Making data visualization non-optional

Author: ajosh0504

docs/20-dev-env/1-dev-env-setup.mdx

@@ -20,76 +20,79 @@ In this lab, we will be using Jupyter Notebooks, which is an interactive Python
 
 ## Option 1: GitHub Codespaces
 
-You will be working in a Jupyter Notebook in a GitHub Codespace throughout this lab. A codespace is a cloud-hosted, containerized development environment that comes pre-configured with all the tools you need to run this lab.
+You will be working in GitHub Codespaces throughout this lab. A codespace is a cloud-hosted, containerized development environment that comes pre-configured with all the tools you need to run this lab.
 
 Navigate to [this](https://github.com/codespaces/new/mongodb-developer/genai-devday-notebooks?quickstart=1) link. You will be prompted to sign into GitHub if you haven't already. Once signed in, click the **Create new codespace** button to create a new codespace.
 
-<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/3-create-codespace.png" alt="Start a codespace" />
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/3-create-codespace.png" alt="Start a codespace" />
 
-Let it run for a few seconds as it prepares a Docker container with all the required libraries and a MongoDB cluster. Once the environment is built, you should see a VS Code IDE with a list of files in the Explorer tab. 
+Let it run for a few seconds as it prepares a Docker container with all the required libraries and a MongoDB cluster.
 
-In the Explorer menu, under `notebooks`, click on the file named `ai-rag-lab.ipynb` to open the Jupyter Notebook for this lab.
+### Connect to the MongoDB cluster
 
-<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/2-nav-notebook.png" alt="Navigate to the notebook" />
+Once the environment is built, you will be prompted to connect to the MongoDB cluster via MongoDB's VSCode extension. This will allow you to visualize the data that we will import into the cluster later in the lab, directly from the VSCode IDE.
 
-Next, select the Python interpreter by clicking **Select Kernel** at the top right of the IDE.
+To connect to the cluster, click the **Connect** button.
 
-<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/4-select-kernel.png" alt="Select kernel" />
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/4-click-connect.png" alt="Click Connect" />
 
-In the modal that appears, click **Python environments...** and select the interpreter that is marked as **Recommended** or **Global Env**.
+In the modal that appears at the top of the screen, enter the string `mongodb://admin:mongodb@mongodb:27017/` and hit `Enter` on your keyboard.
 
-<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/5-python-env-modal.png" alt="Select Python Environments" />
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/5-enter-connection-string.png" alt="Enter connection string" />
 
-<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/6-select-recommended.png" alt="Select recommended interpreter" />
+If the connection was successful, you should see **Connected to: mongodb:27017** with a green indicator.
 
-That's it! You're ready for the lab!
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/6-connection-successful.png" alt="Connection successful" />
 
-<details>
-  <summary>🦹 Use the MongoDB for VS Code extension to interact with the local MongoDB cluster from the VSCode IDE. </summary>
+You can view the default and any additional collections we create on the cluster during the lab by clicking the leaf icon in the left navigation bar of the IDE.
+
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/7-view-collections.png" alt="View collections" />
 
-    In the left navigation bar of the IDE, click on the leaf icon. Under Connections, click the **Add Connection** button.
+### Jupyter Notebook setup
 
-    <Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/7-add-connection.png" alt="Add MongoDB connection" />
+You will be filling code in a Jupyter Notebook during this lab, so let's get set up with that next!
 
-    In the tab that appears, click the **Connect** button.
+Click on the files icon in the left navigation bar. In the Explorer menu, under `notebooks`, click on the file named `ai-rag-lab.ipynb` to open the Jupyter Notebook for this lab.
 
-    <Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/8-click-connect.png" alt="Click Connect" />
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/2-nav-notebook.png" alt="Navigate to the notebook" />
 
-    In the modal that appears at the top of the screen, enter the string `mongodb://admin:mongodb@mongodb:27017/` and hit `Enter` on your keyboard.
+Next, select the Python interpreter by clicking **Select Kernel** at the top right of the IDE.
 
-    <Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/9-enter-connection-string.png" alt="Enter connection string" />
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/8-select-kernel.png" alt="Select kernel" />
 
-    If the connection was successful, you should see the cluster and its default databases under Connections. Any additional databases and collections created during the lab will appear here as well.
+In the modal that appears, click **Python environments...** and select the interpreter that is marked as **Recommended** or **Global Env**.
 
-    <Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/1-dev-env-setup/10-connections.png" alt="View connections" />
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/9-python-env-modal.png" alt="Select Python Environments" />
 
-</details>
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/10-select-recommended.png" alt="Select recommended interpreter" />
+
+That's it! You're ready for the lab!
 
 ## Option 2: Run locally
 
 :::caution
 During the lab, we will use GitHub Codespaces. These instructions are here just in case you can't use Codespaces or if you really, really, really want a local installation.
 :::
 
-To run the lab locally, follow the steps below:
+To run the lab locally, follow the steps below: 
 
 * Clone the [GitHub repo](https://github.com/mongodb-developer/genai-devday-notebooks.git) for this lab by executing the following command from the terminal:
 
 ```
 git clone https://github.com/mongodb-developer/genai-devday-notebooks.git
 ```
 
-* `cd` into the cloned directory:
+* `cd` into the `notebooks` directory of the cloned repository:
 
 ```
-cd genai-devday-notebooks
+cd genai-devday-notebooks/notebooks
 ```
 
 * Create and activate a Python virtual environment:
 
 ```
-python -m venv mongodb-ai-rag-lab
-source mongodb-ai-rag-lab/bin/activate
+python -m venv ai-rag-lab
+source ai-rag-lab/bin/activate
 ```
 
 * Install the dependencies for this lab:
@@ -107,4 +110,4 @@ jupyter notebook
 
 * In the browser tab that pops up, open the file named `ai-rag-lab.ipynb`.
 
-<Screenshot url="localhost:8888/tree" src="img/screenshots/20-dev-env/1-dev-env-setup/11-jupyter-notebook.png" alt="Jupyter Notebook" />
+<Screenshot url="localhost:8888/tree" src="img/screenshots/20-dev-env/2-dev-env-setup/11-jupyter-notebook.png" alt="Jupyter Notebook" />

docs/20-dev-env/2-setup-pre-reqs.mdx

@@ -1,6 +1,6 @@
 # 👐 Setup prerequisites
 
-Replace any placeholders and run the cells under **Step 1: Setup prerequisites** section in the notebook.
+Run the cells under **Step 1: Setup prerequisites** section in the notebook.
 
 :::info
 

docs/30-prepare-the-data/4-ingest-data.mdx

@@ -19,20 +19,16 @@ collection.insert_many(embedded_docs)
 </div>
 </details>
 
-<details>
-  <summary>🦹 Visually verify that the data has been imported into the local MongoDB cluster. </summary>
-
-    Click the leaf icon in the left navigation bar of the IDE.
+To visually verify that the data has been imported into the MongoDB cluster, click the leaf icon in the left navigation bar of the IDE.
 
-    Ensure that you see a database called _mongodb_genai_devday_rag_, and a collection named _knowledge_base_ under it. 
+Ensure that you see a database called _mongodb_genai_devday_rag_, and a collection named _knowledge_base_ under it. 
 
-    <Screenshot url="https://github.com/codespaces" src="img/screenshots/30-prepare-the-data/4-ingest-data/1-verify-collection.png" alt="Verify collection" />
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-prepare-the-data/4-ingest-data/1-verify-collection.png" alt="Verify collection" />
 
-    Click the **>** arrow next to _knowledge_base_ and note the number of documents.
+Click the **>** arrow next to _knowledge_base_ and note the number of documents.
 
-    <Screenshot url="https://github.com/codespaces" src="img/screenshots/30-prepare-the-data/4-ingest-data/2-document-count.png" alt="Verify document count" />
-    
-    To preview a document, click the **>** arrow next to _Documents_ and select a document from the list.
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-prepare-the-data/4-ingest-data/2-document-count.png" alt="Verify document count" />
+
+To preview a document, click the **>** arrow next to _Documents_ and select a document from the list.
 
-    <Screenshot url="https://github.com/codespaces" src="img/screenshots/30-prepare-the-data/4-ingest-data/3-preview-document.png" alt="Preview document" />
-</details>
+<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-prepare-the-data/4-ingest-data/3-preview-document.png" alt="Preview document" />

docs/40-perform-vector-search/2-create-vector-index.mdx

@@ -2,17 +2,4 @@
 
 To retrieve documents from MongoDB using vector search, you must configure a vector search index on the collection into which you ingested your data. In this lab, you will programmatically create vector search indexes using MongoDB's Python driver.
 
-Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 6: Create a vector search index** section in the notebook to create a vector search index.
-
-The answers for code blocks in this section are as follows:
-
-**CODE_BLOCK_6**
-
-<details>
-<summary>Answer</summary>
-<div>
-```python
-collection.create_search_index(model=model)
-```
-</div>
-</details>
+Run the cells under the **Step 6: Create a vector search index** section in the notebook to create a vector search index.

docs/40-perform-vector-search/3-vector-search.mdx

@@ -6,7 +6,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 7:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_7**
+**CODE_BLOCK_6**
 
 <details>
 <summary>Answer</summary>
@@ -17,7 +17,7 @@ get_embedding(user_query)
 </div>
 </details>
 
-**CODE_BLOCK_8**
+**CODE_BLOCK_7**
 
 <details>
 <summary>Answer</summary>
@@ -45,7 +45,7 @@ get_embedding(user_query)
 </div>
 </details>
 
-**CODE_BLOCK_9**
+**CODE_BLOCK_8**
 
 <details>
 <summary>Answer</summary>

docs/40-perform-vector-search/4-pre-filtering.mdx

@@ -10,7 +10,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **🦹‍
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_10**
+**CODE_BLOCK_9**
 
 <details>
 <summary>Answer</summary>
@@ -35,7 +35,7 @@ The answers for code blocks in this section are as follows:
 </div>
 </details>
 
-**CODE_BLOCK_11**
+**CODE_BLOCK_10**
 
 <details>
 <summary>Answer</summary>
@@ -64,7 +64,7 @@ The answers for code blocks in this section are as follows:
 </div>
 </details>
 
-**CODE_BLOCK_12**
+**CODE_BLOCK_11**
 
 <details>
 <summary>Answer</summary>
@@ -90,7 +90,7 @@ The answers for code blocks in this section are as follows:
 </div>
 </details>
 
-**CODE_BLOCK_13**
+**CODE_BLOCK_12**
 
 <details>
 <summary>Answer</summary>

docs/50-build-rag-app/1-build-rag-app.mdx

@@ -6,7 +6,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 8:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_14**
+**CODE_BLOCK_13**
 
 <details>
 <summary>Answer</summary>
@@ -17,7 +17,7 @@ vector_search(user_query)
 </div>
 </details>
 
-**CODE_BLOCK_15**
+**CODE_BLOCK_14**
 
 <details>
 <summary>Answer</summary>
@@ -28,7 +28,7 @@ create_prompt(user_query)
 </div>
 </details>
 
-**CODE_BLOCK_16**
+**CODE_BLOCK_15**
 
 <details>
 <summary>Answer</summary>

docs/50-build-rag-app/2-add-reranking.mdx

@@ -6,7 +6,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **🦹‍
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_17**
+**CODE_BLOCK_16**
 
 <details>
 <summary>Answer</summary>

docs/60-add-memory/1-add-memory.mdx

@@ -6,7 +6,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 9:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_18**
+**CODE_BLOCK_17**
 
 <details>
 <summary>Answer</summary>
@@ -17,7 +17,7 @@ history_collection.create_index("session_id")
 </div>
 </details>
 
-**CODE_BLOCK_19**
+**CODE_BLOCK_18**
 
 <details>
 <summary>Answer</summary>
@@ -28,7 +28,7 @@ history_collection.insert_one(message)
 </div>
 </details>
 
-**CODE_BLOCK_20**
+**CODE_BLOCK_19**
 
 <details>
 <summary>Answer</summary>
@@ -39,7 +39,7 @@ history_collection.find({"session_id": session_id}).sort("timestamp", 1)
 </div>
 </details>
 
-**CODE_BLOCK_21**
+**CODE_BLOCK_20**
 
 <details>
 <summary>Answer</summary>
@@ -50,7 +50,7 @@ retrieve_session_history(session_id)
 </div>
 </details>
 
-**CODE_BLOCK_22**
+**CODE_BLOCK_21**
 
 <details>
 <summary>Answer</summary>
@@ -61,7 +61,7 @@ retrieve_session_history(session_id)
 </div>
 </details>
 
-**CODE_BLOCK_23**
+**CODE_BLOCK_22**
 
 <details>
 <summary>Answer</summary>