Update flow names in NewFlowModal and documentation

docs/docs/guides/rag-with-astradb.mdx

@@ -1,7 +1,6 @@
 import ThemedImage from "@theme/ThemedImage";
 import useBaseUrl from "@docusaurus/useBaseUrl";
 import ZoomableImage from "/src/theme/ZoomableImage.js";
-import DownloadableJsonFile from "/src/theme/DownloadableJsonFile.js";
 import Admonition from "@theme/Admonition";
 
 # 🌟 RAG with AstraDB
@@ -22,10 +21,7 @@ TLDR;
 - Visit the [Astra](https://astra.datastax.com) website and create a free account
 - Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true)
 - Create a new database, get a **Token** and the **API Endpoint**
-- <DownloadableJsonFile
+- Click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components
-    title="Download AstraDB RAG Flows"
-    source="/data/AstraDB-RAG-Flows.json"
-  />
 - Import the project into Langflow by dropping it on the Canvas or My Collection page
 - Update the **Token** and **API Endpoint** in the **AstraDB** components
 - Update the OpenAI API key in the **OpenAI** components
@@ -79,13 +75,9 @@ Now we are all set to start building our RAG application using AstraDB and Langf
 
 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/Logspace/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you.
 
-You'll still need to get the Project file and import it so, let's get to that.
+## Open the Vector Store RAG Project
 
-## Import AstraDB RAG Flows
+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 AstraDB.
-
-To get started, you will need to <DownloadableJsonFile title="download the AstraDB RAG Flows project file" source="/data/AstraDB-RAG-Flows.json" />.
-
-Once you have downloaded the project file, you can import it into Langflow by dropping it on the Canvas or My Collection page.
 
 <ZoomableImage
   alt="Docusaurus themed image"
@@ -138,7 +130,7 @@ And run it! This will ingest the Text data from your file into the AstraDB datab
   style={{ width: "90%" }}
 />
 
-Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries.
+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.
 
 The RAG flow is a bit more complex. It consists of:
 

docs/docs/index.mdx

@@ -17,7 +17,6 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
   style={{ width: "100%" }}
 />
 
-
 ## 🚀 First steps
 
 ## Installation
@@ -37,9 +36,9 @@ pip install langflow -U
 Or you can install a pre-release version using:
 
 ```bash
-pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre"
+pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
 # or
-pip install langflow --pre -U
+pip install langflow --pre --force-reinstall
 ```
 
 ### ⛓️ Running Langflow
@@ -67,14 +66,12 @@ Remember to use a Chromium-based browser for the best experience. You'll be pres
   style={{ width: "100%" }}
 />
 
-
 From here, just name your Space, define the visibility (Public or Private), and click on `Duplicate Space` to start the installation process. When that is done, you'll be redirected to the Space's main page to start using Langflow right away!
 
 Once you get Langflow running, click on New Project in the top right corner of the screen. Langflow provides a range of example flows to help you get started.
 
 To quickly try one of them, open a starter example, set up your API keys and click ⚡ Run, on the bottom right corner of the canvas. This will open up Langflow's Interaction Panel with the chat console, text inputs, and outputs.
 
-
 ### 🖥️ Command Line Interface (CLI)
 
 Langflow provides a command-line interface (CLI) for easy management and configuration.
@@ -91,4 +88,4 @@ Find more information about the available options by running:
 
 ```bash
 langflow --help
-```
+```

src/frontend/src/modals/NewFlowModal/components/undrawCards/index.tsx

@@ -83,7 +83,7 @@ export default function UndrawCardComponent({
             }}
           />
         );
-      case "Prompt Chaining":
+      case "Vector Store RAG":
         return (
           <PromptChaining
             style={{

src/frontend/src/modals/NewFlowModal/index.tsx

@@ -52,18 +52,18 @@ export default function NewFlowModal({
               flow={examples.find((e) => e.name == "Document QA")!}
             />
           )}
-          {examples.find((e) => e.name == "Prompt Chaining") && (
-            <UndrawCardComponent
-              key={1}
-              flow={examples.find((e) => e.name == "Prompt Chaining")!}
-            />
-          )}
           {examples.find((e) => e.name == "Blog Writer") && (
             <UndrawCardComponent
               key={1}
               flow={examples.find((e) => e.name == "Blog Writer")!}
             />
           )}
+          {examples.find((e) => e.name == "Vector Store RAG") && (
+            <UndrawCardComponent
+              key={1}
+              flow={examples.find((e) => e.name == "Vector Store RAG")!}
+            />
+          )}
         </div>
       </BaseModal.Content>
     </BaseModal>