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...

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?

#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

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-...

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.

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

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.