69 lines
3.3 KiB
Markdown
69 lines
3.3 KiB
Markdown
<!-- Title -->
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# ⛓️ LangFlow
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~ A User Interface For [LangChain](https://github.com/hwchase17/langchain) ~
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<p>
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<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/logspace-ai/langflow" />
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<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/logspace-ai/langflow" />
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<img alt="" src="https://img.shields.io/github/repo-size/logspace-ai/langflow" />
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<img alt="GitHub Issues" src="https://img.shields.io/github/issues/logspace-ai/langflow" />
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<img alt="GitHub Pull Requests" src="https://img.shields.io/github/issues-pr/logspace-ai/langflow" />
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<img alt="Github License" src="https://img.shields.io/github/license/logspace-ai/langflow" />
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</p>
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<a href="https://github.com/logspace-ai/langflow">
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<img width="100%" src="https://github.com/logspace-ai/langflow/blob/main/img/langflow-demo.gif?raw=true"></a>
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LangFlow is a GUI for [LangChain](https://github.com/hwchase17/langchain), designed with [react-flow](https://github.com/wbkd/react-flow) to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chat box.
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## 📦 Installation
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### <b>Locally</b>
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You can install LangFlow from pip:
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`pip install langflow`
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Next, run:
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`langflow`
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### Deploy Langflow on Google Cloud Platform
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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**](GCP_DEPLOYMENT.md) document.
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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.
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[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
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## 🎨 Creating Flows
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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](https://langchain.readthedocs.io/en/latest/reference.html) to choose from, including LLMs, prompt serializers, agents, and chains.
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Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
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Once you're done, you can export your flow as a JSON file to use with LangChain.
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To do so, click the "Export" button in the top right corner of the canvas, then
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in Python, you can load the flow with:
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```python
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from langflow import load_flow_from_json
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flow = load_flow_from_json("path/to/flow.json")
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# Now you can use it like any chain
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flow("Hey, have you heard of LangFlow?")
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```
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## 👋 Contributing
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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](./CONTRIBUTING.md) and help make LangFlow more accessible.
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[](https://star-history.com/#logspace-ai/langflow&Date)
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## 📄 License
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LangFlow is released under the MIT License. See the LICENSE file for details.
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