460 lines
No EOL
15 KiB
JSON
460 lines
No EOL
15 KiB
JSON
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"title": "Buffer Memory",
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"sidebar": "docs"
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"id": "examples/conversation-chain",
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"title": "Conversation Chain",
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"description": "This example shows how to instantiate a simple ConversationChain component using a Language Model (LLM). Once the Node Status turns green 🟢, the chat will be ready to take in user messages. Here, we used ChatOpenAI to act as the required LLM input, but you can use any LLM for this purpose.",
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"sidebar": "docs"
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"examples/csv-loader": {
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"id": "examples/csv-loader",
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"title": "CSV Loader",
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"description": "The VectoStoreAgent component retrieves information from one or more vector stores. This example shows a VectoStoreAgent connected to a CSV file through the Chroma vector store. Process description:",
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"sidebar": "docs"
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},
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"id": "examples/how-upload-examples",
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"title": "📚 How to Upload Examples?",
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"description": "We welcome all examples that can help our community learn and explore LangFlow's capabilities.",
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"sidebar": "docs"
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"title": "MidJourney Prompt Chain",
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"examples/multiple-vectorstores": {
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"title": "Multiple Vector Stores",
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"description": "The example below shows an agent operating with two vector stores built upon different data sources.",
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"sidebar": "docs"
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"examples/python-function": {
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"id": "examples/python-function",
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"title": "Python Function",
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"description": "LangFlow allows you to create a customized tool using the PythonFunction connected to a Tool component. In this example, Regex is used in Python to validate a pattern.",
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"sidebar": "docs"
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"examples/serp-api-tool": {
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"id": "examples/serp-api-tool",
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"title": "Serp API Tool",
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"description": "The Serp API (Search Engine Results Page) allows developers to scrape results from search engines such as Google, Bing and Yahoo, and can be used as in LangFlow through the Search component.",
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"sidebar": "docs"
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},
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"getting-started/creating-flows": {
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"id": "getting-started/creating-flows",
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"title": "🎨 Creating Flows",
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"description": "Compose",
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"sidebar": "docs"
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},
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"getting-started/hugging-face-spaces": {
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"id": "getting-started/hugging-face-spaces",
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"title": "🤗 HuggingFace Spaces",
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"description": "A fully featured version of LangFlow can be accessed via HuggingFace spaces with no installation required.",
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"sidebar": "docs"
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},
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"getting-started/installation": {
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"id": "getting-started/installation",
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"title": "📦 How to install?",
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"description": "Installation",
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"sidebar": "docs"
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},
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"guidelines/collection": {
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"id": "guidelines/collection",
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"title": "Collection",
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"description": "A collection is a snapshot of the flows available in the database. You can download your entire collection for local storage and upload it anytime for future use.",
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"sidebar": "docs"
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},
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"guidelines/components": {
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"id": "guidelines/components",
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"title": "Component",
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"description": "Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain documentation section.",
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"sidebar": "docs"
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},
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"guidelines/features": {
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"id": "guidelines/features",
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"title": "Features",
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"description": "When you click for New Project, you will see on the top left corner of the screen, some options such as Import, Export, Code and Save, as displayed in the image below:",
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"sidebar": "docs"
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},
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"index": {
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"id": "index",
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"title": "👋 Welcome to LangFlow",
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"description": "LangFlow is an easy way to prototype LangChain flows. The drag-and-drop feature allows quick and effortless experimentation, while the built-in chat interface facilitates real-time interaction. It provides options to edit prompt parameters, create chains and agents, track thought processes, and export flows.",
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"sidebar": "docs"
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}
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} |