Chat and RAG with Tabular Databases Using Knowledge Graph and LLM Agents

Подписаться 3,5 тыс.

Просмотров 7 тыс.

50% 1

In this video, together we will go through all the steps to construct a #knowledgegraph from Tabular Datasets and design a ChatBot APP to interact with the Knowledge Graph using natural language. For this purpose, we will use Knowledge Graph LLM agents and the GPT model. We will design a Chatbot that can:
1. Chat with Graph DB using an improved LLM agent
2. Chat with Graph DB using a simple LLM agent
3. RAG with Graph DB
Moreover, in this video, I will show you the second RAG approach for interacting with Tabular data but this time, using the knowledge graph. The code is available on the Github repository.
🚀 GitHub Repositories:
Advanced Q&A and RAG series: github.com/Farzad-R/Advanced-...
LLM-Zero-To-Hundred Series: github.com/Farzad-R/LLM-Zero-...
00:00:00 Intro - (Presentation)
00:00:17 Table of Contents - (Presentation)
00:01:55 Why Knowledge Graph? - (Presentation)
09:35 Project schema walk-through - (Presentation)
00:06:14 LLM Model Matters - (Presentation)
00:07:26 Series schema (RAG vs Q&A) - (Presentation)
00:08:05 Knowledge Graph Fundamentals - (Presentation)
00:10:29 How to Construct Knowledge Graph - (Presentation)
00:14:12 ChatBot Schema walk-through - (Presentation)
00:16:02 Knowledge Graph Agent schema walk-through - (Presentation)
00:18:00 Second RAG approach for tabular data - (Presentation)
00:18:24 Knowledge Graph for Movie Dataset - (Presentation)
00:21:41 Knowledge Graph for Microsoft medical chatbot - (Presentation)
00:22:52 ChatBot demo
00:23:36 Graph database installation and configuration
00:32:27 Code structure walk-through
00:33:25 Verify your OpenAI and Neo4j connection
00:34:39 Download the Movide dataset and generate synthetic data
00:37:15 Construct the knowledge graph from the Movie dataset
00:45:50 Creating and populating the Vector Index in Graph Database
00:51:23 Q&A with GraphDB populated with Knowledge Graph of the Tabular Data (designing the simple and improved agent)
01:07:47 RAG with GraphDB
01:13:22 Testing the ChatBot
01:17:10 Microsoft Medical Chatbot walk-through
01:22:52 Ending notes
Frameworks: #langchain , #openai, gradio, #neo4j,
#chatbot #rag #llm #agent #python #gpt

Наука

Опубликовано:

26 июн 2024

Ссылка:

Скачать:

Готовим ссылку...

Добавить в:

Мой плейлист

Посмотреть позже

Комментарии : 42

@alexanderroodt5052 Месяц назад

A++ video. Very informative and detailed.

@airoundtable Месяц назад

Thanks! I am glad you liked the video

@usmanahmed1073 19 часов назад

Video full of Knowledge and well explained. I am looking for you channel to grow more !

@airoundtable 16 часов назад

Thanks! I appreciate the kind words. I am glad that the video was helpful

@TooyAshy-100 Месяц назад

Thank you to Farzad-R, for providing such insightful content on the latest advancements in AI, including RAG using Knowledge Graphs and LLM Agents; it has been incredibly informative and inspiring!

@airoundtable Месяц назад

Thanks for the kind words! I'm glad the content was helpful

@thedatascientist-lg4ls 28 дней назад

You are super!!!. No 15 mins BS others call projects. Thank you so much.

@airoundtable 28 дней назад

Appreciate it! I am glad you liked the video. Thanks for watching

@DefensorVieira 19 дней назад

you have no idea how useful this tutorial is...thank you very much.

@airoundtable 19 дней назад

Thanks, I am glad it helped!

@sumitpawar000 7 дней назад

Solid content as usual 🙂🚀

@airoundtable 6 дней назад

Thanks!

@ChathurangaBW Месяц назад

Excellent video. thank you so much!

@airoundtable Месяц назад

Thanks! Glad it was helpful!

@awakenwithoutcoffee Месяц назад

appreciate it brother. Looking forward learning more together.

@airoundtable Месяц назад

Thanks!

@sachinrajora6753 28 дней назад

Great video! Totally worth watching 💯

@airoundtable 28 дней назад

Thanks! I am glad you liked it

@wassfila 28 дней назад

really great content, useful, focused, original !

@airoundtable 28 дней назад

Thanks! I am glad you liked the video and the content

@mohsenghafari7652 12 дней назад

thanks farzad.

@airoundtable 11 дней назад

Thanks Mohsen!

@ChathurangaBW Месяц назад

keep it up !

@just-another-man Месяц назад

Thank you for the thorough course. You saved me a lot of time and effort to start working with Neojs. Here's an idea for improvement: Some questions may require thinking step by step, and for each step you may need to make a query to retrieve information from the database. Although there may be one question, and to answer it you may need to make several queries to the database, including, in some cases, queries to a vector database and in some to a graph database. You might consider using function calling and further improving the prompt

@airoundtable 29 дней назад

Thanks for watching! I am glad the video was helpful. You are right and that is a very good point. As I mentioned in the video all these agents can be combined and used in bigger systems together to solve more complex problems as the one that you mentioned. Thanks for the insight!

@fionalau2920 17 дней назад

This video is really helpful to those who are stuck with RAG and tabular data. Quick question, when would you use Graph agent, and when would you use SQL agent? And what if you do if you have a mix of text and tabular data?

@airoundtable 16 дней назад

Thanks! The main difference is that a SQL LLM agent is good for querying databases. However, if you are planning to extract specific information and details from a series of data and you are looking to connect some data points to create more meaningful data paths, knowledge graph (KG) is the way to go. KG is for more specialized use-cases imo.

@ahmed_hefnawy Месяц назад

As usual, a useful and powerful video.. This is exactly what we need - I have a suggestion to make a detailed video about: Chat With Document with Knowledge graph data base | Converting Document to KG and query to cypher. - personally I need it, because I'm working on my Master project in critical data that Doesn't accept any LLM hallucination, in addition to RAG retrieving limitations ... kindly keep in that in your mind, and I'm waiting for that. Thanks for your effort :)

@airoundtable Месяц назад

Thanks! Happy to hear the content was useful. That is indeed the subject of the next video (RAG with knowledge Graph on PDF and text files). I am writing the code for it. Just to mention, it won't be focused on Q&A as I described how we can perform Q&A with documents using knowledge graph through the Microsoft project that I explained in this video. The next video will be focused on RAG so it will have some uncertainty at the end due to the interinsic characteristics of RAG. But if you want your project to be accurate, I recommend a similiar approach as the Microsoft project on text data.

@sajinmohammed.p.e.5 3 дня назад

I see you are creating a Vector Index at 48:56. But Are you using that anywhere? I see that you are using the embeddings created further down the program. Then I wonder why you create the Vector Index?

@airoundtable 3 дня назад

I am adding the vector embeddings of my data to that vector index at 49:50. Then I use that vector index for RAG in this chatbot

@thedatascientist-lg4ls 13 дней назад

How about connecting to an existing database rather than creating one as you showed? And also, what if the existing database contains both numeric values and letters. For example, what is the status of the customer order? and How many orders did the customer requests? What framework is best to use?

@airoundtable 13 дней назад

It depends on the structure of the data. If it is a tabular data or sql data the best way to interact with it is using sql agents. I have a detailed video for those agents. If for getting the answer, the knowledge among multiple databases need to be used together, then graph agents can be the better choice. And finally, graph agents only work with graph database. In case you have other databases such as SQL, I recommend you to watch my other video that is focused on those databases. "Chat with tabular data using sql agents"

@drm2005 14 дней назад

Can we use the Grok api or the performance will not be the same with open ai ?

@airoundtable 14 дней назад

i never tested Grok. I am not sure which one would perform better (I have a sense that GPT4 would perform better). But please feel free to test and check the results

@darkmatter9583 День назад

could you be my mentor?? your knowledge is awesome, i would like to learn thar wat

@airoundtable День назад

Hello Darkmatter9583. Thanks! I answered your other message

@BiXmaTube Месяц назад

Hi, Is there a way to reach you by email? Thanks

@airoundtable Месяц назад

Hi. Yes, you can find my email and social media links here: farzad-r.github.io/

@BiXmaTube 23 дня назад

@@airoundtable Appreciate that. I just sent you an email. Looking forward to hearing from you.

@darkmatter9583 День назад

can you be my teacher? 🙏

@airoundtable День назад

Hi Darkmatter9583. I would be happy to help. You can go through the tutorials and ask your questions. I work on multiple projects at the moment but I will respond to questions whenever I can. In case you would like a head start in the field, send me a description of your background and what you want to accomplish. I will try to guide you in the right direction. You can send me a message on Linkedin.