items.append(str(values))
|
||||
|
||||
# Execute search for each question
|
||||
for idx, question in enumerate(all_items):
|
||||
try:
|
||||
# Prepare search parameters based on storage type
|
||||
search_params = {
|
||||
"group_id": group_id,
|
||||
"question": question,
|
||||
"return_raw_results": True
|
||||
}
|
||||
|
||||
# Add storage-specific parameters
|
||||
if storage_type == "rag" and user_rag_memory_id:
|
||||
retrieve_chunks_result = knowledge_retrieval(question, kb_config,[str(group_id)])
|
||||
try:
|
||||
retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
|
||||
clean_content = '\n\n'.join(retrieval_knowledge)
|
||||
cleaned_query=question
|
||||
raw_results=clean_content
|
||||
logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
|
||||
except:
|
||||
clean_content = ''
|
||||
raw_results=''
|
||||
cleaned_query = question
|
||||
logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
|
||||
else:
|
||||
clean_content, cleaned_query, raw_results = await search_service.execute_hybrid_search(
|
||||
**search_params, memory_config=memory_config
|
||||
)
|
||||
|
||||
databases_anser.append({
|
||||
"Query_small": cleaned_query,
|
||||
"Result_small": clean_content,
|
||||
"_intermediate": {
|
||||
"type": "search_result",
|
||||
"query": cleaned_query,
|
||||
"raw_results": raw_results,
|
||||
"index": idx + 1,
|
||||
"total": len(all_items)
|
||||
}
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve: hybrid_search failed for question '{question}': {e}",
|
||||
exc_info=True
|
||||
)
|
||||
# Continue with empty result for this question
|
||||
databases_anser.append({
|
||||
"Query_small": question,
|
||||
"Result_small": ""
|
||||
})
|
||||
|
||||
# Build initial database data structure
|
||||
databases_data = {
|
||||
"Query": original,
|
||||
"Expansion_issue": databases_anser
|
||||
}
|
||||
|
||||
# Collect intermediate outputs before deduplication
|
||||
intermediate_outputs = []
|
||||
for item in databases_anser:
|
||||
if '_intermediate' in item:
|
||||
intermediate_outputs.append(item['_intermediate'])
|
||||
|
||||
# Deduplicate and merge results
|
||||
deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
|
||||
deduplicated_data_merged = merge_to_key_value_pairs(
|
||||
deduplicated_data,
|
||||
'Query_small',
|
||||
'Result_small'
|
||||
)
|
||||
|
||||
# Restructure for Verify/Retrieve_Summary compatibility
|
||||
keys, val = [], []
|
||||
for item in deduplicated_data_merged:
|
||||
for items_key, items_value in item.items():
|
||||
keys.append(items_key)
|
||||
val.append(items_value)
|
||||
|
||||
send_verify = []
|
||||
for i, j in zip(keys, val, strict=False):
|
||||
send_verify.append({
|
||||
"Query_small": i,
|
||||
"Answer_Small": j
|
||||
})
|
||||
|
||||
dup_databases = {
|
||||
"Query": original,
|
||||
"Expansion_issue": send_verify,
|
||||
"_intermediate_outputs": intermediate_outputs # Preserve intermediate outputs
|
||||
}
|
||||
|
||||
logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
|
||||
|
||||
else:
|
||||
# Handle string context (simple query)
|
||||
query = str(context).strip()
|
||||
|
||||
try:
|
||||
# Prepare search parameters based on storage type
|
||||
search_params = {
|
||||
"group_id": group_id,
|
||||
"question": query,
|
||||
"return_raw_results": True
|
||||
}
|
||||
|
||||
# Add storage-specific parameters
|
||||
if storage_type == "rag" and user_rag_memory_id:
|
||||
retrieve_chunks_result = knowledge_retrieval(query, kb_config,[str(group_id)])
|
||||
try:
|
||||
retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
|
||||
clean_content = '\n\n'.join(retrieval_knowledge)
|
||||
cleaned_query = query
|
||||
raw_results = clean_content
|
||||
logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
|
||||
except:
|
||||
clean_content = ''
|
||||
raw_results = ''
|
||||
cleaned_query = query
|
||||
logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
|
||||
else:
|
||||
clean_content, cleaned_query, raw_results = await search_service.execute_hybrid_search(
|
||||
**search_params, memory_config=memory_config
|
||||
)
|
||||
# Keep structure for Verify/Retrieve_Summary compatibility
|
||||
dup_databases = {
|
||||
"Query": cleaned_query,
|
||||
"Expansion_issue": [{
|
||||
"Query_small": cleaned_query,
|
||||
"Answer_Small": clean_content,
|
||||
"_intermediate": {
|
||||
"type": "search_result",
|
||||
"query": cleaned_query,
|
||||
"raw_results": raw_results,
|
||||
"index": 1,
|
||||
"total": 1
|
||||
}
|
||||
}]
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve: hybrid_search failed for query '{query}': {e}",
|
||||
exc_info=True
|
||||
)
|
||||
# Return empty results on failure
|
||||
dup_databases = {
|
||||
"Query": query,
|
||||
"Expansion_issue": []
|
||||
}
|
||||
|
||||
logger.info(
|
||||
f"Retrieval: {storage_type}--{user_rag_memory_id}--Query={dup_databases.get('Query', '')}, "
|
||||
f"Expansion_issue count={len(dup_databases.get('Expansion_issue', []))}"
|
||||
)
|
||||
|
||||
# Build result with intermediate outputs
|
||||
result = {
|
||||
"context": dup_databases,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
|
||||
# Add intermediate outputs list if they exist
|
||||
intermediate_outputs = dup_databases.get('_intermediate_outputs', [])
|
||||
if intermediate_outputs:
|
||||
result['_intermediates'] = intermediate_outputs
|
||||
logger.info(f"Adding {len(intermediate_outputs)} intermediate outputs to result")
|
||||
else:
|
||||
logger.warning("No intermediate outputs found in dup_databases")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"context": {
|
||||
"Query": "",
|
||||
"Expansion_issue": []
|
||||
},
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
finally:
|
||||
# Log execution time
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Retrieval', duration)
|
||||
@@ -1,640 +0,0 @@
|
||||
"""
|
||||
Summary Tools for data summarization.
|
||||
|
||||
This module contains MCP tools for summarizing retrieved data and generating responses.
|
||||
LLM clients are constructed from MemoryConfig when needed.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
|
||||
from app.core.logging_config import get_agent_logger, log_time
|
||||
from app.core.memory.agent.mcp_server.mcp_instance import mcp
|
||||
from app.core.memory.agent.mcp_server.models.summary_models import (
|
||||
RetrieveSummaryResponse,
|
||||
SummaryResponse,
|
||||
)
|
||||
from app.core.memory.agent.mcp_server.server import get_context_resource
|
||||
from app.core.memory.agent.utils.messages_tool import (
|
||||
Resolve_username,
|
||||
Summary_messages_deal,
|
||||
)
|
||||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||||
from app.core.rag.nlp.search import knowledge_retrieval
|
||||
from app.db import get_db_context
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from dotenv import load_dotenv
|
||||
from mcp.server.fastmcp import Context
|
||||
|
||||
load_dotenv()
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Summary(
|
||||
ctx: Context,
|
||||
context: str,
|
||||
usermessages: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
storage_type: str = "",
|
||||
user_rag_memory_id: str = "",
|
||||
) -> dict:
|
||||
"""
|
||||
Summarize the verified data.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: JSON string containing verified data
|
||||
usermessages: User messages identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type for the workspace (optional)
|
||||
user_rag_memory_id: User RAG memory identifier (optional)
|
||||
|
||||
Returns:
|
||||
dict: Contains 'status' and 'summary_result'
|
||||
"""
|
||||
start = time.time()
|
||||
|
||||
try:
|
||||
# Extract services from context
|
||||
template_service = get_context_resource(ctx, "template_service")
|
||||
session_service = get_context_resource(ctx, "session_service")
|
||||
|
||||
# Get LLM client from memory_config
|
||||
with get_db_context() as db:
|
||||
factory = MemoryClientFactory(db)
|
||||
llm_client = factory.get_llm_client_from_config(memory_config)
|
||||
|
||||
# Resolve session ID
|
||||
sessionid = Resolve_username(usermessages)
|
||||
|
||||
# Process context to extract answer and query
|
||||
answer_small, query = await Summary_messages_deal(context)
|
||||
|
||||
|
||||
start_time= time.time()
|
||||
history = await session_service.get_history(sessionid, apply_id, group_id)
|
||||
end_time=time.time()
|
||||
logger.info(f"Retrieve_Summary-REDIS搜索：{end_time - start_time}")
|
||||
data = {
|
||||
"query": query,
|
||||
"history": history,
|
||||
"retrieve_info": answer_small
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Summary: initialization failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "error",
|
||||
"summary_result": "信息不足，无法回答"
|
||||
}
|
||||
|
||||
try:
|
||||
# Render template
|
||||
system_prompt = await template_service.render_template(
|
||||
template_name='summary_prompt.jinja2',
|
||||
operation_name='summary',
|
||||
data=data,
|
||||
query=query
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Template rendering failed for Summary: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "error",
|
||||
"message": f"Prompt rendering failed: {str(e)}"
|
||||
}
|
||||
|
||||
try:
|
||||
# Call LLM with structured response
|
||||
structured = await llm_client.response_structured(
|
||||
messages=[{"role": "system", "content": system_prompt}],
|
||||
response_model=SummaryResponse
|
||||
)
|
||||
|
||||
aimessages = structured.query_answer or ""
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"LLM call failed for Summary: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
aimessages = ""
|
||||
|
||||
try:
|
||||
# Save session
|
||||
if aimessages != "":
|
||||
await session_service.save_session(
|
||||
user_id=sessionid,
|
||||
query=query,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
ai_response=aimessages
|
||||
)
|
||||
logger.info(f"sessionid: {aimessages} 写入成功")
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"sessionid: {sessionid} 写入失败，错误信息：{str(e)}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "error",
|
||||
"message": str(e)
|
||||
}
|
||||
|
||||
# Cleanup duplicate sessions
|
||||
await session_service.cleanup_duplicates()
|
||||
|
||||
# Use fallback if empty
|
||||
if aimessages == '':
|
||||
aimessages = '信息不足，无法回答'
|
||||
|
||||
logger.info(f"Summary after verification: {aimessages}")
|
||||
|
||||
# Log execution time
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Summary', duration)
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"summary_result": aimessages,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Retrieve_Summary(
|
||||
ctx: Context,
|
||||
context: dict,
|
||||
usermessages: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
storage_type: str = "",
|
||||
user_rag_memory_id: str = "",
|
||||
) -> dict:
|
||||
"""
|
||||
Summarize data directly from retrieval results.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: Dictionary containing Query and Expansion_issue from Retrieve
|
||||
usermessages: User messages identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type for the workspace (optional)
|
||||
user_rag_memory_id: User RAG memory identifier (optional)
|
||||
|
||||
Returns:
|
||||
dict: Contains 'status' and 'summary_result'
|
||||
"""
|
||||
start = time.time()
|
||||
|
||||
try:
|
||||
# Extract services from context
|
||||
template_service = get_context_resource(ctx, "template_service")
|
||||
session_service = get_context_resource(ctx, "session_service")
|
||||
|
||||
# Get LLM client from memory_config
|
||||
with get_db_context() as db:
|
||||
factory = MemoryClientFactory(db)
|
||||
llm_client = factory.get_llm_client_from_config(memory_config)
|
||||
|
||||
# Resolve session ID
|
||||
sessionid = Resolve_username(usermessages)
|
||||
|
||||
|
||||
|
||||
# Handle both 'content' and 'context' keys (LangGraph uses 'content')
|
||||
logger.debug(f"Retrieve_Summary: raw context type={type(context)}, keys={list(context.keys()) if isinstance(context, dict) else 'N/A'}")
|
||||
|
||||
if isinstance(context, dict):
|
||||
if "content" in context:
|
||||
inner = context["content"]
|
||||
# If it's a JSON string, parse it
|
||||
if isinstance(inner, str):
|
||||
try:
|
||||
parsed = json.loads(inner)
|
||||
logger.info("Retrieve_Summary: successfully parsed JSON")
|
||||
except json.JSONDecodeError:
|
||||
# Try unescaping first
|
||||
try:
|
||||
unescaped = inner.encode('utf-8').decode('unicode_escape')
|
||||
parsed = json.loads(unescaped)
|
||||
logger.info("Retrieve_Summary: parsed after unescaping")
|
||||
except (json.JSONDecodeError, UnicodeDecodeError) as e:
|
||||
logger.error(
|
||||
f"Retrieve_Summary: parsing failed even after unescape: {e}"
|
||||
)
|
||||
context_dict = {"Query": "", "Expansion_issue": []}
|
||||
parsed = None
|
||||
|
||||
if parsed:
|
||||
# Check if parsed has 'context' wrapper
|
||||
if isinstance(parsed, dict) and "context" in parsed:
|
||||
context_dict = parsed["context"]
|
||||
else:
|
||||
context_dict = parsed
|
||||
elif isinstance(inner, dict):
|
||||
context_dict = inner
|
||||
else:
|
||||
context_dict = {"Query": "", "Expansion_issue": []}
|
||||
elif "context" in context:
|
||||
context_dict = context["context"] if isinstance(context["context"], dict) else context
|
||||
else:
|
||||
context_dict = context
|
||||
else:
|
||||
context_dict = {"Query": "", "Expansion_issue": []}
|
||||
|
||||
query = context_dict.get("Query", "")
|
||||
expansion_issue = context_dict.get("Expansion_issue", [])
|
||||
|
||||
logger.debug(f"Retrieve_Summary: query='{query}', expansion_issue count={len(expansion_issue)}")
|
||||
logger.debug(f"Retrieve_Summary: expansion_issue={expansion_issue[:2] if expansion_issue else 'empty'}")
|
||||
|
||||
# Extract retrieve_info from expansion_issue
|
||||
retrieve_info = []
|
||||
for item in expansion_issue:
|
||||
# Check for both Answer_Small and Answer_Small (typo) for backward compatibility
|
||||
answer = None
|
||||
if isinstance(item, dict):
|
||||
if "Answer_Small" in item:
|
||||
answer = item["Answer_Small"]
|
||||
|
||||
|
||||
if answer is not None:
|
||||
# Handle both string and list formats
|
||||
if isinstance(answer, list):
|
||||
# Join list of characters/strings into a single string
|
||||
retrieve_info.append(''.join(str(x) for x in answer))
|
||||
elif isinstance(answer, str):
|
||||
retrieve_info.append(answer)
|
||||
else:
|
||||
retrieve_info.append(str(answer))
|
||||
|
||||
# Join all retrieve_info into a single string
|
||||
retrieve_info_str = '\n\n'.join(retrieve_info) if retrieve_info else ""
|
||||
|
||||
start_time=time.time()
|
||||
history = await session_service.get_history(sessionid, apply_id, group_id)
|
||||
# Override with empty list for now (as in original)
|
||||
end_time=time.time()
|
||||
logger.info(f"Retrieve_Summary-REDIS搜索：{end_time - start_time}")
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve_Summary: initialization failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "error",
|
||||
"summary_result": "信息不足，无法回答"
|
||||
}
|
||||
|
||||
try:
|
||||
# Render template
|
||||
system_prompt = await template_service.render_template(
|
||||
template_name='Retrieve_Summary_prompt.jinja2',
|
||||
operation_name='retrieve_summary',
|
||||
query=query,
|
||||
history=history,
|
||||
retrieve_info=retrieve_info_str
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Template rendering failed for Retrieve_Summary: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "error",
|
||||
"message": f"Prompt rendering failed: {str(e)}"
|
||||
}
|
||||
|
||||
try:
|
||||
# Call LLM with structured response
|
||||
structured = await llm_client.response_structured(
|
||||
messages=[{"role": "system", "content": system_prompt}],
|
||||
response_model=RetrieveSummaryResponse
|
||||
)
|
||||
|
||||
# Handle case where structured response might be None or incomplete
|
||||
if structured and hasattr(structured, 'data') and structured.data:
|
||||
aimessages = structured.data.query_answer or ""
|
||||
else:
|
||||
logger.warning("Structured response is None or incomplete, using default message")
|
||||
aimessages = "信息不足，无法回答"
|
||||
|
||||
|
||||
# Check for insufficient information response
|
||||
if '信息不足，无法回答' not in str(aimessages) or str(aimessages)!="":
|
||||
# Save session
|
||||
await session_service.save_session(
|
||||
user_id=sessionid,
|
||||
query=query,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
ai_response=aimessages
|
||||
)
|
||||
logger.info(f"sessionid: {aimessages} 写入成功")
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve_Summary: LLM call failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
aimessages = ""
|
||||
# Cleanup duplicate sessions
|
||||
await session_service.cleanup_duplicates()
|
||||
|
||||
# Use fallback if empty
|
||||
if aimessages == '':
|
||||
aimessages = '信息不足，无法回答'
|
||||
|
||||
logger.info(f"Summary after retrieval: {aimessages}")
|
||||
|
||||
# Log execution time
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Retrieval summary', duration)
|
||||
|
||||
# Emit intermediate output for frontend
|
||||
return {
|
||||
"status": "success",
|
||||
"summary_result": aimessages,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "retrieval_summary",
|
||||
"summary": aimessages,
|
||||
"query": query,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Input_Summary(
|
||||
ctx: Context,
|
||||
context: str,
|
||||
usermessages: str,
|
||||
search_switch: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
storage_type: str = "",
|
||||
user_rag_memory_id: str = "",
|
||||
) -> dict:
|
||||
"""
|
||||
Generate a quick summary for direct input without verification.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: String containing the input sentence
|
||||
usermessages: User messages identifier
|
||||
search_switch: Search switch value for routing ('2' for summaries only)
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type for the workspace (e.g., 'rag', 'vector')
|
||||
user_rag_memory_id: User RAG memory identifier
|
||||
|
||||
Returns:
|
||||
dict: Contains 'query_answer' with the summary result
|
||||
"""
|
||||
start = time.time()
|
||||
logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
|
||||
|
||||
try:
|
||||
# Extract services from context
|
||||
session_service = get_context_resource(ctx, "session_service")
|
||||
search_service = get_context_resource(ctx, "search_service")
|
||||
|
||||
# Resolve session ID
|
||||
sessionid = Resolve_username(usermessages) or ""
|
||||
sessionid = sessionid.replace('call_id_', '')
|
||||
|
||||
start_time=time.time()
|
||||
history = await session_service.get_history(
|
||||
str(sessionid),
|
||||
str(apply_id),
|
||||
str(group_id)
|
||||
)
|
||||
end_time=time.time()
|
||||
logger.info(f"Input_Summary-REDIS搜索：{end_time - start_time}")
|
||||
# Override with empty list for now (as in original)
|
||||
|
||||
# Log the raw context for debugging
|
||||
logger.info(f"Input_Summary: Received context type={type(context)}, value={context[:200] if isinstance(context, str) else context}")
|
||||
|
||||
# Extract sentence from context
|
||||
# Context can be a string or might contain the sentence in various formats
|
||||
try:
|
||||
# Try to parse as JSON first
|
||||
if isinstance(context, str) and (context.startswith('{') or context.startswith('[')):
|
||||
try:
|
||||
import json
|
||||
context_dict = json.loads(context)
|
||||
if isinstance(context_dict, dict):
|
||||
query = context_dict.get('sentence', context_dict.get('content', context))
|
||||
else:
|
||||
query = context
|
||||
except json.JSONDecodeError:
|
||||
# Not valid JSON, try regex
|
||||
match = re.search(r"'sentence':\s*['\"]?(.*?)['\"]?\s*,", context)
|
||||
query = match.group(1) if match else context
|
||||
else:
|
||||
query = context
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to extract query from context: {e}")
|
||||
query = context
|
||||
|
||||
# Clean query
|
||||
query = str(query).strip().strip("\"'")
|
||||
|
||||
logger.debug(f"Input_Summary: Extracted query='{query}' from context type={type(context)}")
|
||||
|
||||
# Execute search based on search_switch and storage_type
|
||||
try:
|
||||
logger.info(f"search_switch: {search_switch}, storage_type: {storage_type}")
|
||||
|
||||
# Prepare search parameters based on storage type
|
||||
search_params = {
|
||||
"group_id": group_id,
|
||||
"question": query,
|
||||
"return_raw_results": True
|
||||
}
|
||||
|
||||
# Add storage-specific parameters
|
||||
|
||||
# Retrieval
|
||||
if search_switch == '2':
|
||||
search_params["include"] = ["summaries"]
|
||||
if storage_type == "rag" and user_rag_memory_id:
|
||||
raw_results = []
|
||||
retrieve_info = ""
|
||||
kb_config={
|
||||
"knowledge_bases": [
|
||||
{
|
||||
"kb_id": user_rag_memory_id,
|
||||
"similarity_threshold": 0.7,
|
||||
"vector_similarity_weight": 0.5,
|
||||
"top_k": 10,
|
||||
"retrieve_type": "participle"
|
||||
}
|
||||
],
|
||||
"merge_strategy": "weight",
|
||||
"reranker_id":os.getenv('reranker_id'),
|
||||
"reranker_top_k": 10
|
||||
}
|
||||
|
||||
retrieve_chunks_result = knowledge_retrieval(query, kb_config,[str(group_id)])
|
||||
try:
|
||||
retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
|
||||
retrieve_info = '\n\n'.join(retrieval_knowledge)
|
||||
raw_results=[retrieve_info]
|
||||
logger.info(f"Input_Summary: Using RAG storage with memory_id={user_rag_memory_id}")
|
||||
except:
|
||||
retrieve_info=''
|
||||
raw_results=['']
|
||||
logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
|
||||
else:
|
||||
retrieve_info, question, raw_results = await search_service.execute_hybrid_search(
|
||||
**search_params, memory_config=memory_config
|
||||
)
|
||||
logger.info("Input_Summary: Using summary for retrieval")
|
||||
else:
|
||||
retrieve_info, question, raw_results = await search_service.execute_hybrid_search(
|
||||
**search_params, memory_config=memory_config
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Input_Summary: hybrid_search failed, using empty results: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
retrieve_info, question, raw_results = "", query, []
|
||||
|
||||
# Return retrieved information directly without LLM processing
|
||||
# Use the raw retrieved info as the answer
|
||||
aimessages = retrieve_info if retrieve_info else "信息不足，无法回答"
|
||||
|
||||
logger.info(f"Quick answer (no LLM): {storage_type}--{user_rag_memory_id}--{aimessages[:500]}...")
|
||||
|
||||
# Emit intermediate output for frontend
|
||||
return {
|
||||
"status": "success",
|
||||
"summary_result": aimessages,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "input_summary",
|
||||
"title": "快速答案",
|
||||
"summary": aimessages,
|
||||
"query": query,
|
||||
"raw_results": raw_results,
|
||||
"search_mode": "quick_search",
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Input_Summary failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "fail",
|
||||
"summary_result": "信息不足，无法回答",
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
finally:
|
||||
# Log execution time
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Retrieval', duration)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Summary_fails(
|
||||
ctx: Context,
|
||||
context: str,
|
||||
usermessages: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
storage_type: str = "",
|
||||
user_rag_memory_id: str = ""
|
||||
) -> dict:
|
||||
"""
|
||||
Handle workflow failure when summary cannot be generated.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: Failure context string
|
||||
usermessages: User messages identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
storage_type: Storage type for the workspace (optional)
|
||||
user_rag_memory_id: User RAG memory identifier (optional)
|
||||
|
||||
Returns:
|
||||
dict: Contains 'query_answer' with failure message
|
||||
"""
|
||||
try:
|
||||
# Extract services from context
|
||||
session_service = get_context_resource(ctx, 'session_service')
|
||||
|
||||
# Parse session ID from usermessages
|
||||
usermessages_parts = usermessages.split('_')[1:]
|
||||
sessionid = '_'.join(usermessages_parts[:-1])
|
||||
|
||||
# Cleanup duplicate sessions
|
||||
await session_service.cleanup_duplicates()
|
||||
|
||||
logger.info("没有相关数据")
|
||||
logger.debug(f"Summary_fails called with apply_id: {apply_id}, group_id: {group_id}")
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"summary_result": "没有相关数据",
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Summary_fails failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "fail",
|
||||
"summary_result": "没有相关数据",
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"error": str(e)
|
||||
}
|
||||
@@ -1,174 +0,0 @@
|
||||
"""
|
||||
Verification Tools for data verification.
|
||||
|
||||
This module contains MCP tools for verifying retrieved data.
|
||||
"""
|
||||
import time
|
||||
|
||||
from app.core.logging_config import get_agent_logger, log_time
|
||||
from app.core.memory.agent.mcp_server.mcp_instance import mcp
|
||||
from app.core.memory.agent.mcp_server.server import get_context_resource
|
||||
from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
|
||||
from app.core.memory.agent.utils.messages_tool import (
|
||||
Resolve_username,
|
||||
Retrieve_verify_tool_messages_deal,
|
||||
Verify_messages_deal,
|
||||
)
|
||||
from app.core.memory.agent.utils.verify_tool import VerifyTool
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from jinja2 import Template
|
||||
from mcp.server.fastmcp import Context
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def Verify(
|
||||
ctx: Context,
|
||||
context: dict,
|
||||
usermessages: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
storage_type: str = "",
|
||||
user_rag_memory_id: str = ""
|
||||
) -> dict:
|
||||
"""
|
||||
Verify the retrieved data.
|
||||
|
||||
Args:
|
||||
ctx: FastMCP context for dependency injection
|
||||
context: Dictionary containing query and expansion issues
|
||||
usermessages: User messages identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type for the workspace (optional)
|
||||
user_rag_memory_id: User RAG memory identifier (optional)
|
||||
|
||||
Returns:
|
||||
dict: Contains 'status' and 'verified_data' with verification results
|
||||
"""
|
||||
start = time.time()
|
||||
|
||||
|
||||
try:
|
||||
# Extract services from context
|
||||
session_service = get_context_resource(ctx, 'session_service')
|
||||
|
||||
# Load verification prompt template
|
||||
file_path = PROJECT_ROOT_ + '/agent/utils/prompt/split_verify_prompt.jinja2'
|
||||
|
||||
# Read template file directly (VerifyTool expects raw template content)
|
||||
from app.core.memory.agent.utils.messages_tool import read_template_file
|
||||
system_prompt = await read_template_file(file_path)
|
||||
|
||||
|
||||
|
||||
# Resolve session ID
|
||||
sessionid = Resolve_username(usermessages)
|
||||
|
||||
# Get conversation history
|
||||
history = await session_service.get_history(sessionid, apply_id, group_id)
|
||||
|
||||
template = Template(system_prompt)
|
||||
system_prompt = template.render(history=history, sentence=context)
|
||||
|
||||
# Process context to extract query and results
|
||||
Query_small, Result_small, query = await Verify_messages_deal(context)
|
||||
|
||||
# Build query list for verification
|
||||
query_list = []
|
||||
for query_small, anser in zip(Query_small, Result_small, strict=False):
|
||||
query_list.append({
|
||||
'Query_small': query_small,
|
||||
'Answer_Small': anser
|
||||
})
|
||||
|
||||
messages = {
|
||||
"Query": query,
|
||||
"Expansion_issue": query_list
|
||||
}
|
||||
|
||||
|
||||
|
||||
# Call verification workflow with LLM model ID from memory_config
|
||||
verify_tool = VerifyTool(
|
||||
system_prompt=system_prompt,
|
||||
verify_data=messages,
|
||||
llm_model_id=str(memory_config.llm_model_id)
|
||||
)
|
||||
verify_result = await verify_tool.verify()
|
||||
|
||||
# Parse LLM verification result with error handling
|
||||
try:
|
||||
messages_deal = await Retrieve_verify_tool_messages_deal(
|
||||
verify_result,
|
||||
history,
|
||||
query
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Retrieve_verify_tool_messages_deal parsing failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
# Fallback to avoid 500 errors
|
||||
messages_deal = {
|
||||
"data": {
|
||||
"query": query,
|
||||
"expansion_issue": []
|
||||
},
|
||||
"split_result": "failed",
|
||||
"reason": str(e),
|
||||
"history": history,
|
||||
}
|
||||
|
||||
logger.info(f"Verification result: {messages_deal}")
|
||||
|
||||
# Emit intermediate output for frontend
|
||||
return {
|
||||
"status": "success",
|
||||
"verified_data": messages_deal,
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"_intermediate": {
|
||||
"type": "verification",
|
||||
"title": "Data Verification",
|
||||
"result": messages_deal.get("split_result", "unknown"),
|
||||
"reason": messages_deal.get("reason", ""),
|
||||
"query": query,
|
||||
"verified_count": len(query_list),
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Verify failed: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return {
|
||||
"status": "error",
|
||||
"message": str(e),
|
||||
"storage_type": storage_type,
|
||||
"user_rag_memory_id": user_rag_memory_id,
|
||||
"verified_data": {
|
||||
"data": {
|
||||
"query": "",
|
||||
"expansion_issue": []
|
||||
},
|
||||
"split_result": "failed",
|
||||
"reason": str(e),
|
||||
"history": [],
|
||||
}
|
||||
}
|
||||
|
||||
finally:
|
||||
# Log execution time
|
||||
end = time.time()
|
||||
try:
|
||||
duration = end - start
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
log_time('Verification', duration)
|
||||
@@ -1,114 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import requests
|
||||
|
||||
# from qcloud_cos import CosConfig, CosS3Client
|
||||
# from qcloud_cos.cos_exception import CosClientError, CosServiceError
|
||||
|
||||
# from config.paths import BASE_DIR
|
||||
BASE_DIR = os.path.dirname(os.path.realpath(sys.argv[0]))
|
||||
|
||||
class OSSUploader:
|
||||
"""对象存储文件上传工具类"""
|
||||
|
||||
def __init__(self, env):
|
||||
api = {
|
||||
"test": "https://testlingqi.redbearai.com/api/user/file/common/upload/v2/anon",
|
||||
"prod": "https://lingqi.redbearai.com/api/user/file/common/upload/v2/anon"
|
||||
}
|
||||
self.api = api.get(env, "https://testlingqi.redbearai.com/api/user/file/common/upload/v2/anon")
|
||||
self.privacy = "false"
|
||||
self.headers = {
|
||||
"User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
|
||||
'AppleWebKit/537.36 (KHTML, like Gecko)'
|
||||
' Chrome/133.0.6833.84 Safari/537.36'
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _generate_object_key(file_path, prefix='xhs_'):
|
||||
"""
|
||||
生成对象存储的Key
|
||||
|
||||
:param file_path: 本地文件路径
|
||||
:param prefix: 存储前缀，用于分类存储
|
||||
:return: 生成的对象Key
|
||||
"""
|
||||
# 文件md5值.后缀名
|
||||
filename = os.path.basename(file_path)
|
||||
filename = f"{filename}"
|
||||
|
||||
# 组合成完整的对象Key
|
||||
return f"{prefix}{filename}"
|
||||
|
||||
def upload_image(self, file_name, prefix='jd_'):
|
||||
"""
|
||||
上传文件到COS并返回可访问的URL
|
||||
|
||||
:param file_url: 文件路径
|
||||
:param file_name: 文件名称
|
||||
:param media_type: 文件类型
|
||||
:param prefix: 存储前缀，用于分类存储
|
||||
:return: 文件访问URL
|
||||
"""
|
||||
# 检查文件是否存在
|
||||
|
||||
|
||||
|
||||
file_path = os.path.join(BASE_DIR, file_name)
|
||||
|
||||
# response = requests.get(url, headers=self.headers, stream=True)
|
||||
|
||||
# if response.status_code == 200:
|
||||
# with open(file_path, "wb") as f:
|
||||
# for chunk in response.iter_content(1024): # 分块写入，避免内存占用过大
|
||||
# f.write(chunk)
|
||||
# else:
|
||||
# raise Exception(f"文件下载失败,{file_name}")
|
||||
|
||||
# 生成对象Key
|
||||
object_key = self._generate_object_key(file_path, prefix +file_name.split('.')[-1])
|
||||
|
||||
try:
|
||||
upload_response = requests.post(
|
||||
self.api,
|
||||
data={
|
||||
"privacy": self.privacy,
|
||||
"fileName": object_key,
|
||||
}
|
||||
)
|
||||
|
||||
if upload_response.status_code != 200:
|
||||
raise Exception('上传接口请求失败')
|
||||
resp = upload_response.json()
|
||||
name = resp["data"]["name"]
|
||||
file_url = resp["data"]["path"]
|
||||
policy = resp["data"]["policy"]
|
||||
with open(file_path, 'rb') as f:
|
||||
oss_push_resp = requests.post(
|
||||
policy["host"],
|
||||
files={
|
||||
"key": policy["dir"],
|
||||
"OSSAccessKeyId": policy["accessid"],
|
||||
"name": name,
|
||||
"policy": policy["policy"],
|
||||
"success_action_status": 200,
|
||||
"signature": policy["signature"],
|
||||
"file": f,
|
||||
}
|
||||
)
|
||||
if oss_push_resp.status_code == 200:
|
||||
return file_url
|
||||
raise Exception("OSS上传失败")
|
||||
except Exception:
|
||||
raise Exception(f"上传失败: \n{traceback.format_exc()}")
|
||||
finally:
|
||||
print('success')
|
||||
# os.remove(file_path)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
cos_uploader = OSSUploader("prod")
|
||||
url =cos_uploader.upload_image('./example01.jpg')
|
||||
print(url)
|
||||
@@ -1,121 +0,0 @@
|
||||
import asyncio
|
||||
import re
|
||||
|
||||
from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_, picture_model_requests,Picture_recognize, Voice_recognize
|
||||
from app.core.memory.agent.utils.messages_tool import read_template_file
|
||||
|
||||
import requests
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
# file_urls = [
|
||||
# "https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_female2.wav",
|
||||
# "https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_male2.wav",
|
||||
# ]
|
||||
class Vico_recognition:
|
||||
def __init__(self,file_urls):
|
||||
self.api_key=''
|
||||
self.backend_model_name=''
|
||||
self.api_base=''
|
||||
self.file_urls=file_urls
|
||||
|
||||
# 提交文件转写任务，包含待转写文件url列表
|
||||
async def submit_task(self) -> str:
|
||||
self.api_key, self.backend_model_name, self.api_base =await Voice_recognize()
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
"X-DashScope-Async": "enable",
|
||||
}
|
||||
data = {
|
||||
"model": self.backend_model_name,
|
||||
"input": {"file_urls": self.file_urls},
|
||||
"parameters": {
|
||||
"channel_id": [0],
|
||||
"vocabulary_id": "vocab-Xxxx",
|
||||
},
|
||||
}
|
||||
# 录音文件转写服务url
|
||||
service_url = (
|
||||
"https://dashscope.aliyuncs.com/api/v1/services/audio/asr/transcription"
|
||||
)
|
||||
response = requests.post(
|
||||
service_url, headers=headers, data=json.dumps(data)
|
||||
)
|
||||
|
||||
# 打印响应内容
|
||||
if response.status_code == 200:
|
||||
return response.json()["output"]["task_id"]
|
||||
else:
|
||||
print("task failed!")
|
||||
print(response.json())
|
||||
return None
|
||||
|
||||
async def download_transcription_result(self, transcription_url):
|
||||
"""
|
||||
Args:
|
||||
transcription_url (str): 转写结果文件URL
|
||||
Returns:
|
||||
dict: 转写结果内容
|
||||
"""
|
||||
try:
|
||||
response = requests.get(transcription_url)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except Exception as e:
|
||||
print(f"下载转写结果失败: {e}")
|
||||
return None
|
||||
|
||||
# 循环查询任务状态直到成功
|
||||
async def wait_for_complete(self,task_id):
|
||||
self.api_key, self.backend_model_name, self.api_base = await Voice_recognize()
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
"X-DashScope-Async": "enable",
|
||||
}
|
||||
|
||||
pending = True
|
||||
while pending:
|
||||
# 查询任务状态服务url
|
||||
service_url = f"https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}"
|
||||
response = requests.post(
|
||||
service_url, headers=headers
|
||||
)
|
||||
if response.status_code == 200:
|
||||
status = response.json()['output']['task_status']
|
||||
if status == 'SUCCEEDED':
|
||||
print("task succeeded!")
|
||||
pending = False
|
||||
return response.json()['output']['results']
|
||||
elif status == 'RUNNING' or status == 'PENDING':
|
||||
pass
|
||||
else:
|
||||
print("task failed!")
|
||||
pending = False
|
||||
else:
|
||||
print("query failed!")
|
||||
pending = False
|
||||
time.sleep(0.1)
|
||||
async def run(self):
|
||||
self.api_key, self.backend_model_name, self.api_base = await Voice_recognize()
|
||||
task_id=await self.submit_task()
|
||||
result=await self.wait_for_complete(task_id)
|
||||
result_context=[]
|
||||
for i in result:
|
||||
transcription_url=i['transcription_url']
|
||||
print(f"转写URL: {transcription_url}")
|
||||
|
||||
# 下载并打印转写内容
|
||||
content = await self.download_transcription_result(transcription_url)
|
||||
if content:
|
||||
content=json.dumps(content, indent=2, ensure_ascii=False)
|
||||
context=re.findall(r'"text": "(.*?)"', content)
|
||||
result_context.append(context[0])
|
||||
result=''.join(result_context)
|
||||
return (result)
|
||||
|
||||
|
||||
|
||||
|
||||
277
api/app/core/memory/agent/services/optimized_llm_service.py
Normal file
277
api/app/core/memory/agent/services/optimized_llm_service.py
Normal file
@@ -0,0 +1,277 @@
|
||||
"""
|
||||
优化的LLM服务类，用于压缩和统一LLM调用
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Any, Dict, List, Optional, Type, TypeVar, Union
|
||||
from pydantic import BaseModel
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||||
from app.core.memory.llm_tools.openai_client import OpenAIClient
|
||||
|
||||
T = TypeVar('T', bound=BaseModel)
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
class OptimizedLLMService:
|
||||
"""
|
||||
优化的LLM服务类，提供统一的LLM调用接口
|
||||
|
||||
特性：
|
||||
1. 客户端复用 - 避免重复创建LLM客户端
|
||||
2. 批量处理 - 支持并发处理多个请求
|
||||
3. 错误处理 - 统一的错误处理和降级策略
|
||||
4. 性能优化 - 缓存和连接池优化
|
||||
"""
|
||||
|
||||
def __init__(self, db_session: Session):
|
||||
self.db_session = db_session
|
||||
self.client_factory = MemoryClientFactory(db_session)
|
||||
self._client_cache: Dict[str, OpenAIClient] = {}
|
||||
|
||||
def _get_cached_client(self, llm_model_id: str) -> OpenAIClient:
|
||||
"""获取缓存的LLM客户端，避免重复创建"""
|
||||
if llm_model_id not in self._client_cache:
|
||||
self._client_cache[llm_model_id] = self.client_factory.get_llm_client(llm_model_id)
|
||||
return self._client_cache[llm_model_id]
|
||||
|
||||
async def structured_response(
|
||||
self,
|
||||
llm_model_id: str,
|
||||
system_prompt: str,
|
||||
response_model: Type[T],
|
||||
user_message: Optional[str] = None,
|
||||
fallback_value: Optional[Any] = None
|
||||
) -> T:
|
||||
"""
|
||||
统一的结构化响应接口
|
||||
|
||||
Args:
|
||||
llm_model_id: LLM模型ID
|
||||
system_prompt: 系统提示词
|
||||
response_model: 响应模型类
|
||||
user_message: 用户消息（可选）
|
||||
fallback_value: 失败时的降级值
|
||||
|
||||
Returns:
|
||||
结构化响应对象
|
||||
"""
|
||||
try:
|
||||
llm_client = self._get_cached_client(llm_model_id)
|
||||
|
||||
messages = [{"role": "system", "content": system_prompt}]
|
||||
if user_message:
|
||||
messages.append({"role": "user", "content": user_message})
|
||||
|
||||
logger.debug(f"LLM调用: model={llm_model_id}, prompt_length={len(system_prompt)}")
|
||||
|
||||
structured = await llm_client.response_structured(
|
||||
messages=messages,
|
||||
response_model=response_model
|
||||
)
|
||||
|
||||
if structured is None:
|
||||
logger.warning(f"LLM返回None，使用降级值")
|
||||
return self._create_fallback_response(response_model, fallback_value)
|
||||
|
||||
return structured
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"结构化响应失败: {e}", exc_info=True)
|
||||
return self._create_fallback_response(response_model, fallback_value)
|
||||
|
||||
async def batch_structured_response(
|
||||
self,
|
||||
llm_model_id: str,
|
||||
requests: List[Dict[str, Any]],
|
||||
response_model: Type[T],
|
||||
max_concurrent: int = 5
|
||||
) -> List[T]:
|
||||
"""
|
||||
批量处理结构化响应
|
||||
|
||||
Args:
|
||||
llm_model_id: LLM模型ID
|
||||
requests: 请求列表，每个请求包含system_prompt等参数
|
||||
response_model: 响应模型类
|
||||
max_concurrent: 最大并发数
|
||||
|
||||
Returns:
|
||||
结构化响应列表
|
||||
"""
|
||||
semaphore = asyncio.Semaphore(max_concurrent)
|
||||
|
||||
async def process_single_request(request: Dict[str, Any]) -> T:
|
||||
async with semaphore:
|
||||
return await self.structured_response(
|
||||
llm_model_id=llm_model_id,
|
||||
system_prompt=request.get('system_prompt', ''),
|
||||
response_model=response_model,
|
||||
user_message=request.get('user_message'),
|
||||
fallback_value=request.get('fallback_value')
|
||||
)
|
||||
|
||||
tasks = [process_single_request(req) for req in requests]
|
||||
return await asyncio.gather(*tasks)
|
||||
|
||||
async def simple_response(
|
||||
self,
|
||||
llm_model_id: str,
|
||||
system_prompt: str,
|
||||
user_message: Optional[str] = None,
|
||||
fallback_message: str = "信息不足，无法回答"
|
||||
) -> str:
|
||||
"""
|
||||
简单的文本响应接口
|
||||
|
||||
Args:
|
||||
llm_model_id: LLM模型ID
|
||||
system_prompt: 系统提示词
|
||||
user_message: 用户消息（可选）
|
||||
fallback_message: 失败时的降级消息
|
||||
|
||||
Returns:
|
||||
响应文本
|
||||
"""
|
||||
try:
|
||||
llm_client = self._get_cached_client(llm_model_id)
|
||||
|
||||
messages = [{"role": "system", "content": system_prompt}]
|
||||
if user_message:
|
||||
messages.append({"role": "user", "content": user_message})
|
||||
|
||||
response = await llm_client.response(messages=messages)
|
||||
|
||||
if not response or not response.strip():
|
||||
return fallback_message
|
||||
|
||||
return response.strip()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"简单响应失败: {e}", exc_info=True)
|
||||
return fallback_message
|
||||
|
||||
def _create_fallback_response(self, response_model: Type[T], fallback_value: Optional[Any]) -> T:
|
||||
"""创建降级响应"""
|
||||
try:
|
||||
if fallback_value is not None:
|
||||
if isinstance(fallback_value, response_model):
|
||||
return fallback_value
|
||||
elif isinstance(fallback_value, dict):
|
||||
return response_model(**fallback_value)
|
||||
|
||||
# 尝试创建空的响应模型
|
||||
if hasattr(response_model, 'root'):
|
||||
# RootModel类型
|
||||
return response_model([])
|
||||
else:
|
||||
# 普通BaseModel类型
|
||||
return response_model()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"创建降级响应失败: {e}")
|
||||
# 最后的降级策略
|
||||
if hasattr(response_model, 'root'):
|
||||
return response_model([])
|
||||
else:
|
||||
return response_model()
|
||||
|
||||
def clear_cache(self):
|
||||
"""清理客户端缓存"""
|
||||
self._client_cache.clear()
|
||||
|
||||
|
||||
class LLMServiceMixin:
|
||||
"""
|
||||
LLM服务混入类，为节点提供便捷的LLM调用方法
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self._llm_service: Optional[OptimizedLLMService] = None
|
||||
|
||||
def get_llm_service(self, db_session: Session) -> OptimizedLLMService:
|
||||
"""获取LLM服务实例"""
|
||||
if self._llm_service is None:
|
||||
self._llm_service = OptimizedLLMService(db_session)
|
||||
return self._llm_service
|
||||
|
||||
async def call_llm_structured(
|
||||
self,
|
||||
state: Dict[str, Any],
|
||||
db_session: Session,
|
||||
system_prompt: str,
|
||||
response_model: Type[T],
|
||||
user_message: Optional[str] = None,
|
||||
fallback_value: Optional[Any] = None
|
||||
) -> T:
|
||||
"""
|
||||
便捷的结构化LLM调用方法
|
||||
|
||||
Args:
|
||||
state: 状态字典，包含memory_config
|
||||
db_session: 数据库会话
|
||||
system_prompt: 系统提示词
|
||||
response_model: 响应模型类
|
||||
user_message: 用户消息（可选）
|
||||
fallback_value: 失败时的降级值
|
||||
|
||||
Returns:
|
||||
结构化响应对象
|
||||
"""
|
||||
memory_config = state.get('memory_config')
|
||||
if not memory_config:
|
||||
raise ValueError("State中缺少memory_config")
|
||||
|
||||
llm_model_id = memory_config.llm_model_id
|
||||
if not llm_model_id:
|
||||
raise ValueError("Memory config中缺少llm_model_id")
|
||||
|
||||
llm_service = self.get_llm_service(db_session)
|
||||
return await llm_service.structured_response(
|
||||
llm_model_id=llm_model_id,
|
||||
system_prompt=system_prompt,
|
||||
response_model=response_model,
|
||||
user_message=user_message,
|
||||
fallback_value=fallback_value
|
||||
)
|
||||
|
||||
async def call_llm_simple(
|
||||
self,
|
||||
state: Dict[str, Any],
|
||||
db_session: Session,
|
||||
system_prompt: str,
|
||||
user_message: Optional[str] = None,
|
||||
fallback_message: str = "信息不足，无法回答"
|
||||
) -> str:
|
||||
"""
|
||||
便捷的简单LLM调用方法
|
||||
|
||||
Args:
|
||||
state: 状态字典，包含memory_config
|
||||
db_session: 数据库会话
|
||||
system_prompt: 系统提示词
|
||||
user_message: 用户消息（可选）
|
||||
fallback_message: 失败时的降级消息
|
||||
|
||||
Returns:
|
||||
响应文本
|
||||
"""
|
||||
memory_config = state.get('memory_config')
|
||||
if not memory_config:
|
||||
raise ValueError("State中缺少memory_config")
|
||||
|
||||
llm_model_id = memory_config.llm_model_id
|
||||
if not llm_model_id:
|
||||
raise ValueError("Memory config中缺少llm_model_id")
|
||||
|
||||
llm_service = self.get_llm_service(db_session)
|
||||
return await llm_service.simple_response(
|
||||
llm_model_id=llm_model_id,
|
||||
system_prompt=system_prompt,
|
||||
user_message=user_message,
|
||||
fallback_message=fallback_message
|
||||
)
|
||||
@@ -4,22 +4,19 @@ Parameter Builder for constructing tool call arguments.
|
||||
This service provides tool-specific parameter transformation logic
|
||||
to build correct arguments for each tool type.
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
class ParameterBuilder:
|
||||
"""Service for building tool call arguments based on tool type."""
|
||||
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the parameter builder."""
|
||||
logger.info("ParameterBuilder initialized")
|
||||
|
||||
|
||||
def build_tool_args(
|
||||
self,
|
||||
tool_name: str,
|
||||
@@ -28,9 +25,8 @@ class ParameterBuilder:
|
||||
search_switch: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
memory_config: MemoryConfig,
|
||||
storage_type: Optional[str] = None,
|
||||
user_rag_memory_id: Optional[str] = None,
|
||||
user_rag_memory_id: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Build tool arguments based on tool type.
|
||||
@@ -49,7 +45,6 @@ class ParameterBuilder:
|
||||
search_switch: Search routing parameter
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
memory_config: MemoryConfig object containing all configuration
|
||||
storage_type: Storage type for the workspace (optional)
|
||||
user_rag_memory_id: User RAG memory ID for knowledge base retrieval (optional)
|
||||
|
||||
@@ -60,19 +55,18 @@ class ParameterBuilder:
|
||||
base_args = {
|
||||
"usermessages": tool_call_id,
|
||||
"apply_id": apply_id,
|
||||
"group_id": group_id,
|
||||
"memory_config": memory_config,
|
||||
"group_id": group_id
|
||||
}
|
||||
|
||||
|
||||
# Always add storage_type and user_rag_memory_id (with defaults if None)
|
||||
base_args["storage_type"] = storage_type if storage_type is not None else ""
|
||||
base_args["user_rag_memory_id"] = user_rag_memory_id if user_rag_memory_id is not None else ""
|
||||
|
||||
# Tool-specific argument construction
|
||||
if tool_name in ["Verify", "Summary", "Summary_fails", "Retrieve_Summary", "Problem_Extension"]:
|
||||
# These tools expect dict context
|
||||
if tool_name in ["Verify","Summary", "Summary_fails",'Retrieve_Summary']:
|
||||
# Verify expects dict context
|
||||
return {
|
||||
"context": content if isinstance(content, dict) else {"content": content},
|
||||
"context": content if isinstance(content, dict) else {},
|
||||
**base_args
|
||||
}
|
||||
|
||||
@@ -4,31 +4,21 @@ Search Service for executing hybrid search and processing results.
|
||||
This service provides clean search result processing with content extraction
|
||||
and deduplication.
|
||||
"""
|
||||
|
||||
from typing import TYPE_CHECKING, List, Optional, Tuple
|
||||
from typing import List, Tuple, Optional
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.src.search import run_hybrid_search
|
||||
from app.core.memory.utils.data.text_utils import escape_lucene_query
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
class SearchService:
|
||||
"""Service for executing hybrid search and processing results."""
|
||||
|
||||
def __init__(self, memory_config: "MemoryConfig" = None):
|
||||
"""
|
||||
Initialize the search service.
|
||||
|
||||
Args:
|
||||
memory_config: Optional MemoryConfig for embedding model configuration.
|
||||
If not provided, must be passed to execute_hybrid_search.
|
||||
"""
|
||||
self.memory_config = memory_config
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the search service."""
|
||||
logger.info("SearchService initialized")
|
||||
|
||||
def extract_content_from_result(self, result: dict) -> str:
|
||||
@@ -103,49 +93,40 @@ class SearchService:
|
||||
self,
|
||||
group_id: str,
|
||||
question: str,
|
||||
limit: int = 15,
|
||||
limit: int = 5,
|
||||
search_type: str = "hybrid",
|
||||
include: Optional[List[str]] = None,
|
||||
rerank_alpha: float = 0.6,
|
||||
activation_boost_factor: float = 0.8,
|
||||
rerank_alpha: float = 0.4,
|
||||
output_path: str = "search_results.json",
|
||||
return_raw_results: bool = False,
|
||||
memory_config: "MemoryConfig" = None,
|
||||
memory_config = None
|
||||
) -> Tuple[str, str, Optional[dict]]:
|
||||
"""
|
||||
Execute hybrid search with two-stage ranking.
|
||||
|
||||
Stage 1: Filter by content relevance (BM25 + Embedding)
|
||||
Stage 2: Rerank by activation values (ACTR)
|
||||
Execute hybrid search and return clean content.
|
||||
|
||||
Args:
|
||||
group_id: Group identifier for filtering
|
||||
group_id: Group identifier for filtering results
|
||||
question: Search query text
|
||||
limit: Max results per category (default: 15)
|
||||
search_type: "hybrid", "keyword", or "embedding" (default: "hybrid")
|
||||
include: Result types (default: ["statements", "chunks", "entities", "summaries"])
|
||||
rerank_alpha: BM25 weight (default: 0.6)
|
||||
activation_boost_factor: Activation impact on memory strength (default: 0.8)
|
||||
output_path: JSON output path (default: "search_results.json")
|
||||
return_raw_results: Return full metadata (default: False)
|
||||
memory_config: MemoryConfig for embedding model
|
||||
limit: Maximum number of results to return (default: 5)
|
||||
search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid")
|
||||
include: List of result types to include (default: ["statements", "chunks", "entities", "summaries"])
|
||||
rerank_alpha: Weight for BM25 scores in reranking (default: 0.4)
|
||||
output_path: Path to save search results (default: "search_results.json")
|
||||
return_raw_results: If True, also return the raw search results as third element (default: False)
|
||||
memory_config: Memory configuration object (required)
|
||||
|
||||
Returns:
|
||||
Tuple[str, str, Optional[dict]]: (clean_content, cleaned_query, raw_results)
|
||||
Tuple of (clean_content, cleaned_query, raw_results)
|
||||
raw_results is None if return_raw_results=False
|
||||
"""
|
||||
if include is None:
|
||||
include = ["statements", "chunks", "entities", "summaries"]
|
||||
|
||||
# Use provided memory_config or fall back to instance config
|
||||
config = memory_config or self.memory_config
|
||||
if not config:
|
||||
raise ValueError("memory_config is required for search - either pass it to __init__ or execute_hybrid_search")
|
||||
|
||||
|
||||
# Clean query
|
||||
cleaned_query = self.clean_query(question)
|
||||
|
||||
|
||||
try:
|
||||
# Execute search using memory_config
|
||||
# Execute search
|
||||
answer = await run_hybrid_search(
|
||||
query_text=cleaned_query,
|
||||
search_type=search_type,
|
||||
@@ -153,9 +134,8 @@ class SearchService:
|
||||
limit=limit,
|
||||
include=include,
|
||||
output_path=output_path,
|
||||
memory_config=config,
|
||||
rerank_alpha=rerank_alpha,
|
||||
activation_boost_factor=activation_boost_factor,
|
||||
memory_config=memory_config,
|
||||
rerank_alpha=rerank_alpha
|
||||
)
|
||||
|
||||
# Extract results based on search type and include parameter
|
||||
@@ -3,12 +3,22 @@ Template Service for loading and rendering Jinja2 templates.
|
||||
|
||||
This service provides centralized template management with caching and error handling.
|
||||
"""
|
||||
|
||||
import os
|
||||
from functools import lru_cache
|
||||
from typing import Optional
|
||||
from jinja2 import Environment, FileSystemLoader, Template, TemplateNotFound
|
||||
|
||||
from app.core.logging_config import get_agent_logger, log_prompt_rendering
|
||||
from jinja2 import (
|
||||
Environment,
|
||||
FileSystemLoader,
|
||||
Template,
|
||||
TemplateNotFound,
|
||||
)
|
||||
|
||||
from app.core.logging_config import (
|
||||
get_agent_logger,
|
||||
log_prompt_rendering,
|
||||
)
|
||||
|
||||
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
@@ -1,7 +0,0 @@
|
||||
"""Agent utilities."""
|
||||
|
||||
from app.core.memory.agent.utils.multimodal import MultimodalProcessor
|
||||
|
||||
__all__ = [
|
||||
"MultimodalProcessor",
|
||||
]
|
||||
56
api/app/core/memory/agent/utils/llm_client_pool.py
Normal file
56
api/app/core/memory/agent/utils/llm_client_pool.py
Normal file
@@ -0,0 +1,56 @@
|
||||
|
||||
import asyncio
|
||||
from typing import Dict, Optional
|
||||
from app.core.memory.utils.llm.llm_utils import get_llm_client_fast
|
||||
from app.db import get_db
|
||||
from app.core.logging_config import get_agent_logger
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
class LLMClientPool:
|
||||
"""LLM客户端连接池"""
|
||||
|
||||
def __init__(self, max_size: int = 5):
|
||||
self.max_size = max_size
|
||||
self.pools: Dict[str, asyncio.Queue] = {}
|
||||
self.active_clients: Dict[str, int] = {}
|
||||
|
||||
async def get_client(self, llm_model_id: str):
|
||||
"""获取LLM客户端"""
|
||||
if llm_model_id not in self.pools:
|
||||
self.pools[llm_model_id] = asyncio.Queue(maxsize=self.max_size)
|
||||
self.active_clients[llm_model_id] = 0
|
||||
|
||||
pool = self.pools[llm_model_id]
|
||||
|
||||
try:
|
||||
# 尝试从池中获取客户端
|
||||
client = pool.get_nowait()
|
||||
logger.debug(f"从池中获取LLM客户端: {llm_model_id}")
|
||||
return client
|
||||
except asyncio.QueueEmpty:
|
||||
# 池为空，创建新客户端
|
||||
if self.active_clients[llm_model_id] < self.max_size:
|
||||
db_session = next(get_db())
|
||||
client = get_llm_client_fast(llm_model_id, db_session)
|
||||
self.active_clients[llm_model_id] += 1
|
||||
logger.debug(f"创建新LLM客户端: {llm_model_id}")
|
||||
return client
|
||||
else:
|
||||
# 等待可用客户端
|
||||
logger.debug(f"等待LLM客户端可用: {llm_model_id}")
|
||||
return await pool.get()
|
||||
|
||||
async def return_client(self, llm_model_id: str, client):
|
||||
"""归还LLM客户端到池中"""
|
||||
if llm_model_id in self.pools:
|
||||
try:
|
||||
self.pools[llm_model_id].put_nowait(client)
|
||||
logger.debug(f"归还LLM客户端到池: {llm_model_id}")
|
||||
except asyncio.QueueFull:
|
||||
# 池已满，丢弃客户端
|
||||
self.active_clients[llm_model_id] -= 1
|
||||
logger.debug(f"池已满，丢弃LLM客户端: {llm_model_id}")
|
||||
|
||||
# 全局客户端池
|
||||
llm_client_pool = LLMClientPool()
|
||||
@@ -1,40 +1,12 @@
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from collections import defaultdict
|
||||
from typing import Annotated, TypedDict
|
||||
|
||||
from app.core.memory.agent.utils.messages_tool import read_template_file
|
||||
from app.core.memory.utils.config.config_utils import (
|
||||
get_picture_config,
|
||||
get_voice_config,
|
||||
)
|
||||
|
||||
# Removed global variable imports - use dependency injection instead
|
||||
from dotenv import load_dotenv
|
||||
from langchain_core.messages import AnyMessage
|
||||
from langgraph.graph import add_messages
|
||||
from openai import OpenAI
|
||||
|
||||
PROJECT_ROOT_ = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
async def picture_model_requests(image_url):
|
||||
'''
|
||||
|
||||
Args:
|
||||
image_url:
|
||||
Returns:
|
||||
|
||||
'''
|
||||
file_path = PROJECT_ROOT_ + '/agent/utils/prompt/Template_for_image_recognition_prompt.jinja2 '
|
||||
system_prompt = await read_template_file(file_path)
|
||||
result = await Picture_recognize(image_url,system_prompt)
|
||||
return (result)
|
||||
class WriteState(TypedDict):
|
||||
'''
|
||||
Langgrapg Writing TypedDict
|
||||
@@ -44,39 +16,69 @@ class WriteState(TypedDict):
|
||||
apply_id:str
|
||||
group_id:str
|
||||
errors: list[dict] # Track errors: [{"tool": "tool_name", "error": "message"}]
|
||||
memory_config: object
|
||||
write_result: dict
|
||||
data:str
|
||||
|
||||
class ReadState(TypedDict):
|
||||
'''
|
||||
Langgrapg READING TypedDict
|
||||
name:
|
||||
id:user id
|
||||
loop_count:Traverse times
|
||||
search_switch：type
|
||||
config_id: configuration id for filtering results
|
||||
errors: list of errors that occurred during workflow execution
|
||||
'''
|
||||
messages: Annotated[list[AnyMessage], add_messages] #消息追加的模式增加消息
|
||||
name: str
|
||||
id: str
|
||||
loop_count:int
|
||||
"""
|
||||
LangGraph 工作流状态定义
|
||||
|
||||
Attributes:
|
||||
messages: 消息列表，支持自动追加
|
||||
loop_count: 遍历次数
|
||||
search_switch: 搜索类型开关
|
||||
group_id: 组标识
|
||||
config_id: 配置ID，用于过滤结果
|
||||
data: 从content_input_node传递的内容数据
|
||||
spit_data: 从Split_The_Problem传递的分解结果
|
||||
tool_calls: 工具调用请求列表
|
||||
tool_results: 工具执行结果列表
|
||||
memory_config: 内存配置对象
|
||||
"""
|
||||
messages: Annotated[list[AnyMessage], add_messages] # 消息追加模式
|
||||
loop_count: int
|
||||
search_switch: str
|
||||
user_id: str
|
||||
apply_id: str
|
||||
group_id: str
|
||||
config_id: str
|
||||
errors: list[dict] # Track errors: [{"tool": "tool_name", "error": "message"}]
|
||||
|
||||
|
||||
data: str # 新增字段用于传递内容
|
||||
spit_data: dict # 新增字段用于传递问题分解结果
|
||||
problem_extension:dict
|
||||
storage_type: str
|
||||
user_rag_memory_id: str
|
||||
llm_id: str
|
||||
embedding_id: str
|
||||
memory_config: object # 新增字段用于传递内存配置对象
|
||||
retrieve:dict
|
||||
RetrieveSummary: dict
|
||||
InputSummary: dict
|
||||
verify: dict
|
||||
SummaryFails: dict
|
||||
summary: dict
|
||||
class COUNTState:
|
||||
'''
|
||||
The number of times the workflow dialogue retrieval content has no correct message recall traversal
|
||||
'''
|
||||
"""
|
||||
工作流对话检索内容计数器
|
||||
|
||||
用于记录工作流对话检索内容没有正确消息召回遍历的次数。
|
||||
"""
|
||||
|
||||
def __init__(self, limit: int = 5):
|
||||
"""
|
||||
初始化计数器
|
||||
|
||||
Args:
|
||||
limit: 最大计数限制，默认为5
|
||||
"""
|
||||
self.total: int = 0 # 当前累加值
|
||||
self.limit: int = limit # 最大上限
|
||||
|
||||
def add(self, value: int = 1):
|
||||
"""累加数字，如果达到上限就保持最大值"""
|
||||
def add(self, value: int = 1) -> None:
|
||||
"""
|
||||
累加数字，如果达到上限就保持最大值
|
||||
|
||||
Args:
|
||||
value: 要累加的值，默认为1
|
||||
"""
|
||||
self.total += value
|
||||
print(f"[COUNTState] 当前值: {self.total}")
|
||||
if self.total >= self.limit:
|
||||
@@ -84,21 +86,19 @@ class COUNTState:
|
||||
self.total = self.limit # 达到上限不再增加
|
||||
|
||||
def get_total(self) -> int:
|
||||
"""获取当前累加值"""
|
||||
"""
|
||||
获取当前累加值
|
||||
|
||||
Returns:
|
||||
当前累加值
|
||||
"""
|
||||
return self.total
|
||||
|
||||
def reset(self):
|
||||
def reset(self) -> None:
|
||||
"""手动重置累加值"""
|
||||
self.total = 0
|
||||
print("[COUNTState] 已重置为 0")
|
||||
|
||||
|
||||
def merge_to_key_value_pairs(data, query_key, result_key):
|
||||
grouped = defaultdict(list)
|
||||
for item in data:
|
||||
grouped[item[query_key]].append(item[result_key])
|
||||
return [{key: values} for key, values in grouped.items()]
|
||||
|
||||
def deduplicate_entries(entries):
|
||||
seen = set()
|
||||
deduped = []
|
||||
@@ -109,70 +109,37 @@ def deduplicate_entries(entries):
|
||||
deduped.append(entry)
|
||||
return deduped
|
||||
|
||||
def merge_to_key_value_pairs(data, query_key, result_key):
|
||||
grouped = defaultdict(list)
|
||||
for item in data:
|
||||
grouped[item[query_key]].append(item[result_key])
|
||||
return [{key: values} for key, values in grouped.items()]
|
||||
|
||||
|
||||
async def Picture_recognize(image_path, PROMPT_TICKET_EXTRACTION, picture_model_name: str) -> str:
|
||||
def convert_extended_question_to_question(data):
|
||||
"""
|
||||
Updated to eliminate global variables in favor of explicit parameters.
|
||||
|
||||
递归地将数据中的 extended_question 字段转换为 question 字段
|
||||
|
||||
Args:
|
||||
image_path: Path to image file
|
||||
PROMPT_TICKET_EXTRACTION: Extraction prompt
|
||||
picture_model_name: Picture model name (required, no longer from global variables)
|
||||
data: 要转换的数据（可能是字典、列表或其他类型）
|
||||
|
||||
Returns:
|
||||
转换后的数据
|
||||
"""
|
||||
try:
|
||||
model_config = get_picture_config(picture_model_name)
|
||||
except Exception as e:
|
||||
err = f"LLM配置不可用：{str(e)}。请检查 config.json 和 runtime.json。"
|
||||
logger.error(err)
|
||||
return err
|
||||
api_key = os.getenv(model_config["api_key"]) # 从环境变量读取对应后端的 API key
|
||||
backend_model_name = model_config["llm_name"].split("/")[-1]
|
||||
api_base=model_config['api_base']
|
||||
|
||||
logger.info(f"model_name: {backend_model_name}")
|
||||
logger.info(f"api_key set: {'yes' if api_key else 'no'}")
|
||||
logger.info(f"base_url: {model_config['api_base']}")
|
||||
|
||||
client = OpenAI(
|
||||
api_key=api_key, base_url=api_base,
|
||||
)
|
||||
completion = client.chat.completions.create(
|
||||
model=backend_model_name,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url":image_path,
|
||||
},
|
||||
{"type": "text",
|
||||
"text": PROMPT_TICKET_EXTRACTION}
|
||||
]
|
||||
}
|
||||
])
|
||||
picture_text = completion.choices[0].message.content
|
||||
picture_text = picture_text.replace('```json', '').replace('```', '')
|
||||
picture_text = json.loads(picture_text)
|
||||
return (picture_text['statement'])
|
||||
|
||||
async def Voice_recognize(voice_model_name: str):
|
||||
"""
|
||||
Updated to eliminate global variables in favor of explicit parameters.
|
||||
|
||||
Args:
|
||||
voice_model_name: Voice model name (required, no longer from global variables)
|
||||
"""
|
||||
try:
|
||||
model_config = get_voice_config(voice_model_name)
|
||||
except Exception as e:
|
||||
err = f"LLM配置不可用：{str(e)}。请检查 config.json 和 runtime.json。"
|
||||
logger.error(err)
|
||||
return err
|
||||
api_key = os.getenv(model_config["api_key"]) # 从环境变量读取对应后端的 API key
|
||||
backend_model_name = model_config["llm_name"].split("/")[-1]
|
||||
api_base = model_config['api_base']
|
||||
return api_key,backend_model_name,api_base
|
||||
|
||||
|
||||
if isinstance(data, dict):
|
||||
# 创建新字典来存储转换后的数据
|
||||
converted = {}
|
||||
for key, value in data.items():
|
||||
if key == 'extended_question':
|
||||
# 将 extended_question 转换为 question
|
||||
converted['question'] = convert_extended_question_to_question(value)
|
||||
else:
|
||||
# 递归处理其他字段
|
||||
converted[key] = convert_extended_question_to_question(value)
|
||||
return converted
|
||||
elif isinstance(data, list):
|
||||
# 递归处理列表中的每个元素
|
||||
return [convert_extended_question_to_question(item) for item in data]
|
||||
else:
|
||||
# 其他类型直接返回
|
||||
return data
|
||||
@@ -1,33 +0,0 @@
|
||||
import os
|
||||
from app.core.config import settings
|
||||
|
||||
def get_mcp_server_config():
|
||||
"""
|
||||
Get the MCP server configuration.
|
||||
|
||||
Uses MCP_SERVER_URL environment variable if set (for Docker),
|
||||
otherwise falls back to SERVER_IP and MCP_PORT (for local development).
|
||||
"""
|
||||
# Get MCP port from environment (default: 8081)
|
||||
mcp_port = os.getenv("MCP_PORT", "8081")
|
||||
|
||||
# In Docker: MCP_SERVER_URL=http://mcp-server:8081
|
||||
# In local dev: uses SERVER_IP (127.0.0.1 or localhost)
|
||||
mcp_server_url = os.getenv("MCP_SERVER_URL")
|
||||
|
||||
if mcp_server_url:
|
||||
# Docker environment: use full URL from environment
|
||||
base_url = mcp_server_url
|
||||
else:
|
||||
# Local development: build URL from SERVER_IP and MCP_PORT
|
||||
base_url = f"http://{settings.SERVER_IP}:{mcp_port}"
|
||||
|
||||
mcp_server_config = {
|
||||
"data_flow": {
|
||||
"url": f"{base_url}/sse",
|
||||
"transport": "sse",
|
||||
"timeout": 15000,
|
||||
"sse_read_timeout": 15000,
|
||||
}
|
||||
}
|
||||
return mcp_server_config
|
||||
@@ -1,260 +0,0 @@
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from typing import Any, List
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from langchain_core.messages import AnyMessage
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
def _to_openai_messages(msgs: List[AnyMessage]) -> List[dict]:
|
||||
out = []
|
||||
for m in msgs:
|
||||
if hasattr(m, "content"):
|
||||
out.append({"role": "user", "content": getattr(m, "content", "")})
|
||||
elif isinstance(m, dict) and "role" in m and "content" in m:
|
||||
out.append(m)
|
||||
else:
|
||||
out.append({"role": "user", "content": str(m)})
|
||||
return out
|
||||
|
||||
|
||||
def _extract_content(resp: Any) -> str:
|
||||
"""Extract LLM content and sanitize to raw JSON/text.
|
||||
|
||||
- Supports both object and dict response shapes.
|
||||
- Removes leading role labels (e.g., "Assistant:").
|
||||
- Strips Markdown code fences like ```json ... ```.
|
||||
- Attempts to isolate the first valid JSON array/object block when extra text is present.
|
||||
"""
|
||||
|
||||
def _to_text(r: Any) -> str:
|
||||
try:
|
||||
# 对象形式: resp.choices[0].message.content
|
||||
if hasattr(r, "choices") and getattr(r, "choices", None):
|
||||
msg = r.choices[0].message
|
||||
if hasattr(msg, "content"):
|
||||
return msg.content
|
||||
if isinstance(msg, dict) and "content" in msg:
|
||||
return msg["content"]
|
||||
# 字典形式: resp["choices"][0]["message"]["content"]
|
||||
if isinstance(r, dict):
|
||||
return r.get("choices", [{}])[0].get("message", {}).get("content", "")
|
||||
except Exception:
|
||||
pass
|
||||
return str(r)
|
||||
|
||||
def _clean_text(text: str) -> str:
|
||||
s = str(text).strip()
|
||||
# 移除可能的角色前缀
|
||||
s = re.sub(r"^\s*(Assistant|assistant)\s*:\s*", "", s)
|
||||
# 提取 ```json ... ``` 代码块
|
||||
m = re.search(r"```json\s*(.*?)\s*```", s, flags=re.S | re.I)
|
||||
if m:
|
||||
s = m.group(1).strip()
|
||||
# 如果仍然包含多余文本，尝试截取第一个 JSON 数组/对象片段
|
||||
if not (s.startswith("{") or s.startswith("[")):
|
||||
left = s.find("[")
|
||||
right = s.rfind("]")
|
||||
if left != -1 and right != -1 and right > left:
|
||||
s = s[left:right + 1].strip()
|
||||
else:
|
||||
left = s.find("{")
|
||||
right = s.rfind("}")
|
||||
if left != -1 and right != -1 and right > left:
|
||||
s = s[left:right + 1].strip()
|
||||
return s
|
||||
|
||||
raw = _to_text(resp)
|
||||
return _clean_text(raw)
|
||||
|
||||
def Resolve_username(usermessages):
|
||||
'''
|
||||
Extract username
|
||||
Args:
|
||||
usermessages: user name
|
||||
|
||||
Returns:
|
||||
|
||||
'''
|
||||
usermessages = usermessages.split('_')[1:]
|
||||
sessionid = '_'.join(usermessages[:-1])
|
||||
return sessionid
|
||||
|
||||
|
||||
# TODO: USE app.core.memory.src.utils.render_template instead
|
||||
async def read_template_file(template_path: str) -> str:
|
||||
"""
|
||||
读取模板文件
|
||||
|
||||
Args:
|
||||
template_path: 模板文件路径
|
||||
|
||||
Returns:
|
||||
模板内容字符串
|
||||
|
||||
Note:
|
||||
建议使用 app.core.memory.utils.template_render 中的统一模板渲染功能
|
||||
"""
|
||||
try:
|
||||
with open(template_path, "r", encoding="utf-8") as f:
|
||||
return f.read()
|
||||
except FileNotFoundError:
|
||||
logger.error(f"模板文件未找到: {template_path}")
|
||||
raise
|
||||
except IOError as e:
|
||||
logger.error(f"读取模板文件失败: {template_path}, 错误: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
async def Problem_Extension_messages_deal(context):
|
||||
'''
|
||||
Extract data
|
||||
Args:
|
||||
context:
|
||||
Returns:
|
||||
'''
|
||||
extent_quest = []
|
||||
original = context.get('original', '')
|
||||
messages = context.get('context', '')
|
||||
|
||||
# Handle empty or non-string messages
|
||||
if not messages:
|
||||
return extent_quest, original
|
||||
|
||||
if isinstance(messages, str):
|
||||
try:
|
||||
messages = json.loads(messages)
|
||||
except json.JSONDecodeError:
|
||||
# If JSON parsing fails, return empty list
|
||||
return extent_quest, original
|
||||
|
||||
if isinstance(messages, list):
|
||||
for message in messages:
|
||||
question = message.get('question', '')
|
||||
type = message.get('type', '')
|
||||
extent_quest.append({"role": "user", "content": f"问题:{question}；问题类型：{type}"})
|
||||
|
||||
return extent_quest, original
|
||||
|
||||
|
||||
async def Retriev_messages_deal(context):
|
||||
'''
|
||||
Extract data
|
||||
Args:
|
||||
context:
|
||||
Returns:
|
||||
'''
|
||||
logger.info(f"Retriev_messages_deal input: type={type(context)}, value={str(context)[:500]}")
|
||||
|
||||
if isinstance(context, dict):
|
||||
logger.info(f"Retriev_messages_deal: context is dict with keys={list(context.keys())}")
|
||||
if 'context' in context or 'original' in context:
|
||||
content = context.get('context', {})
|
||||
original = context.get('original', '')
|
||||
logger.info(f"Retriev_messages_deal output: content_type={type(content)}, content={str(content)[:300]}, original='{original[:50] if original else ''}'")
|
||||
return content, original
|
||||
|
||||
# Return empty defaults if context is not a dict or doesn't have expected keys
|
||||
logger.warning(f"Retriev_messages_deal: context missing expected keys, returning empty defaults")
|
||||
return {}, ''
|
||||
|
||||
async def Verify_messages_deal(context):
|
||||
'''
|
||||
Extract data
|
||||
Args:
|
||||
context:
|
||||
Returns:
|
||||
'''
|
||||
|
||||
query = context['context']['Query']
|
||||
Query_small_list = context['context']['Expansion_issue']
|
||||
Result_small = []
|
||||
Query_small = []
|
||||
for i in Query_small_list:
|
||||
Result_small.append(i['Answer_Small'][0])
|
||||
Query_small.append(i['Query_small'])
|
||||
return Query_small, Result_small, query
|
||||
|
||||
|
||||
async def Summary_messages_deal(context):
|
||||
'''
|
||||
Extract data
|
||||
Args:
|
||||
context:
|
||||
Returns:
|
||||
'''
|
||||
messages = str(context).replace('\\n', '').replace('\n', '').replace('\\', '')
|
||||
query = re.findall(r'"query": (.*?),', messages)[0]
|
||||
query = query.replace('[', '').replace(']', '').strip()
|
||||
matches = re.findall(r'"answer_small"\s*:\s*"(\[.*?\])"', messages)
|
||||
answer_small_texts = []
|
||||
for m in matches:
|
||||
try:
|
||||
parsed = json.loads(m)
|
||||
for item in parsed:
|
||||
answer_small_texts.append(item.strip().replace('\\', '').replace('[', '').replace(']', ''))
|
||||
except Exception:
|
||||
answer_small_texts.append(m.strip().replace('\\', '').replace('[', '').replace(']', ''))
|
||||
|
||||
return answer_small_texts, query
|
||||
|
||||
|
||||
async def VerifyTool_messages_deal(context):
|
||||
'''
|
||||
Extract data
|
||||
Args:
|
||||
context:
|
||||
Returns:
|
||||
'''
|
||||
messages = str(context).replace('\\n', '').replace('\n', '').replace('\\', '')
|
||||
content_messages = messages.split('"context":')[1].replace('""', '"')
|
||||
messages = str(content_messages).split("name='Retrieve'")[0]
|
||||
query = re.findall('"Query": "(.*?)"', messages)[0]
|
||||
Query_small = re.findall('"Query_small": "(.*?)"', messages)
|
||||
Result_small = re.findall('"Result_small": "(.*?)"', messages)
|
||||
return Query_small, Result_small, query
|
||||
|
||||
|
||||
async def Retrieve_Summary_messages_deal(context):
|
||||
pass
|
||||
|
||||
|
||||
async def Retrieve_verify_tool_messages_deal(context, history, query):
|
||||
'''
|
||||
Extract data
|
||||
Args:
|
||||
context:
|
||||
Returns:
|
||||
'''
|
||||
results = []
|
||||
# 统一转为字符串，避免 None 或非字符串导致正则报错
|
||||
text = str(context)
|
||||
blocks = re.findall(r'\{(.*?)\}', text, flags=re.S)
|
||||
for block in blocks:
|
||||
query_small = re.search(r'"Query_small"\s*:\s*"([^"]*)"', block)
|
||||
answer_small = re.search(r'"Answer_Small"\s*:\s*(\[[^\]]*\])', block)
|
||||
status = re.search(r'"status"\s*:\s*"([^"]*)"', block)
|
||||
query_answer = re.search(r'"Query_answer"\s*:\s*"([^"]*)"', block)
|
||||
|
||||
results.append({
|
||||
"query_small": query_small.group(1) if query_small else None,
|
||||
"answer_small": answer_small.group(1) if answer_small else None,
|
||||
# 将缺失的 status 统一为空字符串，后续用字符串判定，避免 NoneType 错误
|
||||
"status": status.group(1) if status else "",
|
||||
"query_answer": query_answer.group(1) if query_answer else None
|
||||
})
|
||||
result = []
|
||||
for r in results:
|
||||
# 统一按字符串判定状态，兼容大小写和缺失情况
|
||||
status_str = str(r.get('status', '')).strip().lower()
|
||||
if status_str == 'false':
|
||||
continue
|
||||
else:
|
||||
result.append(r)
|
||||
split_result = 'failed' if not result else 'success'
|
||||
result = {"data": {"query": query, "expansion_issue": result}, "split_result": split_result, "reason": "",
|
||||
"history": history}
|
||||
return result
|
||||
194
api/app/core/memory/agent/utils/messages_tools.py
Normal file
194
api/app/core/memory/agent/utils/messages_tools.py
Normal file
@@ -0,0 +1,194 @@
|
||||
from typing import List, Dict, Any
|
||||
from app.core.logging_config import get_agent_logger
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
async def read_template_file(template_path: str) -> str:
|
||||
"""
|
||||
读取模板文件
|
||||
|
||||
Args:
|
||||
template_path: 模板文件路径
|
||||
|
||||
Returns:
|
||||
模板内容字符串
|
||||
|
||||
Note:
|
||||
建议使用 app.core.memory.utils.template_render 中的统一模板渲染功能
|
||||
"""
|
||||
try:
|
||||
with open(template_path, "r", encoding="utf-8") as f:
|
||||
return f.read()
|
||||
except FileNotFoundError:
|
||||
logger.error(f"模板文件未找到: {template_path}")
|
||||
raise
|
||||
except IOError as e:
|
||||
logger.error(f"读取模板文件失败: {template_path}, 错误: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
def reorder_output_results(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
重新排序输出结果，将 retrieval_summary 类型的数据放到最后面
|
||||
|
||||
Args:
|
||||
results: 原始输出结果列表
|
||||
|
||||
Returns:
|
||||
重新排序后的结果列表
|
||||
"""
|
||||
retrieval_summaries = []
|
||||
other_results = []
|
||||
|
||||
# 分离 retrieval_summary 和其他类型的结果
|
||||
for result in results:
|
||||
if 'summary' in result.get('type'):
|
||||
retrieval_summaries.append(result)
|
||||
else:
|
||||
other_results.append(result)
|
||||
|
||||
# 将 retrieval_summary 放到最后
|
||||
return other_results + retrieval_summaries
|
||||
|
||||
def optimize_search_results(intermediate_outputs):
|
||||
"""
|
||||
优化检索结果，合并多个搜索结果，过滤空结果，统一格式
|
||||
|
||||
Args:
|
||||
intermediate_outputs: 原始的中间输出列表
|
||||
|
||||
Returns:
|
||||
优化后的检索结果列表
|
||||
"""
|
||||
optimized_results = []
|
||||
|
||||
for item in intermediate_outputs:
|
||||
if not item or item == [] or item == {}:
|
||||
continue
|
||||
|
||||
# 检查是否是搜索结果类型
|
||||
if isinstance(item, dict) and item.get('type') == 'search_result':
|
||||
raw_results = item.get('raw_results', {})
|
||||
|
||||
# 如果 raw_results 为空，跳过
|
||||
if not raw_results or raw_results == [] or raw_results == {}:
|
||||
continue
|
||||
|
||||
# 创建优化后的结果结构
|
||||
optimized_item = {
|
||||
"type": "search_result",
|
||||
"title": f"检索结果 ({item.get('index', 1)}/{item.get('total', 1)})",
|
||||
"query": item.get('query', ''),
|
||||
"raw_results": {},
|
||||
"index": item.get('index', 1),
|
||||
"total": item.get('total', 1)
|
||||
}
|
||||
|
||||
# 合并所有搜索结果类型到一个 raw_results 中
|
||||
merged_raw_results = {}
|
||||
|
||||
# 处理 time_search
|
||||
if 'time_search' in raw_results and raw_results['time_search']:
|
||||
merged_raw_results['time_search'] = raw_results['time_search']
|
||||
|
||||
# 处理 keyword_search
|
||||
if 'keyword_search' in raw_results and raw_results['keyword_search']:
|
||||
merged_raw_results['keyword_search'] = raw_results['keyword_search']
|
||||
|
||||
# 处理 embedding_search
|
||||
if 'embedding_search' in raw_results and raw_results['embedding_search']:
|
||||
merged_raw_results['embedding_search'] = raw_results['embedding_search']
|
||||
|
||||
# 处理 combined_summary
|
||||
if 'combined_summary' in raw_results and raw_results['combined_summary']:
|
||||
merged_raw_results['combined_summary'] = raw_results['combined_summary']
|
||||
|
||||
# 处理 reranked_results
|
||||
if 'reranked_results' in raw_results and raw_results['reranked_results']:
|
||||
merged_raw_results['reranked_results'] = raw_results['reranked_results']
|
||||
|
||||
# 如果合并后的结果不为空，添加到优化结果中
|
||||
if merged_raw_results:
|
||||
optimized_item['raw_results'] = merged_raw_results
|
||||
optimized_results.append(optimized_item)
|
||||
else:
|
||||
# 非搜索结果类型，直接添加
|
||||
optimized_results.append(item)
|
||||
|
||||
return optimized_results
|
||||
|
||||
|
||||
def merge_multiple_search_results(intermediate_outputs):
|
||||
"""
|
||||
将多个搜索结果合并为一个统一的搜索结果
|
||||
|
||||
Args:
|
||||
intermediate_outputs: 原始的中间输出列表
|
||||
|
||||
Returns:
|
||||
合并后的结果列表
|
||||
"""
|
||||
search_results = []
|
||||
other_results = []
|
||||
|
||||
# 分离搜索结果和其他结果
|
||||
for item in intermediate_outputs:
|
||||
if isinstance(item, dict) and item.get('type') == 'search_result':
|
||||
raw_results = item.get('raw_results', {})
|
||||
# 只保留有内容的搜索结果
|
||||
if raw_results and raw_results != [] and raw_results != {}:
|
||||
search_results.append(item)
|
||||
else:
|
||||
other_results.append(item)
|
||||
|
||||
# 如果没有搜索结果，返回原始结果
|
||||
if not search_results:
|
||||
return intermediate_outputs
|
||||
|
||||
# 如果只有一个搜索结果，优化格式后返回
|
||||
if len(search_results) == 1:
|
||||
optimized = optimize_search_results(search_results)
|
||||
return other_results + optimized
|
||||
|
||||
# 合并多个搜索结果
|
||||
merged_raw_results = {}
|
||||
all_queries = []
|
||||
|
||||
for result in search_results:
|
||||
query = result.get('query', '')
|
||||
if query:
|
||||
all_queries.append(query)
|
||||
|
||||
raw_results = result.get('raw_results', {})
|
||||
|
||||
# 合并各种搜索类型的结果
|
||||
for search_type in ['time_search', 'keyword_search', 'embedding_search', 'combined_summary',
|
||||
'reranked_results']:
|
||||
if search_type in raw_results and raw_results[search_type]:
|
||||
if search_type not in merged_raw_results:
|
||||
merged_raw_results[search_type] = raw_results[search_type]
|
||||
else:
|
||||
# 如果是字典类型，需要合并
|
||||
if isinstance(raw_results[search_type], dict) and isinstance(merged_raw_results[search_type], dict):
|
||||
for key, value in raw_results[search_type].items():
|
||||
if key not in merged_raw_results[search_type]:
|
||||
merged_raw_results[search_type][key] = value
|
||||
elif isinstance(value, list) and isinstance(merged_raw_results[search_type][key], list):
|
||||
merged_raw_results[search_type][key].extend(value)
|
||||
elif isinstance(raw_results[search_type], list):
|
||||
if isinstance(merged_raw_results[search_type], list):
|
||||
merged_raw_results[search_type].extend(raw_results[search_type])
|
||||
else:
|
||||
merged_raw_results[search_type] = raw_results[search_type]
|
||||
|
||||
# 创建合并后的结果
|
||||
if merged_raw_results:
|
||||
merged_result = {
|
||||
"type": "search_result",
|
||||
"title": f"合并检索结果 (共{len(search_results)}个查询)",
|
||||
"query": " | ".join(all_queries),
|
||||
"raw_results": merged_raw_results,
|
||||
"index": 1,
|
||||
"total": 1
|
||||
}
|
||||
return other_results + [merged_result]
|
||||
|
||||
return other_results
|
||||
@@ -1,38 +0,0 @@
|
||||
|
||||
|
||||
# project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
# sys.path.insert(0, project_root)
|
||||
|
||||
# load_dotenv()
|
||||
|
||||
# async def llm_client_chat(messages: List[dict]) -> str:
|
||||
# """使用 OpenAI 兼容接口进行对话，返回内容字符串。"""
|
||||
# try:
|
||||
# cfg = get_model_config(SELECTED_LLM_ID)
|
||||
# rb_config = RedBearModelConfig(
|
||||
# model_name=cfg["model_name"],
|
||||
# provider=cfg["provider"],
|
||||
# api_key=cfg["api_key"],
|
||||
# base_url=cfg["base_url"],
|
||||
# )
|
||||
# client = OpenAIClient(model_config=rb_config, type_="chat")
|
||||
|
||||
# except Exception as e:
|
||||
# logger.error(f"获取模型配置失败：{e}")
|
||||
# err = f"获取模型配置失败：{str(e)}。请检查!!!"
|
||||
# return err
|
||||
# try:
|
||||
# response = await client.chat(messages)
|
||||
# print(f"model_tool's llm_client_chat response ======>:\n {response}")
|
||||
# return _extract_content(response)
|
||||
# # return _extract_content(result)
|
||||
# except Exception as e:
|
||||
# logger.error(f"LLM调用失败：{str(e)}。请检查 model_name、api_key、api_base 是否正确。")
|
||||
# return f"LLM调用失败：{str(e)}。请检查 model_name、api_key、api_base 是否正确。"
|
||||
|
||||
# async def main(image_url):
|
||||
# await llm_client_chat(image_url)
|
||||
#
|
||||
# # 运行主函数
|
||||
# asyncio.run(main(['https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_male2.wav']))
|
||||
#
|
||||
@@ -1,131 +0,0 @@
|
||||
"""
|
||||
Multimodal input processor for handling image and audio content.
|
||||
|
||||
This module provides utilities for detecting and processing multimodal inputs
|
||||
(images and audio files) by converting them to text using appropriate models.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import List
|
||||
|
||||
from app.core.memory.agent.multimodal.speech_model import Vico_recognition
|
||||
from app.core.memory.agent.utils.llm_tools import picture_model_requests
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MultimodalProcessor:
|
||||
"""
|
||||
Processor for handling multimodal inputs (images and audio).
|
||||
|
||||
This class detects image and audio file paths in input content and converts
|
||||
them to text using appropriate recognition models.
|
||||
"""
|
||||
|
||||
# Supported file extensions
|
||||
IMAGE_EXTENSIONS = ['.jpg', '.png']
|
||||
AUDIO_EXTENSIONS = [
|
||||
'aac', 'amr', 'avi', 'flac', 'flv', 'm4a', 'mkv', 'mov',
|
||||
'mp3', 'mp4', 'mpeg', 'ogg', 'opus', 'wav', 'webm', 'wma', 'wmv'
|
||||
]
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the multimodal processor."""
|
||||
pass
|
||||
|
||||
def is_image(self, content: str) -> bool:
|
||||
"""
|
||||
Check if content is an image file path.
|
||||
|
||||
Args:
|
||||
content: Input string to check
|
||||
|
||||
Returns:
|
||||
True if content ends with a supported image extension
|
||||
|
||||
Examples:
|
||||
>>> processor = MultimodalProcessor()
|
||||
>>> processor.is_image("photo.jpg")
|
||||
True
|
||||
>>> processor.is_image("document.pdf")
|
||||
False
|
||||
"""
|
||||
if not isinstance(content, str):
|
||||
return False
|
||||
|
||||
content_lower = content.lower()
|
||||
return any(content_lower.endswith(ext) for ext in self.IMAGE_EXTENSIONS)
|
||||
|
||||
def is_audio(self, content: str) -> bool:
|
||||
"""
|
||||
Check if content is an audio file path.
|
||||
|
||||
Args:
|
||||
content: Input string to check
|
||||
|
||||
Returns:
|
||||
True if content ends with a supported audio extension
|
||||
|
||||
Examples:
|
||||
>>> processor = MultimodalProcessor()
|
||||
>>> processor.is_audio("recording.mp3")
|
||||
True
|
||||
>>> processor.is_audio("video.mp4")
|
||||
True
|
||||
>>> processor.is_audio("document.txt")
|
||||
False
|
||||
"""
|
||||
if not isinstance(content, str):
|
||||
return False
|
||||
|
||||
content_lower = content.lower()
|
||||
return any(content_lower.endswith(f'.{ext}') for ext in self.AUDIO_EXTENSIONS)
|
||||
|
||||
async def process_input(self, content: str) -> str:
|
||||
"""
|
||||
Process input content, converting images/audio to text if needed.
|
||||
|
||||
This method detects if the input is an image or audio file and converts
|
||||
it to text using the appropriate recognition model. If processing fails
|
||||
or the content is not multimodal, it returns the original content.
|
||||
|
||||
Args:
|
||||
content: Input string (may be file path or regular text)
|
||||
|
||||
Returns:
|
||||
Text content (original or converted from image/audio)
|
||||
|
||||
Examples:
|
||||
>>> processor = MultimodalProcessor()
|
||||
>>> await processor.process_input("photo.jpg")
|
||||
"Recognized text from image..."
|
||||
|
||||
>>> await processor.process_input("Hello world")
|
||||
"Hello world"
|
||||
"""
|
||||
if not isinstance(content, str):
|
||||
logger.warning(f"[MultimodalProcessor] Content is not a string: {type(content)}")
|
||||
return str(content)
|
||||
|
||||
try:
|
||||
# Check for image input
|
||||
if self.is_image(content):
|
||||
logger.info(f"[MultimodalProcessor] Detected image input: {content}")
|
||||
result = await picture_model_requests(content)
|
||||
logger.info(f"[MultimodalProcessor] Image recognition result: {result[:100]}...")
|
||||
return result
|
||||
|
||||
# Check for audio input
|
||||
if self.is_audio(content):
|
||||
logger.info(f"[MultimodalProcessor] Detected audio input: {content}")
|
||||
result = await Vico_recognition([content]).run()
|
||||
logger.info(f"[MultimodalProcessor] Audio recognition result: {result[:100]}...")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[MultimodalProcessor] Error processing multimodal input: {e}", exc_info=True)
|
||||
logger.info("[MultimodalProcessor] Falling back to original content")
|
||||
return content
|
||||
|
||||
# Return original content if not multimodal
|
||||
return content
|
||||
56
api/app/core/memory/agent/utils/performance_monitor.py
Normal file
56
api/app/core/memory/agent/utils/performance_monitor.py
Normal file
@@ -0,0 +1,56 @@
|
||||
|
||||
import time
|
||||
import json
|
||||
from collections import defaultdict
|
||||
from typing import Dict, List
|
||||
from app.core.logging_config import get_agent_logger
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
class ProblemExtensionMonitor:
|
||||
"""Problem_Extension性能监控器"""
|
||||
|
||||
def __init__(self):
|
||||
self.metrics = defaultdict(list)
|
||||
self.slow_queries = []
|
||||
self.error_count = 0
|
||||
|
||||
def record_execution(self, duration: float, question_count: int, success: bool):
|
||||
"""记录执行指标"""
|
||||
self.metrics['durations'].append(duration)
|
||||
self.metrics['question_counts'].append(question_count)
|
||||
|
||||
if not success:
|
||||
self.error_count += 1
|
||||
|
||||
# 记录慢查询（超过10秒）
|
||||
if duration > 10.0:
|
||||
self.slow_queries.append({
|
||||
'duration': duration,
|
||||
'question_count': question_count,
|
||||
'timestamp': time.time()
|
||||
})
|
||||
|
||||
def get_stats(self) -> Dict:
|
||||
"""获取统计信息"""
|
||||
durations = self.metrics['durations']
|
||||
if not durations:
|
||||
return {"message": "暂无数据"}
|
||||
|
||||
return {
|
||||
"total_executions": len(durations),
|
||||
"avg_duration": sum(durations) / len(durations),
|
||||
"max_duration": max(durations),
|
||||
"min_duration": min(durations),
|
||||
"slow_queries_count": len(self.slow_queries),
|
||||
"error_rate": self.error_count / len(durations) if durations else 0,
|
||||
"recent_slow_queries": self.slow_queries[-5:] # 最近5个慢查询
|
||||
}
|
||||
|
||||
def log_stats(self):
|
||||
"""记录统计信息到日志"""
|
||||
stats = self.get_stats()
|
||||
logger.info(f"Problem_Extension性能统计: {json.dumps(stats, indent=2)}")
|
||||
|
||||
# 全局监控器实例
|
||||
performance_monitor = ProblemExtensionMonitor()
|
||||
@@ -0,0 +1,81 @@
|
||||
|
||||
你是一个高效的问题拆分助手，任务是根据用户提供的原始问题和问题类型，生成可操作的扩展问题，用于精确回答原问题。请严格遵循以下规则：
|
||||
|
||||
角色：
|
||||
- 你是“问题拆分专家”，专注于逻辑、信息完整性和可操作性。
|
||||
- 你能够结合【历史信息】、【上下文】、【背景知识】进行分析，以保持问题拆分的连贯性和相关性。
|
||||
- 如果历史信息或上下文与当前问题无关，可忽略。
|
||||
|
||||
---
|
||||
|
||||
### 历史信息参考
|
||||
在生成扩展问题时，你可以参考以下历史数据（如果提供）：
|
||||
- 历史对话或任务的主题；
|
||||
- 历史中出现的关键实体（时间、人物、地点、研究主题等）；
|
||||
- 历史中已解答的问题（避免重复）；
|
||||
- 历史推理链（保持逻辑一致性）。
|
||||
|
||||
> 如果没有提供历史信息，则仅根据当前输入问题进行分析。
|
||||
输入历史信息内容:{{history}}
|
||||
|
||||
## User Input
|
||||
{% if questions is string %}
|
||||
{{ questions }}
|
||||
{% else %}
|
||||
{% for question in questions %}
|
||||
- {{ question }}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
需求：
|
||||
- 如果问题是单跳问题（单步可答），直接保留原问题提取重要提问部分作为拆分/扩展问题。
|
||||
- 如果问题是多跳问题（需多个信息点才能回答），对问题进行扩展拆分。
|
||||
- 扩展问题必须完整覆盖原问题的所有关键要素，包括时间、主体、动作、目标等，不得遗漏。
|
||||
- 扩展问题不得冗余：避免重复询问相同信息或过度拆分同一主题。
|
||||
- 扩展问题必须高度相关：每个子问题直接服务于原问题，不引入未提及的新概念、人物或细节。
|
||||
- 扩展问题必须可操作：每个子问题能在有限资源下独立解答。
|
||||
- 子问题数量不超过4个。
|
||||
- 拆分问题的时候可以考虑输入的历史内容，以保持逻辑连贯。
|
||||
比如：输入历史信息内容:[{'Query': '4月27日，我和你推荐过一本书，书名是什么？', 'ANswer': '张曼玉推荐了《小王子》'}]
|
||||
拆分问题：4月27日，我和你推荐过一本书，书名是什么？，可以拆分为：4月27日，张曼玉推荐过一本书，书名是什么？
|
||||
|
||||
|
||||
|
||||
输出要求：
|
||||
- 仅输出 JSON 数组，不要包含任何解释或代码块。
|
||||
- 每个元素包含：
|
||||
- `original_question`: 原始问题
|
||||
- `extended_question`: 扩展后的问题
|
||||
- `type`: 类型（事实检索/澄清/定义/比较/行动建议）
|
||||
- `reason`: 生成该扩展问题的简短理由
|
||||
- 使用标准 ASCII 双引号，无换行；确保字符串正确关闭并以逗号分隔。
|
||||
|
||||
示例：
|
||||
输入：
|
||||
[
|
||||
"问题：今年诺贝尔物理学奖的获奖者是谁，他们因为什么贡献获奖？；问题类型：多跳",
|
||||
]
|
||||
|
||||
输出：
|
||||
[
|
||||
{
|
||||
"original_question": "今年诺贝尔物理学奖的获奖者是谁，他们因为什么贡献获奖？",
|
||||
"extended_question": "今年诺贝尔物理学奖的获奖者有哪些人？",
|
||||
"type": "多跳",
|
||||
"reason": "输出原问题的关键要素"
|
||||
},
|
||||
{
|
||||
"original_question": "今年诺贝尔物理学奖的获奖者是谁，他们因为什么贡献获奖？",
|
||||
"extended_question": "今年诺贝尔物理学奖的获奖者是因哪些具体贡献获奖的？",
|
||||
"type": "多跳",
|
||||
"reason": "输出原问题的关键要素"
|
||||
}
|
||||
]
|
||||
**Output format**
|
||||
**CRITICAL JSON FORMATTING REQUIREMENTS:**
|
||||
1. Use only standard ASCII double quotes (") for JSON structure - never use Chinese quotation marks ("") or other Unicode quotes
|
||||
2. If the extracted statement text contains quotation marks, escape them properly using backslashes (\")
|
||||
3. Ensure all JSON strings are properly closed and comma-separated
|
||||
4. Do not include line breaks within JSON string values
|
||||
|
||||
The output language should always be the same as the input language.{{ json_schema }}
|
||||
@@ -1,13 +1,10 @@
|
||||
# 角色
|
||||
你是一个专业的问答助手，擅长基于检索信息和历史对话回答用户问题。
|
||||
|
||||
# 任务
|
||||
根据提供的上下文信息回答用户的问题。
|
||||
|
||||
# 输入信息
|
||||
- 历史对话：{{history}}
|
||||
- 检索信息：{{retrieve_info}}
|
||||
|
||||
## User Query
|
||||
{{query}}
|
||||
|
||||
|
||||
@@ -9,8 +9,8 @@
|
||||
3. 判断Answer_Small和Query_Small之间分析出来的关系状态
|
||||
4. 如果是True保留，否则不要相对应的问题和回答
|
||||
5. 输出，需要严格按照模版
|
||||
输入：{{history}}
|
||||
历史消息：{"history":{{sentence}}}
|
||||
输入：{{sentence}}
|
||||
历史消息：{"history":{{history}}}
|
||||
### 第一步 获取用户的输入
|
||||
获取用户的输入提取对应的Query_Small和Answer_Small
|
||||
### 第二步 分析验证
|
||||
|
||||
169
api/app/core/memory/agent/utils/session_tools.py
Normal file
169
api/app/core/memory/agent/utils/session_tools.py
Normal file
@@ -0,0 +1,169 @@
|
||||
"""
|
||||
Session Service for managing user sessions and conversation history.
|
||||
|
||||
This service provides clean Redis interactions with error handling and
|
||||
session management utilities.
|
||||
"""
|
||||
from typing import List, Optional
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.utils.redis_tool import RedisSessionStore
|
||||
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
class SessionService:
|
||||
"""Service for managing user sessions and conversation history."""
|
||||
|
||||
def __init__(self, store: RedisSessionStore):
|
||||
"""
|
||||
Initialize the session service.
|
||||
|
||||
Args:
|
||||
store: Redis session store instance
|
||||
"""
|
||||
self.store = store
|
||||
logger.info("SessionService initialized")
|
||||
|
||||
def resolve_user_id(self, session_string: str) -> str:
|
||||
"""
|
||||
Extract user ID from session string.
|
||||
|
||||
Handles formats like:
|
||||
- 'call_id_user123' -> 'user123'
|
||||
- 'prefix_id_user456_suffix' -> 'user456_suffix'
|
||||
|
||||
Args:
|
||||
session_string: Session identifier string
|
||||
|
||||
Returns:
|
||||
Extracted user ID
|
||||
"""
|
||||
try:
|
||||
# Split by '_id_' and take everything after it
|
||||
parts = session_string.split('_id_')
|
||||
if len(parts) > 1:
|
||||
return parts[1]
|
||||
|
||||
# Fallback: return original string
|
||||
return session_string
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Failed to parse user ID from session string '{session_string}': {e}"
|
||||
)
|
||||
return session_string
|
||||
|
||||
async def get_history(
|
||||
self,
|
||||
user_id: str,
|
||||
apply_id: str,
|
||||
group_id: str
|
||||
) -> List[dict]:
|
||||
"""
|
||||
Retrieve conversation history from Redis.
|
||||
|
||||
Args:
|
||||
user_id: User identifier
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
|
||||
Returns:
|
||||
List of conversation history items with Query and Answer keys
|
||||
Returns empty list if no history found or on error
|
||||
"""
|
||||
try:
|
||||
history = self.store.find_user_apply_group(user_id, apply_id, group_id)
|
||||
|
||||
# Validate history structure
|
||||
if not isinstance(history, list):
|
||||
logger.warning(
|
||||
f"Invalid history format for user {user_id}, "
|
||||
f"apply {apply_id}, group {group_id}: expected list, got {type(history)}"
|
||||
)
|
||||
return []
|
||||
|
||||
return history
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Failed to retrieve history for user {user_id}, "
|
||||
f"apply {apply_id}, group {group_id}: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
# Return empty list on error to allow execution to continue
|
||||
return []
|
||||
|
||||
async def save_session(
|
||||
self,
|
||||
user_id: str,
|
||||
query: str,
|
||||
apply_id: str,
|
||||
group_id: str,
|
||||
ai_response: str
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
Save conversation turn to Redis.
|
||||
|
||||
Args:
|
||||
user_id: User identifier
|
||||
query: User query/message
|
||||
apply_id: Application identifier
|
||||
group_id: Group identifier
|
||||
ai_response: AI response/answer
|
||||
|
||||
Returns:
|
||||
Session ID if successful, None on error
|
||||
"""
|
||||
try:
|
||||
# Validate required fields
|
||||
if not user_id:
|
||||
logger.warning("Cannot save session: user_id is empty")
|
||||
return None
|
||||
|
||||
if not query:
|
||||
logger.warning("Cannot save session: query is empty")
|
||||
return None
|
||||
|
||||
# Save session
|
||||
session_id = self.store.save_session(
|
||||
userid=user_id,
|
||||
messages=query,
|
||||
apply_id=apply_id,
|
||||
group_id=group_id,
|
||||
aimessages=ai_response
|
||||
)
|
||||
|
||||
logger.info(f"Session saved successfully: {session_id}")
|
||||
return session_id
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Failed to save session for user {user_id}: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
return None
|
||||
|
||||
async def cleanup_duplicates(self) -> int:
|
||||
"""
|
||||
Remove duplicate session entries.
|
||||
|
||||
Duplicates are identified by matching:
|
||||
- sessionid
|
||||
- user_id (id field)
|
||||
- group_id
|
||||
- messages
|
||||
- aimessages
|
||||
|
||||
Returns:
|
||||
Number of duplicate sessions deleted
|
||||
"""
|
||||
try:
|
||||
deleted_count = self.store.delete_duplicate_sessions()
|
||||
logger.info(f"Cleaned up {deleted_count} duplicate sessions")
|
||||
return deleted_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to cleanup duplicate sessions: {e}", exc_info=True)
|
||||
return 0
|
||||
117
api/app/core/memory/agent/utils/template_tools.py
Normal file
117
api/app/core/memory/agent/utils/template_tools.py
Normal file
@@ -0,0 +1,117 @@
|
||||
"""
|
||||
Template Service for loading and rendering Jinja2 templates.
|
||||
|
||||
This service provides centralized template management with caching and error handling.
|
||||
"""
|
||||
# 标准库
|
||||
import os
|
||||
from functools import lru_cache
|
||||
|
||||
from jinja2 import Environment, FileSystemLoader, Template, TemplateNotFound
|
||||
|
||||
from app.core.logging_config import get_agent_logger, log_prompt_rendering
|
||||
|
||||
|
||||
logger = get_agent_logger(__name__)
|
||||
|
||||
|
||||
class TemplateRenderError(Exception):
|
||||
"""Exception raised when template rendering fails."""
|
||||
|
||||
def __init__(self, template_name: str, error: Exception, variables: dict):
|
||||
self.template_name = template_name
|
||||
self.error = error
|
||||
self.variables = variables
|
||||
super().__init__(
|
||||
f"Failed to render template '{template_name}': {str(error)}"
|
||||
)
|
||||
|
||||
|
||||
class TemplateService:
|
||||
"""Service for loading and rendering Jinja2 templates with caching."""
|
||||
|
||||
def __init__(self, template_root: str):
|
||||
"""
|
||||
Initialize the template service.
|
||||
|
||||
Args:
|
||||
template_root: Root directory containing template files
|
||||
"""
|
||||
self.template_root = template_root
|
||||
self.env = Environment(
|
||||
loader=FileSystemLoader(template_root),
|
||||
autoescape=False # Disable autoescape for prompt templates
|
||||
)
|
||||
logger.info(f"TemplateService initialized with root: {template_root}")
|
||||
|
||||
@lru_cache(maxsize=128)
|
||||
def _load_template(self, template_name: str) -> Template:
|
||||
"""
|
||||
Load a template from disk with caching.
|
||||
|
||||
Args:
|
||||
template_name: Relative path to template file
|
||||
|
||||
Returns:
|
||||
Loaded Jinja2 Template object
|
||||
|
||||
Raises:
|
||||
TemplateNotFound: If template file doesn't exist
|
||||
"""
|
||||
try:
|
||||
return self.env.get_template(template_name)
|
||||
except TemplateNotFound as e:
|
||||
expected_path = os.path.join(self.template_root, template_name)
|
||||
logger.error(
|
||||
f"Template not found: {template_name}. "
|
||||
f"Expected path: {expected_path}"
|
||||
)
|
||||
raise
|
||||
|
||||
async def render_template(
|
||||
self,
|
||||
template_name: str,
|
||||
operation_name: str,
|
||||
**variables
|
||||
) -> str:
|
||||
"""
|
||||
Load and render a Jinja2 template.
|
||||
|
||||
Args:
|
||||
template_name: Relative path to template file
|
||||
operation_name: Name for logging (e.g., "split_the_problem")
|
||||
**variables: Template variables to render
|
||||
|
||||
Returns:
|
||||
Rendered template string
|
||||
|
||||
Raises:
|
||||
TemplateRenderError: If template loading or rendering fails
|
||||
"""
|
||||
try:
|
||||
# Load template (cached)
|
||||
template = self._load_template(template_name)
|
||||
|
||||
# Render template
|
||||
rendered = template.render(**variables)
|
||||
|
||||
# Log rendered prompt
|
||||
log_prompt_rendering(operation_name, rendered)
|
||||
|
||||
return rendered
|
||||
|
||||
except TemplateNotFound as e:
|
||||
logger.error(
|
||||
f"Template rendering failed for {operation_name} "
|
||||
f"({template_name}): Template not found",
|
||||
exc_info=True
|
||||
)
|
||||
raise TemplateRenderError(template_name, e, variables)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Template rendering failed for {operation_name} "
|
||||
f"({template_name}): {e}",
|
||||
exc_info=True
|
||||
)
|
||||
raise TemplateRenderError(template_name, e, variables)
|
||||
@@ -1,10 +1,9 @@
|
||||
"""
|
||||
Type classification utility for distinguishing read/write operations.
|
||||
"""
|
||||
from app.core.config import settings
|
||||
from app.core.logging_config import get_agent_logger, log_prompt_rendering
|
||||
from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
|
||||
from app.core.memory.agent.utils.messages_tool import read_template_file
|
||||
from app.core.memory.agent.utils.messages_tools import read_template_file
|
||||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||||
from app.db import get_db_context
|
||||
from jinja2 import Template
|
||||
|
||||
@@ -1,49 +0,0 @@
|
||||
import os
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
from sqlalchemy.orm import Session
|
||||
import logging
|
||||
import json
|
||||
|
||||
from app.db import get_db
|
||||
from app.models.retrieval_info import RetrievalInfo
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
async def write_to_database(host_id: uuid.UUID, data: Any) -> str:
|
||||
"""
|
||||
将数据写入数据库
|
||||
:param host_id: 宿主 ID
|
||||
:param data: 要写入的数据
|
||||
:return: 写入数据库的结果
|
||||
"""
|
||||
# 从数据库会话中获取会话
|
||||
db: Session = next(get_db())
|
||||
try:
|
||||
if isinstance(data, (dict, list)):
|
||||
serialized = json.dumps(data, ensure_ascii=False)
|
||||
elif isinstance(data, str):
|
||||
serialized = data
|
||||
else:
|
||||
serialized = str(data)
|
||||
|
||||
new_retrieval_info = RetrievalInfo(
|
||||
# host_id=host_id,
|
||||
host_id=uuid.UUID("2f6ff1eb-50c7-4765-8e89-e4566be19122"),
|
||||
retrieve_info=serialized,
|
||||
created_at=datetime.now()
|
||||
)
|
||||
db.add(new_retrieval_info)
|
||||
db.commit()
|
||||
logger.info(f"success to write data to database, host_id: {host_id}, retrieve_info: {serialized}")
|
||||
return "success to write data to database"
|
||||
except Exception as e:
|
||||
db.rollback()
|
||||
logger.error(f"failed to write data to database, host_id: {host_id}, retrieve_info: {data}, error: {e}")
|
||||
raise e
|
||||
finally:
|
||||
try:
|
||||
db.close()
|
||||
except Exception:
|
||||
pass
|
||||
@@ -7,14 +7,12 @@ pipeline. Only MemoryConfig is needed - clients are constructed internally.
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from app.core.logging_config import get_agent_logger
|
||||
from app.core.memory.agent.utils.get_dialogs import get_chunked_dialogs
|
||||
from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import (
|
||||
ExtractionOrchestrator,
|
||||
)
|
||||
from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import (
|
||||
memory_summary_generation,
|
||||
)
|
||||
from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ExtractionOrchestrator
|
||||
from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import memory_summary_generation
|
||||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||||
from app.core.memory.utils.log.logging_utils import log_time
|
||||
from app.db import get_db_context
|
||||
@@ -23,7 +21,7 @@ from app.repositories.neo4j.add_nodes import add_memory_summary_nodes
|
||||
from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j
|
||||
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
|
||||
from app.schemas.memory_config_schema import MemoryConfig
|
||||
from dotenv import load_dotenv
|
||||
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
@@ -34,5 +34,4 @@ class EmotionSuggestionsRequest(BaseModel):
|
||||
|
||||
class EmotionGenerateSuggestionsRequest(BaseModel):
|
||||
"""生成个性化情绪建议请求"""
|
||||
group_id: str = Field(..., description="组ID")
|
||||
config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）")
|
||||
end_user_id: str = Field(..., description="终端用户ID")
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,27 +1,27 @@
|
||||
import asyncio
|
||||
import trio
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
import uuid
|
||||
from datetime import datetime, timezone
|
||||
from math import ceil
|
||||
from typing import Any, Dict, List, Optional
|
||||
import re
|
||||
|
||||
import redis
|
||||
import requests
|
||||
import trio
|
||||
|
||||
# Import a unified Celery instance
|
||||
from app.celery_app import celery_app
|
||||
from app.core.config import settings
|
||||
from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb
|
||||
from app.core.rag.graphrag.utils import get_llm_cache, set_llm_cache
|
||||
from app.core.rag.llm.chat_model import Base
|
||||
from app.core.rag.llm.cv_model import QWenCV
|
||||
from app.core.rag.llm.embedding_model import OpenAIEmbed
|
||||
from app.core.rag.llm.sequence2txt_model import QWenSeq2txt
|
||||
from app.core.rag.models.chunk import DocumentChunk
|
||||
from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb
|
||||
from app.core.rag.prompts.generator import question_proposal
|
||||
from app.core.rag.vdb.elasticsearch.elasticsearch_vector import (
|
||||
ElasticSearchVectorFactory,
|
||||
@@ -486,6 +486,10 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
|
||||
Raises:
|
||||
Exception on failure
|
||||
"""
|
||||
from app.core.logging_config import get_logger
|
||||
logger = get_logger(__name__)
|
||||
|
||||
logger.info(f"[CELERY WRITE] Starting write task - group_id={group_id}, config_id={config_id}, storage_type={storage_type}")
|
||||
start_time = time.time()
|
||||
|
||||
# Resolve config_id if None
|
||||
@@ -506,8 +510,14 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
|
||||
async def _run() -> str:
|
||||
db = next(get_db())
|
||||
try:
|
||||
logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory")
|
||||
service = MemoryAgentService()
|
||||
return await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id)
|
||||
result = await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id)
|
||||
logger.info(f"[CELERY WRITE] Write completed successfully: {result}")
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"[CELERY WRITE] Write failed: {e}", exc_info=True)
|
||||
raise
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
@@ -532,6 +542,8 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
|
||||
result = loop.run_until_complete(_run())
|
||||
elapsed_time = time.time() - start_time
|
||||
|
||||
logger.info(f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}")
|
||||
|
||||
return {
|
||||
"status": "SUCCESS",
|
||||
"result": result,
|
||||
@@ -548,6 +560,9 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
|
||||
detailed_error = "; ".join(error_messages)
|
||||
else:
|
||||
detailed_error = str(e)
|
||||
|
||||
logger.error(f"[CELERY WRITE] Task failed - elapsed_time={elapsed_time:.2f}s, error={detailed_error}", exc_info=True)
|
||||
|
||||
return {
|
||||
"status": "FAILURE",
|
||||
"error": detailed_error,
|
||||
|
||||
@@ -1,32 +1,5 @@
|
||||
version: '3.9'
|
||||
|
||||
services:
|
||||
# MCP Server - standalone service
|
||||
mcp-server:
|
||||
image: redbear-mem-open:latest
|
||||
container_name: mcp-server
|
||||
ports:
|
||||
- "8081:8081" # MCP server port
|
||||
env_file:
|
||||
- .env
|
||||
environment:
|
||||
- SERVER_IP=0.0.0.0 # Bind to all interfaces
|
||||
volumes:
|
||||
- ./files:/files
|
||||
- /etc/localtime:/etc/localtime:ro
|
||||
command: python -m app.core.memory.agent.mcp_server.server
|
||||
healthcheck:
|
||||
test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8081/sse')"]
|
||||
interval: 10s
|
||||
timeout: 5s
|
||||
retries: 5
|
||||
start_period: 30s
|
||||
restart: unless-stopped
|
||||
networks:
|
||||
- default
|
||||
- celery
|
||||
|
||||
# FastAPI application - connects to MCP server
|
||||
# FastAPI application
|
||||
api:
|
||||
image: redbear-mem-open:latest
|
||||
container_name: api
|
||||
@@ -35,37 +8,31 @@ services:
|
||||
env_file:
|
||||
- .env
|
||||
environment:
|
||||
- MCP_SERVER_URL=http://mcp-server:8081 # Back to using container name
|
||||
- SERVER_IP=0.0.0.0 # Ensure MCP server binds to all interfaces
|
||||
- SERVER_IP=0.0.0.0
|
||||
# 如果代码里必须要 MCP_SERVER_URL，可以先注释或指向占位
|
||||
# - MCP_SERVER_URL=
|
||||
volumes:
|
||||
- ./files:/files
|
||||
- /etc/localtime:/etc/localtime:ro
|
||||
command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload --log-level debug
|
||||
depends_on:
|
||||
mcp-server:
|
||||
condition: service_healthy
|
||||
restart: unless-stopped
|
||||
networks:
|
||||
- default
|
||||
- celery
|
||||
|
||||
# Celery worker - connects to MCP server
|
||||
# Celery worker
|
||||
worker:
|
||||
image: redbear-mem-open:latest
|
||||
container_name: worker
|
||||
env_file:
|
||||
- .env
|
||||
environment:
|
||||
- MCP_SERVER_URL=http://mcp-server:8081 # Back to using container name
|
||||
volumes:
|
||||
- ./files:/files
|
||||
- /etc/localtime:/etc/localtime:ro
|
||||
command: celery -A app.celery_worker.celery_app worker --loglevel=info
|
||||
depends_on:
|
||||
mcp-server:
|
||||
condition: service_healthy
|
||||
restart: unless-stopped
|
||||
networks:
|
||||
- celery
|
||||
|
||||
networks:
|
||||
celery:
|
||||
celery:
|
||||
|
||||
@@ -18,174 +18,180 @@ import type { TestParams } from '@/views/MemoryConversation'
|
||||
import type { EndUser } from '@/views/UserMemoryDetail/types'
|
||||
import { handleSSE, type SSEMessage } from '@/utils/stream'
|
||||
|
||||
// 记忆对话
|
||||
// Memory conversation
|
||||
export const readService = (query: TestParams) => {
|
||||
return request.post('/memory/read_service', query)
|
||||
}
|
||||
/****************** 记忆看板 相关接口 *******************************/
|
||||
// 记忆看板-记忆总量
|
||||
/****************** Memory Dashboard APIs *******************************/
|
||||
// Memory Dashboard - Total memory count
|
||||
export const getTotalMemoryCount = () => {
|
||||
return request.get(`/dashboard/total_memory_count`)
|
||||
}
|
||||
// 记忆看板-知识库类型分布
|
||||
// Memory Dashboard - Knowledge base type distribution
|
||||
export const getKbTypes = () => {
|
||||
return request.get(`/memory/stats/types`)
|
||||
}
|
||||
// 记忆看板-热门记忆标签
|
||||
// Memory Dashboard - Hot memory tags
|
||||
export const getHotMemoryTags = () => {
|
||||
return request.get(`/memory-storage/analytics/hot_memory_tags`)
|
||||
}
|
||||
// 记忆看板-最近活动统计
|
||||
// Memory Dashboard - Recent activity statistics
|
||||
export const getRecentActivityStats = () => {
|
||||
return request.get(`/memory-storage/analytics/recent_activity_stats`)
|
||||
}
|
||||
// 记忆看板-记忆增长趋势
|
||||
// Memory Dashboard - Memory growth trend
|
||||
export const getMemoryIncrement = (limit: number) => {
|
||||
return request.get(`/dashboard/memory_increment`, { limit })
|
||||
}
|
||||
// 记忆看板-API调用趋势
|
||||
// Memory Dashboard - API call trend
|
||||
export const getApiTrend = () => {
|
||||
return request.get(`/dashboard/api_increment`)
|
||||
}
|
||||
// 记忆看板-总数据
|
||||
// Memory Dashboard - Total data
|
||||
export const getDashboardData = () => {
|
||||
return request.get(`/dashboard/dashboard_data`)
|
||||
}
|
||||
/*************** end 记忆看板 相关接口 ******************************/
|
||||
/*************** end Memory Dashboard APIs ******************************/
|
||||
|
||||
|
||||
/****************** 用户记忆 相关接口 *******************************/
|
||||
/****************** User Memory APIs *******************************/
|
||||
export const userMemoryListUrl = '/dashboard/end_users'
|
||||
export const getUserMemoryList = () => {
|
||||
return request.get(userMemoryListUrl)
|
||||
}
|
||||
// 用户记忆-用户记忆总量
|
||||
// User Memory - Total end users
|
||||
export const getTotalEndUsers = () => {
|
||||
return request.get(`/dashboard/total_end_users`)
|
||||
}
|
||||
// 用户记忆-用户详情
|
||||
// User Memory - User profile
|
||||
export const getUserProfile = (end_user_id: string) => {
|
||||
return request.get(`/memory/analytics/user_profile`, { end_user_id })
|
||||
}
|
||||
|
||||
// 用户记忆-记忆洞察
|
||||
// User Memory - Memory insight
|
||||
export const getMemoryInsightReport = (end_user_id: string) => {
|
||||
return request.get(`/memory-storage/analytics/memory_insight/report`, { end_user_id })
|
||||
}
|
||||
// 用户记忆-用户摘要
|
||||
// User Memory - User summary
|
||||
export const getUserSummary = (end_user_id: string) => {
|
||||
return request.get(`/memory-storage/analytics/user_summary`, { end_user_id })
|
||||
}
|
||||
// 记忆分类
|
||||
// Memory classification
|
||||
export const getNodeStatistics = (end_user_id: string) => {
|
||||
return request.get(`/memory-storage/analytics/node_statistics`, { end_user_id })
|
||||
}
|
||||
// 基本信息
|
||||
// Basic information
|
||||
export const getEndUserProfile = (end_user_id: string) => {
|
||||
return request.get(`/memory-storage/read_end_user/profile`, { end_user_id })
|
||||
}
|
||||
export const updatedEndUserProfile = (values: EndUser) => {
|
||||
return request.post(`/memory-storage/updated_end_user/profile`, values)
|
||||
}
|
||||
// 用户记忆-关系网络
|
||||
// User Memory - Relationship network
|
||||
export const getMemorySearchEdges = (end_user_id: string) => {
|
||||
return request.get(`/memory-storage/analytics/graph_data`, { end_user_id })
|
||||
}
|
||||
// 用户记忆-用户兴趣分布
|
||||
// User Memory - User interest distribution
|
||||
export const getHotMemoryTagsByUser = (end_user_id: string) => {
|
||||
return request.get(`/memory/analytics/hot_memory_tags/by_user`, { end_user_id })
|
||||
}
|
||||
// 用户记忆-记忆总量
|
||||
// User Memory - Total memory count
|
||||
export const getTotalMemoryCountByUser = (end_user_id: string) => {
|
||||
return request.get(`/memory-storage/search`, { end_user_id })
|
||||
}
|
||||
// RAG 用户记忆-记忆总量
|
||||
// RAG User Memory - Total memory count
|
||||
export const getTotalRagMemoryCountByUser = (end_user_id: string) => {
|
||||
return request.get(`/dashboard/current_user_rag_total_num`, { end_user_id })
|
||||
}
|
||||
// RAG 用户记忆-用户摘要
|
||||
// RAG User Memory - User summary
|
||||
export const getChunkSummaryTag = (end_user_id: string) => {
|
||||
return request.get(`/dashboard/chunk_summary_tag`, { end_user_id })
|
||||
}
|
||||
// RAG 用户记忆-记忆洞察
|
||||
// RAG User Memory - Memory insight
|
||||
export const getChunkInsight = (end_user_id: string) => {
|
||||
return request.get(`/dashboard/chunk_insight`, { end_user_id })
|
||||
}
|
||||
// RAG 用户记忆-存储内容
|
||||
// RAG User Memory - Storage content
|
||||
export const getRagContent = (end_user_id: string) => {
|
||||
return request.get(`/dashboard/rag_content`, { end_user_id, limit: 20 })
|
||||
}
|
||||
// 情感分布分析
|
||||
// Emotion distribution analysis
|
||||
export const getWordCloud = (group_id: string) => {
|
||||
return request.post(`/memory/emotion-memory/wordcloud`, { group_id, limit: 20 })
|
||||
}
|
||||
// 高频情绪关键词
|
||||
// High-frequency emotion keywords
|
||||
export const getEmotionTags = (group_id: string) => {
|
||||
return request.post(`/memory/emotion-memory/tags`, { group_id, limit: 20 })
|
||||
}
|
||||
// 情绪健康指数
|
||||
// Emotion health index
|
||||
export const getEmotionHealth = (group_id: string) => {
|
||||
return request.post(`/memory/emotion-memory/health`, { group_id, limit: 20 })
|
||||
}
|
||||
// 个性化建议
|
||||
// Personalized suggestions
|
||||
export const getEmotionSuggestions = (group_id: string) => {
|
||||
return request.post(`/memory/emotion-memory/suggestions`, { group_id, limit: 20 })
|
||||
}
|
||||
export const generateSuggestions = (end_user_id: string) => {
|
||||
return request.post(`/memory/emotion-memory/generate_suggestions`, { end_user_id })
|
||||
}
|
||||
export const analyticsRefresh = (end_user_id: string) => {
|
||||
return request.post('/memory-storage/analytics/generate_cache', { end_user_id })
|
||||
}
|
||||
// 遗忘
|
||||
// Forgetting stats
|
||||
export const getForgetStats = (group_id: string) => {
|
||||
return request.get(`/memory/forget-memory/stats`, { group_id })
|
||||
}
|
||||
// 隐性记忆-偏好
|
||||
// Implicit Memory - Preferences
|
||||
export const getImplicitPreferences = (end_user_id: string) => {
|
||||
return request.get(`/memory/implicit-memory/preferences/${end_user_id}`)
|
||||
}
|
||||
// 隐性记忆-核心特质
|
||||
// Implicit Memory - Core traits
|
||||
export const getImplicitPortrait = (end_user_id: string) => {
|
||||
return request.get(`/memory/implicit-memory/portrait/${end_user_id}`)
|
||||
}
|
||||
// 隐性记忆-兴趣领域分布
|
||||
// Implicit Memory - Interest areas distribution
|
||||
export const getImplicitInterestAreas = (end_user_id: string) => {
|
||||
return request.get(`/memory/implicit-memory/interest-areas/${end_user_id}`)
|
||||
}
|
||||
// 隐性记忆-用户习惯分析
|
||||
// Implicit Memory - User habits analysis
|
||||
export const getImplicitHabits = (end_user_id: string) => {
|
||||
return request.get(`/memory/implicit-memory/habits/${end_user_id}`)
|
||||
}
|
||||
// 短期记忆
|
||||
export const generateProfile = (end_user_id: string) => {
|
||||
return request.post(`/memory/implicit-memory/generate_profile`, { end_user_id })
|
||||
}
|
||||
// Short-term memory
|
||||
export const getShortTerm = (end_user_id: string) => {
|
||||
return request.get(`/memory/short/short_term`, { end_user_id })
|
||||
}
|
||||
// 感知记忆-视觉记忆
|
||||
// Perceptual Memory - Visual memory
|
||||
export const getPerceptualLastVisual = (end_user: string) => {
|
||||
return request.get(`/memory/perceptual/${end_user}/last_visual`)
|
||||
}
|
||||
// 感知记忆-音频记忆
|
||||
// Perceptual Memory - Audio memory
|
||||
export const getPerceptualLastListen = (end_user: string) => {
|
||||
return request.get(`/memory/perceptual/${end_user}/last_listen`)
|
||||
}
|
||||
// 感知记忆-文本记忆
|
||||
// Perceptual Memory - Text memory
|
||||
export const getPerceptualLastText = (end_user: string) => {
|
||||
return request.get(`/memory/perceptual/${end_user}/last_text`)
|
||||
}
|
||||
// 感知记忆-感知记忆时间线
|
||||
// Perceptual Memory - Perceptual memory timeline
|
||||
export const getPerceptualTimeline = (end_user: string) => {
|
||||
return request.get(`/memory/perceptual/${end_user}/timeline`)
|
||||
}
|
||||
// 情景记忆-总览
|
||||
// Episodic Memory - Overview
|
||||
export const getEpisodicOverview = (data: { end_user_id: string; time_range: string; episodic_type: string; } ) => {
|
||||
return request.post(`/memory/episodic-memory/overview`, data)
|
||||
}
|
||||
export const getEpisodicDetail = (data: { end_user_id: string; summary_id: string; } ) => {
|
||||
return request.post(`/memory/episodic-memory/details`, data)
|
||||
}
|
||||
// 关系演化
|
||||
// Relationship evolution
|
||||
export const getRelationshipEvolution = (data: { id: string; label: string; } ) => {
|
||||
return request.get(`/memory-storage/memory_space/relationship_evolution`, data)
|
||||
}
|
||||
// 共同记忆时间线
|
||||
// Shared memory timeline
|
||||
export const getTimelineMemories = (data: { id: string; label: string; }) => {
|
||||
return request.get(`/memory-storage/memory_space/timeline_memories`, data)
|
||||
}
|
||||
@@ -207,72 +213,72 @@ export const getConversationDetail = (end_user: string, conversation_id: string)
|
||||
export const forgetTrigger = (data: { max_merge_batch_size: number; min_days_since_access: number; end_user_id: string;}) => {
|
||||
return request.post(`/memory/forget-memory/trigger`, data)
|
||||
}
|
||||
/*************** end 用户记忆 相关接口 ******************************/
|
||||
/*************** end User Memory APIs ******************************/
|
||||
|
||||
/****************** 记忆管理 相关接口 *******************************/
|
||||
// 记忆管理-获取所有配置
|
||||
/****************** Memory Management APIs *******************************/
|
||||
// Memory Management - Get all configurations
|
||||
export const memoryConfigListUrl = '/memory-storage/read_all_config'
|
||||
export const getMemoryConfigList = () => {
|
||||
return request.get(memoryConfigListUrl)
|
||||
}
|
||||
// 记忆管理-创建配置
|
||||
// Memory Management - Create configuration
|
||||
export const createMemoryConfig = (values: MemoryFormData) => {
|
||||
return request.post('/memory-storage/create_config', values)
|
||||
}
|
||||
// 记忆管理-更新配置
|
||||
// Memory Management - Update configuration
|
||||
export const updateMemoryConfig = (values: MemoryFormData) => {
|
||||
return request.post('/memory-storage/update_config', values)
|
||||
}
|
||||
// 记忆管理-删除配置
|
||||
// Memory Management - Delete configuration
|
||||
export const deleteMemoryConfig = (config_id: number) => {
|
||||
return request.delete(`/memory-storage/delete_config?config_id=${config_id}`)
|
||||
}
|
||||
// 遗忘引擎-获取配置
|
||||
// Forgetting Engine - Get configuration
|
||||
export const getMemoryForgetConfig = (config_id: number | string) => {
|
||||
return request.get('/memory/forget-memory/read_config', { config_id })
|
||||
}
|
||||
// 遗忘引擎-更新配置
|
||||
// Forgetting Engine - Update configuration
|
||||
export const updateMemoryForgetConfig = (values: ForgetConfigForm) => {
|
||||
return request.post('/memory/forget-memory/update_config', values)
|
||||
}
|
||||
// 记忆萃取引擎-获取配置
|
||||
// Memory Extraction Engine - Get configuration
|
||||
export const getMemoryExtractionConfig = (config_id: number | string) => {
|
||||
return request.get('/memory-storage/read_config_extracted', { config_id: config_id })
|
||||
}
|
||||
// 记忆萃取引擎-更新配置
|
||||
// Memory Extraction Engine - Update configuration
|
||||
export const updateMemoryExtractionConfig = (values: ExtractionConfigForm) => {
|
||||
return request.post('/memory-storage/update_config_extracted', values)
|
||||
}
|
||||
// 记忆萃取引擎-试运行
|
||||
// Memory Extraction Engine - Pilot run
|
||||
export const pilotRunMemoryExtractionConfig = (values: { config_id: number | string; dialogue_text: string; }, onMessage?: (data: SSEMessage[]) => void) => {
|
||||
return handleSSE('/memory-storage/pilot_run', values, onMessage)
|
||||
}
|
||||
// 情绪引擎-获取配置
|
||||
// Emotion Engine - Get configuration
|
||||
export const getMemoryEmotionConfig = (config_id: number | string) => {
|
||||
return request.get('/memory/emotion/read_config', { config_id: config_id })
|
||||
}
|
||||
// 情绪引擎-更新配置
|
||||
// Emotion Engine - Update configuration
|
||||
export const updateMemoryEmotionConfig = (values: EmotionConfig) => {
|
||||
return request.post('/memory/emotion/updated_config', values)
|
||||
}
|
||||
// 反思引擎-获取配置
|
||||
// Reflection Engine - Get configuration
|
||||
export const getMemoryReflectionConfig = (config_id: number | string) => {
|
||||
return request.get('/memory/reflection/configs', { config_id: config_id })
|
||||
}
|
||||
// 反思引擎-更新配置
|
||||
// Reflection Engine - Update configuration
|
||||
export const updateMemoryReflectionConfig = (values: SelfReflectionEngineConfig) => {
|
||||
return request.post('/memory/reflection/save', values)
|
||||
}
|
||||
// 反思引擎-试运行
|
||||
// Reflection Engine - Pilot run
|
||||
export const pilotRunMemoryReflectionConfig = (values: { config_id: number | string; language_type: string; }) => {
|
||||
return request.get('/memory/reflection/run', values)
|
||||
}
|
||||
|
||||
/*************** end 记忆管理 相关接口 ******************************/
|
||||
/*************** end Memory Management APIs ******************************/
|
||||
|
||||
|
||||
/****************** API参数 相关接口 *******************************/
|
||||
/****************** API Parameters APIs *******************************/
|
||||
export const getMemoryApi = () => {
|
||||
return request.get('/memory/docs/api')
|
||||
}
|
||||
/*************** end API参数 相关接口 ******************************/
|
||||
/*************** end API Parameters APIs ******************************/
|
||||
@@ -1,10 +1,13 @@
|
||||
import { useEffect, useState, type FC, type Key } from 'react';
|
||||
import { Select } from 'antd'
|
||||
import type { SelectProps, DefaultOptionType } from 'antd/es/select'
|
||||
import { Select } from 'antd';
|
||||
import type { SelectProps, DefaultOptionType } from 'antd/es/select';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { request } from '@/utils/request';
|
||||
|
||||
// 定义API响应类型
|
||||
interface OptionType {
|
||||
[key: string]: Key | string | number;
|
||||
}
|
||||
|
||||
interface ApiResponse<T> {
|
||||
items?: T[];
|
||||
}
|
||||
@@ -20,19 +23,16 @@ interface CustomSelectProps extends Omit<SelectProps, 'filterOption'> {
|
||||
format?: (items: OptionType[]) => OptionType[];
|
||||
showSearch?: boolean;
|
||||
optionFilterProp?: string;
|
||||
// 其他SelectProps属性
|
||||
onChange?: SelectProps<Key, DefaultOptionType>['onChange'];
|
||||
value?: SelectProps<Key, DefaultOptionType>['value'];
|
||||
disabled?: boolean;
|
||||
style?: React.CSSProperties;
|
||||
className?: string;
|
||||
filterOption?: (inputValue: string, option?: DefaultOptionType) => boolean;
|
||||
}
|
||||
interface OptionType {
|
||||
[key: string]: Key | string | number;
|
||||
}
|
||||
|
||||
const defaultFilterOption = (inputValue: string, option?: DefaultOptionType): boolean => {
|
||||
if (!option || !inputValue) return true;
|
||||
const label = String(option.children || option.label || '');
|
||||
return label.toLowerCase().includes(inputValue.toLowerCase());
|
||||
};
|
||||
|
||||
const CustomSelect: FC<CustomSelectProps> = ({
|
||||
onChange,
|
||||
url,
|
||||
params,
|
||||
valueKey = 'value',
|
||||
@@ -42,42 +42,37 @@ const CustomSelect: FC<CustomSelectProps> = ({
|
||||
allTitle,
|
||||
format,
|
||||
showSearch = false,
|
||||
optionFilterProp = 'label',
|
||||
filterOption,
|
||||
...props
|
||||
}) => {
|
||||
const { t } = useTranslation();
|
||||
const [options, setOptions] = useState<OptionType[]>([]);
|
||||
|
||||
// 默认模糊搜索函数
|
||||
const defaultFilterOption = (inputValue: string, option?: DefaultOptionType) => {
|
||||
if (!option || !inputValue) return true;
|
||||
const label = String(option.children || option.label || '');
|
||||
return label.toLowerCase().includes(inputValue.toLowerCase());
|
||||
};
|
||||
// 组件挂载时获取初始数据
|
||||
const [options, setOptions] = useState<OptionType[]>([]);
|
||||
|
||||
useEffect(() => {
|
||||
request.get<ApiResponse<OptionType>>(url, params).then((res) => {
|
||||
const data = res;
|
||||
setOptions(Array.isArray(data) ? data || [] : Array.isArray(data?.items) ? data.items || [] : []);
|
||||
const data = Array.isArray(res) ? res : res?.items || [];
|
||||
setOptions(data);
|
||||
});
|
||||
}, []);
|
||||
}, [url, params]);
|
||||
|
||||
const displayOptions = format ? format(options) : options;
|
||||
|
||||
return (
|
||||
<Select
|
||||
placeholder={placeholder ? placeholder : t('common.select')}
|
||||
onChange={onChange}
|
||||
<Select
|
||||
placeholder={placeholder || t('common.select')}
|
||||
defaultValue={hasAll ? null : undefined}
|
||||
showSearch={showSearch}
|
||||
filterOption={filterOption || defaultFilterOption}
|
||||
{...props}
|
||||
>
|
||||
{hasAll && (<Select.Option>{allTitle || t('common.all')}</Select.Option>)}
|
||||
{(format ? format(options) : options)?.map(option => (
|
||||
{hasAll && <Select.Option value={null}>{allTitle || t('common.all')}</Select.Option>}
|
||||
{displayOptions.map((option) => (
|
||||
<Select.Option key={option[valueKey]} value={option[valueKey]}>
|
||||
{String(option[labelKey])}
|
||||
</Select.Option>
|
||||
))}
|
||||
</Select>
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
export default CustomSelect;
|
||||
@@ -1967,6 +1967,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re
|
||||
value: 'Value',
|
||||
addCase: 'Add Condition',
|
||||
addVariable: 'Add Variables',
|
||||
output: 'Output Variable'
|
||||
},
|
||||
|
||||
clear: 'Clear',
|
||||
|
||||
@@ -658,8 +658,8 @@ export const zh = {
|
||||
priority: '结构化整合',
|
||||
addTool: '添加工具',
|
||||
tool: '工具',
|
||||
variableConfig: '配置变量'
|
||||
},
|
||||
// 角色管理相关翻译
|
||||
role: {
|
||||
roleManagement: '角色管理',
|
||||
roleId: '角色ID',
|
||||
@@ -2061,6 +2061,7 @@ export const zh = {
|
||||
value: '值',
|
||||
addCase: '添加条件',
|
||||
addVariable: '添加变量',
|
||||
output: '输出变量'
|
||||
},
|
||||
|
||||
clear: '清空',
|
||||
|
||||
@@ -332,21 +332,6 @@
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 19,
|
||||
"parent": 0,
|
||||
"code": "member",
|
||||
"label": "成员管理",
|
||||
"i18nKey": "menu.memberManagement",
|
||||
"path": "/member",
|
||||
"enable": true,
|
||||
"display": true,
|
||||
"level": 1,
|
||||
"sort": 0,
|
||||
"icon": null,
|
||||
"iconActive": null,
|
||||
"subs": null
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"parent": 0,
|
||||
@@ -377,6 +362,21 @@
|
||||
"iconActive": null,
|
||||
"subs": null
|
||||
},
|
||||
{
|
||||
"id": 19,
|
||||
"parent": 0,
|
||||
"code": "member",
|
||||
"label": "成员管理",
|
||||
"i18nKey": "menu.memberManagement",
|
||||
"path": "/member",
|
||||
"enable": true,
|
||||
"display": true,
|
||||
"level": 1,
|
||||
"sort": 0,
|
||||
"icon": null,
|
||||
"iconActive": null,
|
||||
"subs": null
|
||||
},
|
||||
{
|
||||
"id": 12,
|
||||
"parent": 0,
|
||||
|
||||
@@ -1,8 +1,47 @@
|
||||
import { message } from 'antd';
|
||||
import i18n from '@/i18n'
|
||||
import { cookieUtils } from './request'
|
||||
import { refreshToken } from '@/api/user'
|
||||
import { clearAuthData } from './auth'
|
||||
const API_PREFIX = '/api'
|
||||
|
||||
// Token refresh state
|
||||
let isRefreshing = false;
|
||||
let refreshPromise: Promise<string> | null = null;
|
||||
|
||||
// Refresh token function for SSE
|
||||
const refreshTokenForSSE = async (): Promise<string> => {
|
||||
if (isRefreshing && refreshPromise) {
|
||||
return refreshPromise;
|
||||
}
|
||||
|
||||
isRefreshing = true;
|
||||
refreshPromise = (async () => {
|
||||
try {
|
||||
const refresh_token = cookieUtils.get('refreshToken');
|
||||
if (!refresh_token) {
|
||||
throw new Error(i18n.t('common.refreshTokenNotExist'));
|
||||
}
|
||||
const response: any = await refreshToken();
|
||||
const newToken = response.access_token;
|
||||
cookieUtils.set('authToken', newToken);
|
||||
return newToken;
|
||||
} catch (error) {
|
||||
clearAuthData();
|
||||
message.warning(i18n.t('common.loginExpired'));
|
||||
if (!window.location.hash.includes('#/login')) {
|
||||
window.location.href = `/#/login`;
|
||||
}
|
||||
throw error;
|
||||
} finally {
|
||||
isRefreshing = false;
|
||||
refreshPromise = null;
|
||||
}
|
||||
})();
|
||||
|
||||
return refreshPromise;
|
||||
};
|
||||
|
||||
export interface SSEMessage {
|
||||
event?: string
|
||||
data?: string | object
|
||||
@@ -66,62 +105,66 @@ function parseDataContent(dataContent: string): string | object {
|
||||
}
|
||||
}
|
||||
|
||||
const makeSSERequest = async (url: string, data: any, token: string, config = { headers: {} }) => {
|
||||
return fetch(`${API_PREFIX}${url}`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${token}`,
|
||||
...config.headers,
|
||||
},
|
||||
body: JSON.stringify(data)
|
||||
});
|
||||
};
|
||||
|
||||
export const handleSSE = async (url: string, data: any, onMessage?: (data: SSEMessage[]) => void, config = { headers: {} }) => {
|
||||
try {
|
||||
const token = cookieUtils.get('authToken');
|
||||
const response = await fetch(`${API_PREFIX}${url}`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${token}`,
|
||||
...config.headers,
|
||||
},
|
||||
body: JSON.stringify(data)
|
||||
});
|
||||
let token = cookieUtils.get('authToken');
|
||||
let response = await makeSSERequest(url, data, token || '', config);
|
||||
|
||||
const { status } = response
|
||||
|
||||
switch(status) {
|
||||
switch (response.status) {
|
||||
case 401:
|
||||
if (url?.includes('/public')) {
|
||||
return message.warning(i18n.t('common.publicApiCannotRefreshToken'));
|
||||
}
|
||||
window.location.href = `/#/login`;
|
||||
break;
|
||||
default:
|
||||
if (!response.body) throw new Error('No response body');
|
||||
|
||||
const reader = response.body.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
let buffer = ''; // 添加缓冲区来处理不完整的消息
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
const chunk = decoder.decode(value, { stream: true });
|
||||
buffer += chunk;
|
||||
|
||||
// 处理完整的事件
|
||||
const events = buffer.split('\n\n');
|
||||
buffer = events.pop() || ''; // 保留最后一个可能不完整的事件
|
||||
|
||||
for (const event of events) {
|
||||
if (event.trim() && onMessage) {
|
||||
onMessage(parseSSEToJSON(event) ?? {});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 处理剩余的缓冲区内容
|
||||
if (buffer.trim() && onMessage) {
|
||||
onMessage(parseSSEToJSON(buffer) ?? {});
|
||||
try {
|
||||
const newToken = await refreshTokenForSSE();
|
||||
response = await makeSSERequest(url, data, newToken, config);
|
||||
} catch (refreshError) {
|
||||
return;
|
||||
}
|
||||
break;
|
||||
}
|
||||
if (!response.body) throw new Error('No response body');
|
||||
|
||||
const reader = response.body.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
let buffer = ''; // 添加缓冲区来处理不完整的消息
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
const chunk = decoder.decode(value, { stream: true });
|
||||
buffer += chunk;
|
||||
|
||||
// 处理完整的事件
|
||||
const events = buffer.split('\n\n');
|
||||
buffer = events.pop() || ''; // 保留最后一个可能不完整的事件
|
||||
|
||||
for (const event of events) {
|
||||
if (event.trim() && onMessage) {
|
||||
onMessage(parseSSEToJSON(event) ?? {});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 处理剩余的缓冲区内容
|
||||
if (buffer.trim() && onMessage) {
|
||||
onMessage(parseSSEToJSON(buffer) ?? {});
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Request failed:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
};
|
||||
@@ -13,26 +13,25 @@ import type {
|
||||
Config,
|
||||
ModelConfig,
|
||||
AgentRef,
|
||||
KnowledgeBase,
|
||||
KnowledgeConfig,
|
||||
Variable,
|
||||
MemoryConfig,
|
||||
AiPromptModalRef,
|
||||
Source,
|
||||
ToolOption
|
||||
ChatVariableConfigModalRef
|
||||
} from './types'
|
||||
import type { Variable } from './components/VariableList/types'
|
||||
import type { KnowledgeConfig } from './components/Knowledge/types'
|
||||
import type { Model } from '@/views/ModelManagement/types'
|
||||
import { getModelList } from '@/api/models';
|
||||
import { saveAgentConfig } from '@/api/application'
|
||||
import Knowledge from './components/Knowledge'
|
||||
import VariableList from './components/VariableList'
|
||||
import Knowledge from './components/Knowledge/Knowledge'
|
||||
import VariableList from './components/VariableList/VariableList'
|
||||
import { getApplicationConfig } from '@/api/application'
|
||||
import { getKnowledgeBaseList } from '@/api/knowledgeBase'
|
||||
import { memoryConfigListUrl } from '@/api/memory'
|
||||
import CustomSelect from '@/components/CustomSelect'
|
||||
import aiPrompt from '@/assets/images/application/aiPrompt.png'
|
||||
import AiPromptModal from './components/AiPromptModal'
|
||||
import ToolList from './components/ToolList'
|
||||
import ToolList from './components/ToolList/ToolList'
|
||||
import ChatVariableConfigModal from './components/ChatVariableConfigModal';
|
||||
|
||||
const DescWrapper: FC<{desc: string, className?: string}> = ({desc, className}) => {
|
||||
return (
|
||||
@@ -66,7 +65,7 @@ const SwitchWrapper: FC<{ title: string, desc?: string, name: string | string[];
|
||||
</div>
|
||||
)
|
||||
}
|
||||
const SelectWrapper: FC<{ title: string, desc: string, name: string, url: string }> = ({ title, desc, name, url }) => {
|
||||
const SelectWrapper: FC<{ title: string, desc: string, name: string | string[], url: string }> = ({ title, desc, name, url }) => {
|
||||
const { t } = useTranslation();
|
||||
return (
|
||||
<>
|
||||
@@ -77,6 +76,7 @@ const SelectWrapper: FC<{ title: string, desc: string, name: string, url: string
|
||||
className="rb:mb-0!"
|
||||
>
|
||||
<CustomSelect
|
||||
placeholder={t('common.pleaseSelect')}
|
||||
url={url}
|
||||
hasAll={false}
|
||||
valueKey='config_id'
|
||||
@@ -99,54 +99,22 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
const [modelList, setModelList] = useState<Model[]>([])
|
||||
const [defaultModel, setDefaultModel] = useState<Model | null>(null)
|
||||
const [chatList, setChatList] = useState<ChatData[]>([])
|
||||
const [formData, setFormData] = useState<{
|
||||
default_model_config_id?: string,
|
||||
model_parameters?: Config['model_parameters'],
|
||||
tools: ToolOption[],
|
||||
} | null>(null)
|
||||
const values = Form.useWatch<{
|
||||
memoryEnabled: boolean;
|
||||
memory_content?: string | number;
|
||||
} & Config>([], form)
|
||||
|
||||
const [knowledgeConfig, setKnowledgeConfig] = useState<KnowledgeConfig>({ knowledge_bases: [] })
|
||||
const [variableList, setVariableList] = useState<Variable[]>([])
|
||||
const values = Form.useWatch<Config>([], form)
|
||||
const [isSave, setIsSave] = useState(false)
|
||||
const initialized = useRef(false)
|
||||
const [toolList, setToolList] = useState<ToolOption[]>([])
|
||||
|
||||
// 初始化完成标记
|
||||
useEffect(() => {
|
||||
if (data && values && formData) {
|
||||
if (data) {
|
||||
initialized.current = true
|
||||
}
|
||||
}, [data, values, formData])
|
||||
}, [data])
|
||||
|
||||
useEffect(() => {
|
||||
if (!initialized.current) return
|
||||
if (isSave) return
|
||||
setIsSave(true)
|
||||
}, [knowledgeConfig])
|
||||
useEffect(() => {
|
||||
if (!initialized.current) return
|
||||
if (isSave) return
|
||||
setIsSave(true)
|
||||
}, [variableList])
|
||||
useEffect(() => {
|
||||
if (!initialized.current) return
|
||||
if (isSave) return
|
||||
setIsSave(true)
|
||||
}, [formData])
|
||||
useEffect(() => {
|
||||
if (!initialized.current) return
|
||||
if (isSave) return
|
||||
setIsSave(true)
|
||||
}, [values])
|
||||
useEffect(() => {
|
||||
if (!initialized.current) return
|
||||
if (isSave) return
|
||||
setIsSave(true)
|
||||
}, [toolList])
|
||||
|
||||
useEffect(() => {
|
||||
getModels()
|
||||
@@ -157,68 +125,19 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
setLoading(true)
|
||||
getApplicationConfig(id as string).then(res => {
|
||||
const response = res as Config
|
||||
setData({
|
||||
...response,
|
||||
tools: Array.isArray(response.tools) ? response.tools : []
|
||||
})
|
||||
const { memory, tools } = response
|
||||
let allTools = Array.isArray(response.tools) ? response.tools : []
|
||||
form.setFieldsValue({
|
||||
...response,
|
||||
memoryEnabled: memory?.enabled || false,
|
||||
memory_content: memory?.memory_content ? Number(memory?.memory_content) : undefined,
|
||||
tools: Array.isArray(tools) ? tools : []
|
||||
tools: allTools
|
||||
})
|
||||
setFormData({
|
||||
default_model_config_id: response.default_model_config_id,
|
||||
model_parameters: response.model_parameters || {},
|
||||
tools: Array.isArray(tools) ? tools : []
|
||||
setData({
|
||||
...response,
|
||||
tools: allTools
|
||||
})
|
||||
if (response?.knowledge_retrieval?.knowledge_bases?.length) {
|
||||
getDefaultKnowledgeList(response)
|
||||
}
|
||||
if (response?.tools?.length) {
|
||||
setToolList(response?.tools)
|
||||
}
|
||||
}).finally(() => {
|
||||
setLoading(false)
|
||||
})
|
||||
}
|
||||
const getDefaultKnowledgeList = (data: Config) => {
|
||||
if (!data || !data.knowledge_retrieval || !data.knowledge_retrieval?.knowledge_bases?.length) {
|
||||
return
|
||||
}
|
||||
const initialList = [...(data?.knowledge_retrieval?.knowledge_bases || [])]
|
||||
getKnowledgeBaseList(undefined, {
|
||||
kb_ids: initialList.map(vo => vo.kb_id).join(','),
|
||||
page: 1,
|
||||
pagesize: 100,
|
||||
})
|
||||
.then(res => {
|
||||
const list = res.items || []
|
||||
const knowledge_bases: KnowledgeBase[] = list.map(item => {
|
||||
const filterItem = initialList.find(vo => vo.kb_id === item.id)
|
||||
return {
|
||||
...item,
|
||||
...filterItem
|
||||
}
|
||||
})
|
||||
setKnowledgeConfig(prev => ({
|
||||
...prev,
|
||||
knowledge_bases: [...knowledge_bases]
|
||||
}))
|
||||
setData((prev) => {
|
||||
prev = prev as Config
|
||||
const knowledge_retrieval: KnowledgeConfig = {
|
||||
...(prev?.knowledge_retrieval || {}),
|
||||
knowledge_bases: [...knowledge_bases]
|
||||
}
|
||||
return {
|
||||
...(prev || {}),
|
||||
knowledge_retrieval
|
||||
}
|
||||
})
|
||||
})
|
||||
}
|
||||
|
||||
const refresh = (vo: ModelConfig, type: Source) => {
|
||||
if (type === 'model') {
|
||||
@@ -227,15 +146,7 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
default_model_config_id,
|
||||
model_parameters: {...rest}
|
||||
})
|
||||
setFormData((prevState) => {
|
||||
const prev = prevState as Config
|
||||
return {
|
||||
...(prev || {}),
|
||||
default_model_config_id,
|
||||
model_parameters: {...rest}
|
||||
};
|
||||
})
|
||||
if (default_model_config_id === formData?.default_model_config_id) {
|
||||
if (default_model_config_id === values?.default_model_config_id) {
|
||||
setChatList([{
|
||||
label: vo.label || '',
|
||||
model_config_id: default_model_config_id || '',
|
||||
@@ -279,24 +190,20 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
// 保存Agent配置
|
||||
const handleSave = (flag = true) => {
|
||||
if (!isSave || !data) return Promise.resolve()
|
||||
const { memoryEnabled, memory_content, ...rest } = values
|
||||
const { knowledge_bases = [], ...knowledgeRest } = knowledgeConfig || {}
|
||||
|
||||
|
||||
const { memory, knowledge_retrieval, tools, ...rest } = values
|
||||
const { knowledge_bases = [], ...knowledgeRest } = knowledge_retrieval || {}
|
||||
const { memory_content } = memory || {}
|
||||
// 从原数据中获取memory的其他必要属性
|
||||
const originalMemory = data.memory || ({} as MemoryConfig)
|
||||
|
||||
const params: Config = {
|
||||
...data,
|
||||
...rest,
|
||||
...(formData || {}),
|
||||
memory: {
|
||||
...originalMemory,
|
||||
enabled: memoryEnabled,
|
||||
...memory,
|
||||
memory_content: memory_content ? String(memory_content) : '',
|
||||
max_history: originalMemory.max_history || '',
|
||||
},
|
||||
variables: variableList || [],
|
||||
knowledge_retrieval: knowledge_bases.length > 0 ? {
|
||||
...data.knowledge_retrieval,
|
||||
...knowledgeRest,
|
||||
@@ -305,14 +212,12 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
...(item.config || {})
|
||||
}))
|
||||
} as KnowledgeConfig : null,
|
||||
tools: toolList.map(vo => ({
|
||||
tools: tools.map(vo => ({
|
||||
tool_id: vo.tool_id,
|
||||
operation: vo.operation,
|
||||
enabled: vo.enabled
|
||||
}))
|
||||
}
|
||||
|
||||
console.log('params', rest, params)
|
||||
|
||||
return new Promise((resolve, reject) => {
|
||||
saveAgentConfig(data.app_id, params)
|
||||
@@ -338,8 +243,8 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
modelConfigModalRef.current?.handleOpen('chat')
|
||||
}
|
||||
useEffect(() => {
|
||||
if (formData?.default_model_config_id && modelList.length > 0) {
|
||||
const filterValue = modelList.find(item => item.id === formData.default_model_config_id)
|
||||
if (values?.default_model_config_id && modelList.length > 0) {
|
||||
const filterValue = modelList.find(item => item.id === values.default_model_config_id)
|
||||
setDefaultModel(filterValue as Model | null)
|
||||
setChatList([{
|
||||
label: filterValue?.name || '',
|
||||
@@ -348,7 +253,7 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
list: []
|
||||
}])
|
||||
}
|
||||
}, [modelList, formData?.default_model_config_id])
|
||||
}, [modelList, values?.default_model_config_id])
|
||||
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleSave
|
||||
@@ -360,8 +265,31 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
}
|
||||
const updatePrompt = (value: string) => {
|
||||
form.setFieldValue('system_prompt', value)
|
||||
const variables = value.match(/\{\{([^}]+)\}\}/g)?.map(match => match.slice(2, -2)) || []
|
||||
const uniqueVariables = [...new Set(variables)]
|
||||
const newVariableList: Variable[] = uniqueVariables.map((name, index) => ({
|
||||
index,
|
||||
type: 'text',
|
||||
name,
|
||||
display_name: name,
|
||||
required: false
|
||||
}))
|
||||
updateVariableList(newVariableList)
|
||||
}
|
||||
|
||||
const updateVariableList = (list: Variable[]) => {
|
||||
form.setFieldValue('variables', [...list])
|
||||
setChatVariables([...list])
|
||||
}
|
||||
const chatVariableConfigModalRef = useRef<ChatVariableConfigModalRef>(null)
|
||||
const [chatVariables, setChatVariables] = useState<Variable[]>([])
|
||||
const handleOpenVariableConfig = () => {
|
||||
chatVariableConfigModalRef.current?.handleOpen(chatVariables)
|
||||
}
|
||||
const handleSaveChatVariable = (values: Variable[]) => {
|
||||
setChatVariables(values)
|
||||
}
|
||||
console.log('values', values)
|
||||
return (
|
||||
<>
|
||||
{loading && <Spin fullscreen></Spin>}
|
||||
@@ -379,8 +307,9 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
</Space>
|
||||
</div>
|
||||
<Form form={form}>
|
||||
<Form.Item name="default_model_config_id" hidden noStyle></Form.Item>
|
||||
<Form.Item name="model_parameters" hidden noStyle></Form.Item>
|
||||
<Space size={16} direction="vertical" style={{ width: '100%' }}>
|
||||
{/* 提示词 */}
|
||||
<Card title={t('application.promptConfiguration')}>
|
||||
<div className="rb:flex rb:items-center rb:justify-between rb:mb-2.75">
|
||||
<div className="rb:font-medium rb:leading-5">
|
||||
@@ -406,36 +335,31 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
</Form.Item>
|
||||
</Card>
|
||||
|
||||
{/* 知识库 */}
|
||||
<Knowledge
|
||||
data={data?.knowledge_retrieval || { knowledge_bases: [] }}
|
||||
onUpdate={setKnowledgeConfig}
|
||||
/>
|
||||
<Form.Item name="knowledge_retrieval" noStyle>
|
||||
<Knowledge />
|
||||
</Form.Item>
|
||||
|
||||
{/* 记忆配置 */}
|
||||
<Card title={t('application.memoryConfiguration')}>
|
||||
<Space size={24} direction='vertical' style={{ width: '100%' }}>
|
||||
<SwitchWrapper title="dialogueHistoricalMemory" desc="dialogueHistoricalMemoryDesc" name="memoryEnabled" />
|
||||
<SwitchWrapper title="dialogueHistoricalMemory" desc="dialogueHistoricalMemoryDesc" name={['memory', 'enabled']} />
|
||||
<SelectWrapper
|
||||
title="selectMemoryContent"
|
||||
desc="selectMemoryContentDesc"
|
||||
name="memory_content"
|
||||
name={['memory', 'memory_content']}
|
||||
url={memoryConfigListUrl}
|
||||
/>
|
||||
</Space>
|
||||
</Card>
|
||||
|
||||
{/* 变量配置 */}
|
||||
<VariableList
|
||||
data={data?.variables}
|
||||
onUpdate={setVariableList}
|
||||
/>
|
||||
<Form.Item name="variables">
|
||||
<VariableList />
|
||||
</Form.Item>
|
||||
|
||||
{/* 工具配置 */}
|
||||
<ToolList
|
||||
data={data?.tools || []}
|
||||
onUpdate={setToolList}
|
||||
/>
|
||||
<Form.Item name="tools">
|
||||
<ToolList />
|
||||
</Form.Item>
|
||||
</Space>
|
||||
</Form>
|
||||
</Col>
|
||||
@@ -444,6 +368,9 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
{t('application.debuggingAndPreview')}
|
||||
|
||||
<Space size={10}>
|
||||
<Button type="primary" ghost onClick={handleOpenVariableConfig}>
|
||||
{t('application.variableConfig')}
|
||||
</Button>
|
||||
<Button type="primary" ghost onClick={handleAddModel}>
|
||||
+ {t('application.addModel')}
|
||||
</Button>
|
||||
@@ -463,7 +390,7 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
|
||||
<ModelConfigModal
|
||||
modelList={modelList}
|
||||
data={formData as Config}
|
||||
data={values}
|
||||
ref={modelConfigModalRef}
|
||||
refresh={refresh}
|
||||
/>
|
||||
@@ -472,6 +399,10 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
|
||||
defaultModel={defaultModel}
|
||||
refresh={updatePrompt}
|
||||
/>
|
||||
<ChatVariableConfigModal
|
||||
ref={chatVariableConfigModalRef}
|
||||
refresh={handleSaveChatVariable}
|
||||
/>
|
||||
</>
|
||||
);
|
||||
});
|
||||
|
||||
@@ -3,18 +3,21 @@ import RbCard from '@/components/RbCard/Card'
|
||||
|
||||
interface CardProps {
|
||||
title?: string | ReactNode;
|
||||
subTitle?: string | ReactNode;
|
||||
children: ReactNode;
|
||||
extra?: ReactNode;
|
||||
}
|
||||
|
||||
const Card: FC<CardProps> = ({
|
||||
title,
|
||||
subTitle,
|
||||
children,
|
||||
extra,
|
||||
}) => {
|
||||
return (
|
||||
<RbCard
|
||||
title={title}
|
||||
subTitle={subTitle}
|
||||
extra={extra}
|
||||
headerType="borderL"
|
||||
headerClassName="rb:before:bg-[#155EEF]! rb:before:h-[19px]"
|
||||
|
||||
@@ -0,0 +1,101 @@
|
||||
import { forwardRef, useImperativeHandle, useState } from 'react';
|
||||
import { Form, Input, InputNumber } from 'antd';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
import type { ChatVariableConfigModalRef } from '../types'
|
||||
import type { Variable } from './VariableList/types'
|
||||
import RbModal from '@/components/RbModal'
|
||||
|
||||
interface VariableEditModalProps {
|
||||
refresh: (values: Variable[]) => void;
|
||||
}
|
||||
|
||||
const ChatVariableConfigModal = forwardRef<ChatVariableConfigModalRef, VariableEditModalProps>(({
|
||||
refresh,
|
||||
}, ref) => {
|
||||
const { t } = useTranslation();
|
||||
const [visible, setVisible] = useState(false);
|
||||
const [form] = Form.useForm<{variables: Variable[]}>();
|
||||
const [loading, setLoading] = useState(false)
|
||||
const [initialValues, setInitialValues] = useState<Variable[]>([])
|
||||
|
||||
// 封装取消方法，添加关闭弹窗逻辑
|
||||
const handleClose = () => {
|
||||
setVisible(false);
|
||||
form.resetFields();
|
||||
setLoading(false)
|
||||
};
|
||||
|
||||
const handleOpen = (values: Variable[]) => {
|
||||
console.log('values', values)
|
||||
setVisible(true);
|
||||
form.setFieldsValue({variables: values})
|
||||
setInitialValues([...values])
|
||||
};
|
||||
// 封装保存方法，添加提交逻辑
|
||||
const handleSave = () => {
|
||||
form.validateFields().then((values) => {
|
||||
refresh([
|
||||
...(values?.variables ?? []),
|
||||
])
|
||||
handleClose()
|
||||
})
|
||||
}
|
||||
|
||||
// 暴露给父组件的方法
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleOpen,
|
||||
handleClose
|
||||
}));
|
||||
|
||||
console.log(form.getFieldValue('variables'))
|
||||
|
||||
return (
|
||||
<RbModal
|
||||
title={t('application.variableConfig')}
|
||||
open={visible}
|
||||
onCancel={handleClose}
|
||||
okText={t('common.save')}
|
||||
onOk={handleSave}
|
||||
confirmLoading={loading}
|
||||
>
|
||||
<Form
|
||||
form={form}
|
||||
layout="horizontal"
|
||||
scrollToFirstError={{ behavior: 'instant', block: 'end', focus: true }}
|
||||
>
|
||||
<Form.List name="variables">
|
||||
{(fields) => (
|
||||
<>
|
||||
{fields.map(({ name }, index) => {
|
||||
const field = initialValues[index]
|
||||
return (
|
||||
<Form.Item
|
||||
key={name}
|
||||
name={[name, 'value']}
|
||||
label={`${field.name}·${field.display_name}`}
|
||||
rules={[
|
||||
{ required: field.required, message: t('common.pleaseEnter') },
|
||||
]}
|
||||
>
|
||||
{
|
||||
field.type === 'text' && <Input placeholder={t('common.pleaseEnter')} />
|
||||
}
|
||||
{
|
||||
field.type === 'number' && <InputNumber placeholder={t('common.pleaseEnter')} className="rb:w-full!" onChange={(value) => form.setFieldValue(['variables', name, 'value'], value)} />
|
||||
}
|
||||
{
|
||||
field.type === 'paragraph' && <Input.TextArea placeholder={t('common.pleaseEnter')} />
|
||||
}
|
||||
</Form.Item>
|
||||
)
|
||||
})}
|
||||
</>
|
||||
)}
|
||||
</Form.List>
|
||||
</Form>
|
||||
</RbModal>
|
||||
);
|
||||
});
|
||||
|
||||
export default ChatVariableConfigModal;
|
||||
@@ -2,7 +2,6 @@ import { type FC, useRef, useState, useEffect } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { Space, Button, List } from 'antd'
|
||||
import knowledgeEmpty from '@/assets/images/application/knowledgeEmpty.svg'
|
||||
import Card from './Card'
|
||||
import type {
|
||||
KnowledgeConfigForm,
|
||||
KnowledgeConfig,
|
||||
@@ -11,14 +10,16 @@ import type {
|
||||
KnowledgeModalRef,
|
||||
KnowledgeConfigModalRef,
|
||||
KnowledgeGlobalConfigModalRef,
|
||||
} from '../types'
|
||||
} from './types'
|
||||
import Empty from '@/components/Empty'
|
||||
import KnowledgeListModal from './KnowledgeListModal'
|
||||
import KnowledgeConfigModal from './KnowledgeConfigModal'
|
||||
import KnowledgeGlobalConfigModal from './KnowledgeGlobalConfigModal'
|
||||
import Tag from '@/components/Tag'
|
||||
import { getKnowledgeBaseList } from '@/api/knowledgeBase'
|
||||
import Card from '../Card'
|
||||
|
||||
const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig) => void}> = ({data, onUpdate}) => {
|
||||
const Knowledge: FC<{value?: KnowledgeConfig; onChange?: (config: KnowledgeConfig) => void}> = ({value = {knowledge_bases: []}, onChange}) => {
|
||||
const { t } = useTranslation()
|
||||
const knowledgeModalRef = useRef<KnowledgeModalRef>(null)
|
||||
const knowledgeConfigModalRef = useRef<KnowledgeConfigModalRef>(null)
|
||||
@@ -27,12 +28,31 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
|
||||
const [editConfig, setEditConfig] = useState<KnowledgeConfig>({} as KnowledgeConfig)
|
||||
|
||||
useEffect(() => {
|
||||
if (data) {
|
||||
setEditConfig({ ...(data || {}) })
|
||||
const knowledge_bases = [...(data.knowledge_bases || [])]
|
||||
setKnowledgeList(knowledge_bases)
|
||||
if (value && JSON.stringify(value) !== JSON.stringify(editConfig)) {
|
||||
setEditConfig({ ...(value || {}) })
|
||||
const knowledge_bases = [...(value.knowledge_bases || [])]
|
||||
|
||||
// 检查是否有knowledge_bases缺少name字段
|
||||
const basesWithoutName = knowledge_bases.filter(base => !base.name)
|
||||
if (basesWithoutName.length > 0) {
|
||||
// 调用接口获取完整的知识库信息
|
||||
getKnowledgeBaseList().then(res => {
|
||||
const fullBases = knowledge_bases.map(base => {
|
||||
if (!base.name) {
|
||||
const fullBase = res.items.find((item: any) => item.id === base.kb_id)
|
||||
return fullBase ? { ...base, ...fullBase } : base
|
||||
}
|
||||
return base
|
||||
})
|
||||
setKnowledgeList(fullBases)
|
||||
}).catch(() => {
|
||||
setKnowledgeList(knowledge_bases)
|
||||
})
|
||||
} else {
|
||||
setKnowledgeList(knowledge_bases)
|
||||
}
|
||||
}
|
||||
}, [data])
|
||||
}, [value])
|
||||
|
||||
const handleKnowledgeConfig = () => {
|
||||
knowledgeGlobalConfigModalRef.current?.handleOpen()
|
||||
@@ -43,7 +63,7 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
|
||||
const handleDeleteKnowledge = (id: string) => {
|
||||
const list = knowledgeList.filter(item => item.id !== id)
|
||||
setKnowledgeList([...list])
|
||||
onUpdate({
|
||||
onChange && onChange({
|
||||
...editConfig,
|
||||
knowledge_bases: [...list],
|
||||
})
|
||||
@@ -65,7 +85,7 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
|
||||
list = [...values as KnowledgeBase[]]
|
||||
}
|
||||
setKnowledgeList([...list])
|
||||
onUpdate({
|
||||
onChange && onChange({
|
||||
...editConfig,
|
||||
knowledge_bases: [...list],
|
||||
})
|
||||
@@ -77,14 +97,14 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
|
||||
config: {...values as KnowledgeConfigForm}
|
||||
}
|
||||
setKnowledgeList([...list])
|
||||
onUpdate({
|
||||
onChange && onChange({
|
||||
...editConfig,
|
||||
knowledge_bases: [...list],
|
||||
})
|
||||
} else if (type === 'rerankerConfig') {
|
||||
const rerankerValues = values as RerankerConfig
|
||||
setEditConfig(prev => ({ ...prev, ...rerankerValues }))
|
||||
onUpdate({
|
||||
onChange && onChange({
|
||||
...editConfig,
|
||||
...rerankerValues,
|
||||
reranker_id: rerankerValues.rerank_model ? rerankerValues.reranker_id : undefined,
|
||||
@@ -93,55 +113,54 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
|
||||
}
|
||||
}
|
||||
return (
|
||||
<Card
|
||||
<Card
|
||||
title={t('application.knowledgeBaseAssociation')}
|
||||
extra={
|
||||
<Button style={{padding: '0 8px', height: '24px'}} onClick={() => handleKnowledgeConfig()}>{t('application.globalConfig')}</Button>
|
||||
<Space>
|
||||
<Button style={{ padding: '0 8px', height: '24px' }} onClick={handleKnowledgeConfig}>{t('workflow.config.knowledge-retrieval.recallConfig')}</Button>
|
||||
<Button style={{ padding: '0 8px', height: '24px' }} onClick={handleAddKnowledge}>+</Button>
|
||||
</Space>
|
||||
}
|
||||
>
|
||||
<div className="rb:flex rb:items-center rb:justify-between rb:mb-3">
|
||||
<div className="rb:font-medium rb:leading-5">{t('application.associatedKnowledgeBase')}</div>
|
||||
<Button style={{padding: '0 8px', height: '24px'}} onClick={handleAddKnowledge}>+{t('application.addKnowledgeBase')}</Button>
|
||||
</div>
|
||||
|
||||
{knowledgeList.length === 0
|
||||
? <Empty url={knowledgeEmpty} size={88} subTitle={t('application.knowledgeEmpty')} />
|
||||
:
|
||||
<List
|
||||
grid={{ gutter: 12, column: 1 }}
|
||||
dataSource={knowledgeList}
|
||||
renderItem={(item) => (
|
||||
<List.Item>
|
||||
<div key={item.id} className="rb:flex rb:items-center rb:justify-between rb:p-[12px_16px] rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg">
|
||||
<div className="rb:font-medium rb:leading-4">
|
||||
{item.name}
|
||||
<Tag color={item.status === 1 ? 'success' : item.status === 0 ? 'default' : 'error'} className="rb:ml-2">
|
||||
{item.status === 1 ? t('common.enable') : item.status === 0 ? t('common.disabled') : t('common.deleted')}
|
||||
</Tag>
|
||||
<div className="rb:mt-1 rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-5">{t('application.contains', {include_count: item.doc_num})}</div>
|
||||
renderItem={(item) => {
|
||||
if (!item.id) return null
|
||||
return (
|
||||
<List.Item>
|
||||
<div key={item.id} className="rb:flex rb:items-center rb:justify-between rb:p-[12px_16px] rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg">
|
||||
<div className="rb:font-medium rb:leading-4">
|
||||
{item.name}
|
||||
<Tag color={item.status === 1 ? 'success' : item.status === 0 ? 'default' : 'error'} className="rb:ml-2">
|
||||
{item.status === 1 ? t('common.enable') : item.status === 0 ? t('common.disabled') : t('common.deleted')}
|
||||
</Tag>
|
||||
<div className="rb:mt-1 rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-5">{t('application.contains', {include_count: item.doc_num})}</div>
|
||||
</div>
|
||||
<Space size={12}>
|
||||
<div
|
||||
className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/editBorder.svg')] rb:hover:bg-[url('@/assets/images/editBg.svg')]"
|
||||
onClick={() => handleEditKnowledge(item)}
|
||||
></div>
|
||||
<div
|
||||
className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/deleteBorder.svg')] rb:hover:bg-[url('@/assets/images/deleteBg.svg')]"
|
||||
onClick={() => handleDeleteKnowledge(item.id)}
|
||||
></div>
|
||||
</Space>
|
||||
</div>
|
||||
<Space size={12}>
|
||||
<div
|
||||
className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/editBorder.svg')] rb:hover:bg-[url('@/assets/images/editBg.svg')]"
|
||||
onClick={() => handleEditKnowledge(item)}
|
||||
></div>
|
||||
<div
|
||||
className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/deleteBorder.svg')] rb:hover:bg-[url('@/assets/images/deleteBg.svg')]"
|
||||
onClick={() => handleDeleteKnowledge(item.id)}
|
||||
></div>
|
||||
</Space>
|
||||
</div>
|
||||
</List.Item>
|
||||
)}
|
||||
</List.Item>
|
||||
)
|
||||
}}
|
||||
/>
|
||||
}
|
||||
{/* 全局设置 */}
|
||||
<KnowledgeGlobalConfigModal
|
||||
data={editConfig}
|
||||
ref={knowledgeGlobalConfigModalRef}
|
||||
refresh={refresh}
|
||||
/>
|
||||
{/* 知识库列表 */}
|
||||
<KnowledgeListModal
|
||||
ref={knowledgeModalRef}
|
||||
selectedList={knowledgeList}
|
||||
@@ -2,7 +2,7 @@ import { forwardRef, useEffect, useImperativeHandle, useState } from 'react';
|
||||
import { Form, Select, InputNumber } from 'antd';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
import type { KnowledgeConfigModalRef, KnowledgeBase, KnowledgeConfigForm } from '../types'
|
||||
import type { KnowledgeConfigModalRef, KnowledgeBase, KnowledgeConfigForm, RetrieveType } from './types'
|
||||
import RbModal from '@/components/RbModal'
|
||||
import RbSlider from '@/components/RbSlider'
|
||||
import { formatDateTime } from '@/utils/format';
|
||||
@@ -12,7 +12,7 @@ const FormItem = Form.Item;
|
||||
interface KnowledgeConfigModalProps {
|
||||
refresh: (values: KnowledgeConfigForm, type: 'knowledgeConfig') => void;
|
||||
}
|
||||
const retrieveTypes = ['participle', 'semantic', 'hybrid']
|
||||
const retrieveTypes: RetrieveType[] = ['participle', 'semantic', 'hybrid']
|
||||
|
||||
const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfigModalProps>(({
|
||||
refresh,
|
||||
@@ -33,8 +33,11 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
|
||||
const handleOpen = (data: KnowledgeBase) => {
|
||||
form.setFieldsValue({
|
||||
retrieve_type: retrieveTypes[0],
|
||||
retrieve_type: data?.config?.retrieve_type || retrieveTypes[0],
|
||||
kb_id: data.id,
|
||||
top_k: data?.config?.top_k || 5,
|
||||
similarity_threshold: data?.config?.similarity_threshold || 0.5,
|
||||
vector_similarity_weight: data?.config?.vector_similarity_weight || 0.5,
|
||||
...(data || {}),
|
||||
...(data?.config || {}),
|
||||
})
|
||||
@@ -62,12 +65,10 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
|
||||
useEffect(() => {
|
||||
if (values?.retrieve_type) {
|
||||
const initialValues = Object.keys(values).map(key => {
|
||||
return {
|
||||
[key as keyof KnowledgeConfigForm]: (key === 'kb_id' || key === 'retrieve_type') ? values[key] : undefined
|
||||
}
|
||||
})
|
||||
form.resetFields(initialValues)
|
||||
const fieldsToReset = Object.keys(values).filter(key =>
|
||||
key !== 'kb_id' && key !== 'retrieve_type'
|
||||
) as (keyof KnowledgeConfigForm)[];
|
||||
form.resetFields(fieldsToReset);
|
||||
}
|
||||
}, [values?.retrieve_type])
|
||||
|
||||
@@ -84,12 +85,12 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
layout="vertical"
|
||||
>
|
||||
{data && (
|
||||
<div className="rb:mb-[24px] rb:flex rb:items-center rb:justify-between rb:border rb:rounded-[8px] rb:p-[17px_16px] rb:cursor-pointer rb:bg-[#F0F3F8] rb:border-[#DFE4ED] rb:text-[#212332]">
|
||||
<div className="rb:text-[16px] rb:leading-[22px]">
|
||||
<div className="rb:mb-6 rb:flex rb:items-center rb:justify-between rb:border rb:rounded-lg rb:p-[17px_16px] rb:cursor-pointer rb:bg-[#F0F3F8] rb:border-[#DFE4ED] rb:text-[#212332]">
|
||||
<div className="rb:text-[16px] rb:leading-5.5">
|
||||
{data.name}
|
||||
<div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167] rb:mt-[8px]">{t('application.contains', {include_count: data.doc_num})}</div>
|
||||
<div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167] rb:mt-2">{t('application.contains', {include_count: data.doc_num})}</div>
|
||||
</div>
|
||||
<div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167]">{formatDateTime(data.updated_at, 'YYYY-MM-DD HH:mm:ss')}</div>
|
||||
<div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167]">{formatDateTime(data.updated_at, 'YYYY-MM-DD HH:mm:ss')}</div>
|
||||
</div>
|
||||
)}
|
||||
<FormItem name="kb_id" hidden />
|
||||
@@ -114,8 +115,14 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
label={t('application.top_k')}
|
||||
rules={[{ required: true, message: t('common.pleaseEnter') }]}
|
||||
extra={t('application.top_k_desc')}
|
||||
initialValue={5}
|
||||
>
|
||||
<InputNumber style={{ width: '100%' }} />
|
||||
<InputNumber
|
||||
style={{ width: '100%' }}
|
||||
min={1}
|
||||
max={20}
|
||||
onChange={(value) => form.setFieldValue('top_k', value)}
|
||||
/>
|
||||
</FormItem>
|
||||
{/* 语义相似度阈值 similarity_threshold */}
|
||||
{values?.retrieve_type === 'semantic' && (
|
||||
@@ -123,6 +130,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
name="similarity_threshold"
|
||||
label={t('application.similarity_threshold')}
|
||||
extra={t('application.similarity_threshold_desc')}
|
||||
initialValue={0.5}
|
||||
>
|
||||
<RbSlider
|
||||
max={1.0}
|
||||
@@ -137,6 +145,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
name="vector_similarity_weight"
|
||||
label={t('application.vector_similarity_weight')}
|
||||
extra={t('application.vector_similarity_weight_desc')}
|
||||
initialValue={0.5}
|
||||
>
|
||||
<RbSlider
|
||||
max={1.0}
|
||||
@@ -152,6 +161,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
name="similarity_threshold"
|
||||
label={t('application.similarity_threshold')}
|
||||
extra={t('application.similarity_threshold_desc1')}
|
||||
initialValue={0.5}
|
||||
>
|
||||
<RbSlider
|
||||
max={1.0}
|
||||
@@ -163,6 +173,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
|
||||
name="vector_similarity_weight"
|
||||
label={t('application.vector_similarity_weight')}
|
||||
extra={t('application.vector_similarity_weight_desc1')}
|
||||
initialValue={0.5}
|
||||
>
|
||||
<RbSlider
|
||||
max={1.0}
|
||||
@@ -2,7 +2,7 @@ import { forwardRef, useImperativeHandle, useState, useEffect } from 'react';
|
||||
import { Form, InputNumber, Switch } from 'antd';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
import type { RerankerConfig, KnowledgeGlobalConfigModalRef } from '../types'
|
||||
import type { RerankerConfig, KnowledgeGlobalConfigModalRef } from './types'
|
||||
import RbModal from '@/components/RbModal'
|
||||
import CustomSelect from '@/components/CustomSelect'
|
||||
import { getModelListUrl } from '@/api/models'
|
||||
@@ -71,18 +71,18 @@ const KnowledgeGlobalConfigModal = forwardRef<KnowledgeGlobalConfigModalRef, Kno
|
||||
form={form}
|
||||
layout="vertical"
|
||||
>
|
||||
<div className="rb:text-[#5B6167] rb:mb-[24px]">{t('application.globalConfigDesc')}</div>
|
||||
<div className="rb:text-[#5B6167] rb:mb-6">{t('application.globalConfigDesc')}</div>
|
||||
|
||||
{/* 结果重排 */}
|
||||
<div className="rb:flex rb:items-center rb:justify-between rb:my-[24px]">
|
||||
<div className="rb:text-[14px] rb:font-medium rb:leading-[20px]">
|
||||
<div className="rb:flex rb:items-center rb:justify-between rb:my-6">
|
||||
<div className="rb:text-[14px] rb:font-medium rb:leading-5">
|
||||
{t('application.rerankModel')}
|
||||
<div className="rb:mt-[4px] rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-[16px]">{t('application.rerankModelDesc')}</div>
|
||||
<div className="rb:mt-1 rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-4">{t('application.rerankModelDesc')}</div>
|
||||
</div>
|
||||
<FormItem
|
||||
name="rerank_model"
|
||||
valuePropName="checked"
|
||||
className="rb:mb-[0px]!"
|
||||
className="rb:mb-0!"
|
||||
>
|
||||
<Switch />
|
||||
</FormItem>
|
||||
@@ -110,7 +110,12 @@ const KnowledgeGlobalConfigModal = forwardRef<KnowledgeGlobalConfigModalRef, Kno
|
||||
rules={[{ required: true, message: t('common.pleaseEnter') }]}
|
||||
extra={t('application.reranker_top_k_desc')}
|
||||
>
|
||||
<InputNumber style={{ width: '100%' }} min={1} max={20} />
|
||||
<InputNumber
|
||||
style={{ width: '100%' }}
|
||||
min={1}
|
||||
max={20}
|
||||
onChange={(value) => form.setFieldValue('reranker_top_k', value)}
|
||||
/>
|
||||
</FormItem>
|
||||
</>}
|
||||
</Form>
|
||||
@@ -2,7 +2,7 @@ import { forwardRef, useEffect, useImperativeHandle, useState } from 'react';
|
||||
import { Space, List } from 'antd';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import clsx from 'clsx'
|
||||
import type { KnowledgeModalRef, KnowledgeBase } from '../types'
|
||||
import type { KnowledgeModalRef, KnowledgeBase } from './types'
|
||||
import type { KnowledgeBaseListItem } from '@/views/KnowledgeBase/types'
|
||||
import RbModal from '@/components/RbModal'
|
||||
import { getKnowledgeBaseList } from '@/api/knowledgeBase'
|
||||
@@ -39,12 +39,13 @@ const KnowledgeListModal = forwardRef<KnowledgeModalRef, KnowledgeModalProps>(({
|
||||
setQuery({})
|
||||
setSelectedIds([])
|
||||
setSelectedRows([])
|
||||
getList()
|
||||
};
|
||||
|
||||
useEffect(() => {
|
||||
getList()
|
||||
}, [query.keywords])
|
||||
if (visible) {
|
||||
getList()
|
||||
}
|
||||
}, [query.keywords, visible])
|
||||
const getList = () => {
|
||||
getKnowledgeBaseList(undefined, {
|
||||
...query,
|
||||
@@ -124,15 +125,15 @@ const KnowledgeListModal = forwardRef<KnowledgeModalRef, KnowledgeModalProps>(({
|
||||
dataSource={filterList}
|
||||
renderItem={(item: KnowledgeBase) => (
|
||||
<List.Item>
|
||||
<div key={item.id} className={clsx("rb:flex rb:items-center rb:justify-between rb:border rb:rounded-[8px] rb:p-[17px_16px] rb:cursor-pointer rb:hover:bg-[#F0F3F8]", {
|
||||
<div key={item.id} className={clsx("rb:flex rb:items-center rb:justify-between rb:border rb:rounded-lg rb:p-[17px_16px] rb:cursor-pointer rb:hover:bg-[#F0F3F8]", {
|
||||
"rb:bg-[rgba(21,94,239,0.06)] rb:border-[#155EEF] rb:text-[#155EEF]": selectedIds.includes(item.id),
|
||||
"rb:border-[#DFE4ED] rb:text-[#212332]": !selectedIds.includes(item.id),
|
||||
})} onClick={() => handleSelect(item)}>
|
||||
<div className="rb:text-[16px] rb:leading-[22px]">
|
||||
<div className="rb:text-[16px] rb:leading-5.5">
|
||||
{item.name}
|
||||
<div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167] rb:mt-[8px]">{t('application.contains', {include_count: item.doc_num})}</div>
|
||||
<div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167] rb:mt-2">{t('application.contains', {include_count: item.doc_num})}</div>
|
||||
</div>
|
||||
<div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167]">{formatDateTime(item.created_at, 'YYYY-MM-DD HH:mm:ss')}</div>
|
||||
<div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167]">{formatDateTime(item.created_at, 'YYYY-MM-DD HH:mm:ss')}</div>
|
||||
</div>
|
||||
</List.Item>
|
||||
)}
|
||||
@@ -0,0 +1,30 @@
|
||||
import type { KnowledgeBaseListItem } from '@/views/KnowledgeBase/types'
|
||||
export interface RerankerConfig {
|
||||
rerank_model?: boolean | undefined;
|
||||
reranker_id?: string | undefined;
|
||||
reranker_top_k?: number | undefined;
|
||||
}
|
||||
export type RetrieveType = 'participle' | 'semantic' | 'hybrid'
|
||||
export interface KnowledgeConfigForm {
|
||||
kb_id?: string;
|
||||
similarity_threshold?: number;
|
||||
vector_similarity_weight?: number;
|
||||
top_k?: number;
|
||||
retrieve_type?: RetrieveType;
|
||||
}
|
||||
export interface KnowledgeBase extends KnowledgeBaseListItem, KnowledgeConfigForm {
|
||||
config?: KnowledgeConfigForm
|
||||
}
|
||||
export interface KnowledgeConfig extends RerankerConfig {
|
||||
knowledge_bases: KnowledgeBase[];
|
||||
}
|
||||
|
||||
export interface KnowledgeConfigModalRef {
|
||||
handleOpen: (data: KnowledgeBase) => void;
|
||||
}
|
||||
export interface KnowledgeGlobalConfigModalRef {
|
||||
handleOpen: () => void;
|
||||
}
|
||||
export interface KnowledgeModalRef {
|
||||
handleOpen: (config?: KnowledgeConfig[]) => void;
|
||||
}
|
||||
@@ -1,22 +1,22 @@
|
||||
import { type FC, useRef, useState, useEffect } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { Space, Button, List, Switch } from 'antd'
|
||||
import Card from './Card'
|
||||
import Card from '../Card'
|
||||
import type {
|
||||
ToolModalRef,
|
||||
ToolOption
|
||||
} from '../types'
|
||||
} from './types'
|
||||
import Empty from '@/components/Empty'
|
||||
import ToolModal from './ToolModal'
|
||||
import { getToolMethods, getToolDetail } from '@/api/tools'
|
||||
|
||||
const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => void}> = ({data, onUpdate}) => {
|
||||
const ToolList: FC<{ value?: ToolOption[]; onChange?: (config: ToolOption[]) => void}> = ({value, onChange}) => {
|
||||
const { t } = useTranslation()
|
||||
const toolModalRef = useRef<ToolModalRef>(null)
|
||||
const [toolList, setToolList] = useState<ToolOption[]>([])
|
||||
useEffect(() => {
|
||||
if (data) {
|
||||
const processedData = data.map(async (item) => {
|
||||
if (value) {
|
||||
const processedData = value.map(async (item) => {
|
||||
if (!item.label && item.tool_id) {
|
||||
try {
|
||||
const [toolDetail, methods] = await Promise.all([
|
||||
@@ -77,7 +77,7 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
|
||||
|
||||
Promise.all(processedData).then(setToolList)
|
||||
}
|
||||
}, [data])
|
||||
}, [value])
|
||||
|
||||
const handleAddTool = () => {
|
||||
toolModalRef.current?.handleOpen()
|
||||
@@ -85,12 +85,12 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
|
||||
const updateTools = (tool: ToolOption) => {
|
||||
const list = [...toolList, tool]
|
||||
setToolList(list)
|
||||
onUpdate(list)
|
||||
onChange && onChange(list)
|
||||
}
|
||||
const handleDeleteTool = (index: number) => {
|
||||
const list = toolList.filter((_item, idx) => idx !== index)
|
||||
setToolList([...list])
|
||||
onUpdate(list)
|
||||
onChange && onChange(list)
|
||||
}
|
||||
const handleChangeEnabled = (index: number) => {
|
||||
const list = toolList.map((item, idx) => {
|
||||
@@ -103,7 +103,7 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
|
||||
return item
|
||||
})
|
||||
setToolList([...list])
|
||||
onUpdate(list)
|
||||
onChange && onChange(list)
|
||||
}
|
||||
return (
|
||||
<Card
|
||||
@@ -112,7 +112,6 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
|
||||
<Button style={{ padding: '0 8px', height: '24px' }} onClick={handleAddTool}>+{t('application.addTool')}</Button>
|
||||
}
|
||||
>
|
||||
|
||||
{toolList.length === 0
|
||||
? <Empty size={88} />
|
||||
:
|
||||
26
web/src/views/ApplicationConfig/components/ToolList/types.ts
Normal file
26
web/src/views/ApplicationConfig/components/ToolList/types.ts
Normal file
@@ -0,0 +1,26 @@
|
||||
export interface ToolOption {
|
||||
value?: string | number | null;
|
||||
label?: React.ReactNode;
|
||||
description?: string;
|
||||
children?: ToolOption[];
|
||||
isLeaf?: boolean;
|
||||
method_id?: string;
|
||||
operation?: string;
|
||||
parameters?: Parameter[];
|
||||
tool_id?: string;
|
||||
enabled?: boolean;
|
||||
}
|
||||
export interface Parameter {
|
||||
name: string;
|
||||
type: string;
|
||||
description: string;
|
||||
required: boolean;
|
||||
default: any;
|
||||
enum: null | string[];
|
||||
minimum: number;
|
||||
maximum: number;
|
||||
pattern: null | string;
|
||||
}
|
||||
export interface ToolModalRef {
|
||||
handleOpen: () => void;
|
||||
}
|
||||
@@ -1,131 +0,0 @@
|
||||
import { type FC, useRef, useState, useEffect } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { Space, Button, Switch } from 'antd'
|
||||
import variablesEmpty from '@/assets/images/application/variablesEmpty.svg'
|
||||
import Card from './Card'
|
||||
import Table from '@/components/Table';
|
||||
import type { Variable, VariableEditModalRef } from '../types'
|
||||
import Empty from '@/components/Empty'
|
||||
import VariableEditModal from './VariableEditModal'
|
||||
|
||||
interface VariableListProps {
|
||||
data?: Variable[];
|
||||
onUpdate: (data: Variable[]) => void;
|
||||
}
|
||||
const VariableList: FC<VariableListProps> = ({data = [], onUpdate}) => {
|
||||
const { t } = useTranslation()
|
||||
const variableEditModalRef = useRef<VariableEditModalRef>(null)
|
||||
const [variableList, setVariableList] = useState<Variable[]>([])
|
||||
const [maxIndex, setMaxIndex] = useState(0)
|
||||
|
||||
useEffect(() => {
|
||||
if (!data || data.length === 0) return
|
||||
const list = data.map((item, index) => ({
|
||||
...item,
|
||||
index
|
||||
}))
|
||||
setVariableList(list)
|
||||
onUpdate(list)
|
||||
setMaxIndex(list.length)
|
||||
}, [data])
|
||||
|
||||
const handleAddVariable = () => {
|
||||
variableEditModalRef.current?.handleOpen()
|
||||
}
|
||||
const handleSaveVariable = (value: Variable) => {
|
||||
if (value.index !== undefined && value.index >= 0) {
|
||||
const index = variableList.findIndex(item => item.index === value.index)
|
||||
if (index !== -1) {
|
||||
const newData = [...variableList]
|
||||
newData[index] = value
|
||||
setVariableList([...newData])
|
||||
onUpdate([...newData])
|
||||
}
|
||||
} else {
|
||||
const list = [...variableList, {
|
||||
index: maxIndex + 1,
|
||||
...value
|
||||
}]
|
||||
setVariableList(list)
|
||||
onUpdate([...list])
|
||||
setMaxIndex(maxIndex + 1)
|
||||
}
|
||||
}
|
||||
const handleDeleteVariable = (index: number) => {
|
||||
const list = variableList.filter((_, i) => i !== index)
|
||||
setVariableList(list)
|
||||
onUpdate([...list])
|
||||
}
|
||||
return (
|
||||
<Card title={t('application.variableConfiguration')}>
|
||||
<div className="rb:flex rb:items-center rb:justify-between rb:mb-[11px]">
|
||||
<div className="rb:font-medium rb:leading-[20px]">
|
||||
{t('application.VariableManagement')}
|
||||
<span className="rb:font-regular rb:text-[12px] rb:text-[#5B6167]"> ({t('application.VariableManagementDesc')})</span>
|
||||
</div>
|
||||
<Button style={{padding: '0 8px', height: '24px'}} onClick={handleAddVariable}>+{t('application.addVariables')}</Button>
|
||||
</div>
|
||||
|
||||
{/* List */}
|
||||
{variableList.length > 0
|
||||
? (
|
||||
<div className="rb:mt-[12px]">
|
||||
<Table
|
||||
rowKey="index"
|
||||
pagination={false}
|
||||
columns={[
|
||||
{
|
||||
title: t('application.variableType'),
|
||||
dataIndex: 'type',
|
||||
key: 'type',
|
||||
render: (type) => t(`application.${type}`)
|
||||
},
|
||||
{
|
||||
title: t('application.variableKey'),
|
||||
dataIndex: 'name',
|
||||
key: 'name',
|
||||
},
|
||||
{
|
||||
title: t('application.variableName'),
|
||||
dataIndex: 'display_name',
|
||||
key: 'display_name',
|
||||
},
|
||||
{
|
||||
title: t('application.optional'),
|
||||
dataIndex: 'required',
|
||||
key: 'required',
|
||||
render: (required) => <Switch checked={!required} disabled />
|
||||
},
|
||||
{
|
||||
title: t('common.operation'),
|
||||
key: 'action',
|
||||
render: (_, record, index: number) => (
|
||||
<Space size="middle">
|
||||
<Button
|
||||
type="link"
|
||||
onClick={() => variableEditModalRef.current?.handleOpen(record as Variable)}
|
||||
>
|
||||
{t('common.edit')}
|
||||
</Button>
|
||||
<Button type="link" danger onClick={() => handleDeleteVariable(index)}>
|
||||
{t('common.delete')}
|
||||
</Button>
|
||||
</Space>
|
||||
),
|
||||
},
|
||||
]}
|
||||
initialData={variableList as unknown as Record<string, unknown>[]}
|
||||
emptySize={88}
|
||||
/>
|
||||
</div>
|
||||
)
|
||||
: <Empty url={variablesEmpty} size={88} subTitle={t('application.variablesEmpty')} />
|
||||
}
|
||||
<VariableEditModal
|
||||
ref={variableEditModalRef}
|
||||
refreshTable={handleSaveVariable}
|
||||
/>
|
||||
</Card>
|
||||
)
|
||||
}
|
||||
export default VariableList
|
||||
@@ -2,7 +2,7 @@ import { forwardRef, useImperativeHandle, useState } from 'react';
|
||||
import { Form, Input } from 'antd';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
import type { ApiExtensionModalData, ApiExtensionModalRef } from '../types'
|
||||
import type { ApiExtensionModalData, ApiExtensionModalRef } from './types'
|
||||
import RbModal from '@/components/RbModal'
|
||||
|
||||
const FormItem = Form.Item;
|
||||
@@ -2,7 +2,7 @@ import { forwardRef, useImperativeHandle, useState, useRef } from 'react';
|
||||
import { Form, Input, Select, InputNumber, Checkbox, Tag, Divider, Button } from 'antd';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
import type { ApiExtensionModalRef, Variable, VariableEditModalRef } from '../types'
|
||||
import type { ApiExtensionModalRef, Variable, VariableEditModalRef } from './types'
|
||||
import RbModal from '@/components/RbModal'
|
||||
import SortableList from '@/components/SortableList'
|
||||
import ApiExtensionModal from './ApiExtensionModal'
|
||||
@@ -137,7 +137,14 @@ const VariableEditModal = forwardRef<VariableEditModalRef, VariableEditModalProp
|
||||
{ pattern: /^[a-zA-Z_][a-zA-Z0-9_]*$/, message: t('application.invalidVariableName') },
|
||||
]}
|
||||
>
|
||||
<Input placeholder={t('common.enter')} />
|
||||
<Input
|
||||
placeholder={t('common.enter')}
|
||||
onBlur={(e) => {
|
||||
if (!form.getFieldValue('display_name')) {
|
||||
form.setFieldValue('display_name', e.target.value)
|
||||
}
|
||||
}}
|
||||
/>
|
||||
</FormItem>
|
||||
{/* 显示名称 */}
|
||||
<FormItem
|
||||
@@ -0,0 +1,110 @@
|
||||
import { type FC, useRef } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { Space, Button, Switch, Form } from 'antd'
|
||||
import variablesEmpty from '@/assets/images/application/variablesEmpty.svg'
|
||||
import Card from '../Card'
|
||||
import Table from '@/components/Table';
|
||||
import type { Variable, VariableEditModalRef } from './types'
|
||||
import Empty from '@/components/Empty'
|
||||
import VariableEditModal from './VariableEditModal'
|
||||
|
||||
interface VariableListProps {
|
||||
value?: Variable[];
|
||||
onChange?: (value: Variable[]) => void;
|
||||
}
|
||||
const VariableList: FC<VariableListProps> = ({value = [], onChange}) => {
|
||||
const { t } = useTranslation()
|
||||
const variableEditModalRef = useRef<VariableEditModalRef>(null)
|
||||
|
||||
const handleAddVariable = () => {
|
||||
variableEditModalRef.current?.handleOpen()
|
||||
}
|
||||
const handleSaveVariable = (variable: Variable) => {
|
||||
const newList = [...(value || [])]
|
||||
if (variable.index !== undefined && variable.index >= 0) {
|
||||
const index = newList.findIndex(item => item.index === variable.index)
|
||||
if (index !== -1) {
|
||||
newList[index] = variable
|
||||
}
|
||||
} else {
|
||||
newList.push({ ...variable, index: Date.now() })
|
||||
}
|
||||
onChange?.(newList)
|
||||
}
|
||||
return (
|
||||
<Card
|
||||
title={<>
|
||||
{t('application.variableConfiguration')}
|
||||
<span className="rb:font-regular rb:text-[12px] rb:text-[#5B6167]"> ({t('application.VariableManagementDesc')})</span>
|
||||
</>}
|
||||
extra={<Button style={{ padding: '0 8px', height: '24px' }} onClick={handleAddVariable}>+ {t('application.addVariables')}</Button>}
|
||||
>
|
||||
<Form.List name="variables" initialValue={value}>
|
||||
{(fields, { remove }) => {
|
||||
return (
|
||||
<>
|
||||
{fields.length > 0 ? (
|
||||
<div className="rb:mt-3">
|
||||
<Table
|
||||
rowKey="index"
|
||||
pagination={false}
|
||||
columns={[
|
||||
{
|
||||
title: t('application.variableType'),
|
||||
dataIndex: 'type',
|
||||
key: 'type',
|
||||
render: (type) => t(`application.${type}`)
|
||||
},
|
||||
{
|
||||
title: t('application.variableKey'),
|
||||
dataIndex: 'name',
|
||||
key: 'name',
|
||||
},
|
||||
{
|
||||
title: t('application.variableName'),
|
||||
dataIndex: 'display_name',
|
||||
key: 'display_name',
|
||||
},
|
||||
{
|
||||
title: t('application.optional'),
|
||||
dataIndex: 'required',
|
||||
key: 'required',
|
||||
render: (required) => <Switch checked={!required} disabled />
|
||||
},
|
||||
{
|
||||
title: t('common.operation'),
|
||||
key: 'action',
|
||||
render: (_, record, index: number) => (
|
||||
<Space size="middle">
|
||||
<Button
|
||||
type="link"
|
||||
onClick={() => variableEditModalRef.current?.handleOpen(record as Variable)}
|
||||
>
|
||||
{t('common.edit')}
|
||||
</Button>
|
||||
<Button type="link" danger onClick={() => remove(index)}>
|
||||
{t('common.delete')}
|
||||
</Button>
|
||||
</Space>
|
||||
),
|
||||
},
|
||||
]}
|
||||
initialData={value as unknown as Record<string, unknown>[]}
|
||||
emptySize={88}
|
||||
/>
|
||||
</div>
|
||||
) : (
|
||||
<Empty url={variablesEmpty} size={88} subTitle={t('application.variablesEmpty')} />
|
||||
)}
|
||||
</>
|
||||
)
|
||||
}}
|
||||
</Form.List>
|
||||
<VariableEditModal
|
||||
ref={variableEditModalRef}
|
||||
refreshTable={handleSaveVariable}
|
||||
/>
|
||||
</Card>
|
||||
)
|
||||
}
|
||||
export default VariableList
|
||||
@@ -0,0 +1,28 @@
|
||||
export interface Variable {
|
||||
index?: number;
|
||||
name: string;
|
||||
display_name: string;
|
||||
type: string;
|
||||
required: boolean;
|
||||
max_length?: number;
|
||||
description?: string;
|
||||
|
||||
key?: string;
|
||||
default_value?: string;
|
||||
options?: string[];
|
||||
api_extension?: string;
|
||||
hidden?: boolean;
|
||||
value?: any;
|
||||
}
|
||||
export interface VariableEditModalRef {
|
||||
handleOpen: (values?: Variable) => void;
|
||||
}
|
||||
|
||||
export interface ApiExtensionModalData {
|
||||
name: string;
|
||||
apiEndpoint: string;
|
||||
apiKey: string;
|
||||
}
|
||||
export interface ApiExtensionModalRef {
|
||||
handleOpen: () => void;
|
||||
}
|
||||
@@ -1,4 +1,6 @@
|
||||
import type { KnowledgeBaseListItem } from '@/views/KnowledgeBase/types'
|
||||
import type { KnowledgeConfig } from './components/Knowledge/types'
|
||||
import type { Variable } from './components/VariableList/types'
|
||||
import type { ToolOption } from './components/ToolList/types'
|
||||
import type { ChatItem } from '@/components/Chat/types'
|
||||
import type { GraphRef } from '@/views/Workflow/types';
|
||||
import type { ApiKey } from '@/views/ApiKeyManagement/types'
|
||||
@@ -14,55 +16,6 @@ export interface ModelConfig {
|
||||
n: number;
|
||||
stop?: string;
|
||||
}
|
||||
|
||||
/*************** 知识库相关 ******************/
|
||||
export interface RerankerConfig {
|
||||
rerank_model?: boolean | undefined;
|
||||
reranker_id?: string | undefined;
|
||||
reranker_top_k?: number | undefined;
|
||||
}
|
||||
export interface KnowledgeConfigForm {
|
||||
kb_id?: string;
|
||||
similarity_threshold?: number;
|
||||
vector_similarity_weight?: number;
|
||||
top_k?: number;
|
||||
retrieve_type?: 'participle' | 'semantic' | 'hybrid';
|
||||
}
|
||||
export interface KnowledgeBase extends KnowledgeBaseListItem, KnowledgeConfigForm {
|
||||
config?: KnowledgeConfigForm
|
||||
}
|
||||
export interface KnowledgeConfig extends RerankerConfig {
|
||||
knowledge_bases: KnowledgeBase[];
|
||||
}
|
||||
|
||||
export interface KnowledgeConfigModalRef {
|
||||
handleOpen: (data: KnowledgeBase) => void;
|
||||
}
|
||||
export interface KnowledgeGlobalConfigModalRef {
|
||||
handleOpen: () => void;
|
||||
}
|
||||
/*********** end 知识库相关 ******************/
|
||||
|
||||
/*************** 变量相关 ******************/
|
||||
export interface Variable {
|
||||
index?: number;
|
||||
name: string;
|
||||
display_name: string;
|
||||
type: string;
|
||||
required: boolean;
|
||||
max_length?: number;
|
||||
description?: string;
|
||||
|
||||
key: string;
|
||||
default_value?: string;
|
||||
options?: string[];
|
||||
api_extension?: string;
|
||||
hidden?: boolean;
|
||||
}
|
||||
export interface VariableEditModalRef {
|
||||
handleOpen: (values?: Variable) => void;
|
||||
}
|
||||
/*********** end 变量相关 ******************/
|
||||
export interface MemoryConfig {
|
||||
enabled: boolean;
|
||||
memory_content?: string;
|
||||
@@ -131,17 +84,6 @@ export interface ModelConfigModalData {
|
||||
export interface AiPromptModalRef {
|
||||
handleOpen: () => void;
|
||||
}
|
||||
export interface KnowledgeModalRef {
|
||||
handleOpen: (config?: KnowledgeConfig[]) => void;
|
||||
}
|
||||
export interface ApiExtensionModalData {
|
||||
name: string;
|
||||
apiEndpoint: string;
|
||||
apiKey: string;
|
||||
}
|
||||
export interface ApiExtensionModalRef {
|
||||
handleOpen: () => void;
|
||||
}
|
||||
export interface ChatData {
|
||||
label?: string;
|
||||
model_config_id?: string;
|
||||
@@ -206,30 +148,6 @@ export interface AiPromptForm {
|
||||
message?: string;
|
||||
current_prompt?: string;
|
||||
}
|
||||
export interface ToolModalRef {
|
||||
handleOpen: () => void;
|
||||
}
|
||||
|
||||
export interface ToolOption {
|
||||
value?: string | number | null;
|
||||
label?: React.ReactNode;
|
||||
description?: string;
|
||||
children?: ToolOption[];
|
||||
isLeaf?: boolean;
|
||||
method_id?: string;
|
||||
operation?: string;
|
||||
parameters?: Parameter[];
|
||||
tool_id?: string;
|
||||
enabled?: boolean;
|
||||
}
|
||||
export interface Parameter {
|
||||
name: string;
|
||||
type: string;
|
||||
description: string;
|
||||
required: boolean;
|
||||
default: any;
|
||||
enum: null | string[];
|
||||
minimum: number;
|
||||
maximum: number;
|
||||
pattern: null | string;
|
||||
export interface ChatVariableConfigModalRef {
|
||||
handleOpen: (values: Variable[]) => void;
|
||||
}
|
||||
@@ -121,7 +121,7 @@ const EmotionTags: FC = () => {
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
: <Empty size={88} className="rb:h-full" />
|
||||
: <Empty size={88} className="rb:h-full rb:mb-4" />
|
||||
}
|
||||
</RbCard>
|
||||
)
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import { type FC, useEffect, useState } from 'react'
|
||||
import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { useParams } from 'react-router-dom'
|
||||
import { Skeleton, Space, Progress } from 'antd';
|
||||
@@ -20,7 +20,7 @@ interface HabitsItem {
|
||||
specific_examples: string[];
|
||||
}
|
||||
|
||||
const Habits: FC = () => {
|
||||
const Habits = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
|
||||
const { t } = useTranslation()
|
||||
const { id } = useParams()
|
||||
const [loading, setLoading] = useState<boolean>(false)
|
||||
@@ -43,6 +43,9 @@ const Habits: FC = () => {
|
||||
setLoading(false)
|
||||
})
|
||||
}
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleRefresh: getData
|
||||
}));
|
||||
|
||||
return (
|
||||
<>
|
||||
@@ -80,5 +83,5 @@ const Habits: FC = () => {
|
||||
</RbCard>
|
||||
</>
|
||||
)
|
||||
}
|
||||
})
|
||||
export default Habits
|
||||
@@ -1,4 +1,4 @@
|
||||
import { type FC, useEffect, useState } from 'react'
|
||||
import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { useParams } from 'react-router-dom'
|
||||
import { Skeleton, Progress } from 'antd';
|
||||
@@ -23,7 +23,7 @@ interface InterestAreasItem {
|
||||
art: Item;
|
||||
}
|
||||
|
||||
const InterestAreas: FC = () => {
|
||||
const InterestAreas = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
|
||||
const { t } = useTranslation()
|
||||
const { id } = useParams()
|
||||
const [loading, setLoading] = useState<boolean>(false)
|
||||
@@ -47,6 +47,9 @@ const InterestAreas: FC = () => {
|
||||
})
|
||||
}
|
||||
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleRefresh: getData
|
||||
}));
|
||||
return (
|
||||
<RbCard
|
||||
title={t('implicitDetail.interestAreas')}
|
||||
@@ -70,5 +73,5 @@ const InterestAreas: FC = () => {
|
||||
}
|
||||
</RbCard>
|
||||
)
|
||||
}
|
||||
})
|
||||
export default InterestAreas
|
||||
@@ -1,4 +1,4 @@
|
||||
import { type FC, useEffect, useState } from 'react'
|
||||
import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { useParams } from 'react-router-dom'
|
||||
import { Skeleton, Progress } from 'antd';
|
||||
@@ -25,7 +25,7 @@ interface PortraitItem {
|
||||
literature: Item;
|
||||
}
|
||||
|
||||
const Portrait: FC = () => {
|
||||
const Portrait = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
|
||||
const { t } = useTranslation()
|
||||
const { id } = useParams()
|
||||
const [loading, setLoading] = useState<boolean>(false)
|
||||
@@ -49,6 +49,9 @@ const Portrait: FC = () => {
|
||||
})
|
||||
}
|
||||
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleRefresh: getData
|
||||
}));
|
||||
return (
|
||||
<RbCard
|
||||
title={t('implicitDetail.portrait')}
|
||||
@@ -73,5 +76,5 @@ const Portrait: FC = () => {
|
||||
}
|
||||
</RbCard>
|
||||
)
|
||||
}
|
||||
})
|
||||
export default Portrait
|
||||
@@ -1,4 +1,4 @@
|
||||
import { type FC, useEffect, useState, useRef, useMemo } from 'react'
|
||||
import { useEffect, useState, useRef, useMemo, forwardRef, useImperativeHandle } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { useParams } from 'react-router-dom'
|
||||
import { Row, Col, Skeleton } from 'antd'
|
||||
@@ -31,7 +31,7 @@ const generateCategoryColors = (categories: string[]) => {
|
||||
return colors
|
||||
}
|
||||
|
||||
const Preferences: FC = () => {
|
||||
const Preferences = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
|
||||
const { t } = useTranslation()
|
||||
const { id } = useParams()
|
||||
const chartRef = useRef<HTMLDivElement>(null)
|
||||
@@ -138,6 +138,9 @@ const Preferences: FC = () => {
|
||||
return selectedWord !== null && data[selectedWord].tag_name ? <>{data[selectedWord].tag_name}{t('implicitDetail.preferencesDetail')}</> : ''
|
||||
}, [selectedWord, data, t])
|
||||
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleRefresh: getData
|
||||
}));
|
||||
return (
|
||||
<>
|
||||
<div className="rb:bg-[rgba(21,94,239,0.12)] rb:px-4 rb:py-2.5 rb:font-medium rb:leading-5 rb:mb-4 rb:mt-6 rb:rounded-md">{t('forgetDetail.overviewTitle')}</div>
|
||||
@@ -184,6 +187,6 @@ const Preferences: FC = () => {
|
||||
</Row>
|
||||
</>
|
||||
)
|
||||
}
|
||||
})
|
||||
|
||||
export default Preferences
|
||||
@@ -1,4 +1,4 @@
|
||||
import { type FC, useEffect, useState } from 'react'
|
||||
import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { useParams } from 'react-router-dom'
|
||||
|
||||
@@ -18,7 +18,7 @@ interface Suggestions {
|
||||
actionable_steps: string[];
|
||||
}>;
|
||||
}
|
||||
const Suggestions: FC = () => {
|
||||
const Suggestions = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
|
||||
const { t } = useTranslation()
|
||||
const { id } = useParams()
|
||||
const [suggestions, setSuggestions] = useState<Suggestions | null>(null)
|
||||
@@ -37,6 +37,9 @@ const Suggestions: FC = () => {
|
||||
})
|
||||
}
|
||||
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleRefresh: getSuggestionData
|
||||
}));
|
||||
return (
|
||||
<RbCard
|
||||
title={t('statementDetail.suggestions')}
|
||||
@@ -64,6 +67,6 @@ const Suggestions: FC = () => {
|
||||
}
|
||||
</RbCard>
|
||||
)
|
||||
}
|
||||
})
|
||||
|
||||
export default Suggestions
|
||||
@@ -1,34 +1,57 @@
|
||||
import { type FC } from 'react'
|
||||
import { forwardRef, useImperativeHandle, useRef } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import { Row, Col } from 'antd'
|
||||
import { useParams } from 'react-router-dom'
|
||||
|
||||
import Preferences from '../components/Preferences'
|
||||
import Portrait from '../components/Portrait'
|
||||
import InterestAreas from '../components/InterestAreas'
|
||||
import Habits from '../components/Habits'
|
||||
import {
|
||||
generateProfile,
|
||||
} from '@/api/memory'
|
||||
|
||||
const ImplicitDetail: FC = () => {
|
||||
const ImplicitDetail = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
|
||||
const { t } = useTranslation()
|
||||
const { id } = useParams()
|
||||
const preferencesRef = useRef<{ handleRefresh: () => void; }>(null)
|
||||
const portraitRef = useRef<{ handleRefresh: () => void; }>(null)
|
||||
const interestAreasRef = useRef<{ handleRefresh: () => void; }>(null)
|
||||
const habitsRef = useRef<{ handleRefresh: () => void; }>(null)
|
||||
|
||||
const handleRefresh = () => {
|
||||
if (!id) return
|
||||
generateProfile(id)
|
||||
.then(() => {
|
||||
preferencesRef.current?.handleRefresh()
|
||||
portraitRef.current?.handleRefresh()
|
||||
interestAreasRef.current?.handleRefresh()
|
||||
habitsRef.current?.handleRefresh()
|
||||
})
|
||||
}
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleRefresh
|
||||
}));
|
||||
|
||||
return (
|
||||
<div className="rb:h-full rb:max-w-266 rb:mx-auto">
|
||||
<div className="rb:text-[#5B6167] rb:leading-5 rb:mt-3">{t('implicitDetail.title')}</div>
|
||||
|
||||
<Preferences />
|
||||
<Preferences ref={preferencesRef} />
|
||||
|
||||
<div className="rb:bg-[rgba(21,94,239,0.12)] rb:px-3 rb:py-2.5 rb:font-medium rb:leading-5 rb:mb-4 rb:mt-6 rb:rounded-md">{t('implicitDetail.portraitTitle')}</div>
|
||||
<div className="rb:my-3 rb:text-[#5B6167] rb:leading-5">{t('implicitDetail.portraitSubTitle')}</div>
|
||||
<Row gutter={[16, 16]} className="rb:mt-4">
|
||||
<Col span={12}>
|
||||
<Portrait />
|
||||
<Portrait ref={portraitRef} />
|
||||
</Col>
|
||||
<Col span={12}>
|
||||
<InterestAreas />
|
||||
<InterestAreas ref={interestAreasRef} />
|
||||
</Col>
|
||||
</Row>
|
||||
|
||||
<Habits />
|
||||
<Habits ref={habitsRef} />
|
||||
</div>
|
||||
)
|
||||
}
|
||||
})
|
||||
export default ImplicitDetail
|
||||
@@ -1,13 +1,27 @@
|
||||
import { type FC } from 'react'
|
||||
import { forwardRef, useImperativeHandle, useRef } from 'react'
|
||||
import { Row, Col, Space } from 'antd';
|
||||
import { useParams } from 'react-router-dom'
|
||||
|
||||
import WordCloud from '../components/WordCloud'
|
||||
import EmotionTags from '../components/EmotionTags'
|
||||
import Health from '../components/Health'
|
||||
import Suggestions from '../components/Suggestions'
|
||||
import { generateSuggestions } from '@/api/memory'
|
||||
|
||||
|
||||
const StatementDetail: FC = () => {
|
||||
const StatementDetail = forwardRef((_props, ref) => {
|
||||
const { id } = useParams()
|
||||
const suggestionsRef = useRef<{ handleRefresh: () => void; }>(null)
|
||||
const handleRefresh = () => {
|
||||
if (!id) return
|
||||
generateSuggestions(id)
|
||||
.then(() => {
|
||||
suggestionsRef.current?.handleRefresh()
|
||||
})
|
||||
}
|
||||
useImperativeHandle(ref, () => ({
|
||||
handleRefresh
|
||||
}));
|
||||
return (
|
||||
<Row gutter={[16, 16]}>
|
||||
<Col span={12}>
|
||||
@@ -18,10 +32,10 @@ const StatementDetail: FC = () => {
|
||||
</Space>
|
||||
</Col>
|
||||
<Col span={12}>
|
||||
<Suggestions />
|
||||
<Suggestions ref={suggestionsRef} />
|
||||
</Col>
|
||||
</Row>
|
||||
)
|
||||
}
|
||||
})
|
||||
|
||||
export default StatementDetail
|
||||
@@ -24,6 +24,8 @@ const Detail: FC = () => {
|
||||
const navigate = useNavigate()
|
||||
const [name, setName] = useState<string>('')
|
||||
const forgetDetailRef = useRef<{ handleRefresh: () => void }>(null)
|
||||
const statementDetailRef = useRef<{ handleRefresh: () => void }>(null)
|
||||
const implicitDetailRef = useRef<{ handleRefresh: () => void }>(null)
|
||||
|
||||
useEffect(() => {
|
||||
if (!id) return
|
||||
@@ -45,7 +47,17 @@ const Detail: FC = () => {
|
||||
navigate(`/user-memory/detail/${id}/${key}`, { replace: true })
|
||||
}
|
||||
const handleRefresh = () => {
|
||||
forgetDetailRef.current?.handleRefresh()
|
||||
switch(type) {
|
||||
case 'FORGET_MEMORY':
|
||||
forgetDetailRef.current?.handleRefresh()
|
||||
break;
|
||||
case 'EMOTIONAL_MEMORY':
|
||||
statementDetailRef.current?.handleRefresh()
|
||||
break
|
||||
case 'IMPLICIT_MEMORY':
|
||||
implicitDetailRef.current?.handleRefresh()
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
if (type === 'GRAPH') {
|
||||
@@ -67,16 +79,16 @@ const Detail: FC = () => {
|
||||
</div>
|
||||
</Dropdown>
|
||||
}
|
||||
extra={type === 'FORGET_MEMORY' &&
|
||||
extra={['FORGET_MEMORY', 'EMOTIONAL_MEMORY', 'IMPLICIT_MEMORY'].includes(type as string) &&
|
||||
<Button type="primary" ghost className="rb:group rb:h-6! rb:px-2!" onClick={handleRefresh}>
|
||||
<img src={refreshIcon} className="rb:w-4 rb:h-4" />
|
||||
{t('common.refresh')}
|
||||
</Button>}
|
||||
/>
|
||||
<div className="rb:h-[calc(100vh-64px)] rb:overflow-y-auto rb:py-3 rb:px-4">
|
||||
{type === 'EMOTIONAL_MEMORY' && <StatementDetail />}
|
||||
{type === 'EMOTIONAL_MEMORY' && <StatementDetail ref={statementDetailRef} />}
|
||||
{type === 'FORGET_MEMORY' && <ForgetDetail ref={forgetDetailRef} />}
|
||||
{type === 'IMPLICIT_MEMORY' && <ImplicitDetail />}
|
||||
{type === 'IMPLICIT_MEMORY' && <ImplicitDetail ref={implicitDetailRef} />}
|
||||
{type === 'SHORT_TERM_MEMORY' && <ShortTermDetail />}
|
||||
{type === 'PERCEPTUAL_MEMORY' && <PerceptualDetail />}
|
||||
{type === 'EPISODIC_MEMORY' && <EpisodicDetail />}
|
||||
|
||||
@@ -16,6 +16,7 @@ import InitialValuePlugin from './plugin/InitialValuePlugin';
|
||||
import CommandPlugin from './plugin/CommandPlugin';
|
||||
import Jinja2HighlightPlugin from './plugin/Jinja2HighlightPlugin';
|
||||
import LineNumberPlugin from './plugin/LineNumberPlugin';
|
||||
import BlurPlugin from './plugin/BlurPlugin';
|
||||
import { VariableNode } from './nodes/VariableNode'
|
||||
|
||||
interface LexicalEditorProps {
|
||||
@@ -113,8 +114,10 @@ const Editor: FC<LexicalEditorProps> =({
|
||||
display: flex;
|
||||
align-items: flex-start;
|
||||
}
|
||||
.editor-content-with-numbers {
|
||||
.editor-content-wrapper {
|
||||
flex: 1;
|
||||
}
|
||||
.editor-content-with-numbers {
|
||||
white-space: pre-wrap;
|
||||
}
|
||||
.editor-content-with-numbers p {
|
||||
@@ -174,18 +177,20 @@ const Editor: FC<LexicalEditorProps> =({
|
||||
<div className="line-numbers">
|
||||
<div>1</div>
|
||||
</div>
|
||||
<ContentEditable
|
||||
className="editor-content-with-numbers"
|
||||
style={{
|
||||
minHeight: minheight,
|
||||
padding: '4px 0',
|
||||
outline: 'none',
|
||||
resize: 'none',
|
||||
fontSize: fontSize,
|
||||
lineHeight: lineHeight,
|
||||
border: 'none',
|
||||
}}
|
||||
/>
|
||||
<div className="editor-content-wrapper">
|
||||
<ContentEditable
|
||||
className="editor-content-with-numbers"
|
||||
style={{
|
||||
minHeight: minheight,
|
||||
padding: '4px 0',
|
||||
outline: 'none',
|
||||
resize: 'none',
|
||||
fontSize: fontSize,
|
||||
lineHeight: lineHeight,
|
||||
border: 'none',
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
) : (
|
||||
<ContentEditable
|
||||
@@ -207,8 +212,8 @@ const Editor: FC<LexicalEditorProps> =({
|
||||
style={{
|
||||
minHeight: placeHolderMinheight,
|
||||
position: 'absolute',
|
||||
top: variant === 'borderless' ? '0' : '6px',
|
||||
left: enableJinja2 ? '59px' : (variant === 'borderless' ? '0' : '11px'),
|
||||
top: enableJinja2 ? '4px' : variant === 'borderless' ? '0' : '6px',
|
||||
left: enableJinja2 ? '16px' : (variant === 'borderless' ? '0' : '11px'),
|
||||
color: '#A8A9AA',
|
||||
fontSize: fontSize,
|
||||
lineHeight: placeHolderMinheight,
|
||||
@@ -227,6 +232,7 @@ const Editor: FC<LexicalEditorProps> =({
|
||||
<AutocompletePlugin options={options} enableJinja2={enableJinja2} />
|
||||
<CharacterCountPlugin setCount={(count) => { setCount(count) }} onChange={onChange} />
|
||||
<InitialValuePlugin value={value} options={options} enableJinja2={enableJinja2} />
|
||||
{enableJinja2 && <BlurPlugin />}
|
||||
</div>
|
||||
</LexicalComposer>
|
||||
);
|
||||
|
||||
@@ -36,7 +36,7 @@ const VariableComponent: React.FC<{ nodeKey: NodeKey; data: Suggestion }> = ({
|
||||
return (
|
||||
<span
|
||||
onClick={handleClick}
|
||||
className={clsx('rb:border rb:rounded-md rb:bg-white rb:text-[12px] rb:inline-flex rb:items-center rb:py-0.5 rb:px-1.5 rb:mx-0.5 rb:cursor-pointer', {
|
||||
className={clsx('rb:border rb:rounded-md rb:bg-white rb:text-[10px] rb:inline-flex rb:items-center rb:py-0 rb:px-1.5 rb:mx-0.5 rb:cursor-pointer', {
|
||||
'rb:border-[#155EEF]': isSelected,
|
||||
'rb:border-[#DFE4ED]': !isSelected
|
||||
})}
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import { useEffect, useState, type FC } from 'react';
|
||||
import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
|
||||
import { $getSelection, $isRangeSelection } from 'lexical';
|
||||
import { $getSelection, $isRangeSelection, $isTextNode } from 'lexical';
|
||||
|
||||
import { INSERT_VARIABLE_COMMAND } from '../commands';
|
||||
import type { NodeProperties } from '../../../types'
|
||||
@@ -96,7 +96,9 @@ const AutocompletePlugin: FC<{ options: Suggestion[], enableJinja2?: boolean }>
|
||||
const textAfter = nodeText.substring(anchorOffset);
|
||||
const newText = textBefore + `{{${suggestion.value}}}` + textAfter;
|
||||
|
||||
anchorNode.setTextContent(newText);
|
||||
if ($isTextNode(anchorNode)) {
|
||||
anchorNode.setTextContent(newText);
|
||||
}
|
||||
|
||||
// 设置光标位置到插入文本之后
|
||||
const newOffset = textBefore.length + `{{${suggestion.value}}}`.length;
|
||||
@@ -129,6 +131,8 @@ const AutocompletePlugin: FC<{ options: Suggestion[], enableJinja2?: boolean }>
|
||||
}
|
||||
return (
|
||||
<div
|
||||
data-autocomplete-popup="true"
|
||||
onMouseDown={(e) => e.preventDefault()}
|
||||
style={{
|
||||
position: 'fixed',
|
||||
top: popupPosition.top,
|
||||
|
||||
@@ -0,0 +1,33 @@
|
||||
import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
|
||||
import { useEffect } from 'react';
|
||||
import { $setSelection } from 'lexical';
|
||||
|
||||
export default function BlurPlugin() {
|
||||
const [editor] = useLexicalComposerContext();
|
||||
|
||||
useEffect(() => {
|
||||
return editor.registerRootListener((rootElement) => {
|
||||
if (rootElement) {
|
||||
const handleBlur = (e: FocusEvent) => {
|
||||
// 检查是否点击了自动完成弹窗
|
||||
const target = e.target as HTMLElement;
|
||||
console.log('target', target)
|
||||
if (target?.closest('[data-autocomplete-popup="true"]')) {
|
||||
return;
|
||||
}
|
||||
|
||||
editor.update(() => {
|
||||
$setSelection(null);
|
||||
});
|
||||
};
|
||||
|
||||
rootElement.addEventListener('blur', handleBlur);
|
||||
return () => {
|
||||
rootElement.removeEventListener('blur', handleBlur);
|
||||
};
|
||||
}
|
||||
});
|
||||
}, [editor]);
|
||||
|
||||
return null;
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user