569 lines
23 KiB
Python
569 lines
23 KiB
Python
import json
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import os
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import asyncio
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from typing import Union, Optional
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from fastapi import APIRouter, HTTPException, Header
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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import logging
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logger = logging.getLogger('app')
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from utils import (
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Message, ChatRequest, ChatResponse
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)
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from agent.sharded_agent_manager import init_global_sharded_agent_manager
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from utils.api_models import ChatRequestV2
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from agent.prompt_loader import load_guideline_prompt
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from utils.fastapi_utils import (
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process_messages, extract_block_from_system_prompt, format_messages_to_chat_history,
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create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config,
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_get_optimal_batch_size, process_guideline, get_content_from_messages, call_preamble_llm, get_preamble_text, get_language_text,
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create_stream_chunk
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)
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router = APIRouter()
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# 初始化全局助手管理器
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agent_manager = init_global_sharded_agent_manager(
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max_cached_agents=int(os.getenv("MAX_CACHED_AGENTS", "50")),
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shard_count=int(os.getenv("SHARD_COUNT", "16"))
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)
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def get_user_last_message_content(messages: list) -> Optional[dict]:
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"""获取消息列表中的最后一条消息"""
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if not messages or len(messages) == 0:
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return ""
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last_message = messages[-1]
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if last_message and last_message.get('role') == 'user':
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return last_message["content"]
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return ""
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def append_user_last_message(messages: list, content: str) -> bool:
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"""向最后一条用户消息追加内容
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Args:
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messages: 消息列表
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content: 要追加的内容
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condition: 可选条件，如果提供则检查消息角色是否匹配此条件
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Returns:
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bool: 是否成功追加内容
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"""
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if not messages or len(messages) == 0:
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return messages
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last_message = messages[-1]
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if last_message and last_message.get('role') == 'user':
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messages[-1]['content'] += content
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return messages
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async def process_guidelines_and_terms(
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bot_id: str,
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api_key: str,
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model_name: str,
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model_server: str,
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system_prompt: str,
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messages: list,
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agent_manager,
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project_dir: Optional[str],
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generate_cfg: Optional[dict],
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language: str,
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mcp_settings: Optional[list],
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robot_type: str,
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user_identifier: Optional[str]
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) -> tuple:
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"""
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公共函数：处理guideline分析和terms处理，返回agent和analysis结果
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Returns:
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tuple: (agent, processed_system_prompt, guideline_reasoning, terms_analysis)
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"""
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# 提取system_prompt中的guideline和terms
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processed_system_prompt, guidelines, tools, scenarios, terms_list = extract_block_from_system_prompt(system_prompt)
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# # 处理terms
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terms_analysis = ""
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if terms_list:
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logger.info(f"Processing terms: {len(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, bot_id, query_text)
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if terms_analysis:
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processed_system_prompt = 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/{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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# 创建所有任务
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tasks = []
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# 添加agent创建任务
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agent_task = agent_manager.get_or_create_agent(
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bot_id=bot_id,
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project_dir=project_dir,
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model_name=model_name,
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api_key=api_key,
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model_server=model_server,
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generate_cfg=generate_cfg,
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language=language,
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system_prompt=processed_system_prompt,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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tasks.append(agent_task)
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guideline_prompt = ""
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if robot_type == "general_agent":
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if not guidelines:
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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 tools:
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tools = """
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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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if guidelines:
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chat_history = format_messages_to_chat_history(messages)
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guideline_prompt = await load_guideline_prompt(chat_history, guidelines, tools, scenarios, terms_analysis, language, user_identifier)
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guideline_task = process_guideline(
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chat_history=chat_history,
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guideline_prompt=guideline_prompt,
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model_name=model_name,
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api_key=api_key,
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model_server=model_server
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)
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tasks.append(guideline_task)
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# 并发执行所有任务
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all_results = await asyncio.gather(*tasks, return_exceptions=True)
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# 处理结果
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agent = all_results[0] if len(all_results) >0 else None # agent创建的结果
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guideline_reasoning = all_results[1] if len(all_results) >1 else ""
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if guideline_prompt or guideline_reasoning:
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logger.info("Guideline Prompt: %s, Reasoning: %s",
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guideline_prompt.replace('\n', '\\n') if guideline_prompt else "None",
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guideline_reasoning.replace('\n', '\\n') if guideline_reasoning else "None")
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logger.info("System Prompt: %s", processed_system_prompt.replace('\n', '\\n'))
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return agent, processed_system_prompt, guideline_reasoning
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async def enhanced_generate_stream_response(
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agent_manager,
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bot_id: str,
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api_key: str,
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messages: list,
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tool_response: bool,
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model_name: str,
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model_server: str,
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language: str,
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system_prompt: str,
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mcp_settings: Optional[list],
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robot_type: str,
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project_dir: Optional[str],
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generate_cfg: Optional[dict],
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user_identifier: Optional[str]
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):
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"""增强的渐进式流式响应生成器"""
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try:
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# 第一阶段：并行启动preamble_text生成和第二阶段处理
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query_text = get_user_last_message_content(messages)
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chat_history = format_messages_to_chat_history(messages)
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# 创建preamble_text生成任务
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preamble_text, system_prompt = get_preamble_text(language, system_prompt)
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preamble_task = asyncio.create_task(
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call_preamble_llm(chat_history, query_text, preamble_text, language, model_name, api_key, model_server)
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)
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# 创建guideline分析和agent创建任务
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guidelines_task = asyncio.create_task(
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process_guidelines_and_terms(
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bot_id=bot_id,
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api_key=api_key,
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model_name=model_name,
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model_server=model_server,
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system_prompt=system_prompt,
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messages=messages,
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agent_manager=agent_manager,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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language=language,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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)
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# 等待preamble_text任务完成
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try:
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preamble_text = await preamble_task
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# 只有当preamble_text不为空且不为"<empty>"时才输出
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if preamble_text and preamble_text.strip() and preamble_text != "<empty>":
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preamble_content = get_content_from_messages([{"role": "preamble","content": preamble_text + "\n"}], tool_response=tool_response)
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chunk_data = create_stream_chunk(f"chatcmpl-preamble", model_name, preamble_content)
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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logger.info(f"Stream mode: Generated preamble text ({len(preamble_text)} chars)")
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else:
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logger.info("Stream mode: Skipped empty preamble text")
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except Exception as e:
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logger.error(f"Error generating preamble text: {e}")
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# 等待guideline分析任务完成
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agent, system_prompt, guideline_reasoning = await guidelines_task
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# 立即发送guideline_reasoning
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if guideline_reasoning:
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guideline_content = get_content_from_messages([{"role": "assistant","reasoning_content": guideline_reasoning+ "\n"}], tool_response=tool_response)
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chunk_data = create_stream_chunk(f"chatcmpl-guideline", model_name, guideline_content)
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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# 准备最终的消息
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final_messages = messages.copy()
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final_messages = append_user_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}")
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||
|
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if guideline_reasoning:
|
||
# 用###分割guideline_reasoning，取最后一段作为Guidelines
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guidelines_text = guideline_reasoning.split('###')[-1].strip() if guideline_reasoning else ""
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final_messages = append_user_last_message(final_messages, f"\n\nGuidelines:\n{guidelines_text}\nPlease follow these guidelines step by step.")
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||
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# 第三阶段：agent响应流式传输
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logger.info(f"Starting agent stream response")
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accumulated_content = ""
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chunk_id = 0
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for response in agent.run(messages=final_messages):
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previous_content = accumulated_content
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accumulated_content = get_content_from_messages(response, tool_response=tool_response)
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||
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# 计算新增的内容
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if accumulated_content.startswith(previous_content):
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new_content = accumulated_content[len(previous_content):]
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else:
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new_content = accumulated_content
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previous_content = ""
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||
|
||
# 只有当有新内容时才发送chunk
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if new_content:
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if chunk_id == 0:
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logger.info(f"Agent首个Token已生成, 开始流式输出")
|
||
chunk_id += 1
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chunk_data = create_stream_chunk(f"chatcmpl-{chunk_id}", model_name, new_content)
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
|
||
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||
final_chunk = create_stream_chunk(f"chatcmpl-{chunk_id + 1}", model_name, finish_reason="stop")
|
||
yield f"data: {json.dumps(final_chunk, ensure_ascii=False)}\n\n"
|
||
|
||
# 发送结束标记
|
||
yield "data: [DONE]\n\n"
|
||
logger.info(f"Enhanced stream response completed, total chunks: {chunk_id}")
|
||
|
||
except Exception as e:
|
||
import traceback
|
||
error_details = traceback.format_exc()
|
||
logger.error(f"Error in enhanced_generate_stream_response: {str(e)}")
|
||
logger.error(f"Full traceback: {error_details}")
|
||
|
||
error_data = {
|
||
"error": {
|
||
"message": f"Stream error: {str(e)}",
|
||
"type": "internal_error"
|
||
}
|
||
}
|
||
yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
|
||
|
||
|
||
async def create_agent_and_generate_response(
|
||
bot_id: str,
|
||
api_key: str,
|
||
messages: list,
|
||
stream: bool,
|
||
tool_response: bool,
|
||
model_name: str,
|
||
model_server: str,
|
||
language: str,
|
||
system_prompt: Optional[str],
|
||
mcp_settings: Optional[list],
|
||
robot_type: str,
|
||
project_dir: Optional[str] = None,
|
||
generate_cfg: Optional[dict] = None,
|
||
user_identifier: Optional[str] = None
|
||
) -> Union[ChatResponse, StreamingResponse]:
|
||
"""创建agent并生成响应的公共逻辑"""
|
||
if generate_cfg is None:
|
||
generate_cfg = {}
|
||
|
||
# 如果是流式模式，使用增强的流式响应生成器
|
||
if stream:
|
||
return StreamingResponse(
|
||
enhanced_generate_stream_response(
|
||
agent_manager=agent_manager,
|
||
bot_id=bot_id,
|
||
api_key=api_key,
|
||
messages=messages,
|
||
tool_response=tool_response,
|
||
model_name=model_name,
|
||
model_server=model_server,
|
||
language=language,
|
||
system_prompt=system_prompt or "",
|
||
mcp_settings=mcp_settings,
|
||
robot_type=robot_type,
|
||
project_dir=project_dir,
|
||
generate_cfg=generate_cfg,
|
||
user_identifier=user_identifier
|
||
),
|
||
media_type="text/event-stream",
|
||
headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
|
||
)
|
||
|
||
|
||
_, system_prompt = get_preamble_text(language, system_prompt)
|
||
# 使用公共函数处理所有逻辑
|
||
agent, system_prompt, guideline_reasoning = await process_guidelines_and_terms(
|
||
bot_id=bot_id,
|
||
api_key=api_key,
|
||
model_name=model_name,
|
||
model_server=model_server,
|
||
system_prompt=system_prompt,
|
||
messages=messages,
|
||
agent_manager=agent_manager,
|
||
project_dir=project_dir,
|
||
generate_cfg=generate_cfg,
|
||
language=language,
|
||
mcp_settings=mcp_settings,
|
||
robot_type=robot_type,
|
||
user_identifier=user_identifier
|
||
)
|
||
|
||
# 准备最终的消息
|
||
final_messages = messages.copy()
|
||
final_messages = append_user_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}")
|
||
pre_message_list = []
|
||
if guideline_reasoning:
|
||
# 用###分割guideline_reasoning，取最后一段作为Guidelines
|
||
guidelines_text = guideline_reasoning.split('###')[-1].strip() if guideline_reasoning else ""
|
||
final_messages = append_user_last_message(final_messages, f"\n\nGuidelines:\n{guidelines_text}\nPlease follow these guidelines step by step.")
|
||
pre_message_list.append({"role": "assistant","reasoning_content": guideline_reasoning+ "\n"})
|
||
|
||
# 非流式响应
|
||
agent_responses = agent.run_nonstream(final_messages)
|
||
final_responses = pre_message_list + agent_responses
|
||
if final_responses and len(final_responses) > 0:
|
||
# 使用 get_content_from_messages 处理响应，支持 tool_response 参数
|
||
content = get_content_from_messages(final_responses, tool_response=tool_response)
|
||
|
||
# 构造OpenAI格式的响应
|
||
return ChatResponse(
|
||
choices=[{
|
||
"index": 0,
|
||
"message": {
|
||
"role": "assistant",
|
||
"content": content
|
||
},
|
||
"finish_reason": "stop"
|
||
}],
|
||
usage={
|
||
"prompt_tokens": sum(len(msg.get("content", "")) for msg in messages),
|
||
"completion_tokens": len(content),
|
||
"total_tokens": sum(len(msg.get("content", "")) for msg in messages) + len(content)
|
||
}
|
||
)
|
||
else:
|
||
raise HTTPException(status_code=500, detail="No response from agent")
|
||
|
||
|
||
@router.post("/api/v1/chat/completions")
|
||
async def chat_completions(request: ChatRequest, authorization: Optional[str] = Header(None)):
|
||
"""
|
||
Chat completions API similar to OpenAI, supports both streaming and non-streaming
|
||
|
||
Args:
|
||
request: ChatRequest containing messages, model, optional dataset_ids list, required bot_id, system_prompt, mcp_settings, and files
|
||
authorization: Authorization header containing API key (Bearer <API_KEY>)
|
||
|
||
Returns:
|
||
Union[ChatResponse, StreamingResponse]: Chat completion response or stream
|
||
|
||
Notes:
|
||
- dataset_ids: 可选参数，当提供时必须是项目ID列表（单个项目也使用数组格式）
|
||
- bot_id: 必需参数，机器人ID
|
||
- 只有当 robot_type == "catalog_agent" 且 dataset_ids 为非空数组时才会创建机器人项目目录：projects/robot/{bot_id}/
|
||
- robot_type 为其他值（包括默认的 "agent"）时不创建任何目录
|
||
- dataset_ids 为空数组 []、None 或未提供时不创建任何目录
|
||
- 支持多知识库合并，自动处理文件夹重名冲突
|
||
|
||
Required Parameters:
|
||
- bot_id: str - 目标机器人ID
|
||
- messages: List[Message] - 对话消息列表
|
||
Optional Parameters:
|
||
- dataset_ids: List[str] - 源知识库项目ID列表（单个项目也使用数组格式）
|
||
- robot_type: str - 机器人类型，默认为 "agent"
|
||
|
||
Example:
|
||
{"bot_id": "my-bot-001", "messages": [{"role": "user", "content": "Hello"}]}
|
||
{"dataset_ids": ["project-123"], "bot_id": "my-bot-001", "messages": [{"role": "user", "content": "Hello"}]}
|
||
{"dataset_ids": ["project-123", "project-456"], "bot_id": "my-bot-002", "messages": [{"role": "user", "content": "Hello"}]}
|
||
{"dataset_ids": ["project-123"], "bot_id": "my-catalog-bot", "robot_type": "catalog_agent", "messages": [{"role": "user", "content": "Hello"}]}
|
||
"""
|
||
try:
|
||
# v1接口：从Authorization header中提取API key作为模型API密钥
|
||
api_key = extract_api_key_from_auth(authorization)
|
||
|
||
# 获取bot_id（必需参数）
|
||
bot_id = request.bot_id
|
||
if not bot_id:
|
||
raise HTTPException(status_code=400, detail="bot_id is required")
|
||
|
||
# 创建项目目录（如果有dataset_ids且不是agent类型）
|
||
project_dir = create_project_directory(request.dataset_ids, bot_id, request.robot_type)
|
||
|
||
# 收集额外参数作为 generate_cfg
|
||
exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier'}
|
||
generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
|
||
|
||
# 处理消息
|
||
messages = process_messages(request.messages, request.language)
|
||
|
||
# 调用公共的agent创建和响应生成逻辑
|
||
return await create_agent_and_generate_response(
|
||
bot_id=bot_id,
|
||
api_key=api_key,
|
||
messages=messages,
|
||
stream=request.stream,
|
||
tool_response=True,
|
||
model_name=request.model,
|
||
model_server=request.model_server,
|
||
language=request.language,
|
||
system_prompt=request.system_prompt,
|
||
mcp_settings=request.mcp_settings,
|
||
robot_type=request.robot_type,
|
||
project_dir=project_dir,
|
||
generate_cfg=generate_cfg,
|
||
user_identifier=request.user_identifier
|
||
)
|
||
|
||
except Exception as e:
|
||
import traceback
|
||
error_details = traceback.format_exc()
|
||
logger.error(f"Error in chat_completions: {str(e)}")
|
||
logger.error(f"Full traceback: {error_details}")
|
||
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
|
||
|
||
|
||
@router.post("/api/v2/chat/completions")
|
||
async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[str] = Header(None)):
|
||
"""
|
||
Chat completions API v2 with simplified parameters.
|
||
Only requires messages, stream, tool_response, bot_id, and language parameters.
|
||
Other parameters are fetched from the backend bot configuration API.
|
||
|
||
Args:
|
||
request: ChatRequestV2 containing only essential parameters
|
||
authorization: Authorization header for authentication (different from v1)
|
||
|
||
Returns:
|
||
Union[ChatResponse, StreamingResponse]: Chat completion response or stream
|
||
|
||
Required Parameters:
|
||
- bot_id: str - 目标机器人ID
|
||
- messages: List[Message] - 对话消息列表
|
||
|
||
Optional Parameters:
|
||
- stream: bool - 是否流式输出，默认false
|
||
- tool_response: bool - 是否包含工具响应，默认false
|
||
- language: str - 回复语言，默认"ja"
|
||
|
||
Authentication:
|
||
- Requires valid MD5 hash token: MD5(MASTERKEY:bot_id)
|
||
- Authorization header should contain: Bearer {token}
|
||
- Uses MD5 hash of MASTERKEY:bot_id for backend API authentication
|
||
- Optionally uses API key from bot config for model access
|
||
"""
|
||
try:
|
||
# 获取bot_id（必需参数）
|
||
bot_id = request.bot_id
|
||
if not bot_id:
|
||
raise HTTPException(status_code=400, detail="bot_id is required")
|
||
|
||
# v2接口鉴权验证
|
||
expected_token = generate_v2_auth_token(bot_id)
|
||
provided_token = extract_api_key_from_auth(authorization)
|
||
|
||
if not provided_token:
|
||
raise HTTPException(
|
||
status_code=401,
|
||
detail="Authorization header is required for v2 API"
|
||
)
|
||
|
||
if provided_token != expected_token:
|
||
raise HTTPException(
|
||
status_code=403,
|
||
detail=f"Invalid authentication token. Expected: {expected_token[:8]}..., Provided: {provided_token[:8]}..."
|
||
)
|
||
|
||
# 从后端API获取机器人配置（使用v2的鉴权方式）
|
||
bot_config = await fetch_bot_config(bot_id)
|
||
|
||
# v2接口：API密钥优先从后端配置获取，其次才从Authorization header获取
|
||
# 注意：这里的Authorization header已经用于鉴权，不再作为API key使用
|
||
api_key = bot_config.get("api_key")
|
||
|
||
# 创建项目目录（从后端配置获取dataset_ids）
|
||
project_dir = create_project_directory(
|
||
bot_config.get("dataset_ids", []),
|
||
bot_id,
|
||
bot_config.get("robot_type", "general_agent")
|
||
)
|
||
|
||
# 处理消息
|
||
messages = process_messages(request.messages, request.language)
|
||
|
||
# 调用公共的agent创建和响应生成逻辑
|
||
return await create_agent_and_generate_response(
|
||
bot_id=bot_id,
|
||
api_key=api_key,
|
||
messages=messages,
|
||
stream=request.stream,
|
||
tool_response=request.tool_response,
|
||
model_name=bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"),
|
||
model_server=bot_config.get("model_server", ""),
|
||
language=request.language or bot_config.get("language", "ja"),
|
||
system_prompt=bot_config.get("system_prompt"),
|
||
mcp_settings=bot_config.get("mcp_settings", []),
|
||
robot_type=bot_config.get("robot_type", "general_agent"),
|
||
project_dir=project_dir,
|
||
generate_cfg={}, # v2接口不传递额外的generate_cfg
|
||
user_identifier=request.user_identifier
|
||
)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
import traceback
|
||
error_details = traceback.format_exc()
|
||
logger.error(f"Error in chat_completions_v2: {str(e)}")
|
||
logger.error(f"Full traceback: {error_details}")
|
||
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
|