Agent Development Guide

examples/development_lifecycle/README.md

@@ -5,6 +5,7 @@ You will gain an understanding of concepts like caching, the Flyte remote API, A
 
 ```{auto-examples-toc}
 agent_service
+agent_development
 private_images
 task_cache
 task_cache_serialize

examples/development_lifecycle/development_lifecycle/agent_development.py

@@ -0,0 +1,63 @@
+# %% [markdown]
+# # How To Develop Agent Plugin Service?
+#
+# ```{eval-rst}
+# .. tags:: Extensibility, Contribute, Intermediate
+# ```
+#
+# ## Why Plugin Service but not write the service within a task?
+# Flyte can execute external web APIs in two ways: by writing the service within a task or by utilizing FlytePlugins.
+
+# ### Writing the service within a task
+# Suppose you have a workflow that requires the integration of a web API, like ChatGPT.
+#
+# You might have a Python code similar to the one below:
+#
+# ```python
+# @task()
+# def t1(input: str) -> str:
+#     completion = openai.ChatCompletion.create(
+#           model="gpt-3.5-turbo",
+#           messages=[
+#             {"role": "user", "content": input}
+#           ]
+#         )
+#     return completion.choices[0].message
+#
+# @workflow
+# def wf() -> str:
+#     return t1(input="Your Input Message!")
+# ```
+#
+# Here is how the task's lifecycle unfolds:
+# 1. FlytePropeller initiates a pod to execute the task.
+#
+# 2. The task, running within the pod, calls the ChatGPT API.
+#
+# 3. After the task is completed, FlytePropeller terminates the pod.
+#
+# This process can be resource-intensive and also time-consuming, as initiating and terminating pods for each task execution consumes additional resources and needs much time.
+#
+# ### Utilizing FlytePlugins
+# Let's analyze the example above and compare it to the code provided [here](https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/bigquery_plugin/bigquery.html).
+#
+# ```python
+# bigquery_task_no_io = BigQueryTask(
+#     name="sql.bigquery.no_io",
+#     inputs={},
+#     query_template="SELECT 1",
+#     task_config=BigQueryConfig(ProjectID="flyte"),
+# )
+#
+#
+# @workflow
+# def no_io_wf():
+#     return bigquery_task_no_io()
+# ```
+#
+#
+# In this example, the lifecycle of the bigquery task progresses as follows:
+#
+# 1. FlytePlugins invokes the BigQuery API, as seen [here](https://github.com/flyteorg/flyte/tree/master/flyteplugins/go/tasks/plugins/webapi/bigquery).
+#
+# This approach is notably quicker and more resource-efficient.