add python sdk for SL

website/docs/docs/dbt-cloud-apis/sl-api-overview.md

@@ -50,6 +50,12 @@ plan="dbt Cloud Team or Enterprise"
     link="/docs/dbt-cloud-apis/sl-graphql"
     icon="dbt-bit"/>
 
+<Card
+    title="Python SDK"
+    body="Use the Python SDK to interact with the dbt Semantic Layer using Python."
+    link="/docs/dbt-cloud-apis/sl-python"
+    icon="dbt-bit"/>
+
 <Card
     title="Semantic manifest"
     body="Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your Semantic Layer."

website/docs/docs/dbt-cloud-apis/sl-python-sdk.md

@@ -0,0 +1,147 @@
+---
+title: "Python SDK"
+id: sl-python
+description: "Learn how to use the dbt Semantic Layer SDK for Python library to interact with the dbt Semantic Layer."
+tags: [Semantic Layer, APIs]
+keywords: [dbt Cloud, API, dbt Semantic Layer, python]
+sidebar_label: "Python SDK"
+---
+
+The [`dbt-sl-sdk` Python software development kit](https://github.com/dbt-labs/semantic-layer-sdk-python) (SDK) is a Python library that provides you with easy access to the dbt Semantic Layer with Python. It allows developers to interact with the Semantic Layer APIs and query metrics and dimensions in downstream tools.
+
+## Installation
+
+To install the SDK, you'll need to specify optional dependencies depending on whether you want to use it synchronously (backed by [requests](https://github.com/psf/requests/)) or with asynchronous ([asyncio](https://docs.python.org/3/library/asyncio.html) backed by [aiohttp](https://github.com/aio-libs/aiohttp/)).
+
+<Tabs>
+<TabItem value="sync" label="Sync installation">
+
+Sync installation means your program waits for each task to finish before moving on to the next one. 
+
+It's simpler and easier to understand. Suitable for smaller tasks or when your program doesn't need to handle many tasks at the same time.
+
+```bash
+pip install dbt-sl-sdk[sync]
+```
+</TabItem>
+
+<TabItem value="async" label="Async installation">
+
+Async installation means your program can start a task and then move on to other tasks while waiting for the first one to finish. This can handle many tasks at once without waiting, making it faster and more efficient for larger tasks or when you need to manage multiple tasks at the same time. 
+
+For more details, refer to [asyncio](https://docs.python.org/3/library/asyncio.html).
+
+```bash
+pip install dbt-sl-sdk[async]
+```
+
+Since the [Python ADBC driver](https://github.com/apache/arrow-adbc/tree/main/python/adbc_driver_manager) doesn't yet support asyncio natively, `dbt-sl-sdk` uses a [`ThreadPoolExecutor`](https://github.com/dbt-labs/semantic-layer-sdk-python/blob/5e52e1ca840d20a143b226ae33d194a4a9bc008f/dbtsl/api/adbc/client/asyncio.py#L62) to run `query` and `list dimension-values` (all operations that are done with ADBC).  This is why you might see multiple Python threads spawning.
+
+If you're using async frameworks like [FastAPI](https://fastapi.tiangolo.com/) or [Strawberry](https://github.com/strawberry-graphql/strawberry), installing the sync version of the SDK will block your event loop and can significantly slow down your program. In this case, we strongly recommend using async installation.
+
+</TabItem>
+</Tabs>
+
+## Usage
+To run operations against the Semantic Layer APIs, instantiate (create an instance of) a `SemanticLayerClient` with your specific [API connection parameters](/docs/dbt-cloud-apis/sl-api-overview):
+
+```python
+from dbtsl import SemanticLayerClient
+
+client = SemanticLayerClient(
+    environment_id=123,
+    auth_token="<your-semantic-layer-api-token>",
+    host="semantic-layer.cloud.getdbt.com",
+)
+
+# query the first metric by `metric_time`
+def main():
+    with client.session():
+        metrics = client.metrics()
+        table = client.query(
+            metrics=[metrics[0].name],
+            group_by=["metric_time"],
+        )
+        print(table)
+
+main()
+```
+
+**Note**: All method calls that reach out to the APIs need to be within a `client.session()` context manager. This allows the client to establish a connection to the APIs only once and reuse the same connection between API calls. 
+
+We recommend creating an application-wide session and reuse the same session throughout the application for optimal performance. Creating a session per request is discouraged and inefficient.
+
+### asyncio usage
+If you're using asyncio, import `AsyncSemanticLayerClient` from `dbtsl.asyncio`. The APIs of `SemanticLayerClient` and `AsyncSemanticLayerClient` are identical, but the async version has async methods that you need to `await`.
+
+```python
+import asyncio
+from dbtsl.asyncio import AsyncSemanticLayerClient
+
+client = AsyncSemanticLayerClient(
+    environment_id=123,
+    auth_token="<your-semantic-layer-api-token>",
+    host="semantic-layer.cloud.getdbt.com",
+)
+
+async def main():
+    async with client.session():
+        metrics = await client.metrics()
+        table = await client.query(
+            metrics=[metrics[0].name],
+            group_by=["metric_time"],
+        )
+        print(table)
+
+asyncio.run(main())
+
+```
+
+## Integrate with dataframe libraries
+
+The SDK returns all query data as [pyarrow](https://arrow.apache.org/docs/python/index.html) tables. 
+
+The SDK library doesn't come bundled with [Polars](https://pola.rs/) or [Pandas](https://pandas.pydata.org/). If you use these libraries, add them as dependencies in your project.
+
+To use the data with libraries like Polars or Pandas, manually convert the data into the desired format. For example:
+
+#### If you're using pandas
+
+```python
+# ... initialize client
+
+arrow_table = client.query(...)
+pandas_df = arrow_table.to_pandas()
+
+```
+
+#### If you're using polars
+
+```python
+import polars as pl
+
+# ... initialize client
+
+arrow_table = client.query(...)
+polars_df = pl.from_arrow(arrow_table)
+```
+
+## Usage examples
+For additional usage examples, check out our [usage examples](https://github.com/dbt-labs/semantic-layer-sdk-python/tree/main/examples), some which include:
+
+- [Fetching dimension values sync](https://github.com/dbt-labs/semantic-layer-sdk-python/blob/main/examples/fetch_dimension_values_sync.py)
+- Fetching metrics [async](https://github.com/dbt-labs/semantic-layer-sdk-python/blob/main/examples/fetch_metric_async.py) and [sync](https://github.com/dbt-labs/semantic-layer-sdk-python/blob/main/examples/fetch_metric_sync.py)
+- [List saved queries async](https://github.com/dbt-labs/semantic-layer-sdk-python/blob/main/examples/list_saved_queries_async.py)
+
+## Disable telemetry
+By default, the SDK sends some [platform-related information](https://github.com/dbt-labs/semantic-layer-sdk-python/blob/main/dbtsl/env.py) to dbt Labs. To opt out, set the `PLATFORM.anonymous` attribute to `True`:
+
+```python
+from dbtsl.env import PLATFORM
+PLATFORM.anonymous = True
+
+# ... initialize client
+```
+
+## Contribute
+To contribute to this project, check out our [contribution guidelines](https://github.com/dbt-labs/semantic-layer-sdk-python/blob/main/CONTRIBUTING.md) and open a GitHub [issue](https://github.com/dbt-labs/semantic-layer-sdk-python/issues) or [pull request](https://github.com/dbt-labs/semantic-layer-sdk-python/pulls). 

website/sidebars.js

@@ -669,6 +669,7 @@ const sidebarSettings = {
             "docs/dbt-cloud-apis/sl-api-overview",
             "docs/dbt-cloud-apis/sl-jdbc",
             "docs/dbt-cloud-apis/sl-graphql",
+            "docs/dbt-cloud-apis/sl-python",
             "docs/dbt-cloud-apis/sl-manifest",
           ],
         },