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Simply using OpenAI tools is not interesting

very insighful, would love to see more such videos.

How to return neo4j subgraph image when stremlit's response?

Hi, since you mentioned pricing, especially how expensive GPT-4 is, is using the Diffbot API free? Thanks.

I've just completed some experiments using Microsoft's GraphRAG and your description describes my results exactly, "garbage-in-garbage-out". Without a consistently solid knowledge graph there's not much an LLM can have knowledge of. Thanks for sharing your project. I'll take a look at that.

I feel like a Noob in the field of AI, after seeing your video. But the video is great

Hello, thank you very much for posting the video, I am very interested in the part where you also show the graoh with in the chatnot, what python packahe is that please?(,y apologies if the question is redundant, I couldn't find it in other comments)

Subbed for Mr. Beast counting 💗

you're so pretty

Can you make a video on how to generate knowledge graphs for pdf books like DSM 5

Hi Leann Thank you! Your videos are an amazing source of inspiration! keep up the good work!

Thanks for this video, subscribed! :)

Excellent video, clear explanation, please do post more in the gen ai and knowledge graph space

go to the presentation by Amy Hodler (and tell her I said hello)

Are you creating embeddings on top of the knowledge graph for RAG??

Can you please share the code for the application you built to visualize the knowledge graph ?

Hey Leann, first of all great explanation with some insights (specially on Diffbot). You got a new subscriber 👍 I'm going to work on a RAG based project which will use Neo4J as a Graph Database. I've went through other comments and your answers to them. But still wanted to know few things: 1. Here you took the example of speaker and what they have spoken (and their interest/expertise etc...) which is working fine. But what if I have some PDF docs of roughly 50-70 pages with some rules and regulations and want to use them as a custom knowledge base from my RAG project? Is knowledge graph database is good choice? why not simple Vector DB (such as milvus db)? 2. Assuming I must use Graph database, how do I efficiently chunk the PDFs and store into graph notes and relations? So that if users asks any query then user should get correct answer. 3. If the docs are related to rules and regulations, then what will be the nodes and relationships between them? Because here in your example, nodes were speaker, their expertise etc... I understand that you might not have perfect answer for all of these above but I'd like to have some point of view. Hope you find my comment and reply me once you get a time. Thanks for reading and your time.😊

英文發音清楚。速度剛好。 如果內容文字部分再放大些。會更清晰喔

太棒了。 已訂閱追蹤

Thanks for this video

It's nice to see my old co-worker Michelle randomly popping up in a video. I hope you were able to meet her. She is great!

I have tested for few critical document to get some answer using standard RAG and to be honest, didnt enjoy the performance so much.

Yes, to physically present yourself in multiple locations at the same time is quite challenging. My understanding is that it requires you to achieve presence on the fourth dimension. Once there, you can then enter multiple three-dimensional spaces at the same time. I wish I could do that, though I suspect it would be really disorienting at first! Best wishes! Also, I learn something new with every one of your videos! Thank you! I really like your approach!

You are an excellent presenter. Thank you. We do however need to find you a better background music. It's giving pharmaceutical commercial and the levels are a little too high over your voice. Still great though. You have excellent stage presence and a clear voice.

Can we implement the azure open ai creds like api key, model name, endpoint, type and version in the ipynb file and run it? Also please mention the dependency libraries of the functions.py file as visual, Node, Edge, Cypher_graph are not getting initialised in VSC while running the file....

I'm interested in creating a Little Logical Model based upon the command structure of an application and then using agents take voice to text and text to cmd. maybe with a coresponding graph view updated with current information avaiable in another window on another display screen.

This is very insightful Leann.. cheers from South Africa

Great content! I am a complete beginner: I have a Neo4j db already populated, I want to "only" do the chatbot portion connected with GPT4. Would you mind guiding me on which .py I should use in this usecase? In the meantime, I am getting a "UnboundLocalError: local variable 'nodes' referenced before assignment". Not sure what to do... Thanks!!

This is so good, Leann! I'm just jumping into the field of Knowledge Graphs! This will be huge for RAG applications! Why did you stop publishing videos?

Thank you for amazing short video, I am eagerly waiting for you to make a video on how to convert csv data into knowledge graphs and answers questions on the csv files

thank you for the crisp view on KG+RAG, can we create KG on multiple csv files , currently csv agents were lacking behind to answer questions based on content, they only search for matching column for the question rather content passed.

Thanks for the awesome video! I was trying to reproduce your code but got an error because the "text_for_kg()" function was not defined. Any chance you can help me understand where this functions comes from? Great content and great editing! Thank you

Great! Waiting for more of your videos!

Love this!

Diffbot sets a pretty high bar for entering this project, any thought/plan to utilise open source project instead? Thanks!

useful content, no word wasted!

Very nice content! support support 🇹🇼

Can RAGs become efficient enough to do data analysis over text tables and csvs? I'm planning to build one so wanted to know if this is possible.

Great video! However, I would completely replace DiffBot with an open source solution. There are many NER models, SpanMarkerNER to name one, since most of the entities you showed in the video are Person, Location, and Org, which libraries like SpaCy and setFit are pretty good for them. Using LLM with few shot learning would be another option. Overall, very nice video.

It can be done by hand, but automatisation of this human feature is impressing. Good video!

It's cool! Considering the current long context window of LLMs that pass the "Needle In A Haystack" (NIAH) test with flying colors, makes creating Neo4j graphs rather a human domain-learning and verified knowledge collection activity, useful for science and formal domain exploration, rather than ad-hoc knowledge explorations. One thing, Neo4j is not enough to represent complex hierarchical relationships that need hypergraphs. Check Dgraph (open source software).

Great video!

I just had to be the 1,000th.. Congrats..

Cool! New sub from me 😊

Content was good but found face filters visually distracting.

I'm a newbie on these matters discussed here, but I really do appreciate the way your MyGraph RAG AI Assistant work, responding with text AND graph. Can you tell me a bit more on how you did accomplish this? (I'm especially interested in the graph that got generated!). Hope that's not a stupid question?

nice sharing

KG are key for providing context to RAG. Still, I see the OWL/RDFs path outperforming LPG as it enables the user to explicitly define semantics and infer knowledge

Really awesome, Thank you for this video

Thanks.