cleanup-extra-files
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Mendon Kissling
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import ThemedImage from "@theme/ThemedImage";
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import useBaseUrl from "@docusaurus/useBaseUrl";
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import ZoomableImage from "/src/theme/ZoomableImage.js";
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import Admonition from "@theme/Admonition";
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# 🌟 RAG with Astra DB
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This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **Astra DB** and **Langflow**.
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[Astra DB](https://www.datastax.com/products/datastax-astra?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=astradb) is a cloud-native database built on Apache Cassandra that is optimized for the cloud. It is a fully managed database-as-a-service that simplifies operations and reduces costs. Astra DB is built on the same technology that powers the largest Cassandra deployments in the world.
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In this guide, we will use Astra DB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses.
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<Admonition type="tip">
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This guide assumes that you have Langflow up and running. If you are new to
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Langflow, you can check out the [Getting Started](/) guide.
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</Admonition>
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TLDR;
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- [Create a free Astra DB account](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account)
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- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
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- Create a new database, get a **Token** and the **API Endpoint**
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- Click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components
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- Import the project into Langflow by dropping it on the Canvas or My Collection page
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- Update the **Token** and **API Endpoint** in the **Astra DB** components
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- Update the OpenAI API key in the **OpenAI** components
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- Run the ingestion flow which is the one that uses the **Astra DB** component
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- Click on the ⚡ _Run_ button and start interacting with your RAG application
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# First things first
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## Create an Astra DB Database
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To get started, you will need to [create an Astra DB database](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-an-astradb-database).
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Once you have created an account, you will be taken to the Astra DB dashboard. Click on the **Create Database** button.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-create-database.png",
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dark: "img/astra-create-database.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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Now you will need to configure your database. Choose the **Serverless (Vector)** deployment type, and pick a Database name, provider and region.
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After you have configured your database, click on the **Create Database** button.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-configure-deployment.png",
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dark: "img/astra-configure-deployment.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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Once your database is initialized, to the right of the page, you will see the _Database Details_ section which contains a button for you to copy the **API Endpoint** and another to generate a **Token**.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-generate-token.png",
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dark: "img/astra-generate-token.png",
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}}
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style={{ width: "50%", margin: "20px auto" }}
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/>
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Now we are all set to start building our RAG application using Astra DB and Langflow.
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## (Optional) Duplicate the Langflow 1.0 HuggingFace Space
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If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you.
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## Open the Vector Store RAG Project
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To get started, click on the **New Project** button and look for the **Vector Store RAG** project. This will open a starter project with the necessary components to run a RAG application using Astra DB.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/drag-and-drop-flow.png",
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dark: "img/drag-and-drop-flow.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the Astra DB database.
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Your first step should be to understand what each flow does and how they interact with each other.
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The ingestion flow consists of:
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- **Files** component that uploads a text file to Langflow
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- **Recursive Character Text Splitter** component that splits the text into smaller chunks
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- **OpenAIEmbeddings** component that generates embeddings for the text chunks
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- **Astra DB** component that stores the text chunks in the Astra DB database
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-ingestion-flow.png",
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dark: "img/astra-ingestion-flow.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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Now, let's update the **Astra DB** and **Astra DB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-ingestion-fields.png",
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dark: "img/astra-ingestion-fields.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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And run it! This will ingest the Text data from your file into the Astra DB database.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-ingestion-run.png",
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dark: "img/astra-ingestion-run.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries. It will define all of the steps from getting the User's input to generating a response and displaying it in the Interaction Panel.
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The RAG flow is a bit more complex. It consists of:
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- **Chat Input** component that defines where to put the user input coming from the Interaction Panel
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- **OpenAI Embeddings** component that generates embeddings from the user input
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- **Astra DB Search** component that retrieves the most relevant Records from the Astra DB database
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- **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Interaction Panel
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- One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Interaction Panel
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- **Prompt** component that takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model
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- **OpenAI** component that generates a response to the prompt
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- **Chat Output** component that displays the response in the Interaction Panel
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-rag-flow.png",
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dark: "img/astra-rag-flow.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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To run it all we have to do is click on the ⚡ _Run_ button and start interacting with your RAG application.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-rag-flow-run.png",
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dark: "img/astra-rag-flow-run.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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This opens the Interaction Panel where you can chat your data.
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Because this flow has a **Chat Input** and a **Text Output** component, the Panel displays a chat input at the bottom and the Extracted Chunks section on the left.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-rag-flow-interaction-panel.png",
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dark: "img/astra-rag-flow-interaction-panel.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved records.
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: "img/astra-rag-flow-interaction-panel-interaction.png",
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dark: "img/astra-rag-flow-interaction-panel-interaction.png",
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}}
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style={{ width: "80%", margin: "20px auto" }}
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/>
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And that's it! You have successfully ran a RAG application using Astra DB and Langflow.
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# Conclusion
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In this guide, we have learned how to run a RAG application using Astra DB and Langflow.
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We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows.
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