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.
Enabling SSL for Database in IBM SPSS CaDS on Liberty Server — Post-Installation Guide
IBM Data Science in Practice
MAY 19, 2025
Enabling SSL for Database in IBM SPSS CaDS on Liberty ServerPost-Installation Guide If youve recently installed the SPSS Collaboration and Deployment Services (CaDS) on IBM Liberty and are wondering how to securely connect to your database via SSL, this blog is for you. Why Enable SSL for DB Connections? Microsoft SQL Server).
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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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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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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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Automating complex document processing: How Onity Group built an intelligent solution using Amazon Bedrock
AWS Machine Learning Blog
MAY 20, 2025
In the mortgage servicing industry, efficient document processing can mean the difference between business growth and missed opportunities. Onity processes millions of pages across hundreds of document types annually, including legal documents such as deeds of trust where critical information is often contained within dense text.
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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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Intelligent document processing
Dataconomy
APRIL 30, 2025
Intelligent document processing (IDP) is transforming the way businesses manage their documentation and data management processes. By harnessing the power of emerging technologies, organizations can automate the extraction and handling of data from various document types, significantly enhancing operational workflows.
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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Unify structured data in Amazon Aurora and unstructured data in Amazon S3 for insights using Amazon Q
AWS Machine Learning Blog
NOVEMBER 20, 2024
Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. The solution combines data from an Amazon Aurora MySQL-Compatible Edition database and data stored in an Amazon Simple Storage Service (Amazon S3) bucket.
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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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Improve search results for AI using Amazon OpenSearch Service as a vector database with Amazon Bedrock
FEBRUARY 21, 2025
Search applications include ecommerce websites, document repository search, customer support call centers, customer relationship management, matchmaking for gaming, and application search. AWS recommends Amazon OpenSearch Service as a vector database for Amazon Bedrock as the building blocks to power your solution for these workloads.
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Multi-tenancy in RAG applications in a single Amazon Bedrock knowledge base with metadata filtering
AWS Machine Learning Blog
APRIL 7, 2025
Additionally, we dive into integrating common vector database solutions available for Amazon Bedrock Knowledge Bases and how these integrations enable advanced metadata filtering and querying capabilities. Using the query embedding and the metadata filter, relevant documents are retrieved from the knowledge base.
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Master Vector Embeddings with Weaviate – A Comprehensive Series for You!
Data Science Dojo
JANUARY 22, 2025
Heres how embeddings power these advanced systems: Semantic Understanding LLMs use embeddings to represent words, sentences, and entire documents in a way that captures their semantic meaning. The process enables the models to find the most relevant sections of a document or dataset, improving the accuracy and relevance of their outputs.
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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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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud
NOVEMBER 27, 2024
While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. or a later version) database.
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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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Perplexity acquires Carbon, a Seattle startup that helps developers connect data sources to LLMs
DECEMBER 18, 2024
. “Carbon will make it easier for Perplexity’s answer engine to be informed by diverse sources of information, whether that data resides in internal databases, cloud storage, or document repositories.” ” Carbon raised a $1.3 million seed round in 2023.
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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.
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 […].
Building a Multimodal RAG Pipeline using Gemma 3 and Docling
Analytics Vidhya
MARCH 28, 2025
In this tutorial, we explore how to set up and execute a sophisticated retrieval-augmented generation (RAG) pipeline in Google Colab.
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From keywords to conversations: Reimagining document discovery with Amazon Bedrock
MAY 5, 2025
By combining LLMs and RAG on Amazon Bedrock , organizations can transform static document troves into dynamic, intuitive interfaces for discovery. Users must rely on specific phrases and terminology to find relevant documents, which becomes challenging when searching for complex information requiring deeper language understanding.
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Implement RAG while meeting data residency requirements using AWS hybrid and edge services
JANUARY 14, 2025
The documents uploaded to the knowledge base on the rack might be private and sensitive documents, so they wont be transferred to the AWS Region and will remain completely local on the Outpost rack. This vector database will store the vector representations of your documents, serving as a key component of your local Knowledge Base.
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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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MongoRAG: Leveraging MongoDB Atlas as a Vector Database with Databricks-Deployed Embedding Model and LLMs for Retrieval-Augmented Generation
Towards AI
JANUARY 29, 2025
Retrieval Augmented Generation generally consists of Three major steps, I will explain them briefly down below – Information Retrieval The very first step involves retrieving relevant information from a knowledge base, database, or vector database, where we store the embeddings of the data from which we will retrieve information.
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Manage access controls in generative AI-powered search applications using Amazon OpenSearch Service and Amazon Cognito
NOVEMBER 19, 2024
A common adoption pattern is to introduce document search tools to internal teams, especially advanced document searches based on semantic search. In a real-world scenario, organizations want to make sure their users access only documents they are entitled to access. The following diagram depicts the solution architecture.
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Search enterprise data assets using LLMs backed by knowledge graphs
NOVEMBER 27, 2024
Customers want to search through all of the data and applications across their organization, and they want to see the provenance information for all of the documents retrieved. Enhance the JSON format metadata to JSON-LD format by adding context, and load the data to an Amazon Neptune Serverless database as RDF triples. raw_customer".
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Fauna Service Winding Down
Hacker News
MARCH 19, 2025
The truly serverless database that combines the power of a relational database with the flexibility of JSON documents.
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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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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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Accelerate AWS Well-Architected reviews with Generative AI
MARCH 4, 2025
We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. An interactive chat interface allows deeper exploration of both the original document and generated content.
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Top 10 Python packages you need to master to maximize your coding productivity
Data Science Dojo
MAY 1, 2023
It is designed to simplify the process of working with databases by providing a consistent and high-level interface. It offers a set of utilities and abstractions that make it easier to interact with relational databases using SQL queries. BeautifulSoup BeautifulSoup is a Python library for parsing HTML and XML documents.
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What is an LLM Bootcamp? What Does Data Science Dojo Offer for Your Success?
Data Science Dojo
NOVEMBER 5, 2024
It covers a range of topics including generative AI, LLM basics, natural language processing, vector databases, prompt engineering, and much more. You get a chance to work on various projects that involve practical exercises with vector databases, embeddings, and deployment frameworks.
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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs
AWS Machine Learning Blog
NOVEMBER 7, 2024
Access to car manuals and technical documentation helps the agent provide additional context for curated guidance, enhancing the quality of customer interactions. The workflow includes the following steps: Documents (owner manuals) are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket.
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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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