Replay the interface recording packets, orchestrate and run them based on the LCEL using the langChain.
Project description
caseflow
使用langchain 的LCEL语法来编排用例组成,每一步,每一个用例都是runnable对象。
每个步骤或用例都可使用 lanngchain的callbackmanager、retry等
每个自动化用例都可装饰成agent tool,供agent使用。此agent即为具有AI的RPA(Robotic Process Automation)。
用例采用接口录制har文件转接口自动化用例,并完成用例参数化。(todo: 此转换过程也可交由agent来自动转换)
安装
pip install caseflow
示例
import asyncio
from langchain_core.globals import set_verbose
from caseflow import CaseStep
from caseflow.run_case import parse_har
set_verbose(True)
stepJson_1 = parse_har("file/request.step_1.har")
stepJson_2 = parse_har("file/request.step_2.har")
flow = (
CaseStep(step_json_file_path=stepJson_1)
| CaseStep(step_json_file_path=stepJson_2)
)
# invoke
result = flow.invoke({})
print(result)
# ainvoke
result = asyncio.run(
flow.ainvoke(
{},
config={
"callbacks": [
# CaseStepStdOutCallbackHandler(),
]
},
)
)
print(result)
# stream
for chunk in flow.stream({}):
print(chunk)
# astream
async def stream_output():
async for chunk in flow.astream({}):
print(chunk)
asyncio.run(stream_output())
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