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https://github.com/hatchet-dev/hatchet.git
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d8b6843dec
* feat: add stream event model * docs: how to work with db models * feat: put stream event * chore: rm comments * feat: add stream resource type * feat: enqueue stream event * fix: contracts * feat: protos * chore: set properties correctly for typing * fix: stream example * chore: rm old example * fix: async on * fix: bytea type * fix: worker * feat: put stream data * feat: stream type * fix: correct queue * feat: streaming payloads * fix: cleanup * fix: validation * feat: example file streaming * chore: rm unused query * fix: tenant check and read only consumer * fix: check tenant-steprun relation * Update prisma/schema.prisma Co-authored-by: abelanger5 <belanger@sas.upenn.edu> * chore: generate protos * chore: rename migration * release: 0.20.0 * feat(go-sdk): implement streaming in go --------- Co-authored-by: gabriel ruttner <gabe@hatchet.run> Co-authored-by: abelanger5 <belanger@sas.upenn.edu>
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
from hatchet_sdk import Hatchet, Context
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from dotenv import load_dotenv
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import base64
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import os
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load_dotenv()
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hatchet = Hatchet(debug=True)
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@hatchet.workflow(on_events=["man:create"])
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class ManualTriggerWorkflow:
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@hatchet.step()
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def step1(self, context: Context):
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res = context.playground('res', "HELLO")
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# Get the directory of the current script
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# Construct the path to the image file relative to the script's directory
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image_path = os.path.join(script_dir, "image.jpeg")
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# Load the image file
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with open(image_path, "rb") as image_file:
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image_data = image_file.read()
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print(len(image_data))
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# Encode the image data as base64
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base64_image = base64.b64encode(image_data).decode('utf-8')
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# Stream the base64-encoded image data
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context.put_stream(base64_image)
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context.sleep(3)
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print("executed step1")
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return {"step1": "data1 "+res}
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@hatchet.step(parents=["step1"], timeout='4s')
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def step2(self, context):
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print("started step2")
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context.sleep(1)
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print("finished step2")
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return {"step2": "data2"}
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workflow = ManualTriggerWorkflow()
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worker = hatchet.worker('manual-worker', max_runs=4)
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worker.register_workflow(workflow)
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worker.start()
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