skills.rest
archetypeai
archetypeaiOFFICIAL
Explore all skills by this author →
WebsiteGitHub

embedding-from-file

Stream time-series CSV data to generate embeddings in the Archetype AI Embedding Lens.

Updated Apr 9, 2026
Search Tags:#api#streaming#csv#sse#embedding#lens#timeseries
One-click install
npx skills add https://github.com/archetypeai/newton-earthquake-demo --skill embedding-from-file
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: embedding-from-file
Source: https://github.com/archetypeai/newton-earthquake-demo/tree/main/.claude/skills/embedding-from-file
Command: npx skills add https://github.com/archetypeai/newton-earthquake-demo --skill embedding-from-file

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Run an Embedding Lens by streaming sensor data from a CSV file. Use when extracting embeddings from time-series CSV data for visualization, clustering, dimensionality reduction, or similarity analysis.

Core Features & Use Cases

  • Streaming sensor data to Embedding Lens in windows
  • Collect embeddings and flatten for downstream analytics
  • Visualization, clustering, anomaly detection on time-series embeddings

Quick Start

Start streaming the CSV data to the Embedding Lens to generate 3072D embeddings for each window.

Frequently Asked Questions about embedding-from-file

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate embeddings from CSV time-series data?▼

Stream CSV time-series data in windows to the Embedding Lens to generate 3072D embeddings for each window, collecting vectors for downstream visualization, clustering, or similarity analysis.

Can I stream CSV sensor data in windows using Python and JavaScript?▼

Yes, both Python and JavaScript implementations support streaming CSV sensor data in windows, handling API client setup, session management, and SSE consumption to collect embedding vectors.

What is the best way to visualize and cluster time-series embeddings?▼

Flatten collected 3072D embedding vectors from the SSE stream to perform dimensionality reduction, clustering, anomaly detection, or similarity analysis on your time-series data.

Does the Embedding Lens API support SSE for streaming embeddings?▼

Yes, the Embedding Lens API uses SSE for streaming embeddings, requiring session management and SSE consumption to collect and persist embedding vectors during windowed data streaming.

Why do I need windowed data streaming for time-series embeddings?▼

Windowed data streaming segments continuous time-series data into discrete chunks, enabling the Embedding Lens to generate separate 3072D embedding vectors for each window to capture temporal patterns.

How do I persist embedding vectors after streaming CSV data?▼

Collect embedding vectors from the SSE stream during windowed CSV data streaming, then flatten and persist them for downstream analytics, visualization, or similarity analysis.

Related Skills

Explore More Data & Analytics Skills→
archetypeaiarchetypeai

newton-machine-state-from-file

Stream time-series CSV data to Archetype AI's Machine State Lens for n-shot state classification.

Official
Advanced
haJ1thaJ1t

embedding-manager

Generate and store dense and sparse embeddings for similarity search.

Community
Advanced
archetypeaiarchetypeai

embedding-from-sensor

Stream real-time IMU sensor data to an Embedding Lens for live embeddings.

Official
Advanced
dralkhdralkh
74

umap-learn

Reduce high-dimensional data into low-dimensional manifold embeddings with UMAP.

Community
Advanced