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增加bot_manager

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朱潮 2026-01-28 23:32:34 +08:00
parent f1107ea35a
commit 4c70857ff6
7 changed files with 1632 additions and 7 deletions
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80
agent/agent_config.py
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@ -223,6 +223,86 @@ class AgentConfig:
config.safe_print()
return config
@classmethod
async def from_v3_request(cls, request, bot_config: Dict, project_dir: Optional[str] = None, messages: Optional[List] = None, language: Optional[str] = None):
"""从v3请求创建配置 - 从数据库读取所有配置"""
# 延迟导入避免循环依赖
from .logging_handler import LoggingCallbackHandler
from utils.fastapi_utils import get_preamble_text
from utils.settings import (
MEM0_ENABLED,
MEM0_SEMANTIC_SEARCH_TOP_K,
)
from .checkpoint_utils import prepare_checkpoint_message
from .checkpoint_manager import get_checkpointer_manager
from utils.log_util.context import g
if messages is None:
messages = []
# 从全局上下文获取 trace_id
trace_id = None
try:
trace_id = getattr(g, 'trace_id', None)
except LookupError:
pass
# 从数据库配置获取语言（如果没有传递）
if language is None:
language = bot_config.get("language", "zh")
# 处理 system_prompt 和 preamble
system_prompt_from_db = bot_config.get("system_prompt", "")
preamble_text, system_prompt = get_preamble_text(language, system_prompt_from_db)
# 获取 robot_type
robot_type = bot_config.get("robot_type", "general_agent")
if robot_type == "catalog_agent":
robot_type = "deep_agent"
# 从数据库配置获取其他参数
enable_thinking = bot_config.get("enable_thinking", False)
enable_memori = bot_config.get("enable_memori", False)
config = cls(
bot_id=request.bot_id,
api_key=bot_config.get("api_key", ""),
model_name=bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"),
model_server=bot_config.get("model_server", ""),
language=language,
system_prompt=system_prompt,
mcp_settings=bot_config.get("mcp_settings", []),
robot_type=robot_type,
user_identifier=bot_config.get("user_identifier", ""),
session_id=request.session_id,
enable_thinking=enable_thinking,
project_dir=project_dir,
stream=request.stream,
tool_response=bot_config.get("tool_response", True),
generate_cfg={}, # v3接口不传递额外的generate_cfg
logging_handler=LoggingCallbackHandler(),
messages=messages,
_origin_messages=messages,
preamble_text=preamble_text,
dataset_ids=bot_config.get("dataset_ids", []),
enable_memori=enable_memori,
memori_semantic_search_top_k=bot_config.get("memori_semantic_search_top_k", MEM0_SEMANTIC_SEARCH_TOP_K),
trace_id=trace_id,
)
# 在创建 config 时尽早准备 checkpoint 消息
if config.session_id:
try:
manager = get_checkpointer_manager()
checkpointer = manager.checkpointer
if checkpointer:
await prepare_checkpoint_message(config, checkpointer)
except Exception as e:
logger.warning(f"Failed to load checkpointer: {e}")
config.safe_print()
return config
def invoke_config(self):
"""返回Langchain需要的配置字典"""
config = {}

3
agent/prompt_loader.py
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@ -86,6 +86,9 @@ async def load_system_prompt_async(project_dir: str, language: str = None, syste
"""
from agent.config_cache import config_cache
# 初始化 prompt 为空字符串，避免未定义错误
prompt = ""
# 获取语言显示名称
language_display_map = {
'zh': '中文',

12
fastapi_app.py
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@ -71,7 +71,7 @@ from utils.log_util.logger import init_with_fastapi
logger = logging.getLogger('app')
# Import route modules
from routes import chat, files, projects, system, skill_manager, database
from routes import chat, files, projects, system, skill_manager, database, bot_manager
@asynccontextmanager
@ -118,7 +118,14 @@ async def lifespan(app: FastAPI):
except Exception as e:
logger.warning(f"Mem0 initialization failed (continuing without): {e}")
# 5. 启动 checkpoint 清理调度器
# 5. 初始化 Bot Manager 表
try:
await bot_manager.init_bot_manager_tables()
logger.info("Bot Manager tables initialized")
except Exception as e:
logger.warning(f"Bot Manager table initialization failed (non-fatal): {e}")
# 6. 启动 checkpoint 清理调度器
if CHECKPOINT_CLEANUP_ENABLED:
# 启动时立即执行一次清理
try:
@ -175,6 +182,7 @@ app.include_router(projects.router)
app.include_router(system.router)
app.include_router(skill_manager.router)
app.include_router(database.router)
app.include_router(bot_manager.router)
# 注册文件管理API路由
app.include_router(file_manager_router)

1250
routes/bot_manager.py Normal file
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File diff suppressed because it is too large Load Diff

95
routes/chat.py
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@ -10,10 +10,10 @@ logger = logging.getLogger('app')
from utils import (
Message, ChatRequest, ChatResponse, BatchSaveChatRequest, BatchSaveChatResponse
)
from utils.api_models import ChatRequestV2
from utils.api_models import ChatRequestV2, ChatRequestV3
from utils.fastapi_utils import (
process_messages,
create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config,
create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config, fetch_bot_config_from_db,
call_preamble_llm,
create_stream_chunk
)
@ -654,6 +654,97 @@ async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[st
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
@router.post("/api/v3/chat/completions")
async def chat_completions_v3(request: ChatRequestV3, authorization: Optional[str] = Header(None)):
"""
Chat completions API v3 - 从数据库读取配置
与 v2 相比，v3 从本地数据库读取所有配置参数，而不是从后端 API。
前端只需要传递 bot_id 和 messages，其他配置从数据库自动读取。
Args:
request: ChatRequestV3 包含 bot_id, messages, stream, session_id
authorization: 可选的认证头
Returns:
Union[ChatResponse, StreamingResponse]: Chat completion response or stream
Required Parameters:
- bot_id: str - 目标机器人ID（用户创建时填写的ID）
- messages: List[Message] - 对话消息列表
Optional Parameters:
- stream: bool - 是否流式输出，默认false
- session_id: str - 会话ID，用于保存聊天历史
Configuration (from database):
- model: 模型名称
- api_key: API密钥
- model_server: 模型服务器地址
- language: 回复语言
- tool_response: 是否包含工具响应
- system_prompt: 系统提示词
- robot_type: 机器人类型
- dataset_ids: 数据集ID列表
- mcp_settings: MCP服务器配置
- user_identifier: 用户标识符
Authentication:
- 可选的 Authorization header（如果需要验证）
"""
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")
# 可选的鉴权验证（如果传递了 authorization header）
if authorization:
expected_token = generate_v2_auth_token(bot_id)
provided_token = extract_api_key_from_auth(authorization)
if provided_token and provided_token != expected_token:
logger.warning(f"Invalid auth token provided for v3 API, but continuing anyway")
# 从数据库获取机器人配置
bot_config = await fetch_bot_config_from_db(bot_id)
# 构造类 v2 的请求格式
# 从数据库配置中提取参数
language = bot_config.get("language", "zh")
# 创建项目目录（从数据库配置获取）
project_dir = create_project_directory(
bot_config.get("dataset_ids", []),
bot_id,
bot_config.get("robot_type", "general_agent"),
bot_config.get("skills", [])
)
# 处理消息
messages = process_messages(request.messages, language)
# 创建 AgentConfig 对象
# 需要构造一个兼容 v2 的配置对象
config = await AgentConfig.from_v3_request(
request,
bot_config,
project_dir,
messages,
language
)
# 调用公共的agent创建和响应生成逻辑
return await create_agent_and_generate_response(config)
except HTTPException:
raise
except Exception as e:
import traceback
error_details = traceback.format_exc()
logger.error(f"Error in chat_completions_v3: {str(e)}")
logger.error(f"Full traceback: {error_details}")
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
# ============================================================================
# 聊天历史查询接口
# ============================================================================

15
utils/api_models.py
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@ -68,6 +68,21 @@ class ChatRequestV2(BaseModel):
session_id: Optional[str] = None
class ChatRequestV3(BaseModel):
"""
v3 API 请求模型 - 从数据库读取配置
所有配置参数从数据库读取，前端只需传递：
- bot_id: Bot 的用户ID（用于从数据库查找配置）
- messages: 对话消息列表
- session_id: 可选的会话ID
"""
messages: List[Message]
bot_id: str
stream: Optional[bool] = False
session_id: Optional[str] = None
class FileProcessRequest(BaseModel):
unique_id: str
files: Optional[Dict[str, List[str]]] = Field(default=None, description="Files organized by key groups. Each key maps to a list of file paths (supports zip files)")

178
utils/fastapi_utils.py
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@ -446,6 +446,184 @@ async def fetch_bot_config(bot_id: str) -> Dict[str, Any]:
)
async def fetch_bot_config_from_db(bot_user_id: str) -> Dict[str, Any]:
"""
从本地数据库获取机器人配置
Args:
bot_user_id: Bot 的用户ID（bot_id 字段，不是 UUID）
Returns:
Dict[str, Any]: 包含所有配置参数的字典，格式与 fetch_bot_config 兼容
"""
try:
from agent.db_pool_manager import get_db_pool_manager
pool = get_db_pool_manager().pool
async with pool.connection() as conn:
async with conn.cursor() as cursor:
# 首先根据 bot_user_id 查找 bot 的 UUID
await cursor.execute(
"SELECT id, name FROM bots WHERE bot_id = %s",
(bot_user_id,)
)
bot_row = await cursor.fetchone()
if not bot_row:
raise HTTPException(
status_code=404,
detail=f"Bot with bot_id '{bot_user_id}' not found"
)
bot_uuid = bot_row[0]
# 查询 bot_settings
await cursor.execute(
"""
SELECT model_id,
language, robot_type, dataset_ids, system_prompt, user_identifier,
enable_memori, tool_response, skills
FROM bot_settings WHERE bot_id = %s
""",
(bot_uuid,)
)
settings_row = await cursor.fetchone()
if not settings_row:
# 没有设置，使用默认值
logger.warning(f"No settings found for bot {bot_user_id}, using defaults")
return {
"model": "qwen3-next",
"api_key": "",
"model_server": "",
"language": "zh",
"robot_type": "general_agent",
"dataset_ids": [],
"system_prompt": "",
"user_identifier": "",
"enable_memori": False,
"tool_response": True,
"skills": []
}
# 解析结果
columns = [
'model_id',
'language', 'robot_type', 'dataset_ids', 'system_prompt', 'user_identifier',
'enable_memori', 'tool_response', 'skills'
]
config = dict(zip(columns, settings_row))
# 根据 model_id 查询模型信息
model_id = config['model_id']
if model_id:
await cursor.execute(
"""
SELECT model, server, api_key
FROM models WHERE id = %s
""",
(model_id,)
)
model_row = await cursor.fetchone()
if model_row:
config['model'] = model_row[0]
config['model_server'] = model_row[1]
config['api_key'] = model_row[2]
else:
logger.warning(f"Model with id {model_id} not found, using defaults")
config['model'] = "qwen3-next"
config['model_server'] = ""
config['api_key'] = ""
else:
# 没有选择模型，使用默认值
config['model'] = "qwen3-next"
config['model_server'] = ""
config['api_key'] = ""
# 处理 dataset_ids (可能是 JSON 数组字符串或逗号分隔字符串)
dataset_ids = config['dataset_ids']
if dataset_ids:
if isinstance(dataset_ids, str):
if dataset_ids.startswith('['):
import json
try:
config['dataset_ids'] = json.loads(dataset_ids)
except json.JSONDecodeError:
config['dataset_ids'] = [d.strip() for d in dataset_ids.split(',')]
else:
config['dataset_ids'] = [d.strip() for d in dataset_ids.split(',')]
else:
config['dataset_ids'] = []
# 处理 skills (逗号分隔字符串)
skills = config.get('skills', '')
if skills:
if isinstance(skills, str):
config['skills'] = [s.strip() for s in skills.split(',') if s.strip()]
else:
config['skills'] = []
else:
config['skills'] = []
# 查询 MCP 服务器配置
await cursor.execute(
"""
SELECT name, type, config, enabled
FROM mcp_servers WHERE bot_id = %s AND enabled = true
""",
(bot_uuid,)
)
mcp_rows = await cursor.fetchall()
mcp_servers = []
for mcp_row in mcp_rows:
mcp_name = mcp_row[0]
mcp_type = mcp_row[1]
mcp_config = mcp_row[2]
# 如果 config 是 JSONB/字符串，解析它
if isinstance(mcp_config, str):
try:
mcp_config = json.loads(mcp_config)
except json.JSONDecodeError:
mcp_config = {}
mcp_servers.append({
"name": mcp_name,
"type": mcp_type,
"config": mcp_config
})
# 格式化为 mcp_settings 格式 (兼容 v2 API)
if mcp_servers:
mcp_settings_value = []
for server in mcp_servers:
server_config = server.get("config", {})
server_type = server_config.pop("server_type", server["type"])
mcp_settings_value.append({
"mcpServers": {
server_type: server_config
}
})
config["mcp_settings"] = mcp_settings_value
else:
config["mcp_settings"] = []
return config
except HTTPException:
raise
except Exception as e:
logger.error(f"Error fetching bot config from database: {e}")
import traceback
logger.error(traceback.format_exc())
raise HTTPException(
status_code=500,
detail=f"Failed to fetch bot config from database: {str(e)}"
)
async def _sync_call_llm(llm_config, messages) -> str:
"""同步调用LLM的辅助函数，在线程池中执行 - 使用LangChain"""
try: