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Gabriel Luiz Freitas Almeida 2024-06-24 17:09:06 -03:00
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4
.github/workflows/docker-build.yml vendored
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@ -67,7 +67,7 @@ jobs:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and Push Docker Image
uses: docker/build-push-action@v5
uses: docker/build-push-action@v6
with:
context: .
push: true
@ -102,7 +102,7 @@ jobs:
- name: Wait for Docker Hub to propagate (for backend)
run: sleep 120
- name: Build and push ${{ matrix.component }}
uses: docker/build-push-action@v5
uses: docker/build-push-action@v6
with:
context: .
push: true

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.github/workflows/pre-release-base.yml vendored
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@ -69,7 +69,7 @@ jobs:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push
uses: docker/build-push-action@v5
uses: docker/build-push-action@v6
with:
context: .
push: true

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.github/workflows/pre-release-langflow.yml vendored
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@ -75,7 +75,7 @@ jobs:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push
uses: docker/build-push-action@v5
uses: docker/build-push-action@v6
with:
context: .
push: true
@ -86,7 +86,7 @@ jobs:
langflowai/langflow:${{ needs.release.outputs.version }}
langflowai/langflow:1.0-alpha
- name: Build and push (frontend)
uses: docker/build-push-action@v5
uses: docker/build-push-action@v6
with:
context: .
push: true
@ -99,7 +99,7 @@ jobs:
- name: Wait for Docker Hub to propagate
run: sleep 120
- name: Build and push (backend)
uses: docker/build-push-action@v5
uses: docker/build-push-action@v6
with:
context: .
push: true

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README.md
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@ -64,10 +64,6 @@ You can install Langflow with pip:
```shell
# Make sure you have >=Python 3.10 installed on your system.
# Install the pre-release version (recommended for the latest updates)
python -m pip install langflow --pre --force-reinstall
# or stable version
python -m pip install langflow -U
```
@ -107,11 +103,8 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google
## Deploy on Railway
Use this template to deploy Langflow 1.0 Preview on Railway:
Use this template to deploy Langflow 1.0 on Railway:
[![Deploy 1.0 Preview on Railway](https://railway.app/button.svg)](https://railway.app/template/UsJ1uB?referralCode=MnPSdg)
Or this one to deploy Langflow 0.6.x:
[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg)

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docs/docs/deployment/kubernetes.md → docs/docs/deployment/kubernetes.mdx
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@ -1,5 +1,11 @@
import Admonition from "@theme/Admonition";
# Kubernetes
<Admonition type="warning" title="warning">
This page may contain outdated information. It will be updated as soon as possible.
</Admonition>
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)

4
docs/docs/getting-started/install-langflow.mdx
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@ -5,10 +5,6 @@ import Admonition from "@theme/Admonition";
# 📦 Install Langflow
<Admonition type="warning" title="warning">
This page may contain outdated information. It will be updated as soon as possible.
</Admonition>
<Admonition type="info">
Langflow **requires** Python version 3.10 or greater and
[pip](https://pypi.org/project/pip/) or

4
docs/docs/getting-started/possible-installation-issues.mdx
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@ -2,10 +2,6 @@ import Admonition from "@theme/Admonition";
# ❗️ Common Installation Issues
<Admonition type="warning" title="warning">
This page may contain outdated information. It will be updated as soon as possible.
</Admonition>
This is a list of possible issues that you may encounter when installing Langflow and how to solve them.
## _`No module named 'langflow.__main__'`_

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docs/docs/getting-started/quickstart.mdx
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@ -6,10 +6,6 @@ import Admonition from "@theme/Admonition";
# ⚡️ Quickstart
<Admonition type="warning" title="warning">
This page may contain outdated information. It will be updated as soon as possible.
</Admonition>
This guide demonstrates how to build a basic flow and modify the prompt for different outcomes.
## Prerequisites

194
docs/docs/getting-started/rag-with-astradb.mdx
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@ -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
<Admonition type="warning" title="warning">
This page may contain outdated information. It will be updated as soon as possible.
</Admonition>
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.
<Admonition type="tip">
This guide assumes that you have Langflow up and running. If you are new to
Langflow, you can check out the [Getting Started](/) guide.
</Admonition>
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.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/astra-create-database.png",
dark: "img/astra-create-database.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
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.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/astra-configure-deployment.png",
dark: "img/astra-configure-deployment.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
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**.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/astra-generate-token.png",
dark: "img/astra-generate-token.png",
}}
style={{ width: "50%", margin: "20px auto" }}
/>
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.
<ZoomableImage
alt="Drag-and-drop"
sources={{
light: "img/drag-and-drop-flow.png",
dark: "img/drag-and-drop-flow.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
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
<ZoomableImage
alt="Astra Ingestion"
sources={{
light: "img/astra-ingestion-flow.png",
dark: "img/astra-ingestion-flow.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
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.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/astra-ingestion-fields.png",
dark: "img/astra-ingestion-fields.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
And run it! This will ingest the Text data from your file into the Astra DB database.
<ZoomableImage
alt="Astra Run"
sources={{
light: "img/astra-ingestion-run.png",
dark: "img/astra-ingestion-run.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
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
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/astra-rag-flow.png",
dark: "img/astra-rag-flow.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
To run it all we have to do is click on the ⚡ _Run_ button and start interacting with your RAG application.
<ZoomableImage
alt="Astra RAG"
sources={{
light: "img/astra-rag-flow-run.png",
dark: "img/astra-rag-flow-run.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
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.
<ZoomableImage
alt="Playground"
sources={{
light: "img/astra-rag-flow-interaction-panel.png",
dark: "img/astra-rag-flow-interaction-panel.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved data.
<ZoomableImage
alt="Astra Playground"
sources={{
light: "img/astra-rag-flow-interaction-panel-interaction.png",
dark: "img/astra-rag-flow-interaction-panel-interaction.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
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.

4
docs/docs/getting-started/workspace.mdx
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@ -6,10 +6,6 @@ import Admonition from "@theme/Admonition";
# 🎨 Langflow Workspace
<Admonition type="warning" title="warning">
This page may contain outdated information. It will be updated as soon as possible.
</Admonition>
## 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.

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docs/docs/integrations/langsmith/intro.mdx
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@ -5,10 +5,6 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
# LangSmith
<Admonition type="warning" title="warning">
This page may contain outdated information. It will be updated as soon as possible.
</Admonition>
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

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docs/docs/whats-new/a-new-chapter-langflow.mdx
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@ -1,6 +1,15 @@
import ZoomableImage from "/src/theme/ZoomableImage.js";
# A new chapter for Langflow
# 1.0 - A new chapter for Langflow
<ZoomableImage
alt="1.0"
sources={{
light: "/img/langflow-1-0.png",
dark: "/img/langflow-1-0.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
## First things first

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docs/src/css/custom.css
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@ -240,3 +240,7 @@ body {
.ch-scrollycoding-step-content {
min-height: 70px;
}
.theme-doc-sidebar-item-category.theme-doc-sidebar-item-category-level-2.menu__list-item:not(:first-child) {
margin-top: 0.25rem!important;
}

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