Swati Bhatnagar on LinkedIn: When building GraphRAG, you may want to explicitly define the graph… (2024)

Creating a Pipeline for Generating Synthetic Data for Fine-Tuning Custom Embedding Models: 👀Step 1: Create a Knowledge Base: Start with preparing your domain specific knowledge base, such as PDFs or other documents containing information. Convert the content of these documents into a plain text format.Step 2: Chunk the Data: Divide your text data into manageable chunks of approximately 256 tokens each (chunk size used in RAG later).Step 3: Generate Questions Using LLM: Use a Language Model (LLM) to generate K questions for each chunk of text. The questions should be answerable based on the content within the chunk. Example prompt: "Generate five questions that can be answered using the following text: [insert chunk here]."Step 4: Optionally Generate Hard Negative Examples: Create hard negative examples by generating questions that are similar to the correct questions but have answers that are incorrect or misleading. Alternatively, use random other samples from the batch as negative examples during training (in-batch negatives).Step 5: Deduplicate and Filter Pairs: Remove “duplicate” question-context pairs to ensure uniqueness. Use the LLM to judge and filter out lower-quality pairs by defining custom rubrics for quality assessment.Step 6: Fine-Tune Embedding Models: Use the prepared data to fine-tune your embedding models with Sentence Transformers 3.0Blog post on how to fine-tune Embedding Models: https://lnkd.in/eNTNpNDJ

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We’re excited to launch a huge feature making LlamaIndex the framework for building knowledge graphs with LLMs: The Property Graph Index 💫(There’s a lot of stuff to unpack here, let’s start from the top)You now have a sophisticated set of tools to construct and query a knowledge graph with LLMs:1. You can extract out a knowledge graph according to a set of extractors. These extractors include defining a pre-defined schema of entities/relationships/properties, defining a set of node relationship with LlamaIndex constructs, or implicitly figuring out the schema using an LLM.2. You can now query a knowledge graph with a huge host of different retrievers that can be combined: keywords, vector search, text-to-cypher, and more.3. You can include the text along with the entities/relationships during retrieval4. You can perform joint vector search/graph search even if your graph store doesn’t support vectors! We’ve created robust abstractions to plug in both a graph store as well as a separate vector store.5. You have full customizability: We’ve made it easy/intuitive for you to define your own extractors and retrievers.Labelled Property Graph: a KG representation with nodes + relationships. Each node/relationship has a label and an arbitrary set of properties.Why you care: This is a robust representation of knowledge graphs that extends way beyond just triplets - allows you to treat KGs as a superset of vector search. Each text node can be represented by a vector representation similar to a vector db, but also link to other nodes through relationships.Our initial launch was done in collaboration with our partners from Neo4j. Huge shoutout to Tomaz Bratanic for creating a detailed integration guide as well as extensive guidance on how to refactor our abstractions.Our blog post: https://lnkd.in/gMVsQj-HFull guide in the docs: https://lnkd.in/g8pfxGV4Usage guide: https://lnkd.in/gZcF8sfiBasic notebook: https://lnkd.in/g3S5RZ7qAdvanced notebook (shows extraction according to a schema): https://lnkd.in/gqcAaq3HUsing Neo4j with our property graphs: https://lnkd.in/gV2bNmbS

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Excited to announce my new book on "Retrieval Augmented Generation (RAG)" is now available! Dive into the world of language models and learn about embedding vectors, Sentence BERT, and more. Perfect for those keen to understand AI's workings with practical examples.Get your copy on Amazon and let's connect here to discuss yourinsights! https://lnkd.in/d5pnRCve#AI #MachineLearning #DataScience #NaturalLanguageProcessing#ComputationalLinguistics #TechReads #Innovation #NaturalLanguageProcessing #NLP #TechInnovation #ArtificialIntelligence#DeepLearning #TechCommunity #Education #AIResearch #TechBooks#Innovationn

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Unlocking the Potential of GANs: A Deep Dive into Revolutionary Image Processing TechniquesAre you fascinated by the incredible capabilities of Generative Adversarial Networks (GANs) in image processing? 1. Enhancing Image Resolution Using Super-Resolution GANs: Theory and ApplicationDive into the technical wizardry behind Super-Resolution GANs (SRGANs), understanding their sophisticated architecture and the theoretical foundations that enable them to magnify image resolution without compromising on quality. This session is perfect for those who love to push the boundaries of how sharp an image can get!2. Applying SRGAN for Human Face Enhancement: Techniques and ExamplesLearn about the specific application of SRGANs in enhancing the resolution and details of human facial images. This discussion includes practical examples and case studies, illustrating the transformative impact of high-resolution techniques in real-world scenarios.3. Introduction to DiscoGANs: Bridging Domains without Detailed OutputsGet introduced to DiscoGANs and discover how they enable fascinating domain transitions in image data. This session focuses on the conceptual understanding, offering insights into the versatile applications of these networks.4. Gender Transformation with DiscoGANs: From Male to FemaleExplore the intriguing use of DiscoGANs for gender transformations in images. This topic delves into the technical challenges and societal implications, providing a comprehensive overview of the capabilities of GANs in creating realistic and respectful image transformations.5. Transforming Selfies into Sketches with Custom Cartoon GANsImagine turning your selfie into a fine sketch. This discussion highlights how custom Cartoon GANs are being tailored to convert everyday photos into artistic expressions, enriching the intersection of AI and art.6. Creating Personalized Bitmojis from Selfies Using Cartoon GANsDiscover the playful side of GANs where we discuss converting selfies into personalized Bitmoji-style cartoons. This session covers the creative and technical aspects, offering a glimpse into personalized digital avatars.7. Leveraging Context Encoders for Image Restoration in TensorFlow 2Focusing on TensorFlow 2, this topic will explain the use of context encoders for advanced image restoration tasks like inpainting. Perfect for those interested in the technical implementation of GANs within a popular framework.8. Using Pix2Pix GAN for Transforming Aerial Images to MapsLearn about the application of Pix2Pix GAN for converting aerial photographs into detailed maps, a powerful tool for cartographers and developers alike.Engage in discussions, share your thoughts, and expand your understanding of what GANs can achieve. Whether your interest lies in theoretical aspects or practical applications, there's something here for everyone to learn and be inspired by!

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Virtual prototyping in the aerospace industry is tapping into the power of advanced AI models, like the Lemma Gemma model. These models provide a wealth of benefits, enhancing how aircraft are designed and tested. Here's a brief overview of how technologies inspired by models such as Lemma Gemma are revolutionizing aerospace:Advanced Simulations: AI models similar to Lemma Gemma can simulate complex aerospace scenarios, allowing engineers to test and refine aircraft designs in a virtual environment. This means potential issues can be spotted and addressed without costly physical prototypes.Efficiency and Prediction: These models use vast amounts of data to predict outcomes accurately, much like Lemma Gemma predicts text sequences. In aerospace, this ability helps in optimizing designs for better performance and safety while reducing unnecessary costs.Rapid Prototyping: Just as Lemma Gemma can quickly generate coherent text, AI in aerospace allows for swift iterations of design prototypes. This accelerates the development cycle, helping new designs reach the market faster.Innovative Problem Solving: AI models can identify complex patterns and propose solutions, similar to how Lemma Gemma handles linguistic challenges. In aerospace, this means better troubleshooting and innovation during the design phase.Customization and Adaptation: Like Lemma Gemma adapts to different linguistic contexts, virtual prototyping can tailor aircraft designs to specific requirements, ensuring compliance with international standards and customer needs.For a deeper understanding of how Lemma Gemma and similar AI technologies influence aerospace prototyping, consider reading the detailed paper on this topic. It provides comprehensive insights and examples of AI's transformative role in aerospace engineering, showcasing both current applications and future potential.

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Exploring the Essence of a Customer-Centric CultureIn the ever-evolving landscape of business, placing the customer at the heart of every strategy is paramount. This visual encapsulates the vital components that shape a truly customer-centric culture, a philosophy I'm deeply passionate about.The core principles are clear:Map the customer journey and lifecycle for in-depth understanding.Refine the operating model to enable customer-centricity.Align technologies and processes to enhance customer engagement.Engage executives and leaders to champion the customer's perspective.Focus on development areas that drive behavior change towards customer-centricity.Integrate various business unit cultures to harmonize with the customer-first approach.Transform culture from the top down, ensuring that every level of the organization is involved.Measure change using key metrics to track progress and impact.Incorporate customer feedback directly into processes and behaviors, closing the loop between customer experience and business evolution.My commitment to fostering a customer-centric environment is unwavering. I'm seeking an opportunity where my strategic insight and dedication can contribute to the growth and transformation of a company that prioritizes its customers just as I do.

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