diff --git a/docs/30-aws/3-sagemaker-studio.mdx b/docs/30-aws/3-sagemaker-studio.mdx index 671ad3f..07e8866 100644 --- a/docs/30-aws/3-sagemaker-studio.mdx +++ b/docs/30-aws/3-sagemaker-studio.mdx @@ -24,7 +24,8 @@ In the Sagemaker Studio console, select the **Git** dropdown from the menu bar a -In the modal that appears, paste this URL in the **Git repository URL** box, uncheck **Open README files.**, and click Clone: https://github.com/mongodb-developer/mdb-aws-agents-lab-notebooks/ +In the modal that appears, paste the following URL in the **Git repository URL** box, click the **Clone https://...** message from the drop-down, and click the **Clone** button: +https://github.com/mongodb-developer/mdb-aws-agents-lab-notebooks.git @@ -32,4 +33,8 @@ Once the repo is cloned, you will see the content of the repo in the sidebar. ** +When prompted to select the notebook environment, proceed wih the defaults by clicking the **Select** button. + + + That's it! You are ready to start building your AI agent! \ No newline at end of file diff --git a/docs/50-agent-tools/2-import-data.mdx b/docs/50-agent-tools/2-import-data.mdx deleted file mode 100644 index ff05e96..0000000 --- a/docs/50-agent-tools/2-import-data.mdx +++ /dev/null @@ -1,7 +0,0 @@ -# 👐 Import data - -The MongoDB learning assistant has two tools- a vector search tool to retrieve information to answer questions about MongoDB, and another tool to get the content of articles in our Developer Center for summarization. - -Let's import the data required by these tools into two MongoDB collections. This is as simple as making a `GET` request to a serverless function that we have created for you. - -Run the cells under the **Step 3: Import data** section in the notebook to import the data required by our agent's tools, into MongoDB collections. \ No newline at end of file diff --git a/docs/50-agent-tools/2-jupyter_notebooks.mdx b/docs/50-agent-tools/2-jupyter_notebooks.mdx new file mode 100644 index 0000000..b4563e9 --- /dev/null +++ b/docs/50-agent-tools/2-jupyter_notebooks.mdx @@ -0,0 +1,25 @@ +# 📘 Running code in Jupyter Notebooks + +Jupyter Notebooks are 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. + +To run a cell, highlight it and click the Run icon in the toolbar at the top. + + + +When a cell is running, you will see a * in the square brackets ([ ]) against the cell. + + + +When a cell is finished running successfully, you will see a number appear in the [ ] with no error traceback after the cell. + + + +If an error occurred while running a cell, you will see an error traceback after the cell. + + + +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 in the toolbar at the top. + + \ No newline at end of file diff --git a/docs/20-mongodb-atlas/3-get-connection-string.mdx b/docs/50-agent-tools/3-get-connection-string.mdx similarity index 78% rename from docs/20-mongodb-atlas/3-get-connection-string.mdx rename to docs/50-agent-tools/3-get-connection-string.mdx index dcfafca..ef4373a 100644 --- a/docs/20-mongodb-atlas/3-get-connection-string.mdx +++ b/docs/50-agent-tools/3-get-connection-string.mdx @@ -9,20 +9,14 @@ In the Atlas UI, navigate to the **Overview** page. In the **Clusters section**, -A modal will display several ways to connect to your database. +A modal will display several ways to connect to your database. Select **Drivers**. -Select **Compass**. While we won't be using Compass to import the data, it's an easy way to see your connection string. +Look for your connection string. It should look something like `mongodb+srv://:@/` -Look for your connection string. It should look something like: - -``` -mongodb+srv://:@/ -``` - Click the copy button next to your connection string to copy it to your clipboard. Paste the connection string somewhere safe. :::tip diff --git a/docs/50-agent-tools/4-import-data.mdx b/docs/50-agent-tools/4-import-data.mdx new file mode 100644 index 0000000..b729012 --- /dev/null +++ b/docs/50-agent-tools/4-import-data.mdx @@ -0,0 +1,20 @@ +import Link from "@site/src/components/Link"; +import Screenshot from "@site/src/components/Screenshot"; + +# 👐 Import data + +The MongoDB learning assistant has two tools- a vector search tool to retrieve information to answer questions about MongoDB, and another tool to get the content of articles in our Developer Center for summarization. + +Let's import the data required by these tools into two MongoDB collections. This is as simple as making a `GET` request to a serverless AWS Lambda function that we have created for you. + +Run the cells under the **Step 3: Import data** section in the notebook to import the data required by our agent's tools, into MongoDB collections. + +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 the cluster you just created and click **Browse collections**. + + + +Ensure that you see a database called _mongodb_agents_lab_, and two collections namely _chunked_articles_ and _full_articles_ under it. Note the number and format of documents in both the collections. + + + + \ No newline at end of file diff --git a/docs/50-agent-tools/3-create-vector-search-index.mdx b/docs/50-agent-tools/5-create-vector-search-index.mdx similarity index 51% rename from docs/50-agent-tools/3-create-vector-search-index.mdx rename to docs/50-agent-tools/5-create-vector-search-index.mdx index bb98cff..5f1c7ca 100644 --- a/docs/50-agent-tools/3-create-vector-search-index.mdx +++ b/docs/50-agent-tools/5-create-vector-search-index.mdx @@ -1,3 +1,6 @@ +import Link from "@site/src/components/Link"; +import Screenshot from "@site/src/components/Screenshot"; + # 👐 Create a vector search index To retrieve documents using vector search, you must configure a vector search index on the collection you want to perform vector search against. @@ -26,4 +29,12 @@ mongodb_client[DB_NAME][VS_COLLECTION_NAME] vs_collection.create_search_index(model=model) ``` - \ No newline at end of file + + +To verify that the index was 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