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feat(agent): update SSE event schema and handle tool calls from LLM
- Update AI chat API documentation with revised SSE event schema - Add support for multiple tool calls in `tool_call` events - Include `session_id` in all event types for better session tracking - Implement `on_chat_model_end` handler to process LLM tool call decisions - Update example JSON payloads to reflect new schema structure
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@ -242,10 +242,10 @@ Chat API 使用 Server-Sent Events (SSE) 进行流式传输。
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| type | 说明 | 包含字段 |
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|------|------|----------|
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| content | AI 文本内容(流式) | content |
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| tool_call | 工具调用请求 | tool_call (id, name, arguments) |
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| tool_start | 工具开始执行 | tool_name |
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| tool_end | 工具执行完成 | tool_name, tool_output |
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| error | 错误信息 | error |
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| tool_call | LLM 决定调用工具(可能多个) | tool_calls (数组, 每项包含 id, name, args), session_id |
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| tool_start | 工具开始执行 | tool_name, session_id |
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| tool_end | 工具执行完成 | tool_name, tool_output, session_id |
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| error | 错误信息 | error, session_id |
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| done | 流结束 | session_id |
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| heartbeat | 心跳保活 | session_id |
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@ -255,16 +255,29 @@ Chat API 使用 Server-Sent Events (SSE) 进行流式传输。
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// AI 文本流式输出
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{"type": "content", "content": "Hello! How can I help"}
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// 工具调用
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{"type": "tool_call", "tool_call": {"id": "call_123", "function": {"name": "GNS3TopologyTool", "arguments": {"project_id": "xxx"}}}}
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// LLM 决定调用工具(单个或多个)
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{
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"type": "tool_call",
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"tool_calls": [
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{
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"id": "call_123",
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"name": "execute_multiple_device_commands",
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"args": {
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"device_names": ["R1", "R2"],
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"commands": ["show version"]
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}
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}
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],
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"session_id": "xxx"
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}
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// 工具开始
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{"type": "tool_start", "tool_name": "GNS3TopologyTool", "session_id": "xxx"}
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// 工具开始执行
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{"type": "tool_start", "tool_name": "execute_multiple_device_commands", "session_id": "xxx"}
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// 工具完成
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{"type": "tool_end", "tool_name": "GNS3TopologyTool", "tool_output": "{...}", "session_id": "xxx"}
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// 工具执行完成
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{"type": "tool_end", "tool_name": "execute_multiple_device_commands", "tool_output": "{...}", "session_id": "xxx"}
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// 完成
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// 流结束
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{"type": "done", "session_id": "xxx"}
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// 错误
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@ -410,6 +410,28 @@ class AgentService:
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if content:
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return {"type": "content", "content": content}
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elif event_type == "on_chat_model_end":
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# LLM call completed, check if it decided to call tools
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output = data.get("output", {})
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if hasattr(output, "tool_calls") and output.tool_calls:
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# Extract tool calls information
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tool_calls_data = []
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for tc in output.tool_calls:
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# Convert to dict if it's an object
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tc_dict = tc if isinstance(tc, dict) else tc.model_dump()
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tool_calls_data.append(
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{
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"id": tc_dict.get("id", ""),
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"name": tc_dict.get("name", ""),
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"args": tc_dict.get("args", {}),
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}
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)
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return {
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"type": "tool_call",
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"tool_calls": tool_calls_data,
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"session_id": session_id,
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}
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elif event_type == "on_tool_start":
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# Tool execution started
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return {"type": "tool_start", "tool_name": event.get("name", ""), "session_id": session_id}
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