b3155a58a9This pull request adds a new feature to the flow editor that allows users to easily import example flows from the [logspace-ai/langflow_examples](https://github.com/logspace-ai/langflow_examples) repository on GitHub. The feature is accessible via the import example button Clicking on the "Import Examples" button opens a dialog box that displays a list of available example flows from the GitHub repository. Users can select one example to import, and the flow editor will automatically add the selected flow to the user's current project. This feature saves users time and effort by providing a convenient way to explore and utilize pre-built flows. Additionally, this feature promotes collaboration and community involvement by encouraging users to contribute their own flows to the repository for others to use and benefit from. |
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| CONTRIBUTING.md | ||
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| docker-compose.debug.yml | ||
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| GCP_DEPLOYMENT.md | ||
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⛓️ LangFlow
~ A User Interface For LangChain ~
LangFlow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chat box.
📦 Installation
Locally
You can install LangFlow from pip:
pip install langflow
Next, run:
python -m langflow
or
langflow
Deploy Langflow on Google Cloud Platform
Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) using Google Cloud Shell. The guide is available in the Langflow in Google Cloud Platform document.
Alternatively, click the "Open in Cloud Shell" button below to launch Google Cloud Shell, clone the Langflow repository, and start an interactive tutorial that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project.
🎨 Creating Flows
Creating flows with LangFlow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. LangFlow provides a range of LangChain components to choose from, including LLMs, prompt serializers, agents, and chains.
Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
Once you're done, you can export your flow as a JSON file to use with LangChain. To do so, click the "Export" button in the top right corner of the canvas, then in Python, you can load the flow with:
from langflow import load_flow_from_json
flow = load_flow_from_json("path/to/flow.json")
# Now you can use it like any chain
flow("Hey, have you heard of LangFlow?")
👋 Contributing
We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our contributing guidelines and help make LangFlow more accessible.
📄 License
LangFlow is released under the MIT License. See the LICENSE file for details.