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+# How to use LangFlow to ingest data into Astra DB to be used as the knowledge source of Agent Knowledge 
+This document explains how to use LangFlow to ingest data into Astra DB, to be used as the knowledge source of Agent Knowledge.
+
+## Before you begin
+1. Sign up for Astra DB
+    * To sign up for Astra DB, see [Sign up for Astra DB](https://astra.datastax.com/)  
+2. Get access to LangFlow
+    * To install LangFlow Desktop, see [Install LangFlow Desktop](https://www.langflow.org/desktop)
+    * To sign up for managed LangFlow, see [Sign up for managed LangFlow](https://astra.datastax.com/langflow)
+
+## Table of contents
+* [Step 1: Prepare Astra DB and collect the connection information](#step-1-prepare-astra-db-and-collect-the-connection-information)
+  * [Prepare Astra DB](#prepare-astra-db)
+  * [Collect the connection information](#collect-the-connection-information)
+* [Step 2: Use LangFlow to ingest data into Astra DB](#step-2-use-langflow-to-ingest-data-into-astra-db)
+  * [Add the Astra DB component](#add-the-astra-db-component)
+  * [Add other components](#add-other-components)
+  * [Connect the components and run the ingestion](#connect-the-components-and-run-the-ingestion)
+* [Step 3: Connect to Agent Knowledge in watsonx Orchestrate](#step-3-connect-to-agent-knowledge-in-watsonx-orchestrate)
+
+## Step 1: Prepare Astra DB and collect the connection information
+### Prepare Astra DB
+1. Login to Astra DB
+2. Create a new database, or select an existing database
+3. Go to `Data Explorer` tab > `Collections and Tables` drop down, select `Create collection`
+4. Enter `Collection name`, toggle on `Vector-enabled collection`, select `Embedding generation method`, `Embedding model`, `Dimension`, `Similarity metric`, click `Create collection`
+<img src="./assets/create-astra-db-collection.png" width="1080" height="574" />
+
+### Collect the connection information
+#### Token
+1. Login to Astra DB
+2. On the upper right of the portal, click `Settings` > `Tokens`
+3. Choose `Role`, `Description`, `Expiration`, and click on `Generate token`
+4. Take a note of the generated token 
+
+#### Database name and collection name
+Take a note of the database name and the collection name as used in the `Prepare Astra DB` section.
+
+## Step 2: Use LangFlow to ingest data into Astra DB
+You can ingest data into Milvus vector database either through watsonx.ai or by using custom code.
+### Add the `Astra DB` component
+1. Launch LangFlow and create a project
+2. In `Components` > `Vector Stores`, drag and drop `Astra DB` to the canvas
+3. In the `Astra DB` component, fill in `Astra DB Application Token`
+4. Select `Database` and `Collection` as used in `Step 1`
+
+### Add other components
+1. In `Components` > `Data`, drag and drop `File` to the canvas, and choose the file(s) to upload
+2. In `Components` > `Processing`, drag and drop `Split text` to the canvas, and enter `Chunk Overlap` and `Chunk Size`
+3. In `Components` > `Outputs`, drag and drop `Chat Output` to the canvas
+
+### Connect the components and run the ingestion
+1. Connect all the components as shown in the screen capture below
+2. Click `Playground` > `Run Flow` to ingest the data
+<img src="./assets/use-langflow-to-ingest-data.png" width="1080" height="574" />
+
+**NOTE: By default, `_id` and `$vectorize` are the two main fields created in the Astra DB collection schema. When setting up Astra DB as content repository in Agent Knowledge, you must configure the `Title` and `Body` fields with these two fields.**
+
+
+## Step 3: Connect to Agent Knowledge in watsonx Orchestrate
+
+This option allows you to integrate with your Astra DB service through the Agent Knowledge feature of watsonx Orchestrate.
+
+For detailed instructions on setting up Astra DB through the Agent Knowledge feature of watsonx Orchestrate, see [Connecting to an Astra DB content repository](https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=agents-connecting-astra-db-content-repository).
+