GraphRAG - Ultimate RAG Engine - Semantic Search, Embeddings, Vector Search, & More!
AI Summary
Summary: Introduction to Graph Rag by Microsoft AI
- Graph Rag Overview:
- Open source data pipeline and transformation suite.
- Extracts structured data from unstructured text using large language models (LLMs).
- Enhances LLMs with external knowledge for more relevant answers (retrieval augmented generation - RAG).
- Reduces LLM hallucination, adhering to reliable context information.
- Applications of RAG:
- Question answering, information extraction, recommendations, sentiment analysis, summarization.
- Operates within private, local storage.
- Graph Rag vs. Traditional RAG:
- Integrates text extraction, network analysis, and LLM prompting/summarization.
- Uses knowledge graphs for improved accuracy and relevance.
- Connects disparate information, synthesizes insights, outperforms baseline RAG.
- Suited for advanced data analysis and question answering.
- World of AI Solutions Update:
- A team offering AI solutions for businesses and personal use cases.
- Services include automation and business operation assistance.
- Getting Started with Graph Rag:
- Install Python, Pip, and clone the repository.
- Recommended IDE: Visual Studio Code.
- Install Graph Rag using Pip.
- Export API key for OpenAI or configure for other LLMs like GRO or AMA.
- Create an input folder and index documents for RAG system.
- Configure
.ymlfile for desired output format and settings.- Run the code to start interacting with the indexed documents.
- Additional Resources:
- Demo video explaining Graph Rag in detail.
- Links to resources and further information in the video description.
- Patreon page for subscriptions and updates.
- Consultation bookings available.
- Conclusion:
- Graph Rag is considered the best open-source RAG system currently available.
- Encourages following on Patreon and Twitter for AI news updates.
- Invites viewers to subscribe, like, and watch previous videos for more AI content.