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feat(tutorials/semantic-layer): add semantic layer tab scaffolding to tutorials (#4566)
* feat(tutorials/semantic-layer): add semantic layer tab scaffolding to tutorials * feat(tutorials/semantic-layer): remove v0 and joins * Update apps/docs/docs/tutorials/quickstart.md Co-authored-by: Ícaro Guerra <[email protected]> * Update apps/docs/docs/tutorials/quickstart.md Co-authored-by: Ícaro Guerra <[email protected]> * Update apps/docs/docs/tutorials/quickstart.md Co-authored-by: Ícaro Guerra <[email protected]> * Update apps/docs/docs/tutorials/quickstart.md Co-authored-by: Ícaro Guerra <[email protected]> * Update apps/docs/docs/tutorials/quickstart.md Co-authored-by: Ícaro Guerra <[email protected]> * Update: apps/docs/docs/tutorials/quickstart.md Co-authored-by: Ícaro Guerra <[email protected]> * feat(tutorials/semantic-layer): add vo and v1 * feat(tutorials/semantic-layer): add vo and v1 * feat(tutorials/semantic-layer): add vo and v1 --------- Co-authored-by: Ícaro Guerra <[email protected]>
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‎apps/docs/docs/tutorials/quickstart.md‎

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@@ -4,6 +4,9 @@ description: Get started with pyoso and common query patterns for our most popul
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sidebar_position: 2
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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This tutorial walks through the basics of using [pyoso](../get-started/python.md) to explore the OSO data lake. This tutorial is designed to be run locally in a Jupyter notebook. We also have guides for using pyoso in different notebook environments [here](../guides/notebooks/).
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:::tip
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Get a preview of all available models:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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client.to_pandas("SELECT * FROM models_v0 LIMIT 5")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = semantic.select(
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"models"
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).limit(5)
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```
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</TabItem>
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</Tabs>
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Get the list of stable production models:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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client.to_pandas("SELECT * FROM models_v0 WHERE model_name LIKE '%_v1'")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = oso.semantic.select(
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"models",
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).where(
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"models.model_name LIKE '%_v1'"
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)
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```
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</TabItem>
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</Tabs>
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Get the list of less stable models:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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client.to_pandas("SELECT * FROM models_v0 WHERE model_name LIKE '%_v0'")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = oso.semantic.select(
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"models",
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).where(
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"models.model_name LIKE '%_v0'"
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)
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```
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</TabItem>
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</Tabs>
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## Query Popular Models
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Get a list of projects:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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client.to_pandas("SELECT * FROM projects_v1 LIMIT 5")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = oso.semantic.select(
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"projects",
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).limit(5)
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```
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</TabItem>
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</Tabs>
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Look up a specific project:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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client.to_pandas("SELECT * FROM projects_v1 WHERE project_name = 'opensource-observer'")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = oso.semantic.select(
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"projects",
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).where(
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"projects.project_name = 'opensource-observer'"
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)
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```
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</TabItem>
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</Tabs>
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Get all artifacts owned by a project:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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client.to_pandas("SELECT * FROM artifacts_by_project_v1 WHERE project_name = 'opensource-observer'")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = oso.semantic.select(
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"artifacts_by_project",
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).where(
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"artifacts_by_project.project_name = 'opensource-observer'"
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)
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```
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</TabItem>
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</Tabs>
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Get available key metrics for OSO:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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client.to_pandas("""
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SELECT
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""")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = oso.semantic.select(
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"metrics.id",
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"metrics.name",
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"metrics.display_name",
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"key_metrics_by_project.sample_date",
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"key_metrics_by_project.amount",
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"key_metrics_by_project.unit",
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).where(
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"key_metrics_by_project.unit.project_id = 'UuWbpo5bpL5QsYvlukUWNm2uE8HFjxQxzCM0e+HMZfk='"
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)
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```
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</TabItem>
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</Tabs>
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Get a set of key metrics for a few projects:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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MY_PROJECTS = ['opensource-observer', 'huggingface', 'wevm']
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MY_METRICS = ['GITHUB_stars_over_all_time', 'GITHUB_forks_over_all_time']
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""")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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MY_PROJECTS = ['opensource-observer', 'huggingface', 'wevm']
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MY_METRICS = ['GITHUB_stars_over_all_time', 'GITHUB_forks_over_all_time']
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oso = Client() # ensure OSO_API_KEY is set
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query = oso.semantic.select(
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"metrics.id",
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"metrics.name",
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"metrics.display_name",
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"key_metrics_by_project.sample_date",
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"key_metrics_by_project.amount",
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"key_metrics_by_project.unit",
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).where(
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"key_metrics_by_project.unit.project_id = MY_PROJECTS"
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)
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```
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</TabItem>
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</Tabs>
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Get timeseries metrics for OSO:
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<Tabs>
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<TabItem value="python-sql" label="Python">
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```python
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df_stars = client.to_pandas("""
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SELECT
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""")
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```
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</TabItem>
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<TabItem value="python-semantic" label="Semantic Layer">
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```python
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query = (
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oso.semantic.select(
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"timeseries_metrics_by_project.metric_id",
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"metrics.metric_name",
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"metrics.display_name",
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"timeseries_metrics_by_project.sample_date",
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"timeseries_metrics_by_project.amount",
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"timeseries_metrics_by_project.unit",
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)
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.where(
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"projects.project_name = 'opensource-observer'",
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"metrics.metric_name = 'GITHUB_stars_daily'"
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)
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)
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```
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</TabItem>
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</Tabs>
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Add a cumulative column:
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```python

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