chore: lint Python files

.github/workflows/test.yaml

@@ -136,8 +136,8 @@ jobs:
                     done
 
 
-    lint_code:
-        name: Lint app code
+    lint_code_js:
+        name: Lint JavaScript code
         runs-on: ubuntu-latest
         steps:
             -   name: Checkout Source code
@@ -159,4 +159,35 @@ jobs:
                 env:
                     NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
 
-            -   run: npm run lint:code
+            -   run: npm run lint:code:js
+
+
+    lint_code_py:
+        name: Lint Python code
+        runs-on: ubuntu-latest
+        steps:
+            -   name: Checkout source code
+                uses: actions/checkout@v5
+
+            -   name: Get changed files
+                id: changed-files
+                uses: tj-actions/changed-files@v47
+                with:
+                    files: '**/*.{md,mdx}'
+                    files_ignore: '!sources/api/*.{md,mdx}'
+                    separator: ","
+
+            -   name: Get uv
+                uses: astral-sh/setup-uv@v7
+
+            -   name: List and lint changed files
+                env:
+                    ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }}
+                run: |
+                    IFS=',' read -ra FILE_ARRAY <<< "$ALL_CHANGED_FILES"
+                    for file in "${FILE_ARRAY[@]}"; do
+                      uv run --with doccmd --with ruff doccmd --language=py --language=python --command="ruff check --quiet" "$file"
+                    done
+
+            # TODO: once we fix existing issues, switch to checking the whole sources/ directory
+            # -   run: npm run lint:code:py

package.json

@@ -38,8 +38,11 @@
         "lint:fix": "npm run lint:md:fix && npm run lint:code:fix",
         "lint:md": "markdownlint '**/*.md'",
         "lint:md:fix": "markdownlint '**/*.md' --fix",
-        "lint:code": "eslint .",
-        "lint:code:fix": "eslint . --fix",
+        "lint:code": "npm run lint:code:js && npm run lint:code:py",
+        "lint:code:fix": "npm run lint:code:js:fix",
+        "lint:code:js": "eslint .",
+        "lint:code:js:fix": "eslint . --fix",
+        "lint:code:py": "uv run --with doccmd --with ruff doccmd --language=py --command=\"ruff check --quiet\" ./sources",
         "postinstall": "patch-package",
         "postbuild": "node ./scripts/joinLlmsFiles.mjs && node ./scripts/indentLlmsFile.mjs"
     },

sources/academy/platform/getting_started/apify_client.md

@@ -40,21 +40,20 @@ pip install apify-client
 
 After installing the package, let's make a file named **client** and import the Apify client like so:
 
+<!-- group doccmd[all]: start -->
+
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```js
-// client.js
+```js title="client.js"
 import { ApifyClient } from 'apify-client';
 ```
 
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```py
-# client.py
+```py title="client.py"
 from apify_client import ApifyClient
-
 ```
 
 </TabItem>
@@ -70,17 +69,14 @@ Before we can use the client though, we must create a new instance of the `Apify
 <TabItem value="Node.js" label="Node.js">
 
 ```js
-const client = new ApifyClient({
-    token: 'YOUR_TOKEN',
-});
+const client = new ApifyClient({ token: 'YOUR_TOKEN' });
 ```
 
 </TabItem>
 <TabItem value="Python" label="Python">
 
 ```py
 client = ApifyClient(token='YOUR_TOKEN')
-
 ```
 
 </TabItem>
@@ -108,7 +104,6 @@ run = client.actor('YOUR_USERNAME/adding-actor').call(run_input={
     'num1': 4,
     'num2': 2
 })
-
 ```
 
 </TabItem>
@@ -136,7 +131,6 @@ const dataset = client.dataset(run.defaultDatasetId);
 
 ```py
 dataset = client.dataset(run['defaultDatasetId'])
-
 ```
 
 </TabItem>
@@ -149,7 +143,6 @@ Finally, we can download the items in the dataset by using the **list items** fu
 
 ```js
 const { items } = await dataset.listItems();
-
 console.log(items);
 ```
 
@@ -158,21 +151,20 @@ console.log(items);
 
 ```py
 items = dataset.list_items().items
-
 print(items)
-
 ```
 
 </TabItem>
 </Tabs>
 
+<!-- group doccmd[all]: end -->
+
 The final code for running the Actor and fetching its dataset items looks like this:
 
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```js
-// client.js
+```js title="client.js"
 import { ApifyClient } from 'apify-client';
 
 const client = new ApifyClient({
@@ -185,17 +177,14 @@ const run = await client.actor('YOUR_USERNAME/adding-actor').call({
 });
 
 const dataset = client.dataset(run.defaultDatasetId);
-
 const { items } = await dataset.listItems();
-
 console.log(items);
 ```
 
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```py
-# client.py
+```py title="client.py"
 from apify_client import ApifyClient
 
 client = ApifyClient(token='YOUR_TOKEN')
@@ -206,11 +195,8 @@ actor = client.actor('YOUR_USERNAME/adding-actor').call(run_input={
 })
 
 dataset = client.dataset(run['defaultDatasetId'])
-
 items = dataset.list_items().items
-
 print(items)
-
 ```
 
 </TabItem>
@@ -224,19 +210,26 @@ Let's change these two Actor settings via the Apify client using the [`actor.upd
 
 First, we'll create a pointer to our Actor, similar to before (except this time, we won't be using `.call()` at the end):
 
+<!-- group doccmd[all]: start -->
+
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
 ```js
+import { ApifyClient } from 'apify-client';
+const client = new ApifyClient({ token: 'YOUR_TOKEN' });
+
 const actor = client.actor('YOUR_USERNAME/adding-actor');
 ```
 
 </TabItem>
 <TabItem value="Python" label="Python">
 
 ```py
-actor = client.actor('YOUR_USERNAME/adding-actor')
+from apify_client import ApifyClient
+client = ApifyClient(token='YOUR_TOKEN')
 
+actor = client.actor('YOUR_USERNAME/adding-actor')
 ```
 
 </TabItem>
@@ -261,13 +254,18 @@ await actor.update({
 <TabItem value="Python" label="Python">
 
 ```py
-actor.update(default_run_build='latest', default_run_memory_mbytes=256, default_run_timeout_secs=20)
-
+actor.update(
+    default_run_build='latest',
+    default_run_memory_mbytes=256,
+    default_run_timeout_secs=20,
+)
 ```
 
 </TabItem>
 </Tabs>
 
+<!-- group doccmd[all]: end -->
+
 After running the code, go back to the **Settings** page of **adding-actor**. If your default options now look like this, then it worked!:
 
 ![New run defaults](./images/new-defaults.jpg)

sources/platform/integrations/ai/agno.md

@@ -38,7 +38,7 @@ While our examples use OpenAI, Agno supports other LLM providers as well. You'll
 - _Python environment_: Ensure Python is installed (version 3.8+ recommended).
 - _Required packages_: Install the following dependencies in your terminal:
 
-```bash
+```shell
 pip install agno apify-client
 ```
 

sources/platform/integrations/ai/crewai.md

@@ -30,7 +30,7 @@ This guide demonstrates how to integrate Apify Actors with CrewAI by building a
 - **OpenAI API key**: To power the agents in CrewAI, you need an OpenAI API key. Get one from the [OpenAI platform](https://platform.openai.com/account/api-keys).
 - **Python packages**: Install the following Python packages:
 
-    ```bash
+    ```shell
     pip install 'crewai[tools]' langchain-apify langchain-openai
     ```
 

sources/platform/integrations/ai/haystack.md

@@ -19,7 +19,7 @@ The last step will be to retrieve the most similar documents.
 This example uses the Apify-Haystack Python integration published on [PyPi](https://pypi.org/project/apify-haystack/).
 Before we start with the integration, we need to install all dependencies:
 
-```bash
+```shell
 pip install apify-haystack haystack-ai
 ```
 

sources/platform/integrations/ai/langchain.md

@@ -20,7 +20,9 @@ If you prefer to use JavaScript, you can follow the  [JavaScript LangChain docum
 
 Before we start with the integration, we need to install all dependencies:
 
-`pip install langchain langchain-openai langchain-apify`
+```shell
+pip install langchain langchain-openai langchain-apify
+```
 
 After successful installation of all dependencies, we can start writing code.
 

sources/platform/integrations/ai/langflow.md

@@ -43,13 +43,13 @@ Langflow can either be installed locally or used in the cloud. The cloud version
 
 First, install the Langflow platform using Python package and project manager [uv](https://docs.astral.sh/uv/):
 
-```bash
+```shell
 uv pip install langflow
 ```
 
 After installing Langflow, you can start the platform:
 
-```bash
+```shell
 uv run langflow run
 ```
 

sources/platform/integrations/ai/langgraph.md

@@ -32,7 +32,7 @@ This guide will demonstrate how to use Apify Actors with LangGraph by building a
 
 - **Python packages**:  You need to install the following Python packages:
 
-    ```bash
+    ```shell
     pip install langgraph langchain-apify langchain-openai
     ```
 

sources/platform/integrations/ai/llama.md

@@ -22,7 +22,9 @@ You can integrate Apify dataset or Apify Actor with LlamaIndex.
 
 Before we start with the integration, we need to install all dependencies:
 
-`pip install apify-client llama-index-core llama-index-readers-apify`
+```shell
+pip install apify-client llama-index-core llama-index-readers-apify
+```
 
 After successfully installing all dependencies, we can start writing Python code.
 

sources/platform/integrations/ai/milvus.md

@@ -66,7 +66,7 @@ Another way to interact with Milvus is through the [Apify Python SDK](https://do
 
 1. Install the Apify Python SDK by running the following command:
 
-    ```py
+    ```shell
     pip install apify-client
     ```
 

sources/platform/integrations/ai/openai_assistants.md

@@ -29,7 +29,7 @@ The image below provides an overview of the entire process:
 
 Before we start creating the assistant, we need to install all dependencies:
 
-```bash
+```shell
 pip install apify-client openai
 ```
 
@@ -260,7 +260,7 @@ For more information on automating this process, check out the blog post [How we
 
 Before we start, we need to install all dependencies:
 
-```bash
+```shell
 pip install apify-client openai
 ```
 

sources/platform/integrations/ai/pinecone.md

@@ -74,7 +74,9 @@ Another way to interact with Pinecone is through the [Apify Python SDK](https://
 
 1. Install the Apify Python SDK by running the following command:
 
-    `pip install apify-client`
+    ```shell
+    pip install apify-client
+    ```
 
 1. Create a Python script and import all the necessary modules:
 

sources/platform/integrations/ai/qdrant.md

@@ -68,7 +68,7 @@ Another way to interact with Qdrant is through the [Apify Python SDK](https://do
 
 1. Install the Apify Python SDK by running the following command:
 
-    ```py
+    ```shell
     pip install apify-client
     ```