1 files changed, 40 insertions, 0 deletions
diff --git a/server/continuedev/plugins/recipes/CreatePipelineRecipe/main.py b/server/continuedev/plugins/recipes/CreatePipelineRecipe/main.py
new file mode 100644
index 00000000..56e6f055
--- /dev/null
+++ b/server/continuedev/plugins/recipes/CreatePipelineRecipe/main.py
@@ -0,0 +1,40 @@
+from textwrap import dedent
+
+from ....core.main import Step
+from ....core.sdk import ContinueSDK
+from ....core.steps import MessageStep, WaitForUserInputStep
+from .steps import RunQueryStep, SetupPipelineStep, ValidatePipelineStep
+
+
+class CreatePipelineRecipe(Step):
+    hide: bool = True
+
+    async def run(self, sdk: ContinueSDK):
+        text_observation = await sdk.run_step(
+            MessageStep(
+                name="Building your first dlt pipeline",
+                message=dedent(
+                    """\
+                This recipe will walk you through the process of creating a dlt pipeline for your chosen data source. With the help of Continue, you will:
+                - Create a Python virtual environment with dlt installed
+                - Run `dlt init` to generate a pipeline template
+                - Write the code to call the API
+                - Add any required API keys to the `secrets.toml` file
+                - Test that the API call works
+                - Load the data into a local DuckDB instance
+                - Write a query to view the data"""
+                ),
+            )
+            >> WaitForUserInputStep(
+                prompt="What API do you want to load data from? (e.g. weatherapi.com, chess.com)"
+            )
+        )
+        await sdk.run_step(
+            SetupPipelineStep(api_description=text_observation.text)
+            >> ValidatePipelineStep()
+            >> RunQueryStep()
+            >> MessageStep(
+                name="Congrats!",
+                message="You've successfully created your first dlt pipeline! 🎉",
+            )
+        )