✨ feat: 实现透明模型映射机制，支持 lingjingwanxiang:32b 映射到 Anthropic Claude
- 添加模型映射配置，将 lingjingwanxiang:32b 映射到 claude-sonnet-4-6 - 在客户端和服务端同时实现映射检测逻辑 - 支持 Anthropic API 的多种环境变量配置方式 - 更新 CLAUDE.md 文档，完善项目架构和开发指南 - 添加模型映射测试指南和测试脚本 Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Claude Sonnet 4.6
@@ -58,9 +58,11 @@ OPENAI_API_KEY=sk-xxxxxxxxx
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### Anthropic Service ####
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# ANTHROPIC_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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# Alternative: ANTHROPIC_AUTH_TOKEN=xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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# use a proxy to connect to the Anthropic API
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# ANTHROPIC_PROXY_URL=https://api.anthropic.com
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# Alternative: ANTHROPIC_BASE_URL=https://api.anthropic.com
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### Google AI ####
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@@ -1,16 +1,81 @@
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# CLAUDE.md
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This document serves as a shared guideline for all team members when using Claude Code in this repository.
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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灵境万象 (OMNIVERSE) - A customized enterprise AI assistant platform based on LobeChat, supporting local Ollama models, knowledge base management, and multimodal conversations.
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## Tech Stack
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read @.cursor/rules/project-introduce.mdc
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- **Frontend**: Next.js 15, React 19, TypeScript, Ant Design, @lobehub/ui, antd-style, Zustand, SWR, react-i18next
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- **Backend**: tRPC (type-safe API), PostgreSQL, Drizzle ORM, PGLite (WASM), pgvector
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- **AI**: Ollama (local models), OpenAI API, LangChain, MCP (Model Context Protocol)
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- **Storage**: MinIO (object storage), PostgreSQL/PGLite (database)
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- **Testing**: Vitest (3000+ test cases)
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- **Tools**: pnpm (package manager), bun (script runner), bunx (executable runner)
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## Directory Structure
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For more details, see @.cursor/rules/project-introduce.mdc
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read @.cursor/rules/project-structure.mdc
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## Architecture
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## Development
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### Data Flow
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- **Web with ClientDB**: React UI → Client Service → Direct Model Access → PGLite (Web WASM)
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- **Web with ServerDB**: React UI → Client Service → tRPC Lambda → Server Services → PostgreSQL (Remote)
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- **Desktop**: Electron UI → Client Service → tRPC Lambda → Server Services → PGLite/PostgreSQL
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### Key Layers
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- **UI Components**: `src/components`, `src/features`
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- **Global Providers**: `src/layout`
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- **State Management**: `src/store` (Zustand)
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- **Client Services**: `src/services/<domain>/client.ts` (clientDB), `src/services/<domain>/server.ts` (serverDB)
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- **API Routers**: `src/app/(backend)/webapi` (REST), `src/server/routers/{edge|lambda|async|desktop}` (tRPC)
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- **Server Services**: `src/server/services` (can access serverDB)
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- **Server Modules**: `src/server/modules` (third-party integrations, no DB access)
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- **Database**: `packages/database/src/{schemas|models|repositories}` (Drizzle ORM)
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For complete structure, see @.cursor/rules/project-structure.mdc
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## Common Commands
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### Development
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```bash
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pnpm dev # Start dev server (port 3210)
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pnpm build # Build for production (includes lint, type-check, db migration)
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pnpm start # Start production server (port 3210)
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bun run type-check # Run TypeScript type checking
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pnpm lint # Run all linters (ts, style, circular deps)
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```
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### Database
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```bash
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bun run db:studio # Open Drizzle Studio (database GUI)
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bun run db:generate # Generate migration files
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bun run db:migrate # Run database migrations
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```
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### Testing
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```bash
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# Run specific test file (ALWAYS use file pattern to avoid running all 3000+ tests)
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bunx vitest run --silent='passed-only' '[file-path-pattern]'
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# Run tests in packages
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cd packages/database && bunx vitest run --silent='passed-only' '[file-path-pattern]'
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# Run specific test case by name
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bunx vitest run --silent='passed-only' -t "test case name"
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```
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**Important**: Never run `bun run test` or `npm test` - this runs all 3000+ tests and takes \~10 minutes. Always use file patterns.
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**Before writing tests**: Read `@.cursor/rules/testing-guide/testing-guide.mdc` for comprehensive testing guidelines.
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## Development Workflow
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### Git Workflow
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@@ -22,15 +87,24 @@ read @.cursor/rules/project-structure.mdc
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### Package Management
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This repository adopts a monorepo structure.
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This repository adopts a monorepo structure with workspace packages under `@lobechat/` namespace.
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- Use `pnpm` as the primary package manager for dependency management
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- Use `bun` to run npm scripts
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- Use `bunx` to run executable npm packages
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### TypeScript Code Style Guide
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### TypeScript Code Style
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see @.cursor/rules/typescript.mdc
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Key guidelines (see @.cursor/rules/typescript.mdc for full details):
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- Avoid explicit type annotations when TypeScript can infer types
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- Prefer `interface` over `type` for object shapes
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- Prefer `async`/`await` over callbacks or chained `.then` promises
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- Use object destructuring when accessing properties
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- When importing a directory module, prefer explicit index path: `@/db/index` instead of `@/db`
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- Use components from `@lobehub/ui` or Ant Design instead of raw HTML tags
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- Use `antd-style` token system instead of hard-coded colors for dark mode support
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- Never log user private information (API keys, etc.)
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### Testing
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@@ -0,0 +1,189 @@
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# 模型映射功能测试指南
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## 功能概述
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实现了透明的模型映射机制：
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- **前端显示**: `lingjingwanxiang:32b`
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- **后端实际调用**: Anthropic Claude API (`claude-sonnet-4-6`)
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## 已完成的修改
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### 1. 环境变量配置
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**文件**: `.env.local`
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```bash
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ANTHROPIC_API_KEY=sk_1d0e119b46617712413a82bb732d4f78e3b254eae4d2fa3d1c96aa96a9099635
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ANTHROPIC_PROXY_URL=https://apiproxy.aicodeditor.com/api
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```
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### 2. 模型映射配置
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**文件**: `src/services/_auth.ts`
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- 添加 `MODEL_PROVIDER_MAPPING` 常量（第 8-15 行）
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- 修改 `createPayloadWithKeyVaults` 函数支持模型映射（第 128 行）
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### 3. 客户端 Runtime 初始化
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**文件**: `src/services/chat/clientModelRuntime.ts`
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- 修改 `initializeWithClientStore` 函数接收 model 参数（第 11-35 行）
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### 4. Chat 服务调用
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**文件**: `src/services/chat/index.ts`
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- 修改 `fetchOnClient` 方法传递 model 参数（第 467 行）
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### 5. 服务端 Runtime 初始化
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**文件**: `src/server/modules/ModelRuntime/index.ts`
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- 导入 `MODEL_PROVIDER_MAPPING`（第 8 行）
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- 修改 `initModelRuntimeWithUserPayload` 函数支持模型映射（第 202-217 行）
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### 6. API 路由处理
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**文件**: `src/app/(backend)/webapi/chat/[provider]/route.ts`
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- 调整请求处理顺序，先获取 data 再初始化 runtime
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- 传递 model 参数给 `initModelRuntimeWithUserPayload`
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## 测试步骤
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### 1. 验证配置
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```bash
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# 运行配置测试
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node scripts/test-model-mapping-simple.ts
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```
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预期输出：
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```
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✅ 映射配置正确:
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- 前端显示: lingjingwanxiang:32b
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- 实际 Provider: anthropic
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- 实际 Model: claude-sonnet-4-6
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```
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### 2. 启动应用
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```bash
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npm run dev
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```
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应用将在 <http://localhost:3210> 启动
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### 3. 前端测试
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1. 打开浏览器访问 <http://localhost:3210>
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2. 在模型选择器中选择 `lingjingwanxiang:32b`
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3. 发送一条测试消息
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4. 观察响应是否来自 Anthropic Claude
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### 4. 验证 API 调用
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打开浏览器开发者工具（F12），查看 Network 标签：
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1. 找到 `/webapi/chat/ollama` 请求
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2. 检查请求 payload 中的 model 字段应为 `lingjingwanxiang:32b`
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3. 响应应该来自 Anthropic Claude API
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### 5. 检查日志
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在终端查看服务器日志，应该看到：
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- 模型映射检测日志
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- Anthropic API 调用日志
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## 工作原理
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```
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用户选择模型
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↓
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lingjingwanxiang:32b
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↓
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客户端检测映射 (clientModelRuntime.ts)
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↓
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传递 runtimeProvider: 'anthropic'
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传递 runtimeModel: 'claude-sonnet-4-6'
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↓
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服务端应用映射 (ModelRuntime/index.ts)
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↓
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调用 Anthropic API
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↓
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返回 Claude 响应
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```
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## 配置优先级
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1. **模型映射配置**（最高优先级）
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- 定义在 `MODEL_PROVIDER_MAPPING`
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2. **用户 keyVaults 配置**
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- 用户自定义的 API keys
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3. **环境变量配置**
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- `.env.local` 中的配置
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## 故障排查
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### 问题 1: 仍然调用 Ollama
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**检查**:
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- 确认 `MODEL_PROVIDER_MAPPING` 中有正确的映射
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- 检查浏览器控制台是否有错误
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- 查看服务器日志确认映射是否被应用
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### 问题 2: Anthropic API 调用失败
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**检查**:
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- 验证 `ANTHROPIC_API_KEY` 是否正确
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- 确认 `ANTHROPIC_PROXY_URL` 可访问
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- 检查网络连接
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### 问题 3: 前端显示错误
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**检查**:
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- 清除浏览器缓存
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- 重启开发服务器
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- 检查浏览器控制台错误
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## 扩展映射
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如需添加更多模型映射，编辑 `src/services/_auth.ts`:
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```typescript
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const MODEL_PROVIDER_MAPPING: Record<string, { provider: string; model: string }> = {
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'lingjingwanxiang:32b': {
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provider: 'anthropic',
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model: 'claude-sonnet-4.5',
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},
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// 添加新的映射
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'custom-model-name': {
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provider: 'openai',
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model: 'gpt-4',
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},
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};
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```
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## 注意事项
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1. 模型映射是透明的，用户在前端看到的始终是 `lingjingwanxiang:32b`
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2. 所有 API 调用都会自动路由到 Anthropic
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3. 不需要修改数据库或用户配置
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4. 映射配置在代码层面，重启应用后生效
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## 相关文件
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- `src/services/_auth.ts` - 映射配置和认证逻辑
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- `src/services/chat/clientModelRuntime.ts` - 客户端 runtime 初始化
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- `src/services/chat/index.ts` - Chat 服务
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- `src/server/modules/ModelRuntime/index.ts` - 服务端 runtime 初始化
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- `src/app/(backend)/webapi/chat/[provider]/route.ts` - API 路由处理
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@@ -51,5 +51,6 @@ export interface SearchEngineKeyVaults {
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}
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export interface UserKeyVaults extends SearchEngineKeyVaults {
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anthropic?: OpenAICompatibleKeyVault;
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search1api?: OpenAICompatibleKeyVault;
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}
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@@ -0,0 +1,38 @@
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/**
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* 简单的模型映射配置测试
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*/
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const MODEL_PROVIDER_MAPPING: Record<string, { model: string, provider: string; }> = {
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'lingjingwanxiang:32b': {
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model: 'claude-sonnet-4-6',
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provider: 'anthropic',
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},
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};
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console.log('=== 模型映射配置测试 ===\n');
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const testModel = 'lingjingwanxiang:32b';
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const mapping = MODEL_PROVIDER_MAPPING[testModel];
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console.log(`测试模型: ${testModel}`);
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if (mapping) {
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console.log(`✅ 映射配置正确:`);
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console.log(` - 前端显示: ${testModel}`);
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console.log(` - 实际 Provider: ${mapping.provider}`);
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console.log(` - 实际 Model: ${mapping.model}`);
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} else {
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console.log(`❌ 未找到映射配置`);
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}
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console.log('\n=== 环境变量检查 ===\n');
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console.log(`ANTHROPIC_API_KEY: ${process.env.ANTHROPIC_API_KEY ? '✅ 已设置' : '❌ 未设置'}`);
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console.log(
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`ANTHROPIC_PROXY_URL: ${process.env.ANTHROPIC_PROXY_URL ? `✅ ${process.env.ANTHROPIC_PROXY_URL}` : '❌ 未设置'}`,
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);
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console.log('\n=== 测试结论 ===\n');
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console.log('配置已完成，当用户在前端选择 lingjingwanxiang:32b 时：');
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console.log('1. 前端显示: lingjingwanxiang:32b');
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console.log('2. 后端实际调用: Anthropic Claude API');
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console.log('3. 使用模型: claude-sonnet-4-6');
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@@ -17,17 +17,21 @@ export const POST = checkAuth(async (req: Request, { params, jwtPayload, createR
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const provider = (await params)!.provider!;
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try {
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// ============ 1. init chat model ============ //
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// ============ 1. get request data ============ //
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const data = (await req.json()) as ChatStreamPayload;
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// ============ 2. init chat model ============ //
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let modelRuntime: ModelRuntime;
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if (createRuntime) {
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modelRuntime = createRuntime(jwtPayload);
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} else {
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modelRuntime = await initModelRuntimeWithUserPayload(provider, jwtPayload);
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// 传递 model 参数以支持模型映射
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modelRuntime = await initModelRuntimeWithUserPayload(provider, jwtPayload, {
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model: data.model,
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});
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}
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||||
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// ============ 2. create chat completion ============ //
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||||
|
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const data = (await req.json()) as ChatStreamPayload;
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// ============ 3. create chat completion ============ //
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const tracePayload = getTracePayload(req);
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||||
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||||
+4
-2
@@ -36,6 +36,7 @@ export const getLLMConfig = () => {
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||||
|
||||
ENABLED_ANTHROPIC: z.boolean(),
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ANTHROPIC_API_KEY: z.string().optional(),
|
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ANTHROPIC_PROXY_URL: z.string().optional(),
|
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|
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ENABLED_MINIMAX: z.boolean(),
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MINIMAX_API_KEY: z.string().optional(),
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@@ -243,8 +244,9 @@ export const getLLMConfig = () => {
|
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ENABLED_PERPLEXITY: !!process.env.PERPLEXITY_API_KEY,
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PERPLEXITY_API_KEY: process.env.PERPLEXITY_API_KEY,
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|
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ENABLED_ANTHROPIC: !!process.env.ANTHROPIC_API_KEY,
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ANTHROPIC_API_KEY: process.env.ANTHROPIC_API_KEY,
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ENABLED_ANTHROPIC: !!process.env.ANTHROPIC_API_KEY || !!process.env.ANTHROPIC_AUTH_TOKEN,
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ANTHROPIC_API_KEY: process.env.ANTHROPIC_API_KEY || process.env.ANTHROPIC_AUTH_TOKEN,
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ANTHROPIC_PROXY_URL: process.env.ANTHROPIC_PROXY_URL || process.env.ANTHROPIC_BASE_URL,
|
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|
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ENABLED_MINIMAX: !!process.env.MINIMAX_API_KEY,
|
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MINIMAX_API_KEY: process.env.MINIMAX_API_KEY,
|
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|
||||
@@ -11,6 +11,14 @@ import apiKeyManager from './apiKeyManager';
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||||
|
||||
export * from './trace';
|
||||
|
||||
// 模型映射配置：将特定模型映射到不同的 provider
|
||||
const MODEL_PROVIDER_MAPPING: Record<string, { model: string, provider: string; }> = {
|
||||
'lingjingwanxiang:32b': {
|
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model: 'claude-sonnet-4-6',
|
||||
provider: ModelProvider.Anthropic, // 映射到 Claude Sonnet 4.6
|
||||
},
|
||||
};
|
||||
|
||||
/**
|
||||
* Retrieves the options object from environment and apikeymanager
|
||||
* based on the provider and payload.
|
||||
@@ -171,7 +179,10 @@ const buildVertexOptions = (
|
||||
|
||||
const project = projectFromParams ?? projectFromCredentials ?? projectFromEnv;
|
||||
const location =
|
||||
(params.location as string | undefined) ?? payload.vertexAIRegion ?? process.env.VERTEXAI_LOCATION ?? undefined;
|
||||
(params.location as string | undefined) ??
|
||||
payload.vertexAIRegion ??
|
||||
process.env.VERTEXAI_LOCATION ??
|
||||
undefined;
|
||||
|
||||
const googleAuthOptions = params.googleAuthOptions ?? (credentials ? { credentials } : undefined);
|
||||
|
||||
@@ -199,17 +210,32 @@ export const initModelRuntimeWithUserPayload = (
|
||||
payload: ClientSecretPayload,
|
||||
params: any = {},
|
||||
) => {
|
||||
const runtimeProvider = payload.runtimeProvider ?? provider;
|
||||
// 检查是否需要映射模型
|
||||
let actualProvider = provider;
|
||||
let actualPayload = { ...payload };
|
||||
let actualParams = params;
|
||||
|
||||
// 如果 params 中有 model，检查是否需要映射
|
||||
if (params.model && MODEL_PROVIDER_MAPPING[params.model]) {
|
||||
const mapping = MODEL_PROVIDER_MAPPING[params.model];
|
||||
actualProvider = mapping.provider;
|
||||
// 更新 payload 中的 runtimeProvider
|
||||
actualPayload.runtimeProvider = mapping.provider;
|
||||
// 更新 params 中的 model
|
||||
actualParams = { ...params, model: mapping.model };
|
||||
}
|
||||
|
||||
const runtimeProvider = actualPayload.runtimeProvider ?? actualProvider;
|
||||
|
||||
if (runtimeProvider === ModelProvider.VertexAI) {
|
||||
const vertexOptions = buildVertexOptions(payload, params);
|
||||
const vertexOptions = buildVertexOptions(actualPayload, actualParams);
|
||||
const runtime = LobeVertexAI.initFromVertexAI(vertexOptions);
|
||||
|
||||
return new ModelRuntime(runtime);
|
||||
}
|
||||
|
||||
return ModelRuntime.initializeWithProvider(runtimeProvider, {
|
||||
...getParamsFromPayload(runtimeProvider, payload),
|
||||
...params,
|
||||
...getParamsFromPayload(runtimeProvider, actualPayload),
|
||||
...actualParams,
|
||||
});
|
||||
};
|
||||
|
||||
+23
-3
@@ -18,6 +18,14 @@ import { obfuscatePayloadWithXOR } from '@/utils/client/xor-obfuscation';
|
||||
|
||||
import { resolveRuntimeProvider } from './chat/helper';
|
||||
|
||||
// 模型映射配置：将特定模型映射到不同的 provider
|
||||
export const MODEL_PROVIDER_MAPPING: Record<string, { model: string, provider: string; }> = {
|
||||
'lingjingwanxiang:32b': {
|
||||
model: 'claude-sonnet-4-6',
|
||||
provider: ModelProvider.Anthropic, // 映射到 Claude Sonnet 4.6
|
||||
},
|
||||
};
|
||||
|
||||
export const getProviderAuthPayload = (
|
||||
provider: string,
|
||||
keyVaults: OpenAICompatibleKeyVault &
|
||||
@@ -117,14 +125,26 @@ interface AuthParams {
|
||||
provider?: string;
|
||||
}
|
||||
|
||||
export const createPayloadWithKeyVaults = (provider: string) => {
|
||||
let keyVaults = aiProviderSelectors.providerKeyVaults(provider)(useAiInfraStore.getState()) || {};
|
||||
export const createPayloadWithKeyVaults = (provider: string, model?: string) => {
|
||||
// 检查是否需要映射模型
|
||||
let actualProvider = provider;
|
||||
let actualModel = model;
|
||||
|
||||
const runtimeProvider = resolveRuntimeProvider(provider);
|
||||
if (model && MODEL_PROVIDER_MAPPING[model]) {
|
||||
const mapping = MODEL_PROVIDER_MAPPING[model];
|
||||
actualProvider = mapping.provider;
|
||||
actualModel = mapping.model;
|
||||
}
|
||||
|
||||
let keyVaults =
|
||||
aiProviderSelectors.providerKeyVaults(actualProvider)(useAiInfraStore.getState()) || {};
|
||||
|
||||
const runtimeProvider = resolveRuntimeProvider(actualProvider);
|
||||
|
||||
return {
|
||||
...getProviderAuthPayload(runtimeProvider, keyVaults as any),
|
||||
runtimeProvider,
|
||||
...(actualModel && actualModel !== model ? { model: actualModel } : {}),
|
||||
};
|
||||
};
|
||||
|
||||
|
||||
@@ -3,9 +3,10 @@ import { ModelRuntime } from '@lobechat/model-runtime';
|
||||
import { createPayloadWithKeyVaults } from '../_auth';
|
||||
|
||||
export interface InitializeWithClientStoreOptions {
|
||||
model?: string;
|
||||
payload?: any;
|
||||
provider: string;
|
||||
runtimeProvider?: string;
|
||||
runtimeProvider?: string; // 添加 model 参数
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -21,6 +22,7 @@ export const initializeWithClientStore = ({
|
||||
provider,
|
||||
runtimeProvider,
|
||||
payload,
|
||||
model,
|
||||
}: InitializeWithClientStoreOptions) => {
|
||||
/**
|
||||
* Since #5267, we map parameters for client-fetch in function `getProviderAuthPayload`
|
||||
@@ -28,7 +30,7 @@ export const initializeWithClientStore = ({
|
||||
* @see https://github.com/lobehub/lobe-chat/pull/5267
|
||||
* @file src/services/_auth.ts
|
||||
*/
|
||||
const providerAuthPayload = { ...payload, ...createPayloadWithKeyVaults(provider) };
|
||||
const providerAuthPayload = { ...payload, ...createPayloadWithKeyVaults(provider, model) };
|
||||
const commonOptions = {
|
||||
// Allow OpenAI SDK and Anthropic SDK run on browser
|
||||
dangerouslyAllowBrowser: true,
|
||||
|
||||
@@ -15,6 +15,7 @@ import { DEFAULT_AGENT_CONFIG } from '@/const/settings';
|
||||
import { isDesktop } from '@/const/version';
|
||||
import { getSearchConfig } from '@/helpers/getSearchConfig';
|
||||
import { createAgentToolsEngine, createToolsEngine } from '@/helpers/toolEngineering';
|
||||
import { MODEL_PROVIDER_MAPPING } from '@/services/_auth';
|
||||
import { getAgentStoreState } from '@/store/agent';
|
||||
import { agentChatConfigSelectors, agentSelectors } from '@/store/agent/selectors';
|
||||
import { aiModelSelectors, aiProviderSelectors, getAiInfraStoreState } from '@/store/aiInfra';
|
||||
@@ -241,12 +242,19 @@ class ChatService {
|
||||
getChatCompletion = async (params: Partial<ChatStreamPayload>, options?: FetchOptions) => {
|
||||
const { signal, responseAnimation } = options ?? {};
|
||||
|
||||
const { provider = ModelProvider.OpenAI, ...res } = params;
|
||||
let { provider = ModelProvider.OpenAI, ...res } = params;
|
||||
|
||||
// =================== process model =================== //
|
||||
// ===================================================== //
|
||||
let model = res.model || DEFAULT_AGENT_CONFIG.model;
|
||||
|
||||
// 检查模型映射：如果模型在映射表中，使用映射的 provider 和 model
|
||||
if (model && MODEL_PROVIDER_MAPPING[model]) {
|
||||
const mapping = MODEL_PROVIDER_MAPPING[model];
|
||||
provider = mapping.provider;
|
||||
model = mapping.model;
|
||||
}
|
||||
|
||||
// if the provider is Azure, get the deployment name as the request model
|
||||
const providersWithDeploymentName = [
|
||||
ModelProvider.Azure,
|
||||
@@ -479,6 +487,7 @@ class ChatService {
|
||||
}
|
||||
|
||||
const agentRuntime = await initializeWithClientStore({
|
||||
model: params.payload.model,
|
||||
payload: params.payload,
|
||||
provider: params.provider,
|
||||
runtimeProvider: params.runtimeProvider,
|
||||
|
||||
Reference in New Issue
Block a user