fastembed

What problem does it solve?

This Skill solves slow, dependency-heavy embedding generation by providing a lightweight way to convert text into vectors locally for semantic search and matching.

Core Features & Use Cases

• Dense text embeddings (CPU-friendly): Convert large volumes of text into fixed-size vectors using ONNX Runtime without GPUs.
• Batch embedding at scale: Embed thousands of documents efficiently with configurable batching and parallelism.
• Query vs document workflows: Support symmetric and asymmetric retrieval patterns for similarity search and skill matching.
• EOS-ready integration patterns: Supports an EOS-style three-tier flow (FastEmbed local, optional cloud fallback, keyword fallback) to keep retrieval working even when embeddings fail.

Real-world example: You maintain a catalog of skills and notes inside your system, and you want an AI to automatically select the most relevant skill or retrieve the most similar past interactions based on a user’s task description.

Quick Start

Use the fastembed tool to embed your task description and compare it against pre-embedded skill vectors for semantic retrieval.