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弃用langgrpah更换deepagent

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zcr
2026-03-11 21:45:46 +08:00
parent c862121b48
commit 7042d428fa
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src/routers/deep_agent_chat.py Normal file
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import logging
import uuid
import json
from typing import AsyncGenerator
from fastapi import APIRouter
from fastapi.responses import StreamingResponse
from src.schemas.chat import ChatRequest, HistoryResponse, HistoryItem
from langchain_core.messages import HumanMessage, SystemMessage, AIMessageChunk, ToolMessage, AIMessage, ToolMessageChunk
from src.server.deep_agent.agents.main_agent import build_main_agent
router = APIRouter(prefix="/chat", tags=["Furniture Design Chat"])
logger = logging.getLogger(__name__)
@router.post("/deep_agent_stream")
async def chat_stream(request: ChatRequest):
"""
### 家具设计流式对话接口 (SSE)
通过此接口与 AI 家具设计专家团队进行实时沟通。支持 **记忆持久化** 和 **历史回溯分叉**。
#### 1. 核心功能
* **实时反馈**: 采用 Server-Sent Events (SSE) 技术，实时推送主管、设计师、视觉专家等节点的思考过程。
* **上下文记忆**: 传入 `thread_id` 即可恢复之前的对话进度。
* **版本分溯**: 传入 `checkpoint_id` 可准确定位到历史中的某一轮，并从该点开启新的设计分支。
#### 2. 请求参数
* `message`: 用户的设计意图（如：'我想设计一个极简风格的橡木办公桌'）。
* `thread_id`: (可选) 现有项目的唯一标识。若不传，系统将自动分配并返回。
* `checkpoint_id`: (可选) 历史快照 ID。
* `config_params`: (可选) 对话配置参数
* `need_suggestion`: (可选) 是否需要建议按钮,需要建议的频率，0-1的浮点数
* `use_report`: (可选) 是否需要使用report功能 true/false
#### 3. 响应流说明 (Data Format)
响应以 `data: ` 开头的 JSON 字符串流形式发送：
- **Session Start**: `{"thread_id": "...", "status": "start"}`
- **Node Message**: `{"node": "Designer", "content": "...", "checkpoint_id": "..."}`
- **Session End**: `{"status": "end"}`
- **is_delta**: False/True，表示这个消息不是完整内容，只是 AI 正在生成的一小段内容（一个字、一个词、一句话），需要前端把这些片段拼接起来才能得到完整的回答。
#### 4. 请求示例
```
{
"message": "设计一款北欧风格的躺椅."
}
{
"message": "就以上信息直接生成sketch.",
"thread_id": "187e58af"
}
{
"message": "不要躺椅,要桌子",
"thread_id": "187e58af",
"checkpoint_id": "1f101aa2-8f24-6e2a-8001-2952c3a7447a"
}
```
### 5. 响应流说明
所有响应均以 data: 开头，JSON 字符串格式，末尾以 \n\n 结束
响应流包含三种类型的事件：会话开始、节点消息、会话结束
会话开始:
```
{
"thread_id": "str",
"is_branch": "boolean",
"status": "start"
}
```
节点消息:
```
{
"node": "节点名称（如Designer/Researcher/Main）",
"content": "消息内容",
"checkpoint_id": "快照ID",
"is_delta": "boolean",
"type": "消息类型",
"suggestions": "建议列表（可选）",
"tool_name": "工具名称（可选）",
"tool_call_chunk": "工具调用片段（可选）",
"tool_call_id": "工具调用ID（可选）"
}
```
报告增量消息：
```
{
"node": "Researcher",
"type": "report_delta",
"content": "报告内容增量",
"is_delta": true,
"checkpoint_id": "xxx"
}
```
AI 消息片段：
```
{
"node": "Designer",
"content": "设计建议内容",
"checkpoint_id": "xxx",
"is_delta": true,
"type": "delta",
"tool_call_chunk": {...}
}
```
工具执行结果：
```
{
"node": "ToolExecutor",
"content": "工具执行结果",
"checkpoint_id": "xxx",
"is_delta": false,
"type": "tool_result",
"tool_name": "ImageGenerator",
"tool_call_id": "yyy"
}
```
"""
logger.info(f"chat request data: {request}")
source_thread_id = request.thread_id
checkpoint_id = request.checkpoint_id
# 构建主agent
main_agent = build_main_agent(request.use_report)
# 1. 確定目標 thread_id
is_branching = source_thread_id and checkpoint_id
target_thread_id = str(uuid.uuid4())[:8] if is_branching else (source_thread_id or str(uuid.uuid4())[:8])
# 2. 配置參數
temp = request.config_params.temperature if request.config_params else 0.7
need_suggestion = request.need_suggestion,
current_config = {
"recursion_limit": 120,
"configurable": {
"thread_id": target_thread_id,
"llm_temperature": temp,
"use_report": request.use_report,
}
}
# 3. 初始化消息 + 系統提示 TODO 写入数据库
initial_messages = []
if not source_thread_id or is_branching:
if request.config_params:
cp = request.config_params
system_prompt = (
f"Current furniture design background settings：\n"
f"- type: {cp.type}\n"
f"- space/region: {cp.region}\n"
f"- style tendency: {cp.style}\n"
f"Please strictly follow the above settings in subsequent conversations。"
)
initial_messages.append(SystemMessage(content=system_prompt))
# 4. 處理分支（從歷史 checkpoint 複製狀態）
if is_branching:
source_config = {
"configurable": {
"thread_id": source_thread_id,
"checkpoint_id": checkpoint_id
}
}
older_state = await main_agent.aget_state(source_config)
combined_values = older_state.values.copy()
if initial_messages:
combined_values["messages"] = list(combined_values.get("messages", [])) + initial_messages
await main_agent.aupdate_state(current_config, combined_values)
async def event_generator() -> AsyncGenerator[str, None]:
yield f"data: {json.dumps({'thread_id': target_thread_id, 'is_branch': is_branching, 'status': 'start'}, ensure_ascii=False)}\n\n"
new_messages = initial_messages[:] if not source_thread_id else []
new_messages.append(HumanMessage(content=request.message))
input_data = {
"messages": new_messages,
}
current_cp_id = None
async for stream in main_agent.astream(
input_data,
config=current_config,
stream_mode=["updates", "messages", "custom"], # 确保包含 "values"
subgraphs=True
):
# logger.info(f"Received event: {event}")
_, mode, chunks = stream
if mode == "updates":
# TODO 补充
print(f"[updates] {chunks}")
elif mode == "messages":
token, metadata = chunks
subagent_name = metadata.get('lc_agent_name', None)
payload_out = {
"node": subagent_name,
# "checkpoint_id": current_cp_id or "unknown", TODO 替换为checkpoint_idns
"is_delta": False,
"content": "",
"suggestions": [],
"type": ""
}
if isinstance(token, AIMessageChunk): # 默认回复 思考内容
reasoning = [b for b in token.content_blocks if b["type"] == "reasoning"]
text = [b for b in token.content_blocks if b["type"] == "text"]
if reasoning:
payload_out.update({
"type": "reasoning",
"is_delta": True,
"content": text,
"tool_call_chunk": token.tool_call_chunks[0] if token.tool_call_chunks else None
})
elif text:
payload_out.update({
"type": "text",
"is_delta": True,
"content": text,
"tool_call_chunk": token.tool_call_chunks[0] if token.tool_call_chunks else None
})
else:
payload_out.update({
"is_delta": True,
"tool_call_chunk": token.tool_call_chunks[0] if token.tool_call_chunks else None
})
yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
elif isinstance(token, ToolMessageChunk): # 工具返回
text = [b for b in token.content_blocks if b["type"] == "text"]
payload_out.update({
"type": "tool_text",
"is_delta": False,
"content": text,
"tool_name": token.name,
})
yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
elif isinstance(token, ToolMessage): # 工具返回
text = [b for b in token.content_blocks if b["type"] == "text"]
payload_out.update({
"type": "tool_text",
"is_delta": False,
"content": text,
"tool_name": token.name,
})
yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
else:
continue
elif mode == "custom":
token, metadata = chunks
subagent_name = metadata.get('lc_agent_name', None)
payload_out = {
"node": subagent_name,
# "checkpoint_id": current_cp_id or "unknown", TODO 替换为checkpoint_idns
"is_delta": False,
"content": "",
"suggestions": [],
"type": ""
}
delta = chunks.get("delta", "").strip()
if delta:
payload_out.update({
"type": chunks.get("type", ""),
"is_delta": True,
"content": delta,
})
yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
# elif channel == "updates":
# # 处理 updates（非 interrupt 的部分）
# if isinstance(payload, dict):
# for update_node, update_content in payload.items():
# # 处理 reducer 包裹的值
# if isinstance(update_content, dict):
# for k, v in update_content.items():
# if hasattr(v, "value"):
# update_content[k] = v.value
#
# # 序列化 messages
# if isinstance(update_content, dict) and "messages" in update_content:
# msgs = []
# for m in update_content["messages"]:
# msgs.append({
# "type": m.__class__.__name__,
# "content": getattr(m, "content", ""),
# "name": getattr(m, "name", None),
# "tool_calls": getattr(m, "tool_calls", None),
# })
# update_content["messages"] = msgs
#
# yield f"data: {json.dumps({
# "node": "Supervisor", # 或 update_node
# "type": "updates",
# "content": update_content,
# "is_delta": False,
# "checkpoint_id": current_cp_id,
# }, ensure_ascii=False)}\n\n"
#
# elif channel == "custom":
else:
yield f"data: {json.dumps({'status': 'end'}, ensure_ascii=False)}\n\n"
return StreamingResponse(event_generator(), media_type="text/event-stream")
@router.get("/history/{thread_id}", response_model=HistoryResponse)
async def get_chat_history(thread_id: str):
"""
### 获取项目设计历史记录
此接口用于拉取指定 `thread_id` 下的所有历史状态快照。它是实现 **“版本回溯”** 和 **“方案对比”** 的核心数据来源。
#### 1. 功能说明
* **快照列表**: 返回该项目从启动至今的所有关键节点（Checkpoints）。
* **版本定位**: 每个历史点都包含一个唯一的 `checkpoint_id`。
* **数据回溯**: 客户端获取此列表后，可以引导用户选择任意一个版本，并将其 `checkpoint_id` 传回 `/chat/stream` 接口以开启新的设计分支。
#### 2. 路径参数
* `thread_id`: 设计项目的唯一标识符（由 `/chat/stream` 首次调用时生成或指定）。
#### 3. 返回字段定义
* `thread_id`: 当前查询的项目ID。
* `history`: 历史记录数组，包含：
- `checkpoint_id`: 必填，回溯时使用的关键凭证。
- `last_message`: 该阶段的最后一条消息摘要（方便前端预览）。
- `node`: 产生该快照的节点名称（如 Designer, Visualizer）。
- `timestamp`: 逻辑步骤序号。
#### 4. 响应示例
```json
{
"thread_id": "proj_001",
"history": [
{
"checkpoint_id": "d82f3a12",
"last_message": "我想设计一款北欧风书架",
"node": "Supervisor",
"timestamp": 1
},
{
"checkpoint_id": "f4k92m1a",
"last_message": "建议使用浅色橡木材质，增加简约感...",
"node": "Designer",
"timestamp": 2
}
]
}
```
"""
config = {"configurable": {"thread_id": thread_id}, }
history_data = []
async for state in main_agent.aget_state_history(config):
msg_content = "Initial"
if state.values and "messages" in state.values:
msgs = state.values["messages"]
if msgs and len(msgs) > 0:
last_msg = msgs[-1]
# 获取内容并做摘要截断
content = getattr(last_msg, "content", str(last_msg))
msg_content = content
history_data.append(HistoryItem(
checkpoint_id=state.config["configurable"]["checkpoint_id"],
last_message=msg_content,
node=state.metadata.get("source"),
timestamp=state.metadata.get("step")
))
return HistoryResponse(thread_id=thread_id, history=history_data)
# try:
# except Exception as e:
# raise HTTPException(status_code=404, detail=f"History not found: {str(e)}")