aida agent (基础版)搭建完成
This commit is contained in:
47
app/api/api_fashion_agent.py
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47
app/api/api_fashion_agent.py
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@@ -0,0 +1,47 @@
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import json
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import logging
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from fastapi import APIRouter, HTTPException
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from fastapi.responses import StreamingResponse
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from app.schemas.fashion_agent import FashionAgentRequest
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from app.service.fashion_agent.service import FashionAgentService
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router = APIRouter()
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logger = logging.getLogger()
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@router.post("/agent/stream")
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async def fashion_agent_stream(request_item: FashionAgentRequest):
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"""
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服装设计 Agent(流式输出)
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- **message**: 用户输入的消息(必填)
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- **user_id**: 用户ID,默认 "agent"
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- **enable_thinking**: 是否开启思考模式
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- **call_print**: 是否直接调用 print 生成印花
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- **print_need_prompt_generation**: print 是否需要 LLM 生成 prompt
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- **call_logo**: 是否直接调用 logo 生成装饰图案
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- **call_sketch**: 是否直接调用 sketch 生成草图
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- **sketch_need_prompt_generation**: sketch 是否需要 LLM 生成 prompt
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- **call_design**: 是否直接调用 design 生成设计系列
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- **design_request_data**: design 请求参数(objects, process_id, requestId, callback_url)
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- **call_trending**: 是否直接调用 trending 趋势分析
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- **call_explor**: 是否直接调用 explorer 灵感探索
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- **provider**: 图片源 (pexels/unsplash),默认 unsplash
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返回 SSE 事件流:
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- **tools** 事件:工具调用的 started/finished 状态
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- **custom** 事件:design 工具的逐个生成结果
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- **Done** 事件:流结束标记
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"""
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try:
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logger.info(f"fashion_agent stream request: {json.dumps(request_item.model_dump(), indent=4, ensure_ascii=False)}")
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service = FashionAgentService()
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return StreamingResponse(
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service.run_stream(request_item),
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media_type="text/event-stream",
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)
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except Exception as e:
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logger.warning(f"fashion_agent stream exception: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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@@ -4,6 +4,7 @@ from app.api import api_brand_dna
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from app.api import api_clothing_seg
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from app.api import api_clothing_seg
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from app.api import api_design
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from app.api import api_design
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from app.api import api_design_pre_processing
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from app.api import api_design_pre_processing
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from app.api import api_fashion_agent
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from app.api import api_generate_image
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from app.api import api_generate_image
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from app.api import api_mannequins_edit
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from app.api import api_mannequins_edit
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from app.api import api_pose_transform
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from app.api import api_pose_transform
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@@ -28,6 +29,7 @@ router.include_router(api_mannequins_edit.router, tags=['api_mannequins_edit'],
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router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api")
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router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api")
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router.include_router(api_clothing_seg.router, tags=['api_clothing_seg'], prefix="/api")
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router.include_router(api_clothing_seg.router, tags=['api_clothing_seg'], prefix="/api")
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router.include_router(api_sketch_to_garment.router, tags=['sketch_to_garment'], prefix="/api")
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router.include_router(api_sketch_to_garment.router, tags=['sketch_to_garment'], prefix="/api")
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router.include_router(api_fashion_agent.router, tags=['fashion_agent'], prefix="/api")
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"""停用"""
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"""停用"""
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# from app.api import api_chat_robot
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# from app.api import api_chat_robot
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27
app/schemas/fashion_agent.py
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27
app/schemas/fashion_agent.py
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from typing import Optional
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from pydantic import BaseModel, Field
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class FashionAgentRequest(BaseModel):
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"""服装设计 Agent 请求"""
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message: str = Field(default="", description="用户输入的消息")
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user_id: str = Field(default="test-agent", description="用户ID,用于生成图片存储路径")
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enable_thinking: bool = Field(default=False, description="模型思考是否开启")
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call_print: bool = Field(default=False, description="是否直接调用 print 生成印花")
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print_need_prompt_generation: bool = Field(default=False, description="print 是否需要 LLM 生成 prompt")
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call_logo: bool = Field(default=False, description="是否直接调用 logo 生成装饰图案")
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call_sketch: bool = Field(default=False, description="是否直接调用 sketch 生成草图")
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sketch_need_prompt_generation: bool = Field(default=False, description="sketch 是否需要 LLM 生成 prompt")
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call_design: bool = Field(default=False, description="是否直接调用 design 生成设计系列")
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design_request_data: dict = Field(default={}, description="design 请求参数")
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call_trending: bool = Field(default=False, description="是否直接调用 trending 趋势分析")
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call_explor: bool = Field(default=False, description="是否直接调用 explorer 灵感探索")
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provider: Optional[str] = Field(default="unsplash", description="图片源: pexels 或 unsplash")
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@@ -294,12 +294,11 @@ def design_generate_v2(request_data):
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# 打印结果
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# 打印结果
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logger.info(response.text)
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logger.info(response.text)
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for step, object in enumerate(objects_data):
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t = threading.Thread(target=process_object, args=(object, callback_url))
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threads.append(t)
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t.start()
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t.start()
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# 等待所有线程完成，释放局部变量（图片等大对象）
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for t in threads:
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t.join()
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def post_request(url, data=None, json_data=None, headers=None, auth=None, timeout=5):
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def post_request(url, data=None, json_data=None, headers=None, auth=None, timeout=5):
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"""
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"""
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@@ -320,611 +319,3 @@ def post_request(url, data=None, json_data=None, headers=None, auth=None, timeou
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except requests.RequestException as e:
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except requests.RequestException as e:
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print(f"POST请求出错: {e}")
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print(f"POST请求出错: {e}")
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return None
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return None
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if __name__ == "__main__":
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object_data = {
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"objects": [
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{
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"basic": {
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"body_point_test": {
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"waistband_right": [203, 249],
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"hand_point_right": [229, 343],
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"waistband_left": [119, 248],
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"hand_point_left": [97, 343],
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"shoulder_left": [108, 107],
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"shoulder_right": [212, 107],
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},
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"layer_order": False,
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"scale_bag": 0.7,
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"scale_earrings": 0.16,
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"self_template": True,
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"single_overall": "overall",
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"switch_category": "",
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},
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"items": [
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{
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"color": "28 26 26",
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"icon": "none",
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"image_id": 98419,
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"offset": [1, 1],
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"path": "aida-sys-image/images/female/dress/0825000526.jpg",
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"print": {
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"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
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"overall": {
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"location": [[0.0, 0.0]],
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"print_angle_list": [0.0, 0.0],
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"print_path_list": [],
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"print_scale_list": [0.0, 0.0],
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},
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"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
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},
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"resize_scale": [1.0, 1.0],
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"type": "Dress",
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},
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{
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"body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png",
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"image_id": 67277,
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"offset": [1, 1],
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"resize_scale": [1.0, 1.0],
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"type": "Body",
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},
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],
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},
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{
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"basic": {
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"body_point_test": {
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"waistband_right": [203, 249],
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"hand_point_right": [229, 343],
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"waistband_left": [119, 248],
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"hand_point_left": [97, 343],
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"shoulder_left": [108, 107],
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"shoulder_right": [212, 107],
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},
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"layer_order": False,
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"scale_bag": 0.7,
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"scale_earrings": 0.16,
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"self_template": True,
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"single_overall": "overall",
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"switch_category": "",
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},
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"items": [
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{
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"color": "28 26 26",
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"icon": "none",
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"image_id": 98420,
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"offset": [1, 1],
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"path": "aida-sys-image/images/female/skirt/903000127.jpg",
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"print": {
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"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
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"overall": {
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"location": [[0.0, 0.0]],
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"print_angle_list": [0.0, 0.0],
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"print_path_list": [],
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"print_scale_list": [0.0, 0.0],
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},
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"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
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},
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"resize_scale": [1.0, 1.0],
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"type": "Skirt",
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},
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{
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"color": "28 26 26",
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"icon": "none",
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"image_id": 69140,
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"offset": [1, 1],
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"path": "aida-sys-image/images/female/blouse/0902001100.jpg",
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"print": {
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"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
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"overall": {
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"location": [[0.0, 0.0]],
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"print_angle_list": [0.0, 0.0],
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"print_path_list": [],
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"print_scale_list": [0.0, 0.0],
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},
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"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
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},
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"resize_scale": [1.0, 1.0],
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"type": "Blouse",
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},
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{
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"color": "28 26 26",
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"icon": "none",
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"image_id": 81604,
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"offset": [1, 1],
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"path": "aida-sys-image/images/female/outwear/outwear_p5_729.jpg",
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"print": {
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"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
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"overall": {
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"location": [[0.0, 0.0]],
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"print_angle_list": [0.0, 0.0],
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"print_path_list": [],
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"print_scale_list": [0.0, 0.0],
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},
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"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
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},
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"resize_scale": [1.0, 1.0],
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"type": "Outwear",
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},
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{
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"body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png",
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"image_id": 67277,
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"offset": [1, 1],
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"resize_scale": [1.0, 1.0],
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"type": "Body",
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},
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],
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},
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{
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"basic": {
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"body_point_test": {
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"waistband_right": [203, 249],
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"hand_point_right": [229, 343],
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"waistband_left": [119, 248],
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"hand_point_left": [97, 343],
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"shoulder_left": [108, 107],
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"shoulder_right": [212, 107],
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},
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"layer_order": False,
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"scale_bag": 0.7,
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"scale_earrings": 0.16,
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"self_template": True,
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"single_overall": "overall",
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"switch_category": "",
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},
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"items": [
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{
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"color": "28 26 26",
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"icon": "none",
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"image_id": 63964,
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"offset": [1, 1],
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"path": "aida-sys-image/images/female/outwear/0825001572.jpg",
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"print": {
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"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
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"overall": {
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"location": [[0.0, 0.0]],
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"print_angle_list": [0.0, 0.0],
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"print_path_list": [],
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"print_scale_list": [0.0, 0.0],
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},
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"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
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},
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"resize_scale": [1.0, 1.0],
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"type": "Outwear",
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},
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{
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"color": "28 26 26",
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"icon": "none",
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"image_id": 98421,
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"offset": [1, 1],
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"path": "aida-sys-image/images/female/blouse/blouse_506.jpg",
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"print": {
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"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
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"overall": {
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"location": [[0.0, 0.0]],
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"print_angle_list": [0.0, 0.0],
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"print_path_list": [],
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"print_scale_list": [0.0, 0.0],
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},
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"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
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},
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"resize_scale": [1.0, 1.0],
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"type": "Blouse",
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},
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{
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"color": "28 26 26",
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"icon": "none",
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"image_id": 98422,
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"offset": [1, 1],
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"path": "aida-sys-image/images/female/trousers/0628001244.jpg",
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"print": {
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"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
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"overall": {
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"location": [[0.0, 0.0]],
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"print_angle_list": [0.0, 0.0],
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"print_path_list": [],
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"print_scale_list": [0.0, 0.0],
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},
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"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
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},
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"resize_scale": [1.0, 1.0],
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"type": "Trousers",
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},
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{
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"body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png",
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"image_id": 67277,
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"offset": [1, 1],
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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|
||||||
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||||||
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|
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
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|
||||||
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|
|
||||||
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|
|
||||||
{
|
|
||||||
"color": "28 26 26",
|
|
||||||
"icon": "none",
|
|
||||||
"image_id": 68988,
|
|
||||||
"offset": [1, 1],
|
|
||||||
"path": "aida-sys-image/images/female/trousers/0825000403.jpg",
|
|
||||||
"print": {
|
|
||||||
"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
|
|
||||||
"overall": {
|
|
||||||
"location": [[0.0, 0.0]],
|
|
||||||
"print_angle_list": [0.0, 0.0],
|
|
||||||
"print_path_list": [],
|
|
||||||
"print_scale_list": [0.0, 0.0],
|
|
||||||
},
|
|
||||||
"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
|
|
||||||
},
|
|
||||||
"resize_scale": [1.0, 1.0],
|
|
||||||
"type": "Trousers",
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png",
|
|
||||||
"image_id": 67277,
|
|
||||||
"offset": [1, 1],
|
|
||||||
"resize_scale": [1.0, 1.0],
|
|
||||||
"type": "Body",
|
|
||||||
},
|
|
||||||
],
|
|
||||||
},
|
|
||||||
],
|
|
||||||
"process_id": "123",
|
|
||||||
}
|
|
||||||
start_time = time.time()
|
|
||||||
X = design_generate(object_data)
|
|
||||||
print(time.time() - start_time)
|
|
||||||
print(X)
|
|
||||||
|
|||||||
0
app/service/fashion_agent/__init__.py
Normal file
0
app/service/fashion_agent/__init__.py
Normal file
159
app/service/fashion_agent/graph_node/design_graph/graph.py
Normal file
159
app/service/fashion_agent/graph_node/design_graph/graph.py
Normal file
@@ -0,0 +1,159 @@
|
|||||||
|
import logging
|
||||||
|
from typing import Annotated, Required, TypedDict
|
||||||
|
|
||||||
|
from langchain_core.messages import AIMessage, AnyMessage
|
||||||
|
from langgraph.graph import END, START, StateGraph
|
||||||
|
from langgraph.graph.message import add_messages
|
||||||
|
|
||||||
|
from app.service.fashion_agent.graph_node.design_graph.tools import design_tool # noqa: E402
|
||||||
|
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
"""定义状态"""
|
||||||
|
|
||||||
|
|
||||||
|
class DesignState(TypedDict):
|
||||||
|
messages: Required[Annotated[list[AnyMessage], add_messages]]
|
||||||
|
|
||||||
|
design_request_data: dict = {}
|
||||||
|
request_objects: list[dict] = []
|
||||||
|
results: list[dict] = []
|
||||||
|
|
||||||
|
|
||||||
|
"""节点"""
|
||||||
|
|
||||||
|
|
||||||
|
def run_design_node(state: DesignState) -> dict:
|
||||||
|
"""调用 design_tool 执行设计任务，逐个推送结果"""
|
||||||
|
from langgraph.config import get_stream_writer
|
||||||
|
|
||||||
|
writer = get_stream_writer()
|
||||||
|
|
||||||
|
request_data = state.get("design_request_data")
|
||||||
|
request_objects = request_data.get("objects")
|
||||||
|
|
||||||
|
results = []
|
||||||
|
for item in design_tool.invoke({"objects": request_objects}):
|
||||||
|
logger.info(f"design result: {item}")
|
||||||
|
results.append(item)
|
||||||
|
writer({"event": "tool-output-delta", "tool_name": "design_tool", "type": "design_result", "data": item})
|
||||||
|
|
||||||
|
writer({"event": "tool-finished", "tool_name": "design_tool", "type": "design_result", "data": results})
|
||||||
|
result_text = f"设计完成，共处理 {len(results)} 个对象"
|
||||||
|
return {"results": results, "messages": [AIMessage(content=result_text)]}
|
||||||
|
|
||||||
|
|
||||||
|
"""构建 Graph"""
|
||||||
|
|
||||||
|
|
||||||
|
def build_design_graph():
|
||||||
|
"""构建 design graph"""
|
||||||
|
workflow = StateGraph(DesignState)
|
||||||
|
|
||||||
|
workflow.add_node("run_design", run_design_node)
|
||||||
|
|
||||||
|
workflow.add_edge(START, "run_design")
|
||||||
|
workflow.add_edge("run_design", END)
|
||||||
|
|
||||||
|
return workflow.compile()
|
||||||
|
|
||||||
|
|
||||||
|
design_graph = build_design_graph()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
request_data = {
|
||||||
|
"objects": [
|
||||||
|
{
|
||||||
|
"basic": {
|
||||||
|
"body_point_test": {
|
||||||
|
"waistband_right": [203, 249],
|
||||||
|
"hand_point_right": [229, 343],
|
||||||
|
"waistband_left": [119, 248],
|
||||||
|
"hand_point_left": [97, 343],
|
||||||
|
"shoulder_left": [108, 107],
|
||||||
|
"relation_type": "System",
|
||||||
|
"shoulder_right": [212, 107],
|
||||||
|
"relation_id": 1020356,
|
||||||
|
},
|
||||||
|
"layer_order": False,
|
||||||
|
"scale_bag": 0.7,
|
||||||
|
"scale_earrings": 0.16,
|
||||||
|
"self_template": False,
|
||||||
|
"single_overall": "overall",
|
||||||
|
"switch_category": "",
|
||||||
|
},
|
||||||
|
"items": [
|
||||||
|
{
|
||||||
|
"color": "209 196 171",
|
||||||
|
"image_id": 84093,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"path": "aida-users/89/sketchboard/female/Outwear/0943d209-7ce0-408c-bc61-83f15da94138.png",
|
||||||
|
"print": {
|
||||||
|
"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
|
||||||
|
"overall": {
|
||||||
|
"location": [[0.0, 0.0]],
|
||||||
|
"print_angle_list": [0.0, 0.0],
|
||||||
|
"print_path_list": [],
|
||||||
|
"print_scale_list": [[0.0, 0.0]],
|
||||||
|
},
|
||||||
|
"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
|
||||||
|
},
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Outwear",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"color": "63 71 73",
|
||||||
|
"image_id": 100496,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"path": "aida-sys-image/images/female/blouse/0628001684.jpg",
|
||||||
|
"print": {
|
||||||
|
"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
|
||||||
|
"overall": {
|
||||||
|
"location": [[0.0, 0.0]],
|
||||||
|
"print_angle_list": [0.0, 0.0],
|
||||||
|
"print_path_list": [],
|
||||||
|
"print_scale_list": [[0.0, 0.0]],
|
||||||
|
},
|
||||||
|
"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
|
||||||
|
},
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Blouse",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"color": "111 78 63",
|
||||||
|
"gradient": "aida-gradient/f69b98e8-4248-4f7a-98a2-21bac41bf3e0.png",
|
||||||
|
"image_id": 92193,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"path": "aida-sys-image/images/female/trousers/0825001160.jpg",
|
||||||
|
"print": {
|
||||||
|
"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
|
||||||
|
"overall": {
|
||||||
|
"location": [[0.0, 0.0]],
|
||||||
|
"print_angle_list": [0.0, 0.0],
|
||||||
|
"print_path_list": [],
|
||||||
|
"print_scale_list": [[0.0, 0.0]],
|
||||||
|
},
|
||||||
|
"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
|
||||||
|
},
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Trousers",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png",
|
||||||
|
"image_id": 67277,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Body",
|
||||||
|
},
|
||||||
|
],
|
||||||
|
"objectSign": "65830966",
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"process_id": "4802946666428422",
|
||||||
|
"requestId": "1d1e7641-0d62-4da2-adc0-b4404910723c",
|
||||||
|
"callback_url": "https://api.aida.com.hk/api/third/party/receiveDesignResults",
|
||||||
|
}
|
||||||
|
result = design_graph.invoke({"design_request_data": request_data})
|
||||||
|
print(result)
|
||||||
206
app/service/fashion_agent/graph_node/design_graph/tools.py
Normal file
206
app/service/fashion_agent/graph_node/design_graph/tools.py
Normal file
@@ -0,0 +1,206 @@
|
|||||||
|
import logging
|
||||||
|
import queue
|
||||||
|
import threading
|
||||||
|
|
||||||
|
from langchain.tools import tool
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
from app.service.design_fast.design_generate import process_item, process_layer
|
||||||
|
from app.service.design_fast.utils.synthesis_item import synthesis, synthesis_single, update_base_size_priority
|
||||||
|
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
class DesignModel(BaseModel):
|
||||||
|
objects: list[dict] = Field(description="")
|
||||||
|
|
||||||
|
|
||||||
|
@tool(args_schema=DesignModel, description="design tool")
|
||||||
|
def design_tool(objects: list[dict]):
|
||||||
|
"""design tool"""
|
||||||
|
|
||||||
|
result_queue = queue.Queue()
|
||||||
|
|
||||||
|
def process_object(obj):
|
||||||
|
basic = obj["basic"]
|
||||||
|
design_type = basic.get("design_type", "default")
|
||||||
|
items_response = {
|
||||||
|
"layers": [],
|
||||||
|
"objectSign": obj["objectSign"] if "objectSign" in obj.keys() else "",
|
||||||
|
}
|
||||||
|
if basic["single_overall"] == "overall":
|
||||||
|
item_results = []
|
||||||
|
for item in obj["items"]:
|
||||||
|
item_results.append(process_item(item, basic, design_type))
|
||||||
|
layers = []
|
||||||
|
for item in item_results:
|
||||||
|
process_layer(item, layers)
|
||||||
|
layers = sorted(layers, key=lambda s: s.get("priority", float("inf")))
|
||||||
|
|
||||||
|
layers, new_size = update_base_size_priority(layers)
|
||||||
|
|
||||||
|
for lay in layers:
|
||||||
|
items_response["layers"].append(
|
||||||
|
{
|
||||||
|
"image_category": "body" if lay["name"] == "mannequin" else lay["name"],
|
||||||
|
"position": lay["position"],
|
||||||
|
"priority": lay.get("priority", None),
|
||||||
|
"resize_scale": lay["resize_scale"] if "resize_scale" in lay.keys() else None,
|
||||||
|
"image_size": lay["image"] if lay["image"] is None else lay["image"].size,
|
||||||
|
"gradient_string": lay["gradient_string"] if "gradient_string" in lay.keys() else "",
|
||||||
|
"mask_url": lay["mask_url"],
|
||||||
|
"image_url": lay["image_url"] if "image_url" in lay.keys() else None,
|
||||||
|
"pattern_overall_image_url": (
|
||||||
|
lay["pattern_overall_image_url"] if "pattern_overall_image_url" in lay.keys() else None
|
||||||
|
),
|
||||||
|
"pattern_print_image_url": lay["pattern_print_image_url"] if "pattern_print_image_url" in lay.keys() else None,
|
||||||
|
}
|
||||||
|
)
|
||||||
|
items_response["synthesis_url"] = synthesis(layers, new_size, basic)
|
||||||
|
else:
|
||||||
|
item_result = process_item(obj["items"][0], basic, design_type)
|
||||||
|
items_response["layers"].append(
|
||||||
|
{
|
||||||
|
"image_category": f"{item_result['name']}_front",
|
||||||
|
"image_size": item_result["back_image"].size if item_result["back_image"] else None,
|
||||||
|
"position": None,
|
||||||
|
"priority": 0,
|
||||||
|
"image_url": item_result["front_image_url"],
|
||||||
|
"mask_url": item_result["mask_url"],
|
||||||
|
"gradient_string": item_result["gradient_string"] if "gradient_string" in item_result.keys() else "",
|
||||||
|
"pattern_overall_image_url": (
|
||||||
|
item_result["pattern_overall_image_url"] if "pattern_overall_image_url" in item_result.keys() else None
|
||||||
|
),
|
||||||
|
"pattern_print_image_url": (
|
||||||
|
item_result["pattern_print_image_url"] if "pattern_print_image_url" in item_result.keys() else None
|
||||||
|
),
|
||||||
|
}
|
||||||
|
)
|
||||||
|
items_response["layers"].append(
|
||||||
|
{
|
||||||
|
"image_category": f"{item_result['name']}_back",
|
||||||
|
"image_size": item_result["front_image"].size if item_result["front_image"] else None,
|
||||||
|
"position": None,
|
||||||
|
"priority": 0,
|
||||||
|
"image_url": item_result["back_image_url"],
|
||||||
|
"mask_url": item_result["mask_url"],
|
||||||
|
"gradient_string": item_result["gradient_string"] if "gradient_string" in item_result.keys() else "",
|
||||||
|
"pattern_overall_image_url": (
|
||||||
|
item_result["pattern_overall_image_url"] if "pattern_overall_image_url" in item_result.keys() else None
|
||||||
|
),
|
||||||
|
"pattern_print_image_url": (
|
||||||
|
item_result["pattern_print_image_url"] if "pattern_print_image_url" in item_result.keys() else None
|
||||||
|
),
|
||||||
|
}
|
||||||
|
)
|
||||||
|
items_response["synthesis_url"] = synthesis_single(item_result["front_image"], item_result["back_image"])
|
||||||
|
logger.info(items_response)
|
||||||
|
result_queue.put(items_response)
|
||||||
|
|
||||||
|
# 启动所有线程
|
||||||
|
threads = []
|
||||||
|
for obj in objects:
|
||||||
|
t = threading.Thread(target=process_object, args=(obj,))
|
||||||
|
threads.append(t)
|
||||||
|
t.start()
|
||||||
|
|
||||||
|
# 主线程逐个取出结果 yield
|
||||||
|
finished = 0
|
||||||
|
total = len(objects)
|
||||||
|
while finished < total:
|
||||||
|
result = result_queue.get()
|
||||||
|
yield result
|
||||||
|
finished += 1
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
request_objects = [
|
||||||
|
{
|
||||||
|
"basic": {
|
||||||
|
"body_point_test": {
|
||||||
|
"waistband_right": [203, 249],
|
||||||
|
"hand_point_right": [229, 343],
|
||||||
|
"waistband_left": [119, 248],
|
||||||
|
"hand_point_left": [97, 343],
|
||||||
|
"shoulder_left": [108, 107],
|
||||||
|
"relation_type": "System",
|
||||||
|
"shoulder_right": [212, 107],
|
||||||
|
"relation_id": 1020356,
|
||||||
|
},
|
||||||
|
"layer_order": False,
|
||||||
|
"scale_bag": 0.7,
|
||||||
|
"scale_earrings": 0.16,
|
||||||
|
"self_template": False,
|
||||||
|
"single_overall": "overall",
|
||||||
|
"switch_category": "",
|
||||||
|
},
|
||||||
|
"items": [
|
||||||
|
{
|
||||||
|
"color": "209 196 171",
|
||||||
|
"image_id": 84093,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"path": "aida-users/89/sketchboard/female/Outwear/0943d209-7ce0-408c-bc61-83f15da94138.png",
|
||||||
|
"print": {
|
||||||
|
"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
|
||||||
|
"overall": {
|
||||||
|
"location": [[0.0, 0.0]],
|
||||||
|
"print_angle_list": [0.0, 0.0],
|
||||||
|
"print_path_list": [],
|
||||||
|
"print_scale_list": [[0.0, 0.0]],
|
||||||
|
},
|
||||||
|
"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
|
||||||
|
},
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Outwear",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"color": "63 71 73",
|
||||||
|
"image_id": 100496,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"path": "aida-sys-image/images/female/blouse/0628001684.jpg",
|
||||||
|
"print": {
|
||||||
|
"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
|
||||||
|
"overall": {
|
||||||
|
"location": [[0.0, 0.0]],
|
||||||
|
"print_angle_list": [0.0, 0.0],
|
||||||
|
"print_path_list": [],
|
||||||
|
"print_scale_list": [[0.0, 0.0]],
|
||||||
|
},
|
||||||
|
"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
|
||||||
|
},
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Blouse",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"color": "111 78 63",
|
||||||
|
"gradient": "aida-gradient/f69b98e8-4248-4f7a-98a2-21bac41bf3e0.png",
|
||||||
|
"image_id": 92193,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"path": "aida-sys-image/images/female/trousers/0825001160.jpg",
|
||||||
|
"print": {
|
||||||
|
"element": {"element_angle_list": [], "element_path_list": [], "element_scale_list": [], "location": []},
|
||||||
|
"overall": {
|
||||||
|
"location": [[0.0, 0.0]],
|
||||||
|
"print_angle_list": [0.0, 0.0],
|
||||||
|
"print_path_list": [],
|
||||||
|
"print_scale_list": [[0.0, 0.0]],
|
||||||
|
},
|
||||||
|
"single": {"location": [], "print_angle_list": [], "print_path_list": [], "print_scale_list": []},
|
||||||
|
},
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Trousers",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png",
|
||||||
|
"image_id": 67277,
|
||||||
|
"offset": [1, 1],
|
||||||
|
"resize_scale": [1.0, 1.0],
|
||||||
|
"type": "Body",
|
||||||
|
},
|
||||||
|
],
|
||||||
|
"objectSign": "65830966",
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
result = design_tool.invoke({"objects": request_objects})
|
||||||
|
for item in result:
|
||||||
|
print(item)
|
||||||
138
app/service/fashion_agent/graph_node/explorer_graph/graph.py
Normal file
138
app/service/fashion_agent/graph_node/explorer_graph/graph.py
Normal file
@@ -0,0 +1,138 @@
|
|||||||
|
import asyncio
|
||||||
|
import logging
|
||||||
|
from typing import Annotated, Required, TypedDict
|
||||||
|
|
||||||
|
from langchain.tools import ToolRuntime
|
||||||
|
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, SystemMessage
|
||||||
|
from langchain_qwq import ChatQwen
|
||||||
|
from langgraph.graph import END, START, StateGraph
|
||||||
|
from langgraph.graph.message import add_messages
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
from langchain_core.runnables import RunnableConfig
|
||||||
|
from app.service.fashion_agent.graph_node.explorer_graph.tools import explor_tool
|
||||||
|
from app.service.fashion_agent.init_llm import build_llm
|
||||||
|
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
"""定义状态"""
|
||||||
|
|
||||||
|
|
||||||
|
class ExplorerState(TypedDict):
|
||||||
|
messages: Required[Annotated[list[AnyMessage], add_messages]]
|
||||||
|
input_text: str
|
||||||
|
search_query: str
|
||||||
|
image_results: list[dict] # 每项包含 image_url 和 minio_path
|
||||||
|
provider: str = "unsplash" # 图片源: "pexels" 或 "unsplash"
|
||||||
|
|
||||||
|
|
||||||
|
"""节点"""
|
||||||
|
|
||||||
|
|
||||||
|
def extract_input_node(state: ExplorerState) -> dict:
|
||||||
|
"""从 messages 中提取用户输入"""
|
||||||
|
input_text = state["messages"][0].content if state.get("messages") else ""
|
||||||
|
return {"input_text": input_text}
|
||||||
|
|
||||||
|
|
||||||
|
class SearchQuery(BaseModel):
|
||||||
|
"""搜索关键词"""
|
||||||
|
|
||||||
|
query: str = Field(description="用于搜索灵感图片的英文关键词，简洁有力")
|
||||||
|
|
||||||
|
|
||||||
|
# TODO 要考虑搜索图片失败或者图片不存在的情况， 搜索不到 需要调整搜索词或者拆分搜索词，最终失败的话调用mood board生成工具生成， 保证绝对有图片
|
||||||
|
async def generate_query_node(state: ExplorerState) -> dict:
|
||||||
|
"""使用 LLM 分析用户输入，生成搜索关键词"""
|
||||||
|
input_text = state["input_text"]
|
||||||
|
logger.info(f"[Explorer] 用户输入: {input_text}")
|
||||||
|
llm = build_llm()
|
||||||
|
|
||||||
|
structured_llm = llm.with_structured_output(SearchQuery)
|
||||||
|
|
||||||
|
messages = [
|
||||||
|
SystemMessage(content="""你是一个专业的服装设计师助手。
|
||||||
|
根据用户输入，生成一个英文搜索关键词，用于在图片库中搜索服装设计灵感图片（moodboard）。
|
||||||
|
|
||||||
|
要求：
|
||||||
|
1. 使用英文，简洁有力
|
||||||
|
2. 适合搜索高质量的设计灵感图片
|
||||||
|
|
||||||
|
例如：
|
||||||
|
用户输入："夏季连衣裙，清新风格"
|
||||||
|
输出：summer dress fresh style"""),
|
||||||
|
HumanMessage(content=input_text),
|
||||||
|
]
|
||||||
|
|
||||||
|
result = structured_llm.invoke(messages)
|
||||||
|
logger.info(f"[Explorer] LLM 生成的搜索关键词: {result.query}")
|
||||||
|
return {"search_query": result.query}
|
||||||
|
|
||||||
|
|
||||||
|
async def search_and_upload_node(state: ExplorerState, config: RunnableConfig) -> dict:
|
||||||
|
"""使用搜索关键词获取图片并上传到 minio"""
|
||||||
|
query = state.get("search_query", "")
|
||||||
|
user_id = state.get("user_id", "agent")
|
||||||
|
provider = state.get("provider", "unsplash")
|
||||||
|
|
||||||
|
try:
|
||||||
|
results = await explor_tool.ainvoke({"query": query, "per_page": 4, "user_id": user_id, "method": provider}, config=config)
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"[Explorer] 搜索失败 '{query}': {e}")
|
||||||
|
results = []
|
||||||
|
|
||||||
|
return {"image_results": results}
|
||||||
|
|
||||||
|
|
||||||
|
def summarize_node(state: ExplorerState) -> dict:
|
||||||
|
"""汇总结果"""
|
||||||
|
input_text = state.get("input_text", "")
|
||||||
|
query = state.get("search_query", "")
|
||||||
|
results = state.get("image_results", [])
|
||||||
|
|
||||||
|
result_text = f"【灵感探索 Moodboard】\n\n"
|
||||||
|
result_text += f"基于您的需求：「{input_text}」\n"
|
||||||
|
result_text += f"搜索关键词：{query}\n\n"
|
||||||
|
result_text += f"已为您找到 {len(results)} 张灵感图片：\n"
|
||||||
|
|
||||||
|
for i, item in enumerate(results, 1):
|
||||||
|
result_text += f" {i}. 原图: {item.get('image_url', '')}\n"
|
||||||
|
result_text += f" Minio: {item.get('minio_path', '')}\n"
|
||||||
|
|
||||||
|
return {"messages": [AIMessage(content=result_text)]}
|
||||||
|
|
||||||
|
|
||||||
|
"""构建图"""
|
||||||
|
|
||||||
|
|
||||||
|
def build_explorer_graph():
|
||||||
|
"""构建灵感探索图"""
|
||||||
|
workflow = StateGraph(ExplorerState)
|
||||||
|
|
||||||
|
workflow.add_node("extract_input", extract_input_node)
|
||||||
|
workflow.add_node("generate_query", generate_query_node)
|
||||||
|
workflow.add_node("search_and_upload", search_and_upload_node)
|
||||||
|
workflow.add_node("summarize", summarize_node)
|
||||||
|
|
||||||
|
workflow.add_edge(START, "extract_input")
|
||||||
|
workflow.add_edge("extract_input", "generate_query")
|
||||||
|
workflow.add_edge("generate_query", "search_and_upload")
|
||||||
|
workflow.add_edge("search_and_upload", "summarize")
|
||||||
|
workflow.add_edge("summarize", END)
|
||||||
|
|
||||||
|
return workflow.compile()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
|
||||||
|
async def test():
|
||||||
|
graph = build_explorer_graph()
|
||||||
|
result = await graph.ainvoke(
|
||||||
|
{
|
||||||
|
"messages": [HumanMessage(content="夏季连衣裙，清新自然风格")],
|
||||||
|
"provider": "unsplash",
|
||||||
|
}
|
||||||
|
)
|
||||||
|
print(result["messages"][-1].content)
|
||||||
|
|
||||||
|
asyncio.run(test())
|
||||||
54
app/service/fashion_agent/graph_node/explorer_graph/tools.py
Normal file
54
app/service/fashion_agent/graph_node/explorer_graph/tools.py
Normal file
@@ -0,0 +1,54 @@
|
|||||||
|
from langchain.tools import ToolRuntime, tool
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
from langchain_core.runnables import RunnableConfig
|
||||||
|
from app.service.fashion_agent.graph_node.node_tools.pexels_search import search_photos
|
||||||
|
from app.service.fashion_agent.graph_node.node_tools.unsplash_search import get_random_photos
|
||||||
|
|
||||||
|
|
||||||
|
class SearchInput(BaseModel):
|
||||||
|
"""Input schema for Pexels Search Tool."""
|
||||||
|
|
||||||
|
query: str = Field(description="Search query for Pexels, e.g., 'minimalist fashion moodboard', 'summer dress inspiration'")
|
||||||
|
per_page: int = Field(description="Number of images to return (1-80)", default=4)
|
||||||
|
user_id: str = Field(description="User ID for image storage", default="agent")
|
||||||
|
method: str = Field(description="", default="unsplash")
|
||||||
|
|
||||||
|
|
||||||
|
@tool(args_schema=SearchInput)
|
||||||
|
async def explor_tool(
|
||||||
|
query: str, per_page: int = 4, user_id: str = "agent", method: str = "unsplash", config: RunnableConfig = None
|
||||||
|
) -> list[dict]:
|
||||||
|
"""Search for fashion inspiration images on Unsplash and upload to minio. Returns a list of dicts with image_url and minio_path."""
|
||||||
|
if config:
|
||||||
|
# 方式 1：从 configurable 获取
|
||||||
|
user_id = config.get("configurable", {}).get("user_id", "agent")
|
||||||
|
|
||||||
|
if method == "unsplash":
|
||||||
|
return await get_random_photos(query, count=per_page, user_id=user_id)
|
||||||
|
elif method == "pexels":
|
||||||
|
return await search_photos(query, per_page=per_page, user_id=user_id)
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
async def test():
|
||||||
|
urls = await get_random_photos("summer dress fresh natural style", count=4)
|
||||||
|
print(f"Uploaded {len(urls)} images to minio:")
|
||||||
|
for url in urls:
|
||||||
|
print(f" {url}")
|
||||||
|
|
||||||
|
asyncio.run(test())
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
async def test():
|
||||||
|
urls = await search_photos("minimalist fashion moodboard", per_page=4)
|
||||||
|
print(f"Uploaded {len(urls)} images to minio:")
|
||||||
|
for url in urls:
|
||||||
|
print(f" {url}")
|
||||||
|
|
||||||
|
asyncio.run(test())
|
||||||
152
app/service/fashion_agent/graph_node/logo_graph/graph.py
Normal file
152
app/service/fashion_agent/graph_node/logo_graph/graph.py
Normal file
@@ -0,0 +1,152 @@
|
|||||||
|
import asyncio
|
||||||
|
from typing import Annotated, Required, TypedDict
|
||||||
|
from langchain_core.messages import AIMessage, AnyMessage
|
||||||
|
from langchain_qwq import ChatQwen
|
||||||
|
from langchain_core.messages import HumanMessage, SystemMessage
|
||||||
|
from langgraph.graph import END, START, StateGraph
|
||||||
|
from langgraph.graph.message import add_messages
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
from app.service.fashion_agent.graph_node.node_tools.generate_logo import generate_logo_tool
|
||||||
|
from app.service.fashion_agent.init_llm import qwen_plus_llm
|
||||||
|
|
||||||
|
"""初始化 LLM TODO 将 API Key 替换为环境变量或者配置文件中的值，避免在代码中硬编码敏感信息"""
|
||||||
|
|
||||||
|
|
||||||
|
"""定义状态"""
|
||||||
|
|
||||||
|
|
||||||
|
class LogoState(TypedDict):
|
||||||
|
messages: Required[Annotated[list[AnyMessage], add_messages]]
|
||||||
|
input_text: str
|
||||||
|
user_id: str = "agent"
|
||||||
|
role: str = ""
|
||||||
|
gender: str = ""
|
||||||
|
style: str = ""
|
||||||
|
need_prompt_generation: bool = True # 是否需要使用 prompt 生成节点
|
||||||
|
|
||||||
|
logo_num: int = 1
|
||||||
|
|
||||||
|
logo_prompts: list[str] = []
|
||||||
|
logo_img_urls: list[str] = []
|
||||||
|
|
||||||
|
|
||||||
|
"""生成 Logo 的提示词节点"""
|
||||||
|
|
||||||
|
|
||||||
|
# 定义输出结构
|
||||||
|
class LogoPrompt(BaseModel):
|
||||||
|
"""生成的 Logo 图像提示词"""
|
||||||
|
|
||||||
|
prompts: list[str] = Field(description="用于生成 Logo 的详细提示词")
|
||||||
|
|
||||||
|
|
||||||
|
def extract_input_node(state: LogoState) -> dict:
|
||||||
|
"""从 messages 中提取用户输入"""
|
||||||
|
input_text = state["messages"][0].content if state.get("messages") else ""
|
||||||
|
return {"input_text": input_text}
|
||||||
|
|
||||||
|
|
||||||
|
def generate_logo_prompt_node(state: LogoState) -> dict:
|
||||||
|
"""根据用户输入生成 Logo 的图像生成提示词"""
|
||||||
|
structured_llm = qwen_plus_llm.with_structured_output(LogoPrompt)
|
||||||
|
|
||||||
|
messages = [
|
||||||
|
SystemMessage(content="""从用户输入中提取核心主题词，只输出一个简单的英文单词。
|
||||||
|
例如：
|
||||||
|
- "我想要一个猫咪图案" -> "cat"
|
||||||
|
- "设计一个花朵" -> "flower"
|
||||||
|
- "可爱的狗" -> "dog"
|
||||||
|
只输出单词，不要其他内容。"""),
|
||||||
|
HumanMessage(content=state["input_text"]),
|
||||||
|
]
|
||||||
|
|
||||||
|
result = structured_llm.invoke(messages)
|
||||||
|
prompts = result.prompts
|
||||||
|
|
||||||
|
return {
|
||||||
|
"logo_prompts": prompts,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
"""生成 Logo 图案节点"""
|
||||||
|
|
||||||
|
|
||||||
|
async def generate_logo_img_node(state: LogoState) -> dict:
|
||||||
|
"""根据生成的提示词，生成 Logo 图案"""
|
||||||
|
# 如果 logo_prompts 为空，使用 input_text 作为 prompt
|
||||||
|
prompts = state["logo_prompts"] if state["logo_prompts"] else [state["input_text"]]
|
||||||
|
|
||||||
|
logo_img_urls = []
|
||||||
|
for i in range(state.get("logo_num", 1)):
|
||||||
|
image_url = await generate_logo_tool.ainvoke({"prompt": prompts[i], "user_id": state.get("user_id", "agent")})
|
||||||
|
logo_img_urls.append(image_url)
|
||||||
|
|
||||||
|
result_text = f"Logo 生成完成，共生成 {len(logo_img_urls)} 张图片：\n"
|
||||||
|
return {"logo_img_urls": logo_img_urls, "messages": [AIMessage(content=result_text)]}
|
||||||
|
|
||||||
|
|
||||||
|
"""条件分支 判断是否需要生成 prompt"""
|
||||||
|
|
||||||
|
|
||||||
|
def should_generate_prompt(state: LogoState) -> str:
|
||||||
|
"""条件分支：判断是否需要生成 prompt"""
|
||||||
|
if state.get("need_prompt_generation", True):
|
||||||
|
return "gen_prompt"
|
||||||
|
else:
|
||||||
|
return "gen_logo"
|
||||||
|
|
||||||
|
|
||||||
|
def build_logo_graph():
|
||||||
|
"""构建独立的画像收集 Graph"""
|
||||||
|
|
||||||
|
workflow = StateGraph(LogoState)
|
||||||
|
workflow.add_node("extract_input", extract_input_node)
|
||||||
|
workflow.add_node("gen_prompt", generate_logo_prompt_node)
|
||||||
|
workflow.add_node("gen_logo", generate_logo_img_node)
|
||||||
|
|
||||||
|
# 添加边
|
||||||
|
workflow.add_edge(START, "extract_input")
|
||||||
|
workflow.add_conditional_edges(
|
||||||
|
"extract_input",
|
||||||
|
should_generate_prompt,
|
||||||
|
{
|
||||||
|
"gen_prompt": "gen_prompt",
|
||||||
|
"gen_logo": "gen_logo",
|
||||||
|
},
|
||||||
|
)
|
||||||
|
workflow.add_edge("gen_prompt", "gen_logo")
|
||||||
|
workflow.add_edge("gen_logo", END)
|
||||||
|
|
||||||
|
graph = workflow.compile()
|
||||||
|
|
||||||
|
return graph
|
||||||
|
|
||||||
|
|
||||||
|
async def main(test_input, user_id="agent", need_prompt_generation=True):
|
||||||
|
graph = build_logo_graph()
|
||||||
|
result = await graph.ainvoke(
|
||||||
|
{
|
||||||
|
"input_text": test_input,
|
||||||
|
"user_id": user_id,
|
||||||
|
"logo_prompts": [] if need_prompt_generation else [test_input],
|
||||||
|
"need_prompt_generation": need_prompt_generation,
|
||||||
|
"role": "",
|
||||||
|
"gender": "",
|
||||||
|
"style": "",
|
||||||
|
}
|
||||||
|
)
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 测试示例 1: 需要 prompt 生成（默认）- 简单关键词输入
|
||||||
|
test_input = "我想要一个金毛图案"
|
||||||
|
result = asyncio.run(main(test_input, need_prompt_generation=True))
|
||||||
|
print("=== 需要 prompt 生成 ===")
|
||||||
|
print(f"Result: {result}")
|
||||||
|
|
||||||
|
# 测试示例 2: 直接使用用户提供的 prompt
|
||||||
|
user_prompt = "golden retriever"
|
||||||
|
result = asyncio.run(main(user_prompt, need_prompt_generation=False))
|
||||||
|
print("\n=== 直接使用 prompt ===")
|
||||||
|
print(f"Result: {result}")
|
||||||
@@ -0,0 +1,27 @@
|
|||||||
|
import httpx
|
||||||
|
|
||||||
|
|
||||||
|
async def generate_image(
|
||||||
|
bucket_name="fida-public-bucket",
|
||||||
|
object_name=f"furniture/sketches/123456.png",
|
||||||
|
prompt="Generate a modern minimalist dining chair made of light "
|
||||||
|
"oak wood and white leather, with slim metal legs, photographed "
|
||||||
|
"in a bright Scandinavian living room with natural sunlight, high detail, "
|
||||||
|
"8k resolution.",
|
||||||
|
):
|
||||||
|
request_data = {
|
||||||
|
"input_image_paths": [],
|
||||||
|
"prompt": prompt,
|
||||||
|
"bucket_name": bucket_name,
|
||||||
|
"object_name": object_name,
|
||||||
|
"width": 1024,
|
||||||
|
"height": 1024,
|
||||||
|
}
|
||||||
|
async with httpx.AsyncClient(timeout=120) as client:
|
||||||
|
resp = await client.post(
|
||||||
|
f"http://20.1.1.33:14202/predict",
|
||||||
|
json=request_data,
|
||||||
|
)
|
||||||
|
result = resp.json()
|
||||||
|
image_url = result.get("output_path", None)
|
||||||
|
return image_url
|
||||||
@@ -0,0 +1,79 @@
|
|||||||
|
import asyncio
|
||||||
|
import concurrent.futures
|
||||||
|
import random
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import tritonclient.grpc as grpcclient
|
||||||
|
from langchain.tools import tool
|
||||||
|
from PIL import Image
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
from tritonclient.utils import np_to_triton_dtype
|
||||||
|
from uuid_utils import uuid7
|
||||||
|
from app.core.config import settings
|
||||||
|
from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
|
||||||
|
|
||||||
|
# 模型配置
|
||||||
|
GSL_MODEL_URL = f"{settings.B_4_X_4090_SERVICE_HOST}:10041"
|
||||||
|
GSL_MODEL_NAME = "stable_diffusion_xl_transparent"
|
||||||
|
|
||||||
|
# 线程池用于执行同步推理
|
||||||
|
executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
|
||||||
|
|
||||||
|
|
||||||
|
def _generate_logo_sync(prompt: str) -> Image.Image:
|
||||||
|
"""同步生成 Logo 的内部函数"""
|
||||||
|
seed = random.randint(0, 2**32 - 1)
|
||||||
|
grpc_client = grpcclient.InferenceServerClient(url=GSL_MODEL_URL)
|
||||||
|
|
||||||
|
# 准备输入
|
||||||
|
prompts = [prompt]
|
||||||
|
text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
|
||||||
|
input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
|
||||||
|
input_text.set_data_from_numpy(text_obj)
|
||||||
|
|
||||||
|
negative_prompts = "bad, ugly"
|
||||||
|
text_obj_neg = np.array(negative_prompts, dtype="object").reshape((-1, 1))
|
||||||
|
input_text_neg = grpcclient.InferInput("negative_prompt", text_obj_neg.shape, np_to_triton_dtype(text_obj_neg.dtype))
|
||||||
|
input_text_neg.set_data_from_numpy(text_obj_neg)
|
||||||
|
|
||||||
|
seed_input = np.array(seed, dtype="object").reshape((-1, 1))
|
||||||
|
input_seed = grpcclient.InferInput("seed", seed_input.shape, np_to_triton_dtype(seed_input.dtype))
|
||||||
|
input_seed.set_data_from_numpy(seed_input)
|
||||||
|
|
||||||
|
inputs = [input_text, input_text_neg, input_seed]
|
||||||
|
|
||||||
|
# 同步推理
|
||||||
|
result = grpc_client.infer(model_name=GSL_MODEL_NAME, inputs=inputs)
|
||||||
|
image = result.as_numpy("generated_image")
|
||||||
|
return Image.fromarray(np.squeeze(image.astype(np.uint8)))
|
||||||
|
|
||||||
|
|
||||||
|
async def generate_logo(prompt: str) -> Image.Image:
|
||||||
|
"""异步生成透明背景的 Logo 图片"""
|
||||||
|
loop = asyncio.get_event_loop()
|
||||||
|
return await loop.run_in_executor(executor, _generate_logo_sync, prompt)
|
||||||
|
|
||||||
|
|
||||||
|
class GenerateLogoToolInput(BaseModel):
|
||||||
|
"""Input schema for the Generate Logo Tool."""
|
||||||
|
|
||||||
|
prompt: str = Field(description="Simple keyword for logo generation, e.g., 'cat', 'flower', 'dog'")
|
||||||
|
user_id: str = Field(description="User ID for image storage", default="agent")
|
||||||
|
|
||||||
|
|
||||||
|
@tool(args_schema=GenerateLogoToolInput)
|
||||||
|
async def generate_logo_tool(prompt: str, user_id: str = "agent") -> str:
|
||||||
|
"""Generate a transparent background logo image based on a simple keyword."""
|
||||||
|
|
||||||
|
image = await generate_logo(prompt=prompt)
|
||||||
|
|
||||||
|
# 上传到 minio（使用线程池避免阻塞事件循环）
|
||||||
|
file_name = f"{uuid7()}.png"
|
||||||
|
loop = asyncio.get_event_loop()
|
||||||
|
image_url = await loop.run_in_executor(executor, upload_SDXL_image, image, user_id, "logo", file_name)
|
||||||
|
return image_url
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
result = asyncio.run(generate_logo_tool.ainvoke({"prompt": "golden retriever"}))
|
||||||
|
print(f"Logo saved to: {result}")
|
||||||
@@ -0,0 +1,72 @@
|
|||||||
|
import asyncio
|
||||||
|
import concurrent.futures
|
||||||
|
import io
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
|
||||||
|
import httpx
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
from PIL import Image
|
||||||
|
from uuid_utils import uuid7
|
||||||
|
from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
|
||||||
|
|
||||||
|
load_dotenv()
|
||||||
|
|
||||||
|
logger = logging.getLogger()
|
||||||
|
PEXELS_API_KEY = os.environ.get("PEXELS_API_KEY", "")
|
||||||
|
PEXELS_BASE_URL = os.environ.get("PEXELS_BASE_URL", "")
|
||||||
|
|
||||||
|
# 线程池用于执行同步上传
|
||||||
|
executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
|
||||||
|
|
||||||
|
|
||||||
|
async def search_photos(query: str, per_page: int = 4, user_id: str = "agent") -> list[dict]:
|
||||||
|
"""从 Pexels 搜索图片并上传到 minio
|
||||||
|
|
||||||
|
Args:
|
||||||
|
query: 搜索关键词
|
||||||
|
per_page: 返回图片数量 (1-80)
|
||||||
|
user_id: 用户 ID
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
图片信息列表，每项包含 image_url 和 minio_path
|
||||||
|
"""
|
||||||
|
# 搜索图片
|
||||||
|
async with httpx.AsyncClient(timeout=30) as client:
|
||||||
|
response = await client.get(
|
||||||
|
f"{PEXELS_BASE_URL}/search",
|
||||||
|
headers={"Authorization": PEXELS_API_KEY},
|
||||||
|
params={
|
||||||
|
"query": query,
|
||||||
|
"per_page": per_page,
|
||||||
|
"orientation": "square",
|
||||||
|
"size": "medium",
|
||||||
|
},
|
||||||
|
)
|
||||||
|
|
||||||
|
if response.status_code != 200:
|
||||||
|
raise Exception(f"Pexels API error: {response.status_code} - {response.text}")
|
||||||
|
|
||||||
|
data = response.json()
|
||||||
|
photos = data.get("photos", [])
|
||||||
|
|
||||||
|
# 下载并上传到 minio
|
||||||
|
results = []
|
||||||
|
for photo in photos:
|
||||||
|
try:
|
||||||
|
# 下载图片（使用 large 尺寸）
|
||||||
|
image_url = photo["src"]["original"]
|
||||||
|
async with httpx.AsyncClient(timeout=60) as dl_client:
|
||||||
|
dl_response = await dl_client.get(image_url)
|
||||||
|
image = Image.open(io.BytesIO(dl_response.content))
|
||||||
|
|
||||||
|
# 上传到 minio（使用线程池避免阻塞事件循环）
|
||||||
|
file_name = f"{uuid7()}.jpg"
|
||||||
|
loop = asyncio.get_event_loop()
|
||||||
|
minio_url = await loop.run_in_executor(executor, upload_SDXL_image, image, user_id, "explorer", file_name)
|
||||||
|
results.append({"image_url": image_url, "minio_path": minio_url})
|
||||||
|
logger.info(f"[Explorer] 上传成功: {minio_url}")
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"[Explorer] 上传失败: {e}")
|
||||||
|
|
||||||
|
return results
|
||||||
@@ -0,0 +1,90 @@
|
|||||||
|
import asyncio
|
||||||
|
import concurrent.futures
|
||||||
|
import io
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import httpx
|
||||||
|
|
||||||
|
from PIL import Image
|
||||||
|
from uuid_utils import uuid7
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
|
||||||
|
|
||||||
|
load_dotenv()
|
||||||
|
|
||||||
|
# Unsplash API 配置
|
||||||
|
UNSPLASH_ACCESS_KEY = os.environ.get("UNSPLASH_ACCESS_KEY", "")
|
||||||
|
UNSPLASH_BASE_URL = os.environ.get("UNSPLASH_BASE_URL", "")
|
||||||
|
logger = logging.getLogger()
|
||||||
|
# 线程池用于执行同步上传
|
||||||
|
executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
|
||||||
|
|
||||||
|
|
||||||
|
async def get_random_photos(query: str, count: int = 4, user_id: str = "agent") -> list[dict]:
|
||||||
|
"""从 Unsplash 获取随机图片并上传到 minio
|
||||||
|
|
||||||
|
Args:
|
||||||
|
query: 搜索关键词
|
||||||
|
count: 返回图片数量 (1-30)
|
||||||
|
user_id: 用户 ID
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
图片信息列表，每项包含 image_url 和 minio_path
|
||||||
|
"""
|
||||||
|
# 获取随机图片
|
||||||
|
async with httpx.AsyncClient(timeout=30) as client:
|
||||||
|
response = await client.get(
|
||||||
|
f"{UNSPLASH_BASE_URL}/search/photos",
|
||||||
|
headers={"Authorization": f"Client-ID {UNSPLASH_ACCESS_KEY}"},
|
||||||
|
params={
|
||||||
|
"query": query,
|
||||||
|
"per_page": count,
|
||||||
|
"page": 1,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
|
||||||
|
if response.status_code != 200:
|
||||||
|
raise Exception(f"Unsplash API error: {response.status_code} - {response.text}")
|
||||||
|
|
||||||
|
data = response.json()
|
||||||
|
# /search/photos 返回 {"results": [...], "total": ...}
|
||||||
|
photos = data.get("results", [])
|
||||||
|
|
||||||
|
# 下载并上传到 minio
|
||||||
|
results = []
|
||||||
|
for photo in photos:
|
||||||
|
try:
|
||||||
|
# 下载图片
|
||||||
|
image_url = photo["urls"]["raw"]
|
||||||
|
async with httpx.AsyncClient(timeout=60) as dl_client:
|
||||||
|
dl_response = await dl_client.get(image_url)
|
||||||
|
image = Image.open(io.BytesIO(dl_response.content))
|
||||||
|
|
||||||
|
# 上传到 minio（使用线程池避免阻塞事件循环）
|
||||||
|
file_name = f"{uuid7()}.jpg"
|
||||||
|
loop = asyncio.get_event_loop()
|
||||||
|
minio_url = await loop.run_in_executor(executor, upload_SDXL_image, image, user_id, "explorer", file_name)
|
||||||
|
results.append({"image_url": image_url, "minio_path": minio_url})
|
||||||
|
logger.info(f"[Explorer] 上传成功: {minio_url}")
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"[Explorer] 上传失败: {e}")
|
||||||
|
|
||||||
|
return results
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
async def test():
|
||||||
|
"""测试 Unsplash 搜索"""
|
||||||
|
query = "summer dress fresh natural style"
|
||||||
|
print(f"搜索关键词: {query}")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
|
results = await get_random_photos(query, count=4, user_id="test")
|
||||||
|
print(f"\n找到 {len(results)} 张图片:")
|
||||||
|
for i, item in enumerate(results, 1):
|
||||||
|
print(f" {i}. 原图: {item.get('image_url', '')}")
|
||||||
|
print(f" Minio: {item.get('minio_path', '')}")
|
||||||
|
|
||||||
|
asyncio.run(test())
|
||||||
158
app/service/fashion_agent/graph_node/print_graph/graph.py
Normal file
158
app/service/fashion_agent/graph_node/print_graph/graph.py
Normal file
@@ -0,0 +1,158 @@
|
|||||||
|
import asyncio
|
||||||
|
import logging
|
||||||
|
from typing import Annotated, Required, TypedDict
|
||||||
|
|
||||||
|
from langchain_qwq import ChatQwen
|
||||||
|
from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage
|
||||||
|
from langgraph.graph import END, START, StateGraph
|
||||||
|
from langgraph.graph.message import add_messages
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
from app.service.fashion_agent.init_llm import qwen_plus_llm
|
||||||
|
from app.service.fashion_agent.graph_node.print_graph.tools import generate_print_tool, test
|
||||||
|
|
||||||
|
logger = logging.getLogger()
|
||||||
|
"""定义状态"""
|
||||||
|
|
||||||
|
|
||||||
|
class PrintState(TypedDict):
|
||||||
|
messages: Required[Annotated[list[AnyMessage], add_messages]]
|
||||||
|
input_text: str
|
||||||
|
role: str = ""
|
||||||
|
gender: str = ""
|
||||||
|
style: str = ""
|
||||||
|
print_need_prompt_generation: bool = False # 是否需要使用 prompt 生成节点
|
||||||
|
|
||||||
|
print_num: int = 1
|
||||||
|
|
||||||
|
print_prompts: list[str] = []
|
||||||
|
print_img_urls: list[str] = []
|
||||||
|
|
||||||
|
|
||||||
|
"""生成印花图案的提示词节点"""
|
||||||
|
|
||||||
|
|
||||||
|
# 定义输出结构
|
||||||
|
class PrintPrompt(BaseModel):
|
||||||
|
"""生成的印花图像提示词"""
|
||||||
|
|
||||||
|
prompts: list[str] = Field(description="用于生成印花图案的详细提示词")
|
||||||
|
|
||||||
|
|
||||||
|
def extract_input_node(state: PrintState) -> dict:
|
||||||
|
"""从 messages 中提取用户输入"""
|
||||||
|
input_text = state["messages"][0].content if state.get("messages") else ""
|
||||||
|
return {"input_text": input_text}
|
||||||
|
|
||||||
|
|
||||||
|
def generate_print_prompt_node(state: PrintState) -> dict:
|
||||||
|
"""根据用户输入生成印花图案的图像生成提示词"""
|
||||||
|
structured_llm = qwen_plus_llm.with_structured_output(PrintPrompt)
|
||||||
|
|
||||||
|
messages = [
|
||||||
|
SystemMessage(content=f"""你是一个专业的印花图案设计师。
|
||||||
|
请根据用户输入，生成用于AI图像生成的印花图案提示词。
|
||||||
|
|
||||||
|
要求：
|
||||||
|
1. 提示词应该详细描述印花图案的样式、元素、颜色、布局
|
||||||
|
2. 提示词应该适合用于 Stable Diffusion 图像生成模型
|
||||||
|
3. 提示词应该使用英文，因为图像生成模型对英文理解更好
|
||||||
|
4. 提示词数量为 {state.get("print_num", 1)}
|
||||||
|
"""),
|
||||||
|
HumanMessage(content=state["input_text"]),
|
||||||
|
]
|
||||||
|
|
||||||
|
result = structured_llm.invoke(messages)
|
||||||
|
prompts = result.prompts
|
||||||
|
logger.info(f"[Print Graph] Generated print prompts: {prompts}")
|
||||||
|
return {
|
||||||
|
"print_prompts": prompts,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
"""生成印花图案节点"""
|
||||||
|
|
||||||
|
|
||||||
|
async def generate_print_img_node(state: PrintState) -> dict:
|
||||||
|
"""根据生成的提示词，生成印花图案"""
|
||||||
|
# 如果 print_prompts 为空，使用 input_text 作为 prompt
|
||||||
|
if state.get("print_need_prompt_generation", False):
|
||||||
|
prompts = state["print_prompts"] if state["print_prompts"] else [state["input_text"]]
|
||||||
|
else:
|
||||||
|
input_text = state.get("input_text", "")
|
||||||
|
prompts = [input_text]
|
||||||
|
|
||||||
|
print_img_urls = []
|
||||||
|
for prompt in prompts:
|
||||||
|
image_url = await generate_print_tool.ainvoke({"prompt": prompt})
|
||||||
|
print_img_urls.append(image_url)
|
||||||
|
logger.info(f"[Print Graph] Generated print image URL: {image_url}")
|
||||||
|
|
||||||
|
return {"print_img_urls": print_img_urls}
|
||||||
|
|
||||||
|
|
||||||
|
"""条件分支 判断是否需要生成 prompt"""
|
||||||
|
|
||||||
|
|
||||||
|
def should_generate_prompt(state: PrintState) -> str:
|
||||||
|
"""条件分支：判断是否需要生成 prompt"""
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"[Print Graph] should_generate_prompt: print_need_prompt_generation={state.get('print_need_prompt_generation')}, print_prompts={state.get('print_prompts')}"
|
||||||
|
)
|
||||||
|
if state.get("print_need_prompt_generation", True):
|
||||||
|
return "gen_prompt"
|
||||||
|
else:
|
||||||
|
return "gen_print"
|
||||||
|
|
||||||
|
|
||||||
|
def build_print_graph():
|
||||||
|
|
||||||
|
workflow = StateGraph(PrintState)
|
||||||
|
workflow.add_node("extract_input", extract_input_node)
|
||||||
|
workflow.add_node("gen_prompt", generate_print_prompt_node)
|
||||||
|
workflow.add_node("gen_print", generate_print_img_node)
|
||||||
|
|
||||||
|
# 添加边
|
||||||
|
workflow.add_edge(START, "extract_input")
|
||||||
|
workflow.add_conditional_edges(
|
||||||
|
"extract_input",
|
||||||
|
should_generate_prompt,
|
||||||
|
{
|
||||||
|
"gen_prompt": "gen_prompt",
|
||||||
|
"gen_print": "gen_print",
|
||||||
|
},
|
||||||
|
)
|
||||||
|
workflow.add_edge("gen_prompt", "gen_print")
|
||||||
|
workflow.add_edge("gen_print", END)
|
||||||
|
|
||||||
|
graph = workflow.compile()
|
||||||
|
return graph
|
||||||
|
|
||||||
|
|
||||||
|
async def main(test_input, print_need_prompt_generation=True):
|
||||||
|
graph = build_print_graph()
|
||||||
|
result = await graph.ainvoke(
|
||||||
|
{
|
||||||
|
"input_text": test_input,
|
||||||
|
"print_prompts": [] if print_need_prompt_generation else [test_input],
|
||||||
|
"print_need_prompt_generation": print_need_prompt_generation,
|
||||||
|
"role": "",
|
||||||
|
"gender": "",
|
||||||
|
"style": "",
|
||||||
|
}
|
||||||
|
)
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 测试示例 1: 需要 prompt 生成（默认）
|
||||||
|
test_input = "我想要一个优雅的花卉印花，适合用于连衣裙，颜色以粉色和白色为主"
|
||||||
|
result = asyncio.run(main(test_input, print_need_prompt_generation=True))
|
||||||
|
print("=== 需要 prompt 生成 ===")
|
||||||
|
print(f"Result: {result}")
|
||||||
|
|
||||||
|
# 测试示例 2: 直接使用用户提供的 prompt
|
||||||
|
user_prompt = "Elegant floral print pattern, pink and white colors, suitable for dress fabric, seamless tileable design"
|
||||||
|
result = asyncio.run(main(user_prompt, print_need_prompt_generation=False))
|
||||||
|
print("\n=== 直接使用 prompt ===")
|
||||||
|
print(f"Result: {result}")
|
||||||
39
app/service/fashion_agent/graph_node/print_graph/tools.py
Normal file
39
app/service/fashion_agent/graph_node/print_graph/tools.py
Normal file
@@ -0,0 +1,39 @@
|
|||||||
|
import asyncio
|
||||||
|
|
||||||
|
from langchain.tools import tool
|
||||||
|
from langsmith import uuid7
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
|
||||||
|
from app.service.fashion_agent.graph_node.node_tools.generate_image import generate_image
|
||||||
|
|
||||||
|
|
||||||
|
class GenerateImageToolInput(BaseModel):
|
||||||
|
"""Input schema for the Generate Image Tool."""
|
||||||
|
|
||||||
|
prompt: str = Field(description="Description of the desired image, e.g., 'A cozy living room with warm lighting and natural textures.'")
|
||||||
|
|
||||||
|
|
||||||
|
@tool(args_schema=GenerateImageToolInput)
|
||||||
|
async def generate_print_tool(prompt: str) -> str:
|
||||||
|
"""Generate an image based on the provided prompt."""
|
||||||
|
|
||||||
|
bucket_name = "aida-users"
|
||||||
|
object_name = f"agent_generate_print/{uuid7()}.png"
|
||||||
|
image_url = await generate_image(prompt=prompt, bucket_name=bucket_name, object_name=object_name)
|
||||||
|
return image_url
|
||||||
|
|
||||||
|
|
||||||
|
@tool
|
||||||
|
async def test(text: str):
|
||||||
|
"""测试工具函数，返回固定字符串"""
|
||||||
|
return text
|
||||||
|
|
||||||
|
|
||||||
|
async def run_test():
|
||||||
|
result = await generate_print_tool.ainvoke({"prompt": "A cozy living room with warm lighting and natural textures."})
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
result = asyncio.run(run_test())
|
||||||
|
print(result)
|
||||||
178
app/service/fashion_agent/graph_node/sketch_graph/graph.py
Normal file
178
app/service/fashion_agent/graph_node/sketch_graph/graph.py
Normal file
@@ -0,0 +1,178 @@
|
|||||||
|
import asyncio
|
||||||
|
import logging
|
||||||
|
from typing import Annotated, Required, TypedDict
|
||||||
|
from langchain_qwq import ChatQwen
|
||||||
|
from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage, AIMessage
|
||||||
|
from langgraph.graph import END, START, StateGraph
|
||||||
|
from langgraph.graph.message import add_messages
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
from app.service.fashion_agent.init_llm import qwen_plus_llm
|
||||||
|
|
||||||
|
from app.service.fashion_agent.graph_node.sketch_graph.tools import generate_sketch_tool
|
||||||
|
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
"""定义状态"""
|
||||||
|
|
||||||
|
|
||||||
|
class SketchState(TypedDict):
|
||||||
|
messages: Required[Annotated[list[AnyMessage], add_messages]]
|
||||||
|
input_text: str
|
||||||
|
role: str = ""
|
||||||
|
gender: str = ""
|
||||||
|
style: str = ""
|
||||||
|
sketch_need_prompt_generation: bool = False # 是否需要使用 prompt 生成节点
|
||||||
|
|
||||||
|
sketch_num: int = 1
|
||||||
|
|
||||||
|
sketch_prompts: list[str] = []
|
||||||
|
sketch_img_urls: list[str] = []
|
||||||
|
|
||||||
|
|
||||||
|
"""生成服装草图的提示词节点"""
|
||||||
|
|
||||||
|
|
||||||
|
# 定义输出结构
|
||||||
|
class SketchPrompt(BaseModel):
|
||||||
|
"""生成的印花图像提示词"""
|
||||||
|
|
||||||
|
prompts: list[str] = Field(description="用于生成服装草图的详细提示词")
|
||||||
|
|
||||||
|
|
||||||
|
def extract_input_node(state: SketchState) -> dict:
|
||||||
|
"""从 messages 中提取用户输入"""
|
||||||
|
input_text = state["messages"][0].content if state.get("messages") else ""
|
||||||
|
return {"input_text": input_text}
|
||||||
|
|
||||||
|
|
||||||
|
def generate_sketch_prompt_node(state: SketchState) -> dict:
|
||||||
|
"""根据用户输入生成服装草图的图像生成提示词"""
|
||||||
|
structured_llm = qwen_plus_llm.with_structured_output(SketchPrompt)
|
||||||
|
|
||||||
|
messages = [
|
||||||
|
SystemMessage(content=f"""你是一个专业的服装设计师。
|
||||||
|
请根据用户输入，生成用于AI图像生成的服装草图提示词。
|
||||||
|
|
||||||
|
要求：
|
||||||
|
1. 提示词必须包含：clean black and white line drawing only, pure white background, centered composition
|
||||||
|
2. 提示词应该详细描述服装的廓形、结构、细节
|
||||||
|
3. 提示词应该适合用于 Stable Diffusion 图像生成模型
|
||||||
|
4. 提示词应该使用英文，因为图像生成模型对英文理解更好
|
||||||
|
5. 草图风格必须是黑白线稿，不要添加颜色
|
||||||
|
6. 提示词数量为 {state.get("sketch_num", 1)}
|
||||||
|
"""),
|
||||||
|
HumanMessage(content=state["input_text"]),
|
||||||
|
]
|
||||||
|
|
||||||
|
result = structured_llm.invoke(messages)
|
||||||
|
prompts = result.prompts
|
||||||
|
|
||||||
|
return {
|
||||||
|
"sketch_prompts": prompts,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
"""生成服装草图节点"""
|
||||||
|
|
||||||
|
|
||||||
|
async def generate_sketch_img_node(state: SketchState) -> dict:
|
||||||
|
"""根据生成的提示词，生成服装草图"""
|
||||||
|
# 如果 sketch_need_prompt_generation=False 且 sketch_prompts 为空，使用模板生成 prompt
|
||||||
|
# if not state.get("sketch_need_prompt_generation", False) and not state.get("sketch_prompts"):
|
||||||
|
|
||||||
|
# input_text = state.get("input_text", "")
|
||||||
|
# prompts = [build_sketch_template_prompt(input_text)]
|
||||||
|
# else:
|
||||||
|
# prompts = state["sketch_prompts"] if state["sketch_prompts"] else [state["input_text"]]
|
||||||
|
|
||||||
|
# sketch_img_urls = []
|
||||||
|
# for prompt in prompts:
|
||||||
|
# image_url = await generate_sketch_tool.ainvoke({"prompt": prompt})
|
||||||
|
# sketch_img_urls.append(image_url)
|
||||||
|
|
||||||
|
# result_text = f"服装草图生成完成，共生成 {len(sketch_img_urls)} 张图片：\n" + "\n".join(sketch_img_urls)
|
||||||
|
# return {"sketch_img_urls": sketch_img_urls, "messages": [AIMessage(content=result_text)]}
|
||||||
|
return {"messages": [AIMessage(content="hello")]}
|
||||||
|
|
||||||
|
|
||||||
|
"""条件分支 判断是否需要生成 prompt"""
|
||||||
|
|
||||||
|
|
||||||
|
def should_generate_prompt(state: SketchState) -> str:
|
||||||
|
"""条件分支：判断是否需要生成 prompt"""
|
||||||
|
if state.get("sketch_need_prompt_generation", False):
|
||||||
|
return "gen_prompt"
|
||||||
|
else:
|
||||||
|
return "gen_sketch"
|
||||||
|
|
||||||
|
|
||||||
|
def build_sketch_graph():
|
||||||
|
workflow = StateGraph(SketchState)
|
||||||
|
workflow.add_node("gen_sketch", generate_sketch_img_node)
|
||||||
|
workflow.add_edge(START, "gen_sketch")
|
||||||
|
workflow.add_edge("gen_sketch", END)
|
||||||
|
graph = workflow.compile()
|
||||||
|
return graph
|
||||||
|
|
||||||
|
# workflow = StateGraph(SketchState)
|
||||||
|
# workflow.add_node("extract_input", extract_input_node)
|
||||||
|
# workflow.add_node("gen_prompt", generate_sketch_prompt_node)
|
||||||
|
# workflow.add_node("gen_sketch", generate_sketch_img_node)
|
||||||
|
|
||||||
|
# # 添加边
|
||||||
|
# workflow.add_edge(START, "extract_input")
|
||||||
|
# workflow.add_conditional_edges(
|
||||||
|
# "extract_input",
|
||||||
|
# should_generate_prompt,
|
||||||
|
# {
|
||||||
|
# "gen_prompt": "gen_prompt",
|
||||||
|
# "gen_sketch": "gen_sketch",
|
||||||
|
# },
|
||||||
|
# )
|
||||||
|
# workflow.add_edge("gen_prompt", "gen_sketch")
|
||||||
|
# workflow.add_edge("gen_sketch", END)
|
||||||
|
|
||||||
|
# graph = workflow.compile()
|
||||||
|
# return graph
|
||||||
|
|
||||||
|
|
||||||
|
def build_sketch_template_prompt(input_text: str) -> str:
|
||||||
|
"""构建 sketch prompt 模板"""
|
||||||
|
return f"{input_text}, clean black and white line drawing only, pure white background, centered composition, fashion sketch style"
|
||||||
|
|
||||||
|
|
||||||
|
async def main(test_input, sketch_need_prompt_generation=False):
|
||||||
|
graph = build_sketch_graph()
|
||||||
|
|
||||||
|
# 如果不需要 LLM 生成 prompt，使用模板
|
||||||
|
if not sketch_need_prompt_generation:
|
||||||
|
sketch_prompts = [build_sketch_template_prompt(test_input)]
|
||||||
|
else:
|
||||||
|
sketch_prompts = []
|
||||||
|
|
||||||
|
result = await graph.ainvoke(
|
||||||
|
{
|
||||||
|
"input_text": test_input,
|
||||||
|
"sketch_prompts": sketch_prompts,
|
||||||
|
"sketch_need_prompt_generation": sketch_need_prompt_generation,
|
||||||
|
"role": "",
|
||||||
|
"gender": "",
|
||||||
|
"style": "",
|
||||||
|
}
|
||||||
|
)
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 测试示例 1: 直接使用模板 prompt（默认）
|
||||||
|
test_input = "dress"
|
||||||
|
result = asyncio.run(main(test_input, sketch_need_prompt_generation=False))
|
||||||
|
print("=== 使用模板 prompt ===")
|
||||||
|
print(f"Result: {result}")
|
||||||
|
|
||||||
|
# # 测试示例 2: 使用 LLM 生成 prompt
|
||||||
|
# test_input = "设计一条优雅的A字廓形连衣裙，V领设计，收腰，裙摆到膝盖，适合日常穿着"
|
||||||
|
# result = asyncio.run(main(test_input, sketch_need_prompt_generation=True))
|
||||||
|
# print("\n=== 使用 LLM 生成 prompt ===")
|
||||||
|
# print(f"Result: {result}")
|
||||||
33
app/service/fashion_agent/graph_node/sketch_graph/tools.py
Normal file
33
app/service/fashion_agent/graph_node/sketch_graph/tools.py
Normal file
@@ -0,0 +1,33 @@
|
|||||||
|
import asyncio
|
||||||
|
|
||||||
|
from langchain.tools import tool
|
||||||
|
from langsmith import uuid7
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
|
||||||
|
from app.service.fashion_agent.graph_node.node_tools.generate_image import generate_image
|
||||||
|
|
||||||
|
|
||||||
|
class GenerateImageToolInput(BaseModel):
|
||||||
|
"""Input schema for the Generate Image Tool."""
|
||||||
|
|
||||||
|
prompt: str = Field(description="Description of the desired image, e.g., 'A cozy living room with warm lighting and natural textures.'")
|
||||||
|
|
||||||
|
|
||||||
|
@tool(args_schema=GenerateImageToolInput)
|
||||||
|
async def generate_sketch_tool(prompt: str) -> str:
|
||||||
|
"""Generate an image based on the provided prompt."""
|
||||||
|
|
||||||
|
bucket_name = "fida-public-bucket"
|
||||||
|
object_name = f"test/{uuid7()}.png"
|
||||||
|
image_url = await generate_image(prompt=prompt, bucket_name=bucket_name, object_name=object_name)
|
||||||
|
return image_url
|
||||||
|
|
||||||
|
|
||||||
|
async def run_test():
|
||||||
|
result = await generate_sketch_tool.ainvoke({"prompt": "A cozy living room with warm lighting and natural textures."})
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
result = asyncio.run(run_test())
|
||||||
|
print(result)
|
||||||
@@ -0,0 +1,69 @@
|
|||||||
|
import asyncio
|
||||||
|
from typing import Annotated, Required, TypedDict
|
||||||
|
|
||||||
|
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
|
||||||
|
from langgraph.graph import END, START, StateGraph
|
||||||
|
from langgraph.graph.message import add_messages
|
||||||
|
|
||||||
|
"""定义状态"""
|
||||||
|
|
||||||
|
|
||||||
|
class TrendingState(TypedDict):
|
||||||
|
messages: Required[Annotated[list[AnyMessage], add_messages]]
|
||||||
|
input_text: str
|
||||||
|
|
||||||
|
|
||||||
|
"""节点"""
|
||||||
|
|
||||||
|
|
||||||
|
def extract_input_node(state: TrendingState) -> dict:
|
||||||
|
"""从 messages 中提取用户输入"""
|
||||||
|
input_text = state["messages"][0].content if state.get("messages") else ""
|
||||||
|
return {"input_text": input_text}
|
||||||
|
|
||||||
|
|
||||||
|
async def trending_node(state: TrendingState) -> dict:
|
||||||
|
"""趋势分析节点（占位）"""
|
||||||
|
input_text = state.get("input_text", "")
|
||||||
|
|
||||||
|
# TODO: 接入真实的趋势分析逻辑
|
||||||
|
result_text = (
|
||||||
|
f"【趋势分析】\n基于您的输入「{input_text}」，以下是当前服装设计趋势：\n\n"
|
||||||
|
"1. 极简主义持续流行，黑白灰为主色调\n"
|
||||||
|
"2. 可持续时尚成为主流，环保面料受青睐\n"
|
||||||
|
"3. 复古风格回潮，90年代元素重新流行\n"
|
||||||
|
"4. 功能性与美学结合，运动休闲风持续升温"
|
||||||
|
)
|
||||||
|
|
||||||
|
return {"messages": [AIMessage(content=result_text)]}
|
||||||
|
|
||||||
|
|
||||||
|
"""构建图"""
|
||||||
|
|
||||||
|
|
||||||
|
def build_trending_graph():
|
||||||
|
"""构建趋势分析图"""
|
||||||
|
workflow = StateGraph(TrendingState)
|
||||||
|
|
||||||
|
workflow.add_node("extract_input", extract_input_node)
|
||||||
|
workflow.add_node("trending", trending_node)
|
||||||
|
|
||||||
|
workflow.add_edge(START, "extract_input")
|
||||||
|
workflow.add_edge("extract_input", "trending")
|
||||||
|
workflow.add_edge("trending", END)
|
||||||
|
|
||||||
|
return workflow.compile()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
|
||||||
|
async def test():
|
||||||
|
graph = build_trending_graph()
|
||||||
|
result = await graph.ainvoke(
|
||||||
|
{
|
||||||
|
"messages": [HumanMessage(content="女装连衣裙")],
|
||||||
|
}
|
||||||
|
)
|
||||||
|
print(result["messages"][-1].content)
|
||||||
|
|
||||||
|
asyncio.run(test())
|
||||||
31
app/service/fashion_agent/init_llm.py
Normal file
31
app/service/fashion_agent/init_llm.py
Normal file
@@ -0,0 +1,31 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
from langchain_qwq import ChatQwen
|
||||||
|
|
||||||
|
load_dotenv()
|
||||||
|
|
||||||
|
QWEN_API_KEY_INTL = os.environ.get("QWEN_API_KEY_INTL", "")
|
||||||
|
|
||||||
|
|
||||||
|
def build_llm(enable_thinking: bool = False):
|
||||||
|
llm = ChatQwen(
|
||||||
|
model="qwen3.6-plus",
|
||||||
|
timeout=None,
|
||||||
|
max_retries=2,
|
||||||
|
enable_thinking=enable_thinking,
|
||||||
|
streaming=True,
|
||||||
|
api_key=QWEN_API_KEY_INTL,
|
||||||
|
)
|
||||||
|
return llm
|
||||||
|
|
||||||
|
|
||||||
|
qwen_plus_llm = ChatQwen(
|
||||||
|
model="qwen-plus",
|
||||||
|
timeout=None,
|
||||||
|
max_retries=2,
|
||||||
|
streaming=False,
|
||||||
|
temperature=0.25,
|
||||||
|
top_p=0.8,
|
||||||
|
api_key=QWEN_API_KEY_INTL,
|
||||||
|
)
|
||||||
144
app/service/fashion_agent/main_agent.py
Normal file
144
app/service/fashion_agent/main_agent.py
Normal file
@@ -0,0 +1,144 @@
|
|||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Annotated, Required, TypedDict
|
||||||
|
from deepagents import CompiledSubAgent, create_deep_agent
|
||||||
|
from langchain.agents import create_agent
|
||||||
|
from langchain.tools import tool
|
||||||
|
from langchain_core.messages import AnyMessage, HumanMessage
|
||||||
|
from langchain_qwq import ChatQwen
|
||||||
|
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.design_graph.tools import design_tool
|
||||||
|
from app.service.fashion_agent.graph_node.explorer_graph.tools import explor_tool
|
||||||
|
from app.service.fashion_agent.graph_node.logo_graph.graph import build_logo_graph
|
||||||
|
from app.service.fashion_agent.graph_node.node_tools.generate_logo 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.explorer_graph.graph import build_explorer_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()
|
||||||
|
explorer_graph = build_explorer_graph()
|
||||||
|
|
||||||
|
|
||||||
|
class MainState(TypedDict):
|
||||||
|
# 消息
|
||||||
|
messages: Required[Annotated[list[AnyMessage], add_messages]]
|
||||||
|
|
||||||
|
# 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_explor: 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 = [explor_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_explor"):
|
||||||
|
return "direct_explor"
|
||||||
|
return "llm_agent"
|
||||||
|
|
||||||
|
|
||||||
|
def build_main_graph(enable_thinking: bool = False):
|
||||||
|
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_explor", explorer_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_explor": "direct_explor",
|
||||||
|
},
|
||||||
|
)
|
||||||
|
|
||||||
|
# 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_explor", END)
|
||||||
|
|
||||||
|
return workflow.compile()
|
||||||
|
|
||||||
|
|
||||||
|
agent = build_main_graph()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
async def test_direct():
|
||||||
|
# 直接调用 sketch，跳过 LLM
|
||||||
|
result = await agent.ainvoke(
|
||||||
|
{
|
||||||
|
"messages": [HumanMessage(content="我想设计衬衫，带有猫咪的图案")],
|
||||||
|
"call_sketch": True,
|
||||||
|
"sketch_need_prompt_generation": False,
|
||||||
|
}
|
||||||
|
)
|
||||||
|
print("=== 直接调用 sketch ===")
|
||||||
|
print(result["messages"][-1].content)
|
||||||
|
|
||||||
|
asyncio.run(test_direct())
|
||||||
132
app/service/fashion_agent/service.py
Normal file
132
app/service/fashion_agent/service.py
Normal file
@@ -0,0 +1,132 @@
|
|||||||
|
import json
|
||||||
|
import logging
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
from langgraph.stream import ProtocolEvent, StreamChannel, StreamTransformer
|
||||||
|
from app.service.fashion_agent.main_agent import build_main_graph
|
||||||
|
from langgraph.prebuilt import ToolCallTransformer
|
||||||
|
from typing import AsyncGenerator, TypedDict
|
||||||
|
from langchain_core.messages import HumanMessage
|
||||||
|
from app.schemas.fashion_agent import FashionAgentRequest
|
||||||
|
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
class CustomTransformer(StreamTransformer):
|
||||||
|
required_stream_modes = ("custom",)
|
||||||
|
|
||||||
|
def __init__(self, scope: tuple[str, ...] = ()) -> None:
|
||||||
|
super().__init__(scope)
|
||||||
|
self.log = StreamChannel()
|
||||||
|
|
||||||
|
def init(self) -> dict:
|
||||||
|
return {"custom": self.log}
|
||||||
|
|
||||||
|
def process(self, event: ProtocolEvent) -> bool:
|
||||||
|
if event["method"] == "custom":
|
||||||
|
self.log.push(event["params"]["data"])
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
class FashionAgentService:
|
||||||
|
|
||||||
|
async def run_stream(self, request: FashionAgentRequest) -> AsyncGenerator[str, None]:
|
||||||
|
"""流式运行 agent - 使用 v3 projections"""
|
||||||
|
|
||||||
|
config = {"configurable": {"user_id": request.user_id}}
|
||||||
|
|
||||||
|
agent = build_main_graph(enable_thinking=request.enable_thinking)
|
||||||
|
state = {
|
||||||
|
"messages": [HumanMessage(content=request.message)],
|
||||||
|
"call_print": request.call_print,
|
||||||
|
"call_logo": request.call_logo,
|
||||||
|
"call_sketch": request.call_sketch,
|
||||||
|
"call_design": request.call_design,
|
||||||
|
"call_trending": request.call_trending,
|
||||||
|
"call_explor": request.call_explor,
|
||||||
|
"print_need_prompt_generation": request.print_need_prompt_generation,
|
||||||
|
"sketch_need_prompt_generation": request.sketch_need_prompt_generation,
|
||||||
|
"design_request_data": request.design_request_data,
|
||||||
|
}
|
||||||
|
|
||||||
|
stream = await agent.astream_events(state, config=config, version="v3", transformers=[ToolCallTransformer, CustomTransformer])
|
||||||
|
|
||||||
|
tool_names = {}
|
||||||
|
filter_tool_name = ["design_tool"]
|
||||||
|
async for event in stream:
|
||||||
|
if event["method"] == "tools":
|
||||||
|
data = event["params"]["data"]
|
||||||
|
tool_call_id = data.get("tool_call_id")
|
||||||
|
|
||||||
|
# 记录 tool_name
|
||||||
|
if data.get("event") == "tool-started":
|
||||||
|
tool_names[tool_call_id] = data.get("tool_name")
|
||||||
|
|
||||||
|
# 通过 ID 查找 tool_name
|
||||||
|
elif data.get("event") == "tool-finished":
|
||||||
|
tool_name = tool_names.get(tool_call_id, "unknown")
|
||||||
|
|
||||||
|
if tool_name in filter_tool_name:
|
||||||
|
continue
|
||||||
|
|
||||||
|
data["tool_name"] = tool_name
|
||||||
|
|
||||||
|
response_event = {"event_type": "tool", "data": data}
|
||||||
|
yield f"data: {json.dumps(response_event, ensure_ascii=False)}\n\n"
|
||||||
|
|
||||||
|
elif event["method"] == "custom":
|
||||||
|
data = event["params"]["data"]
|
||||||
|
response_event = {"event_type": "tool", "data": data}
|
||||||
|
yield f"data: {json.dumps(response_event, ensure_ascii=False)}\n\n"
|
||||||
|
|
||||||
|
elif event["method"] == "messages":
|
||||||
|
data = event["params"]["data"][0]
|
||||||
|
if not isinstance(data, dict):
|
||||||
|
continue
|
||||||
|
if data.get("event") != "content-block-delta":
|
||||||
|
continue
|
||||||
|
block = data.get("delta") or {}
|
||||||
|
|
||||||
|
if block.get("type") == "text-delta":
|
||||||
|
response_event = {"event_type": "messages", "data": {"event": data["event"]} | block}
|
||||||
|
yield f"data: {json.dumps(response_event, ensure_ascii=False)}\n\n"
|
||||||
|
elif block.get("type") == "reasoning-delta":
|
||||||
|
response_event = {"event_type": "messages", "data": {"event": data["event"]} | block}
|
||||||
|
yield f"data: {json.dumps(response_event, ensure_ascii=False)}\n\n"
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
# print(f"----------------{event}")
|
||||||
|
response_event = {"event_type": "done"}
|
||||||
|
yield f"data: {response_event}"
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
async def test_stream():
|
||||||
|
"""测试流式调用"""
|
||||||
|
|
||||||
|
with open("app/service/fashion_agent/graph_node/design_graph/design_request_data.json", encoding="utf-8") as f:
|
||||||
|
request_data = json.load(f)
|
||||||
|
|
||||||
|
service = FashionAgentService()
|
||||||
|
|
||||||
|
print("=" * 50)
|
||||||
|
print("测试流式输出")
|
||||||
|
print("=" * 50)
|
||||||
|
request = FashionAgentRequest(
|
||||||
|
message="生成一张草莓图案",
|
||||||
|
call_print=True,
|
||||||
|
# print_need_prompt_generation=False,
|
||||||
|
# call_sketch=True,
|
||||||
|
# sketch_need_prompt_generation=False,
|
||||||
|
# call_logo=True,
|
||||||
|
# call_explor=True,
|
||||||
|
# call_design=True,
|
||||||
|
# design_request_data=request_data,
|
||||||
|
)
|
||||||
|
async for chunk in service.run_stream(request):
|
||||||
|
print(chunk, end="")
|
||||||
|
|
||||||
|
# 运行测试
|
||||||
|
asyncio.run(test_stream())
|
||||||
@@ -92,7 +92,7 @@ if __name__ == "__main__":
|
|||||||
# url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
|
# url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
|
||||||
# url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png"
|
# url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png"
|
||||||
# url = "aida-users/89/single_logo/123-89.png"
|
# url = "aida-users/89/single_logo/123-89.png"
|
||||||
url = "aida-collection-element/26293/Sketchboard/b503d482-3334-46e7-9dee-44e380fb4294.png"
|
url = "aida-users/agent/logo/019e91c4-7b1f-74e2-b716-d652713101cd.png"
|
||||||
|
|
||||||
# url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png"
|
# url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png"
|
||||||
read_type = "2"
|
read_type = "2"
|
||||||
|
|||||||
@@ -22,6 +22,7 @@ dependencies = [
|
|||||||
"langchain-community>=0.4.1",
|
"langchain-community>=0.4.1",
|
||||||
"langchain-qwq>=0.3.5",
|
"langchain-qwq>=0.3.5",
|
||||||
"langgraph>=1.0.5",
|
"langgraph>=1.0.5",
|
||||||
|
"langgraph-api>=0.4.28",
|
||||||
"langgraph-cli[inmem,redis]<=0.4.26",
|
"langgraph-cli[inmem,redis]<=0.4.26",
|
||||||
"load>=1.0.14",
|
"load>=1.0.14",
|
||||||
"load-dotenv>=0.1.0",
|
"load-dotenv>=0.1.0",
|
||||||
|
|||||||
215
uv.lock
generated
215
uv.lock
generated
@@ -83,7 +83,7 @@ wheels = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "aiohttp"
|
name = "aiohttp"
|
||||||
version = "3.14.0"
|
version = "3.14.1"
|
||||||
source = { registry = "https://pypi.org/simple" }
|
source = { registry = "https://pypi.org/simple" }
|
||||||
dependencies = [
|
dependencies = [
|
||||||
{ name = "aiohappyeyeballs" },
|
{ name = "aiohappyeyeballs" },
|
||||||
@@ -95,26 +95,26 @@ dependencies = [
|
|||||||
{ name = "typing-extensions" },
|
{ name = "typing-extensions" },
|
||||||
{ name = "yarl" },
|
{ name = "yarl" },
|
||||||
]
|
]
|
||||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/ab/93ce242f899b68c51b0578c027aafa791ab3614cb9345fa5d37b5f5c8e3e/aiohttp-3.14.0.tar.gz", hash = "sha256:2882de819734c715fd1b9c11c97e09fa020d14438203d1d354d8ed1702791c9b", size = 7940674, upload-time = "2026-06-01T19:41:02.763Z" }
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sdist = { url = "https://files.pythonhosted.org/packages/82/78/8ea7308cac6934de8c74a14f3d5f65d1c89287426688be79538d0e5c013d/aiohttp-3.14.1.tar.gz", hash = "sha256:307f2cff90a764d329e77040603fa032db89c5c24fdad50c4c15334cba744035", size = 7955794, upload-time = "2026-06-07T21:09:35.529Z" }
|
||||||
wheels = [
|
wheels = [
|
||||||
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||||||
{ url = "https://files.pythonhosted.org/packages/21/e2/62634b7fff918ed98c3c6b2f0e70d520f7f28846cb412d451b04354c6459/aiohttp-3.14.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:dbec68ce61b64cb73cab4d33df9433427b1713c8bcccb181dce695c1b6f8e87c", size = 506966, upload-time = "2026-06-01T19:37:50.014Z" },
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{ url = "https://files.pythonhosted.org/packages/c2/82/280619e0bd7bf2454987e19282616e84762255dd9c8468f62382e8c191f1/aiohttp-3.14.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bcfb80a2cc36fba2534e5e5b5264dc7ae6fcd9bf15256da3e53d2f499e6fa29d", size = 512310, upload-time = "2026-06-07T21:06:42.207Z" },
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||||||
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{ url = "https://files.pythonhosted.org/packages/55/b2/2aac325583aaa1353045f96dffa586d8a34e8322e14a7ba49cffeb103ab4/aiohttp-3.14.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:27fd7c91e51729b4f7e1577865fa6d34c9adccbc39aabe9000285b48af9f0ec2", size = 512448, upload-time = "2026-06-07T21:06:43.813Z" },
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||||||
{ url = "https://files.pythonhosted.org/packages/01/d5/405a0ae4e6b081754a3609c1c97c63a950e000a2def16046f1e736933a0e/aiohttp-3.14.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cb6c657104393b5fbff01a5f59b2023db74058a8077d94475d6c25d03882a108", size = 1762420, upload-time = "2026-06-01T19:37:53.839Z" },
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||||||
{ url = "https://files.pythonhosted.org/packages/ae/1d/e05a7c896b15a6bc6fb8fc5319eb437861c2c49c34559ef928add6590315/aiohttp-3.14.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:46fbbec4e4fab7428d4396a3823f9320e4560aa3113b89eeebce712c27c9ed5a", size = 1733672, upload-time = "2026-06-01T19:37:55.791Z" },
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{ url = "https://files.pythonhosted.org/packages/b5/d3/d9fe1c9ec7557ab4d0d82bebaa728c6418f0b93295ec2f4ab015f7710cc7/aiohttp-3.14.1-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f5e6ff2bdbb8f4cd3fbe41f99e25bbcd58e3bf9f13d3dd31a11e7917251cc77a", size = 1740884, upload-time = "2026-06-07T21:06:47.413Z" },
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||||||
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{ url = "https://files.pythonhosted.org/packages/c1/dc/f2cecfaf9337ba3e63f181500814ff502aa3d00d9c7ec93a9d23d10a27b2/aiohttp-3.14.1-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2f73e01dc37122325caf079982621262f96d74823c179038a82fddfc50359264", size = 1810034, upload-time = "2026-06-07T21:06:50.165Z" },
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||||||
{ url = "https://files.pythonhosted.org/packages/80/50/e85bdaba0be59ca4838005ebfef4048fcdd5f35a02b07057a9a123394440/aiohttp-3.14.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3c7139100fbaae76515b73051d8f0aa3a3ff02e415eec8a8eee8e2223d9ba955", size = 1902125, upload-time = "2026-06-01T19:38:00.225Z" },
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{ url = "https://files.pythonhosted.org/packages/66/d7/2ff65c5e65c0d7476daf7e15c032e0805e36811185b9623e3238ad6c763e/aiohttp-3.14.1-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:bb2c0c80d431c0d03f2c7dbf125150fedd4f0de17366a7ca33f7ccb822391842", size = 1904054, upload-time = "2026-06-07T21:06:52.035Z" },
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||||||
{ url = "https://files.pythonhosted.org/packages/19/d8/51de5c6b971c27bb1ef620293b8d1ca611ec78736b34b3f6ccf68e4c8785/aiohttp-3.14.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:78d6f9286a629ce52728430afe18f8ed2b6c39a1fddb3802d7244b9983910ad2", size = 1783112, upload-time = "2026-06-01T19:38:02.641Z" },
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{ url = "https://files.pythonhosted.org/packages/20/9c/d445818389df371f56d141d881153ba23183c4735a03f7356ffb43f7757d/aiohttp-3.14.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3e6fc1a85fa7194a1a7d19f44e8609180f4a8eb5fa4c7ed8b4355f080fad235c", size = 1790278, upload-time = "2026-06-07T21:06:54.049Z" },
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||||||
{ url = "https://files.pythonhosted.org/packages/73/ae/b4402bfde77e43dfb1b6ccff83c7b7ab63ed06b50c4754f0c5423fb374fe/aiohttp-3.14.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cc3c3e12cdaeb92d7dcf13db00e9f6b1956b910e47256e696df1cfa946d02159", size = 1586356, upload-time = "2026-06-01T19:38:04.637Z" },
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{ url = "https://files.pythonhosted.org/packages/4d/aa/bf04cb4d865fc6101c2229a294ad744973b72e513fdc5a6b791e6983d72a/aiohttp-3.14.1-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:686b6c0d3911ec387b444ddf5dc62fb7f7c0a7d5186a7861626496a5ab4aff95", size = 1591795, upload-time = "2026-06-07T21:06:55.911Z" },
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||||||
{ url = "https://files.pythonhosted.org/packages/bc/05/750a3265ca4dc54a460bd0cb1121a8f2ce9171fce4a135fb47ea7fd594d2/aiohttp-3.14.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4d6a998191f5ebe3b8c28463ff72bc030250008b3193c402464efadd08b5ca02", size = 1723119, upload-time = "2026-06-01T19:38:06.713Z" },
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{ url = "https://files.pythonhosted.org/packages/dc/b4/4dac0038960427ba832f6609dfb4ea5437d7fd80c72001b9e48f834f428b/aiohttp-3.14.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c6fa4dc7ad6f8109c70bb1499e589f76b0b792baf39f9b017eb92c8a81d0a199", size = 1728397, upload-time = "2026-06-07T21:06:57.777Z" },
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||||||
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{ url = "https://files.pythonhosted.org/packages/2b/f9/7cd4e8ad7aa3b75f17d56bb5498dd604a93d4e6eece822ba0568c413fff0/aiohttp-3.14.1-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:87a5eea1b2a5e21e1ebdbb33ad4165359189327e63fc4e4894693e7f821ac817", size = 1766504, upload-time = "2026-06-07T21:07:00.009Z" },
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||||||
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||||||
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|
||||||
[[package]]
|
[[package]]
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]
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]
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|
||||||
[[package]]
|
[[package]]
|
||||||
|
|||||||
Reference in New Issue
Block a user