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feat 接入report

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This commit is contained in:
zcr
2026-03-04 19:03:12 +08:00
parent 62b4505261
commit 972c3803a7
6 changed files with 246 additions and 261 deletions
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1
pyproject.toml
View File

@@ -37,4 +37,5 @@ dependencies = [
"dashscope>=1.25.13",
"prompt>=0.4.1",
"langchain-qwq>=0.3.4",
"asyncio>=4.0.0",
]

269
src/routers/chat.py
View File

@@ -7,7 +7,7 @@ from fastapi import APIRouter
from fastapi.responses import StreamingResponse
from src.schemas.chat import ChatRequest, HistoryResponse, HistoryItem
from src.server.agent.graph import app # 导入已经 compile 好的 graph
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.messages import HumanMessage, SystemMessage, AIMessageChunk, ToolMessage, AIMessage
router = APIRouter(prefix="/chat", tags=["Furniture Design Chat"])
logger = logging.getLogger(__name__)
@@ -40,6 +40,8 @@ async def chat_stream(request: ChatRequest):
- **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
```
{
@@ -57,16 +59,77 @@ async def chat_stream(request: ChatRequest):
"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.debug(f"chat request data: {request}")
source_thread_id = request.thread_id
checkpoint_id = request.checkpoint_id
# 1. 确定目标 thread_id
# 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. 配置参数
# 2. 配置參數
temp = request.config_params.temperature if request.config_params else 0.7
current_config = {
"recursion_limit": 100,
@@ -77,7 +140,7 @@ async def chat_stream(request: ChatRequest):
}
}
# 3. 初始化消息 + 系统提示
# 3. 初始化消息 + 系統提示
initial_messages = []
if not source_thread_id or is_branching:
if request.config_params:
@@ -91,7 +154,7 @@ async def chat_stream(request: ChatRequest):
)
initial_messages.append(SystemMessage(content=system_prompt))
# 4. 处理分支（从历史 checkpoint 复制状态）
# 4. 處理分支（從歷史 checkpoint 複製狀態）
if is_branching:
source_config = {
"configurable": {
@@ -109,7 +172,7 @@ async def chat_stream(request: ChatRequest):
# 初始事件
yield f"data: {json.dumps({'thread_id': target_thread_id, 'is_branch': is_branching, 'status': 'start'}, ensure_ascii=False)}\n\n"
# 构造输入
# 構造輸入（保持不變）
new_messages = initial_messages[:] if not source_thread_id else []
new_messages.append(HumanMessage(content=request.message))
@@ -119,130 +182,90 @@ async def chat_stream(request: ChatRequest):
"use_report": request.use_report,
}
# 使用 astream_events v2 + stream_subgraphs=True 来捕获 DeepAgents 内部流式事件
async for event in app.astream_events(
# ─── 重點改這裡 ───────────────────────────────────────
async for event in app.astream(
input_data,
version="v2",
config=current_config,
stream_subgraphs=True,
stream_mode=["custom", "updates", "messages"], # 推薦組合
subgraphs=True
# 不再需要，行為已包含
):
event_kind = event["event"]
# 获取当前 checkpoint_id（安全方式，避免 KeyError）
# 取得 checkpoint_id（可選，視前端是否真的需要）
latest_state = await app.aget_state(current_config)
configurable = latest_state.config.get("configurable", {})
current_cp_id = configurable.get("checkpoint_id", "") # 如果没有，返回空字符串
current_cp_id = configurable.get("checkpoint_id", "")
# ────────────────────────────────────────────────
# 1. LLM token 流式输出（主图或子图的逐 token）
# ────────────────────────────────────────────────
if event_kind == "on_chat_model_stream":
chunk = event["data"].get("chunk")
if chunk and chunk.content:
node_name = event.get("name", "Unknown")
# 判断是否来自 Researcher 子图
namespace = event.get("parent_ids", []) or event.get("namespace", [])
if any("Researcher" in str(ns) for ns in namespace):
node_name = "Researcher"
payload = {
"node": node_name,
"content": chunk.content,
"is_delta": True,
"checkpoint_id": current_cp_id,
"image_url": None,
"suggestions": []
}
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
# ────────────────────────────────────────────────
# 2. 自定义事件（report_delta 等）
# ────────────────────────────────────────────────
elif event_kind == "on_custom_event":
custom_data = event["data"]
if isinstance(custom_data, dict):
if custom_data.get("type") == "report_delta":
payload = {
"node": "Researcher",
"content": custom_data.get("delta", ""),
"is_delta": True,
"checkpoint_id": current_cp_id,
"image_url": None,
"suggestions": []
}
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
# 可选：报告开始/完成/错误等状态提示
elif custom_data.get("type") in ("report_start", "report_complete", "report_error"):
status_msg = {
"report_start": "Start generating reports...",
"report_complete": "Report generation completed",
"report_error": f"Report generation failed: {custom_data.get('message', '')}"
}.get(custom_data["type"], "")
payload = {
"node": "Researcher",
"content": status_msg,
"is_delta": False,
"checkpoint_id": current_cp_id,
"image_url": None,
"suggestions": []
}
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
# ────────────────────────────────────────────────
# 3. 节点启动 / 工具启动（进度提示）
# ────────────────────────────────────────────────
elif event_kind in {"on_tool_start", "on_tool_end"}:
tool_name = event.get("name", "unknown_tool")
tool_data = event.get("data", {})
tool_input = tool_data.get("input", "")
tool_output = tool_data.get("output", "")
if event_kind == "on_tool_start":
payload = {
"node": tool_name,
"content": tool_input,
"is_delta": False,
"checkpoint_id": current_cp_id,
"image_url": None,
"suggestions": []
}
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
else:
if tool_name == "generate_furniture" and isinstance(tool_output, str):
payload = {
"node": tool_name,
"content": "Design sketch has been generated for you.", # 给用户友好的文字提示
"image_url": tool_output, # 直接传 URL 给前端显示
"is_delta": False, # 这是一个完整事件，不是增量
"checkpoint_id": current_cp_id,
"suggestions": []
}
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
elif tool_name == "topic_research":
payload = {
"node": tool_name,
"content": "Visiting...", # 给用户友好的文字提示
"image_url": None, # 直接传 URL 给前端显示
"search_list": tool_output.content,
"is_delta": False, # 这是一个完整事件，不是增量
"checkpoint_id": current_cp_id,
"suggestions": []
}
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
if len(event) == 3:
namespace, channel, payload = event
if event[1] == "custom":
if isinstance(payload, dict) and payload.get("type") in ("report_delta", "report_start", "report_error", "report_save_warning", "report_complete"):
delta = payload.get("delta", "").strip()
if delta:
yield f"data: {json.dumps({
'node': 'Researcher',
'type': 'report_delta',
'content': delta,
'is_delta': True,
'checkpoint_id': current_cp_id,
}, ensure_ascii=False)}\n\n"
if event[1] == "messages":
if namespace:
node_name = namespace[-1] if isinstance(namespace, tuple) else namespace
if ':' in node_name:
node_name = node_name.split(':')[0]
else:
# 可选：其他工具的通用处理（debug 或显示结果）
if tool_output:
payload = {
"node": tool_name,
"content": f"tool {tool_name} Execution completed：{str(tool_output)[:200]}...", # 截断避免过长
"is_delta": False,
"checkpoint_id": current_cp_id,
"image_url": None,
"suggestions": []
}
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
# 流结束
node_name = "Main"
message, metadata = payload
node_name = metadata.get("langgraph_node", node_name)
# 3. 处理不同类型的 message
payload_out = {
"node": node_name,
"checkpoint_id": current_cp_id, # 你之前已经获取了
"is_delta": False,
"content": "",
"suggestions": [],
"type": "unknown"
}
if isinstance(message, AIMessageChunk):
if message.tool_call_chunks:
payload_out.update({
"type": "delta",
"is_delta": True,
"content": message.content,
# 如果有 tool call chunk，也可以在这里处理
"tool_call_chunk": message.tool_call_chunks[0] if message.tool_call_chunks else None
})
yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
elif isinstance(message, ToolMessage):
# 工具执行结果（完整的一次性输出）
payload_out.update({
"type": "tool_result",
"is_delta": False,
"content": message.content,
"tool_name": message.name,
"tool_call_id": message.tool_call_id
})
# 特殊处理：如果内容看起来是用户画像或特定格式
if "实时用户画像" in message.content:
payload_out["type"] = "user_persona"
yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
elif isinstance(message, AIMessage):
# 完整 AIMessage（不常见在 messages 模式下，但以防万一）
payload_out.update({
"type": "complete_message",
"is_delta": False,
"content": message.content
})
yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
else:
# 其他未知类型，记录日志
print(f"未知消息类型: {type(message)}", message)
continue
# 流結束
yield f"data: {json.dumps({'status': 'end'}, ensure_ascii=False)}\n\n"
return StreamingResponse(event_generator(), media_type="text/event-stream")

159
src/server/agent/agents.py
View File

@@ -2,7 +2,7 @@ from pathlib import Path
from typing import AsyncGenerator, Dict, Any
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage, AIMessage
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage, AIMessage, AIMessageChunk
from langchain_core.runnables import RunnableConfig
from langchain_qwq import ChatQwen
@@ -26,6 +26,7 @@ MAIN_DIR = Path(__file__).resolve().parent
PROJECT_ROOT = MAIN_DIR
model = ChatQwen(
enable_thinking=False,
model="qwen3.5-flash",
max_tokens=3_000,
timeout=None,
@@ -48,6 +49,7 @@ research_agent = create_deep_agent(
def get_model(config: RunnableConfig):
temp = config["configurable"].get("llm_temperature", 0.5)
return ChatQwen(
enable_thinking=False,
model="qwen3.5-flash",
max_tokens=3_000,
timeout=None,
@@ -114,110 +116,69 @@ async def researcher_node(
)]
}
async for event in research_agent.astream_events(
async for chunk in research_agent.astream(
{"messages": messages[-12:]},
version="v2",
config=config,
stream_subgraphs=True
config=config
):
event_type = event["event"]
name = event.get("name", "未知")
if event["event"] == "on_custom_event":
custom_data = event["data"]
# 你的 writer 发的是 dict，所以这里 custom_data 就是你写的 {"type": "report_delta", "delta": "..."}
if isinstance(custom_data, dict) and custom_data.get("type") == "report_delta":
delta = custom_data.get("delta", "")
print(delta, end="", flush=True) # 实时打印，不换行
# ────────────── 工具结束事件：重点处理并 yield 输出 ──────────────
if event["event"] in {"on_tool_start", "on_tool_end"}:
tool_name = event.get("name", "未知")
is_start = event["event"] == "on_tool_start"
if is_start:
tool_input = event["data"].get("input", {})
current_step = f"正在執行工具：{tool_name}"
print(f"| {current_step} | {tool_input}")
yield {
"messages": [AIMessage(
content=full_content,
name="Researcher",
additional_kwargs={
"current_step": current_step,
"tool_name": tool_name,
"tool_input": tool_input,
"tool_status": "start",
"streaming": True
}
)]
}
else: # on_tool_end
tool_output = event["data"].get("output", "")
current_step = f"工具 {tool_name} 已完成"
print(f"| {current_step} | {tool_output}")
yield {
"messages": [AIMessage(
content=full_content,
name="Researcher",
additional_kwargs={
"current_step": current_step,
"tool_name": tool_name,
"tool_output": tool_output,
"tool_status": "end",
"streaming": True
}
)]
}
# ────────────── LLM 内容生成（保持原有逻辑） ──────────────
elif event_type == "on_chat_model_stream":
chunk = event["data"]["chunk"].content or ""
if chunk:
print(chunk, end="", flush=True)
full_content += chunk
if "\n" in chunk or len(full_content) % 4 == 0:
yield {
"messages": [AIMessage(
content=full_content,
name="Researcher",
additional_kwargs={
"current_step": current_step,
"streaming": True
}
)]
}
# ────────────── 其他链路事件（可选补充） ──────────────
elif event_type in ("on_chain_start", "on_chain_end"):
status = "开始" if event_type == "on_chain_start" else "完成"
current_step = f"[{status}] {name.upper()}"
if "messages" in chunk and isinstance(chunk["messages"], AIMessageChunk):
yield {
"messages": [AIMessage(
content=full_content,
name="Researcher",
additional_kwargs={
"current_step": current_step,
"streaming": True
}
)]
"messages": chunk["messages"], # 逐 token 追加
# 可以額外 yield 一些 metadata，例如
# "node": "Researcher",
# "status": "thinking"
}
# 最终输出
yield {
"messages": [AIMessage(
content=full_content.strip() or "报告生成完成",
name="Researcher",
additional_kwargs={
"current_step": "报告已完成",
"streaming": False
}
)],
"next": "Suggester"
}
else:
# 其他類型的 chunk
yield chunk
#
# async def researcher_node(
# state: AgentState,
# config: RunnableConfig
# ) -> Dict[str, Any]:
# """
# 薄節點：只判斷是否要跑深度報告，並準備初始訊息
# 真正的 report 生成與 streaming 交給外層或子圖處理
# """
# use_report = config["configurable"].get("use_report", False)
#
# if not use_report:
# return {
# "messages": [AIMessage(
# content="深度報告功能未啟用，請通過前端按鈕觸發。",
# name="Researcher"
# )],
# "next": "Supervisor"
# }
#
# # 發送初始訊息，讓前端馬上看到「正在啟動」
# # initial_msg = AIMessage(
# # content="正在啟動深度報告生成...",
# # name="Researcher",
# # additional_kwargs={
# # "current_step": "正在啟動深度報告生成...",
# # "streaming": True
# # }
# # )
#
# # 方式一：最簡單，直接把 research_agent 當作下一個要執行的東西
# # （假設 research_agent 已 compile 好，且支援 astream）
# # return {
# # "messages": state["messages"] + [initial_msg],
# # # 可以選擇加一個自訂 key 標記
# # "report_in_progress": True,
# # # next 留空或回 Supervisor，由 conditional edges 決定
# # }
#
# # 方式二：如果你想更明確（推薦用 Send，未來好擴充）
# return Send(
# "research_sub_agent", # 你要在 graph.add_node("research_sub_agent", research_agent)
# {
# "messages": state["messages"][-12:],
# "configurable": config["configurable"]
# }
# )
# --- 3. Visualizer Agent (视觉专家) ---
async def visualizer_node(state: AgentState, config: RunnableConfig):
"""负责将自然语言转化为绘图 Prompt 并调用绘图工具"""

4
src/server/agent/graph.py
View File

@@ -2,6 +2,7 @@ from typing import Literal
from langchain_core.messages import AIMessage
from langchain_core.runnables import RunnableConfig
from langchain_qwq import ChatQwen
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
from langgraph.graph import StateGraph, END, START
from pydantic import BaseModel
from pymongo import MongoClient
@@ -91,6 +92,7 @@ client = MongoClient(MONGO_URI)
checkpointer = MongoDBSaver(
client=client["furniture_agent_db"],
db_name="langgraph",
collection_name="checkpoints"
collection_name="checkpoints",
serde=JsonPlusSerializer(pickle_fallback=True), # ← 關鍵這一行
)
app = workflow.compile(checkpointer=checkpointer)

63
src/server/agent/tools/report_generator_tool.py
View File

@@ -3,6 +3,7 @@ import json
import re
from typing import Optional, List, Dict
from langchain_qwq import ChatQwen
from langgraph.config import get_stream_writer
from pydantic import BaseModel, Field
from langchain_core.tools import tool
from langchain_core.messages import SystemMessage, HumanMessage
@@ -15,6 +16,7 @@ from src.core.config import settings
llm = ChatQwen(
enable_thinking=False,
model="qwen3.5-flash",
temperature=0.2,
max_tokens=3_000,
@@ -47,7 +49,7 @@ class ReportInput(BaseModel):
# =========================
@tool("report_generator", args_schema=ReportInput)
async def report_generator(
def report_generator(
report_topic: str,
structured_data: List[Dict],
language: str = "English"
@@ -57,11 +59,11 @@ async def report_generator(
directly from structured retrieval results.
"""
writer = get_stream_writer()
if not structured_data:
return {
"status": "error",
"message": "No structured data provided."
}
error_msg = "Error: No structured data provided."
writer({"type": "report_error", "message": error_msg})
return error_msg
collected_data_str = json.dumps(
structured_data,
@@ -103,55 +105,40 @@ Input Data:
# =========================
# 调用 LLM
# =========================
writer({"type": "report_start", "topic": report_topic, "language": language})
full_report = ""
try:
response = await llm.ainvoke([
for chunk in llm.stream([
SystemMessage(content=system_prompt),
HumanMessage(content=user_prompt)
])
report_content = response.content.strip()
# 清理 markdown block 包裹
report_content = (
report_content
.replace("```markdown", "")
.replace("```", "")
.strip()
)
]):
if chunk.content: # Gemini 返回的 chunk.content
delta = chunk.content
full_report += delta
writer({"type": "report_delta", "delta": delta}) # ← 实时推送给前端
except Exception as e:
return {
"status": "error",
"message": f"LLM generation failed: {str(e)}"
}
error_msg = f"LLM generation failed: {str(e)}"
writer({"type": "report_error", "message": error_msg})
return error_msg
report_content = full_report.strip()
# =========================
# 保存报告
# =========================
output_dir = "workspace/reports"
os.makedirs(output_dir, exist_ok=True)
safe_topic = re.sub(
r'[\\/*?:"<>|]',
"",
report_topic.replace(" ", "_")
)
safe_topic = re.sub(r'[\\/*?:"<>|]', "", report_topic.replace(" ", "_"))
filename = f"{output_dir}/{safe_topic}.md"
try:
with open(filename, "w", encoding="utf-8") as f:
f.write(report_content)
writer({"type": "report_complete", "file_path": filename})
except Exception as e:
return {
"status": "error",
"message": f"Failed to save report: {str(e)}"
}
writer({"type": "report_save_warning", "message": str(e)})
return {
"status": "success",
"file_path": filename,
"message": "Report generated successfully."
}
# 返回完整内容（作为 tool result），同时正文已通过 delta 流式输出
return report_content + f"\n\n✅ Report saved to: {filename}"

11
uv.lock generated
View File

@@ -253,6 +253,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/5c/0a/a72d10ed65068e115044937873362e6e32fab1b7dce0046aeb224682c989/asgiref-3.11.1-py3-none-any.whl", hash = "sha256:e8667a091e69529631969fd45dc268fa79b99c92c5fcdda727757e52146ec133", size = 24345, upload-time = "2026-02-03T13:30:13.039Z" },
]
[[package]]
name = "asyncio"
version = "4.0.0"
source = { registry = "https://pypi.org/simple" }
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@@ -935,6 +944,7 @@ name = "fida"
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source = { virtual = "." }
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{ name = "crawl4ai" },
{ name = "dashscope" },
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@@ -972,6 +982,7 @@ dependencies = [
[package.metadata]
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{ name = "crawl4ai", specifier = ">=0.8.0" },
{ name = "dashscope", specifier = ">=1.25.13" },
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