UMAP Dimensionality Reduction API

This is a high-performance dimensionality reduction microservice using UMAP (Uniform Manifold Approximation and Projection). It provides an efficient way to reduce high-dimensional data to 2D or 3D representations, making it easier to visualize and analyze complex datasets.

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When to Use This Service

Visualizing high-dimensional data in 2D or 3D space
Reducing dimensionality of large datasets for machine learning tasks
Exploring relationships and clusters in complex data
Preprocessing step for other machine learning algorithms

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Common Use Cases

Visualizing word embeddings or document vectors in NLP tasks
Analyzing gene expression data in bioinformatics
Exploring customer segmentation in marketing analytics
Visualizing image embeddings in computer vision tasks

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How to Use

Send a POST request to this endpoint with your data to use the API. The request should include an array of embeddings and optional configuration parameters.

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Basic Example:

\nfetch(\"https://ejfox-umap.web.val.run/\", {\n    method: \"POST\",\n    headers: { \"Content-Type\": \"application/json\" },\n    body: JSON.stringify({\n        embeddings: [[1,2,3], [4,5,6], [7,8,9]],\n        config: { nNeighbors: 15, minDist: 0.1, spread: 1.0 }\n    })\n})\n.then(response => response.json())\n.then(data => console.log(data))\n.catch(error => console.error('Error:', error));\n

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Curl Example:

\n        curl -X POST -H \"Content-Type: application/json\" -d '{\"embeddings\": [[1,2,3], [4,5,6], [7,8,9]], \"config\": { \"nNeighbors\": 15, \"minDist\": 0.1, \"spread\": 1.0 }}' https://ejfox-umap.web.val.run/\n

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Example with OpenAI Embeddings:

This example shows how to use the UMAP service with OpenAI embeddings:

\n// First, generate embeddings using OpenAI API\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nconst openai = new OpenAI();\n\nasync function getEmbeddings(texts) {\n    const response = await openai.embeddings.create({\n        model: \"text-embedding-ada-002\",\n        input: texts,\n    });\n    return response.data.map(item => item.embedding);\n}\n\n// Then, use these embeddings with the UMAP service\nconst texts = [\"Hello world\", \"OpenAI is amazing\", \"UMAP reduces dimensions\"];\nconst embeddings = await getEmbeddings(texts);\n\nfetch(\"/\", {\n    method: \"POST\",\n    headers: { \"Content-Type\": \"application/json\" },\n    body: JSON.stringify({\n        embeddings: embeddings,\n        config: { nNeighbors: 15, minDist: 0.1, spread: 1.0 }\n    })\n})\n.then(response => response.json())\n.then(data => console.log(data))\n.catch(error => console.error('Error:', error));\n

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Live Demo

Click the button below to see a live demo with random data:

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View source: Val Town

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