55 lines
3.2 KiB
Markdown
55 lines
3.2 KiB
Markdown
<!-- Title -->
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# ⛓️ LangFlow
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~ A no-code flow builder for 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="GitHub Language Count" src="https://img.shields.io/github/languages/count/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 Closed Issues" src="https://img.shields.io/github/issues-closed/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 Closed Pull Requests" src="https://img.shields.io/github/issues-pr-closed/logspace-ai/langflow" /> -->
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<!-- <img alt="GitHub Commit Activity (Year)" src="https://img.shields.io/github/commit-activity/y/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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LangFlow is a no-code flow builder for LangChain, designed to provide a drag-and-drop UI, combining the capabilities of LangChain with reactFlow and a chat interface.
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## 📦 Installation
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You can install LangFlow from pip:
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`pip install langflow`
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Next, set the `OPENAI_API_KEY` environment variable using one of the following methods:
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- Use the following command in your terminal: `export OPENAI_API_KEY=your-api-key`.
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- In a Python script or Jupyter notebook, use the following code: `import os; os.environ["OPENAI_API_KEY"] = "your-api-key"`.
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## 🎨 Creating Flows
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Creating flows with LangFlow is easy, thanks to its intuitive drag-and-drop interface. Simply drag components from the sidebar onto the canvas, and connect them together to create your custom NLP pipeline. LangFlow provides a range of pre-built components to choose from, including LLMs, prompt serializers, agents, and chains.
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## 💻 Examples
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LangFlow comes with a number of example flows to help you get started. These examples cover a range of use cases, from chatbots and question-answering systems to data augmentation and model comparison. You can use these examples as a starting point for your own custom flows, or modify them to suit your needs.
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## 🧰 Components
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LangFlow provides support for LangChain main components, including prompts, LLMs, document loaders, utils, chains, indexes, agents, and memory. For each module, we provide examples to get started, how-to guides, reference docs, and conceptual guides.
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For more information on each component, please refer to the [Modules section of the LangChain documentation](https://langchain-docs.example.com/modules).
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## 🔧 Contributing
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We welcome contributions to LangFlow! If you'd like to contribute, please follow our contributing guidelines. You can also get in touch with us via GitHub issues or our community forum.
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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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