add v2 api
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朱潮
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81
README.md
81
README.md
@ -217,6 +217,83 @@ def submit_and_monitor_task():
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submit_and_monitor_task()
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```
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### 4. 项目目录树接口
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#### 获取完整目录树
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**端点**: `GET /api/v1/projects/tree`
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```bash
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# 获取完整目录树
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curl "http://localhost:8001/api/v1/projects/tree"
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# 只显示目录结构（不包含文件）
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curl "http://localhost:8001/api/v1/projects/tree?include_files=false"
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# 只显示data目录
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curl "http://localhost:8001/api/v1/projects/tree?filter_type=data"
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```
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**响应示例**:
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```json
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{
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"success": true,
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"message": "目录树获取成功",
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"tree": {
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"name": "projects",
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"path": "",
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"type": "directory",
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"children": [
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{
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"name": "data",
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"path": "data",
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"type": "directory",
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"children": [
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{
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"name": "1624be71-5432-40bf-9758-f4aecffd4e9c",
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"path": "data/1624be71-5432-40bf-9758-f4aecffd4e9c",
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"type": "directory",
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"children": [...]
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}
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]
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}
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],
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"size": 0,
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"modified_time": 1234567890
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},
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"stats": {
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"total_directories": 15,
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"total_files": 32,
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"total_size": 1048576
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}
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}
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```
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#### 获取子目录树结构
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**端点**: `GET /api/v1/projects/subtree/{sub_path:path}`
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```bash
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# 获取特定项目的目录结构
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curl "http://localhost:8001/api/v1/projects/subtree/data/1624be71-5432-40bf-9758-f4aecffd4e9c"
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# 只显示目录层级
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curl "http://localhost:8001/api/v1/projects/subtree/data/1624be71-5432-40bf-9758-f4aecffd4e9c?include_files=false"
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```
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**参数说明**:
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- `sub_path`: 子目录路径，如 'data/1624be71-5432-40bf-9758-f4aecffd4e9c'
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- `include_files`: 是否包含文件详情（默认true）
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- `max_depth`: 最大深度限制（默认10）
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**功能特性**:
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- 递归构建完整的目录树结构
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- 包含文件大小和修改时间信息
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- 支持过滤文件类型和目录层级
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- 提供统计信息（目录数、文件数、总大小）
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- 安全的错误处理机制
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```
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---
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## 🗃️ 数据包结构
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@ -308,6 +385,10 @@ curl -X POST "http://localhost:8001/api/v1/tasks/cleanup?older_than_days=7"
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- `DELETE /api/v1/task/{task_id}` - 删除任务记录
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- `POST /api/v1/project/cleanup` - 清理项目数据
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### 项目目录树接口
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- `GET /api/v1/projects/tree` - 获取projects文件夹完整目录树结构
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- `GET /api/v1/projects/subtree/{sub_path:path}` - 获取指定子目录的树结构
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### 系统管理接口
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- `GET /api/health` - 健康检查
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- `GET /system/status` - 系统状态
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177
api_v2_example.md
Normal file
177
api_v2_example.md
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@ -0,0 +1,177 @@
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# API v2 Usage Example
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## Overview
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API v2 提供了简化的聊天完成接口，与 v1 接口共享核心逻辑，确保功能一致性和代码维护性。
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## Endpoint
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`POST /api/v2/chat/completions`
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## Description
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This is a simplified version of the chat completions API that only requires essential parameters. All other configuration parameters are automatically fetched from the backend bot configuration API.
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## Code Architecture (重构后的代码结构)
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### 1. 公共函数提取
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- **`process_messages()`**: 处理消息列表，包括[ANSWER]分割和语言指令添加
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- **`create_agent_and_generate_response()`**: 创建agent并生成响应的公共逻辑
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- **`create_project_directory()`**: 创建项目目录的公共逻辑
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- **`extract_api_key_from_auth()`**: 从Authorization header中提取API key
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### 2. 不同的鉴权方式
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- **v1接口**: Authorization header中的API key直接用作模型API密钥
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```bash
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Authorization: Bearer your-model-api-key
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```
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- **v2接口**: 需要有效的MD5哈希令牌进行认证
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```bash
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# 生成鉴权token
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token=$(echo -n "master:your-bot-id" | md5sum | cut -d' ' -f1)
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Authorization: Bearer ${token}
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```
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### 3. 接口设计
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- **`/api/v1/chat/completions`**: 处理 `ChatRequest`，直接使用请求中的所有参数
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- **`/api/v2/chat/completions`**: 处理 `ChatRequestV2`，从后端获取配置参数
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### 4. 设计优势
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- ✅ 最大化代码复用，减少重复逻辑
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- ✅ 保持不同的鉴权方式，满足不同需求
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- ✅ 清晰的函数分离，易于维护和测试
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- ✅ 统一的错误处理和响应格式
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- ✅ 异步HTTP请求，提高并发性能
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- ✅ 使用aiohttp替代requests，避免阻塞
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## Request Format
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### Required Parameters
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- `bot_id`: string - The target robot ID
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- `messages`: array of message objects - Conversation messages
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### Optional Parameters
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- `stream`: boolean - Whether to stream responses (default: false)
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- `tool_response`: boolean - Whether to include tool responses (default: false)
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- `language`: string - Response language (default: "ja")
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### Message Object Format
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```json
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{
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"role": "user" | "assistant" | "system",
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"content": "string"
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}
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```
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## Example Request
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### Basic Request
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```bash
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curl -X POST "http://localhost:8001/api/v2/chat/completions" \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer your-api-key" \
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-d '{
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"bot_id": "1624be71-5432-40bf-9758-f4aecffd4e9c",
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"messages": [
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{
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"role": "user",
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"content": "Hello, how are you?"
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}
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],
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"language": "en",
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"stream": false
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}'
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```
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### Streaming Request
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```bash
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curl -X POST "http://localhost:8001/api/v2/chat/completions" \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer your-api-key" \
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-d '{
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"bot_id": "1624be71-5432-40bf-9758-f4aecffd4e9c",
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"messages": [
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{
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"role": "user",
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"content": "Tell me about yourself"
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}
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],
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"language": "ja",
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"stream": true
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}'
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```
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## Backend Configuration
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The endpoint automatically fetches the following configuration from `{BACKEND_HOST}/v1/agent_bot_config/{bot_id}`:
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- `model`: Model name (e.g., "qwen/qwen3-next-80b-a3b-instruct")
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- `model_server`: Model server URL
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- `dataset_ids`: Array of dataset IDs for knowledge base
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- `system_prompt`: System prompt for the agent
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- `mcp_settings`: MCP configuration settings
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- `robot_type`: Type of robot (e.g., "catalog_agent")
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- `api_key`: API key for model server access
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## Authentication
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### v2 API Authentication (Required)
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The v2 endpoint requires a specific authentication token format:
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**Token Generation:**
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```bash
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# Method 1: Using environment variables (recommended)
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export MASTERKEY="your-master-key"
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export BOT_ID="1624be71-5432-40bf-9758-f4aecffd4e9c"
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token=$(echo -n "${MASTERKEY}:${BOT_ID}" | md5sum | cut -d' ' -f1)
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# Method 2: Direct calculation
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token=$(echo -n "master:1624be71-5432-40bf-9758-f4aecffd4e9c" | md5sum | cut -d' ' -f1)
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```
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**Usage:**
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```bash
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curl -X POST "http://localhost:8001/api/v2/chat/completions" \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer ${token}" \
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-d '{
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"bot_id": "1624be71-5432-40bf-9758-f4aecffd4e9c",
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"messages": [
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{
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"role": "user",
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"content": "Hello"
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}
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]
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}'
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```
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**Authentication Errors:**
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- `401 Unauthorized`: Missing Authorization header
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- `403 Forbidden`: Invalid authentication token
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## Response Format
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Returns the same response format as `/api/v1/chat/completions`:
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### Non-Streaming Response
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```json
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{
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "Response content here"
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},
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"finish_reason": "stop"
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}
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],
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"usage": {
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"prompt_tokens": 10,
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"completion_tokens": 20,
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"total_tokens": 30
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}
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}
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```
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### Streaming Response
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Returns Server-Sent Events (SSE) format compatible with OpenAI's streaming API.
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384
fastapi_app.py
384
fastapi_app.py
@ -3,6 +3,9 @@ import os
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import tempfile
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import shutil
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import uuid
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import hashlib
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import requests
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import aiohttp
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from typing import AsyncGenerator, Dict, List, Optional, Union, Any
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from datetime import datetime
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@ -36,6 +39,9 @@ from utils import (
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get_global_agent_manager, init_global_agent_manager
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)
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# Import ChatRequestV2 directly from api_models
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from utils.api_models import ChatRequestV2
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# Import modified_assistant
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from modified_assistant import update_agent_llm
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@ -122,14 +128,14 @@ app.add_middleware(
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# Models are now imported from utils module
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async def generate_stream_response(agent, messages, request) -> AsyncGenerator[str, None]:
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async def generate_stream_response(agent, messages, tool_response: bool, model: str) -> AsyncGenerator[str, None]:
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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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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=request.tool_response)
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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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@ -146,7 +152,7 @@ async def generate_stream_response(agent, messages, request) -> AsyncGenerator[s
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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": request.model,
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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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@ -163,7 +169,7 @@ async def generate_stream_response(agent, messages, request) -> AsyncGenerator[s
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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": request.model,
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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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@ -441,107 +447,40 @@ async def chat_completions(request: ChatRequest, authorization: Optional[str] =
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{"dataset_ids": ["project-123", "project-456"], "bot_id": "my-bot-002"}
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"""
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try:
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# 从Authorization header中提取API key
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api_key = None
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if authorization:
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# 移除 "Bearer " 前缀
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if authorization.startswith("Bearer "):
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api_key = authorization[7:]
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else:
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api_key = authorization
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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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# 获取dataset_ids（可选参数，当提供时必须是数组）
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dataset_ids_list = request.dataset_ids
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project_dir = None
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# 只有当提供了 dataset_ids 时才创建机器人目录并合并数据
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if dataset_ids_list and len(dataset_ids_list) > 0:
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from utils.multi_project_manager import create_robot_project
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project_dir = create_robot_project(dataset_ids_list, bot_id)
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# 创建项目目录（如果有dataset_ids）
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project_dir = create_project_directory(request.dataset_ids, bot_id)
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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'}
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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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# 从全局管理器获取或创建助手实例（配置读取逻辑已在agent_manager内部处理）
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agent = await agent_manager.get_or_create_agent(
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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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project_dir=project_dir,
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model_name=request.model,
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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=request.tool_response,
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model_name=request.model,
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model_server=request.model_server,
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generate_cfg=generate_cfg,
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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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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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)
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# 构建包含项目信息的消息上下文
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messages = []
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for msg in request.messages:
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if msg.role == "assistant":
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# 对assistant消息进行[ANSWER]分割处理，只保留最后一段
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content_parts = msg.content.split("[ANSWER]")
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if content_parts:
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# 取最后一段非空文本
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last_part = content_parts[-1].strip()
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messages.append({"role": msg.role, "content": last_part})
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else:
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messages.append({"role": msg.role, "content": msg.content})
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else:
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messages.append({"role": msg.role, "content": msg.content})
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# 在最后一条消息的末尾追加回复语言
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if messages and request.language:
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language_map = {
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'zh': '请用中文回复',
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'en': 'Please reply in English',
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'ja': '日本語で回答してください',
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'jp': '日本語で回答してください'
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}
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language_instruction = language_map.get(request.language.lower(), '')
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if language_instruction:
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# 在最后一条消息末尾追加语言指令
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messages[-1]['content'] = messages[-1]['content'] + f"\n\n{language_instruction}。"
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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 request.stream:
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return StreamingResponse(
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generate_stream_response(agent, messages, request),
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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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final_responses = agent.run_nonstream(messages)
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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=request.tool_response)
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# 构造OpenAI格式的响应
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return ChatResponse(
|
||||
choices=[{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": content
|
||||
},
|
||||
"finish_reason": "stop"
|
||||
}],
|
||||
usage={
|
||||
"prompt_tokens": sum(len(msg.content) for msg in request.messages),
|
||||
"completion_tokens": len(content),
|
||||
"total_tokens": sum(len(msg.content) for msg in request.messages) + len(content)
|
||||
}
|
||||
)
|
||||
else:
|
||||
raise HTTPException(status_code=500, detail="No response from agent")
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
@ -551,6 +490,277 @@ async def chat_completions(request: ChatRequest, authorization: Optional[str] =
|
||||
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
|
||||
|
||||
|
||||
async def fetch_bot_config(bot_id: str) -> Dict[str, Any]:
|
||||
"""获取机器人配置从后端API"""
|
||||
try:
|
||||
backend_host = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
|
||||
url = f"{backend_host}/v1/agent_bot_config/{bot_id}"
|
||||
|
||||
auth_token = generate_v2_auth_token(bot_id)
|
||||
headers = {
|
||||
"content-type": "application/json",
|
||||
"authorization": f"Bearer {auth_token}"
|
||||
}
|
||||
print(url,headers)
|
||||
# 使用异步HTTP请求
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(url, headers=headers, timeout=30) as response:
|
||||
if response.status != 200:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Failed to fetch bot config: API returned status code {response.status}"
|
||||
)
|
||||
|
||||
# 解析响应
|
||||
response_data = await response.json()
|
||||
|
||||
if not response_data.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Failed to fetch bot config: {response_data.get('message', 'Unknown error')}"
|
||||
)
|
||||
|
||||
return response_data.get("data", {})
|
||||
|
||||
except aiohttp.ClientError as e:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to connect to backend API: {str(e)}"
|
||||
)
|
||||
except Exception as e:
|
||||
if isinstance(e, HTTPException):
|
||||
raise
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to fetch bot config: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
def process_messages(messages: List[Message], language: Optional[str] = None) -> List[Dict[str, str]]:
|
||||
"""处理消息列表，包括[ANSWER]分割和语言指令添加"""
|
||||
processed_messages = []
|
||||
|
||||
# 处理每条消息
|
||||
for msg in messages:
|
||||
if msg.role == "assistant":
|
||||
# 对assistant消息进行[ANSWER]分割处理，只保留最后一段
|
||||
content_parts = msg.content.split("[ANSWER]")
|
||||
if content_parts:
|
||||
# 取最后一段非空文本
|
||||
last_part = content_parts[-1].strip()
|
||||
processed_messages.append({"role": msg.role, "content": last_part})
|
||||
else:
|
||||
processed_messages.append({"role": msg.role, "content": msg.content})
|
||||
else:
|
||||
processed_messages.append({"role": msg.role, "content": msg.content})
|
||||
|
||||
# 在最后一条消息的末尾追加回复语言
|
||||
if processed_messages and language:
|
||||
language_map = {
|
||||
'zh': '请用中文回复',
|
||||
'en': 'Please reply in English',
|
||||
'ja': '日本語で回答してください',
|
||||
'jp': '日本語で回答してください'
|
||||
}
|
||||
language_instruction = language_map.get(language.lower(), '')
|
||||
if language_instruction:
|
||||
# 在最后一条消息末尾追加语言指令
|
||||
processed_messages[-1]['content'] = processed_messages[-1]['content'] + f"\n\n{language_instruction}。"
|
||||
|
||||
return processed_messages
|
||||
|
||||
|
||||
async def create_agent_and_generate_response(
|
||||
bot_id: str,
|
||||
api_key: str,
|
||||
messages: List[Dict[str, str]],
|
||||
stream: bool,
|
||||
tool_response: bool,
|
||||
model_name: str,
|
||||
model_server: str,
|
||||
language: str,
|
||||
system_prompt: Optional[str],
|
||||
mcp_settings: Optional[List[Dict]],
|
||||
robot_type: str,
|
||||
project_dir: Optional[str] = None,
|
||||
generate_cfg: Optional[Dict] = None
|
||||
) -> Union[ChatResponse, StreamingResponse]:
|
||||
"""创建agent并生成响应的公共逻辑"""
|
||||
if generate_cfg is None:
|
||||
generate_cfg = {}
|
||||
|
||||
# 从全局管理器获取或创建助手实例
|
||||
agent = await agent_manager.get_or_create_agent(
|
||||
bot_id=bot_id,
|
||||
project_dir=project_dir,
|
||||
model_name=model_name,
|
||||
api_key=api_key,
|
||||
model_server=model_server,
|
||||
generate_cfg=generate_cfg,
|
||||
language=language,
|
||||
system_prompt=system_prompt,
|
||||
mcp_settings=mcp_settings,
|
||||
robot_type=robot_type
|
||||
)
|
||||
|
||||
# 根据stream参数决定返回流式还是非流式响应
|
||||
if stream:
|
||||
return StreamingResponse(
|
||||
generate_stream_response(agent, messages, tool_response, model_name),
|
||||
media_type="text/event-stream",
|
||||
headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
|
||||
)
|
||||
else:
|
||||
# 非流式响应
|
||||
final_responses = agent.run_nonstream(messages)
|
||||
|
||||
if final_responses and len(final_responses) > 0:
|
||||
# 使用 get_content_from_messages 处理响应，支持 tool_response 参数
|
||||
content = get_content_from_messages(final_responses, tool_response=tool_response)
|
||||
|
||||
# 构造OpenAI格式的响应
|
||||
return ChatResponse(
|
||||
choices=[{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": content
|
||||
},
|
||||
"finish_reason": "stop"
|
||||
}],
|
||||
usage={
|
||||
"prompt_tokens": sum(len(msg.get("content", "")) for msg in messages),
|
||||
"completion_tokens": len(content),
|
||||
"total_tokens": sum(len(msg.get("content", "")) for msg in messages) + len(content)
|
||||
}
|
||||
)
|
||||
else:
|
||||
raise HTTPException(status_code=500, detail="No response from agent")
|
||||
|
||||
|
||||
def create_project_directory(dataset_ids: List[str], bot_id: str) -> Optional[str]:
|
||||
"""创建项目目录的公共逻辑"""
|
||||
if not dataset_ids:
|
||||
return None
|
||||
|
||||
try:
|
||||
from utils.multi_project_manager import create_robot_project
|
||||
return create_robot_project(dataset_ids, bot_id)
|
||||
except Exception as e:
|
||||
print(f"Error creating project directory: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def extract_api_key_from_auth(authorization: Optional[str]) -> Optional[str]:
|
||||
"""从Authorization header中提取API key"""
|
||||
if not authorization:
|
||||
return None
|
||||
|
||||
# 移除 "Bearer " 前缀
|
||||
if authorization.startswith("Bearer "):
|
||||
return authorization[7:]
|
||||
else:
|
||||
return authorization
|
||||
|
||||
|
||||
def generate_v2_auth_token(bot_id: str) -> str:
|
||||
"""生成v2接口的认证token"""
|
||||
masterkey = os.getenv("MASTERKEY", "master")
|
||||
token_input = f"{masterkey}:{bot_id}"
|
||||
return hashlib.md5(token_input.encode()).hexdigest()
|
||||
|
||||
|
||||
@app.post("/api/v2/chat/completions")
|
||||
async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[str] = Header(None)):
|
||||
"""
|
||||
Chat completions API v2 with simplified parameters.
|
||||
Only requires messages, stream, tool_response, bot_id, and language parameters.
|
||||
Other parameters are fetched from the backend bot configuration API.
|
||||
|
||||
Args:
|
||||
request: ChatRequestV2 containing only essential parameters
|
||||
authorization: Authorization header for authentication (different from v1)
|
||||
|
||||
Returns:
|
||||
Union[ChatResponse, StreamingResponse]: Chat completion response or stream
|
||||
|
||||
Required Parameters:
|
||||
- bot_id: str - 目标机器人ID
|
||||
- messages: List[Message] - 对话消息列表
|
||||
|
||||
Optional Parameters:
|
||||
- stream: bool - 是否流式输出，默认false
|
||||
- tool_response: bool - 是否包含工具响应，默认false
|
||||
- language: str - 回复语言，默认"ja"
|
||||
|
||||
Authentication:
|
||||
- Requires valid MD5 hash token: MD5(MASTERKEY:bot_id)
|
||||
- Authorization header should contain: Bearer {token}
|
||||
- Uses MD5 hash of MASTERKEY:bot_id for backend API authentication
|
||||
- Optionally uses API key from bot config for model access
|
||||
"""
|
||||
try:
|
||||
# 获取bot_id（必需参数）
|
||||
bot_id = request.bot_id
|
||||
if not bot_id:
|
||||
raise HTTPException(status_code=400, detail="bot_id is required")
|
||||
|
||||
# v2接口鉴权验证
|
||||
expected_token = generate_v2_auth_token(bot_id)
|
||||
provided_token = extract_api_key_from_auth(authorization)
|
||||
|
||||
if not provided_token:
|
||||
raise HTTPException(
|
||||
status_code=401,
|
||||
detail="Authorization header is required for v2 API"
|
||||
)
|
||||
|
||||
if provided_token != expected_token:
|
||||
raise HTTPException(
|
||||
status_code=403,
|
||||
detail=f"Invalid authentication token. Expected: {expected_token[:8]}..., Provided: {provided_token[:8]}..."
|
||||
)
|
||||
|
||||
# 从后端API获取机器人配置（使用v2的鉴权方式）
|
||||
bot_config = await fetch_bot_config(bot_id)
|
||||
|
||||
# v2接口：API密钥优先从后端配置获取，其次才从Authorization header获取
|
||||
# 注意：这里的Authorization header已经用于鉴权，不再作为API key使用
|
||||
api_key = bot_config.get("api_key")
|
||||
|
||||
# 创建项目目录（从后端配置获取dataset_ids）
|
||||
project_dir = create_project_directory(bot_config.get("dataset_ids", []), bot_id)
|
||||
|
||||
# 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", "agent"),
|
||||
project_dir=project_dir,
|
||||
generate_cfg={} # v2接口不传递额外的generate_cfg
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
import traceback
|
||||
error_details = traceback.format_exc()
|
||||
print(f"Error in chat_completions_v2: {str(e)}")
|
||||
print(f"Full traceback: {error_details}")
|
||||
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
|
||||
|
||||
|
||||
@app.post("/api/v1/upload")
|
||||
async def upload_file(file: UploadFile = File(...)):
|
||||
"""
|
||||
|
||||
@ -5,6 +5,7 @@ RAG检索MCP服务器
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import hashlib
|
||||
import json
|
||||
import sys
|
||||
import os
|
||||
@ -46,8 +47,14 @@ def rag_retrieve(query: str, top_k: int = 50) -> Dict[str, Any]:
|
||||
]
|
||||
}
|
||||
|
||||
# 获取masterkey并生成认证token
|
||||
masterkey = os.getenv("MASTERKEY", "master")
|
||||
token_input = f"{masterkey}:{bot_id}"
|
||||
auth_token = hashlib.md5(token_input.encode()).hexdigest()
|
||||
|
||||
headers = {
|
||||
"content-type": "application/json"
|
||||
"content-type": "application/json",
|
||||
"authorization": f"Bearer {auth_token}"
|
||||
}
|
||||
data = {
|
||||
"query": query,
|
||||
|
||||
@ -53,6 +53,14 @@ class ChatRequest(BaseModel):
|
||||
robot_type: Optional[str] = "agent"
|
||||
|
||||
|
||||
class ChatRequestV2(BaseModel):
|
||||
messages: List[Message]
|
||||
stream: Optional[bool] = False
|
||||
tool_response: Optional[bool] = False
|
||||
bot_id: str
|
||||
language: Optional[str] = "ja"
|
||||
|
||||
|
||||
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)")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user