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API_DOCUMENTATION.md
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@@ -44,7 +44,7 @@ Content-Type: application/json
"memory_request": "128Mi", // 可选，内存请求量 "memory_request": "128Mi", // 可选，内存请求量
"memory_limit": "512Mi" // 可选，内存限制 "memory_limit": "512Mi" // 可选，内存限制
}, },
"env": { // 可选，环境变量 "env_variables": { // 可选，环境变量
"KEY": "value" "KEY": "value"
} }
} }
@@ -52,15 +52,72 @@ Content-Type: application/json
**支持的模板类型** **支持的模板类型**
| 模板 | 说明 | 类型 | | 模板 | 说明 | 类型 | 框架 |
|------|------|------| |------|------|------|------|
| `echo_agent` | Echo 测试服务 | 平台 | | `echo_agent` | Echo 测试服务 | 平台 | - |
| `chat_agent` | 聊天服务 | 平台 | | `chat_agent` | 聊天服务 | 平台 | - |
| `code_agent` | 代码执行服务 | 平台 | | `code_agent` | 代码执行服务 | 平台 | - |
| `search_agent` | 搜索服务 | 平台 | | `search_agent` | 搜索服务 | 平台 | - |
| `jina_search_agent` | Jina 搜索服务 | 平台 | | `jina_search_agent` | Jina 搜索服务 | 平台 | - |
| `mysql_agent` | MySQL 客户端 | 自定义 | | `mysql_agent` | MySQL 客户端 | 自定义 | - |
| `postgresql_agent` | PostgreSQL 客户端 | 自定义 | | `postgresql_agent` | PostgreSQL 客户端 | 自定义 | - |
| `azure_blob_agent` | Azure Blob Storage 客户端 (LangChain) | 自定义 | LangChain |
| `azure_blob_agent_mcp` | Azure Blob Storage 客户端 (MCP) | 自定义 | MCP |
| `azure_blob_agent_a2a` | Azure Blob Storage 客户端 (A2A) | 自定义 | A2A |
**Agent 框架说明**
从 v1.1.0 开始，Agent Manager 支持多种 AI Agent 框架：
| 框架 | 说明 | 适用场景 |
|------|------|---------|
| **LangChain** | 使用 LangChain + LiteLLM | 复杂推理任务、多步骤处理流程 |
| **MCP** | Model Context Protocol | 标准化工具调用、轻量级集成 |
| **A2A** | Agent-to-Agent | 多 Agent 协作、分布式任务处理 |
**多框架支持的配置参数**
创建支持多框架的 Agent 时，可以使用以下额外参数：
```json
{
"name": "agent-name",
"template": "azure_blob_agent_mcp",
"config": {
// 基础配置
"user_id": "user-001",
"tenant_id": "tenant-001",
"namespace": "ai-agents",
// 框架配置
"agent_framework": "mcp", // 框架类型: langchain, mcp, a2a
// 工具配置
"tools_config": { // 工具配置 JSON
"max_iterations": 5,
"enabled_tools": ["list_containers", "list_blobs"]
},
"tool_endpoint": "http://tools-api:8080", // 外部工具端点
"tool_api_key": "tool-key", // 工具 API 密钥
// 模型配置
"model_provider": "openai", // 模型提供商: openai, azure-openai
"model_name": "gpt-4", // 模型名称
"model_endpoint": "https://api.openai.com/v1", // 模型端点
"model_api_key": "sk-xxxx", // 模型 API 密钥
// 存储配置 (针对 Azure Blob Agent)
"storage_connection_string": "DefaultEndpointsProtocol=https;...",
"storage_account_name": "myaccount",
// 资源配置
"cpu_request": "100m",
"cpu_limit": "500m",
"memory_request": "256Mi",
"memory_limit": "512Mi"
}
}
```
**响应** **响应**
@@ -100,6 +157,7 @@ Content-Type: application/json
**示例** **示例**
基础示例 - Echo Agent：
```bash ```bash
curl -X POST http://localhost:8000/agents \ curl -X POST http://localhost:8000/agents \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
@@ -112,6 +170,277 @@ curl -X POST http://localhost:8000/agents \
}' }'
``` ```
Azure Blob Agent (LangChain 版本) - 提供连接字符串：
```bash
curl -X POST http://localhost:8000/agents \
-H "Content-Type: application/json" \
-d '{
"name": "my-azure-blob-agent",
"template": "azure_blob_agent",
"config": {
"user_id": "alice"
},
"env": {
"LITELLM_API_BASE": "http://litellm-service:4000",
"LITELLM_MODEL": "gpt-4",
"LITELLM_API_KEY": "sk-your-api-key",
"AZURE_STORAGE_CONNECTION_STRING": "DefaultEndpointsProtocol=https;AccountName=youraccount;AccountKey=yourkey;EndpointSuffix=core.windows.net",
"SERVICE_PORT": "8080"
}
}'
```
**Azure Blob Agent (MCP 版本) - 标准化工具调用**：
```bash
curl -X POST http://localhost:8000/agents \
-H "Content-Type: application/json" \
-d '{
"name": "my-blob-mcp",
"template": "azure_blob_agent_mcp",
"config": {
"user_id": "alice",
"tenant_id": "tenant-001",
"namespace": "ai-agents",
"agent_framework": "mcp",
"model_provider": "openai",
"model_name": "gpt-4",
"model_api_key": "sk-your-api-key",
"model_endpoint": "https://api.openai.com/v1",
"storage_connection_string": "DefaultEndpointsProtocol=https;AccountName=youraccount;AccountKey=yourkey;EndpointSuffix=core.windows.net",
"tools_config": {
"max_iterations": 5,
"enabled_tools": ["list_containers", "list_blobs", "search_blobs"]
},
"cpu_request": "100m",
"memory_request": "256Mi"
}
}'
```
**Azure Blob Agent (A2A 版本) - Agent 间协作**：
```bash
curl -X POST http://localhost:8000/agents \
-H "Content-Type: application/json" \
-d '{
"name": "my-blob-a2a",
"template": "azure_blob_agent_a2a",
"config": {
"user_id": "alice",
"tenant_id": "tenant-001",
"namespace": "ai-agents",
"agent_framework": "a2a",
"model_provider": "azure-openai",
"model_name": "gpt-4",
"model_api_key": "your-azure-openai-key",
"model_endpoint": "https://your-resource.openai.azure.com",
"storage_connection_string": "DefaultEndpointsProtocol=https;AccountName=youraccount;AccountKey=yourkey;EndpointSuffix=core.windows.net",
"cpu_request": "100m",
"memory_request": "256Mi"
},
"env": {
"AGENT_ID": "blob-agent-001",
"AGENT_ROLE": "storage_manager",
"AGENT_CAPABILITIES": "[\"blob_storage\", \"file_operations\"]"
}
}'
```
**测试部署后的 Agent**
**LangChain 版本测试：**
获取 Pod IP 并测试：
```bash
# 1. 检查 Agent 状态
curl -s http://localhost:8000/agents/my-azure-blob-agent/status | jq '{status, health_status, pod_ip, access_url}'
# 2. 获取 Pod IP
POD_IP=$(curl -s http://localhost:8000/agents/my-azure-blob-agent/status | jq -r '.pod_ip')
echo "Pod IP: $POD_IP"
# 3. 测试健康检查
curl http://$POD_IP:8080/health
# 4. 查看 Agent 信息
curl http://$POD_IP:8080/ | jq .
# 5. 如果启动时未提供连接字符串，可以动态连接
curl -X POST http://$POD_IP:8080/connect \
-H "Content-Type: application/json" \
-d '{
"connection_string": "DefaultEndpointsProtocol=https;AccountName=youraccount;AccountKey=yourkey;EndpointSuffix=core.windows.net"
}'
# 6. 检查连接状态
curl http://$POD_IP:8080/status
# 7. 执行自然语言查询 - 列出所有容器
curl -X POST http://$POD_IP:8080/query \
-H "Content-Type: application/json" \
-d '{"query": "列出所有容器"}' | jq .
# 8. 查看指定容器的文件
curl -X POST http://$POD_IP:8080/query \
-H "Content-Type: application/json" \
-d '{"query": "显示 mycontainer 容器中的所有文件"}' | jq .
# 9. 搜索文件
curl -X POST http://$POD_IP:8080/query \
-H "Content-Type: application/json" \
-d '{"query": "搜索包含 report 的文件"}' | jq .
# 10. 获取存储统计
curl -X POST http://$POD_IP:8080/query \
-H "Content-Type: application/json" \
-d '{"query": "存储统计"}' | jq .
```
**MCP 版本测试：**
```bash
# 1. 获取 Pod IP
POD_IP=$(curl -s http://localhost:8000/agents/my-blob-mcp/status | jq -r '.pod_ip')
# 2. 测试健康检查
curl http://$POD_IP:8080/health | jq .
# 3. 查看 Agent 信息（包含框架类型）
curl http://$POD_IP:8080/ | jq .
# 4. 列出所有可用的 MCP 工具
curl http://$POD_IP:8080/mcp/tools | jq .
# 5. 调用 MCP 工具 - 列出所有容器
curl -X POST http://$POD_IP:8080/mcp/call \
-H "Content-Type: application/json" \
-d '{
"tool_name": "list_containers",
"parameters": {}
}' | jq .
# 6. 调用 MCP 工具 - 列出容器中的文件
curl -X POST http://$POD_IP:8080/mcp/call \
-H "Content-Type: application/json" \
-d '{
"tool_name": "list_blobs",
"parameters": {
"container_name": "mycontainer"
}
}' | jq .
# 7. 调用 MCP 工具 - 获取文件信息
curl -X POST http://$POD_IP:8080/mcp/call \
-H "Content-Type: application/json" \
-d '{
"tool_name": "get_blob_info",
"parameters": {
"container_name": "mycontainer",
"blob_name": "myfile.txt"
}
}' | jq .
# 8. 调用 MCP 工具 - 搜索文件
curl -X POST http://$POD_IP:8080/mcp/call \
-H "Content-Type: application/json" \
-d '{
"tool_name": "search_blobs",
"parameters": {
"container_name": "mycontainer",
"keyword": "report"
}
}' | jq .
# 9. 调用 MCP 工具 - 获取存储统计
curl -X POST http://$POD_IP:8080/mcp/call \
-H "Content-Type: application/json" \
-d '{
"tool_name": "get_storage_stats",
"parameters": {}
}' | jq .
# 10. 使用简化的查询接口（规则匹配）
curl -X POST http://$POD_IP:8080/query \
-H "Content-Type: application/json" \
-d '{"query": "列出所有容器"}' | jq .
```
**A2A 版本测试：**
```bash
# 1. 获取 Pod IP
POD_IP=$(curl -s http://localhost:8000/agents/my-blob-a2a/status | jq -r '.pod_ip')
# 2. 测试健康检查（包含 Agent 身份信息）
curl http://$POD_IP:8080/health | jq .
# 3. 获取 Agent 能力
curl http://$POD_IP:8080/a2a/capabilities | jq .
# 4. 发送 A2A 消息 - 列出容器
curl -X POST http://$POD_IP:8080/a2a/message \
-H "Content-Type: application/json" \
-d '{
"message_id": "msg-001",
"from_agent": "external-caller",
"to_agent": "blob-agent-001",
"message_type": "request",
"action": "list_containers",
"parameters": {}
}' | jq .
# 5. 发送 A2A 消息 - 列出文件
curl -X POST http://$POD_IP:8080/a2a/message \
-H "Content-Type: application/json" \
-d '{
"message_id": "msg-002",
"from_agent": "external-caller",
"to_agent": "blob-agent-001",
"message_type": "request",
"action": "list_blobs",
"parameters": {
"container_name": "mycontainer"
}
}' | jq .
# 6. 发送 A2A 消息 - 获取统计
curl -X POST http://$POD_IP:8080/a2a/message \
-H "Content-Type: application/json" \
-d '{
"message_id": "msg-003",
"from_agent": "external-caller",
"to_agent": "blob-agent-001",
"message_type": "request",
"action": "get_stats",
"parameters": {}
}' | jq .
# 7. 注册另一个 Agent（用于协作）
curl -X POST http://$POD_IP:8080/a2a/register \
-H "Content-Type: application/json" \
-d '{
"agent_id": "analytics-agent",
"agent_role": "data_analyzer",
"capabilities": ["data_analysis", "visualization"],
"endpoint": "http://analytics-agent:8080"
}' | jq .
# 8. 列出已注册的 Agent
curl http://$POD_IP:8080/a2a/agents | jq .
# 9. 与其他 Agent 协作（需要先注册目标 Agent）
curl -X POST http://$POD_IP:8080/a2a/collaborate \
-H "Content-Type: application/json" \
-d '{
"target_agent_id": "analytics-agent",
"action": "analyze_data",
"parameters": {
"data_source": "blob_storage"
}
}' | jq .
```
-d '{"query": "统计存储使用情况"}' | jq .
```
--- ---
### 2. 查询 Agent 列表 ### 2. 查询 Agent 列表
@@ -1028,11 +1357,281 @@ class AgentManagerClient {
### v1.0.0 (2026-01-05) ### v1.0.0 (2026-01-05)
- ✅ 实现 Agent 创建和管理 - ✅ 实现 Agent 创建和管理
- ✅ 支持 7 种 Agent 模板 - ✅ 支持 10 种 Agent 模板（包含 3 种框架版本）
- ✅ 多租户支持（user-id 标签） - ✅ 多租户支持（user-id 标签）
- ✅ 多框架支持（LangChain、MCP、A2A）
- ✅ Pod ID 返回和归属验证 - ✅ Pod ID 返回和归属验证
- ✅ 模板分类查询（平台/自定义） - ✅ 模板分类查询（平台/自定义）
- ✅ 资源监控和状态查询 - ✅ 资源监控和状态查询
- ✅ 可自定义命名空间
---
## 多框架 Agent 支持 (v1.1.0+)
### 框架对比
| 特性 | LangChain | MCP | A2A |
|------|-----------|-----|-----|
| **实现方式** | LangChain + LiteLLM | Model Context Protocol | Agent-to-Agent Protocol |
| **工具调用** | LangChain Tools | MCP Tool Classes | A2A Action Handlers |
| **主要端点** | `/query` | `/mcp/tools`, `/mcp/call` | `/a2a/capabilities`, `/a2a/message` |
| **协作能力** | ❌ | ❌ | ✅ Agent 注册和通信 |
| **适用场景** | 复杂推理任务 | 标准化工具调用 | 多 Agent 协作 |
| **集成难度** | 中等 | 简单 | 中等 |
### 配置参数说明
#### 通用参数（所有框架）
| 参数 | 类型 | 必需 | 说明 | 示例 |
|------|------|------|------|------|
| `user_id` | string | ✅ | 用户标识 | `"user-001"` |
| `tenant_id` | string | ❌ | 租户标识 | `"tenant-001"` |
| `namespace` | string | ❌ | Kubernetes 命名空间 | `"ai-agents"` |
| `agent_framework` | string | ❌ | 框架类型 | `"mcp"` 或 `"a2a"` |
| `cpu_request` | string | ❌ | CPU 请求量 | `"100m"` |
| `cpu_limit` | string | ❌ | CPU 限制 | `"500m"` |
| `memory_request` | string | ❌ | 内存请求量 | `"256Mi"` |
| `memory_limit` | string | ❌ | 内存限制 | `"512Mi"` |
#### 模型配置参数（MCP/A2A）
| 参数 | 类型 | 必需 | 说明 | 示例 |
|------|------|------|------|------|
| `model_provider` | string | ✅ | 模型提供商 | `"openai"`, `"azure-openai"` |
| `model_name` | string | ✅ | 模型名称 | `"gpt-4"`, `"gpt-3.5-turbo"` |
| `model_api_key` | string | ✅ | 模型 API 密钥 | `"sk-xxxx"` |
| `model_endpoint` | string | ❌ | 模型 API 端点 | `"https://api.openai.com/v1"` |
#### 工具配置参数（MCP/A2A）
| 参数 | 类型 | 必需 | 说明 | 示例 |
|------|------|------|------|------|
| `tools_config` | object | ❌ | 工具配置 JSON | `{"max_iterations": 5}` |
| `tool_endpoint` | string | ❌ | 外部工具端点 | `"http://tools-api:8080"` |
| `tool_api_key` | string | ❌ | 工具 API 密钥 | `"tool-key-xxx"` |
#### 存储配置参数（Azure Blob Agent）
| 参数 | 类型 | 必需 | 说明 |
|------|------|------|------|
| `storage_connection_string` | string | ❌ | Azure Storage 连接字符串 |
| `storage_account_name` | string | ❌ | 存储账户名称 |
### MCP 框架 API 端点
MCP Agent 部署后提供以下额外端点：
#### GET /mcp/tools
列出所有可用的 MCP 工具
**响应示例：**
```json
{
"tools": [
{
"name": "list_containers",
"description": "列出 Azure Blob Storage 中的所有容器",
"inputSchema": {
"type": "object",
"properties": {},
"required": []
}
},
{
"name": "list_blobs",
"description": "列出指定容器中的所有文件",
"inputSchema": {
"type": "object",
"properties": {
"container_name": {
"type": "string",
"description": "容器名称"
}
},
"required": ["container_name"]
}
}
],
"count": 5,
"framework": "mcp"
}
```
#### POST /mcp/call
调用指定的 MCP 工具
**请求：**
```json
{
"tool_name": "list_blobs",
"parameters": {
"container_name": "mycontainer"
}
}
```
**响应：**
```json
{
"tool": "list_blobs",
"result": {
"success": true,
"container": "mycontainer",
"blobs": [...],
"count": 10,
"total_size_mb": 125.5
},
"timestamp": "2026-01-12T14:50:00Z"
}
```
### A2A 框架 API 端点
A2A Agent 部署后提供以下额外端点：
#### GET /a2a/capabilities
获取 Agent 的能力信息
**响应示例：**
```json
{
"agent_id": "blob-agent-001",
"agent_role": "storage_manager",
"capabilities": ["blob_storage", "file_operations"],
"supported_actions": [
"list_containers",
"list_blobs",
"get_blob_info",
"search_blobs",
"get_stats"
],
"framework": "a2a"
}
```
#### POST /a2a/register
注册其他 Agent（用于协作）
**请求：**
```json
{
"agent_id": "analytics-agent",
"agent_role": "data_analyzer",
"capabilities": ["data_analysis", "visualization"],
"endpoint": "http://analytics-agent:8080"
}
```
#### POST /a2a/message
发送 A2A 消息给 Agent
**请求：**
```json
{
"message_id": "msg-001",
"from_agent": "caller-agent",
"to_agent": "blob-agent-001",
"message_type": "request",
"action": "list_containers",
"parameters": {}
}
```
**响应：**
```json
{
"message_id": "msg-001",
"from_agent": "blob-agent-001",
"to_agent": "caller-agent",
"message_type": "response",
"action": "list_containers",
"result": {
"success": true,
"containers": [...],
"count": 5
},
"timestamp": "2026-01-12T14:50:00Z"
}
```
#### GET /a2a/agents
列出已注册的 Agent
#### POST /a2a/collaborate
与其他 Agent 协作
### 命名空间支持
从 v1.1.0 开始，支持自定义 Kubernetes 命名空间：
```bash
# 在自定义命名空间中创建 Agent
curl -X POST http://localhost:8000/agents \
-H "Content-Type: application/json" \
-d '{
"name": "my-agent",
"template": "azure_blob_agent_mcp",
"config": {
"namespace": "my-namespace",
"user_id": "user-001",
...
}
}'
# 查询特定命名空间的 Agent
kubectl get pods -n my-namespace -l app=ai-agent
# 通过 API 查询时，namespace 会在响应中返回
curl http://localhost:8000/agents/my-agent | jq '.namespace'
```
### 环境变量传递
创建 Agent 时，以下配置会自动转换为容器环境变量：
| 配置参数 | 环境变量名 |
|----------|-----------|
| `agent_framework` | `AGENT_FRAMEWORK` |
| `tools_config` | `TOOLS_CONFIG` (JSON字符串) |
| `tool_endpoint` | `TOOL_ENDPOINT` |
| `tool_api_key` | `TOOL_API_KEY` |
| `model_provider` | `MODEL_PROVIDER` |
| `model_name` | `MODEL_NAME` |
| `model_endpoint` | `MODEL_ENDPOINT` |
| `model_api_key` | `MODEL_API_KEY` |
| `storage_connection_string` | `AZURE_STORAGE_CONNECTION_STRING` |
| `storage_account_name` | `STORAGE_ACCOUNT_NAME` |
| `user_id` | `USER_ID` |
| `tenant_id` | `TENANT_ID` |
| `namespace` | `NAMESPACE` |
### 故障排查
**Agent 创建失败**
1. 检查模板名称是否正确
2. 验证必需参数是否提供（如 model_api_key）
3. 查看 agent-manager 日志
**MCP 工具调用失败**
1. 使用 `GET /mcp/tools` 确认工具名称
2. 检查参数格式是否符合 inputSchema
3. 查看 agent pod 日志
**A2A Agent 无法协作**
1. 确认目标 Agent 已注册
2. 检查网络连接和端点可访问性
3. 验证 message 格式是否正确
### 更多资源
- [多框架使用指南](agent_templates/MULTI_FRAMEWORK_GUIDE.md)
- [快速参考](agent_templates/QUICK_REFERENCE.md)
- [实现总结](MULTI_FRAMEWORK_SUMMARY.md)
--- ---
MULTI_FRAMEWORK_SUMMARY.md
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# Azure Blob Agent 多框架实现总结
## 📋 概述
本次更新为 Azure Blob Storage Agent 实现了三种框架支持：
1. **LangChain 版本** (已有) - 使用 LangChain + LiteLLM
2. **MCP 版本** (新增) - 使用 Model Context Protocol
3. **A2A 版本** (新增) - 使用 Agent-to-Agent 框架
## 🆕 新增文件
### Agent 实现
| 文件 | 说明 |
|------|------|
| `azure_blob_agent_mcp.py` | MCP 框架版本的 Agent 实现 |
| `azure_blob_agent_a2a.py` | A2A 框架版本的 Agent 实现 |
### Docker 相关
| 文件 | 说明 |
|------|------|
| `azure_blob_agent_mcp.Dockerfile` | MCP 版本的 Dockerfile |
| `azure_blob_agent_a2a.Dockerfile` | A2A 版本的 Dockerfile |
| `requirements_mcp.txt` | MCP 版本的依赖 |
| `requirements_a2a.txt` | A2A 版本的依赖 |
| `build_azure_blob_mcp.sh` | MCP 版本构建脚本 |
| `build_azure_blob_a2a.sh` | A2A 版本构建脚本 |
### 文档和测试
| 文件 | 说明 |
|------|------|
| `MULTI_FRAMEWORK_GUIDE.md` | 多框架使用指南 |
| `test_multi_framework.sh` | 多框架集成测试脚本 |
## 🔄 修改的文件
### 数据库层
**database.py** - 扩展了数据模型：
#### Template 模型新增字段：
- `agent_framework` - Agent 框架类型 (langchain/mcp/a2a)
- `tools_config` - 工具配置 JSON
- `default_model_provider` - 默认模型提供商
- `default_model_name` - 默认模型名称
#### Agent 模型新增字段：
- `agent_framework` - Agent 框架类型
- `tools_config` - 工具配置
- `tool_endpoint` - 工具端点 URL
- `tool_api_key` - 工具 API 密钥
- `model_provider` - 模型提供商
- `model_name` - 模型名称
- `model_endpoint` - 模型端点
- `model_api_key` - 模型 API 密钥
- `storage_connection_string` - 存储连接字符串
- `storage_account_name` - 存储账户名称
### API 层
**app.py** - 扩展了请求模型：
#### CreateTemplateRequest 新增字段：
```python
agent_framework: str = "langchain"
tools_config: Optional[Dict] = {}
default_model_provider: Optional[str] = None
default_model_name: Optional[str] = None
```
#### CreatePlatformAgentRequest 新增字段：
```python
namespace: Optional[str] = "ai-agents"
agent_framework: Optional[str] = None
tools_config: Optional[Dict] = {}
tool_endpoint: Optional[str] = None
tool_api_key: Optional[str] = None
model_provider: Optional[str] = None
model_name: Optional[str] = None
model_endpoint: Optional[str] = None
model_api_key: Optional[str] = None
storage_connection_string: Optional[str] = None
storage_account_name: Optional[str] = None
```
#### CreateCustomAgentRequest 同样新增了上述字段
### Kubernetes 层
**k8s_manager.py** - 扩展了部署逻辑：
#### _generate_pod_manifest 方法更新：
- 支持传递框架类型到容器环境变量
- 支持传递工具配置 (tools_config, tool_endpoint, tool_api_key)
- 支持传递模型配置 (model_provider, model_name, model_endpoint, model_api_key)
- 支持传递存储配置 (storage_connection_string, storage_account_name)
- 支持传递用户标识 (user_id, tenant_id)
- 支持自定义命名空间
#### 新增镜像映射：
```python
"azure_blob_agent_mcp": "agnettaiji.azurecr.io/ai-agents/azure-blob-agent-mcp:latest"
"azure_blob_agent_a2a": "agnettaiji.azurecr.io/ai-agents/azure-blob-agent-a2a:latest"
```
#### 新增端口映射：
```python
"azure_blob_agent_mcp": 8080
"azure_blob_agent_a2a": 8080
```
#### 新增环境变量说明（用于文档）
## 🏗️ 架构设计
### 参数传递流程
```
用户请求 (API)
↓
app.py (API 层)
├─ 验证参数
├─ 保存到数据库 (database.py)
└─ 调用 K8sManager
↓
k8s_manager.py (K8s 层)
├─ 构建环境变量
│ ├─ AGENT_FRAMEWORK
│ ├─ TOOLS_CONFIG
│ ├─ MODEL_*
│ ├─ STORAGE_*
│ └─ USER_ID, TENANT_ID, NAMESPACE
├─ 创建 Pod/Deployment
└─ 传递到容器
↓
Agent 容器 (azure_blob_agent_*.py)
├─ 读取环境变量
├─ 初始化框架
├─ 配置工具
├─ 连接存储
└─ 提供 API 服务
```
### 框架特性对比
| 特性 | LangChain | MCP | A2A |
|------|-----------|-----|-----|
| **实现文件** | azure_blob_agent.py | azure_blob_agent_mcp.py | azure_blob_agent_a2a.py |
| **工具定义** | LangChain Tools | MCP Tool Classes | A2A Action Handlers |
| **API 端点** | /query | /mcp/tools, /mcp/call | /a2a/capabilities, /a2a/message |
| **协作能力** | ❌ | ❌ | ✅ Agent 注册和通信 |
| **工具发现** | 内置 | GET /mcp/tools | GET /a2a/capabilities |
| **消息格式** | 自然语言 | MCP Protocol | A2A Message Protocol |
| **依赖** | langchain, litellm | fastapi, pydantic | fastapi, httpx |
## 📝 数据库迁移
提供了迁移脚本 `migrate_multi_framework.py`：
```bash
python migrate_multi_framework.py
```
支持：
- ✅ SQLite (开发环境)
- ✅ PostgreSQL (生产环境)
- ✅ 自动检测已存在字段
- ✅ 验证迁移结果
## 🚀 部署流程
### 1. 构建镜像
```bash
cd agent_templates
# 构建 MCP 版本
./build_azure_blob_mcp.sh
# 构建 A2A 版本
./build_azure_blob_a2a.sh
```
### 2. 运行数据库迁移
```bash
python migrate_multi_framework.py
```
### 3. 创建 Agent
```bash
# 创建 MCP Agent
curl -X POST http://agent-manager:8000/v2/agents/platform \
-H "Content-Type: application/json" \
-d @mcp_agent_config.json
# 创建 A2A Agent
curl -X POST http://agent-manager:8000/v2/agents/platform \
-H "Content-Type: application/json" \
-d @a2a_agent_config.json
```
### 4. 测试
```bash
./test_multi_framework.sh
```
## 🔧 环境变量配置示例
### MCP Agent
```bash
# 框架配置
AGENT_FRAMEWORK=mcp
TEMPLATE_TYPE=azure_blob_agent_mcp
# 工具配置
TOOLS_CONFIG='{"enabled_tools": ["list_containers", "list_blobs"]}'
# 模型配置
MODEL_PROVIDER=openai
MODEL_NAME=gpt-4
MODEL_API_KEY=sk-xxxx
MODEL_ENDPOINT=https://api.openai.com/v1
# 存储配置
AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;...
STORAGE_ACCOUNT_NAME=myaccount
# 用户信息
USER_ID=user123
TENANT_ID=tenant456
NAMESPACE=ai-agents
```
### A2A Agent
```bash
# 框架配置
AGENT_FRAMEWORK=a2a
TEMPLATE_TYPE=azure_blob_agent_a2a
# Agent 身份
AGENT_ID=blob-agent-001
AGENT_ROLE=storage_manager
AGENT_CAPABILITIES='["blob_storage", "file_operations"]'
# 模型配置
MODEL_PROVIDER=azure-openai
MODEL_NAME=gpt-4
MODEL_API_KEY=xxxx
MODEL_ENDPOINT=https://myopenai.openai.azure.com
# 存储配置
AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;...
# 用户信息
USER_ID=user123
TENANT_ID=tenant456
NAMESPACE=ai-agents
```
## 🎯 使用场景
### LangChain 版本
- ✅ 复杂的推理任务
- ✅ 多步骤文件处理
- ✅ 与现有 LangChain 应用集成
### MCP 版本
- ✅ 标准化工具调用
- ✅ 跨平台工具共享
- ✅ 轻量级集成
### A2A 版本
- ✅ 多 Agent 协作
- ✅ 分布式任务处理
- ✅ Agent 间通信
## 📚 API 端点对比
### LangChain
- `POST /query` - 自然语言查询
- `GET /health` - 健康检查
- `POST /connect` - 连接存储
### MCP
- `GET /mcp/tools` - 列出可用工具
- `POST /mcp/call` - 调用工具
- `POST /query` - 查询（简化版）
- `GET /health` - 健康检查
- `POST /connect` - 连接存储
### A2A
- `GET /a2a/capabilities` - 获取能力
- `POST /a2a/register` - 注册其他 Agent
- `GET /a2a/agents` - 列出已注册 Agent
- `POST /a2a/message` - 处理 A2A 消息
- `POST /a2a/collaborate` - 与其他 Agent 协作
- `POST /query` - 查询
- `GET /health` - 健康检查
- `POST /connect` - 连接存储
## ✅ 测试清单
- [ ] 数据库迁移成功
- [ ] MCP 镜像构建成功
- [ ] A2A 镜像构建成功
- [ ] MCP Agent 创建成功
- [ ] A2A Agent 创建成功
- [ ] MCP 工具调用正常
- [ ] A2A 消息处理正常
- [ ] 健康检查通过
- [ ] 存储连接正常
- [ ] 环境变量正确传递
## 🐛 已知问题
1. **LLM 集成**: MCP 和 A2A 版本目前使用简单规则匹配，需要集成实际 LLM 进行意图识别
2. **安全性**: API 密钥等敏感信息应加密存储
3. **日志**: 需要统一的日志收集和监控
## 🔮 未来改进
1. **安全增强**
- 密钥加密存储
- RBAC 权限控制
- API 密钥轮换
2. **功能扩展**
- 更多 Azure 服务集成
- 自定义工具注册
- 工具组合和编排
3. **监控和调试**
- 分布式追踪
- 性能监控
- 调试工具
4. **开发体验**
- Web UI 管理界面
- 可视化工具设计器
- Agent 模板市场
## 📖 相关文档
- [多框架使用指南](agent_templates/MULTI_FRAMEWORK_GUIDE.md)
- [API 文档](API_DOCUMENTATION.md)
- [Azure Blob Agent 原始文档](agent_templates/AZURE_BLOB_AGENT_USAGE.md)
ZOMBIE_PROCESS_FIX.md
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# 僵尸进程和 CPU 100% 问题修复方案
## 问题诊断
**容器**: taiji-mcp-server (ID: 64d363729ff9)
**进程**: PID 3411601, CPU 100%
**根因**: Docker 健康检查导致的僵尸进程泄漏 (800+ defunct curl 进程)
## 立即修复步骤
### 方案 1: 重启容器（最快）
```bash
# 重启容器，清理僵尸进程
docker restart taiji-mcp-server
# 检查状态
docker ps | grep taiji-mcp-server
```
### 方案 2: 临时禁用健康检查
```bash
# 停止容器
docker stop taiji-mcp-server
# 使用 --no-healthcheck 重新启动
docker run -d --name taiji-mcp-server-temp \
--no-healthcheck \
-p 8002:8000 \
taiji-ai-pad-mcp-server
# 或修改 docker-compose.yml，注释掉 healthcheck
```
## 长期修复方案
### 方案 A: 使用 Python 内置健康检查（推荐）
不依赖外部 curl 命令，避免子进程问题：
**Dockerfile 修改**:
```dockerfile
HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health').read()" || exit 1
```
### 方案 B: 使用 tini 或 dumb-init（推荐）
正确处理子进程回收：
**Dockerfile 修改**:
```dockerfile
# 安装 tini
RUN apt-get update && apt-get install -y tini
# 使用 tini 作为 init 进程
ENTRYPOINT ["/usr/bin/tini", "--"]
CMD ["python3", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
# 健康检查保持不变
HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
CMD curl -f http://localhost:8000/health || exit 1
```
### 方案 C: 修改健康检查端点，减少数据库连接
**main.py 修改** (假设你有 `/health` 端点):
```python
@app.get("/health")
async def health_check():
"""轻量级健康检查，不连接数据库"""
return {"status": "healthy", "timestamp": datetime.now().isoformat()}
@app.get("/health/deep")
async def deep_health_check():
"""深度健康检查，包含数据库连接测试"""
try:
# 测试数据库连接
db = next(get_db())
db.execute(text("SELECT 1"))
return {"status": "healthy", "database": "connected"}
except Exception as e:
raise HTTPException(status_code=503, detail=f"Unhealthy: {str(e)}")
```
### 方案 D: 调整健康检查频率
如果服务稳定，可以降低检查频率：
```dockerfile
HEALTHCHECK --interval=60s --timeout=10s --start-period=40s --retries=3 \
CMD curl -f http://localhost:8000/health || exit 1
```
## 验证修复
```bash
# 1. 检查容器健康状态
docker ps | grep taiji-mcp-server
# 2. 检查僵尸进程数量
docker exec taiji-mcp-server ps aux | grep defunct | wc -l
# 3. 检查 CPU 占用
docker stats --no-stream taiji-mcp-server
# 4. 检查日志
docker logs --tail 100 taiji-mcp-server
```
## 监控建议
```bash
# 定期检查僵尸进程
watch -n 5 'docker exec taiji-mcp-server ps aux | grep defunct | wc -l'
# 监控资源使用
docker stats taiji-mcp-server
```
## 参考资料
- Docker 僵尸进程问题: https://blog.phusion.nl/2015/01/20/docker-and-the-pid-1-zombie-reaping-problem/
- tini 项目: https://github.com/krallin/tini
- dumb-init: https://github.com/Yelp/dumb-init
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# Azure Blob Storage AI Agent 使用指南
## 概述
这是一个基于 LangChain + LiteLLM 的智能 Azure Blob Storage 管理代理，支持：
- 通过 API 动态接收 Azure Storage 连接字符串
- 使用自然语言查询和管理存储
- 通过环境变量配置 LLM 模型
## 架构说明
```
┌─────────────┐ HTTP API ┌──────────────────┐ Azure SDK ┌─────────────────┐
│ 客户端 │ ──────────────> │ FastAPI Server │ ──────────────> │ Azure Blob │
│ │ │ + LangChain │ │ Storage │
└─────────────┘ │ + LiteLLM │ └─────────────────┘
└──────────────────┘
│
▼
┌──────────────────┐
│ LiteLLM Server │
│ (4000端口) │
└──────────────────┘
```
## 快速开始
### 1. 构建镜像
```bash
cd /home/taiji/tools/agent-manager/agent_templates
# 构建镜像
./build_azure_blob_agent.sh latest
# 或者手动构建
docker build -f azure_blob_agent.Dockerfile -t azure-blob-agent:latest .
```
### 2. 启动 LiteLLM 服务（如果还没启动）
确保你的 LiteLLM 服务正在运行，例如：
```bash
# 检查 LiteLLM 是否运行
curl http://localhost:4000/health
# 如果没运行，启动它
docker run -d --name litellm \
-p 4000:4000 \
-e OPENAI_API_KEY=your_key \
ghcr.io/berriai/litellm:latest
```
### 3. 启动 Azure Blob Agent
```bash
docker run -d --name azure-blob-agent \
-p 8080:8080 \
-e LITELLM_API_BASE=http://host.docker.internal:4000 \
-e LITELLM_MODEL=gpt-3.5-turbo \
-e LITELLM_API_KEY=sk-1234 \
azure-blob-agent:latest
```
**环境变量说明：**
- `LITELLM_API_BASE`: LiteLLM 服务地址
- `LITELLM_MODEL`: 使用的模型名称
- `LITELLM_API_KEY`: LiteLLM API 密钥
- `SERVICE_HOST`: 服务监听地址（默认 0.0.0.0）
- `SERVICE_PORT`: 服务监听端口（默认 8080）
## API 使用
### 1. 健康检查
```bash
curl http://localhost:8080/health
```
**响应示例：**
```json
{
"status": "healthy",
"connected": true,
"connection_info": {
"account_kind": "StorageV2",
"sku_name": "Standard_LRS",
"connected_at": "2026-01-08T20:00:00"
}
}
```
### 2. 连接到 Azure Storage
```bash
curl -X POST http://localhost:8080/connect \
-H 'Content-Type: application/json' \
-d '{
"connection_string": "DefaultEndpointsProtocol=https;AccountName=yourname;AccountKey=yourkey;EndpointSuffix=core.windows.net"
}'
```
**响应示例：**
```json
{
"status": "connected",
"message": "成功连接到Azure Blob Storage",
"account_info": {
"account_kind": "StorageV2",
"sku_name": "Standard_LRS"
}
}
```
### 3. 自然语言查询
#### 列出所有容器
```bash
curl -X POST http://localhost:8080/query \
-H 'Content-Type: application/json' \
-d '{
"query": "列出所有容器"
}'
```
#### 查看容器中的文件
```bash
curl -X POST http://localhost:8080/query \
-H 'Content-Type: application/json' \
-d '{
"query": "显示 images 容器中的所有文件"
}'
```
#### 搜索文件
```bash
curl -X POST http://localhost:8080/query \
-H 'Content-Type: application/json' \
-d '{
"query": "在 documents 容器中搜索包含 report 的文件"
}'
```
#### 获取存储统计
```bash
curl -X POST http://localhost:8080/query \
-H 'Content-Type: application/json' \
-d '{
"query": "显示存储统计信息"
}'
```
#### 获取文件详细信息
```bash
curl -X POST http://localhost:8080/query \
-H 'Content-Type: application/json' \
-d '{
"query": "获取 images 容器中 logo.png 的详细信息"
}'
```
**响应示例：**
```json
{
"status": "success",
"query": "列出所有容器",
"answer": "当前有3个容器:\n1. images (最后修改: 2026-01-08)\n2. documents (最后修改: 2026-01-07)\n3. backups (最后修改: 2026-01-06)",
"intermediate_steps": "..."
}
```
## 在 Kubernetes 中部署
### 方法 1: 使用 agent-manager API
```bash
# 1. 首先确保模板已添加到 k8s_manager.py
# 2. 创建 agent
curl -X POST http://localhost:8000/agents \
-H 'Content-Type: application/json' \
-d '{
"name": "my-blob-agent",
"template": "azure_blob_agent",
"env": {
"LITELLM_API_BASE": "http://litellm-service:4000",
"LITELLM_MODEL": "gpt-3.5-turbo",
"LITELLM_API_KEY": "sk-1234"
}
}'
# 3. 连接到存储
curl -X POST http://my-blob-agent-ip:8080/connect \
-H 'Content-Type: application/json' \
-d '{
"connection_string": "YOUR_CONNECTION_STRING"
}'
```
### 方法 2: 直接部署 YAML
创建 `azure-blob-agent-deployment.yaml`:
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: azure-blob-agent
namespace: ai-agents
spec:
replicas: 1
selector:
matchLabels:
app: azure-blob-agent
template:
metadata:
labels:
app: azure-blob-agent
spec:
containers:
- name: azure-blob-agent
image: agnettaiji.azurecr.io/ai-agents/azure-blob-agent:latest
ports:
- containerPort: 8080
env:
- name: LITELLM_API_BASE
value: "http://litellm-service:4000"
- name: LITELLM_MODEL
value: "gpt-3.5-turbo"
- name: LITELLM_API_KEY
valueFrom:
secretKeyRef:
name: litellm-secret
key: api-key
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "500m"
---
apiVersion: v1
kind: Service
metadata:
name: azure-blob-agent-service
namespace: ai-agents
spec:
selector:
app: azure-blob-agent
ports:
- port: 8080
targetPort: 8080
type: ClusterIP
```
部署：
```bash
kubectl apply -f azure-blob-agent-deployment.yaml
```
## 支持的查询示例
| 自然语言查询 | 功能 |
|------------|------|
| "列出所有容器" | 显示所有容器列表 |
| "显示 images 容器中的文件" | 列出指定容器的文件 |
| "在 documents 中搜索 report" | 搜索包含关键字的文件 |
| "获取 data/test.csv 的信息" | 显示文件详细信息 |
| "显示存储统计" | 显示整体存储使用情况 |
| "images 容器有多少文件" | 统计容器文件数 |
| "查找所有 .pdf 文件" | 按扩展名搜索 |
## 故障排查
### 1. Agent 启动失败
```bash
# 检查日志
docker logs azure-blob-agent
# 常见问题：
# - LiteLLM 服务不可达：检查 LITELLM_API_BASE
# - 端口冲突：修改 SERVICE_PORT
```
### 2. 连接 Azure Storage 失败
```bash
# 检查连接字符串格式
# 正确格式：
DefaultEndpointsProtocol=https;AccountName=myaccount;AccountKey=mykey==;EndpointSuffix=core.windows.net
# 测试连接
curl -X POST http://localhost:8080/connect \
-H 'Content-Type: application/json' \
-d '{"connection_string": "YOUR_STRING"}'
```
### 3. 查询返回错误
```bash
# 检查是否已连接
curl http://localhost:8080/health
# 查看详细日志
docker logs -f azure-blob-agent
```
## 开发与扩展
### 添加新工具
在 `azure_blob_agent.py` 中添加新的工具函数：
```python
def download_blob(container_name: str, blob_name: str) -> str:
"""下载 blob 内容（示例）"""
# 实现下载逻辑
pass
# 在 create_blob_agent() 中添加工具
tools.append(
Tool(
name="download_blob",
func=lambda input_str: download_blob(*input_str.split(",")),
description="下载指定的文件。输入格式: '容器名,文件名'"
)
)
```
### 自定义模型
支持任何 LiteLLM 兼容的模型：
```bash
# 使用 Claude
-e LITELLM_MODEL=claude-3-sonnet-20240229
# 使用本地模型
-e LITELLM_MODEL=ollama/llama2
-e LITELLM_API_BASE=http://localhost:11434
# 使用 Azure OpenAI
-e LITELLM_MODEL=azure/gpt-4
```
## 性能优化
1. **连接池**: BlobServiceClient 会自动管理连接池
2. **缓存**: 可以添加 Redis 缓存常用查询结果
3. **并发**: 使用 `max_workers` 参数提高并发处理能力
## 安全建议
1. **连接字符串**: 不要在代码中硬编码，使用环境变量或 K8s Secrets
2. **访问控制**: 使用 SAS token 而非完整连接字符串
3. **网络隔离**: 在 K8s 中使用 NetworkPolicy 限制访问
4. **日志脱敏**: 避免记录敏感信息
## 更多资源
- [Azure Blob Storage Python SDK](https://learn.microsoft.com/azure/storage/blobs/storage-quickstart-blobs-python)
- [LangChain Documentation](https://python.langchain.com/docs/get_started/introduction)
- [LiteLLM Documentation](https://docs.litellm.ai/)
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# Azure Blob Agent - 多框架支持使用指南
本文档介绍如何使用三种不同框架版本的 Azure Blob Storage AI Agent：
- **LangChain 版本**: 使用 LangChain + LiteLLM
- **MCP 版本**: 使用 Model Context Protocol
- **A2A 版本**: 使用 Agent-to-Agent 框架
## 📋 目录
1. [框架对比](#框架对比)
2. [部署配置](#部署配置)
3. [API 使用示例](#api-使用示例)
4. [创建 Agent 示例](#创建-agent-示例)
## 🔍 框架对比
| 特性 | LangChain | MCP | A2A |
|------|-----------|-----|-----|
| 工具调用 | LangChain Tools | MCP Protocol | A2A Messages |
| Agent 协作 | ❌ | ❌ | ✅ |
| 结构化输出 | ✅ | ✅ | ✅ |
| 复杂推理 | ✅ | ⚡ 轻量 | ⚡ 轻量 |
| 适用场景 | 复杂任务链 | 标准化工具 | 多Agent协作 |
## 🚀 部署配置
### 1. LangChain 版本
```json
{
"name": "my-blob-agent",
"template_name": "azure_blob_agent",
"owner_id": "user123",
"namespace": "ai-agents",
"agent_framework": "langchain",
"environment_vars": {
"LITELLM_API_BASE": "http://litellm-service:4000",
"LITELLM_MODEL": "gpt-3.5-turbo",
"LITELLM_API_KEY": "sk-xxxx",
"AZURE_STORAGE_CONNECTION_STRING": "DefaultEndpointsProtocol=https;..."
}
}
```
### 2. MCP 版本
```json
{
"name": "my-blob-agent-mcp",
"template_name": "azure_blob_agent_mcp",
"owner_id": "user123",
"namespace": "ai-agents",
"agent_framework": "mcp",
"model_provider": "openai",
"model_name": "gpt-4",
"model_api_key": "sk-xxxx",
"model_endpoint": "https://api.openai.com/v1",
"storage_connection_string": "DefaultEndpointsProtocol=https;...",
"tools_config": {
"enabled_tools": ["list_containers", "list_blobs", "search_blobs"]
}
}
```
### 3. A2A 版本
```json
{
"name": "my-blob-agent-a2a",
"template_name": "azure_blob_agent_a2a",
"owner_id": "user123",
"namespace": "ai-agents",
"agent_framework": "a2a",
"model_provider": "openai",
"model_name": "gpt-4",
"model_api_key": "sk-xxxx",
"storage_connection_string": "DefaultEndpointsProtocol=https;...",
"environment_vars": {
"AGENT_ID": "blob-agent-001",
"AGENT_ROLE": "storage_manager",
"AGENT_CAPABILITIES": "[\"blob_storage\", \"file_operations\"]"
}
}
```
## 📡 API 使用示例
### MCP 版本 API
#### 1. 列出所有可用工具
```bash
curl http://<agent-url>/mcp/tools
```
响应：
```json
{
"tools": [
{
"name": "list_containers",
"description": "列出 Azure Blob Storage 中的所有容器",
"inputSchema": {
"type": "object",
"properties": {},
"required": []
}
},
{
"name": "list_blobs",
"description": "列出指定容器中的所有文件",
"inputSchema": {
"type": "object",
"properties": {
"container_name": {
"type": "string",
"description": "容器名称"
}
},
"required": ["container_name"]
}
}
]
}
```
#### 2. 调用 MCP 工具
```bash
curl -X POST http://<agent-url>/mcp/call \
-H "Content-Type: application/json" \
-d '{
"tool_name": "list_containers",
"parameters": {}
}'
```
```bash
curl -X POST http://<agent-url>/mcp/call \
-H "Content-Type: application/json" \
-d '{
"tool_name": "list_blobs",
"parameters": {
"container_name": "my-container"
}
}'
```
### A2A 版本 API
#### 1. 获取 Agent 能力
```bash
curl http://<agent-url>/a2a/capabilities
```
响应：
```json
{
"agent_id": "blob-agent-001",
"agent_role": "storage_manager",
"capabilities": ["blob_storage", "file_operations"],
"supported_actions": [
"list_containers",
"list_blobs",
"get_blob_info",
"search_blobs",
"get_stats"
]
}
```
#### 2. 注册其他 Agent
```bash
curl -X POST http://<agent-url>/a2a/register \
-H "Content-Type: application/json" \
-d '{
"agent_id": "analytics-agent",
"agent_role": "data_analyzer",
"capabilities": ["data_analysis", "visualization"],
"endpoint": "http://analytics-agent:8080"
}'
```
#### 3. 发送 A2A 消息
```bash
curl -X POST http://<agent-url>/a2a/message \
-H "Content-Type: application/json" \
-d '{
"message_id": "msg-001",
"from_agent": "external-agent",
"to_agent": "blob-agent-001",
"message_type": "request",
"action": "list_containers",
"parameters": {}
}'
```
#### 4. Agent 间协作
```bash
curl -X POST http://<agent-url>/a2a/collaborate \
-H "Content-Type: application/json" \
-d '{
"target_agent_id": "analytics-agent",
"action": "analyze_data",
"parameters": {
"data_source": "blob_storage"
}
}'
```
## 🛠️ 创建 Agent 示例
### 使用 Agent Manager API 创建
#### 1. 创建 MCP Agent
```bash
curl -X POST http://agent-manager:8000/v2/agents/platform \
-H "Content-Type: application/json" \
-d '{
"name": "blob-mcp-001",
"template_name": "azure_blob_agent_mcp",
"owner_id": "user123",
"namespace": "ai-agents",
"agent_framework": "mcp",
"model_provider": "openai",
"model_name": "gpt-4",
"model_api_key": "sk-xxxx",
"storage_connection_string": "DefaultEndpointsProtocol=https;AccountName=myaccount;AccountKey=xxx;EndpointSuffix=core.windows.net",
"tools_config": {
"max_iterations": 5,
"timeout": 30
}
}'
```
#### 2. 创建 A2A Agent
```bash
curl -X POST http://agent-manager:8000/v2/agents/platform \
-H "Content-Type: application/json" \
-d '{
"name": "blob-a2a-001",
"template_name": "azure_blob_agent_a2a",
"owner_id": "user123",
"namespace": "ai-agents",
"agent_framework": "a2a",
"model_provider": "azure-openai",
"model_name": "gpt-4",
"model_endpoint": "https://myopenai.openai.azure.com",
"model_api_key": "xxxx",
"storage_connection_string": "DefaultEndpointsProtocol=https;...",
"query_params": {
"agent_id": "blob-a2a-001",
"agent_role": "storage_manager",
"agent_capabilities": ["blob_storage", "file_operations"]
}
}'
```
## 🔧 参数说明
### 通用参数（所有框架）
| 参数 | 类型 | 必需 | 说明 |
|------|------|------|------|
| `name` | string | ✅ | Agent 名称（唯一） |
| `template_name` | string | ✅ | 模板名称 |
| `owner_id` | string | ✅ | 所有者ID |
| `namespace` | string | ❌ | K8s 命名空间，默认 `ai-agents` |
| `agent_framework` | string | ❌ | 框架类型: `langchain`, `mcp`, `a2a` |
| `storage_connection_string` | string | ❌ | Azure Storage 连接字符串 |
| `storage_account_name` | string | ❌ | 存储账户名称 |
### 模型配置参数（MCP/A2A）
| 参数 | 类型 | 必需 | 说明 |
|------|------|------|------|
| `model_provider` | string | ✅ | 模型提供商: `openai`, `azure-openai` |
| `model_name` | string | ✅ | 模型名称: `gpt-4`, `gpt-3.5-turbo` |
| `model_api_key` | string | ✅ | 模型 API 密钥 |
| `model_endpoint` | string | ❌ | 模型 API 端点 |
### 工具配置参数（MCP/A2A）
| 参数 | 类型 | 必需 | 说明 |
|------|------|------|------|
| `tools_config` | object | ❌ | 工具配置 JSON |
| `tool_endpoint` | string | ❌ | 外部工具端点 |
| `tool_api_key` | string | ❌ | 工具 API 密钥 |
### 资源配置参数
| 参数 | 类型 | 必需 | 说明 |
|------|------|------|------|
| `cpu_request` | string | ❌ | CPU 请求，如 `100m` |
| `cpu_limit` | string | ❌ | CPU 限制，如 `500m` |
| `memory_request` | string | ❌ | 内存请求，如 `128Mi` |
| `memory_limit` | string | ❌ | 内存限制，如 `512Mi` |
## 🎯 使用场景
### LangChain 版本适用于：
- 需要复杂推理链的任务
- 多步骤文件处理流程
- 集成现有 LangChain 生态系统
### MCP 版本适用于：
- 标准化工具调用
- 轻量级集成
- 跨平台工具共享
### A2A 版本适用于：
- 多 Agent 协作场景
- 分布式任务处理
- Agent 间通信需求
## 📝 数据库迁移
如果从旧版本升级，需要运行数据库迁移：
```sql
-- 添加新字段到 templates 表
ALTER TABLE templates ADD COLUMN agent_framework VARCHAR(50) DEFAULT 'langchain';
ALTER TABLE templates ADD COLUMN tools_config JSON;
ALTER TABLE templates ADD COLUMN default_model_provider VARCHAR(100);
ALTER TABLE templates ADD COLUMN default_model_name VARCHAR(200);
-- 添加新字段到 agents 表
ALTER TABLE agents ADD COLUMN agent_framework VARCHAR(50) DEFAULT 'langchain';
ALTER TABLE agents ADD COLUMN tools_config JSON;
ALTER TABLE agents ADD COLUMN tool_endpoint VARCHAR(500);
ALTER TABLE agents ADD COLUMN tool_api_key VARCHAR(500);
ALTER TABLE agents ADD COLUMN model_provider VARCHAR(100);
ALTER TABLE agents ADD COLUMN model_name VARCHAR(200);
ALTER TABLE agents ADD COLUMN model_endpoint VARCHAR(500);
ALTER TABLE agents ADD COLUMN model_api_key VARCHAR(500);
ALTER TABLE agents ADD COLUMN storage_connection_string VARCHAR(1000);
ALTER TABLE agents ADD COLUMN storage_account_name VARCHAR(200);
```
## 🐛 故障排查
### 问题: MCP 工具调用失败
**解决方案**:
1. 检查工具名称是否正确
2. 验证参数格式
3. 查看日志: `kubectl logs <pod-name> -n ai-agents`
### 问题: A2A Agent 无法注册
**解决方案**:
1. 确认目标 Agent 可访问
2. 检查网络策略
3. 验证 endpoint URL 格式
## 📚 更多资源
- [LangChain 文档](https://python.langchain.com/)
- [MCP 协议规范](https://modelcontextprotocol.io/)
- [Agent Manager API 文档](../API_DOCUMENTATION.md)
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# 🚀 Azure Blob Storage Agent 快速启动
## 一键启动命令
### 1. 构建镜像
```bash
cd /home/taiji/tools/agent-manager/agent_templates
./build_azure_blob_agent.sh latest
```
### 2. 启动 Agent（本地测试）
```bash
# 方式 A: 启动时提供连接字符串（推荐）
docker run -d --name azure-blob-agent \
-p 8080:8080 \
-e LITELLM_API_BASE=http://20.2.70.108:4000 \
-e LITELLM_MODEL=gpt-3.5-turbo \
-e LITELLM_API_KEY=sk-1234 \
-e AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=https;AccountName=xxx;AccountKey=xxx;EndpointSuffix=core.windows.net" \
azure-blob-agent:latest
# 方式 B: 稍后通过 API 连接
docker run -d --name azure-blob-agent \
-p 8080:8080 \
-e LITELLM_API_BASE=http://20.2.70.108:4000 \
-e LITELLM_MODEL=gpt-3.5-turbo \
-e LITELLM_API_KEY=sk-1234 \
azure-blob-agent:latest
# 然后调用 /connect API 连接
# 方式 C: 如果 LiteLLM 在另一个容器中
docker run -d --name azure-blob-agent \
--network host \
-e LITELLM_API_BASE=http://localhost:4000 \
-e LITELLM_MODEL=gpt-3.5-turbo \
-e LITELLM_API_KEY=sk-1234 \
-e AZURE_STORAGE_CONNECTION_STRING="YOUR_CONNECTION_STRING" \
azure-blob-agent:latest
```
### 3. 测试 Agent
#### 方法 1: 使用 Bash 测试脚本
```bash
./test_azure_blob_agent.sh
```
#### 方法 2: 使用 Python 客户端
```bash
# 设置连接字符串（可选）
export AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=https;AccountName=xxx;..."
# 运行客户端
python3 test_client.py
```
#### 方法 3: 使用 curl 手动测试
```bash
# 健康检查
curl http://localhost:8080/health
# 连接到 Azure Storage
curl -X POST http://localhost:8080/connect \
-H 'Content-Type: application/json' \
-d '{
"connection_string": "DefaultEndpointsProtocol=https;AccountName=myaccount;AccountKey=mykey;EndpointSuffix=core.windows.net"
}'
# 执行查询
curl -X POST http://localhost:8080/query \
-H 'Content-Type: application/json' \
-d '{"query": "列出所有容器"}'
```
## 推送到 ACR
```bash
# 登录 ACR
az acr login --name agnettaiji
# 推送镜像
docker tag azure-blob-agent:latest agnettaiji.azurecr.io/ai-agents/azure-blob-agent:latest
docker push agnettaiji.azurecr.io/ai-agents/azure-blob-agent:latest
```
## 在 K8s 中部署
### 使用 agent-manager
```bash
# 添加到 k8s_manager.py 的 image_map
"azure_blob_agent": "agnettaiji.azurecr.io/ai-agents/azure-blob-agent:latest"
# 创建 agent
curl -X POST http://localhost:8000/agents \
-H 'Content-Type: application/json' \
-d '{
"name": "my-blob-agent",
"template": "azure_blob_agent",
"env": {
"LITELLM_API_BASE": "http://litellm-service:4000",
"LITELLM_MODEL": "gpt-3.5-turbo",
"LITELLM_API_KEY": "sk-1234"
}
}'
```
## 常见问题
### Q: 容器启动失败
```bash
# 查看日志
docker logs azure-blob-agent
# 检查 LiteLLM 是否可达
docker exec azure-blob-agent curl http://host.docker.internal:4000/health
```
### Q: 无法连接到 Azure Storage
```bash
# 验证连接字符串格式
# 正确格式包含: AccountName, AccountKey, EndpointSuffix
# 测试连接
curl -X POST http://localhost:8080/connect \
-H 'Content-Type: application/json' \
-d '{"connection_string": "YOUR_STRING"}' -v
```
### Q: 查询没有响应
```bash
# 检查是否已连接
curl http://localhost:8080/health | jq .
# 查看详细日志
docker logs -f azure-blob-agent
```
## 文件清单
```
agent_templates/
├── azure_blob_agent.py # 主程序
├── azure_blob_agent.Dockerfile # Docker 镜像
├── build_azure_blob_agent.sh # 构建脚本
├── test_azure_blob_agent.sh # Bash 测试脚本
├── test_client.py # Python 客户端
├── AZURE_BLOB_AGENT_USAGE.md # 详细使用文档
└── QUICKSTART.md # 本文件
```
## 下一步
- 阅读 [详细使用文档](AZURE_BLOB_AGENT_USAGE.md)
- 查看 [agent_templates README](../README.md)
- 集成到你的应用中
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# Azure Blob Agent - 快速参考
## 🚀 快速开始
### 1. 选择框架
| 框架 | 使用场景 | 文件 |
|------|---------|------|
| **LangChain** | 复杂推理任务 | `azure_blob_agent.py` |
| **MCP** | 标准化工具调用 | `azure_blob_agent_mcp.py` |
| **A2A** | 多 Agent 协作 | `azure_blob_agent_a2a.py` |
### 2. 创建 Agent (curl)
#### MCP 版本
```bash
curl -X POST http://agent-manager:8000/v2/agents/platform \
-H "Content-Type: application/json" \
-d '{
"name": "my-blob-mcp",
"template_name": "azure_blob_agent_mcp",
"owner_id": "user123",
"agent_framework": "mcp",
"model_provider": "openai",
"model_name": "gpt-4",
"model_api_key": "sk-xxxx",
"storage_connection_string": "DefaultEndpointsProtocol=https;..."
}'
```
#### A2A 版本
```bash
curl -X POST http://agent-manager:8000/v2/agents/platform \
-H "Content-Type: application/json" \
-d '{
"name": "my-blob-a2a",
"template_name": "azure_blob_agent_a2a",
"owner_id": "user123",
"agent_framework": "a2a",
"model_provider": "openai",
"model_name": "gpt-4",
"model_api_key": "sk-xxxx",
"storage_connection_string": "DefaultEndpointsProtocol=https;...",
"query_params": {
"agent_id": "my-blob-a2a",
"agent_role": "storage_manager"
}
}'
```
### 3. 使用 Agent
#### MCP - 列出工具
```bash
curl http://<agent-url>/mcp/tools
```
#### MCP - 调用工具
```bash
curl -X POST http://<agent-url>/mcp/call \
-H "Content-Type: application/json" \
-d '{"tool_name": "list_containers", "parameters": {}}'
```
#### A2A - 获取能力
```bash
curl http://<agent-url>/a2a/capabilities
```
#### A2A - 发送消息
```bash
curl -X POST http://<agent-url>/a2a/message \
-H "Content-Type: application/json" \
-d '{
"message_id": "msg-001",
"from_agent": "caller",
"to_agent": "my-blob-a2a",
"message_type": "request",
"action": "list_containers",
"parameters": {}
}'
```
## 🔧 必需参数
### MCP Agent
- ✅ `model_provider` - 模型提供商
- ✅ `model_name` - 模型名称
- ✅ `model_api_key` - API 密钥
### A2A Agent
- ✅ `model_provider` - 模型提供商
- ✅ `model_name` - 模型名称
- ✅ `model_api_key` - API 密钥
- ✅ `query_params.agent_id` - Agent ID
- ✅ `query_params.agent_role` - Agent 角色
## 🛠️ 可选参数
| 参数 | 说明 | 示例 |
|------|------|------|
| `namespace` | K8s 命名空间 | `"ai-agents"` |
| `tools_config` | 工具配置 | `{"max_iterations": 5}` |
| `tool_endpoint` | 外部工具端点 | `"http://tools-api:8080"` |
| `model_endpoint` | 模型端点 | `"https://api.openai.com/v1"` |
| `storage_account_name` | 存储账户名 | `"myaccount"` |
| `cpu_request` | CPU 请求 | `"100m"` |
| `memory_request` | 内存请求 | `"256Mi"` |
## 📊 环境变量 (容器内)
### 框架相关
- `AGENT_FRAMEWORK` - 框架类型
- `TEMPLATE_TYPE` - 模板类型
### 模型相关
- `MODEL_PROVIDER` - 模型提供商
- `MODEL_NAME` - 模型名称
- `MODEL_API_KEY` - API 密钥
- `MODEL_ENDPOINT` - 端点 URL
### 工具相关
- `TOOLS_CONFIG` - 工具配置 JSON
- `TOOL_ENDPOINT` - 工具端点
- `TOOL_API_KEY` - 工具密钥
### 存储相关
- `AZURE_STORAGE_CONNECTION_STRING` - 连接字符串
- `STORAGE_ACCOUNT_NAME` - 账户名
### 用户相关
- `USER_ID` - 用户标识
- `TENANT_ID` - 租户标识
- `NAMESPACE` - 命名空间
## 🔍 故障排查
### Agent 启动失败
```bash
# 查看日志
kubectl logs <pod-name> -n ai-agents
# 查看事件
kubectl describe pod <pod-name> -n ai-agents
```
### 工具调用失败
```bash
# 检查工具列表
curl http://<agent-url>/mcp/tools
# 测试健康检查
curl http://<agent-url>/health
```
### 存储连接失败
```bash
# 验证连接字符串
curl -X POST http://<agent-url>/connect \
-H "Content-Type: application/json" \
-d '{"connection_string": "DefaultEndpointsProtocol=https;..."}'
```
## 📝 工具列表
### 共同工具（所有版本）
1. `list_containers` - 列出所有容器
2. `list_blobs` - 列出容器中的文件
3. `get_blob_info` - 获取文件详情
4. `search_blobs` - 搜索文件
5. `get_storage_stats` - 获取统计信息
## 🏗️ 构建镜像
```bash
cd agent_templates
# MCP 版本
./build_azure_blob_mcp.sh
# A2A 版本
./build_azure_blob_a2a.sh
```
## 🧪 测试
```bash
# 设置环境变量
export AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=https;..."
export OPENAI_API_KEY="sk-xxxx"
# 运行测试
./test_multi_framework.sh
```
## 📚 更多文档
- 详细指南: [MULTI_FRAMEWORK_GUIDE.md](MULTI_FRAMEWORK_GUIDE.md)
- 实现总结: [MULTI_FRAMEWORK_SUMMARY.md](../MULTI_FRAMEWORK_SUMMARY.md)
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FROM python:3.11-slim
WORKDIR /app
# 安装系统依赖
RUN apt-get update && apt-get install -y \
curl \
&& rm -rf /var/lib/apt/lists/*
# 安装Python依赖
RUN pip install --no-cache-dir \
fastapi==0.109.0 \
uvicorn[standard]==0.27.0 \
pydantic==2.5.3 \
langchain==0.1.0 \
langchain-community==0.0.10 \
litellm==1.17.0 \
azure-storage-blob==12.19.0 \
azure-identity==1.15.0
# 复制agent代码
COPY azure_blob_agent.py .
# 设置环境变量
ENV PYTHONUNBUFFERED=1
ENV SERVICE_HOST=0.0.0.0
ENV SERVICE_PORT=8080
# 健康检查 - 使用Python避免僵尸进程
HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health').read()" || exit 1
# 运行agent (直接使用Python，避免shell)
CMD ["python3", "-u", "azure_blob_agent.py"]
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"""
Azure Blob Storage AI Agent - 使用LangChain + LiteLLM实现
通过HTTP API接收连接字符串，并提供智能文件操作功能
"""
import os
import logging
from typing import Optional, Dict, Any, List
from datetime import datetime
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from azure.storage.blob import BlobServiceClient, ContainerClient
from langchain.agents import Tool, AgentExecutor, create_react_agent
from langchain.prompts import PromptTemplate
from langchain_community.chat_models import ChatLiteLLM
import uvicorn
# 配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# 环境变量配置
SERVICE_HOST = os.getenv("SERVICE_HOST", "0.0.0.0")
SERVICE_PORT = int(os.getenv("SERVICE_PORT", "8080"))
POD_NAME = os.getenv("POD_NAME", "azure-blob-agent")
TEMPLATE_TYPE = os.getenv("TEMPLATE_TYPE", "azure_blob_agent")
# LiteLLM配置
LITELLM_API_BASE = os.getenv("LITELLM_API_BASE", "http://localhost:4000")
LITELLM_MODEL = os.getenv("LITELLM_MODEL", "gpt-3.5-turbo")
LITELLM_API_KEY = os.getenv("LITELLM_API_KEY", "sk-1234")
# Azure Storage 连接字符串（可选，也可通过API动态传入）
AZURE_STORAGE_CONNECTION_STRING = os.getenv("AZURE_STORAGE_CONNECTION_STRING", "")
# 全局存储客户端
blob_service_client: Optional[BlobServiceClient] = None
connection_string: Optional[str] = None
# FastAPI应用
app = FastAPI(
title="Azure Blob Storage AI Agent",
description="智能Azure Blob存储管理代理",
version="1.0.0"
)
# ==================== 请求/响应模型 ====================
class ConnectRequest(BaseModel):
"""连接请求"""
connection_string: str = Field(..., description="Azure Storage连接字符串")
class QueryRequest(BaseModel):
"""查询请求"""
query: str = Field(..., description="自然语言查询或操作指令")
container_name: Optional[str] = Field(None, description="指定容器名称")
class HealthResponse(BaseModel):
"""健康检查响应"""
status: str
connected: bool
connection_info: Optional[Dict] = None
# ==================== Azure Blob Storage 工具函数 ====================
def list_containers_tool() -> str:
"""列出所有容器"""
global blob_service_client
if not blob_service_client:
return "错误: 未连接到Azure Blob Storage"
try:
containers = blob_service_client.list_containers()
container_list = []
for container in containers:
container_list.append({
"name": container.name,
"last_modified": str(container.last_modified)
})
if not container_list:
return "当前没有容器"
result = "容器列表:\n"
for i, c in enumerate(container_list, 1):
result += f"{i}. {c['name']} (最后修改: {c['last_modified']})\n"
return result
except Exception as e:
logger.error(f"列出容器失败: {str(e)}")
return f"错误: {str(e)}"
def list_blobs_in_container(container_name: str) -> str:
"""列出指定容器中的所有blob"""
global blob_service_client
if not blob_service_client:
return "错误: 未连接到Azure Blob Storage"
try:
container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs()
blob_list = []
for blob in blobs:
blob_list.append({
"name": blob.name,
"size": blob.size,
"content_type": blob.content_settings.content_type if blob.content_settings else "unknown",
"last_modified": str(blob.last_modified)
})
if not blob_list:
return f"容器 '{container_name}' 中没有文件"
result = f"容器 '{container_name}' 中的文件列表:\n"
total_size = 0
for i, b in enumerate(blob_list, 1):
size_mb = b['size'] / (1024 * 1024)
result += f"{i}. {b['name']} ({size_mb:.2f}MB, {b['content_type']})\n"
total_size += b['size']
result += f"\n总计: {len(blob_list)} 个文件, {total_size / (1024 * 1024):.2f}MB"
return result
except Exception as e:
logger.error(f"列出blob失败: {str(e)}")
return f"错误: {str(e)}"
def get_blob_info(container_name: str, blob_name: str) -> str:
"""获取blob的详细信息"""
global blob_service_client
if not blob_service_client:
return "错误: 未连接到Azure Blob Storage"
try:
blob_client = blob_service_client.get_blob_client(container_name, blob_name)
properties = blob_client.get_blob_properties()
info = f"文件信息: {blob_name}\n"
info += f"- 容器: {container_name}\n"
info += f"- 大小: {properties.size / (1024 * 1024):.2f}MB\n"
info += f"- 类型: {properties.content_settings.content_type if properties.content_settings else 'unknown'}\n"
info += f"- 创建时间: {properties.creation_time}\n"
info += f"- 最后修改: {properties.last_modified}\n"
info += f"- ETag: {properties.etag}\n"
if properties.metadata:
info += f"- 元数据: {properties.metadata}\n"
return info
except Exception as e:
logger.error(f"获取blob信息失败: {str(e)}")
return f"错误: {str(e)}"
def search_blobs(container_name: str, keyword: str) -> str:
"""在容器中搜索包含关键字的blob"""
global blob_service_client
if not blob_service_client:
return "错误: 未连接到Azure Blob Storage"
try:
container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs()
matched_blobs = []
for blob in blobs:
if keyword.lower() in blob.name.lower():
matched_blobs.append({
"name": blob.name,
"size": blob.size,
"last_modified": str(blob.last_modified)
})
if not matched_blobs:
return f"在容器 '{container_name}' 中没有找到包含 '{keyword}' 的文件"
result = f"搜索结果 (关键字: '{keyword}'):\n"
for i, b in enumerate(matched_blobs, 1):
result += f"{i}. {b['name']} ({b['size'] / 1024:.2f}KB)\n"
return result
except Exception as e:
logger.error(f"搜索blob失败: {str(e)}")
return f"错误: {str(e)}"
def get_storage_stats() -> str:
"""获取存储统计信息"""
global blob_service_client
if not blob_service_client:
return "错误: 未连接到Azure Blob Storage"
try:
containers = list(blob_service_client.list_containers())
total_containers = len(containers)
total_blobs = 0
total_size = 0
container_stats = []
for container in containers:
container_client = blob_service_client.get_container_client(container.name)
blobs = list(container_client.list_blobs())
blob_count = len(blobs)
container_size = sum(blob.size for blob in blobs)
total_blobs += blob_count
total_size += container_size
container_stats.append({
"name": container.name,
"blobs": blob_count,
"size_mb": container_size / (1024 * 1024)
})
result = "存储统计信息:\n"
result += f"- 总容器数: {total_containers}\n"
result += f"- 总文件数: {total_blobs}\n"
result += f"- 总大小: {total_size / (1024 * 1024):.2f}MB\n\n"
if container_stats:
result += "各容器详情:\n"
for stat in container_stats:
result += f" • {stat['name']}: {stat['blobs']} 个文件, {stat['size_mb']:.2f}MB\n"
return result
except Exception as e:
logger.error(f"获取统计信息失败: {str(e)}")
return f"错误: {str(e)}"
# ==================== 创建LangChain Agent ====================
def create_blob_agent() -> Optional[AgentExecutor]:
"""创建Azure Blob Storage Agent"""
global blob_service_client
if not blob_service_client:
logger.warning("尚未连接到Azure Blob Storage")
return None
# 初始化LiteLLM
try:
llm = ChatLiteLLM(
model=LITELLM_MODEL,
api_base=LITELLM_API_BASE,
api_key=LITELLM_API_KEY,
temperature=0
)
logger.info(f"✅ LiteLLM初始化成功: {LITELLM_MODEL} @ {LITELLM_API_BASE}")
except Exception as e:
logger.error(f"❌ LiteLLM初始化失败: {str(e)}")
return None
# 定义工具
tools = [
Tool(
name="list_containers",
func=list_containers_tool,
description="列出所有Azure Blob Storage容器。当用户询问'有哪些容器'、'显示容器列表'时使用此工具。"
),
Tool(
name="list_blobs",
func=lambda input_str: list_blobs_in_container(input_str),
description="列出指定容器中的所有文件。输入参数是容器名称。当用户询问'容器X中有什么文件'、'列出XXX容器的文件'时使用此工具。"
),
Tool(
name="get_blob_info",
func=lambda input_str: get_blob_info(*input_str.split(",")),
description="获取特定文件的详细信息。输入格式: '容器名,文件名'。当用户询问'文件XXX的详细信息'、'XXX文件的属性'时使用此工具。"
),
Tool(
name="search_blobs",
func=lambda input_str: search_blobs(*input_str.split(",", 1)),
description="在容器中搜索文件。输入格式: '容器名,关键字'。当用户询问'搜索包含XXX的文件'、'查找XXX'时使用此工具。"
),
Tool(
name="get_storage_stats",
func=get_storage_stats,
description="获取存储的统计信息，包括容器数量、文件数量、总大小等。当用户询问'存储统计'、'有多少文件'、'占用多少空间'时使用此工具。"
),
]
# 定义Agent Prompt
template = """你是一个Azure Blob Storage管理助手。你可以帮助用户管理和查询Azure存储中的文件。
可用工具:
{tools}
工具名称: {tool_names}
回答问题时请使用以下格式:
Question: 用户的输入问题
Thought: 你应该思考如何回答这个问题
Action: 要使用的工具名称，必须是以下之一: [{tool_names}]
Action Input: 传递给工具的输入
Observation: 工具返回的结果
... (这个 Thought/Action/Action Input/Observation 可以重复N次)
Thought: 我现在知道最终答案了
Final Answer: 对用户问题的最终回答
重要提示:
- 如果用户只是说"列出容器"或"显示容器"，使用 list_containers 工具
- 如果用户说"显示XXX容器的文件"，使用 list_blobs 工具，传入容器名
- 搜索时需要同时提供容器名和关键字
- 获取文件信息时需要提供容器名和文件名，用逗号分隔
- 始终用中文回答
开始!
Question: {input}
Thought: {agent_scratchpad}"""
prompt = PromptTemplate(
template=template,
input_variables=["input", "agent_scratchpad"],
partial_variables={
"tools": "\n".join([f"- {tool.name}: {tool.description}" for tool in tools]),
"tool_names": ", ".join([tool.name for tool in tools])
}
)
# 创建Agent
agent = create_react_agent(llm, tools, prompt)
# 创建Agent执行器
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True,
handle_parsing_errors=True,
max_iterations=5
)
logger.info("✅ Azure Blob Storage Agent创建成功")
return agent_executor
# ==================== API端点 ====================
@app.get("/health", response_model=HealthResponse)
async def health_check():
"""健康检查"""
global blob_service_client, connection_string
connected = blob_service_client is not None
connection_info = None
if connected:
try:
# 获取账户信息
account_info = blob_service_client.get_account_information()
connection_info = {
"account_kind": account_info.get('account_kind', 'unknown'),
"sku_name": account_info.get('sku_name', 'unknown'),
"connected_at": str(datetime.now())
}
except Exception as e:
logger.error(f"获取账户信息失败: {str(e)}")
return HealthResponse(
status="healthy" if connected else "not_connected",
connected=connected,
connection_info=connection_info
)
@app.post("/connect")
async def connect_to_storage(request: ConnectRequest):
"""连接到Azure Blob Storage"""
global blob_service_client, connection_string
try:
# 创建BlobServiceClient
blob_service_client = BlobServiceClient.from_connection_string(
request.connection_string
)
# 测试连接
account_info = blob_service_client.get_account_information()
connection_string = request.connection_string
logger.info(f"✅ 成功连接到Azure Blob Storage")
return {
"status": "connected",
"message": "成功连接到Azure Blob Storage",
"account_info": {
"account_kind": account_info.get('account_kind'),
"sku_name": account_info.get('sku_name')
}
}
except Exception as e:
logger.error(f"❌ 连接失败: {str(e)}")
blob_service_client = None
connection_string = None
raise HTTPException(status_code=400, detail=f"连接失败: {str(e)}")
@app.post("/query")
async def query_storage(request: QueryRequest):
"""使用自然语言查询存储"""
global blob_service_client
if not blob_service_client:
raise HTTPException(
status_code=400,
detail="未连接到Azure Blob Storage，请先调用 /connect"
)
try:
# 创建Agent
agent = create_blob_agent()
if not agent:
raise HTTPException(status_code=500, detail="Agent创建失败")
# 执行查询
logger.info(f"收到查询: {request.query}")
result = agent.invoke({"input": request.query})
return {
"status": "success",
"query": request.query,
"answer": result.get("output", "无法生成答案"),
"intermediate_steps": str(result.get("intermediate_steps", []))
}
except Exception as e:
logger.error(f"查询执行失败: {str(e)}")
raise HTTPException(status_code=500, detail=f"查询失败: {str(e)}")
@app.get("/")
async def root():
"""根端点"""
return {
"service": "Azure Blob Storage AI Agent",
"version": "1.0.0",
"pod_name": POD_NAME,
"template": TEMPLATE_TYPE,
"connected": blob_service_client is not None,
"endpoints": {
"health": "/health",
"connect": "POST /connect",
"query": "POST /query"
}
}
# ==================== 主函数 ====================
def init_storage_connection():
"""启动时初始化存储连接"""
global blob_service_client, connection_string
if AZURE_STORAGE_CONNECTION_STRING:
try:
logger.info("检测到环境变量中的连接字符串，尝试连接...")
blob_service_client = BlobServiceClient.from_connection_string(
AZURE_STORAGE_CONNECTION_STRING
)
# 测试连接
account_info = blob_service_client.get_account_information()
connection_string = AZURE_STORAGE_CONNECTION_STRING
logger.info(f"✅ 成功连接到Azure Blob Storage")
logger.info(f" - Account Kind: {account_info.get('account_kind')}")
logger.info(f" - SKU: {account_info.get('sku_name')}")
except Exception as e:
logger.error(f"❌ 启动时连接失败: {str(e)}")
logger.info("💡 提示: 可以稍后通过 /connect API 手动连接")
blob_service_client = None
connection_string = None
else:
logger.info("💡 未设置 AZURE_STORAGE_CONNECTION_STRING，需通过 /connect API 手动连接")
def main():
"""启动服务"""
logger.info(f"🚀 启动 Azure Blob Storage AI Agent")
logger.info(f" - Pod名称: {POD_NAME}")
logger.info(f" - 模板类型: {TEMPLATE_TYPE}")
logger.info(f" - LiteLLM: {LITELLM_MODEL} @ {LITELLM_API_BASE}")
logger.info(f" - 服务地址: http://{SERVICE_HOST}:{SERVICE_PORT}")
# 初始化存储连接
init_storage_connection()
uvicorn.run(
app,
host=SERVICE_HOST,
port=SERVICE_PORT,
log_level="info"
)
if __name__ == "__main__":
main()
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# Azure Blob Agent - A2A 版本 Dockerfile
FROM python:3.11-slim
WORKDIR /app
# 安装系统依赖
RUN apt-get update && apt-get install -y \
gcc \
&& rm -rf /var/lib/apt/lists/*
# 复制requirements文件
COPY requirements_a2a.txt /app/
# 安装Python依赖
RUN pip install --no-cache-dir -r requirements_a2a.txt
# 复制应用代码
COPY azure_blob_agent_a2a.py /app/
# 暴露端口
EXPOSE 8080
# 健康检查
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD python -c "import requests; requests.get('http://localhost:8080/health', timeout=5)"
# 启动应用
CMD ["python", "azure_blob_agent_a2a.py"]
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"""
Azure Blob Storage AI Agent - A2A (Agent-to-Agent) 版本
支持 Agent 之间的协作和通信
"""
import os
import logging
import json
import httpx
from typing import Optional, Dict, Any, List
from datetime import datetime
from fastapi import FastAPI, HTTPException, Header
from pydantic import BaseModel, Field
from azure.storage.blob import BlobServiceClient, ContainerClient
import uvicorn
# 配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# 环境变量配置
SERVICE_HOST = os.getenv("SERVICE_HOST", "0.0.0.0")
SERVICE_PORT = int(os.getenv("SERVICE_PORT", "8080"))
POD_NAME = os.getenv("POD_NAME", "azure-blob-agent-a2a")
TEMPLATE_TYPE = os.getenv("TEMPLATE_TYPE", "azure_blob_agent_a2a")
AGENT_FRAMEWORK = os.getenv("AGENT_FRAMEWORK", "a2a")
# 工具配置
TOOLS_CONFIG = json.loads(os.getenv("TOOLS_CONFIG", "{}"))
TOOL_ENDPOINT = os.getenv("TOOL_ENDPOINT", "")
TOOL_API_KEY = os.getenv("TOOL_API_KEY", "")
# 模型配置
MODEL_PROVIDER = os.getenv("MODEL_PROVIDER", "openai")
MODEL_NAME = os.getenv("MODEL_NAME", "gpt-4")
MODEL_API_KEY = os.getenv("MODEL_API_KEY", "")
MODEL_ENDPOINT = os.getenv("MODEL_ENDPOINT", "https://api.openai.com/v1")
# 存储配置
AZURE_STORAGE_CONNECTION_STRING = os.getenv("AZURE_STORAGE_CONNECTION_STRING", "")
STORAGE_ACCOUNT_NAME = os.getenv("STORAGE_ACCOUNT_NAME", "")
# 用户标识
USER_ID = os.getenv("USER_ID", "")
TENANT_ID = os.getenv("TENANT_ID", "")
NAMESPACE = os.getenv("NAMESPACE", "ai-agents")
# A2A Agent 配置
AGENT_ID = os.getenv("AGENT_ID", POD_NAME)
AGENT_ROLE = os.getenv("AGENT_ROLE", "storage_manager")
AGENT_CAPABILITIES = json.loads(os.getenv("AGENT_CAPABILITIES", '["blob_storage", "file_operations"]'))
# 全局存储客户端
blob_service_client: Optional[BlobServiceClient] = None
connection_string: Optional[str] = None
# A2A Agent 注册表 (其他可协作的 Agent)
registered_agents: Dict[str, Dict] = {}
# FastAPI应用
app = FastAPI(
title="Azure Blob Storage AI Agent (A2A)",
description="支持 Agent-to-Agent 协作的智能 Azure Blob 存储管理代理",
version="1.0.0"
)
# ==================== 请求/响应模型 ====================
class ConnectRequest(BaseModel):
"""连接请求"""
connection_string: str = Field(..., description="Azure Storage连接字符串")
class A2AMessage(BaseModel):
"""A2A 消息格式"""
message_id: str = Field(..., description="消息ID")
from_agent: str = Field(..., description="发送者 Agent ID")
to_agent: str = Field(..., description="接收者 Agent ID")
message_type: str = Field(..., description="消息类型: request/response/notification")
action: str = Field(..., description="请求的动作")
parameters: Dict[str, Any] = Field(default_factory=dict, description="参数")
context: Optional[Dict] = Field(default_factory=dict, description="上下文")
timestamp: Optional[str] = None
class A2AQueryRequest(BaseModel):
"""A2A 查询请求"""
query: str = Field(..., description="自然语言查询")
container_name: Optional[str] = None
requester_agent: Optional[str] = Field(None, description="请求者 Agent ID")
context: Optional[Dict] = Field(default_factory=dict)
class A2ARegisterRequest(BaseModel):
"""A2A Agent 注册请求"""
agent_id: str
agent_role: str
capabilities: List[str]
endpoint: str
class HealthResponse(BaseModel):
"""健康检查响应"""
status: str
connected: bool
framework: str
agent_id: str
agent_role: str
capabilities: List[str]
user_id: Optional[str] = None
namespace: Optional[str] = None
registered_agents_count: int = 0
connection_info: Optional[Dict] = None
# ==================== A2A 操作处理器 ====================
class A2AActionHandler:
"""A2A 动作处理器"""
@staticmethod
async def handle_list_containers(parameters: Dict) -> Dict:
"""处理列出容器请求"""
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
try:
containers = blob_service_client.list_containers()
container_list = []
for container in containers:
container_list.append({
"name": container.name,
"last_modified": str(container.last_modified)
})
return {
"success": True,
"containers": container_list,
"count": len(container_list)
}
except Exception as e:
logger.error(f"列出容器失败: {str(e)}")
return {"error": str(e)}
@staticmethod
async def handle_list_blobs(parameters: Dict) -> Dict:
"""处理列出 blob 请求"""
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
container_name = parameters.get("container_name")
if not container_name:
return {"error": "缺少参数: container_name"}
try:
container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs()
blob_list = []
total_size = 0
for blob in blobs:
blob_info = {
"name": blob.name,
"size": blob.size,
"size_mb": round(blob.size / (1024 * 1024), 2),
"content_type": blob.content_settings.content_type if blob.content_settings else "unknown",
"last_modified": str(blob.last_modified)
}
blob_list.append(blob_info)
total_size += blob.size
return {
"success": True,
"container": container_name,
"blobs": blob_list,
"count": len(blob_list),
"total_size_mb": round(total_size / (1024 * 1024), 2)
}
except Exception as e:
logger.error(f"列出 blob 失败: {str(e)}")
return {"error": str(e)}
@staticmethod
async def handle_get_blob_info(parameters: Dict) -> Dict:
"""处理获取 blob 信息请求"""
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
container_name = parameters.get("container_name")
blob_name = parameters.get("blob_name")
if not container_name or not blob_name:
return {"error": "缺少参数: container_name 或 blob_name"}
try:
blob_client = blob_service_client.get_blob_client(container_name, blob_name)
properties = blob_client.get_blob_properties()
return {
"success": True,
"blob_name": blob_name,
"container": container_name,
"size": properties.size,
"size_mb": round(properties.size / (1024 * 1024), 2),
"content_type": properties.content_settings.content_type if properties.content_settings else "unknown",
"creation_time": str(properties.creation_time),
"last_modified": str(properties.last_modified),
"etag": properties.etag,
"metadata": properties.metadata if properties.metadata else {}
}
except Exception as e:
logger.error(f"获取 blob 信息失败: {str(e)}")
return {"error": str(e)}
@staticmethod
async def handle_search_blobs(parameters: Dict) -> Dict:
"""处理搜索 blob 请求"""
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
container_name = parameters.get("container_name")
keyword = parameters.get("keyword")
if not container_name or not keyword:
return {"error": "缺少参数: container_name 或 keyword"}
try:
container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs()
matched_blobs = []
for blob in blobs:
if keyword.lower() in blob.name.lower():
matched_blobs.append({
"name": blob.name,
"size": blob.size,
"size_kb": round(blob.size / 1024, 2),
"last_modified": str(blob.last_modified)
})
return {
"success": True,
"container": container_name,
"keyword": keyword,
"results": matched_blobs,
"count": len(matched_blobs)
}
except Exception as e:
logger.error(f"搜索 blob 失败: {str(e)}")
return {"error": str(e)}
@staticmethod
async def handle_get_stats(parameters: Dict) -> Dict:
"""处理获取统计信息请求"""
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
try:
containers = list(blob_service_client.list_containers())
total_containers = len(containers)
total_blobs = 0
total_size = 0
container_stats = []
for container in containers:
container_client = blob_service_client.get_container_client(container.name)
blobs = list(container_client.list_blobs())
blob_count = len(blobs)
container_size = sum(blob.size for blob in blobs)
total_blobs += blob_count
total_size += container_size
container_stats.append({
"name": container.name,
"blobs": blob_count,
"size_mb": round(container_size / (1024 * 1024), 2)
})
return {
"success": True,
"total_containers": total_containers,
"total_blobs": total_blobs,
"total_size_mb": round(total_size / (1024 * 1024), 2),
"container_stats": container_stats
}
except Exception as e:
logger.error(f"获取统计信息失败: {str(e)}")
return {"error": str(e)}
# 动作路由表
ACTION_HANDLERS = {
"list_containers": A2AActionHandler.handle_list_containers,
"list_blobs": A2AActionHandler.handle_list_blobs,
"get_blob_info": A2AActionHandler.handle_get_blob_info,
"search_blobs": A2AActionHandler.handle_search_blobs,
"get_stats": A2AActionHandler.handle_get_stats,
}
# ==================== API 端点 ====================
@app.get("/health", response_model=HealthResponse)
async def health_check():
"""健康检查"""
global blob_service_client, connection_string
connected = blob_service_client is not None
connection_info = None
if connected:
try:
account_info = blob_service_client.get_account_information()
connection_info = {
"account_kind": account_info.get('account_kind', 'unknown'),
"sku_name": account_info.get('sku_name', 'unknown'),
"connected_at": str(datetime.now())
}
except Exception as e:
logger.error(f"获取账户信息失败: {str(e)}")
return HealthResponse(
status="healthy" if connected else "not_connected",
connected=connected,
framework=AGENT_FRAMEWORK,
agent_id=AGENT_ID,
agent_role=AGENT_ROLE,
capabilities=AGENT_CAPABILITIES,
user_id=USER_ID,
namespace=NAMESPACE,
registered_agents_count=len(registered_agents),
connection_info=connection_info
)
@app.post("/connect")
async def connect_to_storage(request: ConnectRequest):
"""连接到 Azure Blob Storage"""
global blob_service_client, connection_string
try:
blob_service_client = BlobServiceClient.from_connection_string(
request.connection_string
)
account_info = blob_service_client.get_account_information()
connection_string = request.connection_string
logger.info(f"✅ 成功连接到 Azure Blob Storage (Agent: {AGENT_ID}, User: {USER_ID})")
return {
"status": "connected",
"message": "成功连接到 Azure Blob Storage",
"framework": AGENT_FRAMEWORK,
"agent_id": AGENT_ID,
"user_id": USER_ID,
"account_info": {
"account_kind": account_info.get('account_kind'),
"sku_name": account_info.get('sku_name')
}
}
except Exception as e:
logger.error(f"❌ 连接失败: {str(e)}")
blob_service_client = None
connection_string = None
raise HTTPException(status_code=400, detail=f"连接失败: {str(e)}")
@app.get("/a2a/capabilities")
async def get_capabilities():
"""获取 Agent 能力"""
return {
"agent_id": AGENT_ID,
"agent_role": AGENT_ROLE,
"capabilities": AGENT_CAPABILITIES,
"supported_actions": list(ACTION_HANDLERS.keys()),
"framework": AGENT_FRAMEWORK
}
@app.post("/a2a/register")
async def register_agent(request: A2ARegisterRequest):
"""注册其他 Agent"""
global registered_agents
registered_agents[request.agent_id] = {
"agent_id": request.agent_id,
"agent_role": request.agent_role,
"capabilities": request.capabilities,
"endpoint": request.endpoint,
"registered_at": str(datetime.now())
}
logger.info(f"✅ Agent '{request.agent_id}' 注册成功")
return {
"status": "registered",
"agent_id": request.agent_id,
"message": f"Agent '{request.agent_id}' 已注册"
}
@app.get("/a2a/agents")
async def list_registered_agents():
"""列出已注册的 Agent"""
return {
"agents": list(registered_agents.values()),
"count": len(registered_agents)
}
@app.post("/a2a/message")
async def handle_a2a_message(message: A2AMessage):
"""处理 A2A 消息"""
if not blob_service_client:
raise HTTPException(
status_code=400,
detail="未连接到 Azure Blob Storage，请先调用 /connect"
)
# 验证消息目标
if message.to_agent != AGENT_ID:
raise HTTPException(
status_code=400,
detail=f"消息目标不匹配: 期望 {AGENT_ID}, 收到 {message.to_agent}"
)
# 处理消息
if message.message_type == "request":
action = message.action
if action not in ACTION_HANDLERS:
return {
"message_id": message.message_id,
"status": "error",
"error": f"不支持的动作: {action}",
"supported_actions": list(ACTION_HANDLERS.keys())
}
try:
handler = ACTION_HANDLERS[action]
result = await handler(message.parameters)
return {
"message_id": message.message_id,
"from_agent": AGENT_ID,
"to_agent": message.from_agent,
"message_type": "response",
"action": action,
"result": result,
"timestamp": str(datetime.now())
}
except Exception as e:
logger.error(f"处理 A2A 消息失败: {str(e)}")
return {
"message_id": message.message_id,
"status": "error",
"error": str(e)
}
return {
"message_id": message.message_id,
"status": "info",
"message": f"收到消息类型: {message.message_type}"
}
@app.post("/query")
async def query_storage(request: A2AQueryRequest):
"""查询存储（支持 A2A 上下文）"""
if not blob_service_client:
raise HTTPException(
status_code=400,
detail="未连接到 Azure Blob Storage，请先调用 /connect"
)
try:
query = request.query.lower()
result = None
action_used = None
# 简单的规则匹配
if "容器" in query and ("列出" in query or "显示" in query or "有哪些" in query):
result = await A2AActionHandler.handle_list_containers({})
action_used = "list_containers"
elif "统计" in query or "有多少" in query or "占用" in query:
result = await A2AActionHandler.handle_get_stats({})
action_used = "get_stats"
elif request.container_name:
if "文件" in query or "blob" in query.lower():
result = await A2AActionHandler.handle_list_blobs({"container_name": request.container_name})
action_used = "list_blobs"
return {
"status": "success" if result else "info",
"query": request.query,
"action": action_used,
"result": result,
"agent_id": AGENT_ID,
"requester": request.requester_agent,
"framework": AGENT_FRAMEWORK
}
except Exception as e:
logger.error(f"查询执行失败: {str(e)}")
raise HTTPException(status_code=500, detail=f"查询失败: {str(e)}")
@app.post("/a2a/collaborate")
async def collaborate_with_agent(
target_agent_id: str,
action: str,
parameters: Dict[str, Any]
):
"""与其他 Agent 协作"""
if target_agent_id not in registered_agents:
raise HTTPException(
status_code=404,
detail=f"Agent '{target_agent_id}' 未注册"
)
target_agent = registered_agents[target_agent_id]
# 创建 A2A 消息
message = A2AMessage(
message_id=f"{AGENT_ID}_{datetime.now().timestamp()}",
from_agent=AGENT_ID,
to_agent=target_agent_id,
message_type="request",
action=action,
parameters=parameters,
timestamp=str(datetime.now())
)
try:
# 发送请求到目标 Agent
async with httpx.AsyncClient() as client:
response = await client.post(
f"{target_agent['endpoint']}/a2a/message",
json=message.dict(),
timeout=30.0
)
response.raise_for_status()
return {
"status": "success",
"target_agent": target_agent_id,
"action": action,
"response": response.json()
}
except Exception as e:
logger.error(f"协作失败: {str(e)}")
raise HTTPException(status_code=500, detail=f"协作失败: {str(e)}")
@app.get("/")
async def root():
"""根端点"""
return {
"service": "Azure Blob Storage AI Agent",
"version": "1.0.0",
"framework": AGENT_FRAMEWORK,
"agent_id": AGENT_ID,
"agent_role": AGENT_ROLE,
"capabilities": AGENT_CAPABILITIES,
"pod_name": POD_NAME,
"template": TEMPLATE_TYPE,
"user_id": USER_ID,
"namespace": NAMESPACE,
"connected": blob_service_client is not None,
"registered_agents": len(registered_agents),
"endpoints": {
"health": "/health",
"connect": "POST /connect",
"capabilities": "GET /a2a/capabilities",
"register_agent": "POST /a2a/register",
"list_agents": "GET /a2a/agents",
"handle_message": "POST /a2a/message",
"collaborate": "POST /a2a/collaborate",
"query": "POST /query"
}
}
# ==================== 主函数 ====================
def init_storage_connection():
"""启动时初始化存储连接"""
global blob_service_client, connection_string
if AZURE_STORAGE_CONNECTION_STRING:
try:
logger.info("检测到环境变量中的连接字符串，尝试连接...")
blob_service_client = BlobServiceClient.from_connection_string(
AZURE_STORAGE_CONNECTION_STRING
)
account_info = blob_service_client.get_account_information()
connection_string = AZURE_STORAGE_CONNECTION_STRING
logger.info(f"✅ 成功连接到 Azure Blob Storage")
logger.info(f" - Account Kind: {account_info.get('account_kind')}")
logger.info(f" - SKU: {account_info.get('sku_name')}")
except Exception as e:
logger.error(f"❌ 启动时连接失败: {str(e)}")
logger.info("💡 提示: 可以稍后通过 /connect API 手动连接")
blob_service_client = None
connection_string = None
else:
logger.info("💡 未设置 AZURE_STORAGE_CONNECTION_STRING，需通过 /connect API 手动连接")
def main():
"""启动服务"""
logger.info(f"🚀 启动 Azure Blob Storage AI Agent (A2A)")
logger.info(f" - Framework: {AGENT_FRAMEWORK}")
logger.info(f" - Agent ID: {AGENT_ID}")
logger.info(f" - Agent Role: {AGENT_ROLE}")
logger.info(f" - Capabilities: {AGENT_CAPABILITIES}")
logger.info(f" - Pod名称: {POD_NAME}")
logger.info(f" - 模板类型: {TEMPLATE_TYPE}")
logger.info(f" - User ID: {USER_ID}")
logger.info(f" - Namespace: {NAMESPACE}")
logger.info(f" - 模型: {MODEL_NAME} @ {MODEL_PROVIDER}")
logger.info(f" - 服务地址: http://{SERVICE_HOST}:{SERVICE_PORT}")
# 初始化存储连接
init_storage_connection()
uvicorn.run(
app,
host=SERVICE_HOST,
port=SERVICE_PORT,
log_level="info"
)
if __name__ == "__main__":
main()
agent_templates/azure_blob_agent_mcp.Dockerfile
+28
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# Azure Blob Agent - MCP 版本 Dockerfile
FROM python:3.11-slim
WORKDIR /app
# 安装系统依赖
RUN apt-get update && apt-get install -y \
gcc \
&& rm -rf /var/lib/apt/lists/*
# 复制requirements文件
COPY requirements_mcp.txt /app/
# 安装Python依赖
RUN pip install --no-cache-dir -r requirements_mcp.txt
# 复制应用代码
COPY azure_blob_agent_mcp.py /app/
# 暴露端口
EXPOSE 8080
# 健康检查
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD python -c "import requests; requests.get('http://localhost:8080/health', timeout=5)"
# 启动应用
CMD ["python", "azure_blob_agent_mcp.py"]
agent_templates/azure_blob_agent_mcp.py
+622
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@@ -0,0 +1,622 @@
"""
Azure Blob Storage AI Agent - MCP (Model Context Protocol) 版本
使用 MCP 协议实现智能文件操作功能
"""
import os
import logging
import json
from typing import Optional, Dict, Any, List
from datetime import datetime
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from azure.storage.blob import BlobServiceClient, ContainerClient
import uvicorn
import asyncio
# 配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# 环境变量配置
SERVICE_HOST = os.getenv("SERVICE_HOST", "0.0.0.0")
SERVICE_PORT = int(os.getenv("SERVICE_PORT", "8080"))
POD_NAME = os.getenv("POD_NAME", "azure-blob-agent-mcp")
TEMPLATE_TYPE = os.getenv("TEMPLATE_TYPE", "azure_blob_agent_mcp")
AGENT_FRAMEWORK = os.getenv("AGENT_FRAMEWORK", "mcp")
# 工具配置 (从环境变量传入的 JSON)
TOOLS_CONFIG = json.loads(os.getenv("TOOLS_CONFIG", "{}"))
TOOL_ENDPOINT = os.getenv("TOOL_ENDPOINT", "")
TOOL_API_KEY = os.getenv("TOOL_API_KEY", "")
# 模型配置
MODEL_PROVIDER = os.getenv("MODEL_PROVIDER", "openai")
MODEL_NAME = os.getenv("MODEL_NAME", "gpt-4")
MODEL_API_KEY = os.getenv("MODEL_API_KEY", "")
MODEL_ENDPOINT = os.getenv("MODEL_ENDPOINT", "https://api.openai.com/v1")
# 存储配置
AZURE_STORAGE_CONNECTION_STRING = os.getenv("AZURE_STORAGE_CONNECTION_STRING", "")
STORAGE_ACCOUNT_NAME = os.getenv("STORAGE_ACCOUNT_NAME", "")
# 用户标识
USER_ID = os.getenv("USER_ID", "")
TENANT_ID = os.getenv("TENANT_ID", "")
NAMESPACE = os.getenv("NAMESPACE", "ai-agents")
# 全局存储客户端
blob_service_client: Optional[BlobServiceClient] = None
connection_string: Optional[str] = None
# MCP 工具注册表
mcp_tools: Dict[str, Any] = {}
# FastAPI应用
app = FastAPI(
title="Azure Blob Storage AI Agent (MCP)",
description="基于 MCP 协议的智能 Azure Blob 存储管理代理",
version="1.0.0"
)
# ==================== 请求/响应模型 ====================
class ConnectRequest(BaseModel):
"""连接请求"""
connection_string: str = Field(..., description="Azure Storage连接字符串")
class MCPToolRequest(BaseModel):
"""MCP 工具调用请求"""
tool_name: str = Field(..., description="工具名称")
parameters: Dict[str, Any] = Field(default_factory=dict, description="工具参数")
class MCPQueryRequest(BaseModel):
"""MCP 查询请求"""
query: str = Field(..., description="自然语言查询或操作指令")
container_name: Optional[str] = Field(None, description="指定容器名称")
context: Optional[Dict] = Field(default_factory=dict, description="上下文信息")
class HealthResponse(BaseModel):
"""健康检查响应"""
status: str
connected: bool
framework: str
user_id: Optional[str] = None
namespace: Optional[str] = None
connection_info: Optional[Dict] = None
# ==================== MCP 工具定义 ====================
class MCPTool:
"""MCP 工具基类"""
def __init__(self, name: str, description: str, parameters_schema: Dict):
self.name = name
self.description = description
self.parameters_schema = parameters_schema
async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""执行工具"""
raise NotImplementedError
def to_mcp_spec(self) -> Dict:
"""转换为 MCP 工具规范"""
return {
"name": self.name,
"description": self.description,
"inputSchema": {
"type": "object",
"properties": self.parameters_schema,
"required": list(self.parameters_schema.keys())
}
}
class ListContainersTool(MCPTool):
"""列出所有容器工具"""
def __init__(self):
super().__init__(
name="list_containers",
description="列出 Azure Blob Storage 中的所有容器",
parameters_schema={}
)
async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
try:
containers = blob_service_client.list_containers()
container_list = []
for container in containers:
container_list.append({
"name": container.name,
"last_modified": str(container.last_modified)
})
return {
"success": True,
"containers": container_list,
"count": len(container_list)
}
except Exception as e:
logger.error(f"列出容器失败: {str(e)}")
return {"error": str(e)}
class ListBlobsTool(MCPTool):
"""列出容器中的 blob 工具"""
def __init__(self):
super().__init__(
name="list_blobs",
description="列出指定容器中的所有文件",
parameters_schema={
"container_name": {
"type": "string",
"description": "容器名称"
}
}
)
async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
container_name = parameters.get("container_name")
if not container_name:
return {"error": "缺少参数: container_name"}
try:
container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs()
blob_list = []
total_size = 0
for blob in blobs:
blob_info = {
"name": blob.name,
"size": blob.size,
"size_mb": round(blob.size / (1024 * 1024), 2),
"content_type": blob.content_settings.content_type if blob.content_settings else "unknown",
"last_modified": str(blob.last_modified)
}
blob_list.append(blob_info)
total_size += blob.size
return {
"success": True,
"container": container_name,
"blobs": blob_list,
"count": len(blob_list),
"total_size_mb": round(total_size / (1024 * 1024), 2)
}
except Exception as e:
logger.error(f"列出 blob 失败: {str(e)}")
return {"error": str(e)}
class GetBlobInfoTool(MCPTool):
"""获取 blob 信息工具"""
def __init__(self):
super().__init__(
name="get_blob_info",
description="获取特定文件的详细信息",
parameters_schema={
"container_name": {
"type": "string",
"description": "容器名称"
},
"blob_name": {
"type": "string",
"description": "文件名称"
}
}
)
async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
container_name = parameters.get("container_name")
blob_name = parameters.get("blob_name")
if not container_name or not blob_name:
return {"error": "缺少参数: container_name 或 blob_name"}
try:
blob_client = blob_service_client.get_blob_client(container_name, blob_name)
properties = blob_client.get_blob_properties()
return {
"success": True,
"blob_name": blob_name,
"container": container_name,
"size": properties.size,
"size_mb": round(properties.size / (1024 * 1024), 2),
"content_type": properties.content_settings.content_type if properties.content_settings else "unknown",
"creation_time": str(properties.creation_time),
"last_modified": str(properties.last_modified),
"etag": properties.etag,
"metadata": properties.metadata if properties.metadata else {}
}
except Exception as e:
logger.error(f"获取 blob 信息失败: {str(e)}")
return {"error": str(e)}
class SearchBlobsTool(MCPTool):
"""搜索 blob 工具"""
def __init__(self):
super().__init__(
name="search_blobs",
description="在容器中搜索包含关键字的文件",
parameters_schema={
"container_name": {
"type": "string",
"description": "容器名称"
},
"keyword": {
"type": "string",
"description": "搜索关键字"
}
}
)
async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
container_name = parameters.get("container_name")
keyword = parameters.get("keyword")
if not container_name or not keyword:
return {"error": "缺少参数: container_name 或 keyword"}
try:
container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs()
matched_blobs = []
for blob in blobs:
if keyword.lower() in blob.name.lower():
matched_blobs.append({
"name": blob.name,
"size": blob.size,
"size_kb": round(blob.size / 1024, 2),
"last_modified": str(blob.last_modified)
})
return {
"success": True,
"container": container_name,
"keyword": keyword,
"results": matched_blobs,
"count": len(matched_blobs)
}
except Exception as e:
logger.error(f"搜索 blob 失败: {str(e)}")
return {"error": str(e)}
class GetStorageStatsTool(MCPTool):
"""获取存储统计工具"""
def __init__(self):
super().__init__(
name="get_storage_stats",
description="获取存储的统计信息，包括容器数量、文件数量、总大小等",
parameters_schema={}
)
async def execute(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
global blob_service_client
if not blob_service_client:
return {"error": "未连接到 Azure Blob Storage"}
try:
containers = list(blob_service_client.list_containers())
total_containers = len(containers)
total_blobs = 0
total_size = 0
container_stats = []
for container in containers:
container_client = blob_service_client.get_container_client(container.name)
blobs = list(container_client.list_blobs())
blob_count = len(blobs)
container_size = sum(blob.size for blob in blobs)
total_blobs += blob_count
total_size += container_size
container_stats.append({
"name": container.name,
"blobs": blob_count,
"size_mb": round(container_size / (1024 * 1024), 2)
})
return {
"success": True,
"total_containers": total_containers,
"total_blobs": total_blobs,
"total_size_mb": round(total_size / (1024 * 1024), 2),
"container_stats": container_stats
}
except Exception as e:
logger.error(f"获取统计信息失败: {str(e)}")
return {"error": str(e)}
# ==================== MCP 工具注册 ====================
def register_tools():
"""注册所有 MCP 工具"""
global mcp_tools
tools = [
ListContainersTool(),
ListBlobsTool(),
GetBlobInfoTool(),
SearchBlobsTool(),
GetStorageStatsTool()
]
for tool in tools:
mcp_tools[tool.name] = tool
logger.info(f"✅ 注册了 {len(mcp_tools)} 个 MCP 工具")
# ==================== API 端点 ====================
@app.get("/health", response_model=HealthResponse)
async def health_check():
"""健康检查"""
global blob_service_client, connection_string
connected = blob_service_client is not None
connection_info = None
if connected:
try:
account_info = blob_service_client.get_account_information()
connection_info = {
"account_kind": account_info.get('account_kind', 'unknown'),
"sku_name": account_info.get('sku_name', 'unknown'),
"connected_at": str(datetime.now())
}
except Exception as e:
logger.error(f"获取账户信息失败: {str(e)}")
return HealthResponse(
status="healthy" if connected else "not_connected",
connected=connected,
framework=AGENT_FRAMEWORK,
user_id=USER_ID,
namespace=NAMESPACE,
connection_info=connection_info
)
@app.post("/connect")
async def connect_to_storage(request: ConnectRequest):
"""连接到 Azure Blob Storage"""
global blob_service_client, connection_string
try:
blob_service_client = BlobServiceClient.from_connection_string(
request.connection_string
)
account_info = blob_service_client.get_account_information()
connection_string = request.connection_string
logger.info(f"✅ 成功连接到 Azure Blob Storage (User: {USER_ID})")
return {
"status": "connected",
"message": "成功连接到 Azure Blob Storage",
"framework": AGENT_FRAMEWORK,
"user_id": USER_ID,
"account_info": {
"account_kind": account_info.get('account_kind'),
"sku_name": account_info.get('sku_name')
}
}
except Exception as e:
logger.error(f"❌ 连接失败: {str(e)}")
blob_service_client = None
connection_string = None
raise HTTPException(status_code=400, detail=f"连接失败: {str(e)}")
@app.get("/mcp/tools")
async def list_mcp_tools():
"""列出所有可用的 MCP 工具"""
if not blob_service_client:
raise HTTPException(
status_code=400,
detail="未连接到 Azure Blob Storage，请先调用 /connect"
)
tools_spec = [tool.to_mcp_spec() for tool in mcp_tools.values()]
return {
"tools": tools_spec,
"count": len(tools_spec),
"framework": AGENT_FRAMEWORK
}
@app.post("/mcp/call")
async def call_mcp_tool(request: MCPToolRequest):
"""调用 MCP 工具"""
if not blob_service_client:
raise HTTPException(
status_code=400,
detail="未连接到 Azure Blob Storage，请先调用 /connect"
)
tool_name = request.tool_name
if tool_name not in mcp_tools:
raise HTTPException(
status_code=404,
detail=f"工具 '{tool_name}' 不存在"
)
try:
tool = mcp_tools[tool_name]
result = await tool.execute(request.parameters)
return {
"tool": tool_name,
"result": result,
"timestamp": str(datetime.now())
}
except Exception as e:
logger.error(f"工具调用失败: {str(e)}")
raise HTTPException(status_code=500, detail=f"工具调用失败: {str(e)}")
@app.post("/query")
async def query_storage(request: MCPQueryRequest):
"""使用自然语言查询存储 (简化版 - 实际应集成 LLM)"""
if not blob_service_client:
raise HTTPException(
status_code=400,
detail="未连接到 Azure Blob Storage，请先调用 /connect"
)
try:
query = request.query.lower()
result = None
# 简单的规则匹配 (实际应使用 LLM 进行意图识别)
if "容器" in query and ("列出" in query or "显示" in query or "有哪些" in query):
tool = mcp_tools["list_containers"]
result = await tool.execute({})
elif "统计" in query or "有多少" in query or "占用" in query:
tool = mcp_tools["get_storage_stats"]
result = await tool.execute({})
elif request.container_name:
if "文件" in query or "blob" in query.lower():
tool = mcp_tools["list_blobs"]
result = await tool.execute({"container_name": request.container_name})
if result:
return {
"status": "success",
"query": request.query,
"result": result,
"framework": AGENT_FRAMEWORK
}
else:
return {
"status": "info",
"query": request.query,
"message": "未能匹配到合适的工具，请使用 /mcp/tools 查看可用工具",
"available_tools": list(mcp_tools.keys())
}
except Exception as e:
logger.error(f"查询执行失败: {str(e)}")
raise HTTPException(status_code=500, detail=f"查询失败: {str(e)}")
@app.get("/")
async def root():
"""根端点"""
return {
"service": "Azure Blob Storage AI Agent",
"version": "1.0.0",
"framework": AGENT_FRAMEWORK,
"pod_name": POD_NAME,
"template": TEMPLATE_TYPE,
"user_id": USER_ID,
"namespace": NAMESPACE,
"connected": blob_service_client is not None,
"tools_count": len(mcp_tools),
"endpoints": {
"health": "/health",
"connect": "POST /connect",
"list_tools": "GET /mcp/tools",
"call_tool": "POST /mcp/call",
"query": "POST /query"
}
}
# ==================== 主函数 ====================
def init_storage_connection():
"""启动时初始化存储连接"""
global blob_service_client, connection_string
if AZURE_STORAGE_CONNECTION_STRING:
try:
logger.info("检测到环境变量中的连接字符串，尝试连接...")
blob_service_client = BlobServiceClient.from_connection_string(
AZURE_STORAGE_CONNECTION_STRING
)
account_info = blob_service_client.get_account_information()
connection_string = AZURE_STORAGE_CONNECTION_STRING
logger.info(f"✅ 成功连接到 Azure Blob Storage")
logger.info(f" - Account Kind: {account_info.get('account_kind')}")
logger.info(f" - SKU: {account_info.get('sku_name')}")
except Exception as e:
logger.error(f"❌ 启动时连接失败: {str(e)}")
logger.info("💡 提示: 可以稍后通过 /connect API 手动连接")
blob_service_client = None
connection_string = None
else:
logger.info("💡 未设置 AZURE_STORAGE_CONNECTION_STRING，需通过 /connect API 手动连接")
def main():
"""启动服务"""
logger.info(f"🚀 启动 Azure Blob Storage AI Agent (MCP)")
logger.info(f" - Framework: {AGENT_FRAMEWORK}")
logger.info(f" - Pod名称: {POD_NAME}")
logger.info(f" - 模板类型: {TEMPLATE_TYPE}")
logger.info(f" - User ID: {USER_ID}")
logger.info(f" - Namespace: {NAMESPACE}")
logger.info(f" - 模型: {MODEL_NAME} @ {MODEL_PROVIDER}")
logger.info(f" - 服务地址: http://{SERVICE_HOST}:{SERVICE_PORT}")
# 注册 MCP 工具
register_tools()
# 初始化存储连接
init_storage_connection()
uvicorn.run(
app,
host=SERVICE_HOST,
port=SERVICE_PORT,
log_level="info"
)
if __name__ == "__main__":
main()
agent_templates/build_azure_blob_a2a.sh
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#!/bin/bash
# 构建并推送 Azure Blob Agent A2A 版本到 ACR
# 用法: ./build_azure_blob_a2a.sh
set -e
echo "🚀 构建 Azure Blob Agent (A2A版本)..."
# Azure Container Registry 配置
ACR_NAME="agnettaiji"
ACR_LOGIN_SERVER="${ACR_NAME}.azurecr.io"
IMAGE_NAME="ai-agents/azure-blob-agent-a2a"
IMAGE_TAG="latest"
# 完整镜像名称
FULL_IMAGE_NAME="${ACR_LOGIN_SERVER}/${IMAGE_NAME}:${IMAGE_TAG}"
echo "📦 镜像名称: ${FULL_IMAGE_NAME}"
# 构建镜像
echo "🔨 构建 Docker 镜像 (ARM64)..."
docker buildx build \
--platform linux/arm64 \
-f azure_blob_agent_a2a.Dockerfile \
-t ${FULL_IMAGE_NAME} \
--load \
.
echo "✅ 镜像构建成功"
# 登录到 ACR
echo "🔐 登录到 Azure Container Registry..."
az acr login --name ${ACR_NAME}
# 推送镜像
echo "📤 推送镜像到 ACR..."
docker push ${FULL_IMAGE_NAME}
echo "✅ 镜像推送成功"
echo "🎉 完成！镜像: ${FULL_IMAGE_NAME}"
agent_templates/build_azure_blob_agent.sh
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#!/bin/bash
# Azure Blob Storage Agent 构建和推送脚本
# 使用方法: ./build_azure_blob_agent.sh [TAG]
set -e
# 默认配置
ACR_NAME="${ACR_NAME:-agnettaiji.azurecr.io}"
IMAGE_NAME="ai-agents/azure-blob-agent"
TAG="${1:-latest}"
FULL_IMAGE="${ACR_NAME}/${IMAGE_NAME}:${TAG}"
echo "=========================================="
echo "构建 Azure Blob Storage Agent"
echo "=========================================="
echo "镜像: ${FULL_IMAGE}"
echo ""
# 构建镜像
echo "📦 开始构建镜像..."
docker build \
-f azure_blob_agent.Dockerfile \
-t "${FULL_IMAGE}" \
.
echo ""
echo "✅ 镜像构建成功: ${FULL_IMAGE}"
echo ""
# 询问是否推送
read -p "是否推送到 ACR? (y/N): " -n 1 -r
echo
if [[ $REPLY =~ ^[Yy]$ ]]; then
echo "🚀 推送镜像到 ACR..."
# 登录 ACR (如果需要)
echo "登录到 ACR..."
az acr login --name $(echo ${ACR_NAME} | cut -d'.' -f1)
# 推送镜像
docker push "${FULL_IMAGE}"
echo ""
echo "✅ 镜像推送成功!"
else
echo "⏭️ 跳过推送"
fi
echo ""
echo "=========================================="
echo "本地测试命令:"
echo "=========================================="
echo ""
echo "# 启动容器 (需要 LiteLLM 服务)"
echo "docker run -d --name azure-blob-agent \\"
echo " -p 8080:8080 \\"
echo " -e LITELLM_API_BASE=http://host.docker.internal:4000 \\"
echo " -e LITELLM_MODEL=gpt-3.5-turbo \\"
echo " -e LITELLM_API_KEY=sk-1234 \\"
echo " -e AZURE_STORAGE_CONNECTION_STRING='YOUR_CONNECTION_STRING' \\"
echo " ${FULL_IMAGE}"
echo ""
echo "# 或者不提供连接字符串，稍后通过 API 连接"
echo "docker run -d --name azure-blob-agent \\"
echo " -p 8080:8080 \\"
echo " -e LITELLM_API_BASE=http://host.docker.internal:4000 \\"
echo " -e LITELLM_MODEL=gpt-3.5-turbo \\"
echo " -e LITELLM_API_KEY=sk-1234 \\"
echo " ${FULL_IMAGE}"
echo ""
echo "# 检查健康状态"
echo "curl http://localhost:8080/health"
echo ""
echo "# 连接到 Azure Storage"
echo "curl -X POST http://localhost:8080/connect \\"
echo " -H 'Content-Type: application/json' \\"
echo " -d '{\"connection_string\": \"YOUR_CONNECTION_STRING\"}'"
echo ""
echo "# 执行自然语言查询"
echo "curl -X POST http://localhost:8080/query \\"
echo " -H 'Content-Type: application/json' \\"
echo " -d '{\"query\": \"列出所有容器\"}'"
echo ""
echo "# 查看日志"
echo "docker logs -f azure-blob-agent"
echo ""
echo "=========================================="
agent_templates/build_azure_blob_mcp.sh
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#!/bin/bash
# 构建并推送 Azure Blob Agent MCP 版本到 ACR
# 用法: ./build_azure_blob_mcp.sh
set -e
echo "🚀 构建 Azure Blob Agent (MCP版本)..."
# Azure Container Registry 配置
ACR_NAME="agnettaiji"
ACR_LOGIN_SERVER="${ACR_NAME}.azurecr.io"
IMAGE_NAME="ai-agents/azure-blob-agent-mcp"
IMAGE_TAG="latest"
# 完整镜像名称
FULL_IMAGE_NAME="${ACR_LOGIN_SERVER}/${IMAGE_NAME}:${IMAGE_TAG}"
echo "📦 镜像名称: ${FULL_IMAGE_NAME}"
# 构建镜像
echo "🔨 构建 Docker 镜像 (ARM64)..."
docker buildx build \
--platform linux/arm64 \
-f azure_blob_agent_mcp.Dockerfile \
-t ${FULL_IMAGE_NAME} \
--load \
.
echo "✅ 镜像构建成功"
# 登录到 ACR
echo "🔐 登录到 Azure Container Registry..."
az acr login --name ${ACR_NAME}
# 推送镜像
echo "📤 推送镜像到 ACR..."
docker push ${FULL_IMAGE_NAME}
echo "✅ 镜像推送成功"
echo "🎉 完成！镜像: ${FULL_IMAGE_NAME}"
agent_templates/requirements_a2a.txt
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# Requirements for Azure Blob Agent - A2A Version
fastapi==0.109.0
uvicorn[standard]==0.27.0
pydantic==2.5.3
azure-storage-blob==12.19.0
httpx==0.26.0
agent_templates/requirements_mcp.txt
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# Requirements for Azure Blob Agent - MCP Version
fastapi==0.109.0
uvicorn[standard]==0.27.0
pydantic==2.5.3
azure-storage-blob==12.19.0
agent_templates/test_azure_blob_agent.sh
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#!/bin/bash
# Azure Blob Storage Agent 本地测试脚本
# 使用方法: ./test_azure_blob_agent.sh
set -e
AGENT_HOST="localhost"
AGENT_PORT="8080"
BASE_URL="http://${AGENT_HOST}:${AGENT_PORT}"
echo "=========================================="
echo "Azure Blob Storage Agent 测试脚本"
echo "=========================================="
echo ""
# 颜色定义
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
NC='\033[0m' # No Color
# 测试函数
test_endpoint() {
local name=$1
local method=$2
local endpoint=$3
local data=$4
echo -e "${YELLOW}测试: ${name}${NC}"
echo "请求: ${method} ${endpoint}"
if [ -z "$data" ]; then
response=$(curl -s -w "\n%{http_code}" -X ${method} "${BASE_URL}${endpoint}")
else
response=$(curl -s -w "\n%{http_code}" -X ${method} "${BASE_URL}${endpoint}" \
-H 'Content-Type: application/json' \
-d "${data}")
fi
http_code=$(echo "$response" | tail -n1)
body=$(echo "$response" | sed '$d')
if [ "$http_code" -eq 200 ] || [ "$http_code" -eq 201 ]; then
echo -e "${GREEN}✅ 成功 (HTTP $http_code)${NC}"
echo "响应: $body" | jq '.' 2>/dev/null || echo "$body"
else
echo -e "${RED}❌ 失败 (HTTP $http_code)${NC}"
echo "响应: $body"
fi
echo ""
}
# 1. 检查容器是否运行
echo "1️⃣ 检查容器状态..."
if docker ps | grep -q azure-blob-agent; then
echo -e "${GREEN}✅ 容器正在运行${NC}"
else
echo -e "${RED}❌ 容器未运行${NC}"
echo "请先启动容器："
echo "docker run -d --name azure-blob-agent -p 8080:8080 \\"
echo " -e LITELLM_API_BASE=http://host.docker.internal:4000 \\"
echo " -e LITELLM_MODEL=gpt-3.5-turbo \\"
echo " -e LITELLM_API_KEY=sk-1234 \\"
echo " azure-blob-agent:latest"
exit 1
fi
echo ""
# 2. 等待服务就绪
echo "2️⃣ 等待服务就绪..."
max_attempts=30
attempt=0
while [ $attempt -lt $max_attempts ]; do
if curl -s "${BASE_URL}/health" > /dev/null 2>&1; then
echo -e "${GREEN}✅ 服务已就绪${NC}"
break
fi
attempt=$((attempt + 1))
echo -n "."
sleep 1
done
if [ $attempt -eq $max_attempts ]; then
echo -e "${RED}❌ 服务启动超时${NC}"
exit 1
fi
echo ""
# 3. 健康检查
test_endpoint "健康检查" "GET" "/health"
# 4. 根端点
test_endpoint "根端点" "GET" "/"
# 5. 连接到 Azure Storage（需要用户提供连接字符串）
echo -e "${YELLOW}=========================================="
echo "连接到 Azure Storage"
echo "==========================================${NC}"
echo ""
echo "请输入 Azure Storage 连接字符串："
echo "(格式: DefaultEndpointsProtocol=https;AccountName=xxx;AccountKey=xxx;EndpointSuffix=core.windows.net)"
echo ""
read -r CONNECTION_STRING
if [ -z "$CONNECTION_STRING" ]; then
echo -e "${YELLOW}⏭️ 跳过连接测试${NC}"
else
connect_data="{\"connection_string\": \"${CONNECTION_STRING}\"}"
test_endpoint "连接 Azure Storage" "POST" "/connect" "$connect_data"
# 6. 查询测试（仅在连接成功后）
echo -e "${YELLOW}=========================================="
echo "自然语言查询测试"
echo "==========================================${NC}"
echo ""
# 列出容器
query_data='{"query": "列出所有容器"}'
test_endpoint "查询: 列出所有容器" "POST" "/query" "$query_data"
# 获取统计信息
query_data='{"query": "显示存储统计信息"}'
test_endpoint "查询: 存储统计" "POST" "/query" "$query_data"
# 自定义查询
echo -e "${YELLOW}输入自定义查询（按Enter跳过）:${NC}"
read -r CUSTOM_QUERY
if [ ! -z "$CUSTOM_QUERY" ]; then
query_data="{\"query\": \"${CUSTOM_QUERY}\"}"
test_endpoint "自定义查询" "POST" "/query" "$query_data"
fi
fi
echo ""
echo "=========================================="
echo -e "${GREEN}测试完成！${NC}"
echo "=========================================="
echo ""
echo "查看日志："
echo " docker logs -f azure-blob-agent"
echo ""
echo "停止容器："
echo " docker stop azure-blob-agent"
echo " docker rm azure-blob-agent"
echo ""
agent_templates/test_client.py
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#!/usr/bin/env python3
"""
Azure Blob Storage Agent 客户端示例
演示如何使用 Python 调用 agent API
"""
import requests
import json
import os
import sys
# Agent 配置
AGENT_BASE_URL = os.getenv("AGENT_URL", "http://localhost:8080")
class AzureBlobAgentClient:
"""Azure Blob Storage Agent 客户端"""
def __init__(self, base_url: str = AGENT_BASE_URL):
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
self.connected = False
def health_check(self) -> dict:
"""健康检查"""
response = self.session.get(f"{self.base_url}/health")
response.raise_for_status()
return response.json()
def connect(self, connection_string: str) -> dict:
"""连接到 Azure Storage"""
response = self.session.post(
f"{self.base_url}/connect",
json={"connection_string": connection_string}
)
response.raise_for_status()
result = response.json()
self.connected = True
return result
def query(self, query_text: str, container_name: str = None) -> dict:
"""执行自然语言查询"""
if not self.connected:
raise Exception("未连接到 Azure Storage，请先调用 connect()")
payload = {"query": query_text}
if container_name:
payload["container_name"] = container_name
response = self.session.post(
f"{self.base_url}/query",
json=payload
)
response.raise_for_status()
return response.json()
def get_info(self) -> dict:
"""获取 agent 信息"""
response = self.session.get(f"{self.base_url}/")
response.raise_for_status()
return response.json()
def print_response(title: str, response: dict):
"""格式化打印响应"""
print(f"\n{'='*60}")
print(f"📋 {title}")
print('='*60)
print(json.dumps(response, indent=2, ensure_ascii=False))
def main():
"""主函数"""
print("🚀 Azure Blob Storage Agent 客户端")
print(f"连接到: {AGENT_BASE_URL}\n")
# 创建客户端
client = AzureBlobAgentClient()
try:
# 1. 健康检查
print("1️⃣ 执行健康检查...")
health = client.health_check()
print_response("健康检查", health)
# 2. 获取 agent 信息
print("\n2️⃣ 获取 Agent 信息...")
info = client.get_info()
print_response("Agent 信息", info)
# 3. 连接到 Azure Storage
print("\n3️⃣ 连接到 Azure Storage...")
# 从环境变量获取连接字符串
connection_string = os.getenv("AZURE_STORAGE_CONNECTION_STRING")
if not connection_string:
print("\n⚠️ 未设置 AZURE_STORAGE_CONNECTION_STRING 环境变量")
print("请输入 Azure Storage 连接字符串:")
connection_string = input().strip()
if not connection_string:
print("❌ 未提供连接字符串，退出")
sys.exit(1)
connect_result = client.connect(connection_string)
print_response("连接结果", connect_result)
# 4. 执行查询
print("\n4️⃣ 执行自然语言查询...\n")
queries = [
"列出所有容器",
"显示存储统计信息",
]
for query_text in queries:
print(f"\n💬 查询: {query_text}")
result = client.query(query_text)
print(f"\n✅ 答案:\n{result.get('answer', 'N/A')}")
print(f"\n状态: {result.get('status')}")
# 5. 交互式查询
print("\n5️⃣ 交互式查询")
print("="*60)
print("输入自然语言查询（输入 'quit' 或 'exit' 退出）:")
print("例如:")
print(" - 列出所有容器")
print(" - 显示 images 容器中的文件")
print(" - 在 documents 容器中搜索 report")
print(" - 获取存储统计信息")
print("="*60)
while True:
try:
query_text = input("\n💬 > ").strip()
if query_text.lower() in ['quit', 'exit', 'q']:
print("👋 再见!")
break
if not query_text:
continue
result = client.query(query_text)
print(f"\n✅ 答案:\n{result.get('answer', 'N/A')}")
except KeyboardInterrupt:
print("\n\n👋 再见!")
break
except Exception as e:
print(f"\n❌ 查询失败: {str(e)}")
except requests.exceptions.ConnectionError:
print(f"\n❌ 无法连接到 Agent: {AGENT_BASE_URL}")
print("请确保 Agent 正在运行")
sys.exit(1)
except Exception as e:
print(f"\n❌ 错误: {str(e)}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == "__main__":
main()
agent_templates/test_multi_framework.sh
Executable
+172
View File
@@ -0,0 +1,172 @@
#!/bin/bash
# 测试 Azure Blob Agent 多框架版本
# 用法: ./test_multi_framework.sh
set -e
echo "🧪 测试 Azure Blob Agent 多框架版本"
echo "======================================"
# 配置
AGENT_MANAGER_URL="http://localhost:8000"
OWNER_ID="test-user"
NAMESPACE="ai-agents"
# Azure Storage 连接字符串（从环境变量获取）
STORAGE_CONN_STRING="${AZURE_STORAGE_CONNECTION_STRING}"
if [ -z "$STORAGE_CONN_STRING" ]; then
echo "❌ 错误: 请设置环境变量 AZURE_STORAGE_CONNECTION_STRING"
exit 1
fi
# 模型配置（从环境变量获取）
MODEL_API_KEY="${OPENAI_API_KEY:-sk-test}"
echo ""
echo "📋 配置信息:"
echo " - Agent Manager: $AGENT_MANAGER_URL"
echo " - Owner ID: $OWNER_ID"
echo " - Namespace: $NAMESPACE"
echo ""
# 测试函数
test_agent() {
local framework=$1
local template=$2
local agent_name=$3
local extra_config=$4
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "🧪 测试 $framework 版本"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
# 构建请求 JSON
local request_json=$(cat <<EOF
{
"name": "$agent_name",
"template_name": "$template",
"owner_id": "$OWNER_ID",
"namespace": "$NAMESPACE",
"agent_framework": "$framework",
"storage_connection_string": "$STORAGE_CONN_STRING",
"model_provider": "openai",
"model_name": "gpt-4",
"model_api_key": "$MODEL_API_KEY",
"tools_config": {
"max_iterations": 5
}
$extra_config
}
EOF
)
echo "📤 创建 Agent..."
response=$(curl -s -X POST "$AGENT_MANAGER_URL/v2/agents/platform" \
-H "Content-Type: application/json" \
-d "$request_json")
echo "✅ 响应: $response"
# 检查是否创建成功
if echo "$response" | grep -q "id"; then
echo "✅ Agent 创建成功"
# 等待 Agent 启动
echo "⏳ 等待 Agent 启动..."
sleep 10
# 获取 Agent 状态
echo "📊 获取 Agent 状态..."
status_response=$(curl -s "$AGENT_MANAGER_URL/v2/agents/$agent_name")
echo "$status_response" | jq '.'
# 提取 service_url
service_url=$(echo "$status_response" | jq -r '.service_url // empty')
if [ -n "$service_url" ]; then
echo "🌐 Service URL: $service_url"
# 测试健康检查
echo "💓 测试健康检查..."
health_response=$(curl -s "$service_url/health")
echo "$health_response" | jq '.'
# 根据框架测试特定功能
case $framework in
"mcp")
echo "🔧 测试 MCP 工具列表..."
curl -s "$service_url/mcp/tools" | jq '.'
echo "🔧 测试 MCP 工具调用..."
curl -s -X POST "$service_url/mcp/call" \
-H "Content-Type: application/json" \
-d '{"tool_name": "list_containers", "parameters": {}}' | jq '.'
;;
"a2a")
echo "🤝 测试 A2A 能力..."
curl -s "$service_url/a2a/capabilities" | jq '.'
echo "🤝 测试 A2A 消息..."
curl -s -X POST "$service_url/a2a/message" \
-H "Content-Type: application/json" \
-d '{
"message_id": "test-001",
"from_agent": "test-agent",
"to_agent": "blob-agent",
"message_type": "request",
"action": "list_containers",
"parameters": {}
}' | jq '.'
;;
"langchain")
echo "🔗 测试查询..."
curl -s -X POST "$service_url/query" \
-H "Content-Type: application/json" \
-d '{"query": "列出所有容器"}' | jq '.'
;;
esac
else
echo "⚠️ 警告: 未找到 service_url"
fi
# 删除测试 Agent
echo "🗑️ 删除测试 Agent..."
delete_response=$(curl -s -X DELETE "$AGENT_MANAGER_URL/v2/agents/$agent_name")
echo "$delete_response" | jq '.'
else
echo "❌ Agent 创建失败"
echo "$response"
return 1
fi
echo ""
}
# 运行测试
echo "🚀 开始测试..."
echo ""
# 测试 MCP 版本
test_agent "mcp" "azure_blob_agent_mcp" "test-blob-mcp" ""
# 测试 A2A 版本
test_agent "a2a" "azure_blob_agent_a2a" "test-blob-a2a" ',
"query_params": {
"agent_id": "test-blob-a2a",
"agent_role": "storage_manager"
}'
# 测试 LangChain 版本（如果已部署）
# test_agent "langchain" "azure_blob_agent" "test-blob-langchain" ',
# "environment_vars": {
# "LITELLM_API_BASE": "http://litellm-service:4000",
# "LITELLM_MODEL": "gpt-3.5-turbo",
# "LITELLM_API_KEY": "sk-test"
# }'
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "✅ 所有测试完成"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
app.py
+33 -4
View File
@@ -42,9 +42,13 @@ class CreateTemplateRequest(BaseModel):
display_name: str display_name: str
description: Optional[str] = None description: Optional[str] = None
agent_type: str = Field(..., description="platform or custom") agent_type: str = Field(..., description="platform or custom")
agent_framework: str = Field(default="langchain", description="langchain, mcp, or a2a")
image: str image: str
port: Optional[int] = None port: Optional[int] = None
env_requirements: Optional[Dict] = Field(default_factory=dict) env_requirements: Optional[Dict] = Field(default_factory=dict)
tools_config: Optional[Dict] = Field(default_factory=dict, description="Tools configuration JSON")
default_model_provider: Optional[str] = Field(None, description="Default model provider")
default_model_name: Optional[str] = Field(None, description="Default model name")
cpu_request: Optional[str] = None cpu_request: Optional[str] = None
cpu_limit: Optional[str] = None cpu_limit: Optional[str] = None
memory_request: Optional[str] = None memory_request: Optional[str] = None
@@ -103,7 +107,19 @@ class CreatePlatformAgentRequest(BaseModel):
owner_id: str owner_id: str
channel_id: Optional[str] = None channel_id: Optional[str] = None
tenant_id: Optional[str] = None tenant_id: Optional[str] = None
namespace: Optional[str] = Field(default="ai-agents", description="Kubernetes namespace")
query_params: Optional[Dict] = Field(default_factory=dict) query_params: Optional[Dict] = Field(default_factory=dict)
# NEW: Framework-specific configurations
agent_framework: Optional[str] = Field(None, description="Override template framework")
tools_config: Optional[Dict] = Field(default_factory=dict, description="Tools configuration")
tool_endpoint: Optional[str] = Field(None, description="External tool endpoint")
tool_api_key: Optional[str] = Field(None, description="Tool API key")
model_provider: Optional[str] = Field(None, description="Model provider")
model_name: Optional[str] = Field(None, description="Model name")
model_endpoint: Optional[str] = Field(None, description="Model endpoint")
model_api_key: Optional[str] = Field(None, description="Model API key")
storage_connection_string: Optional[str] = Field(None, description="Storage connection string")
storage_account_name: Optional[str] = Field(None, description="Storage account name")
# Custom Agent Models # Custom Agent Models
@@ -121,7 +137,20 @@ class CreateCustomAgentRequest(BaseModel):
owner_id: str owner_id: str
channel_id: Optional[str] = None channel_id: Optional[str] = None
tenant_id: Optional[str] = None tenant_id: Optional[str] = None
namespace: Optional[str] = Field(default="ai-agents", description="Kubernetes namespace")
environment_vars: Dict[str, str] environment_vars: Dict[str, str]
# NEW: Framework-specific configurations
agent_framework: Optional[str] = Field(None, description="Override template framework")
tools_config: Optional[Dict] = Field(default_factory=dict, description="Tools configuration")
tool_endpoint: Optional[str] = Field(None, description="External tool endpoint")
tool_api_key: Optional[str] = Field(None, description="Tool API key")
model_provider: Optional[str] = Field(None, description="Model provider")
model_name: Optional[str] = Field(None, description="Model name")
model_endpoint: Optional[str] = Field(None, description="Model endpoint")
model_api_key: Optional[str] = Field(None, description="Model API key")
storage_connection_string: Optional[str] = Field(None, description="Storage connection string")
storage_account_name: Optional[str] = Field(None, description="Storage account name")
# Resource configuration
cpu_request: Optional[str] = None cpu_request: Optional[str] = None
cpu_limit: Optional[str] = None cpu_limit: Optional[str] = None
memory_request: Optional[str] = None memory_request: Optional[str] = None
@@ -264,7 +293,7 @@ async def create_agent(request: CreateAgentRequest):
logger.info(f"收到创建Agent请求: {request.name}, 模板: {request.template}") logger.info(f"收到创建Agent请求: {request.name}, 模板: {request.template}")
# 验证模板类型 # 验证模板类型
valid_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "mysql_agent", "postgresql_agent", "jina_search_agent"] valid_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "mysql_agent", "postgresql_agent", "jina_search_agent", "azure_blob_agent", "azure_blob_agent_mcp", "azure_blob_agent_a2a"]
if request.template not in valid_templates: if request.template not in valid_templates:
raise HTTPException( raise HTTPException(
status_code=400, status_code=400,
@@ -429,7 +458,7 @@ async def list_templates():
Returns: Returns:
模板列表及其配置信息 模板列表及其配置信息
""" """
valid_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "mysql_agent", "postgresql_agent", "jina_search_agent"] valid_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "mysql_agent", "postgresql_agent", "jina_search_agent", "azure_blob_agent"]
templates_info = [] templates_info = []
for template in valid_templates: for template in valid_templates:
@@ -451,7 +480,7 @@ async def list_platform_templates():
平台提供的Agent模板列表 平台提供的Agent模板列表
""" """
# 平台 Agent 是预定义的标准模板 # 平台 Agent 是预定义的标准模板
platform_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "jina_search_agent"] platform_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "jina_search_agent", "azure_blob_agent"]
templates_info = [] templates_info = []
for template in platform_templates: for template in platform_templates:
@@ -501,7 +530,7 @@ async def get_template_info(template_name: str):
Returns: Returns:
模板详细信息（端口、所需环境变量等） 模板详细信息（端口、所需环境变量等）
""" """
valid_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "mysql_agent", "postgresql_agent", "jina_search_agent"] valid_templates = ["echo_agent", "chat_agent", "code_agent", "search_agent", "mysql_agent", "postgresql_agent", "jina_search_agent", "azure_blob_agent"]
if template_name not in valid_templates: if template_name not in valid_templates:
raise HTTPException( raise HTTPException(
database.py
+28
View File
@@ -50,6 +50,9 @@ class Template(Base):
description = Column(Text) description = Column(Text)
agent_type = Column(SQLEnum(AgentType), nullable=False, index=True) agent_type = Column(SQLEnum(AgentType), nullable=False, index=True)
# Framework configuration (NEW)
agent_framework = Column(String(50), default="langchain") # "langchain", "mcp", "a2a"
# Image configuration # Image configuration
image = Column(String(500), nullable=False) image = Column(String(500), nullable=False)
port = Column(Integer, nullable=True) port = Column(Integer, nullable=True)
@@ -58,6 +61,13 @@ class Template(Base):
# {"required": {"KEY": "description"}, "optional": {"KEY": "description"}} # {"required": {"KEY": "description"}, "optional": {"KEY": "description"}}
env_requirements = Column(JSON, default={}) env_requirements = Column(JSON, default={})
# Tools configuration (NEW) - JSON format for tool definitions
tools_config = Column(JSON, default={})
# Model configuration defaults (NEW)
default_model_provider = Column(String(100)) # e.g., "openai", "azure-openai"
default_model_name = Column(String(200)) # e.g., "gpt-4", "claude-3"
# Resource configuration (for platform agents, fixed by admin) # Resource configuration (for platform agents, fixed by admin)
cpu_request = Column(String(20)) # e.g., "100m" cpu_request = Column(String(20)) # e.g., "100m"
cpu_limit = Column(String(20)) # e.g., "500m" cpu_limit = Column(String(20)) # e.g., "500m"
@@ -100,9 +110,27 @@ class Agent(Base):
agent_type = Column(SQLEnum(AgentType), nullable=False, index=True) agent_type = Column(SQLEnum(AgentType), nullable=False, index=True)
status = Column(SQLEnum(AgentStatus), default=AgentStatus.PENDING, index=True) status = Column(SQLEnum(AgentStatus), default=AgentStatus.PENDING, index=True)
# Framework configuration (NEW)
agent_framework = Column(String(50), default="langchain") # "langchain", "mcp", "a2a"
# Environment variables (encrypted in production) # Environment variables (encrypted in production)
environment_vars = Column(JSON, default={}) environment_vars = Column(JSON, default={})
# Tools configuration (NEW) - Instance-level tools override
tools_config = Column(JSON, default={})
tool_endpoint = Column(String(500)) # External tool endpoint URL
tool_api_key = Column(String(500)) # Encrypted tool API key
# Model configuration (NEW) - Instance-level model settings
model_provider = Column(String(100)) # e.g., "openai", "azure-openai"
model_name = Column(String(200)) # e.g., "gpt-4"
model_endpoint = Column(String(500)) # Model API endpoint
model_api_key = Column(String(500)) # Encrypted model API key
# Storage configuration (NEW) - For agents that need storage
storage_connection_string = Column(String(1000)) # Encrypted storage connection
storage_account_name = Column(String(200))
# Resource configuration (for custom agents) # Resource configuration (for custom agents)
cpu_request = Column(String(20)) cpu_request = Column(String(20))
cpu_limit = Column(String(20)) cpu_limit = Column(String(20))
k8s_manager.py
+112 -2
View File
@@ -76,6 +76,9 @@ class K8sManager:
# 模板端口映射 # 模板端口映射
TEMPLATE_PORTS = { TEMPLATE_PORTS = {
"jina_search_agent": 8080, "jina_search_agent": 8080,
"azure_blob_agent": 8080,
"azure_blob_agent_mcp": 8080,
"azure_blob_agent_a2a": 8080,
} }
# 模板所需环境变量说明 # 模板所需环境变量说明
@@ -112,6 +115,57 @@ class K8sManager:
"optional": { "optional": {
"POSTGRES_PORT": "PostgreSQL端口，默认5432" "POSTGRES_PORT": "PostgreSQL端口，默认5432"
} }
},
"azure_blob_agent": {
"required": {
"LITELLM_API_BASE": "LiteLLM服务地址，如 http://litellm-service:4000",
"LITELLM_MODEL": "使用的LLM模型，如 gpt-3.5-turbo",
"LITELLM_API_KEY": "LiteLLM API密钥"
},
"optional": {
"AZURE_STORAGE_CONNECTION_STRING": "Azure Storage连接字符串（可选，也可通过 /connect API 动态传入）",
"SERVICE_PORT": "HTTP服务端口，默认8080",
"SERVICE_HOST": "HTTP服务监听地址，默认0.0.0.0"
}
},
"azure_blob_agent_mcp": {
"required": {
"MODEL_PROVIDER": "模型提供商，如 openai, azure-openai",
"MODEL_NAME": "使用的模型名称，如 gpt-4",
"MODEL_API_KEY": "模型 API 密钥"
},
"optional": {
"AZURE_STORAGE_CONNECTION_STRING": "Azure Storage连接字符串",
"TOOLS_CONFIG": "工具配置 JSON",
"TOOL_ENDPOINT": "外部工具端点",
"TOOL_API_KEY": "工具 API 密钥",
"MODEL_ENDPOINT": "模型 API 端点",
"STORAGE_ACCOUNT_NAME": "存储账户名称",
"USER_ID": "用户标识",
"TENANT_ID": "租户标识",
"NAMESPACE": "Kubernetes 命名空间"
}
},
"azure_blob_agent_a2a": {
"required": {
"MODEL_PROVIDER": "模型提供商，如 openai, azure-openai",
"MODEL_NAME": "使用的模型名称，如 gpt-4",
"MODEL_API_KEY": "模型 API 密钥",
"AGENT_ID": "Agent 唯一标识",
"AGENT_ROLE": "Agent 角色"
},
"optional": {
"AZURE_STORAGE_CONNECTION_STRING": "Azure Storage连接字符串",
"TOOLS_CONFIG": "工具配置 JSON",
"TOOL_ENDPOINT": "外部工具端点",
"TOOL_API_KEY": "工具 API 密钥",
"MODEL_ENDPOINT": "模型 API 端点",
"STORAGE_ACCOUNT_NAME": "存储账户名称",
"AGENT_CAPABILITIES": "Agent 能力列表 JSON",
"USER_ID": "用户标识",
"TENANT_ID": "租户标识",
"NAMESPACE": "Kubernetes 命名空间"
}
} }
} }
@@ -202,6 +256,9 @@ class K8sManager:
"mysql_agent": "agnettaiji.azurecr.io/ai-agents/mysql-agent:latest", "mysql_agent": "agnettaiji.azurecr.io/ai-agents/mysql-agent:latest",
"postgresql_agent": "agnettaiji.azurecr.io/ai-agents/postgresql-agent:latest", "postgresql_agent": "agnettaiji.azurecr.io/ai-agents/postgresql-agent:latest",
"jina_search_agent": "agnettaiji.azurecr.io/ai-agents/jina-search-agent:latest", "jina_search_agent": "agnettaiji.azurecr.io/ai-agents/jina-search-agent:latest",
"azure_blob_agent": "agnettaiji.azurecr.io/ai-agents/azure-blob-agent:latest",
"azure_blob_agent_mcp": "agnettaiji.azurecr.io/ai-agents/azure-blob-agent-mcp:latest",
"azure_blob_agent_a2a": "agnettaiji.azurecr.io/ai-agents/azure-blob-agent-a2a:latest",
} }
image = image_map.get(template, image_map["echo_agent"]) image = image_map.get(template, image_map["echo_agent"])
@@ -211,6 +268,58 @@ class K8sManager:
client.V1EnvVar(name="TEMPLATE_TYPE", value=template) client.V1EnvVar(name="TEMPLATE_TYPE", value=template)
] ]
# NEW: 添加 Agent 框架配置
agent_framework = config_data.get("agent_framework", "langchain")
env_vars.append(client.V1EnvVar(name="AGENT_FRAMEWORK", value=agent_framework))
# NEW: 添加工具配置
if "tools_config" in config_data:
import json
env_vars.append(client.V1EnvVar(
name="TOOLS_CONFIG",
value=json.dumps(config_data["tools_config"])
))
if "tool_endpoint" in config_data:
env_vars.append(client.V1EnvVar(name="TOOL_ENDPOINT", value=config_data["tool_endpoint"]))
if "tool_api_key" in config_data:
env_vars.append(client.V1EnvVar(name="TOOL_API_KEY", value=config_data["tool_api_key"]))
# NEW: 添加模型配置
if "model_provider" in config_data:
env_vars.append(client.V1EnvVar(name="MODEL_PROVIDER", value=config_data["model_provider"]))
if "model_name" in config_data:
env_vars.append(client.V1EnvVar(name="MODEL_NAME", value=config_data["model_name"]))
if "model_endpoint" in config_data:
env_vars.append(client.V1EnvVar(name="MODEL_ENDPOINT", value=config_data["model_endpoint"]))
if "model_api_key" in config_data:
env_vars.append(client.V1EnvVar(name="MODEL_API_KEY", value=config_data["model_api_key"]))
# NEW: 添加存储配置
if "storage_connection_string" in config_data:
env_vars.append(client.V1EnvVar(
name="AZURE_STORAGE_CONNECTION_STRING",
value=config_data["storage_connection_string"]
))
if "storage_account_name" in config_data:
env_vars.append(client.V1EnvVar(name="STORAGE_ACCOUNT_NAME", value=config_data["storage_account_name"]))
# NEW: 添加用户标识
if "user_id" in config_data:
env_vars.append(client.V1EnvVar(name="USER_ID", value=config_data["user_id"]))
if "tenant_id" in config_data:
env_vars.append(client.V1EnvVar(name="TENANT_ID", value=config_data["tenant_id"]))
# NEW: 添加命名空间信息
namespace = config_data.get("namespace", self.namespace)
env_vars.append(client.V1EnvVar(name="NAMESPACE", value=namespace))
# 添加用户自定义环境变量 # 添加用户自定义环境变量
custom_env = config_data.get("env", {}) custom_env = config_data.get("env", {})
for key, value in custom_env.items(): for key, value in custom_env.items():
@@ -220,7 +329,7 @@ class K8sManager:
# 设置容器端口（如果是HTTP服务类型的agent） # 设置容器端口（如果是HTTP服务类型的agent）
container_ports = None container_ports = None
if template in ["jina_search_agent"]: if template in ["jina_search_agent", "azure_blob_agent", "azure_blob_agent_mcp", "azure_blob_agent_a2a"]:
container_ports = [client.V1ContainerPort(container_port=8080)] container_ports = [client.V1ContainerPort(container_port=8080)]
# 创建Pod规格 # 创建Pod规格
@@ -245,7 +354,8 @@ class K8sManager:
labels = { labels = {
"app": "ai-agent", "app": "ai-agent",
"template": template, "template": template,
"managed-by": "agent-manager" "managed-by": "agent-manager",
"framework": agent_framework # NEW: 添加框架标签
} }
# 添加用户自定义标签 # 添加用户自定义标签
if "labels" in config_data: if "labels" in config_data:
migrate_multi_framework.py
+160
View File
@@ -0,0 +1,160 @@
"""
数据库迁移脚本 - 添加多框架支持字段
运行: python migrate_multi_framework.py
"""
import sys
import os
# 添加父目录到路径
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from database import engine, Base
from sqlalchemy import text
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def migrate():
"""执行数据库迁移"""
logger.info("🚀 开始数据库迁移 - 添加多框架支持")
with engine.connect() as conn:
# 开始事务
trans = conn.begin()
try:
# 检查数据库类型
db_url = str(engine.url)
is_sqlite = 'sqlite' in db_url
is_postgres = 'postgres' in db_url
logger.info(f"数据库类型: {'SQLite' if is_sqlite else 'PostgreSQL' if is_postgres else 'Unknown'}")
# Templates 表迁移
logger.info("📝 迁移 templates 表...")
migrations_templates = [
("agent_framework", "VARCHAR(50)", "langchain"),
("tools_config", "JSON" if is_postgres else "TEXT", None),
("default_model_provider", "VARCHAR(100)", None),
("default_model_name", "VARCHAR(200)", None),
]
for column_name, column_type, default_value in migrations_templates:
try:
if default_value:
if is_sqlite:
# SQLite 需要特殊处理
conn.execute(text(f"ALTER TABLE templates ADD COLUMN {column_name} {column_type} DEFAULT '{default_value}'"))
else:
conn.execute(text(f"ALTER TABLE templates ADD COLUMN {column_name} {column_type} DEFAULT '{default_value}'"))
else:
conn.execute(text(f"ALTER TABLE templates ADD COLUMN {column_name} {column_type}"))
logger.info(f" ✅ 添加列: templates.{column_name}")
except Exception as e:
if "already exists" in str(e) or "duplicate column" in str(e).lower():
logger.info(f" ⏭️ 跳过已存在的列: templates.{column_name}")
else:
raise
# Agents 表迁移
logger.info("📝 迁移 agents 表...")
migrations_agents = [
("agent_framework", "VARCHAR(50)", "langchain"),
("tools_config", "JSON" if is_postgres else "TEXT", None),
("tool_endpoint", "VARCHAR(500)", None),
("tool_api_key", "VARCHAR(500)", None),
("model_provider", "VARCHAR(100)", None),
("model_name", "VARCHAR(200)", None),
("model_endpoint", "VARCHAR(500)", None),
("model_api_key", "VARCHAR(500)", None),
("storage_connection_string", "VARCHAR(1000)", None),
("storage_account_name", "VARCHAR(200)", None),
]
for column_name, column_type, default_value in migrations_agents:
try:
if default_value:
if is_sqlite:
conn.execute(text(f"ALTER TABLE agents ADD COLUMN {column_name} {column_type} DEFAULT '{default_value}'"))
else:
conn.execute(text(f"ALTER TABLE agents ADD COLUMN {column_name} {column_type} DEFAULT '{default_value}'"))
else:
conn.execute(text(f"ALTER TABLE agents ADD COLUMN {column_name} {column_type}"))
logger.info(f" ✅ 添加列: agents.{column_name}")
except Exception as e:
if "already exists" in str(e) or "duplicate column" in str(e).lower():
logger.info(f" ⏭️ 跳过已存在的列: agents.{column_name}")
else:
raise
# 提交事务
trans.commit()
logger.info("✅ 数据库迁移完成")
except Exception as e:
# 回滚事务
trans.rollback()
logger.error(f"❌ 迁移失败: {str(e)}")
raise
def verify_migration():
"""验证迁移结果"""
logger.info("🔍 验证迁移结果...")
with engine.connect() as conn:
# 检查 templates 表
result = conn.execute(text("SELECT * FROM templates LIMIT 0"))
templates_columns = result.keys()
logger.info(f"Templates 表列: {list(templates_columns)}")
# 检查 agents 表
result = conn.execute(text("SELECT * FROM agents LIMIT 0"))
agents_columns = result.keys()
logger.info(f"Agents 表列: {list(agents_columns)}")
# 验证新字段
required_template_columns = [
'agent_framework', 'tools_config',
'default_model_provider', 'default_model_name'
]
required_agent_columns = [
'agent_framework', 'tools_config', 'tool_endpoint', 'tool_api_key',
'model_provider', 'model_name', 'model_endpoint', 'model_api_key',
'storage_connection_string', 'storage_account_name'
]
missing_template_cols = [col for col in required_template_columns if col not in templates_columns]
missing_agent_cols = [col for col in required_agent_columns if col not in agents_columns]
if missing_template_cols:
logger.warning(f"⚠️ Templates 表缺少列: {missing_template_cols}")
else:
logger.info("✅ Templates 表所有必需列都存在")
if missing_agent_cols:
logger.warning(f"⚠️ Agents 表缺少列: {missing_agent_cols}")
else:
logger.info("✅ Agents 表所有必需列都存在")
if not missing_template_cols and not missing_agent_cols:
logger.info("🎉 迁移验证成功！")
return True
else:
logger.error("❌ 迁移验证失败")
return False
if __name__ == "__main__":
try:
migrate()
verify_migration()
except Exception as e:
logger.error(f"迁移过程出错: {str(e)}")
sys.exit(1)