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Generating Embeddings for a RAG System

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RAG (Retrieval-Augmented Generation) is one of the hottest topics in AI today. It allows you to retrieve desired outputs from a model without fine-tuning it or modifying its underlying layers and weights . Think of it like this: you send your query along with specific instructions to the AI, and it returns results that are aligned with those instructions . These instructions are what we call a prompt , and the effectiveness of your RAG system largely depends on how well you design the prompt . Another critical component of RAG is fetching relevant information from your source database . This data helps you build the prompt more effectively. To achieve this, you need to retrieve data that is contextually similar to the user’s query , a process known as semantic similarity . This is where a Vector Database comes into play. As discussed in our previous posts, vector embeddings play a crucial role here. Transformers and other NLP models accept embedded vectors of tokens as input, and the...
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Enhancing E-commerce Search with Sentence Transformers

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 Product search is a critical feature for any e-commerce platform, as it directly impacts sales and revenue. The effectiveness of the search function depends on how well it generates diverse and relevant results based on customer queries that plays a key role in meeting customer needs and driving conversions. Most of the e-commerce websites are still using old fashioned search that is driven by traditional engine such as solr, elastic search etc. Imagine a smart AI based search portal which is capable to generate much diversified results instead of just relying on product descriptions and other indexed information.  Here is a Proof of Concept (PoC) for an enhanced search portal that acts like a virtual shopkeeper, delivering more diverse and relevant search results by accurately understanding the intent behind each query. One of the best suitable options available is semantic search There are numerous AI based models to achieve such tasks and sentence transformers are one of t...
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RNN model training with PyTorch

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A Recurrent Neural Network (RNN) is a type of artificial neural network designed for processing sequential data like time series, speech, and text. Unlike traditional neural networks, RNNs have memory that allows them to retain information from previous inputs, making them effective for tasks where context matters.  RNNs played a crucial role for modern AI applications, especially in Natural Language Processing (NLP) and speech processing . Below is a proof of concept demonstrating the training of an RNN model for sentiment analysis using the publicly available IMDB dataset. Process: Install torchtext 0.6.0 Importing the PyTorch libraries # Styrish AI # Installing PyTorch libraries import torch #================================================================= # this will check if GPU is available, the device on which the # code is run is GPU, else CPU. if torch.cuda.is_available():     device = torch.device( "cuda" )     print ( f "GPU is available: {torc...
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Exploring CNN with TensorFlow & Keras

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Convolutional Neural Network or CNN for short, is one of the widely used neural network architecture for image recognition. It’s use cases can be widely extended to various powerful tasks, such as, object detection within an image, image classification, facial recognition, gesture recognition etc. Indeed, Convolutional Neural Networks (CNNs) are designed with some level of resemblance to the image recognition process in the human brain. For instance, In the visual cortex, neurons have local receptive fields, meaning they respond to stimuli only in a specific region of the visual field, which is achieved by CNN using kernels or filters. Both human brain and CNN process the visual information in hierarchical manner. Basic information of an image is extracted via lower level of neurons in human brain, and higher-level neurons integrate the information from lower-level neurons to identify the complex patterns. On the other hand, in CNN, we use multiple convolutional layers to extract hiera...
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Finetuning of Transformers in Natural Language Processing

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Transformers are the essential parts of deep neural network, and widely used in Natural language processing tasks. We have a wide variety of usages where transformers are used in real time scenarios, such as, translations, text generation, question answering and various other NLP tasks. One of the widely used examples of transformer is Chat GPT. More information about transformer architecture and its mechanism can be accessed on page Understanding Transformers (BERT & GPT) . One of the very important processes in transformers is Finetuning. Finetuning is the way for adapting the OOB (out of the box) model for your specific tasks. In other words, it is the process of training a pre-trained model on your specific datasets to adapt the knowledge from new dataset. During fine-tuning, the parameters of the pre-trained model are adjusted based on the task-specific dataset. The goal is to adapt the model’s knowledge to perform well on the particular task of interest. Let’s understand how ...
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Tuning Hyperparameters and visualizing on TensorBoard

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Hyperparameters tuning is one of the most crucial steps of machine or deep learning process. Hyperparameters are configurations for a machine learning model that are not learned from the data but are set before the training process begins. These parameters are essential for controlling the overall behavior of the model. While training a machine learning model, you may have to experiment with different hyperparameters such as learning rate, batch size, dropout size, optimizers etc. in order to achieve the model with best accuracy. Performing experiments with hyperparameters one by one can be a tedious and time-consuming process. For instance, you initiate the training process with a specific combination of hyperparameters, and subsequently, you repeat the procedure with a different set of hyperparameters, and so forth. TensorFlow allows you to run experiments with different sets of hyperparameters in a single execution, enabling you to visualize the metrics on HParam dashboard in Tenso...
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Using TensorBoard with Machine Learning

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TensorBoard is a web-based tool to provide the visualizations and metrices needed during the machine learning process. TensorBoard is tightly integrated with TensorFlow and can be used seamlessly with it. It is highly efficient to track metrices like loss and accuracy, visualizing the model graph, histograms, and much more. Let’s understand more about how to collect metrices on TensorBoard and analyze those. TensorBoard as callback Consider the animal-building classification scenario in our post, Exploring CNN with TensorFlow & Keras . We have included TensorBoard as one of the callbacks while training the model. Callback is a tool to customize and extend the behavior of a model during training, evaluation, or inference, such as, model checkpointing (to periodically save your model during training), data augmentation (to increase the diversity of the training dataset), learning rate adjustments (dynamically adjust the learning rate during training based on certain conditions), T...
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