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Tag: embeddings

  • Vector DB Weaviate Advanced Internal Concepts and Code Snippets

    Weaviate Internal Concepts and Code Snippets This document explores the core internal concepts of Weaviate, an open-source vector database, and provides illustrative code snippets using the Python client library to demonstrate its usage. Internal Concepts of Weaviate Schema and Collections Schema: Defines the structure of your data, including classes (now called Collections in newer versions), Read more

  • Vector DB Pinecone Advanced Internal Concepts and Architecture

    Advanced Pinecone Internal Concepts and Architecture Advanced Pinecone Internal Concepts and Architecture This document builds upon the foundational understanding of Pinecone’s internals and delves into more advanced concepts, complemented by illustrative code snippets and a high-level architectural overview. As Pinecone’s exact architecture is proprietary, these are informed inferences based on advanced vector database techniques and Read more

  • Most Used Data Science Algorithms for Retail Checkout Video Analysis

    Detailed Data Science Algorithms for Retail Checkout Video Analysis Detailed Data Science Algorithms for Retail Checkout Video Analysis This article provides an in-depth look at the data science algorithms employed for analyzing video data from retail checkouts, covering both the computer vision techniques for processing the visual information and the machine learning/statistical methods for extracting Read more

  • Various flavors of Retrieval Augmented Generation (RAG)

    Various Types of RAG The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with several variations and advanced techniques emerging beyond the basic “naive” RAG. I. Based on the Core RAG Pipeline 1. Naive/Standard RAG The user’s query is directly used to retrieve relevant documents, and these are passed to the LLM for generation. Use Read more

  • Top 30 Machine Learning Libraries

    Top 30 Machine Learning Libraries: Details, Links, and Use Cases Here is an expanded list of top machine learning libraries with details, links to their official websites, and common use cases: Core Data Science Libraries NumPy: Fundamental package for numerical computation in Python. Provides support for large, multi-dimensional arrays and matrices, along with a large Read more

  • Implementing Locally running Mistral Chatbot with RAG

    Locally running Mistral Chatbot with RAG Let’s implement a local running chatbot with Mistral LLM using RAG to retrieve documents from a locally running Vector DB that also contains FAQs. Here’s a breakdown of the steps and the Python code to achieve this: Phase 1: Setting Up the Local Environment Install Dependencies: pip install transformers Read more

  • Comparing DynamoDB vs MongoDB for Vector Embedding

    Comparing DynamoDB vs MongoDB for Vector Embedding Both Amazon DynamoDB and MongoDB offer capabilities for working with vector embeddings, but they approach it with different underlying architectures and strengths. Choosing the right database depends on your specific use case, scalability requirements, query patterns, and existing infrastructure. DynamoDB for Vector Embedding DynamoDB, a fully managed NoSQL Read more

  • Comparing Vector DB Embedding Use Cases: Neo4j vs MongoDB

    Comparing Vector DB Embedding Use Cases: Neo4j vs MongoDB Both Neo4j and MongoDB have integrated vector embedding capabilities, but their strengths and ideal use cases differ significantly due to their fundamental data models. Neo4j: The Graph-Centric Approach Focus: Excels at managing and querying highly connected data and relationships. Vector embeddings enhance its ability to perform Read more

  • Detailed Guide to MongoDB Vector Embedding Similarity Search

    Detailed Guide to MongoDB Vector Embedding Similarity Search Performing similarity searches using vector embeddings in MongoDB allows you to find documents that are semantically or conceptually similar based on the numerical representations of their content. This technique is powerful for applications like recommendation systems, semantic search, and anomaly detection. For a general introduction to MongoDB, Read more

  • Detailed Explanation: Vector Embedding vs Feature Store

    Detailed Explanation: Vector Embedding vs Feature Store Vector Embeddings: Deep Dive Detailed Explanation: At its core, a vector embedding is a way to represent complex data as a point in a multi-dimensional space. The magic lies in how these representations are learned or constructed. The goal is to capture the underlying semantic meaning, relationships, and Read more