diff --git a/docs/docs/deployment/kubernetes.md b/docs/docs/deployment/kubernetes.mdx similarity index 98% rename from docs/docs/deployment/kubernetes.md rename to docs/docs/deployment/kubernetes.mdx index 8648354e2..8896ab875 100644 --- a/docs/docs/deployment/kubernetes.md +++ b/docs/docs/deployment/kubernetes.mdx @@ -1,5 +1,11 @@ +import Admonition from "@theme/Admonition"; + # Kubernetes + +This page may contain outdated information. It will be updated as soon as possible. + + This guide will help you get LangFlow up and running in Kubernetes cluster, including the following steps: - Install [LangFlow as IDE](#langflow-ide) in a Kubernetes cluster (for development) diff --git a/docs/docs/getting-started/install-langflow.mdx b/docs/docs/getting-started/install-langflow.mdx index b94b3468c..58bd2fca0 100644 --- a/docs/docs/getting-started/install-langflow.mdx +++ b/docs/docs/getting-started/install-langflow.mdx @@ -5,10 +5,6 @@ import Admonition from "@theme/Admonition"; # 📦 Install Langflow - -This page may contain outdated information. It will be updated as soon as possible. - - Langflow **requires** Python version 3.10 or greater and [pip](https://pypi.org/project/pip/) or diff --git a/docs/docs/getting-started/possible-installation-issues.mdx b/docs/docs/getting-started/possible-installation-issues.mdx index 21e6a5d1a..0d4de5175 100644 --- a/docs/docs/getting-started/possible-installation-issues.mdx +++ b/docs/docs/getting-started/possible-installation-issues.mdx @@ -2,10 +2,6 @@ import Admonition from "@theme/Admonition"; # ❗️ Common Installation Issues - -This page may contain outdated information. It will be updated as soon as possible. - - This is a list of possible issues that you may encounter when installing Langflow and how to solve them. ## _`No module named 'langflow.__main__'`_ diff --git a/docs/docs/getting-started/quickstart.mdx b/docs/docs/getting-started/quickstart.mdx index e6151c1eb..7d4f15573 100644 --- a/docs/docs/getting-started/quickstart.mdx +++ b/docs/docs/getting-started/quickstart.mdx @@ -6,10 +6,6 @@ import Admonition from "@theme/Admonition"; # ⚡️ Quickstart - -This page may contain outdated information. It will be updated as soon as possible. - - This guide demonstrates how to build a basic flow and modify the prompt for different outcomes. ## Prerequisites diff --git a/docs/docs/getting-started/rag-with-astradb.mdx b/docs/docs/getting-started/rag-with-astradb.mdx deleted file mode 100644 index 015f50f3d..000000000 --- a/docs/docs/getting-started/rag-with-astradb.mdx +++ /dev/null @@ -1,194 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# 🌟 RAG with Astra DB - - -This page may contain outdated information. It will be updated as soon as possible. - - -This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **Astra DB** and **Langflow**. - -[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. - -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. - - - This guide assumes that you have Langflow up and running. If you are new to - Langflow, you can check out the [Getting Started](/) guide. - - -TLDR; - -- [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) -- Create a new database, get a **Token** and the **API Endpoint** -- Start Langflow and click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components -- Import the project into Langflow by dropping it on the Workspace or My Collection page -- Update the **Token** and **API Endpoint** in the **Astra DB** components -- Update the OpenAI API key in the **OpenAI** components -- Run the ingestion flow which is the one that uses the **Astra DB** component -- Click on the ⚡ _Run_ button and start interacting with your RAG application - -# First things first - -## Create an Astra DB Database - -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). - -Once you have created an account, you will be taken to the Astra DB dashboard. Click on the **Create Database** button. - - - -Now you will need to configure your database. Choose the **Serverless (Vector)** deployment type, and pick a Database name, provider and region. - -After you have configured your database, click on the **Create Database** button. - - - -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**. - - - -Now we are all set to start building our RAG application using Astra DB and Langflow. - -## Open the Vector Store RAG Project - -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. - - - -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. - -Your first step should be to understand what each flow does and how they interact with each other. - -The ingestion flow consists of: - -- **Files** component that uploads a text file to Langflow -- **Recursive Character Text Splitter** component that splits the text into smaller chunks -- **OpenAIEmbeddings** component that generates embeddings for the text chunks -- **Astra DB** component that stores the text chunks in the Astra DB database - - - -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. - - - -And run it! This will ingest the Text data from your file into the Astra DB database. - - - -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 Playground. - -The RAG flow is a bit more complex. It consists of: - -- **Chat Input** component that defines where to put the user input coming from the Playground -- **OpenAI Embeddings** component that generates embeddings from the user input -- **Astra DB Search** component that retrieves the most relevant Data from the Astra DB database -- **Text Output** component that turns the Data into Text by concatenating them and also displays it in the Playground - - One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Playground -- **Prompt** component that takes in the user input and the retrieved Data as text and builds a prompt for the OpenAI model -- **OpenAI** component that generates a response to the prompt -- **Chat Output** component that displays the response in the Playground - - - -To run it all we have to do is click on the ⚡ _Run_ button and start interacting with your RAG application. - - - -This opens the Playground where you can chat your data. - -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. - - - -Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved data. - - - -And that's it! You have successfully ran a RAG application using Astra DB and Langflow. - -# Conclusion - -In this guide, we have learned how to run a RAG application using Astra DB and Langflow. -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. diff --git a/docs/docs/getting-started/workspace.mdx b/docs/docs/getting-started/workspace.mdx index e6951a5e1..374faca74 100644 --- a/docs/docs/getting-started/workspace.mdx +++ b/docs/docs/getting-started/workspace.mdx @@ -6,10 +6,6 @@ import Admonition from "@theme/Admonition"; # 🎨 Langflow Workspace - -This page may contain outdated information. It will be updated as soon as possible. - - ## The Langflow Workspace Interface The **Langflow Workspace** is where you assemble new flows and create AIs by connecting and running components. To get started, click on **New Project**. You can either build a flow from scratch (Blank Flow) or choose from pre-built starter examples. diff --git a/docs/docs/integrations/langsmith/intro.mdx b/docs/docs/integrations/langsmith/intro.mdx index 02f474e67..68f28a891 100644 --- a/docs/docs/integrations/langsmith/intro.mdx +++ b/docs/docs/integrations/langsmith/intro.mdx @@ -5,10 +5,6 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; # LangSmith - -This page may contain outdated information. It will be updated as soon as possible. - - LangSmith is a full-lifecycle DevOps service from LangChain that provides monitoring and observability. To integrate with Langflow, just add your LangChain API key as a Langflow environment variable and you are good to go! ## Step-by-step Configuration