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ajosh0504
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Making data viz elements non-optional
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docs/30-import-data/1-import-data.mdx

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@@ -6,18 +6,14 @@ Let's import the data required by these tools into two MongoDB collections.
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Run the cells under the **Step 2: Import data into MongoDB** section in the notebook to import the data required by the agent's tools, into MongoDB collections.
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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 local 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_agents_, and two collections named _mongodb_docs_ and _mongodb_docs_embeddings_ under it.
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Ensure that you see a database called _mongodb_genai_devday_agents_, and two collections named _mongodb_docs_ and _mongodb_docs_embeddings_ under it.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/1-verify-collections.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-collections.png" alt="Verify collection" />
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Click the **>** arrow next to each collection and note the number of documents in it.
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Click the **>** arrow next to each collection and note the number of documents in it.
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/2-full-collection.png" alt="Verify full collection document count" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/2-full-collection.png" alt="Verify full collection document count" />
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<Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/3-embedded-collection.png" alt="Verify embedded collection document count" />
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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-embedded-collection.png" alt="Verify embedded collection document count" />

docs/40-agent-tools/2-create-vector-search-index.mdx

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To retrieve documents using vector search, you must configure a vector search index on the collection you want to perform vector search against.
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Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 3: Create a vector search index** section in the notebook to create a vector search index.
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The answers for code blocks in this section are as follows:
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**CODE_BLOCK_1**
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<details>
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<summary>Answer</summary>
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<div>
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```python
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vs_collection.create_search_index(model=model)
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```
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</div>
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</details>
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Run the cells under the **Step 3: Create a vector search index** section in the notebook to create a vector search index.

docs/40-agent-tools/3-create-agent-tools.mdx

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## Vector search tool
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**CODE_BLOCK_2**
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**CODE_BLOCK_1**
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<details>
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<summary>Answer</summary>
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</div>
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**CODE_BLOCK_2**
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## Get page content tool
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docs/50-create-agent/3-instantiate-llm.mdx

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The answers for code blocks in this section are as follows:
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**CODE_BLOCK_9**
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**CODE_BLOCK_88**
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docs/50-create-agent/4-define-graph-nodes.mdx

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**CODE_BLOCK_11**
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**CODE_BLOCK_10**
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**CODE_BLOCK_11**
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docs/50-create-agent/6-build-and-execute-graph.mdx

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**CODE_BLOCK_14**
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**CODE_BLOCK_13**
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docs/60-adding-memory/2-adding-memory.mdx

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**CODE_BLOCK_18**
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