Merge branch 'dev' into feature/ui_settings
This commit is contained in:
cristhianzl
commit
8303d367a7
187 changed files with 5702 additions and 2378 deletions
2
.github/workflows/deploy_gh-pages.yml
vendored
2
.github/workflows/deploy_gh-pages.yml
vendored
|
|
@ -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: ${{ secrets.GITHUB_TOKEN }}
|
||||
# Build output to publish to the `gh-pages` branch:
|
||||
|
|
|
|||
2
.github/workflows/pre-release-base.yml
vendored
2
.github/workflows/pre-release-base.yml
vendored
|
|
@ -74,4 +74,4 @@ jobs:
|
|||
push: true
|
||||
file: ./build_and_push_base.Dockerfile
|
||||
tags: |
|
||||
logspace/langflow:base-${{ needs.release.outputs.version }}
|
||||
langflowai/langflow:base-${{ needs.release.outputs.version }}
|
||||
|
|
|
|||
9
.github/workflows/pre-release-langflow.yml
vendored
9
.github/workflows/pre-release-langflow.yml
vendored
|
|
@ -53,7 +53,7 @@ jobs:
|
|||
run: |
|
||||
make publish main=true
|
||||
- name: Upload Artifact
|
||||
uses: actions/upload-artifact@v2
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
|
|
@ -63,6 +63,7 @@ jobs:
|
|||
runs-on: ubuntu-latest
|
||||
needs: release
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v3
|
||||
- name: Set up Docker Buildx
|
||||
|
|
@ -79,15 +80,15 @@ jobs:
|
|||
push: true
|
||||
file: ./build_and_push.Dockerfile
|
||||
tags: |
|
||||
logspace/langflow:${{ needs.release.outputs.version }}
|
||||
logspace/langflow:1.0-alpha
|
||||
langflowai/langflow:${{ needs.release.outputs.version }}
|
||||
langflowai/langflow:1.0-alpha
|
||||
|
||||
create_release:
|
||||
name: Create Release
|
||||
runs-on: ubuntu-latest
|
||||
needs: [docker_build, release]
|
||||
steps:
|
||||
- uses: actions/download-artifact@v2
|
||||
- uses: actions/download-artifact@v4
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
|
|
|
|||
4
.github/workflows/release.yml
vendored
4
.github/workflows/release.yml
vendored
|
|
@ -52,8 +52,8 @@ jobs:
|
|||
push: true
|
||||
file: ./build_and_push.Dockerfile
|
||||
tags: |
|
||||
logspace/langflow:${{ steps.check-version.outputs.version }}
|
||||
logspace/langflow:latest
|
||||
langflowai/langflow:${{ steps.check-version.outputs.version }}
|
||||
langflowai/langflow:latest
|
||||
- name: Create Release
|
||||
uses: ncipollo/release-action@v1
|
||||
with:
|
||||
|
|
|
|||
4
.github/workflows/typescript_test.yml
vendored
4
.github/workflows/typescript_test.yml
vendored
|
|
@ -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: ${{ env.NODE_VERSION }}
|
||||
|
||||
|
|
|
|||
|
|
@ -69,7 +69,7 @@ services:
|
|||
- traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.middlewares=${STACK_NAME?Variable not set}-www-redirect,${STACK_NAME?Variable not set}-https-redirect
|
||||
|
||||
backend: &backend
|
||||
image: "logspace/langflow:latest"
|
||||
image: "langflowai/langflow:latest"
|
||||
depends_on:
|
||||
- db
|
||||
- broker
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ services:
|
|||
- "7860:7860"
|
||||
volumes:
|
||||
- ./:/app
|
||||
command: bash -c "uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --loop asyncio",
|
||||
command: bash -c "uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --loop asyncio"
|
||||
networks:
|
||||
- langflow
|
||||
frontend:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
FROM logspace/langflow:latest
|
||||
FROM langflowai/langflow:latest
|
||||
|
||||
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ The Docker Compose configuration spins up two services: `langflow` and `postgres
|
|||
|
||||
### LangFlow Service
|
||||
|
||||
The `langflow` service uses the `logspace/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service.
|
||||
The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service.
|
||||
|
||||
Environment variables:
|
||||
|
||||
|
|
@ -62,4 +62,4 @@ Volumes:
|
|||
|
||||
## Switching to a Specific LangFlow Version
|
||||
|
||||
If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `logspace/langflow:latest` to `logspace/langflow:1.0-alpha`.
|
||||
If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`.
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ version: "3.8"
|
|||
|
||||
services:
|
||||
langflow:
|
||||
image: logspace/langflow:latest
|
||||
image: langflowai/langflow:latest
|
||||
ports:
|
||||
- "7860:7860"
|
||||
depends_on:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
FROM logspace/langflow:1.0-alpha
|
||||
FROM langflowai/langflow:1.0-alpha
|
||||
|
||||
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ version: "3.8"
|
|||
|
||||
services:
|
||||
langflow:
|
||||
image: logspace/langflow:1.0-alpha
|
||||
image: langflowai/langflow:1.0-alpha
|
||||
ports:
|
||||
- "7860:7860"
|
||||
depends_on:
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ To use a custom component, follow these steps:
|
|||
|
||||
<Admonition type="info" label="Tip">
|
||||
|
||||
For an in-depth explanation of custom components, their rules, and applications, make sure to read [Custom Component guidelines](../guidelines/custom-component).
|
||||
For an in-depth explanation of custom components, their rules, and applications, make sure to read [Custom Component guidelines](../administration/custom-component).
|
||||
|
||||
</Admonition>
|
||||
|
||||
|
|
@ -57,7 +57,7 @@ The CustomComponent class serves as the foundation for creating custom component
|
|||
|
||||
<Admonition type="info">
|
||||
Unlike Langchain types, base Python types do not add a
|
||||
[handle](../guidelines/components) to the field by default. To add handles,
|
||||
[handle](../administration/components) to the field by default. To add handles,
|
||||
use the _`input_types`_ key in the _`build_config`_ method.
|
||||
</Admonition>
|
||||
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ The `PromptTemplate` component allows users to create prompts and define variabl
|
|||
<Admonition type="info">
|
||||
Once a variable is defined in the prompt template, it becomes a component
|
||||
input of its own. Check out [Prompt
|
||||
Customization](../guidelines/prompt-customization) to learn more.
|
||||
Customization](../administration/prompt-customization) to learn more.
|
||||
</Admonition>
|
||||
|
||||
- **template:** Template used to format an individual request.
|
||||
|
|
|
|||
|
|
@ -159,7 +159,7 @@ Now, let's add the [parameters](focus://11[20:55]) and the [return type](focus:/
|
|||
|
||||
- _`flow_name`_ is the name of the flow we want to run.
|
||||
- _`document`_ is the input document to be passed to that flow.
|
||||
- Since _`Document`_ is a Langchain type, it will add an input [handle](../guidelines/components) to the component ([see more](../components/custom)).
|
||||
- Since _`Document`_ is a Langchain type, it will add an input [handle](../administration/components) to the component ([see more](../components/custom)).
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -242,7 +242,7 @@ class FlowRunner(CustomComponent):
|
|||
|
||||
```
|
||||
|
||||
You can load this flow using _`get_flow`_ and set a _`tweaks`_ dictionary to customize it. Find more about tweaks in our [features guidelines](../guidelines/features#code).
|
||||
You can load this flow using _`get_flow`_ and set a _`tweaks`_ dictionary to customize it. Find more about tweaks in our [features guidelines](../administration/features#code).
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
27
docs/docs/getting-started/huggingface-spaces.mdx
Normal file
27
docs/docs/getting-started/huggingface-spaces.mdx
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# 🤗 HuggingFace Spaces
|
||||
|
||||
Hugging Face provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow without any local installation required.
|
||||
|
||||
The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true) or [Langflow 1.0 Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
|
||||
Remember to use a Chromium-based browser for the best experience. You'll be presented with the following screen:
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/duplicate-space.png",
|
||||
dark: "img/duplicate-space.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
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.
|
||||
77
docs/docs/getting-started/install-langflow.mdx
Normal file
77
docs/docs/getting-started/install-langflow.mdx
Normal file
|
|
@ -0,0 +1,77 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# 📦 Install Langflow
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 is also available in a [HuggingFace Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) if you'd rather try it out before installing locally.
|
||||
</Admonition>
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Langflow requires the following programs installed on your system.
|
||||
|
||||
* [Python 3.10](https://www.python.org/downloads/release/python-3100/)
|
||||
|
||||
* [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/)
|
||||
|
||||
## Install Langflow
|
||||
|
||||
To install Langflow:
|
||||
|
||||
pip:
|
||||
```bash
|
||||
python -m pip install langflow -U
|
||||
```
|
||||
|
||||
pipx:
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python
|
||||
```
|
||||
Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually.
|
||||
|
||||
|
||||
## Install Langflow pre-release
|
||||
|
||||
Use `--force-reinstall` to ensure you have the latest version of Langflow and its dependencies.
|
||||
|
||||
To install a pre-release version of Langflow:
|
||||
|
||||
pip:
|
||||
```bash
|
||||
python -m pip install langflow --pre --force-reinstall
|
||||
```
|
||||
|
||||
pipx:
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
|
||||
```
|
||||
|
||||
## Having a problem?
|
||||
|
||||
If you encounter a problem, see [Possible Installation Issues](/migration/possible-installation-issues).
|
||||
|
||||
To get help in the Langflow CLI:
|
||||
|
||||
```bash
|
||||
python -m langflow --help
|
||||
```
|
||||
|
||||
## ⛓️ Run Langflow
|
||||
|
||||
1. To run Langflow, enter the following command.
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
2. Confirm that a local Langflow instance starts by visiting `http://127.0.0.1:7860` in your browser.
|
||||
```bash
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
3. Continue on to the [Quickstart](./quickstart.mdx).
|
||||
10
docs/docs/getting-started/new-to-llms.mdx
Normal file
10
docs/docs/getting-started/new-to-llms.mdx
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
# 📚 New to LLMs?
|
||||
|
||||
Large Language Models, or LLMs, are part of an exciting new world in computing.
|
||||
|
||||
We made Langflow for anyone to create with LLMs, and hope you'll feel comfortable installing Langflow and [getting started](./quickstart.mdx).
|
||||
|
||||
If you want to learn more about LLMs, prompt engineering, and AI models, Langflow recommends [promptingguide.ai](https://promptingguide.ai), an open-source repository of prompt engineering content maintained by AI experts.
|
||||
PromptingGuide offers content for [beginners](https://www.promptingguide.ai/introduction/basics) and [experts](https://www.promptingguide.ai/techniques/cot), as well as the latest [research papers](https://www.promptingguide.ai/papers) and [test results](https://www.promptingguide.ai/research) fueling AI's progress.
|
||||
|
||||
Wherever you are on your AI journey, it's helpful to keep Prompting Guide open in a tab.
|
||||
119
docs/docs/getting-started/quickstart.mdx
Normal file
119
docs/docs/getting-started/quickstart.mdx
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
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";
|
||||
|
||||
# ⚡️ Quickstart
|
||||
|
||||
This quickstart demonstrates how to install Langflow, run it locally, build a basic prompt flow, and modify that prompt for different outcomes.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
* [Python 3.10](https://www.python.org/downloads/release/python-3100/)
|
||||
|
||||
* [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/)
|
||||
|
||||
* [OpenAI API key](https://platform.openai.com)
|
||||
|
||||
## Install Langflow
|
||||
|
||||
<Admonition type="info">
|
||||
Langflow v1.0 is also available in a [HuggingFace Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) if you'd rather try it out before installing locally. This quickstart will run there, too.
|
||||
</Admonition>
|
||||
|
||||
1. To install Langflow, enter the following command in pip or pipx:
|
||||
|
||||
pip:
|
||||
```bash
|
||||
python -m pip install langflow -U
|
||||
```
|
||||
|
||||
pipx:
|
||||
```bash
|
||||
pipx install langflow --python python3.10 --fetch-missing-python
|
||||
```
|
||||
Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually.
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
3. Go to `http://127.0.0.1:7860` and confirm the Langflow UI is available.
|
||||
|
||||
<Admonition type="info">
|
||||
If you encounter a problem, see [Possible Installation Issues](/migration/possible-installation-issues).
|
||||
</Admonition>
|
||||
|
||||
## Create the basic prompting project
|
||||
|
||||
Now that you have Langflow installed and running, let us formally welcome you to Langflow!👋
|
||||
|
||||
You will use Langflow's prompt tools to issue prompts to the OpenAI LLM.
|
||||
|
||||
Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks.
|
||||
|
||||
By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems.
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Basic Prompting**.
|
||||
3. The **Basic Prompting** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/quickstart.png",
|
||||
dark: "img/quickstart.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow allows you to chat with the **OpenAI** component via a **Prompt** component.
|
||||
Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.`
|
||||
This should be interesting...
|
||||
|
||||
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**.
|
||||
|
||||
## Run the basic prompting flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot responds in a markedly piratical manner!
|
||||
|
||||
## Modify the prompt for a different result
|
||||
|
||||
1. To modify your prompt results, in the **Prompt** template, click the **Template** field.
|
||||
The **Edit Prompt** window opens.
|
||||
2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.`
|
||||
3. Run the basic prompting flow again.
|
||||
The response will be markedly different.
|
||||
|
||||
## Next steps
|
||||
|
||||
Well done! You've built your first prompt in Langflow. 🎉
|
||||
|
||||
By adding Langflow components to this prompt, you can build all sorts of interesting flows.
|
||||
|
||||
* [Memory chatbot](/starter-projects/memory-chatbot.mdx)
|
||||
* [Blog writer](/starter-projects/blog-writer.mdx)
|
||||
* [Document QA](/starter-projects/document-qa.mdx)
|
||||
|
||||
|
||||
|
|
@ -130,18 +130,18 @@ 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"
|
||||
|
|
@ -163,7 +163,7 @@ 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.
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,9 @@ import Admonition from "@theme/Admonition";
|
|||
|
||||
# 👋 Welcome to Langflow
|
||||
|
||||
Langflow is an easy way to build from simple to complex AI applications. It is a low-code platform that allows you to integrate AI into everything you do.
|
||||
Langflow is a low-code platform that allows you to integrate AI into everything you do.
|
||||
|
||||
Use Langflow's simple but powerful UI to build any AI application you can dream up, from simple to complex.
|
||||
|
||||
{" "}
|
||||
|
||||
|
|
@ -20,96 +22,17 @@ Langflow is an easy way to build from simple to complex AI applications. It is a
|
|||
|
||||
## 🚀 First steps
|
||||
|
||||
## Installation
|
||||
- [Install Langflow](/getting-started/install-langflow) - Install and start a local Langflow server.
|
||||
|
||||
Make sure you have **Python 3.10** installed on your system.
|
||||
- [Quickstart](/getting-started/quickstart) - Install Langflow, create a flow, and run it.
|
||||
|
||||
You can install **Langflow** with [pipx](https://pipx.pypa.io/stable/installation/) or with pip.
|
||||
- [HuggingFace Spaces](/getting-started/huggingface-spaces) - Duplicate the Langflow preview space and try it out before you install.
|
||||
|
||||
Pipx can fetch the missing Python version for you, but you can also install it manually.
|
||||
- [New to LLMs?](/getting-started/new-to-llms) - Learn more about LLMs, prompting, and more at [promptingguide.ai](https://promptingguide.ai).
|
||||
|
||||
```bash
|
||||
# Remember to check if you have Python 3.10 installed
|
||||
python -m pip install langflow -U
|
||||
# or
|
||||
pipx install langflow --python python3.10 --fetch-missing-python
|
||||
```
|
||||
## Learn more about Langflow 1.0
|
||||
|
||||
Or you can install a pre-release version using:
|
||||
Learn more about the exciting changes in Langflow 1.0, and how to migrate your existing Langflow projects.
|
||||
|
||||
```bash
|
||||
python -m pip install langflow --pre --force-reinstall
|
||||
# or
|
||||
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
|
||||
```
|
||||
|
||||
<Admonition type="tip">
|
||||
<p>
|
||||
Please, check out our [Possible Installation Issues
|
||||
section](/migration/possible-installation-issues) if you encounter any
|
||||
problems.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
We recommend using --force-reinstall to ensure you have the latest version of Langflow and its dependencies.
|
||||
|
||||
### ⛓️ Running Langflow
|
||||
|
||||
Langflow can be run in a variety of ways, including using the command-line interface (CLI) or HuggingFace Spaces.
|
||||
|
||||
```bash
|
||||
python -m langflow run # or langflow --help
|
||||
```
|
||||
|
||||
#### 🤗 HuggingFace Spaces
|
||||
|
||||
Hugging Face provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow without any local installation required.
|
||||
|
||||
The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true) or [Langflow 1.0 Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
|
||||
|
||||
Remember to use a Chromium-based browser for the best experience. You'll be presented with the following screen:
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/duplicate-space.png",
|
||||
dark: "img/duplicate-space.png",
|
||||
}}
|
||||
style={{ width: "100%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
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.
|
||||
|
||||
#### Usage
|
||||
|
||||
You can run the Langflow using the following command:
|
||||
|
||||
```bash
|
||||
langflow run [OPTIONS]
|
||||
```
|
||||
|
||||
Find more information about the available options by running:
|
||||
|
||||
```bash
|
||||
python -m langflow --help
|
||||
```
|
||||
|
||||
## Find out more about 1.0
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We are currently working on updating the documentation for Langflow 1.0.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
To get you learning more about what's new and why you should be excited about Langflow 1.0,
|
||||
go to [A new chapter for Langflow](/whats-new/a-new-chapter-langflow) and also come back often
|
||||
to check out our [migration guides](/whats-new/migrating-to-one-point-zero) as we release them.
|
||||
- [A new chapter for Langflow](/whats-new/a-new-chapter-langflow)
|
||||
- [Migration guides](/migration/migrating-to-one-point-zero)
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
||||
|
|
@ -25,3 +25,16 @@ For this error to occur, two scenarios are possible:
|
|||
In this case, you might not be running the correct executable.
|
||||
To solve this issue, you can run the correct executable by running _`python -m langflow run`_ instead of _`langflow run`_ and if that doesn't work, you can try uninstalling langflow and reinstalling it using _`python -m pip install langflow --pre -U`_.
|
||||
2. Some version conflicts might have occurred during the installation process. Run _`python -m pip install langflow --pre -U --force-reinstall`_ to reinstall langflow and its dependencies.
|
||||
|
||||
## _`Something went wrong running migrations. Please, run 'langflow migration --fix'`_
|
||||
|
||||
TLDR;
|
||||
|
||||
- Clear the cache by deleting the contents of the cache folder.
|
||||
This folder can be found at:
|
||||
- **Linux or WSL2 on Windows**: `home/<username>/.cache/langflow/`
|
||||
- **MacOS**: `/Users/<username>/Library/Caches/langflow/`
|
||||
|
||||
If you wish to retain your files, ensure to back them up before clearing the folder.
|
||||
|
||||
This error often occurs when upgrading Langflow, the new version can't override `langflow-pre.db` in `.cache/langflow/`. Clearing the cache removes this file but will also erase your settings.
|
||||
83
docs/docs/starter-projects/basic-prompting.mdx
Normal file
83
docs/docs/starter-projects/basic-prompting.mdx
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Basic prompting
|
||||
|
||||
Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks.
|
||||
|
||||
By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems.
|
||||
|
||||
This article demonstrates how to use Langflow's prompt tools to issue basic prompts to an LLM, and how various prompting strategies can affect your outcomes.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the basic prompting project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Basic Prompting**.
|
||||
3. The **Basic Prompting** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/basic-prompting.png",
|
||||
dark: "img/basic-prompting.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow allows you to chat with the **OpenAI** component via a **Prompt** component.
|
||||
Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.`
|
||||
This should be interesting...
|
||||
|
||||
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**.
|
||||
|
||||
## Run the basic prompting flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot responds in a markedly piratical manner!
|
||||
|
||||
## Modify the prompt for a different result
|
||||
|
||||
1. To modify your prompt results, in the **Prompt** template, click the **Template** field.
|
||||
The **Edit Prompt** window opens.
|
||||
2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.`
|
||||
3. Run the basic prompting flow again.
|
||||
The response will be markedly different.
|
||||
|
||||
|
||||
|
||||
|
||||
92
docs/docs/starter-projects/blog-writer.mdx
Normal file
92
docs/docs/starter-projects/blog-writer.mdx
Normal file
|
|
@ -0,0 +1,92 @@
|
|||
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";
|
||||
|
||||
# Blog writer
|
||||
|
||||
Build a blog writer with OpenAI that uses URLs for reference content.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```bash
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the Blog Writer project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Blog Writer**.
|
||||
3. The **Blog Writer** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/blog-writer.png",
|
||||
dark: "img/blog-writer.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow creates a one-shot prompt flow with **Prompt**, **OpenAI**, and **Chat Output** components, and augments the flow with reference content and instructions from the **URL** and **Instructions** components.
|
||||
|
||||
The **Prompt** component's default **Template** field looks like this:
|
||||
```bash
|
||||
Reference 1:
|
||||
|
||||
{reference_1}
|
||||
|
||||
---
|
||||
|
||||
Reference 2:
|
||||
|
||||
{reference_2}
|
||||
|
||||
---
|
||||
|
||||
{instructions}
|
||||
|
||||
Blog:
|
||||
|
||||
```
|
||||
|
||||
The `{instructions}` value is received from the **Value** field of the **Instructions** component.
|
||||
The `reference_1` and `reference_2` values are received from the **URL** fields of the **URL** components.
|
||||
|
||||
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**.
|
||||
|
||||
## Run the Blog Writer flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can run your one-shot flow.
|
||||
2. Click the **Lighting Bolt** icon to run your flow.
|
||||
3. The **OpenAI** component constructs a blog post with the **URL** items as context.
|
||||
The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs.
|
||||
|
||||
To write about something different, change the values in the **URL** components, and see what the LLM constructs.
|
||||
82
docs/docs/starter-projects/document-qa.mdx
Normal file
82
docs/docs/starter-projects/document-qa.mdx
Normal file
|
|
@ -0,0 +1,82 @@
|
|||
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";
|
||||
|
||||
# Document QA
|
||||
|
||||
Build a question-and-answer chatbot with a document loaded from local memory.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the Document QA project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Document QA**.
|
||||
3. The **Document QA** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/document-qa.png",
|
||||
dark: "img/document-qa.png",
|
||||
}}
|
||||
style={{ width: "80%", margin: "20px auto" }}
|
||||
/>
|
||||
|
||||
This flow creates a basic chatbot with the **Chat Input**, **Prompt**, **OpenAI**, and **Chat Output** components.
|
||||
This chatbot is augmented with the **Files** component, which loads a file from your local machine into the **Prompt** component as `{Document}`.
|
||||
The **Prompt** component is instructed to answer questions based on the contents of `{Document}`.
|
||||
Including a file with the prompt gives the **OpenAI** component context it may not otherwise have access to.
|
||||
|
||||
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**.
|
||||
|
||||
5. To select a document to load, in the **Files** component, click within the **Path** field.
|
||||
1. Select a local file, and then click **Open**.
|
||||
2. The file name appears in the field.
|
||||
<Admonition type="tip">
|
||||
The file must be of an extension type listed [here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13).
|
||||
</Admonition>
|
||||
|
||||
## Run the Document QA flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
For this example, we loaded an error log `.txt` file and asked, "What went wrong?"
|
||||
The bot responded:
|
||||
```
|
||||
The issue occurred during the execution of migrations in the application. Specifically, an error was raised by the Alembic library, indicating that new upgrade operations were detected that had not been accounted for in the existing migration scripts. The operation in question involved modifying the nullable property of a column (apikey, created_at) in the database, with details about the existing type (DATETIME()), existing server default, and other properties.
|
||||
```
|
||||
|
||||
This result indicates that the bot received the loaded document and understood the context surrounding the vague question. It also correctly identified the issue in the error log, and followed up with appropriate troubleshooting suggestions. Nice!
|
||||
99
docs/docs/starter-projects/memory-chatbot.mdx
Normal file
99
docs/docs/starter-projects/memory-chatbot.mdx
Normal file
|
|
@ -0,0 +1,99 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Memory chatbot
|
||||
|
||||
This flow extends the [basic prompting flow](./basic-prompting.mdx) to include chat memory for unique SessionIDs.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Install Langflow.
|
||||
```bash
|
||||
python -m pip install langflow --pre
|
||||
```
|
||||
|
||||
2. Start a local Langflow instance with the Langflow CLI:
|
||||
```bash
|
||||
langflow run
|
||||
```
|
||||
Or start Langflow with Python:
|
||||
```bash
|
||||
python -m langflow run
|
||||
```
|
||||
|
||||
Result:
|
||||
```
|
||||
│ Welcome to ⛓ Langflow │
|
||||
│ │
|
||||
│ Access http://127.0.0.1:7860 │
|
||||
│ Collaborate, and contribute at our GitHub Repo 🚀 │
|
||||
```
|
||||
|
||||
Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
|
||||
|
||||
3. Create an [OpenAI API key](https://platform.openai.com).
|
||||
|
||||
## Create the memory chatbot project
|
||||
|
||||
1. From the Langflow dashboard, click **New Project**.
|
||||
2. Select **Memory Chatbot**.
|
||||
3. The **Memory Chatbot** flow is created.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/memory-chatbot.png",
|
||||
dark: "img/memory-chatbot.png",
|
||||
}}
|
||||
style={{
|
||||
width: "80%",
|
||||
margin: "20px auto",
|
||||
display: "flex",
|
||||
justifyContent: "center",
|
||||
}}
|
||||
/>
|
||||
|
||||
This flow creates a basic chatbot with the **Chat Input**, **Prompt**, and **OpenAI** components.
|
||||
This chatbot is augmented with the **Chat Memory** component, which stores messages submitted via **Chat Input** and prepends them to subsequent prompts to OpenAI via `{context}`.
|
||||
The **Chat History** component gives the **OpenAI** component a memory of previous questions.
|
||||
|
||||
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**.
|
||||
|
||||
## Run the memory chatbot flow
|
||||
|
||||
1. Click the **Run** button.
|
||||
The **Interaction Panel** opens, where you can converse with your bot.
|
||||
2. Type a message and press Enter.
|
||||
The bot will respond according to the template in the **Prompt** component.
|
||||
3. Type more questions. In the **Outputs** log, your queries are logged in order. Up to 5 queries are stored by default. Try asking `What is the first subject I asked you about?` to see where the LLM's memory disappears.
|
||||
|
||||
## Modify the Session ID field to have multiple conversations
|
||||
|
||||
`SessionID` is a unique identifier in Langchain for a conversation session between a chatbot and a client.
|
||||
A `SessionID` is created when a conversation is initiated, and then associated with all subsequent messages during that session.
|
||||
|
||||
In the **Memory Chatbot** flow you created, the **Chat Memory** component references past interactions with **Chat Input** by **Session ID**.
|
||||
You can demonstrate this by modifying the **Session ID** value to switch between conversation histories.
|
||||
|
||||
1. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value from `MySessionID` to `AnotherSessionID`.
|
||||
2. Click the **Run** button to run your flow.
|
||||
In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button).
|
||||
3. Type a few questions to your bot.
|
||||
4. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value back to `MySessionID`.
|
||||
5. Run your flow.
|
||||
The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`.
|
||||
|
||||
## Store Session ID as a Langflow variable
|
||||
|
||||
To store **Session ID** as a Langflow variable, in the **Session ID** field, click the **Globe** button, and then click **Add New Variable**.
|
||||
|
||||
1. In the **Variable Name** field, enter a name like `customer_chat_emea`.
|
||||
2. In the **Value** field, enter a value like `1B5EBD79-6E9C-4533-B2C8-7E4FF29E983B`.
|
||||
3. Click **Save Variable**.
|
||||
4. Apply this variable to **Chat Input**.
|
||||
|
||||
|
|
@ -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.
|
||||
|
||||
|
|
@ -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
|
||||
|
||||
|
|
@ -61,11 +61,11 @@ We wanted to create start projects that would help you learn about new features
|
|||
|
||||
For now, we have:
|
||||
|
||||
- **[Basic Prompting (Hello, World)](/guides/basic-prompting)**: A simple flow that shows you how to use the Prompt Component and how to talk like a pirate.
|
||||
- **[Vector Store RAG](/guides/rag-with-astradb)**: A flow that shows you how to ingest data into a Vector Store and then use it to run a RAG application.
|
||||
- **[Memory Chatbot](/guides/memory-chatbot)**: This one shows you how to create a simple chatbot that can remember things about the user.
|
||||
- **[Document QA](/guides/document-qa)**: This flow shows you how to build a simple flow that helps you get answers about a document.
|
||||
- **[Blog Writer](/guides/blog-writer)**: Shows you how you can expand on the Prompt variables and be creative about what inputs you add to it.
|
||||
- **[Basic Prompting (Hello, World)](/starter-projects/basic-prompting)**: A simple flow that shows you how to use the Prompt Component and how to talk like a pirate.
|
||||
- **[Vector Store RAG](/tutorials/rag-with-astradb)**: A flow that shows you how to ingest data into a Vector Store and then use it to run a RAG application.
|
||||
- **[Memory Chatbot](/starter-projects/memory-chatbot)**: This one shows you how to create a simple chatbot that can remember things about the user.
|
||||
- **[Document QA](/starter-projects/document-qa)**: This flow shows you how to build a simple flow that helps you get answers about a document.
|
||||
- **[Blog Writer](/starter-projects/blog-writer)**: Shows you how you can expand on the Prompt variables and be creative about what inputs you add to it.
|
||||
|
||||
As always, your feedback is invaluable, so please let us know what you think of the new starter projects and what you would like to see in the future.
|
||||
|
||||
|
|
|
|||
143
docs/sidebars.js
143
docs/sidebars.js
|
|
@ -2,74 +2,49 @@ module.exports = {
|
|||
docs: [
|
||||
{
|
||||
type: "category",
|
||||
label: " Getting Started",
|
||||
label: "What's New?",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"whats-new/a-new-chapter-langflow"
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Getting Started",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"index",
|
||||
"getting-started/cli",
|
||||
// "guides/basic-prompting",
|
||||
// "guides/document-qa",
|
||||
// "guides/blog-writer",
|
||||
// "guides/memory-chatbot",
|
||||
"guides/rag-with-astradb",
|
||||
"getting-started/install-langflow",
|
||||
"getting-started/quickstart",
|
||||
"getting-started/huggingface-spaces",
|
||||
"getting-started/new-to-llms"
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: " What's New",
|
||||
label: "Starter Projects",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"whats-new/a-new-chapter-langflow",
|
||||
"whats-new/migrating-to-one-point-zero",
|
||||
"starter-projects/basic-prompting",
|
||||
"starter-projects/blog-writer",
|
||||
"starter-projects/document-qa",
|
||||
"starter-projects/memory-chatbot"
|
||||
],
|
||||
},
|
||||
|
||||
{
|
||||
type: "category",
|
||||
label: " Migration Guides",
|
||||
label: "Administration",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"migration/possible-installation-issues",
|
||||
// "migration/flow-of-data",
|
||||
"migration/inputs-and-outputs",
|
||||
// "migration/supported-frameworks",
|
||||
// "migration/sidebar-and-interaction-panel",
|
||||
// "migration/new-categories-and-components",
|
||||
"migration/text-and-record",
|
||||
// "migration/custom-component",
|
||||
"migration/compatibility",
|
||||
// "migration/multiple-flows",
|
||||
// "migration/component-status-and-data-passing",
|
||||
// "migration/connecting-output-components",
|
||||
// "migration/renaming-and-editing-components",
|
||||
// "migration/passing-tweaks-and-inputs",
|
||||
"migration/global-variables",
|
||||
// "migration/experimental-components",
|
||||
// "migration/state-management",
|
||||
"administration/login",
|
||||
"administration/api",
|
||||
"administration/cli",
|
||||
"administration/components",
|
||||
"administration/collection",
|
||||
"administration/prompt-customization",
|
||||
"administration/langfuse_integration"
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Guidelines",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"guidelines/login",
|
||||
"guidelines/api",
|
||||
"guidelines/components",
|
||||
// "guidelines/features",
|
||||
"guidelines/collection",
|
||||
"guidelines/prompt-customization",
|
||||
// "guidelines/chat-interface",
|
||||
// "guidelines/chat-widget",
|
||||
// "guidelines/custom-component",
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Extended Components",
|
||||
collapsed: false,
|
||||
items: ["guides/langfuse_integration"],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Core Components",
|
||||
|
|
@ -82,45 +57,69 @@ module.exports = {
|
|||
"components/helpers",
|
||||
"components/vector-stores",
|
||||
"components/embeddings",
|
||||
"components/custom"
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Extended Components",
|
||||
collapsed: false,
|
||||
collapsed: true,
|
||||
items: [
|
||||
"components/agents",
|
||||
"components/chains",
|
||||
"components/loaders",
|
||||
"components/experimental",
|
||||
"components/utilities",
|
||||
"components/memories",
|
||||
"components/model_specs",
|
||||
"components/retrievers",
|
||||
"components/text-splitters",
|
||||
"components/toolkits",
|
||||
"components/tools",
|
||||
"components/tools"
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Example Components",
|
||||
collapsed: true,
|
||||
items: [
|
||||
"examples/flow-runner",
|
||||
"examples/conversation-chain",
|
||||
"examples/buffer-memory",
|
||||
"examples/csv-loader",
|
||||
"examples/searchapi-tool",
|
||||
"examples/serp-api-tool",
|
||||
"examples/python-function"
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Migration Guides",
|
||||
collapsed: false,
|
||||
items: [
|
||||
"migration/possible-installation-issues",
|
||||
"migration/migrating-to-one-point-zero",
|
||||
"migration/inputs-and-outputs",
|
||||
"migration/text-and-record",
|
||||
"migration/compatibility",
|
||||
"migration/global-variables"
|
||||
]
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Tutorials",
|
||||
collapsed: true,
|
||||
items: [
|
||||
"tutorials/chatprompttemplate_guide",
|
||||
"tutorials/loading_document",
|
||||
"tutorials/rag-with-astradb"
|
||||
],
|
||||
},
|
||||
// {
|
||||
// type: "category",
|
||||
// label: "Examples",
|
||||
// collapsed: false,
|
||||
// items: [
|
||||
// // "examples/flow-runner",
|
||||
// // "examples/conversation-chain",
|
||||
// // "examples/buffer-memory",
|
||||
// // "examples/csv-loader",
|
||||
// // "examples/searchapi-tool",
|
||||
// // "examples/serp-api-tool",
|
||||
// // "examples/python-function",
|
||||
// ],
|
||||
// },
|
||||
{
|
||||
type: "category",
|
||||
label: "Deployment",
|
||||
collapsed: false,
|
||||
items: ["deployment/gcp-deployment"],
|
||||
collapsed: true,
|
||||
items: [
|
||||
"deployment/gcp-deployment"
|
||||
],
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
|
|
@ -129,7 +128,7 @@ module.exports = {
|
|||
items: [
|
||||
"contributing/how-contribute",
|
||||
"contributing/github-issues",
|
||||
"contributing/community",
|
||||
"contributing/community"
|
||||
],
|
||||
},
|
||||
],
|
||||
|
|
|
|||
12
docs/static/data/AstraDB-RAG-Flows.json
vendored
12
docs/static/data/AstraDB-RAG-Flows.json
vendored
|
|
@ -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",
|
||||
|
|
|
|||
BIN
docs/static/img/basic-prompting.png
vendored
Normal file
BIN
docs/static/img/basic-prompting.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 271 KiB |
BIN
docs/static/img/blog-writer.png
vendored
Normal file
BIN
docs/static/img/blog-writer.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 524 KiB |
BIN
docs/static/img/document-qa.png
vendored
Normal file
BIN
docs/static/img/document-qa.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 126 KiB |
BIN
docs/static/img/memory-chatbot.png
vendored
Normal file
BIN
docs/static/img/memory-chatbot.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 271 KiB |
BIN
docs/static/img/quickstart.png
vendored
Normal file
BIN
docs/static/img/quickstart.png
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 486 KiB |
2087
poetry.lock
generated
2087
poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "1.0.0a24"
|
||||
version = "1.0.0a30"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Langflow <contact@langflow.org>"]
|
||||
maintainers = [
|
||||
|
|
@ -81,6 +81,7 @@ chromadb = "^0.4.24"
|
|||
langchain-anthropic = "^0.1.6"
|
||||
langchain-astradb = "^0.1.0"
|
||||
langchain-openai = "^0.1.1"
|
||||
zep-python = { version = "^2.0.0rc5", allow-prereleases = true }
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
types-redis = "^4.6.0.5"
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
FROM logspace/backend_build as backend_build
|
||||
FROM langflowai/backend_build as backend_build
|
||||
|
||||
FROM python:3.10-slim
|
||||
WORKDIR /app
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
@ -22,7 +22,6 @@ depends_on: Union[str, Sequence[str], None] = None
|
|||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
table_names = inspector.get_table_names()
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
column_names = [column["name"] for column in inspector.get_columns("variable")]
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
|
|
@ -35,7 +34,6 @@ def upgrade() -> None:
|
|||
def downgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
table_names = inspector.get_table_names()
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
column_names = [column["name"] for column in inspector.get_columns("variable")]
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,59 @@
|
|||
"""Fix nullable
|
||||
|
||||
Revision ID: 6e7b581b5648
|
||||
Revises: 58b28437a398
|
||||
Create Date: 2024-04-30 09:17:45.024688
|
||||
|
||||
"""
|
||||
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
from sqlalchemy.engine.reflection import Inspector
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = "6e7b581b5648"
|
||||
down_revision: Union[str, None] = "58b28437a398"
|
||||
branch_labels: Union[str, Sequence[str], None] = None
|
||||
depends_on: Union[str, Sequence[str], None] = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
table_names = inspector.get_table_names()
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
columns = inspector.get_columns("apikey")
|
||||
column_names = {column["name"]: column for column in columns}
|
||||
with op.batch_alter_table("apikey", schema=None) as batch_op:
|
||||
created_at_column = [column for column in columns if column["name"] == "created_at"][0]
|
||||
if "created_at" in column_names and created_at_column.get("nullable"):
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.DATETIME(),
|
||||
nullable=False,
|
||||
existing_server_default=sa.text("(CURRENT_TIMESTAMP)"), # type: ignore
|
||||
)
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
table_names = inspector.get_table_names()
|
||||
columns = inspector.get_columns("apikey")
|
||||
column_names = {column["name"]: column for column in columns}
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
with op.batch_alter_table("apikey", schema=None) as batch_op:
|
||||
created_at_column = [column for column in columns if column["name"] == "created_at"][0]
|
||||
if "created_at" in column_names and not created_at_column.get("nullable"):
|
||||
batch_op.alter_column(
|
||||
"created_at",
|
||||
existing_type=sa.DATETIME(),
|
||||
nullable=True,
|
||||
existing_server_default=sa.text("(CURRENT_TIMESTAMP)"), # type: ignore
|
||||
)
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
"""Set name and value to not nullable
|
||||
|
||||
Revision ID: c153816fd85f
|
||||
Revises: 1f4d6df60295
|
||||
Create Date: 2024-04-30 14:31:23.898995
|
||||
|
||||
"""
|
||||
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
from sqlalchemy.engine.reflection import Inspector
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = "c153816fd85f"
|
||||
down_revision: Union[str, None] = "1f4d6df60295"
|
||||
branch_labels: Union[str, Sequence[str], None] = None
|
||||
depends_on: Union[str, Sequence[str], None] = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
columns = inspector.get_columns("variable")
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
name_column = [column for column in columns if column["name"] == "name"][0]
|
||||
if name_column and name_column["nullable"]:
|
||||
batch_op.alter_column("name", existing_type=sa.VARCHAR(), nullable=False)
|
||||
value_column = [column for column in columns if column["name"] == "value"][0]
|
||||
if value_column and value_column["nullable"]:
|
||||
batch_op.alter_column("value", existing_type=sa.VARCHAR(), nullable=False)
|
||||
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = Inspector.from_engine(conn) # type: ignore
|
||||
columns = inspector.get_columns("variable")
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
with op.batch_alter_table("variable", schema=None) as batch_op:
|
||||
name_column = [column for column in columns if column["name"] == "name"][0]
|
||||
if name_column and not name_column["nullable"]:
|
||||
batch_op.alter_column("name", existing_type=sa.VARCHAR(), nullable=True)
|
||||
value_column = [column for column in columns if column["name"] == "value"][0]
|
||||
if value_column and not value_column["nullable"]:
|
||||
batch_op.alter_column("name", existing_type=sa.VARCHAR(), nullable=True)
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -8,13 +8,15 @@ from fastapi.responses import StreamingResponse
|
|||
from loguru import logger
|
||||
|
||||
from langflow.api.utils import (
|
||||
build_and_cache_graph,
|
||||
build_and_cache_graph_from_data,
|
||||
build_and_cache_graph_from_db,
|
||||
format_elapsed_time,
|
||||
format_exception_message,
|
||||
get_top_level_vertices,
|
||||
parse_exception,
|
||||
)
|
||||
from langflow.api.v1.schemas import (
|
||||
FlowDataRequest,
|
||||
InputValueRequest,
|
||||
ResultDataResponse,
|
||||
StreamData,
|
||||
|
|
@ -49,9 +51,10 @@ async def try_running_celery_task(vertex, user_id):
|
|||
return vertex
|
||||
|
||||
|
||||
@router.get("/build/{flow_id}/vertices", response_model=VerticesOrderResponse)
|
||||
async def get_vertices(
|
||||
@router.post("/build/{flow_id}/vertices", response_model=VerticesOrderResponse)
|
||||
async def retrieve_vertices_order(
|
||||
flow_id: str,
|
||||
data: Optional[Annotated[Optional[FlowDataRequest], Body(embed=True)]] = None,
|
||||
stop_component_id: Optional[str] = None,
|
||||
start_component_id: Optional[str] = None,
|
||||
chat_service: "ChatService" = Depends(get_chat_service),
|
||||
|
|
@ -62,6 +65,7 @@ async def get_vertices(
|
|||
|
||||
Args:
|
||||
flow_id (str): The ID of the flow.
|
||||
data (Optional[FlowDataRequest], optional): The flow data. Defaults to None.
|
||||
stop_component_id (str, optional): The ID of the stop component. Defaults to None.
|
||||
start_component_id (str, optional): The ID of the start component. Defaults to None.
|
||||
chat_service (ChatService, optional): The chat service dependency. Defaults to Depends(get_chat_service).
|
||||
|
|
@ -76,9 +80,16 @@ async def get_vertices(
|
|||
try:
|
||||
# First, we need to check if the flow_id is in the cache
|
||||
graph = None
|
||||
if cache := await chat_service.get_cache(flow_id):
|
||||
graph = cache.get("result")
|
||||
graph = await build_and_cache_graph(flow_id, session, chat_service, graph)
|
||||
if not data:
|
||||
if cache := await chat_service.get_cache(flow_id):
|
||||
graph = cache.get("result")
|
||||
graph = await build_and_cache_graph_from_db(
|
||||
flow_id=flow_id, session=session, chat_service=chat_service, graph=graph
|
||||
)
|
||||
else:
|
||||
graph = await build_and_cache_graph_from_data(
|
||||
flow_id=flow_id, graph_data=data.model_dump(), chat_service=chat_service
|
||||
)
|
||||
if stop_component_id or start_component_id:
|
||||
try:
|
||||
first_layer = graph.sort_vertices(stop_component_id, start_component_id)
|
||||
|
|
@ -144,7 +155,9 @@ async def build_vertex(
|
|||
if not cache:
|
||||
# If there's no cache
|
||||
logger.warning(f"No cache found for {flow_id}. Building graph starting at {vertex_id}")
|
||||
graph = await build_and_cache_graph(flow_id=flow_id, session=next(get_session()), chat_service=chat_service)
|
||||
graph = await build_and_cache_graph_from_db(
|
||||
flow_id=flow_id, session=next(get_session()), chat_service=chat_service
|
||||
)
|
||||
else:
|
||||
graph = cache.get("result")
|
||||
result_data_response = ResultDataResponse(results={})
|
||||
|
|
|
|||
|
|
@ -130,7 +130,7 @@ async def simplified_run_flow(
|
|||
graph_data = flow.data
|
||||
|
||||
graph_data = process_tweaks(graph_data, input_request.tweaks or {}, stream=stream)
|
||||
graph = Graph.from_payload(graph_data, flow_id=flow_id, user_id=api_key_user.id)
|
||||
graph = Graph.from_payload(graph_data, flow_id=flow_id, user_id=str(api_key_user.id))
|
||||
inputs = [
|
||||
InputValueRequest(components=[], input_value=input_request.input_value, type=input_request.input_type)
|
||||
]
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ class BuildStatus(Enum):
|
|||
|
||||
|
||||
class TweaksRequest(BaseModel):
|
||||
tweaks: Optional[Dict[str, Dict[str, str]]] = Field(default_factory=dict)
|
||||
tweaks: Optional[Dict[str, Dict[str, Any]]] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class UpdateTemplateRequest(BaseModel):
|
||||
|
|
@ -294,3 +294,15 @@ class SimplifiedAPIRequest(BaseModel):
|
|||
)
|
||||
tweaks: Optional[Tweaks] = Field(default=None, description="The tweaks")
|
||||
session_id: Optional[str] = Field(default=None, description="The session id")
|
||||
|
||||
|
||||
# (alias) type ReactFlowJsonObject<NodeData = any, EdgeData = any> = {
|
||||
# nodes: Node<NodeData>[];
|
||||
# edges: Edge<EdgeData>[];
|
||||
# viewport: Viewport;
|
||||
# }
|
||||
# import ReactFlowJsonObject
|
||||
class FlowDataRequest(BaseModel):
|
||||
nodes: List[dict]
|
||||
edges: List[dict]
|
||||
viewport: Optional[dict] = None
|
||||
|
|
|
|||
|
|
@ -37,7 +37,11 @@ def create_variable(
|
|||
variable_dict["user_id"] = current_user.id
|
||||
|
||||
db_variable = Variable.model_validate(variable_dict)
|
||||
if not db_variable.value:
|
||||
if not db_variable.name and not db_variable.value:
|
||||
raise HTTPException(status_code=400, detail="Variable name and value cannot be empty")
|
||||
elif not db_variable.name:
|
||||
raise HTTPException(status_code=400, detail="Variable name cannot be empty")
|
||||
elif not db_variable.value:
|
||||
raise HTTPException(status_code=400, detail="Variable value cannot be empty")
|
||||
encrypted = auth_utils.encrypt_api_key(db_variable.value, settings_service=settings_service)
|
||||
db_variable.value = encrypted
|
||||
|
|
|
|||
|
|
@ -1,10 +1,9 @@
|
|||
import warnings
|
||||
from typing import Optional, Union
|
||||
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.memory import add_messages
|
||||
from langflow.memory import store_message
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
|
|
@ -50,34 +49,16 @@ class ChatComponent(CustomComponent):
|
|||
sender: Optional[str] = None,
|
||||
sender_name: Optional[str] = None,
|
||||
) -> list[Record]:
|
||||
if not message:
|
||||
warnings.warn("No message provided.")
|
||||
return []
|
||||
|
||||
if not session_id or not sender or not sender_name:
|
||||
raise ValueError("All of session_id, sender, and sender_name must be provided.")
|
||||
if isinstance(message, Record):
|
||||
record = message
|
||||
record.data.update(
|
||||
{
|
||||
"session_id": session_id,
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
}
|
||||
)
|
||||
else:
|
||||
record = Record(
|
||||
data={
|
||||
"text": message,
|
||||
"session_id": session_id,
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
},
|
||||
)
|
||||
records = store_message(
|
||||
message,
|
||||
session_id=session_id,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
)
|
||||
|
||||
self.status = record
|
||||
records = add_messages([record])
|
||||
return records[0]
|
||||
self.status = records
|
||||
return records
|
||||
|
||||
def build_with_record(
|
||||
self,
|
||||
|
|
|
|||
51
src/backend/base/langflow/base/memory/memory.py
Normal file
51
src/backend/base/langflow/base/memory/memory.py
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class BaseMemoryComponent(CustomComponent):
|
||||
display_name = "Chat Memory"
|
||||
description = "Retrieves stored chat messages given a specific Session ID."
|
||||
beta: bool = True
|
||||
icon = "history"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"sender": {
|
||||
"options": ["Machine", "User", "Machine and User"],
|
||||
"display_name": "Sender Type",
|
||||
},
|
||||
"sender_name": {"display_name": "Sender Name", "advanced": True},
|
||||
"n_messages": {
|
||||
"display_name": "Number of Messages",
|
||||
"info": "Number of messages to retrieve.",
|
||||
},
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"order": {
|
||||
"options": ["Ascending", "Descending"],
|
||||
"display_name": "Order",
|
||||
"info": "Order of the messages.",
|
||||
"advanced": True,
|
||||
},
|
||||
"record_template": {
|
||||
"display_name": "Record Template",
|
||||
"multiline": True,
|
||||
"info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
raise NotImplementedError
|
||||
|
||||
def add_message(
|
||||
self, sender: str, sender_name: str, text: str, session_id: str, metadata: Optional[dict] = None, **kwargs
|
||||
):
|
||||
raise NotImplementedError
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.memory import get_messages, store_message
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class StoreMessageComponent(CustomComponent):
|
||||
display_name = "Store Message"
|
||||
description = "Stores a chat message given a Session ID."
|
||||
beta: bool = True
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"sender": {
|
||||
"options": ["Machine", "User"],
|
||||
"display_name": "Sender Type",
|
||||
},
|
||||
"sender_name": {"display_name": "Sender Name"},
|
||||
"message": {"display_name": "Message"},
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
sender: str = "User",
|
||||
sender_name: Optional[str] = None,
|
||||
session_id: Optional[str] = None,
|
||||
message: str = "",
|
||||
) -> List[Record]:
|
||||
|
||||
store_message(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
session_id=session_id,
|
||||
message=message,
|
||||
)
|
||||
|
||||
self.status = get_messages(session_id=session_id)
|
||||
return get_messages(session_id=session_id)
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.schema import Record
|
||||
from langflow.field_typing import Text
|
||||
|
||||
class TextOperatorComponent(CustomComponent):
|
||||
display_name = "Text Operator"
|
||||
description = "Compares two text inputs based on a specified condition such as equality or inequality, with optional case sensitivity."
|
||||
|
||||
def build_config(self) -> dict:
|
||||
return {
|
||||
"input_text": {
|
||||
"display_name": "Input Text",
|
||||
"info": "The primary text input for the operation.",
|
||||
},
|
||||
"match_text": {
|
||||
"display_name": "Match Text",
|
||||
"info": "The text input to compare against.",
|
||||
},
|
||||
"operator": {
|
||||
"display_name": "Operator",
|
||||
"info": "The operator to apply for comparing the texts.",
|
||||
"options": ["equals", "not equals", "contains", "starts with", "ends with"],
|
||||
},
|
||||
"case_sensitive": {
|
||||
"display_name": "Case Sensitive",
|
||||
"info": "If true, the comparison will be case sensitive.",
|
||||
"field_type": "bool",
|
||||
"default": False,
|
||||
}
|
||||
}
|
||||
|
||||
def build(self, input_text: Text, match_text: Text, operator: Text, case_sensitive: bool = False) -> Text:
|
||||
if not input_text or not match_text:
|
||||
raise ValueError("Both 'input_text' and 'match_text' must be provided and non-empty.")
|
||||
|
||||
if not case_sensitive:
|
||||
input_text = input_text.lower()
|
||||
match_text = match_text.lower()
|
||||
|
||||
result = False
|
||||
if operator == "equals":
|
||||
result = input_text == match_text
|
||||
elif operator == "not equals":
|
||||
result = input_text != match_text
|
||||
elif operator == "contains":
|
||||
result = match_text in input_text
|
||||
elif operator == "starts with":
|
||||
result = input_text.startswith(match_text)
|
||||
elif operator == "ends with":
|
||||
result = input_text.endswith(match_text)
|
||||
|
||||
if not result:
|
||||
self.stop()
|
||||
self.status = f"{result} \n\n {input_text}"
|
||||
return input_text
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class CombineTextsUnsortedComponent(CustomComponent):
|
||||
display_name = "Combine Texts (Unsorted)"
|
||||
description = "Concatenate text sources into a single text chunk using a specified delimiter."
|
||||
icon = "merge"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"texts": {
|
||||
"display_name": "Texts",
|
||||
"info": "The first text input to concatenate.",
|
||||
},
|
||||
"delimiter": {
|
||||
"display_name": "Delimiter",
|
||||
"info": "A string used to separate the two text inputs. Defaults to a whitespace.",
|
||||
},
|
||||
}
|
||||
|
||||
def build(self, texts: list[str], delimiter: str = " ") -> Text:
|
||||
combined = delimiter.join(texts)
|
||||
self.status = combined
|
||||
return combined
|
||||
|
|
@ -1,12 +1,13 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.helpers.record import records_to_text
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.memory import get_messages
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class MemoryComponent(CustomComponent):
|
||||
class MemoryComponent(BaseMemoryComponent):
|
||||
display_name = "Chat Memory"
|
||||
description = "Retrieves stored chat messages given a specific Session ID."
|
||||
beta: bool = True
|
||||
|
|
@ -42,6 +43,24 @@ class MemoryComponent(CustomComponent):
|
|||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
# Validate kwargs by checking if it contains the correct keys
|
||||
if "sender" not in kwargs:
|
||||
kwargs["sender"] = None
|
||||
if "sender_name" not in kwargs:
|
||||
kwargs["sender_name"] = None
|
||||
if "session_id" not in kwargs:
|
||||
kwargs["session_id"] = None
|
||||
if "limit" not in kwargs:
|
||||
kwargs["limit"] = 5
|
||||
if "order" not in kwargs:
|
||||
kwargs["order"] = "Descending"
|
||||
|
||||
kwargs["order"] = "DESC" if kwargs["order"] == "Descending" else "ASC"
|
||||
if kwargs["sender"] == "Machine and User":
|
||||
kwargs["sender"] = None
|
||||
return get_messages(**kwargs)
|
||||
|
||||
def build(
|
||||
self,
|
||||
sender: Optional[str] = "Machine and User",
|
||||
|
|
@ -51,10 +70,7 @@ class MemoryComponent(CustomComponent):
|
|||
order: Optional[str] = "Descending",
|
||||
record_template: Optional[str] = "{sender_name}: {text}",
|
||||
) -> Text:
|
||||
order = "DESC" if order == "Descending" else "ASC"
|
||||
if sender == "Machine and User":
|
||||
sender = None
|
||||
messages = get_messages(
|
||||
messages = self.get_messages(
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
session_id=session_id,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,30 @@
|
|||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text
|
||||
|
||||
|
||||
class ShouldRunNextComponent(CustomComponent):
|
||||
display_name = "Should Run Next"
|
||||
description = "Determines if a vertex is runnable."
|
||||
|
||||
def build(self, llm: BaseLanguageModel, question: str, context: str, retries: int = 3) -> Text:
|
||||
template = "Given the following question and the context below, answer with a yes or no.\n\n{error_message}\n\nQuestion: {question}\n\nContext: {context}\n\nAnswer:"
|
||||
|
||||
prompt = PromptTemplate.from_template(template)
|
||||
chain = prompt | llm
|
||||
error_message = ""
|
||||
for i in range(retries):
|
||||
result = chain.invoke(dict(question=question, context=context, error_message=error_message))
|
||||
if isinstance(result, BaseMessage):
|
||||
content = result.content
|
||||
elif isinstance(result, str):
|
||||
content = result
|
||||
if isinstance(content, str) and content.lower().strip() in ["yes", "no"]:
|
||||
break
|
||||
condition = str(content).lower().strip() == "yes"
|
||||
self.status = f"Should Run Next: {condition}"
|
||||
if condition is False:
|
||||
self.stop()
|
||||
return context
|
||||
|
|
@ -7,7 +7,7 @@ from langflow.schema import Record
|
|||
|
||||
class ChatInput(ChatComponent):
|
||||
display_name = "Chat Input"
|
||||
description = "Get chat inputs from the Interaction Panel."
|
||||
description = "Get chat inputs from the Playground."
|
||||
icon = "ChatInput"
|
||||
|
||||
def build_config(self):
|
||||
|
|
|
|||
48
src/backend/base/langflow/components/inputs/FileInput.py
Normal file
48
src/backend/base/langflow/components/inputs/FileInput.py
Normal file
|
|
@ -0,0 +1,48 @@
|
|||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.schema import Record
|
||||
|
||||
|
||||
class FileInput(CustomComponent):
|
||||
display_name = "File Input"
|
||||
description = "A generic file input."
|
||||
icon = "file-text"
|
||||
|
||||
def build_config(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"path": {
|
||||
"display_name": "Path",
|
||||
"field_type": "file",
|
||||
"file_types": TEXT_FILE_TYPES,
|
||||
"info": f"Supported file types: {', '.join(TEXT_FILE_TYPES)}",
|
||||
},
|
||||
"silent_errors": {
|
||||
"display_name": "Silent Errors",
|
||||
"advanced": True,
|
||||
"info": "If true, errors will not raise an exception.",
|
||||
},
|
||||
}
|
||||
|
||||
def load_file(self, path: str, silent_errors: bool = False) -> Record:
|
||||
resolved_path = self.resolve_path(path)
|
||||
path_obj = Path(resolved_path)
|
||||
extension = path_obj.suffix[1:].lower()
|
||||
if extension == "doc":
|
||||
raise ValueError("doc files are not supported. Please save as .docx")
|
||||
if extension not in TEXT_FILE_TYPES:
|
||||
raise ValueError(f"Unsupported file type: {extension}")
|
||||
record = parse_text_file_to_record(resolved_path, silent_errors)
|
||||
self.status = record if record else "No data"
|
||||
return record or Record()
|
||||
|
||||
def build(
|
||||
self,
|
||||
path: str,
|
||||
silent_errors: bool = False,
|
||||
) -> Record:
|
||||
record = self.load_file(path, silent_errors)
|
||||
self.status = record
|
||||
return record
|
||||
17
src/backend/base/langflow/components/inputs/JsonInput.py
Normal file
17
src/backend/base/langflow/components/inputs/JsonInput.py
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
from langflow.base.io.text import TextComponent
|
||||
from langflow.field_typing.constants import Data, NestedDict
|
||||
|
||||
class JsonInput(TextComponent):
|
||||
display_name = "JSON Input"
|
||||
description = "JSON Input."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "JSON",
|
||||
"field_type": "NestedDict"
|
||||
}
|
||||
}
|
||||
|
||||
def build(self, input_value: NestedDict) -> NestedDict:
|
||||
return input_value
|
||||
19
src/backend/base/langflow/components/inputs/KeyPairInput.py
Normal file
19
src/backend/base/langflow/components/inputs/KeyPairInput.py
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
from langflow.base.io.text import TextComponent
|
||||
from langflow.field_typing.constants import Data
|
||||
|
||||
|
||||
class KeyPairInput(TextComponent):
|
||||
display_name = "Dictionary Input"
|
||||
description = "Dictionary Input."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Dictionaries",
|
||||
"field_type": "dict",
|
||||
"list": True
|
||||
}
|
||||
}
|
||||
|
||||
def build(self, input_value: dict) -> dict:
|
||||
return input_value
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
# from langflow.field_typing import Data
|
||||
from langflow.schema import Record
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
|
||||
class StringListInput(CustomComponent):
|
||||
display_name = "String List Input"
|
||||
|
||||
def build_config(self):
|
||||
return {"input_value": {"display_name": "String List Input", "field_type": "str", "list": True}}
|
||||
|
||||
def build(self, input_value: list) -> Record:
|
||||
return Record(data=input_value)
|
||||
|
|
@ -6,7 +6,7 @@ from langflow.field_typing import Text
|
|||
|
||||
class TextInput(TextComponent):
|
||||
display_name = "Text Input"
|
||||
description = "Get text inputs from the Interaction Panel."
|
||||
description = "Get text inputs from the Playground."
|
||||
icon = "type"
|
||||
|
||||
def build_config(self):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,137 @@
|
|||
from typing import Optional, cast
|
||||
|
||||
from langchain_community.chat_message_histories.zep import SearchScope, SearchType, ZepChatMessageHistory
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class ZepMessageReaderComponent(BaseMemoryComponent):
|
||||
display_name = "Zep Message Reader"
|
||||
description = "Retrieves stored chat messages from Zep."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"url": {
|
||||
"display_name": "Zep URL",
|
||||
"info": "URL of the Zep instance.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"api_key": {
|
||||
"display_name": "Zep API Key",
|
||||
"info": "API Key for the Zep instance.",
|
||||
"password": True,
|
||||
},
|
||||
"query": {
|
||||
"display_name": "Query",
|
||||
"info": "Query to search for in the chat history.",
|
||||
},
|
||||
"metadata": {
|
||||
"display_name": "Metadata",
|
||||
"info": "Optional metadata to attach to the message.",
|
||||
"advanced": True,
|
||||
},
|
||||
"search_scope": {
|
||||
"options": ["Messages", "Summary"],
|
||||
"display_name": "Search Scope",
|
||||
"info": "Scope of the search.",
|
||||
"advanced": True,
|
||||
},
|
||||
"search_type": {
|
||||
"options": ["Similarity", "MMR"],
|
||||
"display_name": "Search Type",
|
||||
"info": "Type of search.",
|
||||
"advanced": True,
|
||||
},
|
||||
"limit": {
|
||||
"display_name": "Limit",
|
||||
"info": "Limit of search results.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def get_messages(self, **kwargs) -> list[Record]:
|
||||
"""
|
||||
Retrieves messages from the ZepChatMessageHistory memory.
|
||||
|
||||
If a query is provided, the search method is used to search for messages in the memory, otherwise all messages are returned.
|
||||
|
||||
Args:
|
||||
memory (ZepChatMessageHistory): The ZepChatMessageHistory instance to retrieve messages from.
|
||||
query (str, optional): The query string to search for messages. Defaults to None.
|
||||
metadata (dict, optional): Additional metadata to filter the search results. Defaults to None.
|
||||
search_scope (str, optional): The scope of the search. Can be 'messages' or 'summary'. Defaults to 'messages'.
|
||||
search_type (str, optional): The type of search. Can be 'similarity' or 'exact'. Defaults to 'similarity'.
|
||||
limit (int, optional): The maximum number of search results to return. Defaults to None.
|
||||
|
||||
Returns:
|
||||
list[Record]: A list of Record objects representing the search results.
|
||||
"""
|
||||
memory: ZepChatMessageHistory = cast(ZepChatMessageHistory, kwargs.get("memory"))
|
||||
if not memory:
|
||||
raise ValueError("ZepChatMessageHistory instance is required.")
|
||||
query = kwargs.get("query")
|
||||
search_scope = kwargs.get("search_scope", SearchScope.messages).lower()
|
||||
search_type = kwargs.get("search_type", SearchType.similarity).lower()
|
||||
limit = kwargs.get("limit")
|
||||
|
||||
if query:
|
||||
memory_search_results = memory.search(
|
||||
query,
|
||||
search_scope=search_scope,
|
||||
search_type=search_type,
|
||||
limit=limit,
|
||||
)
|
||||
# Get the messages from the search results if the search scope is messages
|
||||
result_dicts = []
|
||||
for result in memory_search_results:
|
||||
result_dict = {}
|
||||
if search_scope == SearchScope.messages:
|
||||
result_dict["text"] = result.message
|
||||
else:
|
||||
result_dict["text"] = result.summary
|
||||
result_dict["metadata"] = result.metadata
|
||||
result_dict["score"] = result.score
|
||||
result_dicts.append(result_dict)
|
||||
results = [Record(data=result_dict) for result_dict in result_dicts]
|
||||
else:
|
||||
messages = memory.messages
|
||||
results = [Record.from_lc_message(message) for message in messages]
|
||||
return results
|
||||
|
||||
def build(
|
||||
self,
|
||||
session_id: Text,
|
||||
url: Optional[Text] = None,
|
||||
api_key: Optional[Text] = None,
|
||||
query: Optional[Text] = None,
|
||||
search_scope: SearchScope = SearchScope.messages,
|
||||
search_type: SearchType = SearchType.similarity,
|
||||
limit: Optional[int] = None,
|
||||
) -> list[Record]:
|
||||
try:
|
||||
from zep_python import ZepClient
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import zep-python package. " "Please install it with `pip install zep-python`."
|
||||
)
|
||||
if url == "":
|
||||
url = None
|
||||
zep_client = ZepClient(api_url=url, api_key=api_key)
|
||||
memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
|
||||
records = self.get_messages(
|
||||
memory=memory,
|
||||
query=query,
|
||||
search_scope=search_scope,
|
||||
search_type=search_type,
|
||||
limit=limit,
|
||||
)
|
||||
self.status = records
|
||||
return records
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
from typing import Optional, TYPE_CHECKING
|
||||
|
||||
|
||||
from langflow.base.memory.memory import BaseMemoryComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
|
||||
|
||||
class ZepMessageWriterComponent(BaseMemoryComponent):
|
||||
display_name = "Zep Message Writer"
|
||||
description = "Writes a message to Zep."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"session_id": {
|
||||
"display_name": "Session ID",
|
||||
"info": "Session ID of the chat history.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"url": {
|
||||
"display_name": "Zep URL",
|
||||
"info": "URL of the Zep instance.",
|
||||
"input_types": ["Text"],
|
||||
},
|
||||
"api_key": {
|
||||
"display_name": "Zep API Key",
|
||||
"info": "API Key for the Zep instance.",
|
||||
"password": True,
|
||||
},
|
||||
"limit": {
|
||||
"display_name": "Limit",
|
||||
"info": "Limit of search results.",
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {
|
||||
"display_name": "Input Record",
|
||||
"info": "Record to write to Zep.",
|
||||
},
|
||||
}
|
||||
|
||||
def add_message(
|
||||
self, sender: Text, sender_name: Text, text: Text, session_id: Text, metadata: dict | None = None, **kwargs
|
||||
):
|
||||
"""
|
||||
Adds a message to the ZepChatMessageHistory memory.
|
||||
|
||||
Args:
|
||||
sender (Text): The type of the message sender. Valid values are "Machine" or "User".
|
||||
sender_name (Text): The name of the message sender.
|
||||
text (Text): The content of the message.
|
||||
session_id (Text): The session ID associated with the message.
|
||||
metadata (dict | None, optional): Additional metadata for the message. Defaults to None.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Raises:
|
||||
ValueError: If the ZepChatMessageHistory instance is not provided.
|
||||
|
||||
"""
|
||||
memory: ZepChatMessageHistory | None = kwargs.pop("memory", None)
|
||||
if memory is None:
|
||||
raise ValueError("ZepChatMessageHistory instance is required.")
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
metadata["sender_name"] = sender_name
|
||||
metadata.update(kwargs)
|
||||
if sender == "Machine":
|
||||
memory.add_ai_message(text, metadata=metadata)
|
||||
elif sender == "User":
|
||||
memory.add_user_message(text, metadata=metadata)
|
||||
else:
|
||||
raise ValueError(f"Invalid sender type: {sender}")
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Record,
|
||||
session_id: Text,
|
||||
url: Optional[Text] = None,
|
||||
api_key: Optional[Text] = None,
|
||||
) -> Record:
|
||||
try:
|
||||
from zep_python import ZepClient
|
||||
from zep_python.langchain import ZepChatMessageHistory
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import zep-python package. " "Please install it with `pip install zep-python`."
|
||||
)
|
||||
if url == "":
|
||||
url = None
|
||||
zep_client = ZepClient(api_url=url, api_key=api_key)
|
||||
memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
|
||||
self.add_message(**input_value.data, memory=memory)
|
||||
self.status = f"Added message to Zep memory for session {session_id}"
|
||||
return input_value
|
||||
|
|
@ -1,6 +1,5 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.llms.base import BaseLanguageModel
|
||||
from langchain_openai import AzureChatOpenAI
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMExce
|
|||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class ChatLiteLLMModelComponent(LCModelComponent):
|
||||
|
|
|
|||
17
src/backend/base/langflow/components/outputs/CSVOutput.py
Normal file
17
src/backend/base/langflow/components/outputs/CSVOutput.py
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.base.io.text import TextComponent
|
||||
from langflow.field_typing import Text, Data
|
||||
|
||||
|
||||
class CSVOutput(TextComponent):
|
||||
display_name = "CSV Output"
|
||||
description = "Used view csv files"
|
||||
|
||||
field_config = {
|
||||
"input_value": {"display_name": "csv","info":"A csv blob","input_types":["Data"]},
|
||||
"separator": {"display_name": "separator","info":"The separator used in the csv file","input_types":["Text"], "field_type":"Text","default_value":";","options":[";", ",", "|"]},
|
||||
}
|
||||
|
||||
def build(self, input_value: Data, separator) -> Data:
|
||||
return {"data": input_value, "separator": separator}
|
||||
|
|
@ -7,7 +7,7 @@ from langflow.schema import Record
|
|||
|
||||
class ChatOutput(ChatComponent):
|
||||
display_name = "Chat Output"
|
||||
description = "Display a chat message in the Interaction Panel."
|
||||
description = "Display a chat message in the Playground."
|
||||
icon = "ChatOutput"
|
||||
|
||||
def build(
|
||||
|
|
|
|||
15
src/backend/base/langflow/components/outputs/ImageOutput.py
Normal file
15
src/backend/base/langflow/components/outputs/ImageOutput.py
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.base.io.text import TextComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
class ImageOutput(TextComponent):
|
||||
display_name = "Image Output"
|
||||
description = "Used view image files"
|
||||
|
||||
field_config = {
|
||||
"input_value": {"display_name": "image","info":"A image url","input_types":["Text"]},
|
||||
}
|
||||
|
||||
def build(self, input_value: Text) -> Text:
|
||||
return input_value
|
||||
17
src/backend/base/langflow/components/outputs/JsonOutput.py
Normal file
17
src/backend/base/langflow/components/outputs/JsonOutput.py
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
from langflow.base.io.text import TextComponent
|
||||
from langflow.field_typing.constants import Data, NestedDict
|
||||
|
||||
class JsonOutput(TextComponent):
|
||||
display_name = "JSON Output"
|
||||
description = "JSON Output."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "JSON",
|
||||
"field_type": "NestedDict"
|
||||
}
|
||||
}
|
||||
|
||||
def build(self, input_value: NestedDict) -> NestedDict:
|
||||
return input_value
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
from langflow.base.io.text import TextComponent
|
||||
from langflow.field_typing.constants import Data
|
||||
|
||||
|
||||
class KeyPairOutput(TextComponent):
|
||||
display_name = "Dictionary Output"
|
||||
description = "Dictionary Output."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {
|
||||
"display_name": "Dictionaries",
|
||||
"field_type": "dict",
|
||||
"list": True
|
||||
}
|
||||
}
|
||||
|
||||
def build(self, input_value: dict) -> dict:
|
||||
return input_value
|
||||
16
src/backend/base/langflow/components/outputs/PDFOutput.py
Normal file
16
src/backend/base/langflow/components/outputs/PDFOutput.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
from typing import Optional
|
||||
|
||||
from langflow.base.io.text import TextComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class PDFOutput(TextComponent):
|
||||
display_name = "PDF Output"
|
||||
description = "Used view pdf files"
|
||||
|
||||
field_config = {
|
||||
"input_value": {"display_name": "pdf","info":"A pdf url","input_types":["Text"]},
|
||||
}
|
||||
|
||||
def build(self, input_value: Text) -> Text:
|
||||
return input_value
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
# from langflow.field_typing import Data
|
||||
from langflow.schema import Record
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
|
||||
class StringListOutput(CustomComponent):
|
||||
display_name = "String List Output"
|
||||
|
||||
def build_config(self):
|
||||
return {"input_value": {"display_name": "String List Output", "field_type": "str", "list": True}}
|
||||
|
||||
def build(self, input_value: list) -> Record:
|
||||
return Record(data=input_value)
|
||||
|
|
@ -6,7 +6,7 @@ from langflow.field_typing import Text
|
|||
|
||||
class TextOutput(TextComponent):
|
||||
display_name = "Text Output"
|
||||
description = "Display a text output in the Interaction Panel."
|
||||
description = "Display a text output in the Playground."
|
||||
icon = "type"
|
||||
|
||||
def build_config(self):
|
||||
|
|
|
|||
|
|
@ -61,10 +61,10 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
|
|||
input_value: Text,
|
||||
search_type: str,
|
||||
url: str,
|
||||
index_name: str,
|
||||
number_of_results: int = 4,
|
||||
search_by_text: bool = False,
|
||||
api_key: Optional[str] = None,
|
||||
index_name: Optional[str] = None,
|
||||
text_key: str = "text",
|
||||
embedding: Optional[Embeddings] = None,
|
||||
attributes: Optional[list] = None,
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ import weaviate # type: ignore
|
|||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore, Weaviate
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
|
@ -50,9 +51,9 @@ class WeaviateVectorStoreComponent(CustomComponent):
|
|||
def build(
|
||||
self,
|
||||
url: str,
|
||||
index_name: str,
|
||||
search_by_text: bool = False,
|
||||
api_key: Optional[str] = None,
|
||||
index_name: Optional[str] = None,
|
||||
text_key: str = "text",
|
||||
embedding: Optional[Embeddings] = None,
|
||||
inputs: Optional[Record] = None,
|
||||
|
|
@ -78,11 +79,13 @@ class WeaviateVectorStoreComponent(CustomComponent):
|
|||
return pascal_case_word
|
||||
|
||||
index_name = _to_pascal_case(index_name) if index_name else None
|
||||
documents = []
|
||||
if not index_name:
|
||||
raise ValueError("Index name is required")
|
||||
documents: list[Document] = []
|
||||
for _input in inputs or []:
|
||||
if isinstance(_input, Record):
|
||||
documents.append(_input.to_lc_document())
|
||||
else:
|
||||
elif isinstance(_input, Document):
|
||||
documents.append(_input)
|
||||
|
||||
if documents and embedding is not None:
|
||||
|
|
|
|||
|
|
@ -232,3 +232,4 @@ output_parsers:
|
|||
custom_components:
|
||||
CustomComponent:
|
||||
documentation: "https://docs.langflow.org/guidelines/custom-component"
|
||||
# documentation: "https://docs.langflow.org/administration/custom-component"
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ import uuid
|
|||
from collections import defaultdict, deque
|
||||
from functools import partial
|
||||
from itertools import chain
|
||||
from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Optional, Type, Union
|
||||
from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Optional, Tuple, Type, Union
|
||||
|
||||
from loguru import logger
|
||||
|
||||
|
|
@ -14,7 +14,7 @@ from langflow.graph.graph.state_manager import GraphStateManager
|
|||
from langflow.graph.graph.utils import process_flow
|
||||
from langflow.graph.schema import InterfaceComponentTypes, RunOutputs
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.vertex.types import ChatVertex, FileToolVertex, LLMVertex, RoutingVertex, StateVertex, ToolkitVertex
|
||||
from langflow.graph.vertex.types import ChatVertex, FileToolVertex, LLMVertex, StateVertex, ToolkitVertex
|
||||
from langflow.interface.tools.constants import FILE_TOOLS
|
||||
from langflow.schema import Record
|
||||
from langflow.schema.schema import INPUT_FIELD_NAME, InputType
|
||||
|
|
@ -75,7 +75,7 @@ class Graph:
|
|||
self.vertices: List[Vertex] = []
|
||||
self.run_manager = RunnableVerticesManager()
|
||||
self._build_graph()
|
||||
self.build_graph_maps()
|
||||
self.build_graph_maps(self.edges)
|
||||
self.define_vertices_lists()
|
||||
self.state_manager = GraphStateManager()
|
||||
|
||||
|
|
@ -130,6 +130,18 @@ class Graph:
|
|||
):
|
||||
vertices_ids.append(vertex_id)
|
||||
successors = self.get_all_successors(vertex, flat=True)
|
||||
# Update run_manager.run_predecessors because we are activating vertices
|
||||
# The run_prdecessors is the predecessor map of the vertices
|
||||
# we remove the vertex_id from the predecessor map whenever we run a vertex
|
||||
# So we need to get all edges of the vertex and successors
|
||||
# and run self.build_adjacency_maps(edges) to get the new predecessor map
|
||||
# that is not complete but we can use to update the run_predecessors
|
||||
edges_set = set()
|
||||
for vertex in [vertex] + successors:
|
||||
edges_set.update(vertex.edges)
|
||||
edges = list(edges_set)
|
||||
new_predecessor_map, _ = self.build_adjacency_maps(edges)
|
||||
self.run_manager.run_predecessors.update(new_predecessor_map)
|
||||
self.vertices_to_run.update(list(map(lambda x: x.id, successors)))
|
||||
self.activated_vertices = vertices_ids
|
||||
self.vertices_to_run.update(vertices_ids)
|
||||
|
|
@ -401,14 +413,20 @@ class Graph:
|
|||
"inactivated_vertices": self.inactivated_vertices,
|
||||
}
|
||||
|
||||
def build_graph_maps(self):
|
||||
def build_graph_maps(self, edges: Optional[List[ContractEdge]] = None, vertices: Optional[List[Vertex]] = None):
|
||||
"""
|
||||
Builds the adjacency maps for the graph.
|
||||
"""
|
||||
self.predecessor_map, self.successor_map = self.build_adjacency_maps()
|
||||
if edges is None:
|
||||
edges = self.edges
|
||||
|
||||
self.in_degree_map = self.build_in_degree()
|
||||
self.parent_child_map = self.build_parent_child_map()
|
||||
if vertices is None:
|
||||
vertices = self.vertices
|
||||
|
||||
self.predecessor_map, self.successor_map = self.build_adjacency_maps(edges)
|
||||
|
||||
self.in_degree_map = self.build_in_degree(edges)
|
||||
self.parent_child_map = self.build_parent_child_map(vertices)
|
||||
|
||||
def reset_inactivated_vertices(self):
|
||||
"""
|
||||
|
|
@ -433,9 +451,9 @@ class Graph:
|
|||
for child_id in self.parent_child_map[vertex_id]:
|
||||
self.mark_branch(child_id, state)
|
||||
|
||||
def build_parent_child_map(self):
|
||||
def build_parent_child_map(self, vertices: List[Vertex]):
|
||||
parent_child_map = defaultdict(list)
|
||||
for vertex in self.vertices:
|
||||
for vertex in vertices:
|
||||
parent_child_map[vertex.id] = [child.id for child in self.get_successors(vertex)]
|
||||
return parent_child_map
|
||||
|
||||
|
|
@ -559,6 +577,7 @@ class Graph:
|
|||
self.update_vertex_from_another(self_vertex, other_vertex)
|
||||
|
||||
self.build_graph_maps()
|
||||
self.define_vertices_lists()
|
||||
self.increment_update_count()
|
||||
return self
|
||||
|
||||
|
|
@ -944,8 +963,6 @@ class Graph:
|
|||
node_name = node_id.split("-")[0]
|
||||
if node_name in ["ChatOutput", "ChatInput"]:
|
||||
return ChatVertex
|
||||
elif node_name in ["ShouldRunNext"]:
|
||||
return RoutingVertex
|
||||
elif node_name in ["SharedState", "Notify", "Listen"]:
|
||||
return StateVertex
|
||||
elif node_base_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP:
|
||||
|
|
@ -1277,17 +1294,17 @@ class Graph:
|
|||
def remove_from_predecessors(self, vertex_id: str):
|
||||
self.run_manager.remove_from_predecessors(vertex_id)
|
||||
|
||||
def build_in_degree(self):
|
||||
in_degree = defaultdict(int)
|
||||
for edge in self.edges:
|
||||
def build_in_degree(self, edges: List[ContractEdge]) -> Dict[str, int]:
|
||||
in_degree: Dict[str, int] = defaultdict(int)
|
||||
for edge in edges:
|
||||
in_degree[edge.target_id] += 1
|
||||
return in_degree
|
||||
|
||||
def build_adjacency_maps(self):
|
||||
def build_adjacency_maps(self, edges: List[ContractEdge]) -> Tuple[Dict[str, List[str]], Dict[str, List[str]]]:
|
||||
"""Returns the adjacency maps for the graph."""
|
||||
predecessor_map = defaultdict(list)
|
||||
successor_map = defaultdict(list)
|
||||
for edge in self.edges:
|
||||
for edge in edges:
|
||||
predecessor_map[edge.target_id].append(edge.source_id)
|
||||
successor_map[edge.source_id].append(edge.target_id)
|
||||
return predecessor_map, successor_map
|
||||
|
|
|
|||
|
|
@ -15,7 +15,6 @@ from langflow.interface.wrappers.base import wrapper_creator
|
|||
from langflow.utils.lazy_load import LazyLoadDictBase
|
||||
|
||||
CHAT_COMPONENTS = ["ChatInput", "ChatOutput", "TextInput", "SessionID"]
|
||||
ROUTING_COMPONENTS = ["ShouldRunNext"]
|
||||
|
||||
|
||||
class VertexTypesDict(LazyLoadDictBase):
|
||||
|
|
@ -51,7 +50,6 @@ class VertexTypesDict(LazyLoadDictBase):
|
|||
**{t: types.CustomComponentVertex for t in custom_component_creator.to_list()},
|
||||
**{t: types.RetrieverVertex for t in retriever_creator.to_list()},
|
||||
**{t: types.ChatVertex for t in CHAT_COMPONENTS},
|
||||
**{t: types.RoutingVertex for t in ROUTING_COMPONENTS},
|
||||
}
|
||||
|
||||
def get_custom_component_vertex_type(self):
|
||||
|
|
|
|||
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Add table
Add a link
Reference in a new issue