Merge pull request #5 from mongodb-developer/post-local-updates

Updates Round #2

.github/CODEOWNERS

@@ -0,0 +1,5 @@
+# These owners will be the default owners for everything in
+# the repo. Unless a later match takes precedence,
+# @global-owner1 and @global-owner2 will be requested for
+# review when someone opens a pull request.
+*       @ajosh0504

docs/20-mongodb-atlas/1-create-account.mdx

@@ -5,7 +5,29 @@ import Screenshot from "@site/src/components/Screenshot";
 
 In this lab, you will learn how to use MongoDB Atlas as a knowledge base as well as a memory provider for RAG applications.
 
-To use MongoDB Atlas, you will need to start by creating an account. **If you already have an account, you can skip the following steps and move on to the next section.**
+To use MongoDB Atlas, you will need to start by creating an account.
+
+<details>
+  <summary>🦹 If you already have an Atlas account, you can use it by creating a new project</summary>
+
+    First, log into <Link to="https://account.mongodb.com/">MongoDB Atlas</Link> using your existing credentials.
+
+    <Screenshot url="https://account.mongodb.com" src={"img/screenshots/20-mongodb-atlas/1-create-account/3-login.png"} alt="Login form" />
+
+    ## Create a new Atlas project
+
+    Once you're in Atlas, open the dropdown on the left-top corner of the page and select **New Project**.
+
+    <Screenshot url="https://cloud.mongodb.com" src={"img/screenshots/20-mongodb-atlas/1-create-account/4-new-project.png"} alt="Create new project" />
+
+    Enter a name for your project and click **Next**.
+
+    <Screenshot url="https://cloud.mongodb.com" src={"img/screenshots/20-mongodb-atlas/1-create-account/5-new-project-name.png"} alt="Create new project" />
+
+    On the next page, you can leave the current user as the project owner, and click **Create Project**.
+
+    Once the project is ready, continue the workshop from <a href="./create-cluster">**deploying a Database Cluster**</a>
+</details>
 
 ## Sign up for MongoDB Atlas
 

docs/20-mongodb-atlas/3-get-connection-string.mdx

@@ -15,10 +15,6 @@ A modal will display several ways to connect to your database. Select **Drivers*
 
 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" />
 
 Click the copy button next to your connection string to copy it to your clipboard. Paste the connection string somewhere safe.

docs/30-fireworks-ai/1-create-account.mdx

@@ -2,14 +2,18 @@ import Screenshot from "@site/src/components/Screenshot";
 
 # 👐 Create an account
 
-In this lab, we will use the _Llama 3 8B Instruct_ model hosted by Fireworks AI. Fireworks gives you $1 credit upon sign up which is plenty to experiment with industry-leading models on their platform.
+In this lab, we will use the _Llama 3 8B Instruct_ model hosted by Fireworks AI.
 
-The easiest way to use the model is via the Fireworks API. But first, you will need to create a Fireworks account. **If you already have an account, you can skip the following steps and move on to the next section.**
+The easiest way to use this model is via the Fireworks API. But first, you will need to create a Fireworks account.
+
+:::tip
+If you already have an account, you can skip the following steps and continue the workshop from <a href="./create-api-key">**create an API key**</a>
+:::
 
 Start by navigating to the [Fireworks AI homepage](https://fireworks.ai/) and click the purple **Get Started Free** button to create an account.
 
 <Screenshot url="https://fireworks.ai/" src="img/screenshots/30-fireworks-ai/1-create-account/1-homepage.png" alt="Get started" />
 
-Click **Login With Google** and authenticate with your Google account. This will provision a new Fireworks account for you.
+Click **Login With Google** and authenticate with your Google account.
 
 <Screenshot url="https://fireworks.ai/" src="img/screenshots/30-fireworks-ai/1-create-account/2-google-login.png" alt="Login" />

docs/30-fireworks-ai/2-create-api-key.mdx

@@ -2,14 +2,12 @@ import Screenshot from "@site/src/components/Screenshot";
 
 # 👐 Create an API key
 
-To use the Fireworks API, you will need an API Key. To create a secure API key, follow the steps below.
+Once logged in, navigate to **API keys** under **User Settings**.
 
-Upon logging in, click the **User** tab.
+<Screenshot url="https://fireworks.ai/" src="img/screenshots/30-fireworks-ai/2-create-api-key/1-api-keys.png" alt="Select API keys" />
 
-<Screenshot url="https://fireworks.ai/" src="img/screenshots/30-fireworks-ai/2-create-api-key/1-api-key.png" alt="Select API keys" />
-
-Click the purple **Create API Key** button. In the modal that appears, enter a name and click **Create Key** to create an API key.
+Click the **Create API Key** button to create a Secure API Key.
 
 <Screenshot url="https://fireworks.ai/" src="img/screenshots/30-fireworks-ai/2-create-api-key/2-create-api-key.png" alt="Create API key" />
 
-When prompted, copy the API key to a safe location.
+In the modal that appears, follow the steps to create an API key and when prompted, copy and paste it somewhere safe.

docs/50-prepare-the-data/1-jupyter-notebooks.mdx

@@ -0,0 +1,31 @@
+# 📘 Running Jupyter Notebooks in Google Colab
+
+Jupyter Notebooks is 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.
+
+In this lab, we will use Colaboratory (a.k.a Colab) which is a hosted Jupyter Notebook service from Google.
+
+To run a cell in Colab, hover over it and click the Run icon that appears against the cell.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-prepare-the-data/1-jupyter-notebooks/1-run-cell.png" alt="Run a cell" />
+
+When a cell is running, you will see a loading spinner appear against the cell. 
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-prepare-the-data/1-jupyter-notebooks/2-running-cell.png" alt="A running cell" />
+
+When a cell is finished running successfully, you will see green check mark appear against the cell.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-prepare-the-data/1-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-prepare-the-data/1-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 that you see against the cell while it is running.
+
+<Screenshot url="https://us-west-2.console.aws.amazon.com/sagemaker/jupyter" src="img/screenshots/50-prepare-the-data/1-jupyter-notebooks/5-interrupt-cell.png" alt="Interrupt cell run" />
+
+:::warning
+The UI might differ slightly if you are running Jupyter Notebooks outside of Google Colab. Refer to the appropriate documentation if running the notebook using a different service.    
+:::

docs/50-prepare-the-data/4-ingest-data.mdx → docs/50-prepare-the-data/5-ingest-data.mdx

@@ -28,4 +28,12 @@ mongodb_client[DB_NAME][COLLECTION_NAME]
 collection.insert_many(embedded_docs)
 ```
 </div>
-</details>
+</details>
+
+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 your cluster and click **Browse collections**.
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/50-prepare-the-data/5-ingest-data/1-browse-collections.png" alt="Browse collections" />
+
+Ensure that you see a database called _mongodb_rag_lab_, and a collection named _knowledge_base_ under it. Note the number and format of documents in the collection.
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/50-prepare-the-data/5-ingest-data/2-verify-collection.png" alt="Verify collection" />

docs/60-perform-semantic-search/2-create-vector-index.mdx

@@ -15,4 +15,12 @@ The answers for code blocks in this section are as follows:
 collection.create_search_index(model=model)
 ```
 </div>
-</details>
+</details>
+
+To verify that the index was created, navigate to **Search Indexes** for the _knowledge_base_ collection.
+
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/60-perform-semantic-search/2-create-vector-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/60-perform-semantic-search/2-create-vector-index/2-index-ready.png" alt="Index ready to use" />

docusaurus.config.js

@@ -97,10 +97,6 @@ const config = {
   favicon: `${favicon}`,
   deploymentBranch: "gh-pages",
   staticDirectories: ["static"],
-  i18n: {
-    defaultLocale: "en",
-    locales: ["en"],
-  },
   customFields: {
     startButtonTitle: `${startButtonTitle}`,
     featureList: featureList,