Files
AiDA_Python/app/service/fashion_agent/main_agent.py
zcr b9163f0b46 重构图像生成和搜索工具;更新主代理来处理输入图像
- 更新了“generate_image.py”以接受输入图像以增强图像生成。
- 修改了`pexels_search.py​​`和`unsplash_search.py​​`以将日志记录和上传路径从“explorer”更改为“explore”。
- 调整了“print_graph”和“sketch_graph”以提取最新的用户输入并处理输入图像以生成打印和草图图像。
- 重构“generate_print_tool”和“generate_sketch_tool”以接受输入图像。
- 更新了“main_agent.py”以包含状态中的输入图像并调整了图形构建过程。
- 增强了“service.py”来管理输入图像并改进了流媒体期间的事件处理。
- 更新了新软件包和版本的“pyproject.toml”和“uv.lock”中的依赖项。
2026-06-17 11:56:53 +08:00

123 lines
4.2 KiB
Python

from typing import Annotated, Required, TypedDict
from langchain.agents import create_agent
from langchain_core.messages import AnyMessage
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
from app.service.fashion_agent.graph_node.design_graph.graph import build_design_graph
from app.service.fashion_agent.graph_node.explore_graph.tools import explore_tool
from app.service.fashion_agent.graph_node.logo_graph.graph import build_logo_graph
from app.service.fashion_agent.graph_node.logo_graph.tools import generate_logo_tool
from app.service.fashion_agent.graph_node.print_graph.graph import build_print_graph
from app.service.fashion_agent.graph_node.print_graph.tools import generate_print_tool
from app.service.fashion_agent.graph_node.sketch_graph.graph import build_sketch_graph
from app.service.fashion_agent.graph_node.sketch_graph.tools import generate_sketch_tool
from app.service.fashion_agent.graph_node.trending_graph.trending_graph import build_trending_graph
from app.service.fashion_agent.graph_node.explore_graph.graph import build_explore_graph
from app.service.fashion_agent.init_llm import build_llm
print_graph = build_print_graph()
logo_graph = build_logo_graph()
sketch_graph = build_sketch_graph()
design_graph = build_design_graph()
trending_graph = build_trending_graph()
explore_graph = build_explore_graph()
class MainState(TypedDict):
# 消息
messages: Required[Annotated[list[AnyMessage], add_messages]]
# 上传图片
input_images: list[str] = []
# 模块控制
call_design: bool = False
call_print: bool = False
call_logo: bool = False
call_sketch: bool = False
call_design: bool = False
call_trending: bool = False
call_explore: bool = False
# design参数
design_request_data: dict = {}
# 模块需求标志
print_need_prompt_generation: bool = False
sketch_need_prompt_generation: bool = False
# 公共参数
role: str = ""
gender: str = ""
style: str = ""
# print模块结果
print_img_urls: list[str] = []
tools = [explore_tool, generate_logo_tool, generate_print_tool, generate_sketch_tool]
def route_node(state: MainState) -> str:
"""根据标志决定走哪条路径"""
if state.get("call_print"):
return "direct_print"
if state.get("call_logo"):
return "direct_logo"
if state.get("call_sketch"):
return "direct_sketch"
if state.get("call_design"):
return "direct_design"
if state.get("call_trending"):
return "direct_trending"
if state.get("call_explore"):
return "direct_explore"
return "llm_agent"
async def build_main_graph(enable_thinking: bool = False, checkpointer=None):
llm = build_llm(enable_thinking=enable_thinking)
chat_agent = create_agent(
model=llm, tools=tools, state_schema=MainState, system_prompt="你是一个专业的服装设计助手。根据用户需求,调用合适的工具完成任务."
)
"""构建主图"""
workflow = StateGraph(MainState)
# 添加节点
workflow.add_node("llm_agent", chat_agent)
workflow.add_node("direct_print", print_graph)
workflow.add_node("direct_logo", logo_graph)
workflow.add_node("direct_sketch", sketch_graph)
workflow.add_node("direct_design", design_graph)
workflow.add_node("direct_trending", trending_graph)
workflow.add_node("direct_explore", explore_graph)
# 条件分支
workflow.add_conditional_edges(
START,
route_node,
{
"llm_agent": "llm_agent",
"direct_print": "direct_print",
"direct_logo": "direct_logo",
"direct_sketch": "direct_sketch",
"direct_design": "direct_design",
"direct_trending": "direct_trending",
"direct_explore": "direct_explore",
},
)
# 所有路径都到 END
workflow.add_edge("llm_agent", END)
workflow.add_edge("direct_print", END)
workflow.add_edge("direct_logo", END)
workflow.add_edge("direct_sketch", END)
workflow.add_edge("direct_design", END)
workflow.add_edge("direct_trending", END)
workflow.add_edge("direct_explore", END)
graph = workflow.compile(checkpointer=checkpointer)
return graph