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docs/40-import-data/1-import-data.mdx

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Let's first import a dataset to use for this lab. We will use a collection of books.
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Run the cells under the **Step 2: Import data** section in the notebook to import the dataset for this lab into a MongoDB collection.
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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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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**.
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docs/50-perform-vector-search/1-generate-embeddings.mdx

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In this lab, you will see how to enable search using text as well as images. We will use [CLIP](https://huggingface.co/sentence-transformers/clip-ViT-B-32), a multimodal embedding model that can handle both images and text.
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Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 3: Generating Embeddings** section in the notebook to see how to embed text and images using the CLIP model.
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Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 3: Generating embeddings** section in the notebook to see how to embed text and images using the CLIP model.
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The answers for code blocks in this section are as follows:
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docs/50-perform-vector-search/2-add-embeddings.mdx

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This is a common scenario you will run into where you might want to update your existing data in MongoDB with embeddings.
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Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 4: Adding Embeddings to Existing Data in Atlas** section in the notebook to add embeddings of the books' cover images to the documents in the `books` collection.
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Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 4: Adding embeddings to existing data in Atlas** section in the notebook to add embeddings of the books' cover images to the documents in the `books` collection.
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The answers for code blocks in this section are as follows:
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docs/70-other-search-techniques/3-hybrid-search.mdx

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You can combine Atlas Vector Search and Atlas Search queries into a hybrid search for unified results. This approach leverages the strengths of both full-text search and vector search to deliver more relevant results.
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Run the cells under **🦹‍♀️ Hybrid Search** section in the notebook to try out hybrid search in MongoDB Atlas.
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Run the cells under **🦹‍♀️ Hybrid search** section in the notebook to try out hybrid search in MongoDB Atlas.
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Refer to our [documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/tutorials/reciprocal-rank-fusion/) to learn more about this technique.

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