
GENKIT . DEV {
}
Title:
Defining AI workflows with Flows | Genkit
Description:
Learn how to define, call, stream, debug, and deploy AI workflows using Genkit Flows in Python.
Website Age:
0 years and 1 months (reg. 2025-05-20).
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Keywords {🔍}
flows, flow, output, streaming, calling, genkit, run, response, deploy, stream, developer, app, input, model, section, theme, titled, async, str, api, python, defining, user, await, return, schema, workflows, deploying, function, schemas, features, call, type, defined, generate, cloud, reference, models, code, deployment, directly, calls, aiflow, def, menusuggestionflowtheme, promptfinvent, menu, item, themed, restaurant,
Topics {✒️}
interrupts retrieval-augmented generation flows support streaming firebase deploy flask generating content chat-based llm interfaces calling flows input started deploy google ai-related task encapsulating ai logic calling flows” page overview defining retrieving contextual information defining flows deploy flows directly web api endpoints debug flows independently response = ai ai features output schemas defined js platform authorization directly calling streaming output prompt=f'invent custom serializable objects flows directly streaming interface supports streaming {theme} themed restaurant run flows post-processing steps parital llm responses called “side channel” flows defined ordinary python code deploying tightly-linked logic output schemas” async def menu_suggestion_flow chat app generative model requests model api streaming flow genkit developer ui model output back define schemas cloud run deploy clicking view trace unlike similar features calling
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