去掉MCP框架，重构
This commit is contained in:
lixinyue
@@ -146,6 +146,8 @@ 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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BROKER_URL: str = os.getenv("BROKER_URL", f"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}/{CELERY_BROKER}")
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RESULT_BACKEND: str = os.getenv("RESULT_BACKEND", f"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}/{CELERY_BACKEND}")
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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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46
api/app/core/memory/agent/config/problem_extension_config.py
Normal file
46
api/app/core/memory/agent/config/problem_extension_config.py
Normal file
@@ -0,0 +1,46 @@
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"""
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Problem_Extension优化配置
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在应用启动时应用这些优化配置
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"""
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# 优化配置
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PROBLEM_EXTENSION_CONFIG = {
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# 缓存配置
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"cache_enabled": True,
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"cache_ttl": 3600, # 1小时
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# 超时配置
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"llm_timeout": 8.0, # 8秒超时
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"max_retries": 1, # 最多重试1次
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# 批处理配置
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"max_questions_per_batch": 10,
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"batch_timeout": 15.0,
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# 性能监控
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"monitoring_enabled": True,
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"slow_query_threshold": 10.0, # 10秒为慢查询
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# 连接池配置
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"client_pool_size": 3,
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# 简化模式
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"use_simplified_prompt": True,
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"skip_history_for_simple_queries": True,
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}
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def apply_optimizations():
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"""应用优化配置"""
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import os
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# 设置环境变量
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for key, value in PROBLEM_EXTENSION_CONFIG.items():
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env_key = f"PROBLEM_EXTENSION_{key.upper()}"
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os.environ[env_key] = str(value)
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print("✅ Problem_Extension优化配置已应用")
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if __name__ == "__main__":
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apply_optimizations()
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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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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}",
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exc_info=True
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)
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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]
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except (IndexError, AttributeError):
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logger.warning(
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f"[create_input_message] Could not extract namespace from session_id: {session_id}"
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)
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namespace = "unknown"
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# Handle verified_data extraction (backward compatibility)
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# This regex-based extraction is kept for compatibility with existing data formats
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if 'verified_data' in str(last_message):
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try:
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messages_last = str(last_message).replace('\\n', '').replace('\\', '')
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query_match = re.findall(r'"query": "(.*?)",', messages_last)
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if query_match:
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last_message = query_match[0]
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logger.debug(
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f"[create_input_message] Extracted query from verified_data: {last_message}"
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)
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except Exception as e:
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logger.warning(
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f"[create_input_message] Failed to extract query from verified_data: {e}"
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)
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# Construct tool call message
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tool_call_id = f"{session_id}_{uuid_str}"
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logger.info(
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f"[create_input_message] Creating tool call for '{tool_name}' "
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f"with ID: {tool_call_id}"
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)
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# Build tool arguments
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tool_args = {
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"sentence": last_message,
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"sessionid": session_id,
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"messages_id": str(uuid_str),
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"search_switch": search_switch,
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"apply_id": apply_id,
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"group_id": group_id,
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"memory_config": memory_config,
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}
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return {
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"messages": [
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AIMessage(
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content="",
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tool_calls=[{
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"name": tool_name,
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"args": tool_args,
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"id": tool_call_id
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}]
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)
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]
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}
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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 @@
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import json
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import time
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from app.core.logging_config import get_agent_logger
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from app.db import get_db
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from app.core.memory.agent.models.problem_models import ProblemExtensionResponse
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from app.core.memory.agent.utils.llm_tools import (
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PROJECT_ROOT_,
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ReadState,
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)
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from app.core.memory.agent.utils.redis_tool import store
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from app.core.memory.agent.utils.session_tools import SessionService
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from app.core.memory.agent.utils.template_tools import TemplateService
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from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
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template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
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db_session = next(get_db())
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logger = get_agent_logger(__name__)
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class ProblemNodeService(LLMServiceMixin):
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"""问题处理节点服务类"""
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def __init__(self):
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super().__init__()
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self.template_service = TemplateService(template_root)
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# 创建全局服务实例
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problem_service = ProblemNodeService()
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async def Split_The_Problem(state: ReadState) -> ReadState:
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"""问题分解节点"""
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# 从状态中获取数据
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content = state.get('data', '')
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group_id = state.get('group_id', '')
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memory_config = state.get('memory_config', None)
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history = await SessionService(store).get_history(group_id, group_id, group_id)
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system_prompt = await problem_service.template_service.render_template(
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template_name='problem_breakdown_prompt.jinja2',
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operation_name='split_the_problem',
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history=history,
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sentence=content
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)
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try:
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# 使用优化的LLM服务
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structured = await problem_service.call_llm_structured(
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state=state,
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db_session=db_session,
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system_prompt=system_prompt,
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response_model=ProblemExtensionResponse,
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fallback_value=[]
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)
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# 添加更详细的日志记录
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logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
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# 验证结构化响应
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if not structured or not hasattr(structured, 'root'):
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logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
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split_result = json.dumps([], ensure_ascii=False)
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elif not structured.root:
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logger.warning("Split_The_Problem: 结构化响应的root为空")
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split_result = json.dumps([], ensure_ascii=False)
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else:
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split_result = json.dumps(
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[item.model_dump() for item in structured.root],
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ensure_ascii=False
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)
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split_result_dict = []
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for index, item in enumerate(json.loads(split_result)):
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split_data = {
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"id": f"Q{index+1}",
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"question": item['extended_question'],
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"type": item['type'],
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"reason": item['reason']
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}
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split_result_dict.append(split_data)
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logger.info(f"Split_The_Problem: 成功生成 {len(structured.root) if structured.root else 0} 个分解项")
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result = {
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"context": split_result,
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"original": content,
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"_intermediate": {
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"type": "problem_split",
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"title": "问题拆分",
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"data": split_result_dict,
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"original_query": content
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}
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}
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except Exception as e:
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logger.error(
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f"Split_The_Problem failed: {e}",
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exc_info=True
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)
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# 提供更详细的错误信息
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error_details = {
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"error_type": type(e).__name__,
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"error_message": str(e),
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"content_length": len(content),
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"llm_model_id": memory_config.llm_model_id if memory_config else None
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}
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logger.error(f"Split_The_Problem error details: {error_details}")
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# 创建默认的空结果
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result = {
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"context": json.dumps([], ensure_ascii=False),
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"original": content,
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"error": str(e),
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"_intermediate": {
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"type": "problem_split",
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"title": "问题拆分",
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"data": [],
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"original_query": content,
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"error": error_details
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}
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}
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# 返回更新后的状态，包含spit_context字段
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return {"spit_data": result}
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async def Problem_Extension(state: ReadState) -> ReadState:
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"""问题扩展节点"""
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# 获取原始数据和分解结果
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start = time.time()
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content = state.get('data', '')
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data = state.get('spit_data', '')['context']
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group_id = state.get('group_id', '')
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storage_type = state.get('storage_type', '')
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user_rag_memory_id = state.get('user_rag_memory_id', '')
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memory_config = state.get('memory_config', None)
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databasets = {}
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try:
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data = json.loads(data)
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for i in data:
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databasets[i['extended_question']] = i['type']
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except (json.JSONDecodeError, KeyError, TypeError) as e:
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logger.error(f"Problem_Extension: 数据解析失败: {e}")
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# 使用空字典作为fallback
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databasets = {}
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data = []
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history = await SessionService(store).get_history(group_id, group_id, group_id)
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system_prompt = await problem_service.template_service.render_template(
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template_name='Problem_Extension_prompt.jinja2',
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operation_name='problem_extension',
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history=history,
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questions=databasets
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)
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try:
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# 使用优化的LLM服务
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response_content = await problem_service.call_llm_structured(
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state=state,
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db_session=db_session,
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system_prompt=system_prompt,
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response_model=ProblemExtensionResponse,
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fallback_value=[]
|
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)
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logger.info(f"Problem_Extension: 开始处理问题扩展，问题数量: {len(databasets)}")
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# 验证结构化响应
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if not response_content or not hasattr(response_content, 'root'):
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logger.warning("Problem_Extension: 结构化响应为空或格式不正确")
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aggregated_dict = {}
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elif not response_content.root:
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logger.warning("Problem_Extension: 结构化响应的root为空")
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aggregated_dict = {}
|
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else:
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# Aggregate results by original question
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aggregated_dict = {}
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for item in response_content.root:
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try:
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key = getattr(item, "original_question", None) or (
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item.get("original_question") if isinstance(item, dict) else None
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)
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value = getattr(item, "extended_question", None) or (
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item.get("extended_question") if isinstance(item, dict) else None
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)
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if not key or not value:
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logger.warning(f"Problem_Extension: 跳过无效项: key={key}, value={value}")
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continue
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aggregated_dict.setdefault(key, []).append(value)
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except Exception as item_error:
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logger.warning(f"Problem_Extension: 处理项目时出错: {item_error}")
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continue
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||||
|
||||
logger.info(f"Problem_Extension: 成功生成 {len(aggregated_dict)} 个扩展问题组")
|
||||
|
||||
except Exception as e:
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logger.error(
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||||
f"LLM call failed for Problem_Extension: {e}",
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||||
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 +1,53 @@
|
||||
|
||||
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,
|
||||
)
|
||||
print('-----------')
|
||||
print(result)
|
||||
print('-----------')
|
||||
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()
|
||||
|
||||
|
||||
@@ -8,11 +8,12 @@ Reference:
|
||||
import logging
|
||||
import html
|
||||
from typing import Any, cast
|
||||
from graspologic.partition import hierarchical_leiden
|
||||
from graspologic.utils import largest_connected_component
|
||||
# from graspologic.partition import hierarchical_leiden
|
||||
# from graspologic.utils import largest_connected_component
|
||||
import networkx as nx
|
||||
from networkx import is_empty
|
||||
|
||||
hierarchical_leiden=''
|
||||
largest_connected_component=''
|
||||
|
||||
def _stabilize_graph(graph: nx.Graph) -> nx.Graph:
|
||||
"""Ensure an undirected graph with the same relationships will always be read the same way."""
|
||||
|
||||
@@ -13,26 +13,26 @@ from threading import Lock
|
||||
from typing import Any, AsyncGenerator, Dict, List, Optional
|
||||
|
||||
import redis
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from app.core.config import settings
|
||||
from app.core.logging_config import get_config_logger, get_logger
|
||||
from app.core.memory.agent.langgraph_graph.read_graph import make_read_graph
|
||||
from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph
|
||||
from app.core.memory.agent.logger_file.log_streamer import LogStreamer
|
||||
from app.core.memory.agent.utils.mcp_tools import get_mcp_server_config
|
||||
from app.core.memory.agent.utils.messages_tools import merge_multiple_search_results, reorder_output_results
|
||||
from app.core.memory.agent.utils.type_classifier import status_typle
|
||||
from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
|
||||
from app.repositories.memory_short_repository import ShortTermMemoryRepository
|
||||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||||
from app.db import get_db_context
|
||||
from app.models.knowledge_model import Knowledge, KnowledgeType
|
||||
from app.repositories.memory_short_repository import ShortTermMemoryRepository
|
||||
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
|
||||
from app.schemas.memory_config_schema import ConfigurationError
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
from app.services.memory_konwledges_server import (
|
||||
write_rag,
|
||||
)
|
||||
from langchain_mcp_adapters.client import MultiServerMCPClient
|
||||
from langchain_mcp_adapters.tools import load_mcp_tools
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy import func
|
||||
from sqlalchemy.orm import Session
|
||||
@@ -55,18 +55,16 @@ class MemoryAgentService:
|
||||
self.user_locks: Dict[str, Lock] = {}
|
||||
self.locks_lock = Lock()
|
||||
|
||||
def writer_messages_deal(self,messages,start_time,group_id,config_id,message):
|
||||
messages = str(messages).replace("'", '"').replace('\\n', '').replace('\n', '').replace('\\', '')
|
||||
countext = re.findall(r'"status": "(.*?)",', messages)[0]
|
||||
def writer_messages_deal(self, messages, start_time, group_id, config_id, message, context):
|
||||
duration = time.time() - start_time
|
||||
|
||||
if countext == 'success':
|
||||
if str(messages) == 'success':
|
||||
logger.info(f"Write operation successful for group {group_id} with config_id {config_id}")
|
||||
# 记录成功的操作
|
||||
if audit_logger:
|
||||
audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=True,
|
||||
duration=duration, details={"message_length": len(message)})
|
||||
return countext
|
||||
return context
|
||||
else:
|
||||
logger.warning(f"Write operation failed for group {group_id}")
|
||||
|
||||
@@ -150,8 +148,26 @@ class MemoryAgentService:
|
||||
else:
|
||||
status = "unknown"
|
||||
|
||||
# Add database connection pool status
|
||||
try:
|
||||
from app.db import get_pool_status
|
||||
pool_status = get_pool_status()
|
||||
logger.info(f"Database pool status: {pool_status}")
|
||||
|
||||
# Check if pool usage is too high
|
||||
if pool_status.get("usage_percent", 0) > 80:
|
||||
logger.warning(f"High database pool usage: {pool_status['usage_percent']}%")
|
||||
status = "warning"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get pool status: {e}")
|
||||
pool_status = {"error": str(e)}
|
||||
|
||||
logger.info(f"Health status: {status}")
|
||||
return {"status": status}
|
||||
return {
|
||||
"status": status,
|
||||
"database_pool": pool_status
|
||||
}
|
||||
|
||||
def get_log_content(self) -> str:
|
||||
"""
|
||||
@@ -308,54 +324,42 @@ class MemoryAgentService:
|
||||
audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg)
|
||||
|
||||
raise ValueError(error_msg)
|
||||
mcp_config = get_mcp_server_config()
|
||||
client = MultiServerMCPClient(mcp_config)
|
||||
|
||||
if storage_type == "rag":
|
||||
result = await write_rag(group_id, message, user_rag_memory_id)
|
||||
return result
|
||||
else:
|
||||
async with client.session("data_flow") as session:
|
||||
logger.debug("Connected to MCP Server: data_flow")
|
||||
tools = await load_mcp_tools(session)
|
||||
workflow_errors = [] # Track errors from workflow
|
||||
|
||||
# Pass memory_config to the graph workflow
|
||||
async with make_write_graph(group_id, tools, group_id, group_id, memory_config=memory_config) as graph:
|
||||
logger.debug("Write graph created successfully")
|
||||
|
||||
try:
|
||||
if storage_type == "rag":
|
||||
result = await write_rag(group_id, message, user_rag_memory_id)
|
||||
return result
|
||||
else:
|
||||
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}
|
||||
|
||||
async for event in graph.astream(
|
||||
{"messages": message, "memory_config": memory_config, "errors": []},
|
||||
stream_mode="values",
|
||||
# 获取节点更新信息
|
||||
async for update_event in graph.astream(
|
||||
initial_state,
|
||||
stream_mode="updates",
|
||||
config=config
|
||||
):
|
||||
messages = event.get('messages')
|
||||
# Capture any errors from the state
|
||||
if event.get('errors'):
|
||||
workflow_errors.extend(event.get('errors', []))
|
||||
|
||||
# Check for workflow errors
|
||||
if workflow_errors:
|
||||
error_details = "; ".join([f"{e['tool']}: {e['error']}" for e in workflow_errors])
|
||||
logger.error(f"Write workflow failed with errors: {error_details}")
|
||||
|
||||
if audit_logger:
|
||||
duration = time.time() - start_time
|
||||
audit_logger.log_operation(
|
||||
operation="WRITE",
|
||||
config_id=config_id,
|
||||
group_id=group_id,
|
||||
success=False,
|
||||
duration=duration,
|
||||
error=error_details
|
||||
)
|
||||
|
||||
raise ValueError(f"Write workflow failed: {error_details}")
|
||||
|
||||
return self.writer_messages_deal(messages, start_time, group_id, config_id, message)
|
||||
|
||||
for node_name, node_data in update_event.items():
|
||||
if 'save_neo4j' == node_name:
|
||||
massages = node_data
|
||||
massagesstatus = massages.get('write_result')['status']
|
||||
contents = massages.get('write_result')
|
||||
return self.writer_messages_deal(massagesstatus, start_time, group_id, config_id, message, contents)
|
||||
except Exception as e:
|
||||
# Ensure proper error handling and logging
|
||||
error_msg = f"Write operation failed: {str(e)}"
|
||||
logger.error(error_msg)
|
||||
if audit_logger:
|
||||
duration = time.time() - start_time
|
||||
audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
|
||||
|
||||
|
||||
async def read_memory(
|
||||
self,
|
||||
group_id: str,
|
||||
@@ -394,8 +398,9 @@ class MemoryAgentService:
|
||||
|
||||
import time
|
||||
start_time = time.time()
|
||||
ori_message=message
|
||||
end_user_id=group_id
|
||||
ori_message=message
|
||||
|
||||
# Resolve config_id if None using end_user's connected config
|
||||
if config_id is None:
|
||||
try:
|
||||
@@ -408,15 +413,15 @@ class MemoryAgentService:
|
||||
raise # Re-raise our specific error
|
||||
logger.error(f"Failed to get connected config for end_user {group_id}: {e}")
|
||||
raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}")
|
||||
|
||||
|
||||
logger.info(f"Read operation for group {group_id} with config_id {config_id}")
|
||||
|
||||
|
||||
# 导入审计日志记录器
|
||||
try:
|
||||
from app.core.memory.utils.log.audit_logger import audit_logger
|
||||
except ImportError:
|
||||
audit_logger = None
|
||||
|
||||
|
||||
# Get group lock to prevent concurrent processing
|
||||
group_lock = self.get_group_lock(group_id)
|
||||
|
||||
@@ -432,7 +437,7 @@ class MemoryAgentService:
|
||||
except ConfigurationError as e:
|
||||
error_msg = f"Failed to load configuration for config_id: {config_id}: {e}"
|
||||
logger.error(error_msg)
|
||||
|
||||
|
||||
# Log failed operation
|
||||
if audit_logger:
|
||||
duration = time.time() - start_time
|
||||
@@ -444,305 +449,133 @@ class MemoryAgentService:
|
||||
duration=duration,
|
||||
error=error_msg
|
||||
)
|
||||
|
||||
|
||||
raise ValueError(error_msg)
|
||||
|
||||
|
||||
# Step 2: Prepare history
|
||||
history.append({"role": "user", "content": message})
|
||||
logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}")
|
||||
|
||||
|
||||
# Step 3: Initialize MCP client and execute read workflow
|
||||
mcp_config = get_mcp_server_config()
|
||||
client = MultiServerMCPClient(mcp_config)
|
||||
|
||||
async with client.session('data_flow') as session:
|
||||
session_start = time.time()
|
||||
logger.debug("Connected to MCP Server: data_flow")
|
||||
|
||||
tools_start = time.time()
|
||||
tools = await load_mcp_tools(session)
|
||||
tools_time = time.time() - tools_start
|
||||
logger.info(f"[PERF] MCP tools loading took: {tools_time:.4f}s")
|
||||
|
||||
outputs = []
|
||||
intermediate_outputs = []
|
||||
seen_intermediates = set() # Track seen intermediate outputs to avoid duplicates
|
||||
|
||||
# Pass memory_config to the graph workflow
|
||||
graph_start = time.time()
|
||||
async with make_read_graph(group_id, tools, search_switch, group_id, group_id, memory_config=memory_config, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id) as graph:
|
||||
graph_init_time = time.time() - graph_start
|
||||
logger.info(f"[PERF] Graph initialization took: {graph_init_time:.4f}s")
|
||||
|
||||
start = time.time()
|
||||
try:
|
||||
async with make_read_graph() as graph:
|
||||
config = {"configurable": {"thread_id": group_id}}
|
||||
workflow_errors = [] # Track errors from workflow
|
||||
|
||||
event_count = 0
|
||||
async for event in graph.astream(
|
||||
{"messages": history, "memory_config": memory_config, "errors": []},
|
||||
stream_mode="values",
|
||||
# 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
|
||||
):
|
||||
event_count += 1
|
||||
event_start = time.time()
|
||||
messages = event.get('messages')
|
||||
# Capture any errors from the state
|
||||
if event.get('errors'):
|
||||
workflow_errors.extend(event.get('errors', []))
|
||||
for node_name, node_data in update_event.items():
|
||||
|
||||
for msg in messages:
|
||||
msg_content = msg.content
|
||||
msg_role = msg.__class__.__name__.lower().replace("message", "")
|
||||
outputs.append({
|
||||
"role": msg_role,
|
||||
"content": msg_content
|
||||
})
|
||||
# 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']
|
||||
|
||||
# Extract intermediate outputs
|
||||
if hasattr(msg, 'content'):
|
||||
try:
|
||||
# Handle MCP content format: [{'type': 'text', 'text': '...'}]
|
||||
content_to_parse = msg_content
|
||||
if isinstance(msg_content, list):
|
||||
for block in msg_content:
|
||||
if isinstance(block, dict) and block.get('type') == 'text':
|
||||
content_to_parse = block.get('text', '')
|
||||
break
|
||||
else:
|
||||
continue # No text block found
|
||||
spit_data = node_data.get('spit_data', {}).get('_intermediate', None)
|
||||
if spit_data and spit_data != [] and spit_data != {}:
|
||||
_intermediate_outputs.append(spit_data)
|
||||
|
||||
# Try to parse content as JSON
|
||||
if isinstance(content_to_parse, str):
|
||||
try:
|
||||
parsed = json.loads(content_to_parse)
|
||||
if isinstance(parsed, dict):
|
||||
# Check for single intermediate output
|
||||
if '_intermediate' in parsed:
|
||||
intermediate_data = parsed['_intermediate']
|
||||
output_key = self._create_intermediate_key(intermediate_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)
|
||||
|
||||
if output_key not in seen_intermediates:
|
||||
seen_intermediates.add(output_key)
|
||||
intermediate_outputs.append(self._format_intermediate_output(intermediate_data))
|
||||
# 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)
|
||||
|
||||
# Check for multiple intermediate outputs (from Retrieve)
|
||||
if '_intermediates' in parsed:
|
||||
for intermediate_data in parsed['_intermediates']:
|
||||
output_key = self._create_intermediate_key(intermediate_data)
|
||||
# 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)
|
||||
|
||||
if output_key not in seen_intermediates:
|
||||
seen_intermediates.add(output_key)
|
||||
intermediate_outputs.append(self._format_intermediate_output(intermediate_data))
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to extract intermediate output: {e}")
|
||||
|
||||
event_time = time.time() - event_start
|
||||
logger.info(f"[PERF] Event {event_count} processing took: {event_time:.4f}s")
|
||||
# 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)
|
||||
|
||||
workflow_duration = time.time() - start
|
||||
session_duration = time.time() - session_start
|
||||
logger.info(f"[PERF] Read graph workflow completed in {workflow_duration}s")
|
||||
logger.info(f"[PERF] Total session duration: {session_duration:.4f}s")
|
||||
logger.info(f"[PERF] Total events processed: {event_count}")
|
||||
# Extract final answer
|
||||
final_answer = ""
|
||||
for messages in outputs:
|
||||
if messages['role'] == 'tool':
|
||||
message = messages['content']
|
||||
_intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
|
||||
|
||||
# Handle MCP content format: [{'type': 'text', 'text': '...'}]
|
||||
if isinstance(message, list):
|
||||
# Extract text from MCP content blocks
|
||||
for block in message:
|
||||
if isinstance(block, dict) and block.get('type') == 'text':
|
||||
message = block.get('text', '')
|
||||
break
|
||||
else:
|
||||
continue # No text block found
|
||||
optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
|
||||
result = reorder_output_results(optimized_outputs)
|
||||
|
||||
try:
|
||||
parsed = json.loads(message) if isinstance(message, str) else message
|
||||
if isinstance(parsed, dict):
|
||||
if parsed.get('status') == 'success':
|
||||
summary_result = parsed.get('summary_result')
|
||||
if summary_result:
|
||||
final_answer = summary_result
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
pass
|
||||
# Log successful operation
|
||||
if audit_logger:
|
||||
duration = time.time() - start_time
|
||||
audit_logger.log_operation(
|
||||
operation="READ",
|
||||
config_id=config_id,
|
||||
group_id=group_id,
|
||||
success=True,
|
||||
duration=duration
|
||||
)
|
||||
|
||||
# 记录成功的操作
|
||||
total_duration = time.time() - start_time
|
||||
|
||||
# Check for workflow errors
|
||||
if workflow_errors:
|
||||
error_details = "; ".join([f"{e['tool']}: {e['error']}" for e in workflow_errors])
|
||||
logger.warning(f"Read workflow completed with errors: {error_details}")
|
||||
retrieved_content = []
|
||||
repo = ShortTermMemoryRepository(db)
|
||||
if str(search_switch).strip() != "2":
|
||||
for intermediate in result:
|
||||
intermediate_type = intermediate['type']
|
||||
if intermediate_type == "search_result":
|
||||
query = intermediate['query']
|
||||
raw_results = intermediate['raw_results']
|
||||
reranked_results = raw_results.get('reranked_results', [])
|
||||
try:
|
||||
statements = [statement['statement'] for statement in
|
||||
reranked_results.get('statements', [])]
|
||||
except Exception:
|
||||
statements = []
|
||||
statements = list(set(statements))
|
||||
retrieved_content.append({query: statements})
|
||||
if retrieved_content == []:
|
||||
retrieved_content = ''
|
||||
if '信息不足，无法回答。' != str(summary) and str(search_switch).strip() != "2": # and retrieved_content!=[]
|
||||
# 使用 upsert 方法
|
||||
repo.upsert(
|
||||
end_user_id=end_user_id, # 确保这个变量在作用域内
|
||||
messages=ori_message,
|
||||
aimessages=summary,
|
||||
retrieved_content=retrieved_content,
|
||||
search_switch=str(search_switch)
|
||||
)
|
||||
print("写入成功")
|
||||
|
||||
return {
|
||||
"answer": summary,
|
||||
"intermediate_outputs": result
|
||||
}
|
||||
except Exception as e:
|
||||
# Ensure proper error handling and logging
|
||||
error_msg = f"Read operation failed: {str(e)}"
|
||||
logger.error(error_msg)
|
||||
if audit_logger:
|
||||
duration = time.time() - start_time
|
||||
audit_logger.log_operation(
|
||||
operation="READ",
|
||||
config_id=config_id,
|
||||
group_id=group_id,
|
||||
success=False,
|
||||
duration=total_duration,
|
||||
error=error_details,
|
||||
details={
|
||||
"search_switch": search_switch,
|
||||
"history_length": len(history),
|
||||
"intermediate_outputs_count": len(intermediate_outputs),
|
||||
"has_answer": bool(final_answer),
|
||||
"errors": workflow_errors
|
||||
}
|
||||
duration=duration,
|
||||
error=error_msg
|
||||
)
|
||||
|
||||
# Raise error if no answer was produced
|
||||
if not final_answer:
|
||||
raise ValueError(f"Read workflow failed: {error_details}")
|
||||
|
||||
if audit_logger and not workflow_errors:
|
||||
audit_logger.log_operation(
|
||||
operation="READ",
|
||||
config_id=config_id,
|
||||
group_id=group_id,
|
||||
success=True,
|
||||
duration=total_duration,
|
||||
details={
|
||||
"search_switch": search_switch,
|
||||
"history_length": len(history),
|
||||
"intermediate_outputs_count": len(intermediate_outputs),
|
||||
"has_answer": bool(final_answer)
|
||||
}
|
||||
)
|
||||
retrieved_content=[]
|
||||
repo = ShortTermMemoryRepository(db)
|
||||
if str(search_switch)!="2":
|
||||
for intermediate in intermediate_outputs:
|
||||
print(intermediate)
|
||||
intermediate_type=intermediate['type']
|
||||
if intermediate_type=="search_result":
|
||||
query=intermediate['query']
|
||||
raw_results=intermediate['raw_results']
|
||||
reranked_results=raw_results.get('reranked_results',[])
|
||||
try:
|
||||
statements=[statement['statement'] for statement in reranked_results.get('statements', [])]
|
||||
except Exception:
|
||||
statements=[]
|
||||
statements=list(set(statements))
|
||||
retrieved_content.append({query:statements})
|
||||
if retrieved_content==[]:
|
||||
retrieved_content=''
|
||||
if '信息不足，无法回答。' != str(final_answer) :#and retrieved_content!=[]
|
||||
# 使用 upsert 方法
|
||||
repo.upsert(
|
||||
end_user_id=end_user_id, # 确保这个变量在作用域内
|
||||
messages=ori_message,
|
||||
aimessages=final_answer,
|
||||
retrieved_content=retrieved_content,
|
||||
search_switch=str(search_switch)
|
||||
)
|
||||
print("写入成功")
|
||||
|
||||
|
||||
|
||||
return {
|
||||
"answer": final_answer,
|
||||
"intermediate_outputs": intermediate_outputs
|
||||
}
|
||||
raise ValueError(error_msg)
|
||||
|
||||
def _create_intermediate_key(self, output: Dict) -> str:
|
||||
"""
|
||||
Create a unique key for an intermediate output to detect duplicates.
|
||||
|
||||
Args:
|
||||
output: Intermediate output dictionary
|
||||
|
||||
Returns:
|
||||
Unique string key for this output
|
||||
"""
|
||||
output_type = output.get('type', 'unknown')
|
||||
|
||||
if output_type == 'problem_split':
|
||||
# Use type + original query as key
|
||||
return f"split:{output.get('original_query', '')}"
|
||||
elif output_type == 'problem_extension':
|
||||
# Use type + original query as key
|
||||
return f"extension:{output.get('original_query', '')}"
|
||||
elif output_type == 'search_result':
|
||||
# Use type + query + index as key
|
||||
return f"search:{output.get('query', '')}:{output.get('index', 0)}"
|
||||
elif output_type == 'retrieval_summary':
|
||||
# Use type + query as key
|
||||
return f"summary:{output.get('query', '')}"
|
||||
elif output_type == 'verification':
|
||||
# Use type + query as key
|
||||
return f"verification:{output.get('query', '')}"
|
||||
elif output_type == 'input_summary':
|
||||
# Use type + query as key
|
||||
return f"input_summary:{output.get('query', '')}"
|
||||
else:
|
||||
# Fallback: use JSON representation
|
||||
import json
|
||||
return json.dumps(output, sort_keys=True)
|
||||
|
||||
def _format_intermediate_output(self, output: Dict) -> Dict:
|
||||
"""Format intermediate output for frontend display."""
|
||||
output_type = output.get('type', 'unknown')
|
||||
|
||||
if output_type == 'problem_split':
|
||||
return {
|
||||
'type': 'problem_split',
|
||||
'title': '问题拆分',
|
||||
'data': output.get('data', []),
|
||||
'original_query': output.get('original_query', '')
|
||||
}
|
||||
elif output_type == 'problem_extension':
|
||||
return {
|
||||
'type': 'problem_extension',
|
||||
'title': '问题扩展',
|
||||
'data': output.get('data', {}),
|
||||
'original_query': output.get('original_query', '')
|
||||
}
|
||||
elif output_type == 'search_result':
|
||||
return {
|
||||
'type': 'search_result',
|
||||
'title': f'检索结果 ({output.get("index", 0)}/{output.get("total", 0)})',
|
||||
'query': output.get('query', ''),
|
||||
'raw_results': output.get('raw_results', ''),
|
||||
'index': output.get('index', 0),
|
||||
'total': output.get('total', 0)
|
||||
}
|
||||
elif output_type == 'retrieval_summary':
|
||||
return {
|
||||
'type': 'retrieval_summary',
|
||||
'title': '检索总结',
|
||||
'summary': output.get('summary', ''),
|
||||
'query': output.get('query', ''),
|
||||
'raw_results': output.get('raw_results'),
|
||||
|
||||
}
|
||||
elif output_type == 'verification':
|
||||
return {
|
||||
'type': 'verification',
|
||||
'title': '数据验证',
|
||||
'result': output.get('result', 'unknown'),
|
||||
'reason': output.get('reason', ''),
|
||||
'query': output.get('query', ''),
|
||||
'verified_count': output.get('verified_count', 0)
|
||||
}
|
||||
elif output_type == 'input_summary':
|
||||
return {
|
||||
'type': 'input_summary',
|
||||
'title': '快速答案',
|
||||
'summary': output.get('summary', ''),
|
||||
'query': output.get('query', ''),
|
||||
'raw_results': output.get('raw_results'),
|
||||
|
||||
}
|
||||
else:
|
||||
return output
|
||||
|
||||
async def classify_message_type(self, message: str, config_id: int, db: Session) -> Dict:
|
||||
"""
|
||||
@@ -850,6 +683,7 @@ class MemoryAgentService:
|
||||
# 获取当前空间下的所有宿主
|
||||
from app.repositories import app_repository, end_user_repository
|
||||
from app.schemas.app_schema import App as AppSchema
|
||||
from app.schemas.end_user_schema import EndUser as EndUserSchema
|
||||
|
||||
# 查询应用并转换为 Pydantic 模型
|
||||
apps_orm = app_repository.get_apps_by_workspace_id(db, current_workspace_id)
|
||||
@@ -1147,43 +981,6 @@ class MemoryAgentService:
|
||||
logger.info("Log streaming completed, cleaning up resources")
|
||||
# LogStreamer uses context manager for file handling, so cleanup is automatic
|
||||
|
||||
# async def get_api_docs(self, file_path: Optional[str] = None) -> Dict[str, Any]:
|
||||
# """
|
||||
# Parse and return API documentation
|
||||
|
||||
# Args:
|
||||
# file_path: Optional path to API docs file. If None, uses default path.
|
||||
|
||||
# Returns:
|
||||
# Dict containing parsed API documentation or error information
|
||||
# """
|
||||
# try:
|
||||
# target = file_path or get_default_docs_path()
|
||||
|
||||
# if not os.path.isfile(target):
|
||||
# return {
|
||||
# "success": False,
|
||||
# "msg": "API文档文件不存在",
|
||||
# "error_code": "DOC_NOT_FOUND",
|
||||
# "data": {"path": target}
|
||||
# }
|
||||
|
||||
# data = parse_api_docs(target)
|
||||
# return {
|
||||
# "success": True,
|
||||
# "msg": "解析成功",
|
||||
# "data": data
|
||||
# }
|
||||
# except Exception as e:
|
||||
# logger.error(f"Failed to parse API docs: {e}")
|
||||
# return {
|
||||
# "success": False,
|
||||
# "msg": "解析失败",
|
||||
# "error_code": "DOC_PARSE_ERROR",
|
||||
# "data": {"error": str(e)}
|
||||
# }
|
||||
|
||||
|
||||
def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, Any]:
|
||||
"""
|
||||
获取终端用户关联的记忆配置
|
||||
@@ -1192,20 +989,18 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
|
||||
1. 根据 end_user_id 获取用户的 app_id
|
||||
2. 获取该应用的最新发布版本
|
||||
3. 从发布版本的 config 字段中提取 memory_config_id
|
||||
4. 根据 memory_config_id 查询配置名称
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
db: 数据库会话
|
||||
|
||||
Returns:
|
||||
包含 memory_config_id、config_name 和相关信息的字典
|
||||
包含 memory_config_id 和相关信息的字典
|
||||
|
||||
Raises:
|
||||
ValueError: 当终端用户不存在或应用未发布时
|
||||
"""
|
||||
from app.models.app_release_model import AppRelease
|
||||
from app.models.data_config_model import DataConfig
|
||||
from app.models.end_user_model import EndUser
|
||||
from sqlalchemy import select
|
||||
|
||||
@@ -1239,31 +1034,15 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
|
||||
memory_obj = config.get('memory', {})
|
||||
memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
|
||||
|
||||
# 4. 根据 memory_config_id 查询配置名称
|
||||
config_name = None
|
||||
if memory_config_id:
|
||||
try:
|
||||
# memory_config_id 可能是整数或字符串，需要转换
|
||||
config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
|
||||
data_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
|
||||
if data_config:
|
||||
config_name = data_config.config_name
|
||||
logger.debug(f"Found config_name: {config_name} for config_id: {config_id}")
|
||||
else:
|
||||
logger.warning(f"DataConfig not found for config_id: {config_id}")
|
||||
except (ValueError, TypeError) as e:
|
||||
logger.warning(f"Invalid memory_config_id format: {memory_config_id}, error: {str(e)}")
|
||||
|
||||
result = {
|
||||
"end_user_id": str(end_user_id),
|
||||
"app_id": str(app_id),
|
||||
"release_id": str(latest_release.id),
|
||||
"release_version": latest_release.version,
|
||||
"memory_config_id": memory_config_id,
|
||||
"memory_config_name": config_name
|
||||
"memory_config_id": memory_config_id
|
||||
}
|
||||
|
||||
logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}, config_name={config_name}")
|
||||
logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}")
|
||||
return result
|
||||
|
||||
|
||||
@@ -1271,126 +1050,112 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
|
||||
"""
|
||||
批量获取多个终端用户关联的记忆配置
|
||||
|
||||
通过优化的查询减少数据库往返次数：
|
||||
1. 一次性查询所有 end_user 及其 app_id
|
||||
2. 批量查询所有相关的 app_release
|
||||
3. 批量查询所有相关的 data_config
|
||||
通过以下流程获取配置：
|
||||
1. 批量查询所有 end_user 及其 app_id
|
||||
2. 批量获取所有应用的最新发布版本
|
||||
3. 从发布版本的 config 字段中提取 memory_config_id 和 memory_config_name
|
||||
|
||||
Args:
|
||||
end_user_ids: 终端用户ID列表
|
||||
db: 数据库会话
|
||||
|
||||
Returns:
|
||||
字典，key 为 end_user_id，value 为配置信息字典
|
||||
对于查询失败的用户，value 包含 error 字段
|
||||
字典，key 为 end_user_id，value 为包含 memory_config_id 和 memory_config_name 的字典
|
||||
格式: {
|
||||
"user_id_1": {"memory_config_id": "xxx", "memory_config_name": "xxx"},
|
||||
"user_id_2": {"memory_config_id": None, "memory_config_name": None},
|
||||
...
|
||||
}
|
||||
"""
|
||||
from app.models.app_release_model import AppRelease
|
||||
from app.models.data_config_model import DataConfig
|
||||
from app.models.end_user_model import EndUser
|
||||
from app.models.memory_config_model import MemoryConfig
|
||||
from sqlalchemy import select
|
||||
|
||||
logger.info(f"Batch getting connected configs for {len(end_user_ids)} end users")
|
||||
logger.info(f"Batch getting connected configs for {len(end_user_ids)} end_users")
|
||||
|
||||
result = {}
|
||||
|
||||
# 1. 批量查询所有 end_user 及其 app_id
|
||||
end_users = db.query(EndUser).filter(EndUser.id.in_(end_user_ids)).all()
|
||||
|
||||
# 构建 end_user_id -> end_user 的映射
|
||||
end_user_map = {str(user.id): user for user in end_users}
|
||||
# 创建 end_user_id 到 app_id 的映射
|
||||
user_to_app = {str(eu.id): eu.app_id for eu in end_users}
|
||||
|
||||
# 记录不存在的用户
|
||||
for user_id in end_user_ids:
|
||||
if user_id not in end_user_map:
|
||||
result[user_id] = {
|
||||
"end_user_id": user_id,
|
||||
"memory_config_id": None,
|
||||
"memory_config_name": None,
|
||||
"error": f"终端用户不存在: {user_id}"
|
||||
}
|
||||
# 获取所有相关的 app_id
|
||||
app_ids = list(set(user_to_app.values()))
|
||||
|
||||
if not end_users:
|
||||
logger.warning("No valid end users found")
|
||||
if not app_ids:
|
||||
logger.warning("No valid app_ids found for the provided end_user_ids")
|
||||
# 返回空配置
|
||||
for user_id in end_user_ids:
|
||||
result[user_id] = {"memory_config_id": None, "memory_config_name": None}
|
||||
return result
|
||||
|
||||
# 2. 批量查询所有相关应用的最新发布版本
|
||||
app_ids = [user.app_id for user in end_users]
|
||||
|
||||
# 2. 批量获取所有应用的最新发布版本
|
||||
# 使用子查询找到每个 app 的最新版本
|
||||
from sqlalchemy import and_
|
||||
from sqlalchemy import func
|
||||
|
||||
# 查询所有相关的活跃发布版本
|
||||
releases = db.query(AppRelease).filter(
|
||||
and_(
|
||||
AppRelease.app_id.in_(app_ids),
|
||||
AppRelease.is_active.is_(True)
|
||||
subq = (
|
||||
select(
|
||||
AppRelease.app_id,
|
||||
func.max(AppRelease.version).label('max_version')
|
||||
)
|
||||
).order_by(AppRelease.app_id, AppRelease.version.desc()).all()
|
||||
.where(AppRelease.app_id.in_(app_ids), AppRelease.is_active.is_(True))
|
||||
.group_by(AppRelease.app_id)
|
||||
.subquery()
|
||||
)
|
||||
|
||||
# 构建 app_id -> latest_release 的映射（每个 app 只保留最新版本）
|
||||
app_release_map = {}
|
||||
for release in releases:
|
||||
app_id_str = str(release.app_id)
|
||||
if app_id_str not in app_release_map:
|
||||
app_release_map[app_id_str] = release
|
||||
stmt = (
|
||||
select(AppRelease)
|
||||
.join(
|
||||
subq,
|
||||
(AppRelease.app_id == subq.c.app_id) & (AppRelease.version == subq.c.max_version)
|
||||
)
|
||||
.where(AppRelease.is_active.is_(True))
|
||||
)
|
||||
|
||||
# 3. 收集所有 memory_config_id
|
||||
latest_releases = db.scalars(stmt).all()
|
||||
|
||||
# 创建 app_id 到 release 的映射
|
||||
app_to_release = {str(release.app_id): release for release in latest_releases}
|
||||
|
||||
# 3. 提取所有 memory_config_id
|
||||
memory_config_ids = []
|
||||
for release in app_release_map.values():
|
||||
for release in latest_releases:
|
||||
config = release.config or {}
|
||||
memory_obj = config.get('memory', {})
|
||||
memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
|
||||
if memory_config_id:
|
||||
try:
|
||||
config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
|
||||
memory_config_ids.append(config_id)
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
memory_config_ids.append(memory_config_id)
|
||||
|
||||
# 4. 批量查询所有 data_config
|
||||
config_name_map = {}
|
||||
# 4. 批量查询 memory_config_name
|
||||
memory_configs = {}
|
||||
if memory_config_ids:
|
||||
data_configs = db.query(DataConfig).filter(
|
||||
DataConfig.config_id.in_(memory_config_ids)
|
||||
).all()
|
||||
config_name_map = {config.config_id: config.config_name for config in data_configs}
|
||||
configs = db.query(MemoryConfig).filter(MemoryConfig.id.in_(memory_config_ids)).all()
|
||||
memory_configs = {str(cfg.id): cfg.config_name for cfg in configs}
|
||||
|
||||
# 5. 组装结果
|
||||
for user in end_users:
|
||||
user_id = str(user.id)
|
||||
app_id = str(user.app_id)
|
||||
|
||||
# 检查是否有发布版本
|
||||
if app_id not in app_release_map:
|
||||
result[user_id] = {
|
||||
"end_user_id": user_id,
|
||||
"memory_config_id": None,
|
||||
"memory_config_name": None,
|
||||
"error": f"应用未发布: {app_id}"
|
||||
}
|
||||
for user_id in end_user_ids:
|
||||
app_id = user_to_app.get(user_id)
|
||||
if not app_id:
|
||||
result[user_id] = {"memory_config_id": None, "memory_config_name": None}
|
||||
continue
|
||||
|
||||
release = app_release_map[app_id]
|
||||
release = app_to_release.get(str(app_id))
|
||||
if not release:
|
||||
result[user_id] = {"memory_config_id": None, "memory_config_name": None}
|
||||
continue
|
||||
|
||||
# 提取 memory_config_id
|
||||
config = release.config or {}
|
||||
memory_obj = config.get('memory', {})
|
||||
memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
|
||||
|
||||
# 获取 config_name
|
||||
config_name = None
|
||||
if memory_config_id:
|
||||
try:
|
||||
config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
|
||||
config_name = config_name_map.get(config_id)
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
memory_config_name = memory_configs.get(memory_config_id) if memory_config_id else None
|
||||
|
||||
result[user_id] = {
|
||||
"end_user_id": user_id,
|
||||
"memory_config_id": memory_config_id,
|
||||
"memory_config_name": config_name
|
||||
"memory_config_name": memory_config_name
|
||||
}
|
||||
|
||||
logger.info(f"Successfully retrieved batch configs: total={len(result)}, with_config={sum(1 for v in result.values() if v.get('memory_config_id'))}")
|
||||
logger.info(f"Successfully retrieved {len(result)} connected configs")
|
||||
return result
|
||||
198
api/clear_celery_queue.py
Normal file
198
api/clear_celery_queue.py
Normal file
@@ -0,0 +1,198 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
清空 Celery 队列中的所有消息
|
||||
|
||||
这个脚本会删除 Redis 中 Celery 队列的所有待处理任务
|
||||
"""
|
||||
|
||||
import redis
|
||||
from app.core.config import settings
|
||||
from app.celery_app import celery_app
|
||||
|
||||
|
||||
def clear_celery_queue():
|
||||
"""清空 Celery 队列"""
|
||||
print("🗑️ 清空 Celery 队列")
|
||||
print("=" * 50)
|
||||
|
||||
try:
|
||||
# 连接到 Redis
|
||||
redis_client = redis.Redis(
|
||||
host=settings.REDIS_HOST,
|
||||
port=settings.REDIS_PORT,
|
||||
password=settings.REDIS_PASSWORD,
|
||||
db=settings.CELERY_BROKER,
|
||||
decode_responses=True
|
||||
)
|
||||
|
||||
# 测试连接
|
||||
redis_client.ping()
|
||||
print("✅ Redis 连接成功")
|
||||
|
||||
# 队列名称
|
||||
queue_name = 'localhost_test_wyl'
|
||||
|
||||
# 获取队列长度
|
||||
queue_length = redis_client.llen(queue_name)
|
||||
print(f"📊 队列 '{queue_name}' 当前长度: {queue_length}")
|
||||
|
||||
if queue_length == 0:
|
||||
print("✅ 队列已经是空的，无需清理")
|
||||
return
|
||||
|
||||
# 确认清空
|
||||
print(f"\n⚠️ 警告: 即将删除 {queue_length} 个待处理任务")
|
||||
confirm = input("确认清空队列? (yes/no): ").strip().lower()
|
||||
|
||||
if confirm not in ['yes', 'y']:
|
||||
print("❌ 操作已取消")
|
||||
return
|
||||
|
||||
# 删除队列
|
||||
deleted_count = redis_client.delete(queue_name)
|
||||
print(f"✅ 已删除队列，删除了 {deleted_count} 个键")
|
||||
|
||||
# 验证队列已清空
|
||||
new_length = redis_client.llen(queue_name)
|
||||
print(f"📊 队列 '{queue_name}' 新长度: {new_length}")
|
||||
|
||||
if new_length == 0:
|
||||
print("✅ 队列已成功清空!")
|
||||
else:
|
||||
print(f"⚠️ 队列仍有 {new_length} 个任务")
|
||||
|
||||
# 清理结果后端（可选）
|
||||
print("\n🧹 清理结果后端...")
|
||||
result_keys = redis_client.keys("celery-task-meta-*")
|
||||
if result_keys:
|
||||
deleted_results = redis_client.delete(*result_keys)
|
||||
print(f"✅ 删除了 {deleted_results} 个任务结果")
|
||||
else:
|
||||
print("✅ 没有待清理的任务结果")
|
||||
|
||||
except redis.ConnectionError as e:
|
||||
print(f"❌ Redis 连接失败: {e}")
|
||||
except Exception as e:
|
||||
print(f"❌ 清空队列失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
def clear_all_celery_data():
|
||||
"""清空所有 Celery 相关数据（包括结果）"""
|
||||
print("\n🗑️ 清空所有 Celery 数据")
|
||||
print("=" * 50)
|
||||
|
||||
try:
|
||||
# 连接到 Redis
|
||||
redis_client = redis.Redis(
|
||||
host=settings.REDIS_HOST,
|
||||
port=settings.REDIS_PORT,
|
||||
password=settings.REDIS_PASSWORD,
|
||||
db=settings.CELERY_BROKER,
|
||||
decode_responses=True
|
||||
)
|
||||
|
||||
# 获取所有 Celery 相关的键
|
||||
all_keys = redis_client.keys("*")
|
||||
celery_keys = [k for k in all_keys if 'celery' in k.lower() or 'localhost_test_wyl' in k]
|
||||
|
||||
print(f"📊 找到 {len(celery_keys)} 个 Celery 相关的键")
|
||||
|
||||
if not celery_keys:
|
||||
print("✅ 没有 Celery 数据需要清理")
|
||||
return
|
||||
|
||||
# 显示键列表
|
||||
print("\n📋 Celery 相关的键:")
|
||||
for key in celery_keys[:10]: # 只显示前10个
|
||||
print(f" - {key}")
|
||||
if len(celery_keys) > 10:
|
||||
print(f" ... 还有 {len(celery_keys) - 10} 个键")
|
||||
|
||||
# 确认清空
|
||||
print(f"\n⚠️ 警告: 即将删除 {len(celery_keys)} 个 Celery 相关的键")
|
||||
confirm = input("确认清空所有 Celery 数据? (yes/no): ").strip().lower()
|
||||
|
||||
if confirm not in ['yes', 'y']:
|
||||
print("❌ 操作已取消")
|
||||
return
|
||||
|
||||
# 删除所有键
|
||||
if celery_keys:
|
||||
deleted_count = redis_client.delete(*celery_keys)
|
||||
print(f"✅ 已删除 {deleted_count} 个键")
|
||||
|
||||
print("✅ 所有 Celery 数据已清空!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 清空失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
def show_queue_info():
|
||||
"""显示队列信息"""
|
||||
print("\n📊 队列信息")
|
||||
print("=" * 50)
|
||||
|
||||
try:
|
||||
# 连接到 Redis
|
||||
redis_client = redis.Redis(
|
||||
host=settings.REDIS_HOST,
|
||||
port=settings.REDIS_PORT,
|
||||
password=settings.REDIS_PASSWORD,
|
||||
db=settings.CELERY_BROKER,
|
||||
decode_responses=True
|
||||
)
|
||||
|
||||
# 队列名称
|
||||
queue_name = 'localhost_test_wyl'
|
||||
|
||||
# 获取队列信息
|
||||
queue_length = redis_client.llen(queue_name)
|
||||
print(f"📊 队列 '{queue_name}' 长度: {queue_length}")
|
||||
|
||||
# 获取结果数量
|
||||
result_keys = redis_client.keys("celery-task-meta-*")
|
||||
print(f"📊 任务结果数量: {len(result_keys)}")
|
||||
|
||||
# 获取所有 Celery 键
|
||||
all_keys = redis_client.keys("*")
|
||||
celery_keys = [k for k in all_keys if 'celery' in k.lower() or 'localhost_test_wyl' in k]
|
||||
print(f"📊 Celery 相关键总数: {len(celery_keys)}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 获取队列信息失败: {e}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
print("🚀 Celery 队列清理工具")
|
||||
print("=" * 50)
|
||||
|
||||
while True:
|
||||
print("\n请选择操作:")
|
||||
print("1. 查看队列信息")
|
||||
print("2. 清空队列（只删除待处理任务）")
|
||||
print("3. 清空所有 Celery 数据（包括结果）")
|
||||
print("4. 退出")
|
||||
|
||||
choice = input("\n请输入选项 (1-4): ").strip()
|
||||
|
||||
if choice == '1':
|
||||
show_queue_info()
|
||||
elif choice == '2':
|
||||
clear_celery_queue()
|
||||
elif choice == '3':
|
||||
clear_all_celery_data()
|
||||
elif choice == '4':
|
||||
print("👋 再见!")
|
||||
break
|
||||
else:
|
||||
print("❌ 无效选项，请重新选择")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user