Graph RAG: Improving RAG with Knowledge Graphs

Discover Microsoft’s groundbreaking GraphRAG, an open-source system combining knowledge graphs with Retrieval Augmented Generation to improve query-focused summarization. I’ll guide you through setting it up on your local machine, demonstrate its functions, and evaluate its cost implications.

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00:00 Introduction to GraphRAG and Its Cost Issue
00:44 Understanding Traditional RAG
01:46 Limitations of Traditional RAG
02:22 Introduction to GraphRAG
02:39 Technical Details of GraphRAG
05:46 Setting Up GraphRAG on Your Local Machine
06:22 Running the Indexing Process
12:00 Running Queries with GraphRAG
14:26 Cost Implications and Alternatives

All Interesting Videos:

[aswarytiwari]

Would really appreciate the comparison of all the Graph based RAG frameworks, like LlamaIndex's Property Graph RAG, MS's GraphRAG etc.

[roguesecurity]

Thanks for sharing this video. It was very informative. I would love to see a comparison between different Graph RAG solutions.

[kennethpinpin]

Excellent concise introduction on GraphRAG and why, how, when it is needed. Please compare this with Neo4j and Llama Index.

[abhijitbarman]

Thanks for sharing this video.I would love to see a comparison between different Graph RAG solutions.

[danielshurman]

Another great choice of important topics and video to show the current best implementations. Thank you!

[VivekHaldar]

Love the focus on cost tradeoff. Thanks!

[wryltxw]

this looks great. yes, would be interested to compare against llamaindex.

[alexisdamnit]

Really good walkthrough of Microsoft's GraphRAG. The associated costs with LLM APIs are definitely costly. But even if you wanted to use local or in-house LLMs you'll have to pay for the infrastructure and/or time to compute.

[Independent_AI]

Great job. Please publish more videos about GraphRAG with other competitors.

[robboerman]

Very useful, also good to share the associated cost. It would be very interesting to see a video comparing the different GraphRAG implementations as you mentioned.

[rajpootatul]

Thanks for sharing this video. It was highly informative. I'd be interested in seeing a comparison of different Graph RAG solutions.

[paulmiller]

Great video and very timely. Please do more like these.

[mehmetbakideniz]

very usefull indeed! please publish more videos comparing different frameworks in terms of accuracy and the cost.

[AyanKhan-dceu]

I think using open source models like llama 3.1 locally will really fit here in the graph rag process. Companies having their own infrastructure to run llms can take advantage of this.

[mehmetnaciakkk]

Thanks for this. Yes, a comparison of Microsft´s Graph Rag with Neo4J´s and Llama Index´s implementations would be great!

[MPReilly]

Excellent! Outstandingly thorough and clear details. Subscribing now.

[mr.anonymus]

comparing them and a short video showcasing it on a local llm would be nice for sure

[IslandDave]

It would be very interesting to see the results of this in global mode compared to dropping the whole book into the prompt of Gemini or Claude if it fit under their token limit. Obviously once you get beyond those limits RAG is required.
Also would be great to see this run fully locally against a standard RAG local solution using the exact same LLM.

[Ayush-tlny]

Can you please tell how can we change the embedding model to use some open source embedding

[DayLearningIT-hzkj]

Please share more about this topic 👍👍 so the most expensive part is in creating the graph ?

Do you think that it is really improved the accuracy of the response ?