From e18a5ea8a0efbaa00c4e56028be5c8ffb3096ab8 Mon Sep 17 00:00:00 2001 From: NirantK Date: Tue, 10 Oct 2023 19:43:58 +0530 Subject: [PATCH] * chore(fastembed.md): fix typo in title, add more descriptive title for FastEmbed article --- qdrant-landing/content/articles/fastembed.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/qdrant-landing/content/articles/fastembed.md b/qdrant-landing/content/articles/fastembed.md index ba6a711d2..788c0614c 100644 --- a/qdrant-landing/content/articles/fastembed.md +++ b/qdrant-landing/content/articles/fastembed.md @@ -1,5 +1,5 @@ --- -title: "FastEmbed: 2x faster Embeddings" +title: "FastEmbed: 2x Faster Embeddings better than OpenAI" short_description: "FastEmbed is a Python library engineered for speed, efficiency, and above all, usability." description: "FastEmbed is a Python library engineered for speed, efficiency, and accuracy. It's more accurate than OpenAI and 1.5x faster than the PyTorch implementation with fewer dependencies" social_preview_image: /articles_data/fastembed/social_preview.png @@ -16,7 +16,6 @@ keywords: - OpenAI Ada - quantized embedding model --- -# FastEmbed In the ever-changing landscape of Data Science and Machine Learning, practitioners often find themselves navigating through a labyrinth of models, libraries, and frameworks. Among the plethora of choices, the need for a specialized, efficient, and easy-to-implement solution for embedding generation is increasingly evident. This is where FastEmbed (docs: [https://qdrant.github.io/fastembed/](https://qdrant.github.io/fastembed/?utm_source=twitter&utm_medium=social&utm_campaign=fastembed&utm_term=fastembed)) comes into play—a Python library engineered for speed, efficiency, and above all, usability. @@ -94,7 +93,7 @@ FastEmbed is fast because of a lot of small things we've taken care of for you: 1. **Quantized Models**: We quantize the models for CPU (and Mac Metal) – giving you the best buck for your compute model. Our models are so small, you can run this in AWS Lambda if you'd like! 2. **1.5x Throughput**: This is the fastest CPU model which beats OpenAI Embedding model as well. And we do so while being 1.5x faster than the Open Source implementation. -![alt_text](images/image1.png "image_tooltip") +![](images/image1.png "image_tooltip") ### Retaining Accuracy and Recall