Michael Hunger 🇪🇺 🇺🇦 @[email protected]

Happy dad of 3 girls GraphAddict @neo4j JavaChampion Code Words Network

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

Soo excited about the #GenAIStack that we @neo4j launched today together with @Docker @langchain and @Ollama_ai

Learn more at

https://t.co/qa9bYSCNuP

Or our live streamed session at @DockerCon at 11:45PT

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@jobergum Documents are just one dimension, across documents you have topic clusters that you can utilize.
And yes @swyx vector similarity forms an "invisible" graph that you can choose to materialize so it's not black box. But I agree we're very much in the space of experiments and eval.

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@jobergum #GraphRAG is a RAG pattern that uses graph data structures.
Metadata is often only one level of attributes or you have to denormalize a lot.
If you have existing structured data combining it with vector search gives you an entry point to fetch more relevant context.

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@attila_ibs @emileifrem Can you please drop me an email with your example. michael at neo4j

[Emil Eifrem @emileifrem]

Folks, seriously. The GraphRAG Ecosystem Tools are frickin' amazing. The KG Builder is a visual cloud service that easily (click click!) turns unstructured data into a knowledge graph! You can point it to some random PDFs, a wikipedia page or a youtube video, and a few seconds later you have the data and concepts from those sources represented as a knowledge graph in your Aura free account. 🤯 This used to take DAYS! It really makes it so easy to get started on your own knowledge graph journey.

https://t.co/UYtUI6KiQ5

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@gillinghammer @emileifrem It provides more relvant context than pure vector rag. You can follow longer patterns of information that might be a few hops away. And if the ingestion aggregates across similar tooics it provides horizontal, cross document topic discovery and summarization to work with

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@attila_ibs @emileifrem Also did you check if Margaret Bryans node was extracted?

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@attila_ibs @emileifrem It currently starts with vector search and expands to entities and their rels from the top 3 chunks. If they are connected both should show up.
We plan to add more retrieval queries.
Did you configure any graph schema?

[LlamaIndex 🦙 @llama_index]

We’re excited to launch a huge feature making @llama_index the framework for building knowledge graphs with LLMs: The Property Graph Index 💫

(There’s a lot of stuff to unpack here, let’s start from the top)

You now have a sophisticated set of tools to construct and query a knowledge graph with LLMs:
1. You can extract out a knowledge graph according to a set of extractors. These extractors include defining a pre-defined schema of entities/relationships/properties, defining a set of node relationship with @llama_index constructs, or implicitly figuring out the schema using an LLM.
2. You can now query a knowledge graph with a huge host of different retrievers that can be combined: keywords, vector search, text-to-cypher, and more.
3. You can include the text along with the entities/relationships during retrieval
4. You can perform joint vector search/graph search even if your graph store doesn’t support vectors! We’ve created robust abstractions to plug in both a graph store as well as a separate vector store.
5. You have full customizability: We’ve made it easy/intuitive for you to define your own extractors and retrievers.

Labelled Property Graph: a KG representation with nodes + relationships. Each node/relationship has a label and an arbitrary set of properties.

Why you care: This is a robust representation of knowledge graphs that extends way beyond just triplets - allows you to treat KGs as a superset of vector search. Each text node can be represented by a vector representation similar to a vector db, but also link to other nodes through relationships.

Our initial launch was done in collaboration with our partners from @neo4j. Huge shoutout to @tb_tomaz for creating a detailed integration guide as well as extensive guidance on how to refactor our abstractions.

Our blog post: https://t.co/iYTf22PQkU

Full guide in the docs: https://t.co/Qe5gEWdPAI

Usage guide: https://t.co/qV8WPEsgvC

Basic notebook: https://t.co/9RQrBwbUTh

Advanced notebook (shows extraction according to a schema): https://t.co/ELTX360w84

Using Neo4j with our property graphs: https://t.co/sBtHruu5Kz

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@deepset_ai @Haystack_AI @neo4j @qdrant_engine @mixedbreadai Can't wait. If you're in #Berlin and interested in #GenAI make sure to come to this meetup. We'll have so much great content. I'll be talking about both creating knowledge graphs w/ LLMs and also GraphRAG with Neo4j. Be there!

[Emil Eifrem @emileifrem]

Ok, this is pretty crazy.

SQL has been the lingua franca of database querying since the dawn of time.

But for the first time in over three decades (!), ISO just published a NEW database query language called GQL -- the Graph Query Language!

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

🎉Our #DuckDBInAction book is almost there, Chapter 10, 11 and Appendix released on the MEAP.
Soon off to print!

@markhneedham @rotnroll666 it was a great collaboration.

Big Thanks to @ryguyrg @jrdntgn @hfmuehleisen & @ManningBooks for your support 🙏

https://t.co/UugOG9jsh2

[MotherDuck @motherduck]

Building text-to-SQL models? Take advantage of our new training dataset featuring 25K samples covering virtually all documented DuckDB features. Now that's something to quack about!

https://t.co/xurj1LqHEi

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@codexeditor Great news Iian! Good luck and have fun.
Check out GLiNER for initial annotation. @markhneedham just did a video on it.
https://t.co/awToZSvbq6

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

@AI21Labs @huggingface @AI21Labs missed an opportunity here to call it #Llambada instead of #Jamba 😂 💃

https://t.co/ChTyqyet53

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

Awesome work by @akollegger and @AndrewYNg introducing knowledge graphs and how to superpower your GenAI applications with #GraphRAG. Take it now.

[DeepLearning.AI @DeepLearningAI]

Learn how to use knowledge graphs to enhance your RAG applications with our new course, built in collaboration with @Neo4j!

Explore the basics of knowledge graphs, use Cypher, Neo4j’s query language, build your own graphs, and more.

👉 Join now: https://t.co/VHL75j52JU

[Jason Lengstorf @jlengstorf]

hi friends!

I find myself approaching the end of the month and I REALLY need to land a new contract, like, this week 😬

any companies looking to partner on content? explainer videos, tutorials, social media, etc. — I'm available! [email protected]

(plz share — thanks!)

[Learn With Jason @LWJShow]

📣 Just Scheduled

If you're like me and you don't know what "GenAI" means, @adamcowley will teach us how to use langchain.js and build our own custom apps.

Details:
https://t.co/vyKdBokb3Q

[JUG Ostfalen @jug_ostfalen]

Nächste Woche findet die erste JUG Ostfalen Veranstaltung 2024 statt. @mesirii wird uns Project Loom vorstellen.
https://t.co/SS6VhlUTZT

[Michael Hunger 🇪🇺 🇺🇦 @[email protected] @mesirii]

Perfect timing. We finished all the main chapters of our book @duckdb 🐥in action. And the reviewers love 💕 it so far.
Hope you do too.

@ManningBooks gives out a 45% discount with NMDUCKDB24LT

https://t.co/hEEVPzGT25

Thanks to @motherduck for the support

[Ruprecht Polenz🇪🇺 @polenz_r]

Alle, die mit unserem „System“ unzufrieden sind, sollten sich diese Rede (8 Min) anhören. Wir können nur produktiv miteinander streiten, wenn wir uns in den Grundsätzen einig bleiben, die #Habeck hier skizziert. Die Notwendigkeit, Prioritäten zu setzen, bestand auch ohne BVerfG.