[Docs] - Vector Store RAG Starter Flow (#1850)

* initial-content

* additional-query

docs/docs/starter-projects/vector-store-rag.mdx

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+import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+import Admonition from "@theme/Admonition";
+
+# Vector store RAG
+
+Retrieval Augmented Generation, or RAG, is a pattern for training LLMs on your data and querying it.
+
+RAG is backed by a **vector store**, a vector database which stores embeddings of the ingested data.
+
+This enables **vector search**, a more powerful and context-aware search.
+
+We've chosen [Astra DB](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) as the vector database for this starter project, but you can follow along with any of Langflow's vector database options.
+
+## Prerequisites
+
+<Admonition type="info">
+  Langflow v1.0 alpha is also available in [HuggingFace Spaces](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true). Try it out or follow the instructions [here](../getting-started/huggingface-spaces) to install it locally.
+</Admonition>
+
+* [Langflow installed](../getting-started/install-langflow.mdx)
+
+* [OpenAI API key](https://platform.openai.com)
+
+* [An Astra DB vector database created](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html) with:
+    * Application token (`AstraCS:WSnyFUhRxsrg…​`)
+    * API endpoint (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`)
+
+## Create the vector store RAG project
+
+1. From the Langflow dashboard, click **New Project**.
+2. Select **Vector Store RAG**.
+3. The **Vector Store RAG** flow is created.
+
+<ZoomableImage
+  alt="Docusaurus themed image"
+  sources={{
+    light: "img/vector-store-rag.png",
+    dark: "img/vector-store-rag.png",
+  }}
+  style={{ width: "80%", margin: "20px auto" }}
+/>
+
+The vector store RAG flow is built of two separate flows.
+
+The **ingestion** flow (bottom of the screen) populates the vector store with data from a local file.
+It ingests data from a file (**File**), splits it into chunks (**Recursive Character Text Splitter**), indexes it in Astra DB (**Astra DB**), and computes embeddings for the chunks (**OpenAI Embeddings**).
+This forms a "brain" for the query flow.
+
+The **query**  flow (top of the screen) allows users to chat with the embedded vector store data. It's a little more complex:
+
+* **Chat Input** component defines where to put the user input coming from the Playground.
+* **OpenAI Embeddings** component generates embeddings from the user input.
+* **Astra DB Search** component retrieves the most relevant Records from the Astra DB database.
+* **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground.
+* **Prompt** component takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model.
+* **OpenAI** component generates a response to the prompt.
+* **Chat Output** component displays the response in the Playground.
+
+4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**.
+    1. In the **Variable Name** field, enter `openai_api_key`.
+    2. In the **Value** field, paste your OpenAI API Key (`sk-...`).
+    3. Click **Save Variable**.
+
+4. To create environment variables for the **Astra DB** and **Astra DB Search** components:
+    1. In the **Token** field, click the **Globe** button, and then click **Add New Variable**.
+    2. In the **Variable Name** field, enter `astra_token`.
+    3. In the **Value** field, paste your Astra application token (`AstraCS:WSnyFUhRxsrg…​`).
+    4. Click **Save Variable**.
+    5. Repeat the above steps for the **API Endpoint** field, pasting your Astra API Endpoint instead (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`).
+    6. Add the global variable to both the **Astra DB** and **Astra DB Search** components.
+
+## Run the vector store RAG flow
+
+1. Click the **Playground** button.
+The **Playground** opens, where you can chat with your data.
+2. Type a message and press Enter. (Try something like "What topics do you know about?")
+3. The bot will respond with a summary of the data you've embedded.
+
+For example, we embedded a PDF of an engine maintenance manual and asked, "How do I change the oil?"
+The bot responds:
+```
+To change the oil in the engine, follow these steps:
+
+Make sure the engine is turned off and cool before starting.
+
+Locate the oil drain plug on the bottom of the engine.
+
+Place a drain pan underneath the oil drain plug to catch the old oil...
+```
+
+We can also get more specific:
+
+```
+User
+What size wrench should I use to remove the oil drain cap?
+
+AI
+You should use a 3/8 inch wrench to remove the oil drain cap.
+```
+
+This is the size the engine manual lists as well. This confirms our flow works, because the query returns the unique knowledge we embedded from the Astra vector store.
+
+
+
+