diff --git a/docs/20-mongodb-atlas/1-create-account.mdx b/docs/20-mongodb-atlas/1-create-account.mdx index dc54af8..eafa5a8 100644 --- a/docs/20-mongodb-atlas/1-create-account.mdx +++ b/docs/20-mongodb-atlas/1-create-account.mdx @@ -5,7 +5,29 @@ import Screenshot from "@site/src/components/Screenshot"; In this lab, you will learn how to use MongoDB Atlas as a knowledge base as well as a memory provider for RAG applications. -To use MongoDB Atlas, you will need to start by creating an account. **If you already have an account, you can skip the following steps and move on to the next section.** +To use MongoDB Atlas, you will need to start by creating an account. + +
+ đŸŠč If you already have an Atlas account, you can use it by creating a new project + + First, log into MongoDB Atlas using your existing credentials. + + + + ## Create a new Atlas project + + Once you're in Atlas, open the dropdown on the left-top corner of the page and select **New Project**. + + + + Enter a name for your project and click **Next**. + + + + On the next page, you can leave the current user as the project owner, and click **Create Project**. + + Once the project is ready, continue the workshop from **deploying a Database Cluster** +
## Sign up for MongoDB Atlas diff --git a/docs/30-fireworks-ai/1-create-account.mdx b/docs/30-fireworks-ai/1-create-account.mdx index 8b0a875..95ec347 100644 --- a/docs/30-fireworks-ai/1-create-account.mdx +++ b/docs/30-fireworks-ai/1-create-account.mdx @@ -2,14 +2,16 @@ import Screenshot from "@site/src/components/Screenshot"; # 👐 Create an account -In this lab, we will use the _Llama 3 8B Instruct_ model hosted by Fireworks AI. Fireworks gives you $1 credit upon sign up which is plenty to experiment with industry-leading models on their platform. +In this lab, we will use the _Llama 3 8B Instruct_ model hosted by Fireworks AI. -The easiest way to use the model is via the Fireworks API. But first, you will need to create a Fireworks account. **If you already have an account, you can skip the following steps and move on to the next section.** +The easiest way to use this model is via the Fireworks API. But first, you will need to create a Fireworks account. + +If you already have an account, you can skip the following steps and continue the workshop from **create an API key** Start by navigating to the [Fireworks AI homepage](https://fireworks.ai/) and click the purple **Get Started Free** button to create an account. -Click **Login With Google** and authenticate with your Google account. This will provision a new Fireworks account for you. +Click **Login With Google** and authenticate with your Google account. \ No newline at end of file diff --git a/docs/30-fireworks-ai/2-create-api-key.mdx b/docs/30-fireworks-ai/2-create-api-key.mdx index 8922633..a3c91c6 100644 --- a/docs/30-fireworks-ai/2-create-api-key.mdx +++ b/docs/30-fireworks-ai/2-create-api-key.mdx @@ -2,14 +2,12 @@ import Screenshot from "@site/src/components/Screenshot"; # 👐 Create an API key -To use the Fireworks API, you will need an API Key. To create a secure API key, follow the steps below. +Once logged in, navigate to **API keys** under **User Settings**. -Upon logging in, click the **User** tab. + - - -Click the purple **Create API Key** button. In the modal that appears, enter a name and click **Create Key** to create an API key. +Click the **Create API Key** button to create a Secure API Key. -When prompted, copy the API key to a safe location. \ No newline at end of file +In the modal that appears, follow the steps to create an API key and when prompted, copy and paste it somewhere safe. \ No newline at end of file diff --git a/docs/50-prepare-the-data/1-jupyter-notebooks.mdx b/docs/50-prepare-the-data/1-jupyter-notebooks.mdx new file mode 100644 index 0000000..fc7c527 --- /dev/null +++ b/docs/50-prepare-the-data/1-jupyter-notebooks.mdx @@ -0,0 +1,31 @@ +# 📘 Running Jupyter Notebooks in Google Colab + +Jupyter Notebooks is 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. + +In this lab, we will use Colaboratory (a.k.a Colab) which is a hosted Jupyter Notebook service from Google. + +To run a cell in Colab, hover over it and click the Run icon that appears against the cell. + + + +When a cell is running, you will see a loading spinner appear against the cell. + + + +When a cell is finished running successfully, you will see green check mark appear against 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 that you see against the cell while it is running. + + + +:::warning +The UI might differ slightly if you are running Jupyter Notebooks outside of Google Colab. Refer to the appropriate documentation if running the notebook using a different service. +::: \ No newline at end of file diff --git a/docs/50-prepare-the-data/1-load-data.mdx b/docs/50-prepare-the-data/2-load-data.mdx similarity index 100% rename from docs/50-prepare-the-data/1-load-data.mdx rename to docs/50-prepare-the-data/2-load-data.mdx diff --git a/docs/50-prepare-the-data/2-chunk-data.mdx b/docs/50-prepare-the-data/3-chunk-data.mdx similarity index 100% rename from docs/50-prepare-the-data/2-chunk-data.mdx rename to docs/50-prepare-the-data/3-chunk-data.mdx diff --git a/docs/50-prepare-the-data/3-embed-data.mdx b/docs/50-prepare-the-data/4-embed-data.mdx similarity index 100% rename from docs/50-prepare-the-data/3-embed-data.mdx rename to docs/50-prepare-the-data/4-embed-data.mdx diff --git a/docs/50-prepare-the-data/4-ingest-data.mdx b/docs/50-prepare-the-data/4-ingest-data.mdx deleted file mode 100644 index 235a98d..0000000 --- a/docs/50-prepare-the-data/4-ingest-data.mdx +++ /dev/null @@ -1,31 +0,0 @@ -import Screenshot from "@site/src/components/Screenshot"; - -# 👐 Ingest data into MongoDB - -The final step to build a MongoDB vector store for our RAG application is to ingest the embedded article chunks into MongoDB. - -Fill in any `` placeholders and run the cells under the **Step 6: Ingest data into MongoDB** section in the notebook to ingest the embedded documents into MongoDB. - -The answers for code blocks in this section are as follows: - -**CODE_BLOCK_6** - -
-Answer -
-```python -mongodb_client[DB_NAME][COLLECTION_NAME] -``` -
-
- -**CODE_BLOCK_7** - -
-Answer -
-```python -collection.insert_many(embedded_docs) -``` -
-
\ No newline at end of file diff --git a/docs/50-prepare-the-data/5-ingest-data.mdx b/docs/50-prepare-the-data/5-ingest-data.mdx new file mode 100644 index 0000000..656476f --- /dev/null +++ b/docs/50-prepare-the-data/5-ingest-data.mdx @@ -0,0 +1,41 @@ +import Screenshot from "@site/src/components/Screenshot"; + +# 👐 Ingest data into MongoDB + +The final step to build a MongoDB vector store for our RAG application is to ingest the embedded article chunks into MongoDB. + +Fill in any `` placeholders and run the cells under the **Step 6: Ingest data into MongoDB** section in the notebook to ingest the embedded documents into MongoDB. + +The answers for code blocks in this section are as follows: + +**CODE_BLOCK_6** + +
+Answer +
+```python +mongodb_client[DB_NAME][COLLECTION_NAME] +``` +
+
+ +**CODE_BLOCK_7** + +
+Answer +
+```python +collection.insert_many(embedded_docs) +``` +
+
+ +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**. + + + +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/60-perform-semantic-search/2-create-vector-index.mdx b/docs/60-perform-semantic-search/2-create-vector-index.mdx index 6c013b5..56a1392 100644 --- a/docs/60-perform-semantic-search/2-create-vector-index.mdx +++ b/docs/60-perform-semantic-search/2-create-vector-index.mdx @@ -15,4 +15,12 @@ The answers for code blocks in this section are as follows: collection.create_search_index(model=model) ``` - \ No newline at end of file + + +To verify that the index was created, navigate to **Search Indexes** for the _knowledge_base_ collection. + + + +The index is ready to use once the status changes from **PENDING** to **READY**. + + \ No newline at end of file diff --git a/static/img/screenshots/20-mongodb-atlas/1-create-account/3-login.png b/static/img/screenshots/20-mongodb-atlas/1-create-account/3-login.png new file mode 100644 index 0000000..b6e678b Binary files /dev/null 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