NoSQL Databases and Their Use Cases
KDnuggets
MARCH 16, 2023
Learn about NoSQL Databases and their types like key-value, document, graph and column family with their use cases.
Ask your Documents with Langchain and Deep Lake!
Analytics Vidhya
SEPTEMBER 14, 2023
Introduction Large Language Models like langchain and deep lake have come a long way in Document Q&A and information retrieval. However, a […] The post Ask your Documents with Langchain and Deep Lake! These models know a lot about the world, but sometimes, they struggle to know when they don’t know something.
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Building Multi-Document Agentic RAG using LLamaIndex
Analytics Vidhya
SEPTEMBER 5, 2024
Enter Multi-Document Agentic RAG – a powerful approach that combines Retrieval-Augmented Generation (RAG) with agent-based systems to create AI that can reason across multiple documents.
A New Era of Text Generation: RAG, LangChain, and Vector Databases
Analytics Vidhya
NOVEMBER 5, 2023
One such groundbreaking approach is Retrieval Augmented Generation (RAG), which combines the power of generative models like GPT (Generative Pretrained Transformer) with the efficiency of vector databases and langchain.
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A Deep Dive into Qdrant, the Rust-Based Vector Database
Analytics Vidhya
NOVEMBER 21, 2023
Introduction Vector Databases have become the go-to place for storing and indexing the representations of unstructured and structured data. In the ever-evolving landscape of […] The post A Deep Dive into Qdrant, the Rust-Based Vector Database appeared first on Analytics Vidhya.
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Build Semantic Search Applications Using Open Source Vector Database ChromaDB
Analytics Vidhya
JULY 18, 2023
Among such tools, today we will learn about the workings and functions of ChromaDB, an open-source vector database to store embeddings from […] The post Build Semantic Search Applications Using Open Source Vector Database ChromaDB appeared first on Analytics Vidhya.
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CRUD Operations in MongoDB
Analytics Vidhya
DECEMBER 13, 2022
Introduction MongoDB is a type of NoSQL Database, that stores data in document format(bson or binary json format). Its advantage over traditional SQL Databases includes the flexibility of schema-design, relaxation of its ACID properties and its distributed data storage capability thus performing better for […].
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Building Custom Q&A Applications Using LangChain and Pinecone Vector Database
Analytics Vidhya
AUGUST 19, 2023
One of the fascinating applications of these models is developing custom question-answering or chatbots that draw from personal or organizational data sources. […] The post Building Custom Q&A Applications Using LangChain and Pinecone Vector Database appeared first on Analytics Vidhya.
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Introduction to Apache CouchDB using Python
Analytics Vidhya
JULY 23, 2022
Introduction Apache CouchDB is an open-source, document-based NoSQL database developed by Apache Software Foundation and used by big companies like Apple, GenCorp Technologies, and Wells Fargo. This article was published as a part of the Data Science Blogathon.
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Vector Streaming: Memory-efficient Indexing with Rust
Analytics Vidhya
SEPTEMBER 17, 2024
Introduction Vector streaming in EmbedAnything is being introduced, a feature designed to optimize large-scale document embedding. Today, I will show how to integrate it with the Weaviate Vector Database for seamless image embedding and search.
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50+ MongoDB Interview Questions and Answers
Analytics Vidhya
JULY 18, 2024
Introduction MongoDB is a NoSQL database offering high performance and scalability. It stores data as documents, similar to JSON objects, allowing for complex structures like nested documents and arrays. It also reduces the need for joins with embedded documents and arrays.
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Understanding the popular database management system: MySQL
Data Science Dojo
MARCH 25, 2024
MySQL is a popular database management system that is used globally and across different domains. It is one of the most popular database management systems (DBMS) globally that supports all major operating systems: Linux, macOS, and Windows. Databases are stored on a server, which is typically a remote computer or a cloud server.
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Understanding the popular database management system: MySQL
Data Science Dojo
MARCH 25, 2024
MySQL is a popular database management system that is used globally and across different domains. It is one of the most popular database management systems (DBMS) globally that supports all major operating systems: Linux, macOS, and Windows. Databases are stored on a server, which is typically a remote computer or a cloud server.
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Top vector databases in market
Data Science Dojo
AUGUST 3, 2023
A vector database is a type of database that stores data as high-dimensional vectors. One way to think about a vector database is as a way of storing and organizing data that is similar to how the human brain stores and organizes memories. Pinecone is a vector database that is designed for machine learning applications.
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Introduction to Elasticsearch using Python
Analytics Vidhya
JULY 18, 2022
Introduction Elasticsearch is primarily a document-based NoSQL database, meaning developers do not need any prior knowledge of SQL to use it. Still, it is much more than just a NoSQL database. This article was published as a part of the Data Science Blogathon.
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How to Develop Serverless Code Using Azure Functions?
Analytics Vidhya
JANUARY 30, 2023
Whether we are analyzing IoT data streams, managing scheduled events, processing document uploads, responding to database changes, etc. Azure functions allow developers […] The post How to Develop Serverless Code Using Azure Functions? appeared first on Analytics Vidhya.
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How To Create An Aggregation Pipeline In MongoDB
Analytics Vidhya
APRIL 12, 2021
Introduction MongoDB is a free open-source No-SQL document database. ArticleVideo Book This article was published as a part of the Data Science Blogathon. The post How To Create An Aggregation Pipeline In MongoDB appeared first on Analytics Vidhya.
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Interactive SQLite Documentation: Experiment with Queries in Real-Time
Hacker News
MARCH 5, 2024
At SQLite Cloud, we are dedicated to making database management as seamless and intuitive as possible. Today, we are thrilled to unveil a groundbreaking addition to our platform - the Interactive SQLite Documentation! Now, alongside our comprehensive.
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Understanding databases: A comprehensive guide to different types for beginners
Data Science Dojo
APRIL 6, 2023
While Python and R are popular for analysis and machine learning, SQL and database management are often overlooked. However, data is typically stored in databases and requires SQL or business intelligence tools for access. In this guide, we provide a comprehensive overview of various types of databases and their differences.
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LLM as Database Administrator
Hacker News
AUGUST 3, 2024
Database administrators (DBAs) play an important role in managing, maintaining and optimizing database systems. However, it is hard and tedious for DBAs to manage a large number of databases and give timely response (waiting for hours is intolerable in many online cases). under 10 minutes compared to hours by a DBA).
Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model
AWS Machine Learning Blog
APRIL 11, 2024
Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Categorizing documents is an important first step in IDP systems.
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Databases are the unsung heroes of AI
Dataconomy
AUGUST 7, 2023
Artificial intelligence is no longer fiction and the role of AI databases has emerged as a cornerstone in driving innovation and progress. An AI database is not merely a repository of information but a dynamic and specialized system meticulously crafted to cater to the intricate demands of AI and ML applications.
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Natural Language Processing Using CNNs for Sentence Classification
Analytics Vidhya
SEPTEMBER 2, 2021
This article was published as a part of the Data Science Blogathon Overview Sentence classification is one of the simplest NLP tasks that have a wide range of applications including document classification, spam filtering, and sentiment analysis. A question database will be used for this article and […].
Intelligent document processing with Amazon Textract, Amazon Bedrock, and LangChain
AWS Machine Learning Blog
OCTOBER 24, 2023
In today’s information age, the vast volumes of data housed in countless documents present both a challenge and an opportunity for businesses. Traditional document processing methods often fall short in efficiency and accuracy, leaving room for innovation, cost-efficiency, and optimizations. However, the potential doesn’t end there.
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Complete roadmap of LlamaIndex to Creating Personalized Q&A Chatbots
Data Science Dojo
SEPTEMBER 28, 2023
It supports a variety of data sources, including APIs, databases, and PDFs. Key components of LlamaIndex: The key components of LlamaIndex are as follows: Data connectors: These components allow LlamaIndex to ingest data from a variety of sources, such as APIs, databases, and PDFs.
Retrieval augmented generation (RAG) – Elevate your large language models experience
Data Science Dojo
DECEMBER 6, 2023
This process is typically facilitated by document loaders, which provide a “load” method for accessing and loading documents into the memory. This involves splitting lengthy documents into smaller chunks that are compatible with the model and produce accurate and clear results.
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A Beginner’s Guide to MongoDB and CRUD Operations
Analytics Vidhya
MAY 31, 2023
Introduction In this guide, we will explore the fundamentals of MongoDB and delve into the essential CRUD (Create, Read, Update, Delete) operations that form the backbone of any database system.
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Exploring the fundamentals of online transaction processing databases
Dataconomy
APRIL 27, 2023
What is an online transaction processing database (OLTP)? But the true power of OLTP databases lies beyond the mere execution of transactions, and delving into their inner workings is to unravel a complex tapestry of data management, high-performance computing, and real-time responsiveness.
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Knowledge Bases in Amazon Bedrock now simplifies asking questions on a single document
AWS Machine Learning Blog
APRIL 26, 2024
Today, we’re introducing the new capability to chat with your document with zero setup in Knowledge Bases for Amazon Bedrock. With this new capability, you can securely ask questions on single documents, without the overhead of setting up a vector database or ingesting data, making it effortless for businesses to use their enterprise data.
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Leaked Midjourney artist database could be a moment of reckoning for AI art
JANUARY 4, 2024
Over 16,000 artists are named in the document.
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The Marvels of Generative AI: Key Concepts and Use Cases
Data Science Dojo
APRIL 29, 2024
Imagine a tool so versatile that it can compose music, generate legal documents, assist in developing vaccines, and even create artwork that seems to have sprung from the brush of a Renaissance master. Example: Searching for similar images in a database based on content. Example: Word vectors are used for sentiment analysis in reviews.
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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents
AWS Machine Learning Blog
AUGUST 9, 2024
Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents.
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Build Custom Retriever using LLamaIndex and Gemini
Analytics Vidhya
APRIL 30, 2024
Chat with Multiple Documents using Gemini LLM is the project use case on which we will build this RAG pipeline. Introduction Retriever is the most important part of the RAG(Retrieval Augmented Generation) pipeline. In this article, you will implement a custom retriever combining Keyword and Vector search retriever using LlamaIndex.
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Overcoming 12 Challenges in Building Production-Ready RAG-based LLM Applications
Data Science Dojo
MARCH 29, 2024
Usually, the ingestion stage consists of the following steps: Collect data Chunk data Generate vector embeddings of chunks Store vector embeddings and chunks in a vector database The efficiency and effectiveness of the data ingestion phase significantly influence the overall performance of the system. Finding the optimal balance is crucial.
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Unleashing the power of LangChain: A comprehensive guide to building custom Q&A chatbots
Data Science Dojo
MAY 22, 2023
Document Loaders and Utils: LangChain’s Document Loaders and Utils modules simplify data access and computation. These embeddings, along with the associated documents, are stored in a vectorstore. This vectorstore enables efficient retrieval of relevant documents based on their embeddings.
Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock
AWS Machine Learning Blog
MAY 1, 2024
This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. For documentation retrieval, Retrieval Augmented Generation (RAG) stands out as a key tool. Virginia) AWS Region. The following diagram illustrates the solution architecture.
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5 tips to ensure your cloud databases are compliant, secure and private
Dataconomy
MAY 21, 2024
Developers, data privacy officers, and IT security teams are under pressure to make sure that cloud databases are not only functional and efficient, but also comply with data privacy legislation and are well protected from malicious actors. The stakes are high when it comes to database compliance. Image credit ) 3.
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Automate document processing with Amazon Bedrock Prompt Flows (preview)
AWS Machine Learning Blog
OCTOBER 29, 2024
Enterprises in industries like manufacturing, finance, and healthcare are inundated with a constant flow of documents—from financial reports and contracts to patient records and supply chain documents. An AWS Lambda function reads the Amazon Textract response and calls an Amazon Bedrock prompt flow to classify the document.
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What is LangChain? Key Features, Tools, and Use Cases
Data Science Dojo
OCTOBER 24, 2024
It also connects effortlessly with collaboration tools like Airtable, Trello, Figma, and Notion, as well as databases including Pandas, MongoDB, and Microsoft databases. For instance, a healthcare application could integrate patient data from a secure database with the latest medical research.
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Mastering Arxiv Searches: A DIY Guide to Building a QA Chatbot with Haystack
Analytics Vidhya
NOVEMBER 3, 2023
Human-like conversational skills of LLMs combined with vector retrieval methods make it much easier to extract answers from large documents. Introduction Question and answering on custom data is one of the most sought-after use cases of Large Language Models.
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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock
AWS Machine Learning Blog
DECEMBER 6, 2023
Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. For example, imagine that you are planning next year’s strategy of an investment company.
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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock
NOVEMBER 15, 2024
By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment. Each table represents a single data store.
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Top 6 Amazon S3 Interview Questions
Analytics Vidhya
MARCH 5, 2023
It stores and retrieves large amounts of data, including photos, movies, documents, and other files, in a durable, accessible, and scalable manner. Introduction S3 is Amazon Web Services cloud-based object storage service (AWS).
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Understanding SQL language: The backbone of relational databases
Dataconomy
SEPTEMBER 16, 2024
The SQL language, or Structured Query Language, is essential for managing and manipulating relational databases. It was designed to retrieve and manage data stored in relational databases. This versatile programming language is widely used by database administrators, developers, and data analysts.
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