I've seen a lot of explanations on similarity measures in vector search but this one by my colleague @dadoonet is by far the most fun! How similar* is Han Solo to: • Princess Leia: very similar • Obi-Wan: meh • Darth Vader: complete opposites Talk slides: david.pilato.fr/talks/2025/2...

What's the most underrated embedding technique you've used? Static embeddings -> speed-improvements Binary quantization -> storage-reduction Late interaction -> added granularity I'm curious about lesser-known approaches that worked surprisingly well.

Roses are red, violets are blue, A good baseline embedding model is all-MiniLM-L6-v2.

Make RAG results more trustworthy with citations. In his latest recipe, @danman966.bsky.social shows you how you can build a RAG pipeline with citations, using: - a @weaviate.bsky.social vector database and - @anthropic.com's Claude 3.5 Sonnet 📌 Code: github.com/weaviate/rec...

Normalize not knowing everything in the AI space. It's evolving fast. I’m sure your to-do list is growing as fast as mine. Here are 3 topics, I want to catch up on this quarter: • AI agents • Fine-tuning embedding models • Multimodality • (If time permits: reinforcement learning) What about you?

I’m trying to wrap my head around multi-agent system architectures. Here are some patterns I’m seeing so far: 1. Type of collaboration: Network vs. hierarchical 2. Type of information flow: Sequential vs. parallel vs. loop 3. Type of functionality: Routing vs. aggregating What else?

Some considerations for choosing a vector dimension: 1. Data complexity 2. Task complexity 3. Dataset size 4. Computational constraints 5. Performance requirements 6. Scalability requirements 7. Latency requirements What else?

#1 Rule of RAG Club: Look at your data. With the new explorer tool, looking at your data got a lot easier in Weaviate Cloud. The explorer tool provides a graphical interface to easily: • Browse collections • Inspect objects, metadata, and vectors Check it out now: https://buff.ly/3KWivSF

You can be GPU poor like me and still fine-tune an LLM. Here’s how you can fine-tune Gemma 2 in a Kaggle notebook on a single T4 GPU: • @kaggle.com offers 30 hours/week of GPUs for free • @unsloth.bsky.social uses 60% less memory to fit it on a T4 GPU 🔗Code: https://buff.ly/4apUUG2

Although I know that Vertical scaling: scaling up (to a more powerful machine) Horizontal scaling: scaling out (to multiple smaller machines) I still always have to take a second to think about it. It’s like the left-right-weakness of system design.

I talk about RAG so much, I could fill a book. So, we did - and you can download it for free. Together with my colleagues Mary & Prajjwal, we curated an e-book of the most effective advanced RAG techniques. Which ones did we miss? Get it now: weaviate.io/ebooks/advan...

Over the holidays, I learned how to fine-tune an LLM. Here’s my entry for the latest @kaggle.com comp. This tutorial shows you: • Fine-tune Gemma 2 • LoRA fine-tuning with @unsloth.bsky.social on T4 GPU • Experiment tracking with @weightsbiases.bsky.social 🔗Code: www.kaggle.com/code/iamleon...

Got myself a little early Christmas present. Although this book is from 2017, I heard so many good things about it this year. Can't wait to dig into this over the holidays. And with that being said, I hope you have some nice and relaxing holidays yourself! See you in the new year!

It’s time to review the AI space in 2024! Here’s what I got right (and what I missed) in my 2024 predictions: ✅ Evaluation ❌ Multimodal foundation models ❌ Fine-tuning open-weight models and quantization ❌ AI agents ✅ RAG lives on ❌ Knowledge graphs medium.com/towards-data...

日本語テキスト向けのハイブリッド検索には日本語テキス用のトークナイザーが必要です。 @weaviate.bsky.socialでは３つのトークナイザーを使用することができます。 一つずつのメリットとデメリットはこちら weaviate.io/blog/hybrid-...

Struggling to keep up with new RAG variants? Here’s a cheat sheet of 7 of the most popular RAG architectures. Which variants did we miss?

ハイブリッド検索とは何？ ハイブリッド検索は、デンスベクトルとスパースベクトルを統合して、それぞれの検索手法の利点を活かします。 この記事では、Weaviateの日本語テキスト向けのハイブリッド検索の説明をします。 - 日本語テキス用のトークナイザーを使用するキーワード検索 - ベクトル検索 - 融合アルゴリズム 詳しくはこちら https://buff.ly/49yMR9K

Look what came in the mail today! This is already the 2nd edition of “Developing apps with GPT-4” by Olivier and Marie-Alice I had the pleasure to review. This edition covers the latest advancements in GPT-4, especially regarding its visual capabilities to build multimodal applications.

It's been two years since the release of ChatGPT. What cool use cases using Generative AI have you seen in the wild so far?

Struggling with RAG over PDF files? You might want to give Docling a try. 𝗪𝗵𝗮𝘁'𝘀 𝗗𝗼𝗰𝗹𝗶𝗻𝗴? • Python package by IBM • OS (MIT license) • PDF, DOCX, PPTX → Markdown, JSON 𝗪𝗵𝘆 𝘂𝘀𝗲 𝗗𝗼𝗰𝗹𝗶𝗻𝗴? • Doesn’t require fancy gear, lots of memory, or cloud services • Works on regular computers or Google Colab Pro

🇯🇵日本初のWeaviateミートアップのお知らせ🇯🇵 本イベントでは、Weaviateの特徴や活用事例を学び、Weaviate CEO @bobvanluijt.bsky.socialとグローバル パートナーシップ責任者 @jobig630.bsky.social やWeaviate Kagome コントリビューター Jun Ohtaniと交流できる場を提供します。 マルチモーダル検索や検索拡張世代（RAG）によるAIのユースケースのお話楽しみにしてます。

Yes, you don’t need a vector database to do vector search. @victorialslocum.bsky.social shows you how - using just numpy. This article covers: • How does vector search work? • How to do vector search from scratch in Python • and more Learn more: weaviate.io/blog/vector-...

Struggling with slow filtered vector search? Here's how Weaviate's researchers 10x'ed query speeds with ACORN:

Hi, I am Leonie! (Yes, that's the handle) I do machine learning at Weaviate and write about it on the Internet. medium.com/@iamleonie You might know me for my monochrome technical visuals.