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Making MongoDB viz elements non-optional
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‎docs/20-dev-env/2-dev-env-setup.mdx

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## Option 1: GitHub Codespaces
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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.
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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.
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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.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/1-create-codespace.png" alt="Start a codespace" />
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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 under the Explorer tab.
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Let it run for a few seconds as it prepares a Docker container with all the required libraries and a MongoDB cluster.
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In the Explorer menu, under `notebooks`, click on the file named `vector-search-lab.ipynb` to open the Jupyter Notebook for this lab.
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### Connect to the MongoDB cluster
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<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" />
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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.
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Next, select the Python interpreter by clicking **Select Kernel** at the top right of the IDE.
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To connect to the cluster, click the **Connect** button.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/3-select-kernel.png" alt="Select kernel" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/2-click-connect.png" alt="Click Connect" />
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In the modal that appears, click **Python environments...** and select the interpreter that is marked as **Recommended** or **Global Env**.
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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.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/4-python-env-modal.png" alt="Select Python Environments" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/3-enter-connection-string.png" alt="Enter connection string" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/5-select-recommended.png" alt="Select recommended interpreter" />
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If the connection was successful, you should see **Connected to: mongodb:27017** with a green indicator.
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That's it! You're ready for the lab!
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/4-connection-successful.png" alt="Connection successful" />
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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.
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<details>
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<summary>🦹 Use the MongoDB for VS Code extension to interact with the local MongoDB cluster from the VSCode IDE. </summary>
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/5-view-collections.png" alt="View collections" />
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In the left navigation bar of the IDE, click on the leaf icon. Under Connections, click the **Add Connection** button.
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### Jupyter Notebook setup
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/6-add-connection.png" alt="Add MongoDB connection" />
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You will be filling code in a Jupyter Notebook during this lab, so let's get set up with that next!
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In the tab that appears, click the **Connect** button.
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Click on the files icon in the left navigation bar. In the Explorer menu, under `notebooks`, click on the file named `vector-search-lab.ipynb` to open the Jupyter Notebook for this lab.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/7-click-connect.png" alt="Click Connect" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/6-nav-notebook.png" alt="Navigate to the notebook" />
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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.
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Next, select the Python interpreter by clicking **Select Kernel** at the top right of the IDE.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/7-select-kernel.png" alt="Select kernel" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/8-enter-connection-string.png" alt="Enter connection string" />
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In the modal that appears, click **Python environments...** and select the interpreter that is marked as **Recommended** or **Global Env**.
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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.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/8-python-env-modal.png" alt="Select Python Environments" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/9-connections.png" alt="View connections" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/20-dev-env/2-dev-env-setup/9-select-recommended.png" alt="Select recommended interpreter" />
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</details>
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That's it! You're ready for the lab!
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## Option 2: Run locally
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git clone https://github.com/mongodb-developer/genai-devday-notebooks.git
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```
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* `cd` into the cloned directory:
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* `cd` into the `notebooks` directory of the cloned repository:
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```
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cd genai-devday-notebooks
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cd genai-devday-notebooks/notebooks
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```
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* Create and activate a Python virtual environment:

‎docs/30-import-data/1-import-data.mdx

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Run the cells under the **Step 2: Import data into MongoDB** section in the notebook to import the dataset for this lab into a MongoDB collection.
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<details>
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<summary>🦹 Visually verify that the data has been imported into the local MongoDB cluster. </summary>
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Click the leaf icon in the left navigation bar of the IDE.
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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.
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Ensure that you see a database called _mongodb_genai_devday_vs_, and a collection named _books_ under it.
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Ensure that you see a database called _mongodb_genai_devday_vs_, and a collection named _books_ under it.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/1-verify-collection.png" alt="Verify collection" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/1-verify-collection.png" alt="Verify collection" />
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Click the **>** arrow next to _books_ and note the number of documents.
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Click the **>** arrow next to _books_ and note the number of documents.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/2-document-count.png" alt="Verify document count" />
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To preview a document, click the **>** arrow next to _Documents_ and select a document from the list.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/2-document-count.png" alt="Verify document count" />
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To preview a document, click the **>** arrow next to _Documents_ and select a document from the list.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/3-preview-document.png" alt="Preview document" />
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</details>
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/3-preview-document.png" alt="Preview document" />

‎docs/40-perform-vector-search/2-add-embeddings.mdx

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</div>
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<details>
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<summary>🦹 Visually verify that embeddings have been added to the documents in the `books` collection. </summary>
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Click on the leaf icon in the left navigation bar of the IDE.
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To visually verify that embeddings have been added to the documents in the `books` collection, click on the leaf icon in the left navigation bar of the IDE.
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Navigate to a document in the _books_ collection and ensure that it has an `embedding` field.
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Navigate to a document in the _books_ collection and ensure that it has an `embedding` field.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/40-perform-vector-search/2-add-embeddings/1-preview-document.png" alt="Preview document" />
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</details>
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/40-perform-vector-search/2-add-embeddings/1-preview-document.png" alt="Preview document" />

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

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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.
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Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 5: Create a vector search index** section in the notebook to create a vector search index on the `books` collection.
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The answers for code blocks in this section are as follows:
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<details>
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<summary>Answer</summary>
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<div>
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collection.create_search_index(model=model)
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```
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Run the cells under the **Step 5: Create a vector search index** section in the notebook to create a vector search index on the `books` collection.

‎docs/40-perform-vector-search/4-run-vector-search-queries.mdx

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The answers for code blocks in this section are as follows:
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**CODE_BLOCK_7**
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**CODE_BLOCK_6**
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‎docs/50-optimizing-vector-search/2-pre-filtering.mdx

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**CODE_BLOCK_9**
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‎docs/50-optimizing-vector-search/3-similarity-functions.mdx

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‎docs/50-optimizing-vector-search/4-vector-quantization.mdx

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‎static/img/screenshots/20-dev-env/2-dev-env-setup/10-jupyter-notebook.png

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‎static/img/screenshots/20-dev-env/2-dev-env-setup/2-click-connect.png

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