can it do downstream task like summarization ?

Excellent concise introduction on GraphRAG！ 如果你更习惯中文，如果对人工智能充满好奇，但却缺乏技术基础？如果你看了很多理论，却没有手搓一个--“九析带你轻松完爆”全新力作《GraphRAG 小白漫画教程》这门课程可以让你在门外汉“三上悠亚”陪伴下，一个周末就理解当下最火爆的AI技术：GraphRAG 的原理、能力价值、环境准备、项目介绍和实战演示，即使你是零基础的萌新也能快速上手，轻松完爆。无需任何经验，适用于各行各业。在整个课程中，你将亲手创建一个基于知识图谱的AI大模型应用。学习点击kzbin.info/www/bejne/ZnjadKihZ6yNiKs

Devs work 2 hours a week. Inefficiencies keep jobs.

Are tech bro puffy best wearing freaks grown in a lab?

Using an AI is like running a continuous code review with junior developers who never seem to learn from the experience.

Stop ruining car brands

I haven't coded for the last 6 months. Ai writes everything.

Great work Vikhyat! Rooting for moondream :)

The demo was impressive. Had no idea what the presentation was till the demo

Einstein never works 😂

They are no different you take any model and just give a good prompt you get the same result

None of them work

Have you tried the $10 Pro plan of Codeium? Are there any differences compared to the free one? According to the information on their website, this plan uses GPT-4o, Codeium models. 6

Wait - are companies actually happy running their models at full precision after creation?! That's an insanely inefficient waste of resources for no gain given modern (2022+) quantisation techniques.

Demo was the coolest part ! Well done !

Does the model also work for extracting data from documents? Thanks!

One of the best talks of the conference

amazing video ai engineer thanks for sharing, please keep up with finding and sharing great content

Moondream is great

Awesome

i never commented on any of the tech videos till with my 8 years of experience. I got an interview in few days and i wanted to use graphRAG but i knew about graphDB but this video made me realize few things that its not the number of technical words you use to explain something but its how you make such complicated concept look easy. Kudos Prof Emil Eifrem. Please release more videos on different concepts on GraphRAG.

What are some good use cases of this tech?

Go vik..to the moon 🌝

Very impressive demo and talk. I have not had any ideas for a multi-modal or vision based ai application but if I do I will be sure moondream is at the top of the list of models to try out.

Good job 👍

Moondream is incredible. Thank you, Vikhyat

Moondream 🙏

Watching the Moondream grow for a half a year now - great progress!

This is a great overview of the transformative impact of Large Language Models and the exciting developments around Retrieval Augmented Generation (RAG). Jerry Liu's talk seems like a must-watch for anyone interested in building and optimizing LLM-powered applications on private data. It's inspiring to see experts like Jerry, with his impressive background in AI research and engineering, sharing insights on how to tackle the challenges of productionizing RAG systems. Looking forward to exploring more at the AI Engineer World's Fair 2024!

🎉

great talk, thanks for sharing

Super interesting

Could have been more render result demo than talks.

Cool😊😊

We love Moondream!

One of the most enjoyable and down-to-earth videos in this series. Great stuff! Thanks!

The first mistake is use a vector db for RAG.

This reminds me NAS - network architecture search. I would call this RAG optimal configuration search.

Thank for sharing 👍

Geez this is must have video to watch liked so much

Thanks for sharing this, amazing to see how useful Braintrust is.

That's pretty cool but the title is a bit misleading as it doesn't touch the actual GraphRAG method (that is, a specific paper and project).

Huggy huggy wuggy

Oh whatever, Chris will make his money and leave a massive pile for the bastard maintainers to clean up. 😂 This is a well marketed PyPy with a pinch of Julia

Slides?

🎯 Key points for quick navigation: 00:00:21 *📖 Introduction to Interactive Session* - Overview of interactive workshop using Python notebook and Neo4j, - Introduction of facilitators aiding the session, - Outline of session structure with emphasis on interaction and hands-on activities. 00:02:09 *🚀 Setup and Preparation* - Instructions to create a Neo4j graph data science sandbox, - Guide to accessing and copying Google Colab notebook for exercises, - Importance of running initial setup tasks for smooth participation. 00:05:22 *🧠 Retrieval Augmented Generation with Graphs Overview* - Explanation of retrieval augmented generation for context enrichment, - Introduction to key components: vector search, graph traversal, and graph data science, - Workshop focus on building a fashion recommendation system using real-world data. 00:09:18 *🛒 Use Case: AI Fashion Assistance* - Building a system to recommend clothing items and generate personalized emails, - Utilizing H&M dataset for practical demonstration, - Potential applications in marketing and customer support scenarios. 00:10:28 *💻 Technology Stack and Course Structure* - Tools involved: OpenAI, Langchain, Colab, Gradio, - Breakdown of session structure from graph building to LLM integration, - Aim to utilize embeddings for enhancing recommendation systems. 00:11:23 *🌐 Graph Model Setup and Data Loading* - Explanation of data model and node types: customers, articles, products, - Instructions to load data using Neo4j and Cypher queries, - Clarification of structural data preparation for graph embeddings. 00:18:25 *🔄 Assistance and Connectivity Issues* - Addressing network and setup issues faced by participants, - Offering personal assistance and guidance on troubleshooting connections, - Encouragement and acknowledgment of participant engagement and progress. 00:25:16 *🤔 Vector Search and Embeddings Introduction* - Introduction to embeddings as data compression for machine learning, - Explanation of embeddings for text, images, and audio, - Discussion on mathematical similarity and importance in data search processes. 27:24 *📊 Embedding and Vector Search Techniques* - Introduction to embedding graph structures alongside audio, text, and video. - Usage of vector search for semantic similarity in graphs and text. - Explanation of Neo4j indices and vector index creation using hnsw. 30:10 *🔍 Creating and Visualizing Vector Indexes* - Steps for creating vector indexes and embedding property using OpenAI. - Use of cosine similarity to perform vector searches in Neo4j. - Demonstration of encoding and searching with example queries. 33:15 *🧩 Embedding Generation vs. Vector Index Creation* - Clarification on generating embeddings and creating vector indexes. - Process of embedding text using OpenAI and indexing in Neo4j. - Practical example of searching via simple prompts using Cipher. 36:24 *⚙️ Integrating LangChain and Advanced Search Techniques* - Integration of LangChain for vector store creation and enhanced search functionality. - Example of searching structured vector data using LangChain. - Discussion on optimization and handling large datasets. 43:20 *🔄 Semantic Search with Graph Patterns* - Explanation of graph patterns and semantic search layers. - Utilizing graph databases for personalized recommendations. - Query examples for visualizing customer purchase data and recommendations. 00:54:48 *📊 Graph-Based Customer Recommendations* - Exploring customer purchase patterns using a graph, - Using machine learning to automate recommendation calculations, - Flexibility in adjusting the time window for filtering purchase data. 00:57:19 *🎯 Vector Search and Score Integration* - Combining vector search with graph traversal to enhance recommendations, - Returning personalized product lists based on purchase similarities, - Using both search and purchase scores for better ranking. 01:03:41 *🔍 Real-Time Data Updates in Recommendations* - Emphasizing real-time updates in recommendation systems, - Flexibility in adjusting query parameters for more accurate results, - Discussing the integration of external batch recommendations. 01:09:03 *🧠 Knowledge Graph Inference Techniques* - Introduction to graph data science and knowledge graph inference, - Explanation of node embeddings and their role in relationship inference, - Use of K-nearest neighbor for relationship prediction and co-purchase analysis. 01:17:56 *🛍️ Techniques for Product Recommendation* - Discusses various strategies for enhancing product recommendations using graph databases, - Emphasizes scaling via customer purchase patterns and multi-hop graph queries, - Mentions the integration of different machine learning models for inference. 01:20:31 *📉 Graph Embedding Challenge & Solutions* - Explains the need to recalculate graph embeddings over time and current efforts for real-time updating, - Highlights the role of Fast RP for efficient computation on large graphs, - Suggests alternative methods like averaging neighborhood embeddings. 01:21:49 *🔍 Dimension and Use Cases of Graph Embeddings* - Talks about the scalability of dimension in graph embeddings relative to use case scale, - Differentiates the application size needed for dimensions compared to text embeddings, - Discusses deployed use cases like entity resolution, fraud detection, and customer segmentation. 01:23:43 *📚 Semantic Retrieval and Functions in LLMs* - Explores semantic data retrieval in graph structures, using text-to-cypher patterns, - Investigates function calling in LLMs for dynamic graph queries, - Describes creating domain-specific query functions for improved relevance and precision. 01:26:19 *🤔 Contextual Understanding of Graph Embeddings* - Describes the motivation and intuition behind graph embeddings, - Connects graph embeddings to traditional ML techniques, focusing on inferring unseen relationships, - Clarifies the representation and scalability that graph embeddings offer for various queries. 01:30:12 *📧 LLMs in Action: Personalized Emails* - Demonstrates using LLMs to generate personalized emails based on graph-based recommendations, - Details the use of search products and recommendation products in a structured prompt, - Illustrates the retrieval and query processes for generating relevant data-driven communications. 01:34:09 *🛠️ Building the Retrieval and Recommendation System* - Walks through the setup of a personalized search and recommendation system using Langchain, - Describes the configuration of retrievers and the process of injecting results into prompts, - Offers a detailed look at the construction of a chain incorporating multiple retrieval steps. 01:37:20 *💻 Application Deployment Demonstration* - Shows the deployment of a demo app using Gradio to run recommendation scenarios, - Discusses the frontend interface and backend processes for generating user-specific outputs, - Highlights the variation in recommendations for different users with similar search queries. Made with HARPA AI

Great presentation!

When was this talk recorded?

The usage of the word generative is incredible. But I'd rather see observed insights than generated.

Really enjoyed the workshop. Thanks for sharing