Files
AiDA_Python/app/service/fashion_agent/service.py

133 lines
4.9 KiB
Python
Raw Normal View History

2026-06-15 14:48:17 +08:00
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())