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feat 1.增加 建议词 机制 2.对话生图实现

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zcr
2026-02-06 11:55:11 +08:00
parent ec195d17e1
commit 3248c45cd4
12 changed files with 655 additions and 85 deletions
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2
config.yaml
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@@ -28,6 +28,8 @@ agents:
1. 根据上下文，编写一个详细的 Stable Diffusion 风格的英文 Prompt。
2. 必须调用 generate_furniture_sketch 工具来生成图片。
注意：如果对话中出现 [SYSTEM_DIRECTIVE] 要求直接绘图，请立即根据已知信息编写 Prompt 并调用 generate_furniture_sketch 工具，不要进行多余的询问。
researcher:
prompt_template: |
你是情报专家，负责检索与整理参考资料并生成报告。

4
pyproject.toml
View File

@@ -6,14 +6,18 @@ requires-python = ">=3.12"
dependencies = [
"fastapi[standard]>=0.128.0",
"gunicorn>=25.0.1",
"image>=1.5.33",
"langchain-core>=1.2.8",
"langchain-google-genai>=4.2.0",
"langgraph>=1.0.7",
"langgraph-checkpoint-mongodb>=0.3.1",
"minio>=7.2.20",
"modality>=0.1.0",
"motor>=3.7.1",
"pydantic>=2.12.5",
"pydantic-settings>=2.12.0",
"pymongo[srv]>=4.15.5",
"python-dotenv>=1.2.1",
"uuid>=1.30",
"uvicorn>=0.40.0",
]

10
src/core/config.py
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@@ -7,13 +7,21 @@ class Settings(BaseSettings):
应用配置类。Pydantic Settings 会自动从环境变量和 .env 文件中加载这些值。
"""
model_config = SettingsConfigDict(
env_file='.env',
env_file='.env_local',
env_file_encoding='utf-8',
extra='ignore' # 忽略环境变量中多余的键
)
# --- google api 配置信息 ---
GOOGLE_GENAI_USE_VERTEXAI: str = Field(default="", description="")
GOOGLE_API_KEY: str = Field(default="", description="")
GOOGLE_CLOUD_PROJECT: str = Field(default="", description="")
GOOGLE_CLOUD_LOCATION: str = Field(default="", description="")
# --- minio 配置信息 ---
MINIO_URL: str = Field(default='', description="")
MINIO_ACCESS: str = Field(default='', description="")
MINIO_SECRET: str = Field(default='', description="")
MINIO_SECURE: bool = Field(default=True, description="")
# --- mongodb配置信息 ---
MONGODB_USERNAME: str = Field(default="", description="")

116
src/routers/chat.py
View File

@@ -4,7 +4,7 @@ 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
from langchain_core.messages import HumanMessage, SystemMessage
router = APIRouter(prefix="/chat", tags=["Furniture Design Chat"])
@@ -57,38 +57,114 @@ async def chat_stream(request: ChatRequest):
# 如果是回溯操作，我们生成一个新的 ID，或者由前端传入一个新的 target_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. 获取配置参数
temp = request.config_params.temperature if request.config_params else 0.7
# 2. 如果是分叉请求，我们需要先“搬家”状态
# 构建基础 Config
current_config = {
"configurable": {
"thread_id": target_thread_id,
"llm_temperature": temp
}
}
# 3. 处理状态初始化与分支
initial_messages = []
# 如果是全新的对话（没有 source_thread_id），或者明确要求分叉
if not source_thread_id or is_branching:
# 如果用户传了标签，构造 SystemMessage 注入上下文
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. 执行分叉逻辑（搬运旧数据）
if is_branching:
# 获取旧状态
source_config = {"configurable": {"thread_id": source_thread_id, "checkpoint_id": checkpoint_id}}
source_config = {
"configurable": {
"thread_id": source_thread_id,
"checkpoint_id": checkpoint_id
}
}
older_state = await app.aget_state(source_config)
# 将旧状态的消息，作为新 thread 的初始值注入
# 注意：这里我们手动把旧消息塞给新 thread
new_config = {"configurable": {"thread_id": target_thread_id}}
await app.aupdate_state(new_config, older_state.values)
# 将旧消息和我们新定义的 SystemMessage 合并
# update_state 会将这些消息推送到新 thread 的存储中
combined_values = older_state.values.copy()
if initial_messages:
combined_values["messages"] = list(combined_values["messages"]) + initial_messages
# 现在的 config 指向新 Thread
current_config = new_config
else:
current_config = {"configurable": {"thread_id": target_thread_id}}
await app.aupdate_state(current_config, combined_values)
async def event_generator():
# 告诉前端：现在是在哪个 Thread 上工作（如果是分叉，前端需要更新本地存储的 ID）
yield f"data: {json.dumps({'thread_id': target_thread_id, 'is_branch': is_branching}, ensure_ascii=False)}\n\n"
# 初始推送状态信息
yield f"data: {json.dumps({'thread_id': target_thread_id, 'is_branch': is_branching, 'status': 'start'}, ensure_ascii=False)}\n\n"
# 构造本次请求的输入
# 如果是第一次开始，且有 initial_messages，则连同 user message 一起发送
# --- 核心逻辑：构造本次请求的消息列表 ---
new_messages = []
if not source_thread_id and initial_messages:
new_messages.extend(initial_messages)
# 添加用户消息
new_messages.append(HumanMessage(content=request.message))
# --- 新增：强制绘图指令注入 ---
# if request.force_sketch:
# force_instruction = HumanMessage(
# content="[SYSTEM_DIRECTIVE]: 用户点击了强制生成按钮。请立即根据当前上下文调用 generate_furniture_sketch 工具生成草图，无需确认。"
# )
# new_messages.append(force_instruction)
input_data = {"messages": new_messages}
async for event in app.astream(
{"messages": [HumanMessage(content=request.message)]},
input_data,
current_config,
stream_mode="updates"
):
# ... 发送流式内容的逻辑保持不变 ...
for node_name, output in event.items():
if "messages" in output:
msg = output["messages"][-1]
# 获取最新 state 以获取 checkpoint_id
state = await app.aget_state(current_config)
yield f"data: {json.dumps({'node': node_name, 'content': msg.content, 'checkpoint_id': state.config['configurable']['checkpoint_id']}, ensure_ascii=False)}\n\n"
current_cp_id = state.config["configurable"].get("checkpoint_id")
# 遍历本次 update 产生的所有消息
for msg in output["messages"]:
payload = {
"node": node_name,
"content": "",
"image_url": None,
"checkpoint_id": current_cp_id,
"suggestions": []
}
# --- 核心改动：提取建议按钮 ---
# 无论是不是 Suggester 节点，只要消息里带了建议就提取
if hasattr(msg, "additional_kwargs") and "suggestions" in msg.additional_kwargs:
payload["suggestions"] = msg.additional_kwargs["suggestions"]
content = msg.content
# 逻辑判断：MinIO 图片处理
if node_name == "Visualizer" and str(content).endswith("png") and "furniture/sketches" in str(content):
payload["image_url"] = content
payload["content"] = "已为您生成设计草图"
else:
payload["content"] = content
# 如果消息既没有文本、也没有图片、也没有建议（比如中间的 ToolCall 消息），则跳过
if not payload["content"] and not payload["image_url"] and not payload["suggestions"]:
continue
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
yield f"data: {json.dumps({'status': 'end'}, ensure_ascii=False)}\n\n"
return StreamingResponse(event_generator(), media_type="text/event-stream")
@@ -147,11 +223,11 @@ async def get_chat_history(thread_id: str):
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[:50] + ("..." if len(content) > 50 else "")
msg_content = content
history_data.append(HistoryItem(
checkpoint_id=state.config["configurable"]["checkpoint_id"],
last_message=msg_content[:50],
last_message=msg_content,
node=state.metadata.get("source"),
timestamp=state.metadata.get("step")
))

11
src/schemas/chat.py
View File

@@ -1,11 +1,20 @@
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, confloat
from typing import Optional, List, Dict, Any
class AgentConfig(BaseModel):
type: str = Field(..., description="家具类型，如：沙发、餐桌")
region: str = Field(..., description="地区/空间，如：客厅、卧室、户外")
style: str = Field(..., description="设计风格，如：极简、工业风、中式")
temperature: confloat(ge=0, le=2.0) = Field(default=0.7, description="模型温度")
class ChatRequest(BaseModel):
message: str = Field(..., description="用户的输入指令")
thread_id: Optional[str] = Field(None, description="会话线程ID，不传则开启新会话")
checkpoint_id: Optional[str] = Field(None, description="回溯点的ID，用于从历史点开启新对话")
config_params: Optional[AgentConfig] = None
# force_sketch: bool = False # 新增：是否强制绘图
class HistoryItem(BaseModel):

101
src/server/agent/agents.py
View File

@@ -1,86 +1,117 @@
import os
from google.oauth2 import service_account
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage, AIMessage
from langchain_core.runnables import RunnableConfig
from langchain_google_genai import ChatGoogleGenerativeAI
from src.server.agent.state import AgentState
from src.server.agent.tools import generate_2025_report_tool, generate_furniture_sketch
from src.server.agent.config_loader import get_agent_prompt
from src.core.config import settings
from src.server.utils.generate_suggestion import generate_chat_suggestions
creds = service_account.Credentials.from_service_account_file(
settings.GOOGLE_GENAI_USE_VERTEXAI,
scopes=["https://www.googleapis.com/auth/cloud-platform"],
)
# 初始化 Gemini 模型 (使用 Flash 以保证速度)
llm = ChatGoogleGenerativeAI(
model="gemini-2.0-flash", temperature=0.5, credentials=creds,
project="aida-461108", location='us-central1', vertexai=True, api_key=settings.GOOGLE_API_KEY
# 辅助函数：根据配置动态获取 LLM
def get_model(config: RunnableConfig):
# 从 configurable 中获取温度，默认为 0.5 (对应你之前的设置)
# 这个 key 必须与你在 chat_stream 路由里定义的 "llm_temperature" 一致
temp = config["configurable"].get("llm_temperature", 0.5)
return ChatGoogleGenerativeAI(
model="gemini-2.0-flash",
temperature=temp,
credentials=creds,
project=settings.GOOGLE_CLOUD_PROJECT,
location=settings.GOOGLE_CLOUD_LOCATION,
vertexai=True,
api_key=settings.GOOGLE_API_KEY
)
# --- 1. Designer Agent (设计顾问) ---
def designer_node(state: AgentState):
async def designer_node(state: AgentState, config: RunnableConfig):
"""负责细化设计需求，提供专业参数"""
model = get_model(config) # 获取带动态温度的模型
messages = state["messages"]
system_text = get_agent_prompt("designer") or """
你是一位资深的家具设计师。你的职责是：
1. 从用户的模糊描述中提取或补充具体的设计参数（尺寸、材质、人体工学数据）。
2. 如果用户想画图，不要直接画，而是先描述清楚细节，然后让 Visualizer 去画。
请以专业的口吻回复。
"""
system_text = get_agent_prompt("designer")
system_prompt = SystemMessage(content=system_text)
response = llm.invoke([system_prompt] + messages)
# 改为异步调用 ainvoke
response = await model.ainvoke([system_prompt] + messages)
return {"messages": [response]}
# --- 2. Researcher Agent (情报专家) ---
def researcher_node(state: AgentState):
async def researcher_node(state: AgentState, config: RunnableConfig):
"""负责调用报告生成工具"""
# 绑定工具给 LLM
model = get_model(config)
tools = [generate_2025_report_tool]
llm_with_tools = llm.bind_tools(tools)
llm_with_tools = model.bind_tools(tools)
messages = state["messages"]
system_text = get_agent_prompt("researcher") or "你是情报专家，负责检索与整理参考资料并生成报告。"
system_text = get_agent_prompt("researcher")
system_prompt = SystemMessage(content=system_text)
response = llm_with_tools.invoke([system_prompt] + messages)
response = await llm_with_tools.ainvoke([system_prompt] + messages)
# 如果模型决定调用工具
if response.tool_calls:
# 这里为了简化，直接在节点内执行工具（LangGraph也可以用 ToolNode）
tool_call = response.tool_calls[0]
if tool_call["name"] == "generate_2025_report_tool":
result = generate_2025_report_tool.invoke(tool_call["args"])
# 这里的工具调用如果也是异步的，建议加 await
result = await generate_2025_report_tool.ainvoke(tool_call["args"])
return {"messages": [response, HumanMessage(content=str(result))]}
return {"messages": [response]}
# --- 3. Visualizer Agent (视觉专家) ---
def visualizer_node(state: AgentState):
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"""
model = get_model(config)
tools = [generate_furniture_sketch]
llm_with_tools = llm.bind_tools(tools)
llm_with_tools = model.bind_tools(tools)
messages = state["messages"]
system_text = get_agent_prompt("visualizer") or """
你是视觉专家。你的目标是生成高质量的家具草图。
步骤：
1. 根据上下文，编写一个详细的 Stable Diffusion 风格的英文 Prompt。
2. 必须调用 generate_furniture_sketch 工具来生成图片。
"""
system_text = get_agent_prompt("visualizer")
# 强制它尝试调用工具
system_prompt = SystemMessage(content=system_text)
response = llm_with_tools.invoke([system_prompt] + messages)
response = await llm_with_tools.ainvoke([system_prompt] + messages)
if response.tool_calls:
tool_call = response.tool_calls[0]
if tool_call["name"] == "generate_furniture_sketch":
result = generate_furniture_sketch.invoke(tool_call["args"])
# 返回工具结果给 LLM，让它生成最终回复
final_msg = f"已为您生成草图，链接如下：{result}"
return {"messages": [response, HumanMessage(content=final_msg)]}
img_url = await generate_furniture_sketch.ainvoke(tool_call["args"])
return {
"messages": [
response,
ToolMessage(content=img_url, tool_call_id=tool_call["id"]) # 标记这是一个图片结果
]
}
return {"messages": [response]}
# --- 4. Suggester Agent (推荐对话专家) ---
async def suggester_node(state: AgentState, config: RunnableConfig):
"""专门生成追问建议的节点，作为流程终点"""
model = get_model(config)
messages = state["messages"]
# 只需要分析最近的对话
suggestions = await generate_chat_suggestions(messages, model)
# 返回一个特殊消息，前端通过解析 additional_kwargs 获取按钮内容
return {
"messages": [
AIMessage(
content="",
additional_kwargs={"suggestions": suggestions},
name="Suggester"
)
]
}

39
src/server/agent/graph.py
View File

@@ -10,14 +10,15 @@ from pymongo import MongoClient
from src.core.config import settings, MONGO_URI
from src.server.agent.state import AgentState
from src.server.agent.agents import designer_node, researcher_node, visualizer_node
from src.server.agent.agents import designer_node, researcher_node, visualizer_node, suggester_node
from langgraph.checkpoint.mongodb import MongoDBSaver
# --- Supervisor (路由逻辑) ---
# 定义路由的输出结构，强制 LLM 选择一个
class RouteResponse(BaseModel):
next: Literal["Designer", "Researcher", "Visualizer", "FINISH"]
# 将 FINISH 替换或增加 Suggester
next: Literal["Designer", "Researcher", "Visualizer", "Suggester"]
creds = service_account.Credentials.from_service_account_file(
@@ -34,30 +35,23 @@ llm_supervisor = ChatGoogleGenerativeAI(
def supervisor_node(state: AgentState):
messages = state["messages"]
if not messages:
return {"next": "FINISH"}
return {"next": "Suggester"}
last_message = messages[-1]
# --- 改进的拦截逻辑 ---
# 如果最后一条消息是 AI 产生的（且没有调用工具），说明专家已经回复完了用户
# 此时我们才拦截并结束，否则会导致专家没机会说话
# --- 拦截逻辑修改 ---
# 如果专家已经回复完了（AIMessage 且无工具调用），则交给 Suggester 生成按钮
if isinstance(last_message, AIMessage) and not last_message.tool_calls:
return {"next": "FINISH"}
return {"next": "Suggester"}
# 如果最后一条是 HumanMessage，说明用户刚说完，Supervisor 必须派发任务
system_prompt = """
你是家具设计团队的主管(Supervisor)。
请根据用户的意图，选择最合适的专家：
- Designer: 设计建议、参数细化、闲聊、问候。
- Visualizer: 绘图、看草图。
- Researcher: 市场报告、趋势。
只需输出专家名称。
system_prompt = """你是家具设计主管。分配任务给专家：
- Designer: 设计建议、参数细化。
- Visualizer: 绘图需求。
- Researcher: 市场报告。
"""
chain = llm_supervisor.with_structured_output(RouteResponse)
decision = chain.invoke([{"role": "system", "content": system_prompt}] + messages)
return {"next": decision.next}
@@ -68,10 +62,11 @@ workflow.add_node("Supervisor", supervisor_node)
workflow.add_node("Designer", designer_node)
workflow.add_node("Researcher", researcher_node)
workflow.add_node("Visualizer", visualizer_node)
workflow.add_node("Suggester", suggester_node) # 新增节点
workflow.add_edge(START, "Supervisor")
# 这里的逻辑是关键：Supervisor 决定去向
# 修改条件边映射
workflow.add_conditional_edges(
"Supervisor",
lambda state: state["next"],
@@ -79,16 +74,18 @@ workflow.add_conditional_edges(
"Designer": "Designer",
"Researcher": "Researcher",
"Visualizer": "Visualizer",
"FINISH": END
"Suggester": "Suggester" # 原本的 FINISH 现在指向 Suggester
}
)
# 重点修改：专家执行完后，必须回到 Supervisor 进行状态检查
# 如果 Supervisor 发现专家刚说完话，它会触发上面的逻辑返回 FINISH
# 专家执行完依然回到 Supervisor
workflow.add_edge("Designer", "Supervisor")
workflow.add_edge("Researcher", "Supervisor")
workflow.add_edge("Visualizer", "Supervisor")
# 重点：Suggester 是整个流程的终点
workflow.add_edge("Suggester", END)
client = MongoClient(MONGO_URI)
checkpointer = MongoDBSaver(
client=client["furniture_agent_db"],

101
src/server/agent/tools.py
View File

@@ -1,4 +1,30 @@
import base64
import uuid
from google.oauth2 import service_account
from langchain_core.tools import tool
from google import genai
from google.genai.types import GenerateContentConfig, Modality
from PIL import Image
from io import BytesIO
from minio import Minio
from src.core.config import settings
from src.server.utils.new_oss_client import oss_upload_image
# 初始化全局凭证和客户端
creds = service_account.Credentials.from_service_account_file(
settings.GOOGLE_GENAI_USE_VERTEXAI,
scopes=["https://www.googleapis.com/auth/cloud-platform"],
)
minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
client = genai.Client(
credentials=creds,
project=settings.GOOGLE_CLOUD_PROJECT,
location=settings.GOOGLE_CLOUD_LOCATION,
vertexai=True
)
# --- 模拟你已经开发好的报告生成功能 ---
@@ -13,13 +39,76 @@ def generate_2025_report_tool(topic: str) -> str:
return f"【报告生成成功】已生成关于 {topic} 的 PDF 报告。核心洞察：2025年趋势倾向于生物嗜好设计(Biophilic Design)和可持续软木材质。"
# --- 绘图工具 ---
# --- 2. 绘图工具 (接入 Nano Banana 逻辑) ---
@tool
def generate_furniture_sketch(prompt: str) -> str:
"""
用于生成家具草图。输入必须是详细的英文绘画提示词(Prompt)。
使用 Gemini 图像生成模型根据详细的英文提示词生成家具设计草图。
"""
print(f"\n[系统日志] 正在调用 Gemini/Imagen 绘图 API，Prompt: {prompt}...")
# 在真实场景中，这里调用 Google Imagen API 或 Midjourney API
# 示例返回一个模拟的图片链接
return "https://furniture-design-db.com/generated_sketch_v1.jpg"
print(f"\n[系统日志] 正在调用 Nano Banana (Gemini Image Gen) ...")
try:
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=(f"Generate a professional furniture design sketch: {prompt}"),
config=GenerateContentConfig(
response_modalities=[Modality.TEXT, Modality.IMAGE],
),
)
image_bytes = None
for part in response.candidates[0].content.parts:
if part.inline_data:
image_bytes = part.inline_data.data
break
if not image_bytes:
return "未能生成图像数据。"
object_name = f"furniture/sketches/{uuid.uuid4()}.png"
bucket = "fida-test" # 替换为你的 bucket 名称
# 3. 调用你的上传函数
upload_res = oss_upload_image(
oss_client=minio_client,
bucket=bucket,
object_name=object_name,
image_bytes=image_bytes
)
if upload_res:
# 4. 构造访问链接 (如果是私有 bucket，需使用 presigned_get_object)
# 这里简单示例为直接访问地址
image_url = f"{bucket}/{object_name}"
return image_url
else:
return "图片生成成功，但上传至存储服务器失败。"
except Exception as e:
return f"绘图流程异常: {str(e)}"
if __name__ == '__main__':
print(generate_furniture_sketch("椅子"))
# creds = service_account.Credentials.from_service_account_file(
# settings.GOOGLE_GENAI_USE_VERTEXAI,
# scopes=["https://www.googleapis.com/auth/cloud-platform"],
# )
# client = genai.Client(
# credentials=creds,
# project=settings.GOOGLE_CLOUD_PROJECT,
# location=settings.GOOGLE_CLOUD_LOCATION,
# vertexai=True
# )
#
# response = client.models.generate_content(
# model="gemini-2.5-flash-image",
# contents=("Generate an image of the Eiffel tower with fireworks in the background."),
# config=GenerateContentConfig(
# response_modalities=[Modality.TEXT, Modality.IMAGE],
# ),
# )
#
# for part in response.candidates[0].content.parts:
# if part.text:
# print(part.text)
# elif part.inline_data:
# image = Image.open(BytesIO((part.inline_data.data)))
# image.save("example-image-eiffel-tower.png")

0
src/server/utils/__init__.py Normal file
View File

43
src/server/utils/generate_suggestion.py Normal file
View File

@@ -0,0 +1,43 @@
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from pydantic import BaseModel, Field
# 定义输出结构，保证稳定性
class SuggestionOutput(BaseModel):
suggestions: list[str] = Field(description="A list of 3 short follow-up questions or actions for the user, max 10 chars each.")
async def generate_chat_suggestions(messages, model) -> list[str]:
"""
根据对话历史生成 3 个推荐追问按钮
"""
# 只需要最近的几次交互即可判断意图
recent_msgs = messages[-4:]
parser = JsonOutputParser(pydantic_object=SuggestionOutput)
prompt = ChatPromptTemplate.from_messages([
("system", """
你是家具设计系统的交互助手。请根据用户的对话历史，预测用户接下来最可能想做的 3 件事。
【判断逻辑】
1. 如果用户已经确定了【类型、材质、风格】但还没有生成过草图 -> 必须推荐 "生成设计草图"。
2. 如果刚生成了草图 -> 推荐 "调整材质"、"查看三维视图"、"下载报价单" 等。
3. 如果用户还在犹豫 -> 推荐具体的风格或材质询问。
请直接输出 JSON 格式，包含 suggestions 字段。按钮文案要简短（中文，不超过8个字）。
"""),
("user", "对话历史：{history}"),
])
chain = prompt | model | parser
try:
# 将消息对象转为字符串喂给模型
history_str = "\n".join([f"{m.type}: {m.content}" for m in recent_msgs])
result = await chain.ainvoke({"history": history_str})
return result.get("suggestions", [])
except Exception as e:
print(f"建议生成失败: {e}")
return []

65
src/server/utils/new_oss_client.py Normal file
View File

@@ -0,0 +1,65 @@
import io
import logging
from io import BytesIO
import urllib3
from PIL import Image
from minio import Minio
from src.core.config import settings
minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
# 自定义 Retry 类
class CustomRetry(urllib3.Retry):
def increment(self, method=None, url=None, response=None, error=None, **kwargs):
# 调用父类的 increment 方法
new_retry = super(CustomRetry, self).increment(method, url, response, error, **kwargs)
# 打印重试信息
logger.info(f"重试连接: {method} {url}，错误: {error}，重试次数: {self.total - new_retry.total}")
return new_retry
logger = logging.getLogger()
timeout = urllib3.Timeout(connect=1, read=10.0) # 连接超时 5 秒，读取超时 10 秒
http_client = urllib3.PoolManager(
num_pools=10, # 设置连接池大小
maxsize=10,
timeout=timeout,
cert_reqs='CERT_REQUIRED', # 需要证书验证
retries=CustomRetry(
total=5,
backoff_factor=0.2,
status_forcelist=[500, 502, 503, 504],
),
)
# 获取图片
def oss_get_image(oss_client, bucket, object_name, data_type):
# cv2 默认全通道读取
image_object = None
try:
image_data = oss_client.get_object(bucket_name=bucket, object_name=object_name)
data_bytes = BytesIO(image_data.read())
image_object = Image.open(data_bytes)
except Exception as e:
logger.warning(f" | 获取图片出现异常 ######: {e}")
return image_object
def oss_upload_image(oss_client, bucket, object_name, image_bytes):
req = None
try:
req = oss_client.put_object(bucket_name=bucket, object_name=object_name, data=io.BytesIO(image_bytes), length=len(image_bytes), content_type='image/png')
except Exception as e:
logger.warning(f" | 上传图片出现异常 ######: {e}")
return req
if __name__ == '__main__':
url = "aida-users/89/sketch/123-89.png"
read_type = "2"
img = oss_get_image(oss_client=minio_client, bucket=url.split('/')[0], object_name=url[url.find('/') + 1:], data_type=read_type)
img.show()
img.save("result.png")

246
uv.lock generated
View File

@@ -1,6 +1,10 @@
version = 1
revision = 3
requires-python = ">=3.12"
resolution-markers = [
"python_full_version >= '3.14'",
"python_full_version < '3.14'",
]
[[package]]
name = "annotated-doc"
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@@ -402,15 +472,19 @@ source = { virtual = "." }
dependencies = [
{ name = "fastapi", extra = ["standard"] },
{ name = "gunicorn" },
{ name = "image" },
{ name = "langchain-core" },
{ name = "langchain-google-genai" },
{ name = "langgraph" },
{ name = "langgraph-checkpoint-mongodb" },
{ name = "minio" },
{ name = "modality" },
{ name = "motor" },
{ name = "pydantic" },
{ name = "pydantic-settings" },
{ name = "pymongo" },
{ name = "python-dotenv" },
{ name = "uuid" },
{ name = "uvicorn" },
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requires-dist = [
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{ name = "gunicorn", specifier = ">=25.0.1" },
{ name = "image", specifier = ">=1.5.33" },
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