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Category: indexing

  • Building Agentic AI Applications on AWS: Detailed Tools and Resources

    Amazon Web Services (AWS) provides a robust and evolving ecosystem for building sophisticated agentic AI applications. These intelligent systems can operate autonomously, plan actions, retain memory, and interact with their environment to achieve specific goals. This detailed guide outlines key AWS services, their functionalities, and relevant links to help you get started, formatted for your… Read more

  • Top 8 Essential Data Structures You Should Know

    Data structures are fundamental building blocks in computer science, enabling efficient organization and manipulation of data. Understanding these structures is crucial for writing effective and performant code. Here are eight of the most commonly used data structures: 1. Arrays (and Python Lists) An array is a contiguous block of memory used to store a collection… Read more

  • Sample Project demonstrating moving Data from Kafka into Tableau

    Here we demonstrate connection from Tableau to Kafka using a most practical approach using a database as a sink via Kafka Connect and then connecting Tableau to that database. Here’s a breakdown with conceptual configuration and Python code snippets: Scenario: We’ll stream JSON data from a Kafka topic (user_activity) into a PostgreSQL database table (user_activity_table)… Read more

  • Parquet “Indexing”

    While Parquet itself doesn’t have traditional database-style indexes that you explicitly create and manage, it leverages its columnar format and metadata to optimize data retrieval, which can be considered a form of implicit indexing. When it comes to joins, Parquet’s efficiency can significantly impact join performance in data processing frameworks. Here’s a breakdown of Parquet… Read more

  • Data Lake vs. Data Lakehouse: Understanding Modern Data Architectures

    Organizations today grapple with ever-increasing volumes and varieties of data. To effectively store, manage, and analyze this data, different architectural approaches have emerged. Two prominent concepts in this landscape are the data lake and the data lakehouse. While both aim to provide a centralized data repository, they differ significantly in their design principles and capabilities.… Read more

  • Loading manuals into a vector database

    Here’s a breakdown of how to load manuals into a vector database, focusing on the key steps and considerations: 1. Choose a Vector Database: Several vector databases are available, each with its own strengths and weaknesses.1 Some popular options include: Consider factors like scalability, ease of use, cost, integration with your existing stack, and specific… Read more

  • Building a Product Manual Chatbot with Amazon OpenSearch and Open-Source LLMs

    This article guides you through building an intelligent chatbot that can answer questions based on your product manuals, leveraging the power of Amazon OpenSearch for semantic search and open-source Large Language Models (LLMs) for generating informative responses. This approach provides a cost-effective and customizable solution without relying on Amazon Bedrock. The Challenge: Navigating through lengthy… Read more

  • Scaling a vector database

    Scaling a vector database is a crucial consideration as your data grows and your query demands increase. Here’s a breakdown of the common strategies and factors involved in scaling vector databases: Why Scaling is Important: Common Scaling Strategies: Techniques for Horizontal Scaling: Factors to Consider When Scaling: Choosing the Right Scaling Strategy: The best scaling… Read more

  • Spring AI chatbot with RAG and FAQ

    Demonstrate the concepts of building a Spring AI chatbot with both general knowledge RAG and an FAQ section into a single comprehensive article.Building a Powerful Spring AI Chatbot with RAG and FAQLarge Language Models (LLMs) offer incredible potential for building intelligent chatbots. However, to create truly useful and context-aware chatbots, especially for specific domains, we… Read more

  • Vector Database Internals

    Vector databases are specialized databases designed to store, manage, and efficiently query high-dimensional vectors. These vectors are numerical representations of data, often generated by machine learning models to capture the semantic meaning of the underlying data (text, images, audio, etc.). Here’s a breakdown of the key internal components and concepts: 1. Vector Embeddings: 2. Data… Read more

  • Tensor

    PyTorch‘s fundamental data structure is the Tensor. It’s the central object for numerical computation in PyTorch, analogous to NumPy’s ndarray but with added capabilities for GPU acceleration and automatic differentiation (crucial for deep learning). Here’s a breakdown of PyTorch’s data structure landscape, with the Tensor at the core: 1. Tensors (torch.Tensor) 2. NumPy Arrays (numpy.ndarray)… Read more