Adding more Atlas screenshots

docs/30-aws/3-sagemaker-studio.mdx

@@ -24,12 +24,17 @@ In the Sagemaker Studio console, select the **Git** dropdown from the menu bar a
 
 <Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/home" src="img/screenshots/30-aws/3-sagemaker-studio/5-clone-repo-dropdown.png" alt="Clone Git Repository dropdown" />
 
-In the modal that appears, paste this URL in the **Git repository URL** box, uncheck **Open README files.**, and click Clone: https://github.com/mongodb-developer/mdb-aws-agents-lab-notebooks/
+In the modal that appears, paste the following URL in the **Git repository URL** box, click the **Clone https://...** message from the drop-down, and click the **Clone** button:
+https://github.com/mongodb-developer/mdb-aws-agents-lab-notebooks.git
 
 <Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/home" src="img/screenshots/30-aws/3-sagemaker-studio/6-clone-repo.png" alt="Cloning the repo" />
 
 Once the repo is cloned, you will see the content of the repo in the sidebar. **Double-click** on the file named **notebook_template.ipynb**. This is where you will fill in code for the rest of the lab.
 
 <Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/home" src="img/screenshots/30-aws/3-sagemaker-studio/7-open-template.png" alt="Opening the notebook template" />
 
+When prompted to select the notebook environment, proceed wih the defaults by clicking the **Select** button.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/home" src="img/screenshots/30-aws/3-sagemaker-studio/8-select-notebook-env.png" alt="Select the notebook environment" />
+
 That's it! You are ready to start building your AI agent!

docs/50-agent-tools/2-jupyter_notebooks.mdx

@@ -0,0 +1,25 @@
+# 📘 Running code in Jupyter Notebooks
+
+Jupyter Notebooks are an interactive Python environment. Cells in a Jupyter notebook are a modular unit of code or text that you can execute and view outputs for.
+
+To run a cell, highlight it and click the Run icon in the toolbar at the top.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-agent-tools/2-jupyter-notebooks/1-run-cell.png" alt="Run a cell" />
+
+When a cell is running, you will see a * in the square brackets ([ ]) against the cell. 
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-agent-tools/2-jupyter-notebooks/2-running-cell.png" alt="A running cell" />
+
+When a cell is finished running successfully, you will see a number appear in the [ ] with no error traceback after the cell.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-agent-tools/2-jupyter-notebooks/3-successful-cell.png" alt="Successful cell run" />
+
+If an error occurred while running a cell, you will see an error traceback after the cell.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-agent-tools/2-jupyter-notebooks/4-error-in-cell.png" alt="Erroneous cell run" />
+
+To fix errors, you may need to update previous cells. If you do, re-run all the cells following the one(s) you updated.
+
+To interrupt a running cell, click the Stop icon in the toolbar at the top.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-agent-tools/2-jupyter-notebooks/5-interrupt-cell.png" alt="Interrupt cell run" />

docs/20-mongodb-atlas/3-get-connection-string.mdx → docs/50-agent-tools/3-get-connection-string.mdx

@@ -9,20 +9,14 @@ In the Atlas UI, navigate to the **Overview** page. In the **Clusters section**,
 
 <Screenshot url="https://cloud.mongodb.com" src="img/screenshots/20-mongodb-atlas/3-get-conn-string/1-connect-button.png" alt="Screenshot of the connect button" />
 
-A modal will display several ways to connect to your database.
+A modal will display several ways to connect to your database. Select **Drivers**.
 
 <Screenshot url="https://cloud.mongodb.com" src="img/screenshots/20-mongodb-atlas/3-get-conn-string/2-connect-modal.png" alt="Screenshot of the connect modal" />
 
-Select **Compass**. While we won't be using Compass to import the data, it's an easy way to see your connection string.
+Look for your connection string. It should look something like `mongodb+srv://<username>:<password>@<cluster-url>/`
 
 <Screenshot url="https://cloud.mongodb.com" src="img/screenshots/20-mongodb-atlas/3-get-conn-string/3-connect-compass.png" alt="Screenshot of the connection string" />
 
-Look for your connection string. It should look something like:
-
-```
-mongodb+srv://<username>:<password>@<cluster-url>/
-```
-
 Click the copy button next to your connection string to copy it to your clipboard. Paste the connection string somewhere safe.
 
 :::tip

docs/50-agent-tools/4-import-data.mdx

@@ -0,0 +1,20 @@
+import Link from "@site/src/components/Link";
+import Screenshot from "@site/src/components/Screenshot";
+
+# 👐 Import data
+
+The MongoDB learning assistant has two tools- a vector search tool to retrieve information to answer questions about MongoDB, and another tool to get the content of articles in our Developer Center for summarization.
+
+Let's import the data required by these tools into two MongoDB collections. This is as simple as making a `GET` request to a serverless AWS Lambda function that we have created for you.
+
+Run the cells under the **Step 3: Import data** section in the notebook to import the data required by our agent's tools, into MongoDB collections.
+
+To verify that the data has been imported into your MongoDB cluster, navigate to the **Overview** page in the Atlas UI. In the **Clusters section**, select the cluster you just created and click **Browse collections**.
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/50-agent-tools/4-import-data/1-browse-collections.png" alt="Browse collections" />
+
+Ensure that you see a database called _mongodb_agents_lab_, and two collections namely _chunked_articles_ and _full_articles_ under it. Note the number and format of documents in both the collections.
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/50-agent-tools/4-import-data/2-chunked-collection.png" alt="Chunked collection" />
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/50-agent-tools/4-import-data/3-full-collection.png" alt="Full collection" />

docs/50-agent-tools/3-create-vector-search-index.mdx → docs/50-agent-tools/5-create-vector-search-index.mdx

@@ -1,3 +1,6 @@
+import Link from "@site/src/components/Link";
+import Screenshot from "@site/src/components/Screenshot";
+
 # 👐 Create a vector search index
 
 To retrieve documents using vector search, you must configure a vector search index on the collection you want to perform vector search against.
@@ -26,4 +29,12 @@ mongodb_client[DB_NAME][VS_COLLECTION_NAME]
 vs_collection.create_search_index(model=model)
 ```
 </div>
-</details>
+</details>
+
+To verify that the index was created, navigate to **Search Indexes** for the _chunked_articles_ collection.
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/50-agent-tools/5-create-vector-search-index/1-nav-search-indexes.png" alt="Navigate to search indexes" />
+
+The index is ready to use once the status changes from **PENDING** to **READY**.
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/50-agent-tools/5-create-vector-search-index/2-index-ready.png" alt="Index ready to use" />