146 lines
6.1 KiB
Python
146 lines
6.1 KiB
Python
from langchain.agents.middleware import AgentState, AgentMiddleware, ModelRequest, ModelResponse
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from langchain_core.messages import convert_to_openai_messages
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from agent.prompt_loader import load_guideline_prompt
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from utils.fastapi_utils import (extract_block_from_system_prompt, format_messages_to_chat_history, get_user_last_message_content)
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from langchain.chat_models import BaseChatModel
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from langgraph.runtime import Runtime
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from typing import Any, Callable
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.outputs import LLMResult
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import logging
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import re
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logger = logging.getLogger('app')
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class GuidelineMiddleware(AgentMiddleware):
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def __init__(self, bot_id: str, model:BaseChatModel, prompt: str, robot_type: str, language: str, user_identifier: str):
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self.model = model
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self.bot_id = bot_id
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processed_system_prompt, guidelines, tool_description, scenarios, terms_list = extract_block_from_system_prompt(prompt)
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self.processed_system_prompt = processed_system_prompt
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self.guidelines = guidelines
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self.tool_description = tool_description
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self.scenarios = scenarios
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self.language = language
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self.user_identifier = user_identifier
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self.robot_type = robot_type
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self.terms_list = terms_list
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if self.robot_type == "general_agent":
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if not self.guidelines:
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self.guidelines = """
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1. General Inquiries
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Condition: User inquiries about products, policies, troubleshooting, factual questions, etc.
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Action: Priority given to invoking the 【Knowledge Base Retrieval】 tool to query the knowledge base.
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2.Social Dialogue
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Condition: User intent involves small talk, greetings, expressions of thanks, compliments, or other non-substantive conversations.
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Action: Provide concise, friendly, and personified natural responses.
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"""
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if not self.tool_description:
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self.tool_description = """
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- **Knowledge Base Retrieval**: For knowledge queries/other inquiries, prioritize searching the knowledge base → rag_retrieve-rag_retrieve
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"""
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def get_guideline_prompt(self, messages: list[dict[str, Any]]) -> str:
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## 处理terms
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terms_analysis = self.get_term_analysis(messages)
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guideline_prompt = ""
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if self.guidelines:
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chat_history = format_messages_to_chat_history(messages)
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query_text = get_user_last_message_content(messages)
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guideline_prompt = load_guideline_prompt(chat_history, query_text, self.guidelines, self.tool_description, self.scenarios, terms_analysis, self.language, self.user_identifier)
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return guideline_prompt
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def get_term_analysis(self, messages: list[dict[str, Any]]) -> str:
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## 处理terms
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terms_analysis = ""
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if self.terms_list:
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logger.info(f"Processing terms: {len(self.terms_list)} terms")
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try:
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from embedding.embedding import process_terms_with_embedding
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query_text = get_user_last_message_content(messages)
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terms_analysis = process_terms_with_embedding(terms_list, self.bot_id, query_text)
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if terms_analysis:
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self.processed_system_prompt = self.processed_system_prompt.replace("#terms#", terms_analysis)
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logger.info(f"Terms analysis completed: {len(terms_analysis)} chars")
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except Exception as e:
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logger.error(f"Error processing terms with embedding: {e}")
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terms_analysis = ""
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else:
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# 当terms_list为空时，删除对应的pkl缓存文件
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try:
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import os
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cache_file = f"projects/cache/{self.bot_id}_terms.pkl"
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if os.path.exists(cache_file):
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os.remove(cache_file)
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logger.info(f"Removed empty terms cache file: {cache_file}")
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except Exception as e:
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logger.error(f"Error removing terms cache file: {e}")
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return terms_analysis
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def before_agent(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
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if not self.guidelines:
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return None
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guideline_prompt = self.get_guideline_prompt(convert_to_openai_messages(state['messages']))
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# 使用回调处理器调用模型
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response = self.model.invoke(
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guideline_prompt,
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config={"callbacks": [BaseCallbackHandler()]}
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)
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# 提取<think>与</think>之间的内容作为thinking
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match = re.search(r'<think>(.*?)</think>', response.content, re.DOTALL)
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response.additional_kwargs["thinking"] = match.group(1).strip() if match else response.content
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messages = state['messages']+[response]
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return {
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"messages": messages
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}
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async def abefore_agent(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
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if not self.guidelines:
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return None
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guideline_prompt = self.get_guideline_prompt(convert_to_openai_messages(state['messages']))
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# 使用回调处理器调用模型
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response = await self.model.ainvoke(
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guideline_prompt,
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config={"callbacks": [BaseCallbackHandler()]}
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)
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# 提取<think>与</think>之间的内容作为thinking
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match = re.search(r'<think>(.*?)</think>', response.content, re.DOTALL)
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response.additional_kwargs["thinking"] = match.group(1).strip() if match else response.content
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messages = state['messages']+[response]
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return {
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"messages": messages
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}
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def wrap_model_call(
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self,
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request: ModelRequest,
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handler: Callable[[ModelRequest], ModelResponse],
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) -> ModelResponse:
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return handler(request.override(system_prompt=self.processed_system_prompt))
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async def awrap_model_call(
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self,
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request: ModelRequest,
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handler: Callable[[ModelRequest], ModelResponse],
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) -> ModelResponse:
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return await handler(request.override(system_prompt=self.processed_system_prompt))
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