GraphRAG: Ultimate RAG Engine - Semantic Search, Embeddings, Vector Search, & More!

Unlock the Power of GraphRAG: The Ultimate RAG Engine for Advanced Semantic Search, Embeddings, Vector Search, and More!
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[Link's Used]:
Github Repo: github.com/microsoft/graphrag
Blog Post: microsoft.github.io/graphrag/
Project Page: www.microsoft.com/en-us/resea...
Research Paper: arxiv.org/pdf/2404.16130
Download Git: git-scm.com/downloads
Download VS Code: code.visualstudio.com/download
Download Python: www.python.org/downloads/
Download Pip: pypi.org/project/pip/
OpenAI API Key: platform.openai.com/api-keys
Original Video Credits: • GraphRAG (v4) demo
🌟 *Introduction:*
Welcome to our deep dive into GraphRAG, the groundbreaking Retrieval-Augmented Generation (RAG) engine that seamlessly combines text extraction, network analysis, and LLM prompting and summarization. Discover why GraphRAG stands out as the ultimate solution for semantic search, embeddings, and vector search. GraphRAG takes text comprehension to the next level by extracting a knowledge graph from raw text, building a community hierarchy, and generating precise summaries. This structured approach offers deeper insights than conventional text searches, making it an invaluable tool for complex data analysis.
🔗 *Better Connectivity:*
GraphRAG connects disparate pieces of information through shared attributes, offering synthesized insights that baseline RAG often misses. This enhanced connectivity transforms how you perceive and interact with complex information.
👍 *Call to Action:*
If you found this video helpful, please give it a thumbs up, subscribe to our channel for more exciting content, and share it with your friends and colleagues!
*Relevant Tags and Keywords:*
GraphRAG, RAG Engine, Semantic Search, Embeddings, Vector Search, Knowledge Graph, Text Extraction, Network Analysis, LLM Prompting, Data Summarization, Microsoft Research, Azure Resources, Solution Accelerator, Enhanced Question-Answering, Complex Datasets, Advanced Analytics
*Hashtags:*
#GraphRAG #semanticsearch #vectorsearch #embeddings #knowledgegraph #llm #dataanalysis #MicrosoftResearch #azure #techinnovation #datascience

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[@artur50]

great video! yet, could you just show a short snippet of code, how to use it with Ollama?

[@janalgos]

awesome video thank you

[@07Mihai07]

Nice video! Keep it up, please!

[@intheworldofai]

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[@rockypunk91]

Do we need to reindex all documents, everytime we add new document.
Is there any way to run it programitically

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[@girijeshthodupunuri1300]

How do you think we can use this in a production application? I noticed indexing documents took me around 3 minutes when I use gpt-3.5-turbo.

[@GeertBaeke]

It's not created for production use. It is an example implementation based on the paper from local to global. Indexing takes a long time because many LLM calls are used to extract entities, relationships and community summaries based on detected communities via the Leiden algorithm. In fact, it's easy to spend 10 to 20 euros simply for indexing a few documents. They do use caching so that a second indexing step does not consume tokens as long as you do not change chunk size etc...

[@opita]

I wonder why this isn't used by the LLM themselves.

[@ObscuredByCIouds]

What do you mean?

[@brettmiddleton5013]

I tried it just gave me prompts

[@brandonvelasquez3530]

Maybe that's in the backlog

[@martinbak]

Is it better than Verba?

[@vitalis]

I never got verba to ingest properly