Satish Chandra Gupta

Enhancing Retrieval-Augmented Generation (RAG) with Graph Databases for Improved Contextual Understanding in Large Language Models: Retrieval-Augmented Generation (RAG) techniques bolster Large Language Model (LLM) performance by incorporating external knowledge. However, traditional retrieval methods often lack the nuance to capture complex relationships and semantic dependencies within vast knowledge collections. This article introduces a novel approach integrating graph databases to represent and retrieve contextual knowledge for use in RAG systems. Graph structures allow for richer modeling of connections between text segments, keywords, and metadata, enabling retrieval strategies based on semantic similarity, indirect associations, and temporal context. Maybe outlining graph construction methods, tailored query design, and demonstrate the benefits of this approach for LLM-based applications. Addresses Core LLM Weakness LLMs & Limited Buffers: Traditional LLMs often struggle with maintaining long-term coherence and understanding complex relationships due to reliance on fixed-size buffers. Your idea strikes at the root of this issue. Flexible Context: Graph databases break the mold of linear representations. They allow for dynamic and multifaceted representations of how information relates, better mirroring how we humans often store and connect knowledge. Practical Advantages Nuanced Retrieval: Your solution goes beyond simple keyword matching. Queries can prioritize semantic similarity, indirect relations, and even temporal context. This could unlock much more relevant information for the LLM to work with. Potential Explainability: Graph structures have the potential to offer some level of traceability in how the LLM arrived at its answer. This is valuable in sensitive domains or for debugging Scalability: While there is overhead, graph databases are designed with handling large, interconnected datasets in mind. Innovative Research Area Emerging Field: The use of graph databases specifically for this purpose in LLMs is a relatively fresh research direction. This presents the potential for significant contributions to the field. RAG Enhancement: Combining graph techniques with Retrieval-Augmented Generation (RAG) is a clever approach with the potential to improve results across multiple application areas. Challenges and Refinement Naturally, such a novel idea will have its hurdles, making it even more interesting from a research perspective: Optimization: Graph operations and complex queries will need careful design to balance efficiency in a real-time LLM setting. Hybrid Approaches: It's likely that a combination of graph-based retrieval and some buffer mechanism for the most immediate interactions will provide the optimal solution. Dataset Suitability: The benefits of this approach would be most pronounced in data domains with rich interconnections and where nuanced semantic understanding is important. #LLM_GraphDB

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This is probably the best in-depth "musing" on building a Generative AI product I've ever read so far! Some notable learnings that I have picked up in this article: - There is trade-off of speed of development vs consistency in UX when parallelizing tasks across multiple teams to build different independent AI agents - Building the RAG pipeline (80%) was a breeze: they built dev sets and fitted them with prompt engineering and in-house models, but the generation bit (20%) was tough - In-house defensive YAML parser; re-prompting LLM to correct its YAML output mistakes adds non-trivial latency + GPU calls - "Poor man's fine-tuning" with Embedding-Based Retrieval (EBR) with in-memory DB to inject response examples into the prompts. Link in the comment

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As we moved from #monolithic to #microservice... the #LLM should also be moving to domain specific Small Language Models (#SLM) which need to be tailored to excel within particular fields by focusing their training on specialized corpora relevant to specific industries or areas of expertise. The SLM will benefit from their reduced size and computational efficiency, making them suitable for applications in domains such as #healthcare, #finance, #legal, and #customer service, where they can handle specific tasks with more precise and contextually appropriate outputs compared to general-purpose models. Any thoughts?

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The latest State of #AI report by #StanfordHAI showcases AI's remarkable progress. AI has surpassed human performance on some benchmarks like image classification and language understanding, but still lags behind on complex cognitive skills. Interestingly, new benchmarks like MMMU for general reasoning are emerging, with most recent tests showing close-to-vertical lines! However, it's essential to understand the limitations and potential pitfalls of such benchmarks. They lack broader context and are narrowly focused on evaluating a single capability. Rigorous and multi-faceted evaluations are essential for enterprise use cases, including meaningful and comprehensive benchmarks tied to real-world applications using business domain-specific datasets. https://lnkd.in/eUuK65u3

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���A super interesting blogpost by Siddharth Jindal on Analytics India Magazine, talking about the uniqueness and potential advantages of Gemma over the LLaMA models when it comes fine-tuning LLMs for Indic languages. Some highlights: 📌This is specifically due to the sentencepiece tokenizer used by Gemma which is able to encode strong representation for scripts like Devanagari. 📌The vocab size of Gemma is 256K whereas the LLaMA-3 has only 128K, this seems to indicate that the coverage is much larger for Gemma! 📌The Gemma model is able to generalize to other languages without the explicit need for continuous pre-training as opposed to LLaMA models where the tokenizer needs to be extended to accomodate the language/s in question. 📌Of course, this dosen't mean that Gemma is the silver bullet, since it does face some stability issues in training. This article definitely was an eye-opener for me and also exciting in terms of working with Gemma for fine-tuning Indic LLMs. Check it out here: https://lnkd.in/dRCEtF9Q Also, I'd love to hear more thoughts from my audience here, about their experiences with Gemma and multilingual LLMs! #artificialintelligence #airesearch #largelanguagemodels

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New post! Probably the most desired enhancement for database privilege management is finally here in 23ai. #database #sql https://lnkd.in/gXkXGWWH

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How are Graph Convolutional Neural Networks (GCNNs) a generalisation of Convolutional Neural Networks (CNNs)? My next blog in the Graph series on "Deep Learning with Graphs" is on the way (well, it's already written on paper) and it's going to be a blockbuster. The beauty of Graph Machine Learning is that the more you read about it, the more you feel that your neurons (real ones!) are learning to think better! And you're already in love with it! Coming to the point: One might recall the convolution operations in a CNN. You have a grid of pixels representing an image. You have a 3 x 3 filter. You slide the filter over the image and carry out aggregation to obtain a new pixel value. You proceed in the same fashion over the whole grid, sliding towards the right , bottom, carrying out the convolution operation and finally getting a new transformed, reduced image through the series of aggregations. You repeat this through the different convolutional layers which together form a Convolutional Neural Network Now think of a Graph. It is not simple grid like an image. The topology of a Graph is arbitrary. There is no notion of left, bottom, right in a Graph like in an image which is represented through a grid of pixels. You cannot slide over a Graph as simplistically you did and performed the convolutional operations in an image. How do you approach the problem? How do you carry out convolutional operations in a Graph? Now take a node in the Graph. Take all the nodes which are the immediate neighbours of this node. The convolutional operation here is: You aggregate the information from all the neighbours and pass it to the node. The information is nothing but the emedfings of the neighbour nodes. You add together the emedding of the node under consideration. You transform the aggregated embedding by multiplying with a weight Matrix and transform the node's own embedding by multiplying with a different weight matrix. This is the typical linear transformation operation in a NN. You apply non linearity - the activation function like ReLu. Now think about a CNN taking the same grid and considering a central pixel. Consider the rest of the pixels around the central pixel. You are essentially doing the same thing: taking information from the neighbour pixels along with the central pixel, aggregating, as I described above for the node in the Graph. Hence a Graph Convolutional Network is a generalisation of CNN The story goes, you do the same for every node on the Graph. Each node in the Graph represented through a different neural network architecture! This is amazing 🔥🔥🔥💓💓💓 #graphs #machinelearning #deeplearning My Technical Blogs Library: 1) https://lnkd.in/ejyWacXn 2) https://lnkd.in/bfrkRjq Follow Ajay Taneja for insights on: Data Science, Data Engineering, Finite Element Analysis, Non Linear Finite Element Analysis, Fatigue, Fracture Mechanics

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Have you wondered about the striking synergies between Large Language Models (LLMs) and Automated Machine Learning(Auto ML) Models? A few years ago, AutoML models made a lot of buzz in the AI domain. Even bigger buzz has been made by LLMs in recent times. There are striking synergies between LLMs and AutoML models. The performance of both LLMs and AutoML models improves with the quantity and quality of data. Both aim to automate aspects of machine learning. LLMs automate text-related tasks, while AutoML models automate the entire machine-learning pipeline. While LLMs are suitable for applications requiring deep language understanding and generation, AutoML models are ideal for users who need efficient and scalable ML solutions across different domains. As the use of LLM applications continues to grow, the next decade is poised to witness a significant shift towards the automation and productization of these models rather than their stand-alone deployment. This evolution in technology is a fascinating prospect, and if you share this interest, I invite you to stay tuned and follow along for more. #artificialintelligence #generativeai #LLMs #AutoML #leadership #futurisits #reviewwithsrujana

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Nice to have 'Top New Release' status on Amazon for the latest book (my 25th) that explores an alternative paradigm for quantum computation. Illustrations (111) by Dr. Narendra Madhav Joshi. My take is that we owe it to ourselves at this early stage of the industry's lifecycle to explore alternative approaches. Here is a brief overview: "This book attempts to reveal something of the vastly different and fundamentally creative quality of computing that must accompany any computation involving the quantum-levels. To linearly project digital computing laurels manifest as increasing speeds and the ability to process vaster amounts of information, as the inevitable trajectory of quantum computing is perhaps, in the aphorism attributed to the Buddha, to look only at the finger and to miss the moon and the sky that it is pointing to. The cover figure highlights all that is being missed, summarized as an egg-like structure synonymous with the term ‘Hiranyagarbha’ in Sanskrit. This depicts the womb of creation abundant with many layers and patterns reflecting luminescence, due to the constant superposition and entanglement inherent to the quantum computation that derives from a light-centered interpretation of quantum dynamics." The book is available here: https://lnkd.in/gXu4H9uW #quantumcomputation #quantumtech #technologyleadership #cosmology #systemsthinking

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Gerhann Pieters brought up this very interesting discussion on ECL modeling. IMO, provisions analysts should always worry about the final impact of parameters models on the ECL. This is basically how their models are proven feasible. But... there is a world of derived analysis that can rise from this - with respect to impacts in both the PnL and the business strategy. That's why major banks set provision steering committees to collect insights and different perspectives.

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Quantized embeddings offer significant advantages for NLP models, including reduced storage requirements, faster computations, lower energy consumption, and improved model interpretability. They are becoming increasingly important for enabling efficient and scalable NLP on resource-constrained devices and cloud-based applications. Read my latest article below

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RAG has now become the go-to skill for being a GenAI Engineer" 💯 In this post, we will break down one of the components of RAG and understand it simply! 👇

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Quentin H. and Yanwei (Wayne) Zhang on the Faire Data team recently did some great work fine-tuning Llama3 model to enhance search relevance. Here are some key insights from the work: • While prompt engineering is valuable, LLM fine-tuning is essential for a domain specific task like understanding semantic search relevance. • Fine-tuned open-source LLMs like Llama3 can deliver impressive performance, scalability, and cost-efficiency compared to using proprietary models. • Effective application of a fine-tuned LLM demands a well-defined problem, robust, high-quality labeled data, and ongoing model refinement. Ground-truth labeled data is crucial for maximizing model performance. • Fine-tuned Llama3-8b achieves performance on par with the larger Llama2–13b model. Read the blog post to dive deeper into these findings! https://lnkd.in/ecP_3E7S We are also hiring (https://lnkd.in/dHfRZcPi) #LLM #AI #Finetuning #LLAMA3 #SearchRelevance #Hiring

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Data Scientists, This is BIG! Snowflake's Arctic LLM is Here – and Open-Source! 🤯 Get your hands on it now: Interactive Demo: Explore Arctic's capabilities with this Streamlit app → https://lnkd.in/gJ7y38Ng Replicate: Ready to experiment? Find it on Replicate → https://lnkd.in/gyDXiMju Why This Matters: Arctic lets you quickly build demos of your models. This is a game-changer for showcasing your work and making your models more accessible! Huge thanks to Snowflake for making this happen! Are you excited to try Arctic? Let me know in the comments! 👇 #LLM #OpenSource #DataScience #Streamlit #Snowflake

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My latest Medium post comparing SetFit, FastFit, and Semantic Router for Few-shot NLP Intent Classification has been published by Towards AI! This post covers: 1. Details of NLP Intent classification and why it is extremely important in the age of LLM chatbots 2. Algorithm details of SetFit, FastFit and Semantic Router 3. Detailed code experiments on a chatbot intent detection dataset 4. Conclusion on the best method as of today. As with most of my posts, it has lots of code you can directly use in your work! This post was jointly written by Shilpa and me, both of us humans 😅 🔗 Medium Friend Link (without paywall): https://lnkd.in/dVfAvjzr Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

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We must leverage AI/LLMs for greater productivity where ever possible. But more and more research is coming out with respect to their limitations. We will all greatly benefit by understanding them before we go too far in deploying such technologies. Will it burst the AI bubble sooner than later? What do you think? https://lnkd.in/eCsagcUs #AI #LLM #ChatGPT #Research

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