532 lines
22 KiB
Python
532 lines
22 KiB
Python
"""LLM configuration management API endpoints."""
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from typing import List, Optional
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from fastapi import APIRouter, Depends, HTTPException, status, Query
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import AIMessage
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from sqlalchemy.orm import Session
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from sqlalchemy import or_, select, delete, update
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from loguru import logger
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from th_agenter.llm.embed.embed_llm import BGEEmbedLLM, EmbedLLM
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from th_agenter.llm.online.online_llm import OnlineLLM
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from ...db.database import get_session
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from ...models.user import User
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from ...models.llm_config import LLMConfig
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from th_agenter.llm.base_llm import LLMConfig_DataClass
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from ...core.simple_permissions import require_super_admin, require_authenticated_user
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from ...schemas.llm_config import (
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LLMConfigCreate, LLMConfigUpdate, LLMConfigResponse,
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LLMConfigTest
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)
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from th_agenter.services.document_processor import get_document_processor
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from utils.util_exceptions import HxfResponse
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router = APIRouter(prefix="/llm-configs", tags=["llm-configs"])
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@router.get("/", response_model=List[LLMConfigResponse], summary="获取大模型配置列表")
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async def get_llm_configs(
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skip: int = Query(0, ge=0),
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limit: int = Query(100, ge=1, le=1000),
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search: Optional[str] = Query(None),
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provider: Optional[str] = Query(None),
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is_active: Optional[bool] = Query(None),
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is_embedding: Optional[bool] = Query(None),
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session: Session = Depends(get_session),
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current_user: User = Depends(require_authenticated_user)
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):
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"""获取大模型配置列表."""
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session.title = "获取大模型配置列表"
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session.desc = f"START: 获取大模型配置列表, skip={skip}, limit={limit}, search={search}, provider={provider}, is_active={is_active}, is_embedding={is_embedding}"
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stmt = select(LLMConfig)
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# 搜索
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if search:
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stmt = stmt.where(
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or_(
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LLMConfig.name.ilike(f"%{search}%"),
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LLMConfig.model_name.ilike(f"%{search}%"),
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LLMConfig.description.ilike(f"%{search}%")
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)
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)
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# 服务商筛选
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if provider:
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stmt = stmt.where(LLMConfig.provider == provider)
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# 状态筛选
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if is_active is not None:
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stmt = stmt.where(LLMConfig.is_active == is_active)
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# 模型类型筛选
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if is_embedding is not None:
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stmt = stmt.where(LLMConfig.is_embedding == is_embedding)
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# 排序
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stmt = stmt.order_by(LLMConfig.name)
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# 分页
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stmt = stmt.offset(skip).limit(limit)
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configs = (await session.execute(stmt)).scalars().all()
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session.desc = f"SUCCESS: 获取 {len(configs)} 个大模型配置 ..."
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return HxfResponse([config.to_dict(include_sensitive=True) for config in configs])
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@router.get("/providers", summary="获取支持的大模型服务商列表")
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async def get_llm_providers(
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session: Session = Depends(get_session),
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current_user: User = Depends(require_authenticated_user)
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):
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"""获取支持的大模型服务商列表."""
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session.desc = "START: 获取支持的大模型服务商列表"
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stmt = select(LLMConfig.provider).distinct()
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providers = (await session.execute(stmt)).scalars().all()
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session.desc = f"SUCCESS: 获取 {len(providers)} 个大模型服务商"
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return HxfResponse([provider for provider in providers if provider])
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@router.get("/active", response_model=List[LLMConfigResponse], summary="获取所有激活的大模型配置")
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async def get_active_llm_configs(
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is_embedding: Optional[bool] = Query(None),
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session: Session = Depends(get_session),
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current_user: User = Depends(require_authenticated_user)
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):
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"""获取所有激活的大模型配置."""
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session.desc = f"START: 获取所有激活的大模型配置, is_embedding={is_embedding}"
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stmt = select(LLMConfig).where(LLMConfig.is_active == True)
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if is_embedding is not None:
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stmt = stmt.where(LLMConfig.is_embedding == is_embedding)
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stmt = stmt.order_by(LLMConfig.created_at)
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configs = (await session.execute(stmt)).scalars().all()
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session.desc = f"SUCCESS: 获取 {len(configs)} 个激活的大模型配置"
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return HxfResponse([config.to_dict(include_sensitive=True) for config in configs])
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@router.get("/default", response_model=LLMConfigResponse, summary="获取默认大模型配置")
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async def get_default_llm_config(
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is_embedding: bool = Query(False, description="是否获取嵌入模型默认配置"),
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session: Session = Depends(get_session),
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current_user: User = Depends(require_authenticated_user)
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):
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"""获取默认大模型配置."""
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session.desc = f"START: 获取默认大模型配置, is_embedding={is_embedding}"
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stmt = select(LLMConfig).where(
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LLMConfig.is_default == True,
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LLMConfig.is_embedding == is_embedding,
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LLMConfig.is_active == True
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)
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config = (await session.execute(stmt)).scalar_one_or_none()
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if not config:
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model_type = "嵌入模型" if is_embedding else "对话模型"
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"未找到默认{model_type}配置"
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)
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session.desc = f"SUCCESS: 获取默认大模型配置, is_embedding={is_embedding}"
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return HxfResponse(config.to_dict(include_sensitive=True))
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@router.get("/{config_id}", response_model=LLMConfigResponse, summary="获取大模型配置详情")
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async def get_llm_config(
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config_id: int,
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session: Session = Depends(get_session),
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current_user: User = Depends(require_authenticated_user)
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):
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"""获取大模型配置详情."""
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stmt = select(LLMConfig).where(LLMConfig.id == config_id)
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config = (await session.execute(stmt)).scalar_one_or_none()
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if not config:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="大模型配置不存在"
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)
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return HxfResponse(config.to_dict(include_sensitive=True))
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@router.post("/", response_model=LLMConfigResponse, status_code=status.HTTP_201_CREATED, summary="创建大模型配置")
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async def create_llm_config(
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config_data: LLMConfigCreate,
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session: Session = Depends(get_session),
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current_user: User = Depends(require_super_admin)
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):
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"""创建大模型配置."""
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# 检查配置名称是否已存在
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# 先保存当前用户名，避免在refresh后访问可能导致MissingGreenlet错误
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username = current_user.username
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session.desc = f"START: 创建大模型配置, name={config_data.name}"
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stmt = select(LLMConfig).where(LLMConfig.name == config_data.name)
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existing_config = (await session.execute(stmt)).scalar_one_or_none()
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if existing_config:
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session.desc = f"ERROR: 配置名称已存在, name={config_data.name}"
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="配置名称已存在"
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)
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# 创建配置对象
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config = LLMConfig_DataClass(
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name=config_data.name,
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provider=config_data.provider,
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model_name=config_data.model_name,
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api_key=config_data.api_key,
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base_url=config_data.base_url,
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max_tokens=config_data.max_tokens,
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temperature=config_data.temperature,
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top_p=config_data.top_p,
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frequency_penalty=config_data.frequency_penalty,
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presence_penalty=config_data.presence_penalty,
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description=config_data.description,
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is_active=config_data.is_active,
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is_default=config_data.is_default,
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is_embedding=config_data.is_embedding,
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extra_config=config_data.extra_config or {}
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)
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# 验证配置
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validation_result = config.validate_config()
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if not validation_result['valid']:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=validation_result['error']
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)
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# 如果设为默认，取消同类型的其他默认配置
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if config_data.is_default:
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stmt = update(LLMConfig).where(
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LLMConfig.is_embedding == config_data.is_embedding
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).values({"is_default": False})
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await session.execute(stmt)
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session.desc = f"验证大模型配置, config_data"
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# 创建配置
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config = LLMConfig_DataClass(
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name=config_data.name,
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provider=config_data.provider,
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model_name=config_data.model_name,
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api_key=config_data.api_key,
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base_url=config_data.base_url,
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max_tokens=config_data.max_tokens,
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temperature=config_data.temperature,
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top_p=config_data.top_p,
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frequency_penalty=config_data.frequency_penalty,
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presence_penalty=config_data.presence_penalty,
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description=config_data.description,
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is_active=config_data.is_active,
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is_default=config_data.is_default,
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is_embedding=config_data.is_embedding,
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extra_config=config_data.extra_config or {}
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)
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# Audit fields are set automatically by SQLAlchemy event listener
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session.add(config)
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await session.commit()
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await session.refresh(config)
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session.desc = f"SUCCESS: 创建大模型配置, name={config.name} by user {username}"
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return HxfResponse(config.to_dict())
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@router.put("/{config_id}", response_model=LLMConfigResponse, summary="更新大模型配置")
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async def update_llm_config(
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config_id: int,
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config_data: LLMConfigUpdate,
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session: Session = Depends(get_session),
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current_user: User = Depends(require_authenticated_user)
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):
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"""更新大模型配置."""
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username = current_user.username
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session.desc = f"START: 更新大模型配置, id={config_id}"
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stmt = select(LLMConfig).where(LLMConfig.id == config_id)
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config = (await session.execute(stmt)).scalar_one_or_none()
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if not config:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="大模型配置不存在"
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)
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# 检查配置名称是否已存在（排除自己）
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if config_data.name and config_data.name != config.name:
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stmt = select(LLMConfig).where(
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LLMConfig.name == config_data.name,
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LLMConfig.id != config_id
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)
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existing_config = (await session.execute(stmt)).scalar_one_or_none()
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if existing_config:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="配置名称已存在"
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)
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# 如果设为默认，取消同类型的其他默认配置
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if config_data.is_default is True:
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# 获取当前配置的embedding类型，如果更新中包含is_embedding则使用新值
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is_embedding = config_data.is_embedding if config_data.is_embedding is not None else config.is_embedding
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stmt = update(LLMConfig).where(
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LLMConfig.is_embedding == is_embedding,
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LLMConfig.id != config_id
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).values({"is_default": False})
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await session.execute(stmt)
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# 更新字段
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update_data = config_data.model_dump(exclude_unset=True)
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was_embedding = config.is_embedding
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for field, value in update_data.items():
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setattr(config, field, value)
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await session.commit()
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await session.refresh(config)
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# 如果设置为默认嵌入模型，更新文档处理器的嵌入模型
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if config.is_embedding and config.is_default:
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try:
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document_processor = await get_document_processor(session)
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await document_processor._init_embeddings(session)
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except Exception as e:
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logger.warning(f"更新文档处理器嵌入模型失败: {str(e)}")
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# 不阻止更新配置，只记录警告
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session.desc = f"SUCCESS: 更新大模型配置, id={config_id} by user {username}"
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return HxfResponse(config.to_dict())
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@router.delete("/{config_id}", status_code=status.HTTP_204_NO_CONTENT, summary="删除大模型配置")
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async def delete_llm_config(
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config_id: int,
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session: Session = Depends(get_session),
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current_user: User = Depends(require_super_admin)
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):
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"""删除大模型配置."""
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username = current_user.username
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session.desc = f"START: 删除大模型配置, id={config_id}"
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stmt = select(LLMConfig).where(LLMConfig.id == config_id)
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config = (await session.execute(stmt)).scalar_one_or_none()
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if not config:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="大模型配置不存在"
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)
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session.desc = f"待删除大模型记录 {config.to_dict()}"
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# TODO: 检查是否有对话或其他功能正在使用该配置
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# 这里可以添加相关的检查逻辑
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# 删除配置
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await session.delete(config)
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await session.commit()
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session.desc = f"SUCCESS: 删除大模型配置成功, id={config_id} by user {username}"
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return HxfResponse({"message": "LLM config deleted successfully"})
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@router.post("/{config_id}/test", summary="测试连接大模型配置")
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async def test_llm_config(
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config_id: int,
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test_data: LLMConfigTest,
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session: Session = Depends(get_session),
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current_user: User = Depends(require_authenticated_user)
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):
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"""测试连接大模型配置."""
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username = current_user.username
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session.desc = f"TEST: 测试连接大模型配置 {config_id} by user {username}"
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stmt = select(LLMConfig).where(LLMConfig.id == config_id)
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config = (await session.execute(stmt)).scalar_one_or_none()
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if not config:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="大模型配置不存在"
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)
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logger.info(f"TEST: 测试连接大模型配置 {config_id} by user {username}")
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config_name = config.name
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# 验证配置
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validation_result = config.validate_config()
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logger.info(f"TEST: 验证大模型配置 {config_name} validation_result = {validation_result}")
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if not validation_result["valid"]:
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return {
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"success": False,
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"message": f"配置验证失败: {validation_result['error']}",
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"details": validation_result
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}
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session.desc = f"准备测试LLM功能 > 测试连接大模型配置 {config.to_dict()}"
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# 尝试创建客户端并发送测试请求
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try:
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# # 这里应该根据不同的服务商创建相应的客户端
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# # 由于具体的客户端实现可能因服务商而异，这里提供一个通用的框架
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test_message = test_data.message or "Hello, this is a test message."
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session.desc = f"准备测试LLM功能 > test_message = {test_message}"
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# 处理 base_url：如果包含 /chat/completions，需要移除
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original_base_url = config.base_url
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if config.base_url and '/chat/completions' in config.base_url:
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# 移除 /chat/completions 后缀，ChatOpenAI 会自动添加
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config.base_url = config.base_url.replace('/chat/completions', '').rstrip('/')
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logger.info(f"调整 base_url: {original_base_url} -> {config.base_url}")
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# 处理 SiliconFlow 的模型名称格式
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original_model_name = config.model_name
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if 'siliconflow' in config.base_url.lower() and '/' not in config.model_name:
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# SiliconFlow 需要 org/model 格式，尝试自动转换
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model_name_lower = config.model_name.lower()
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if 'deepseek' in model_name_lower or 'r1' in model_name_lower:
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# 尝试常见的 DeepSeek 模型格式
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if 'r1' in model_name_lower:
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config.model_name = 'deepseek-ai/DeepSeek-R1'
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elif 'v3' in model_name_lower:
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config.model_name = 'deepseek-ai/DeepSeek-V3'
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else:
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config.model_name = f'deepseek-ai/{config.model_name}'
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logger.info(f"调整 SiliconFlow 模型名称: {original_model_name} -> {config.model_name}")
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if config.is_embedding:
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config.provider = "ollama"
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streaming_llm = BGEEmbedLLM(config)
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else:
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streaming_llm = OnlineLLM(config)
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session.desc = f"创建{'EmbeddingLLM' if config.is_embedding else 'OnlineLLM'}完毕 > 测试连接大模型配置 {config.to_dict()}"
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streaming_llm.load_model() # 加载模型
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session.desc = f"加载模型完毕，模型名称：{config.model_name}，base_url: {config.base_url}，准备测试对话..."
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if config.is_embedding:
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# 测试嵌入模型，使用嵌入API而非聊天API
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test_text = test_message or "Hello, this is a test message for embedding"
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response = streaming_llm.embed_query(test_text)
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else:
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# 测试聊天模型
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from langchain_core.messages import SystemMessage, HumanMessage
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messages = [
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SystemMessage(content="你是一个简洁的助手，回答控制在50字以内"),
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HumanMessage(content=test_message)
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]
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# 使用异步调用
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import asyncio
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if hasattr(streaming_llm.model, 'ainvoke'):
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response = await streaming_llm.model.ainvoke(messages)
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else:
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# 如果模型不支持异步，使用同步调用但在线程中执行
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response = await asyncio.to_thread(streaming_llm.model.invoke, messages)
|
||
session.desc = f"测试连接大模型配置 {config_name} 成功 >>> 响应: {type(response)}"
|
||
|
||
return HxfResponse({
|
||
"success": True,
|
||
"message": "LLM测试成功",
|
||
"request": test_message,
|
||
"response": response.content if hasattr(response, 'content') else response, # 使用转换后的字典
|
||
"latency_ms": 150, # 模拟延迟
|
||
"config_info": config.to_dict()
|
||
})
|
||
|
||
except Exception as test_error:
|
||
import traceback
|
||
error_detail = str(test_error)
|
||
error_traceback = traceback.format_exc()
|
||
logger.error(f"测试LLM配置失败: {error_detail}\n{error_traceback}")
|
||
session.desc = f"ERROR: 测试连接大模型配置 {config.name} 失败, error: {error_detail}"
|
||
|
||
# 提取更详细的错误信息
|
||
if hasattr(test_error, 'response') and hasattr(test_error.response, 'status_code'):
|
||
error_detail = f"HTTP {test_error.response.status_code}: {error_detail}"
|
||
# 如果是模型不存在的错误，提供更详细的提示
|
||
if "Model does not exist" in error_detail or "model" in error_detail.lower():
|
||
original_model = original_model_name if 'original_model_name' in locals() else (config.model_name if 'config' in locals() else "N/A")
|
||
if 'siliconflow' in (original_base_url if 'original_base_url' in locals() else (config.base_url if 'config' in locals() else "")).lower():
|
||
error_detail += f"\n提示：SiliconFlow 的模型名称格式应为 'org/model-name'（如 'deepseek-ai/DeepSeek-R1'），当前配置: {original_model}"
|
||
elif "Not Found" in error_detail or "404" in error_detail:
|
||
error_detail = f"API端点未找到。请检查 base_url 配置是否正确。当前 base_url: {original_base_url if 'original_base_url' in locals() else config.base_url}"
|
||
|
||
return HxfResponse({
|
||
"success": False,
|
||
"message": f"LLM测试失败: {error_detail}",
|
||
"test_message": test_message if 'test_message' in locals() else "N/A",
|
||
"config_info": config.to_dict() if 'config' in locals() else {}
|
||
})
|
||
|
||
@router.post("/{config_id}/toggle-status", summary="切换大模型配置状态")
|
||
async def toggle_llm_config_status(
|
||
config_id: int,
|
||
session: Session = Depends(get_session),
|
||
current_user: User = Depends(require_super_admin)
|
||
):
|
||
"""切换大模型配置状态."""
|
||
username = current_user.username
|
||
session.desc = f"START: 切换大模型配置状态, id={config_id} by user {username}"
|
||
|
||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||
if not config:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="大模型配置不存在"
|
||
)
|
||
|
||
# 切换状态
|
||
config.is_active = not config.is_active
|
||
# Audit fields are set automatically by SQLAlchemy event listener
|
||
|
||
await session.commit()
|
||
await session.refresh(config)
|
||
|
||
status_text = "激活" if config.is_active else "禁用"
|
||
session.desc = f"SUCCESS: 切换大模型配置状态: {config.name} {status_text} by user {username}"
|
||
|
||
return HxfResponse({
|
||
"message": f"配置已{status_text}",
|
||
"is_active": config.is_active
|
||
})
|
||
|
||
|
||
@router.post("/{config_id}/set-default", summary="设置默认大模型配置")
|
||
async def set_default_llm_config(
|
||
config_id: int,
|
||
session: Session = Depends(get_session),
|
||
current_user: User = Depends(require_authenticated_user)
|
||
):
|
||
"""设置默认大模型配置."""
|
||
username = current_user.username
|
||
session.desc = f"START: 设置大模型配置 {config_id} 为默认 by user {username}"
|
||
|
||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||
if not config:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="大模型配置不存在"
|
||
)
|
||
|
||
# 检查配置是否激活
|
||
if not config.is_active:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_400_BAD_REQUEST,
|
||
detail="只能将激活的配置设为默认"
|
||
)
|
||
|
||
# 取消同类型的其他默认配置
|
||
stmt = update(LLMConfig).where(
|
||
LLMConfig.is_embedding == config.is_embedding,
|
||
LLMConfig.id != config_id
|
||
).values({"is_default": False})
|
||
await session.execute(stmt)
|
||
|
||
# 设置当前配置为默认
|
||
config.is_default = True
|
||
config.set_audit_fields(current_user.id, is_update=True)
|
||
|
||
await session.commit()
|
||
await session.refresh(config)
|
||
|
||
model_type = "嵌入模型" if config.is_embedding else "对话模型"
|
||
# 如果设置为默认嵌入模型，更新文档处理器的嵌入模型
|
||
if config.is_embedding:
|
||
try:
|
||
document_processor = await get_document_processor(session)
|
||
await document_processor._init_embeddings(session)
|
||
except Exception as e:
|
||
logger.warning(f"更新文档处理器嵌入模型失败: {str(e)}")
|
||
# 不阻止设置默认配置，只记录警告
|
||
session.desc = f"SUCCESS: 设置大模型配置 {config.name} ({model_type}) 为默认 by user {username}"
|
||
return HxfResponse({
|
||
"message": f"已将 {config.name} 设为默认{model_type}配置",
|
||
"is_default": config.is_default
|
||
}) |