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.eslintrc.json Normal file
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@ -0,0 +1,90 @@
{
"extends": [
"eslint:recommended",
"plugin:react/recommended",
"plugin:prettier/recommended"
],
"plugins": [
"react",
"import-helpers",
"prettier"
],
"parser": "@typescript-eslint/parser",
"parserOptions": {
"project": [
"./tsconfig.node.json",
"./tsconfig.json"
],
"extraFileExtensions:": [
".mdx"
],
"extensions:": [
".mdx"
]
},
"env": {
"browser": true,
"es2021": true
},
"settings": {
"react": {
"version": "detect"
}
},
"rules": {
"no-console": "warn",
"no-self-assign": "warn",
"no-self-compare": "warn",
"complexity": [
"error",
{
"max": 15
}
],
"indent": [
"error",
2,
{
"SwitchCase": 1
}
],
"no-dupe-keys": "error",
"no-invalid-regexp": "error",
"no-undef": "error",
"no-return-assign": "error",
"no-redeclare": "error",
"no-empty": "error",
"no-await-in-loop": "error",
"react/react-in-jsx-scope": 0,
"node/exports-style": [
"error",
"module.exports"
],
"node/file-extension-in-import": [
"error",
"always"
],
"node/prefer-global/buffer": [
"error",
"always"
],
"node/prefer-global/console": [
"error",
"always"
],
"node/prefer-global/process": [
"error",
"always"
],
"node/prefer-global/url-search-params": [
"error",
"always"
],
"node/prefer-global/url": [
"error",
"always"
],
"node/prefer-promises/dns": "error",
"node/prefer-promises/fs": "error"
}
}

2
.github/workflows/deploy_gh-pages.yml vendored
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@ -27,7 +27,7 @@ jobs:
# Popular action to deploy to GitHub Pages:
# Docs: https://github.com/peaceiris/actions-gh-pages#%EF%B8%8F-docusaurus
- name: Deploy to GitHub Pages
uses: peaceiris/actions-gh-pages@v3
uses: peaceiris/actions-gh-pages@v4
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
# Build output to publish to the `gh-pages` branch:

4
.github/workflows/pre-release-langflow.yml vendored
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@ -53,7 +53,7 @@ jobs:
run: |
make publish main=true
- name: Upload Artifact
uses: actions/upload-artifact@v2
uses: actions/upload-artifact@v4
with:
name: dist
path: dist
@ -88,7 +88,7 @@ jobs:
runs-on: ubuntu-latest
needs: [docker_build, release]
steps:
- uses: actions/download-artifact@v2
- uses: actions/download-artifact@v4
with:
name: dist
path: dist

4
.github/workflows/typescript_test.yml vendored
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@ -26,7 +26,7 @@ jobs:
uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v3
uses: actions/setup-node@v4
id: setup-node
with:
node-version: ${{ env.NODE_VERSION }}
@ -99,7 +99,7 @@ jobs:
uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v3
uses: actions/setup-node@v4
with:
node-version: ${{ env.NODE_VERSION }}

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.pre-commit-config.yaml Normal file
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@ -0,0 +1,44 @@
fail_fast: true
repos:
- repo: https://github.com/pre-commit/mirrors-eslint
rev: "v9.1.1"
hooks:
- id: eslint
files: \.[jt]sx?$ # *.js, *.jsx, *.ts and *.tsx
types: [file]
args: ["--fix", "--no-warn-ignored"]
additional_dependencies:
- eslint@9.1.1
- eslint-plugin-prettier
- eslint-config-prettier
- prettier
- eslint-plugin-react@latest
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.1.0
hooks:
- id: check-case-conflict
- id: end-of-file-fixer
- id: mixed-line-ending
args:
- --fix=lf
- id: trailing-whitespace
- id: pretty-format-json
exclude: ^tsconfig.*.json
args:
- --autofix
- --indent=4
- --no-sort-keys
- id: check-merge-conflict
- repo: https://github.com/astral-sh/ruff-pre-commit
# Ruff version.
rev: v0.4.2
hooks:
# Run the linter.
- id: ruff
# Python
files: \.py$
types: [file]
# Run the formatter.
- id: ruff-format
files: \.py$
types: [file]

1
Makefile
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@ -135,6 +135,7 @@ frontendc:
install_backend:
@echo 'Installing backend dependencies'
@poetry install
@poetry run pre-commit install
backend:
@echo 'Setting up the environment'

4
docs/docs/components/inputs.mdx
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@ -47,7 +47,7 @@ If you expose all its fields, it will look like the image below.
style={{ width: "40%", margin: "20px auto" }}
/>
One key capability of the Chat Input component is how it transforms the Interaction Panel into a chat window. This feature is particularly useful for scenarios where user input is required to initiate or influence the flow.
One key capability of the Chat Input component is how it transforms the Playground into a chat window. This feature is particularly useful for scenarios where user input is required to initiate or influence the flow.
<ZoomableImage
alt="Docusaurus themed image"
@ -152,7 +152,7 @@ You can use a template like this: _`"Name: {name}, Age: {age}"`_ to convert the
style={{ width: "50%", margin: "20px auto" }}
/>
The Text Input component gives you the possibility to add an Input field on the Interaction Panel. This is useful because it allows you to define parameters while running and testing your flow.
The Text Input component gives you the possibility to add an Input field on the Playground. This is useful because it allows you to define parameters while running and testing your flow.
<ZoomableImage
alt="Docusaurus themed image"

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docs/docs/getting-started/rag-with-astradb.mdx Normal file
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@ -0,0 +1,195 @@
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 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)
- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
- Create a new database, get a **Token** and the **API Endpoint**
- 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 Canvas 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.
## (Optional) Duplicate the Langflow 1.0 HuggingFace Space
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/Langflow/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you.
## 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="Docusaurus themed image"
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="Docusaurus themed image"
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="Docusaurus themed image"
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 Records from the Astra DB database
- **Text Output** component that turns the Records 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 Records 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="Docusaurus themed image"
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="Docusaurus themed image"
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 records.
<ZoomableImage
alt="Docusaurus themed image"
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.

12
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@ -22,17 +22,17 @@ Use Langflow's simple but powerful UI to build any AI application you can dream
## 🚀 First steps
* [Install Langflow](/getting-started/install-langflow) - Install and start a local Langflow server.
- [Install Langflow](/getting-started/install-langflow) - Install and start a local Langflow server.
* [Quickstart](/getting-started/quickstart) - Install Langflow, create a flow, and run it.
- [Quickstart](/getting-started/quickstart) - Install Langflow, create a flow, and run it.
* [HuggingFace Spaces](/getting-started/huggingface-spaces) - Duplicate the Langflow preview space and try it out before you install.
- [HuggingFace Spaces](/getting-started/huggingface-spaces) - Duplicate the Langflow preview space and try it out before you install.
* [New to LLMs?](/getting-started/new-to-llms) - Learn more about LLMs, prompting, and more at [promptingguide.ai](https://promptingguide.ai).
- [New to LLMs?](/getting-started/new-to-llms) - Learn more about LLMs, prompting, and more at [promptingguide.ai](https://promptingguide.ai).
## Learn more about Langflow 1.0
Learn more about the exciting changes in Langflow 1.0, and how to migrate your existing Langflow projects.
* [A new chapter for Langflow](/whats-new/a-new-chapter-langflow)
* [Migration guides](/migration/migrating-to-one-point-zero)
- [A new chapter for Langflow](/whats-new/a-new-chapter-langflow)
- [Migration guides](/migration/migrating-to-one-point-zero)

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docs/docs/migration/inputs-and-outputs.mdx
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@ -1,7 +1,7 @@
# Inputs and Outputs
TL;DR: Inputs and Outputs are a category of components that are used to define where data comes in and out of your flow. They also
dynamically change the Interaction Panel and can be renamed to make it easier to build and maintain your flows.
dynamically change the Playground and can be renamed to make it easier to build and maintain your flows.
## Introduction
@ -10,8 +10,8 @@ Langflow 1.0 introduces new categories of components called Inputs and Outputs.
Let's start with what they have in common:
- Components in these categories connect to components that have Text or Record inputs or outputs. Some can connect to both but you have to pick what type of data you want to output or input.
- They can be renamed to help you identify them more easily in the Interaction Panel and while using the API.
- They dynamically change the Interaction Panel to make it easier to understand and interact with your flows.
- They can be renamed to help you identify them more easily in the Playground and while using the API.
- They dynamically change the Playground to make it easier to understand and interact with your flows.
Native Langflow Components were created to be powerful tools that work around Langflow's features. They are designed to be easy to use and understand, and to help you build your flows faster.
@ -21,7 +21,7 @@ Let's dive into Inputs and Outputs.
Inputs are components that are used to define where data comes into your flow. They can be used to receive data from the user, from a database, or from any other source that can be converted to Text or Record.
The difference between Chat Input and other Input components is the format of the output, the number of configurable fields, and the way they are displayed in the Interaction Panel.
The difference between Chat Input and other Input components is the format of the output, the number of configurable fields, and the way they are displayed in the Playground.
Chat Input components can output Text or Record. When you want to pass the sender name, or sender to the next component, you can use the Record output, and when you want to pass the message only you can use the Text output. This is useful when saving the message to a database or a memory system like Zep.
@ -29,7 +29,7 @@ You can find out more about it and the other Inputs [here](../components/inputs)
## Outputs
Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Interaction Panel, or to define how the data will be displayed in the Interaction Panel.
Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground.
The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user.

2
docs/docs/migration/migrating-to-one-point-zero.mdx
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@ -55,7 +55,7 @@ Langflow 1.0 continues to support LangChain while also introducing support for m
**Guide coming soon**
## Sidebar Redesign and Customizable Interaction Panel
## Sidebar Redesign and Customizable Playground
We've expanded on the chat experience by creating a customizable interaction panel that allows you to design a panel that fits your needs and interact with it. The sidebar has also been redesigned to provide a more intuitive and user-friendly experience. Explore the new sidebar and interaction panel features to enhance your workflow.

12
docs/docs/tutorials/rag-with-astradb.mdx
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@ -121,18 +121,18 @@ And run it! This will ingest the Text data from your file into the Astra DB data
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 Interaction Panel.
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 Interaction Panel
- **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 Records from the Astra DB database
- **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Interaction Panel
- One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Interaction Panel
- **Text Output** component that turns the Records 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 Records 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 Interaction Panel
- **Chat Output** component that displays the response in the Playground
<ZoomableImage
alt="Docusaurus themed image"
@ -154,7 +154,7 @@ To run it all we have to do is click on the ⚡ _Run_ button and start interacti
style={{ width: "80%", margin: "20px auto" }}
/>
This opens the Interaction Panel where you can chat your data.
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.

10
docs/docs/whats-new/a-new-chapter-langflow.mdx
View file

@ -36,18 +36,18 @@ The caveat is existing projects may need some new Components to get them back to
## Custom Interactions
The moment we decided to make this change, we saw the potential to make Langflow even more yours.
By having a clear definition of Inputs and Outputs, we could build the experience around that which led us to create the **Interaction Panel**.
By having a clear definition of Inputs and Outputs, we could build the experience around that which led us to create the **Playground**.
When building a project testing and debugging is crucial. The Interaction Panel is a tool that changes dynamically based on the Inputs and Outputs you defined in your project.
When building a project testing and debugging is crucial. The Playground is a tool that changes dynamically based on the Inputs and Outputs you defined in your project.
For example, let's say you are building a simple RAG application. Generally, you have an Input, some references that come from a Vector Store Search, a Prompt and the answer.
Now, you could plug the output of your Prompt into a [Text Output](../components/outputs#Text-Output), rename that to "Prompt Result" and see the output of your Prompt in the Interaction Panel.
Now, you could plug the output of your Prompt into a [Text Output](../components/outputs#Text-Output), rename that to "Prompt Result" and see the output of your Prompt in the Playground.
{/* Add image here of the described above */}
This is just one example of how the Interaction Panel can help you build and debug your projects.
This is just one example of how the Playground can help you build and debug your projects.
We have many planned features for the Interaction Panel, and we're excited to see how you use it and what you think of it.
We have many planned features for the Playground, and we're excited to see how you use it and what you think of it.
## An easier start

12
docs/static/data/AstraDB-RAG-Flows.json vendored
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@ -20,7 +20,7 @@
"list": false,
"show": true,
"multiline": true,
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
"fileTypes": [],
"file_path": "",
"password": false,
@ -141,7 +141,7 @@
},
"_type": "CustomComponent"
},
"description": "Get chat inputs from the Interaction Panel.",
"description": "Get chat inputs from the Playground.",
"icon": "ChatInput",
"base_classes": [
"Text",
@ -214,7 +214,7 @@
"list": false,
"show": true,
"multiline": true,
"value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
"value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
"fileTypes": [],
"file_path": "",
"password": false,
@ -249,7 +249,7 @@
},
"_type": "CustomComponent"
},
"description": "Display a text output in the Interaction Panel.",
"description": "Display a text output in the Playground.",
"icon": "type",
"base_classes": [
"object",
@ -1255,7 +1255,7 @@
"list": false,
"show": true,
"multiline": true,
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n",
"value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n",
"fileTypes": [],
"file_path": "",
"password": false,
@ -1399,7 +1399,7 @@
},
"_type": "CustomComponent"
},
"description": "Display a chat message in the Interaction Panel.",
"description": "Display a chat message in the Playground.",
"icon": "ChatOutput",
"base_classes": [
"object",

0
eslint.config.js Normal file
View file

644
poetry.lock generated
View file

@ -469,17 +469,17 @@ files = [
[[package]]
name = "boto3"
version = "1.34.94"
version = "1.34.96"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
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[package.dependencies]
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botocore = ">=1.34.96,<1.35.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@ -488,13 +488,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.34.94"
version = "1.34.96"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
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[package.dependencies]
@ -828,6 +828,17 @@ files = [
[package.dependencies]
pycparser = "*"
[[package]]
name = "cfgv"
version = "3.4.0"
description = "Validate configuration and produce human readable error messages."
optional = false
python-versions = ">=3.8"
files = [
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[[package]]
name = "chardet"
version = "5.2.0"
@ -1604,6 +1615,17 @@ files = [
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[[package]]
name = "distlib"
version = "0.3.8"
description = "Distribution utilities"
optional = false
python-versions = "*"
files = [
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version = "1.9.0"
@ -2491,13 +2513,13 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
[[package]]
name = "google-api-core"
version = "2.18.0"
version = "2.19.0"
description = "Google API client core library"
optional = false
python-versions = ">=3.7"
files = [
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[package.dependencies]
@ -2728,69 +2750,61 @@ test = ["objgraph", "psutil"]
[[package]]
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version = "1.63.0"
description = "HTTP/2-based RPC framework"
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python-versions = ">=3.8"
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version = "0.1.5"
description = "An integration package connecting OpenAI and LangChain"
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python-versions = "<4.0,>=3.8.1"
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version = "0.0.41"
description = "A Python package with a built-in web application"
optional = false
python-versions = ">=3.10,<3.12"
@ -3968,17 +3996,17 @@ url = "src/backend/base"
[[package]]
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version = "2.28.2"
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optional = false
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[[package]]
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version = "0.1.52"
version = "0.1.53"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
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description = "Python bindings for the llama.cpp library"
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version = "0.1.19"
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description = "llama-index readers file integration"
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version = "5.3.1"
description = "Python tool and library for decrypting MS Office files with passwords or other keys"
version = "5.4.0"
description = "Python tool and library for decrypting and encrypting MS Office files using a password or other keys"
optional = false
python-versions = ">=3.8,<4.0"
python-versions = "<4.0,>=3.8"
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tgrep = ["pyparsing"]
twitter = ["twython"]
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]
[package.dependencies]
@ -8505,13 +8567,13 @@ typing = ["mypy (>=1.4)", "rich", "twisted"]
[[package]]
name = "supabase"
version = "2.4.3"
version = "2.4.5"
description = "Supabase client for Python."
optional = false
python-versions = "<4.0,>=3.8"
files = [
{file = "supabase-2.4.3-py3-none-any.whl", hash = "sha256:31b7cf5577471f473e690e6afb1b2f8ffe881a5a81f2c33a5fa7b07ba0fb9857"},
{file = "supabase-2.4.3.tar.gz", hash = "sha256:ee9a5db8ecc3de20fe8351b0d3bc7667809095322915cea753f83db310494001"},
{file = "supabase-2.4.5-py3-none-any.whl", hash = "sha256:100441c36bf3390b040818c636c372a91645d18b6a9e0c12cea061fb00db664c"},
{file = "supabase-2.4.5.tar.gz", hash = "sha256:8520b5a194c6d8fdbdd71b45aefc8b5a42d1a6711a2c693b6d299aeb785e8532"},
]
[package.dependencies]
@ -9547,13 +9609,13 @@ test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)"
[[package]]
name = "validators"
version = "0.28.0"
version = "0.28.1"
description = "Python Data Validation for Humans™"
optional = false
python-versions = ">=3.8"
files = [
{file = "validators-0.28.0-py3-none-any.whl", hash = "sha256:e0184691dea3ba82b52c161ba81d3ec1d8be8da9609f0137d1430b395b366521"},
{file = "validators-0.28.0.tar.gz", hash = "sha256:85bc82511f6ccd0800f4c15d8c0dc546c15e369640c5ea1f24349ba0b3b17815"},
{file = "validators-0.28.1-py3-none-any.whl", hash = "sha256:890c98789ad884037f059af6ea915ec2d667129d509180c2c590b8009a4c4219"},
{file = "validators-0.28.1.tar.gz", hash = "sha256:5ac88e7916c3405f0ce38ac2ac82a477fcf4d90dbbeddd04c8193171fc17f7dc"},
]
[[package]]
@ -9567,6 +9629,26 @@ files = [
{file = "vine-5.1.0.tar.gz", hash = "sha256:8b62e981d35c41049211cf62a0a1242d8c1ee9bd15bb196ce38aefd6799e61e0"},
]
[[package]]
name = "virtualenv"
version = "20.26.1"
description = "Virtual Python Environment builder"
optional = false
python-versions = ">=3.7"
files = [
{file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"},
{file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"},
]
[package.dependencies]
distlib = ">=0.3.7,<1"
filelock = ">=3.12.2,<4"
platformdirs = ">=3.9.1,<5"
[package.extras]
docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
[[package]]
name = "watchfiles"
version = "0.21.0"
@ -9667,13 +9749,13 @@ files = [
[[package]]
name = "weaviate-client"
version = "4.5.6"
version = "4.5.7"
description = "A python native Weaviate client"
optional = false
python-versions = ">=3.8"
files = [
{file = "weaviate_client-4.5.6-py3-none-any.whl", hash = "sha256:bdafbf94343f621ca68bc547b5c9a5272dc6ca7953ad6a228f5ad8179021de68"},
{file = "weaviate_client-4.5.6.tar.gz", hash = "sha256:32a2b328f0a6637228c064e04aa6004c4ba733469b81754ae4598750735a9624"},
{file = "weaviate_client-4.5.7-py3-none-any.whl", hash = "sha256:97958050d215ab64d702e5dd29206c4b855a3372d3ae22f784287a2d0ed085dc"},
{file = "weaviate_client-4.5.7.tar.gz", hash = "sha256:130f7371c4af31949e6f3537683813f0e5adb7ec94a5536e2278768c5fe057bf"},
]
[package.dependencies]
@ -9681,10 +9763,10 @@ authlib = ">=1.2.1,<2.0.0"
grpcio = ">=1.57.0,<2.0.0"
grpcio-health-checking = ">=1.57.0,<2.0.0"
grpcio-tools = ">=1.57.0,<2.0.0"
httpx = "0.27.0"
httpx = ">=0.25.0,<=0.27.0"
pydantic = ">=2.5.0,<3.0.0"
requests = ">=2.30.0,<3.0.0"
validators = "0.28.0"
validators = "0.28.1"
[[package]]
name = "websocket-client"
@ -10259,4 +10341,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.12"
content-hash = "bec34397b534f882551511558c76785c7cd67e6a1eefc1d45f6a64d97175d886"
content-hash = "b3d424bc8e83a9f10a8e71f95e2499b3018711d8edf7a594814b9388e5393a84"

3
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "langflow"
version = "1.0.0a29"
version = "1.0.0a30"
description = "A Python package with a built-in web application"
authors = ["Langflow <contact@langflow.org>"]
maintainers = [
@ -108,6 +108,7 @@ respx = "^0.21.1"
pytest-instafail = "^0.5.0"
pytest-asyncio = "^0.23.0"
pytest-profiling = "^1.7.0"
pre-commit = "^3.7.0"
[tool.poetry.extras]
deploy = ["celery", "redis", "flower"]

88
scripts/setup/update_poetry.sh
View file

@ -32,90 +32,6 @@ case "$OS" in
;;
esac
echo "Detected Operating System: $OS"
# Installation of pipx based on the detected OS
install_pipx() {
case $1 in
macOS)
# macOS installation using Homebrew
command -v brew >/dev/null 2>&1 || exit_with_message "Homebrew is not installed. Please install Homebrew first."
echo "Installing pipx using Homebrew..."
brew install pipx
pipx ensurepath
;;
Linux)
# Linux installation. Further checks are needed to distinguish between distributions
if grep -qEi "(ubuntu|debian)" /etc/*release; then
echo "Installing pipx on Ubuntu/Debian..."
sudo apt update
sudo apt install pipx -y
elif grep -qEi "fedora" /etc/*release; then
echo "Installing pipx on Fedora..."
sudo dnf install pipx -y
else
echo "Installing pipx using pip (other Linux distributions)..."
python3 -m pip install --user pipx
fi
pipx ensurepath
;;
*)
exit_with_message "Unsupported operating system for pipx installation."
;;
esac
}
# Function to fetch the latest version of pipx from GitHub and compare with the installed version
check_for_pipx_update() {
echo "Checking for updates to pipx..."
# Fetch the latest version of pipx, ensuring only to capture the numeric version without 'v' prefix.
local latest_version=$(curl -s https://api.github.com/repos/pypa/pipx/releases/latest | grep '"tag_name":' | sed -E 's/.*"tag_name": "v?([^"]+)".*/\1/')
# Extract the current installed version of pipx.
local current_version=$(pipx --version | grep -oE '[0-9]+\.[0-9]+\.[0-9]+')
if [[ "$latest_version" == "$current_version" ]]; then
echo "You have the latest version of pipx ($current_version)."
else
echo "A newer version of pipx ($latest_version) is available. You have $current_version. Do you want to update? (yes/no)"
read -r user_input
if [[ "$user_input" == "yes" ]]; then
echo "Updating pipx..."
case "$OS" in
macOS)
brew upgrade pipx
;;
Linux)
if grep -qEi "(ubuntu|debian)" /etc/*release; then
sudo apt update
sudo apt install --only-upgrade pipx -y
elif grep -qEi "fedora" /etc/*release; then
sudo dnf upgrade pipx -y
else
python3 -m pip install --user --upgrade pipx
fi
;;
*)
exit_with_message "Unsupported operating system for pipx update."
;;
esac
pipx ensurepath
echo "pipx updated to version $latest_version"
else
echo "Not updating pipx at this time."
fi
fi
}
# Now, modify the existing check to call check_for_pipx_update even if pipx is installed
if ! command -v pipx &> /dev/null; then
echo "Pipx is not installed. Installing..."
install_pipx "$OS"
echo "Pipx installed successfully."
else
echo "Pipx is already installed."
check_for_pipx_update
fi
echo "Checking Poetry installation..."
@ -124,7 +40,7 @@ if ! command -v poetry &> /dev/null
then
echo "Poetry is not installed. Installing..."
# Also install python 3.10 and use
pipx install poetry --python python3.10 --fetch-missing-python
curl -sSL https://install.python-poetry.org | python3 -
echo "Poetry installed successfully."
else
echo "Poetry is already installed."
@ -146,3 +62,5 @@ else
echo "Poetry version is $1 or higher. No need to update."
fi

4
src/backend/base/langflow/alembic/versions/1f4d6df60295_add_default_fields_column.py
View file

@ -1,7 +1,7 @@
"""Add default_fields column
Revision ID: 1f4d6df60295
Revises: 58b28437a398
Revises: 6e7b581b5648
Create Date: 2024-04-29 09:49:46.864145
"""
@ -14,7 +14,7 @@ from sqlalchemy.engine.reflection import Inspector
# revision identifiers, used by Alembic.
revision: str = "1f4d6df60295"
down_revision: Union[str, None] = "58b28437a398"
down_revision: Union[str, None] = "6e7b581b5648"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None

13
src/backend/base/langflow/api/utils.py
View file

@ -201,7 +201,7 @@ def format_elapsed_time(elapsed_time: float) -> str:
return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}"
async def build_and_cache_graph(
async def build_and_cache_graph_from_db(
flow_id: str,
session: Session,
chat_service: "ChatService",
@ -220,6 +220,17 @@ async def build_and_cache_graph(
return graph
async def build_and_cache_graph_from_data(
flow_id: str,
chat_service: "ChatService",
graph_data: dict,
): # -> Graph | Any:
"""Build and cache the graph."""
graph = Graph.from_payload(graph_data, flow_id)
await chat_service.set_cache(flow_id, graph)
return graph
def format_syntax_error_message(exc: SyntaxError) -> str:
"""Format a SyntaxError message for returning to the frontend."""
if exc.text is None:

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