GraphRAG: The Most Incredible RAG Strategy Revealed
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- Опубліковано 3 сер 2024
- 🎥 Welcome to our channel! Today, we dive into the revolutionary Graph RAG from Microsoft, an advanced retrieval-augmented generation system that enhances AI responses by providing relevant context. GraphRAG: The Most Incredible RAG Strategy Revealed
📌 In this video, you will learn:
What is RAG (Retrieval-Augmented Generation)?
Differences between Basic RAG and Graph RAG
How to implement Graph RAG in your application
Step-by-step guide on setting up Graph RAG
Advantages of using Graph RAG over traditional methods
🔍 Key Features:
Entity Extraction
Hierarchy Extraction
Graph Embedding
Community Summarization
Topic Detection
🔧 Setup Steps:
Install the Graph RAG package
Configure API keys and settings
Initialize your project
Upload and process data
Run queries to extract high-quality answers
🔗 Useful Links:
Graph RAG Documentation
GitHub Repository
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Code: mer.vin/2024/07/graphrag-code/
📅 Timestamps:
0:00 Introduction to Graph RAG
1:00 Basics of RAG
1:58 Understanding Graph RAG
3:00 Setting Up Graph RAG
5:01 Integrating Graph RAG with Your Application
7:30 Running Queries and Extracting Data
9:00 Global vs. Local Search
10:27 Conclusion and Next Steps - Навчання та стиль
Great Explanation ❤
Great video👍 just saw today about graphrag. You're one of the first covering this. Looking forward for the next video. Graph visualization would be nice 2. Thanks.
You are the man , thanks again for your videos, we apreciate that
Thank you Mervin!
This is a powerful video on a powerful tech ... waiting to see what you will do with it ...thanks for the good content 🌹🌹🌹
This is great video! Thank you Mervin.
This content is really amazing! Thank you!
Great video! Gonna try this now
Wonderful work..
Knowledge graphs are the future... a definite component to give structure to RAG, reasoning, agentic behavior etc. That why i think LangGraph and LLamaIndex are 2 frameworks to keep up to date with.
I agree but how does LangGraph relate to graphRAG?
Thank you for the introduction so soon after the announcement! I'd be really curious to see how it compares with classic RAG on a large text where we ask for specific data, such as the taxes you'd have to pay on dividends according to the fiscal code.
Excellent intro. I've been looking forward to seeing what MS did with this research
Really like your videos
thx a lot for your work !
Thanks! Great presentation as always! Can you do this using Ollama?
Waiting on ur next video. Please cover setting this up with ollama and openwebui
I can't make it work in this stage
Shallow knowledge on topic
Thank you, Mervin for your video and bringing this into my attention. Amazing to see that you are using Cody. What do you think, could GraphRag bring benefits to code search too?
Amazing content sir. This concept much much needed in current time where native RAG lacks at some point.
I just wanted to ask how did you create Graph visualisation at starting? [2:57]
powerful!
Thanks MP. Can you pl extend this to read csv, pdf, docx and add UI using streamlit too?
i believe it can inherently read CSV since that is basically just raw text in a specific format. I am curious about pdf and docx still
thanks
Awesome video ! Do you know how does it compare with RAPTOR performance wise ?
Hi! Have you try LLamaIndex Graph Rag? What are the main difference between them? Very interesting video bro
What are the use cases for the text genration and embedding models?
Embedding model: Indexing
Text Generation:gpt-4o Summarization
I think text generation is also used here for indexing, does that not involve much cost than naive RAG?
quite amazing isn't it. From the MS presentation it looked promising but the results took 10x more Tokens + 10x longer to generate (70s for 1 answer). How would we tackle this issue, maybe Groq inferencing could reduce the compute time ?
Also: can you elaborate more on local vs global search and when to use which ? for the most accurate response maybe we should combine the two into a final answer (?). Exciting indeed, would love to see more benchmarks. 🙏
Is it possible to add networkx graph into this instead of LLM generated graph! I have a readymade graph on the private dataset?
First Blood 🙌
I wait for the ollama example .... still not sure if i got the definition of community content ..... but awesome video
no ollama support
Anyone know the rough token cost for creating the relationships / user query? seems that it would likely be ~5x the cost of setting up a standard RAG.
Hi currently we are using Pinecone Vector based DB. Can we shift to using graphrag? How it is different from vector DB? And when should we use it? Or how can we utilizes both vector DB and graph db to make outputs better?
Does it work with the Claude models?
How we can see the knowledge graph on UI on Neo4j?
Can you do this demo with tabular data?
Are there any ways in which you can use graphrag for coding tasks or code generation, etc? I know that wasn't their main focus with this, but I wonder if it's possible with this system.
Hi Melvin, how could this be used to optimize responses with the latest best practices and updates about a rust framework like dioxus? Many of these models are outdated and hence present a challenge.
❤
Can it work with Claude 3.5 sonnet?
thank you, this is actually really exciting but is there a way to use sentence transformer embedders instead of openai or azure ? its better in my experience to use a custom embedding model trained on my data , the whole system is amazing but if its kept general it will still underperform custom systems tailored for the data
If we can customise the chunking ( not token based we can actually maybe either have the chunks ready ( usually i do regex ) and use a custom transformer model ( kinda similar how u can do it in Haystack or llamaindex )
this can be really amazing
Great content! Thanks. Knowledge Graphs are superior to flat RAG systems, enabling complex queries that explore relationships between entities. They allow for more challenging questions that require connecting information, like analyzing Scrooge's actions in context. Knowledge Graphs provide structured relationships, not just text chunks, leading to more insightful answers. This approach is effective for Q&A assistants, as users seek more than just facts. Combining Knowledge Graphs with vector data is ideal. To present the real difference, instead of asking a factual question like "Who is Scrooge?", please try "what part of the story shows Scrooge doing wrong?" This requires an argument and connections between facts. Or ask, "Who is Scrooge and what is the most important thing we understand from his reaction in the story?" Such questions need to retrieve and connect information and facts.
Your comment is as valuable as this very valuable video. A big thank you to you and to Mervin for providing such great insights into RAG and GraphRAG!
Hi bro kindly could you make a video on, how can i integrate this GraphRAG on phidata, crewai etc... it would be worth it...
How can this be used practically inside of obsidian, where many people already have a huge database on their own fields of interest? Can you create a tutorial how to implement this in obsidian?
also can you tell for what exact purpose GPT was used here? and how many tokens were you charged for?
How does it fair with CrewAI?
Any Local version of this, like private, without API?
5:47 Can I use this with Claude api?
I just want to know the graphrag will extract the ner and relationship，but the original content will embed to the graphrag？hope some can reply me ❤❤
Why is Graph-RAG more expensive and less effective(namely, slower)? Does it have to search the whole graph for each query?
The question here is , would this not end up in having an issue with context length?
Can it be local?
If you index different documents at different point of time. We end up with multiple artifacts in the output folder.
How should one do a search over all outputs. Like a production level application
merge script
How can we see the graph?
Congrats!
How much cost this process of graph generation using gpt-4o? As I understood, for each chunk you make one request to extract the relation, all right?
I just spent 38$ on a 300 page document with GPT-4o....... Wasnt even a relevant document, just a first test 😥
Just did a single Prompt against this, costet another 2.38$
@@1509skate omg!
Can we do then agentic GraphRAG? I mean having GraphRAG as a query engine tool for an agent?
Yes you can
@@MervinPraison are you thinking about a tutorial using multi-agents and GraphRAG? 😋
all links reference missing 😊
Difference between local and global search is not evident through the example. I think it's assumed that the person watching the video already knows it very well.
Does anyone know a great open source library for a chatbot that is comparable to production chatbots. A lot of enterprise level chatbots are totally lacking in the Gen AI / LLM capabilities but it would be create if developers like us could enhance a base chatbot with our own RAG techniques like GraphRAG
Uhm… a chat window is simply a text field and text above it. That is so simple to do with a few lines of html that this would be a very small open source project 😅
Nice video, but next time try to give a more popular source for retrieving the info, the poor gpt might probably not have any clue about such an unknown book as the one you used...
most common text form is pdf. not txt, not markdown. so how does it deal with REAL documents?
Pdf is not a textformat
anyone checked Ollama?
github?
Interesting - current RAGs are not good enough for me - maybe this method will be more accurate.
anybody got ollama running with graphrag?
Ollama still doesn't support OpenAI API embeddings format, but the LLM part worked. Might need some patching to use 100% local.
Unclear that the results are any better based on what you showed.
Please when you do these , evaluate the response for correctness. That fact that it gives 'something' is not nearly sufficient.
I came for the 3d graph I left empty handed.
respect bro good content. thanks