530 lines
22 KiB
Python
530 lines
22 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 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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call_guideline_llm, _get_optimal_batch_size, process_guideline_batch, get_content_from_messages, call_preamble_llm, get_preamble_text, get_language_text
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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 generate_stream_response(agent, messages, pre_message_list, tool_response: bool, model: str):
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"""生成流式响应"""
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accumulated_content = ""
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chunk_id = 0
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try:
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if len(pre_message_list)>0:
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accumulated_content = get_content_from_messages(pre_message_list, tool_response=tool_response)
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chunk_data = {
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"id": f"chatcmpl-thought",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model,
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"choices": [{
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"index": 0,
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"delta": {
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"content": accumulated_content
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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logger.info(f"开始生成Agent流式响应, model: {model}")
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for response in agent.run(messages=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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# 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")
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chunk_id += 1
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# 构造OpenAI格式的流式响应
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chunk_data = {
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"id": f"chatcmpl-{chunk_id}",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model,
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"choices": [{
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"index": 0,
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"delta": {
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"content": new_content
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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# 发送最终完成标记
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final_chunk = {
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"id": f"chatcmpl-{chunk_id + 1}",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model,
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"choices": [{
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"index": 0,
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"delta": {},
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"finish_reason": "stop"
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}]
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}
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yield f"data: {json.dumps(final_chunk, ensure_ascii=False)}\n\n"
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# 发送结束标记
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yield "data: [DONE]\n\n"
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logger.info(f"Agent流式响应完成, 总共生成 {chunk_id} 个chunks")
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except Exception as e:
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import traceback
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error_details = traceback.format_exc()
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logger.error(f"Error in generate_stream_response: {str(e)}")
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logger.error(f"Full traceback: {error_details}")
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error_data = {
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"error": {
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"message": f"Stream error: {str(e)}",
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"type": "internal_error"
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}
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}
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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async def create_agent_and_generate_response(
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bot_id: str,
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api_key: str,
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messages: list,
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stream: bool,
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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: Optional[str],
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mcp_settings: Optional[list],
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robot_type: str,
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project_dir: Optional[str] = None,
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generate_cfg: Optional[dict] = None,
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user_identifier: Optional[str] = None
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) -> Union[ChatResponse, StreamingResponse]:
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"""创建agent并生成响应的公共逻辑"""
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if generate_cfg is None:
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generate_cfg = {}
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pre_message_list = []
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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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preamble_text = await call_preamble_llm(chat_history, query_text, get_preamble_text(language), language, model_name, api_key, model_server)
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if preamble_text != '':
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pre_message_list.append({"role": "assistant","content": preamble_text})
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# 1. 从system_prompt提取guideline和terms内容
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system_prompt, guidelines_list, terms_list = extract_block_from_system_prompt(system_prompt)
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# 2. 如果有terms内容，先进行embedding（embedding需要缓存起来，这个可以tmp文件缓存，以{bot_id}_terms作为key）embedding实现情参考 @embedding/embedding.py 文件,可以在里面实现。拿到embedding后，可以进行相似性检索，检索方式先使用cos相似度，找到阈值相似性>0.7的匹配项，重新整理为terms_analysis，格式：1) Name: term_name1, Description: desc, Synonyms: syn1, syn2。
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terms_analysis = ""
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if terms_list:
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logger.info(f"terms_list: {terms_list}")
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# 使用embedding进行terms处理
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try:
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from embedding.embedding import process_terms_with_embedding
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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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# 将terms分析结果也添加到消息中
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system_prompt = system_prompt.replace("{terms}", terms_analysis)
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logger.info(f"Generated terms analysis: {terms_analysis[:200]}...") # 只打印前200个字符
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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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# 3. 如果有guideline内容，进行并发处理
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guideline_analysis = ""
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if guidelines_list:
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logger.info(f"guidelines_list: {guidelines_list}")
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guidelines_count = len(guidelines_list)
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if guidelines_count > 0:
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# 获取最优批次数量（并发数）
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batch_count = _get_optimal_batch_size(guidelines_count)
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# 计算每个批次应该包含多少条guideline
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guidelines_per_batch = max(1, guidelines_count // batch_count)
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# 分批处理guidelines - 将字典列表转换为字符串列表以便处理
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batches = []
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for i in range(0, guidelines_count, guidelines_per_batch):
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batch_guidelines = guidelines_list[i:i + guidelines_per_batch]
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# 将格式化为字符串，保持原有的格式以便LLM处理
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batch_strings = []
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for guideline in batch_guidelines:
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guideline_str = f"{guideline['id']}) Condition: {guideline['condition']} Action: {guideline['action']}"
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batch_strings.append(guideline_str)
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batches.append(batch_strings)
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# 确保批次数量不超过要求的并发数
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while len(batches) > batch_count:
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# 将最后一个批次合并到倒数第二个批次
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batches[-2].extend(batches[-1])
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batches.pop()
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logger.info(f"Processing {guidelines_count} guidelines in {len(batches)} batches with {batch_count} concurrent batches")
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# 并发执行所有任务：guideline批次处理 + agent创建
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tasks = []
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# 添加所有guideline批次任务
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for batch in batches:
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task = process_guideline_batch(
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guidelines_batch=batch,
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chat_history=chat_history,
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terms=terms_analysis,
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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(task)
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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=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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# 等待所有任务完成
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all_results = await asyncio.gather(*tasks, return_exceptions=True)
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# 处理结果：最后一个结果是agent，前面的是guideline批次结果
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agent = all_results[-1] # agent创建的结果
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batch_results = all_results[:-1] # guideline批次的结果
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logger.info(f"batch_results:{batch_results}")
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# 合并guideline分析结果，使用JSON格式的checks数组
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all_checks = []
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for i, result in enumerate(batch_results):
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if isinstance(result, Exception):
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logger.error(f"Guideline batch {i} failed: {result}")
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continue
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if result and isinstance(result, dict) and 'checks' in result:
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# 如果是JSON对象且包含checks数组，只保留applies为true的checks
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applicable_checks = [check for check in result['checks'] if check.get('applies') is True]
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all_checks.extend(applicable_checks)
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elif result and isinstance(result, str) and result.strip():
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# 如果是普通文本，保留原有逻辑
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logger.info(f"Non-JSON result from batch {i}: {result}")
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if all_checks:
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# 将checks数组格式化为JSON字符串
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guideline_analysis = "\n".join([item["rationale"] for item in all_checks])
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# guideline_analysis = json.dumps({"checks": all_checks}, ensure_ascii=False)
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logger.info(f"Merged guideline analysis result: {guideline_analysis}")
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# 将分析结果添加到最后一个消息的内容中
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if guideline_analysis:
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messages = append_user_last_message(messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
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else:
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# 3. 从全局管理器获取或创建助手实例
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agent = await 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=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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messages = append_user_last_message(messages, f"\n\nlanguage:{get_language_text(language)}")
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if guideline_analysis != '':
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pre_message_list.append({"role": "assistant","reasoning_content": guideline_analysis})
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# 根据stream参数决定返回流式还是非流式响应
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if stream:
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return StreamingResponse(
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generate_stream_response(agent, messages, pre_message_list, tool_response, model_name),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
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)
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else:
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# 非流式响应
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agent_responses = agent.run_nonstream(messages)
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final_responses = pre_message_list+agent_responses
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if final_responses and len(final_responses) > 0:
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# 使用 get_content_from_messages 处理响应，支持 tool_response 参数
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content = get_content_from_messages(final_responses, tool_response=tool_response)
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# 构造OpenAI格式的响应
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return ChatResponse(
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choices=[{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": content
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},
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"finish_reason": "stop"
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}],
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usage={
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"prompt_tokens": sum(len(msg.get("content", "")) for msg in messages),
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"completion_tokens": len(content),
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"total_tokens": sum(len(msg.get("content", "")) for msg in messages) + len(content)
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}
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)
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else:
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raise HTTPException(status_code=500, detail="No response from agent")
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@router.post("/api/v1/chat/completions")
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async def chat_completions(request: ChatRequest, authorization: Optional[str] = Header(None)):
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"""
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Chat completions API similar to OpenAI, supports both streaming and non-streaming
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Args:
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request: ChatRequest containing messages, model, optional dataset_ids list, required bot_id, system_prompt, mcp_settings, and files
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authorization: Authorization header containing API key (Bearer <API_KEY>)
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Returns:
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Union[ChatResponse, StreamingResponse]: Chat completion response or stream
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Notes:
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- 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）
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- bot_id: 必需参数，机器人ID
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- 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}/
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- robot_type 为其他值（包括默认的 "agent"）时不创建任何目录
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- dataset_ids 为空数组 []、None 或未提供时不创建任何目录
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- 支持多知识库合并，自动处理文件夹重名冲突
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Required Parameters:
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- bot_id: str - 目标机器人ID
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- messages: List[Message] - 对话消息列表
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Optional Parameters:
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- dataset_ids: List[str] - 源知识库项目ID列表（单个项目也使用数组格式）
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- robot_type: str - 机器人类型，默认为 "agent"
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Example:
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{"bot_id": "my-bot-001", "messages": [{"role": "user", "content": "Hello"}]}
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{"dataset_ids": ["project-123"], "bot_id": "my-bot-001", "messages": [{"role": "user", "content": "Hello"}]}
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{"dataset_ids": ["project-123", "project-456"], "bot_id": "my-bot-002", "messages": [{"role": "user", "content": "Hello"}]}
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{"dataset_ids": ["project-123"], "bot_id": "my-catalog-bot", "robot_type": "catalog_agent", "messages": [{"role": "user", "content": "Hello"}]}
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"""
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try:
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# v1接口：从Authorization header中提取API key作为模型API密钥
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api_key = extract_api_key_from_auth(authorization)
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# 获取bot_id（必需参数）
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bot_id = request.bot_id
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if not bot_id:
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raise HTTPException(status_code=400, detail="bot_id is required")
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# 创建项目目录（如果有dataset_ids且不是agent类型）
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project_dir = create_project_directory(request.dataset_ids, bot_id, request.robot_type)
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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# 处理消息
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messages = process_messages(request.messages, request.language)
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# 调用公共的agent创建和响应生成逻辑
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return await create_agent_and_generate_response(
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bot_id=bot_id,
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api_key=api_key,
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messages=messages,
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stream=request.stream,
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tool_response=True,
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model_name=request.model,
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model_server=request.model_server,
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language=request.language,
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system_prompt=request.system_prompt,
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mcp_settings=request.mcp_settings,
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robot_type=request.robot_type,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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user_identifier=request.user_identifier
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)
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except Exception as e:
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import traceback
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error_details = traceback.format_exc()
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logger.error(f"Error in chat_completions: {str(e)}")
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logger.error(f"Full traceback: {error_details}")
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raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
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@router.post("/api/v2/chat/completions")
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async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[str] = Header(None)):
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"""
|
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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)}")
|