AWS Machine Learning Blog

NOVEMBER 18, 2024

Amazon Titan Text Embeddings models generate meaningful semantic representations of documents, paragraphs, and sentences. It supports exact and approximate nearest-neighbor algorithms and multiple storage and matching engines. RAG helps FMs deliver more relevant, accurate, and customized responses.

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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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Data Science Dojo

JULY 15, 2024

K-Nearest Neighbors (KNN): This method classifies a data point based on the majority class of its K nearest neighbors in the training data. Document Clustering: Grouping documents based on topic or content for efficient information retrieval.

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Towards AI

JULY 3, 2024

Community & Support: Verify the availability of documentation and the level of community support. For geographical analysis, Random Forest, Support Vector Machines (SVM), and k-nearest Neighbors (k-NN) are three excellent methods. So, Who Do I Have?

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Flipboard

NOVEMBER 17, 2023

The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. MongoDB Atlas Vector Search uses a technique called k-nearest neighbors (k-NN) to search for similar vectors.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Broadly speaking, a retriever is a module that takes a query as input and outputs relevant documents from one or more knowledge sources relevant to that query. Document ingestion In a RAG architecture, documents are often stored in a vector store. You must use the same embedding model at ingestion time and at search time.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

This centralized system consolidates a wide range of data sources, including detailed reports, FAQs, and technical documents. The system integrates structured data, such as tables containing product properties and specifications, with unstructured text documents that provide in-depth product descriptions and usage guidelines.

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Pickl AI

MARCH 26, 2024

The K Nearest Neighbors (KNN) algorithm of machine learning stands out for its simplicity and effectiveness. What are K Nearest Neighbors in Machine Learning? Definition of KNN Algorithm K Nearest Neighbors (KNN) is a simple yet powerful machine learning algorithm for classification and regression tasks.

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Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

For more information on managing credentials securely, see the AWS Boto3 documentation. For example: aws s3 cp /Users/username/Documents/training/loafers s3://footwear-dataset/ --recursive Confirm the upload : Go back to the S3 console, open your bucket, and verify that the images have been successfully uploaded to the bucket.

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AWS Machine Learning Blog

MARCH 11, 2025

One of the most critical applications for LLMs today is Retrieval Augmented Generation (RAG), which enables AI models to ground responses in enterprise knowledge bases such as PDFs, internal documents, and structured data. Each provisioned node was r7g.4xlarge, FloTorch used HSNW indexing in OpenSearch Service.

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Towards AI

NOVEMBER 6, 2024

Evaluation allows us to select the top embedding models across various dimensions, potentially considering multiple values for k nearest neighbors. Create a Golden Dataset The first step is to create a “golden dataset” comprising queries, relevant context (chunks or documents from the corpus), and ground truth answers.

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Towards AI

JULY 15, 2024

We shall look at various machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can install and call their libraries in R studios, including executing the code. In-depth Documentation- R facilitates repeatability by analyzing data using a script-based methodology.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Intelligent responses and a direct conduit to Druva’s documentation – Users can gain in-depth knowledge about product features and functionalities without manual searches or watching training videos. Dru also suggests follow-up questions to enhance user experience. Dru also suggests resources for further learning.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

These included document translations, inquiries about IDIADAs internal services, file uploads, and other specialized requests. This approach allows for tailored responses and processes for different types of user needs, whether its a simple question, a document translation, or a complex inquiry about IDIADAs services.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

OpenSearch Service allows you to store vectors and other data types in an index, and offers rich functionality that allows you to search for documents using vectors and measuring the semantical relatedness, which we use in this post. Using the k-nearest neighbors (k-NN) algorithm, you define how many images to return in your results.

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Pickl AI

JANUARY 21, 2025

k-Nearest Neighbors (k-NN) k-NN is a simple algorithm that classifies new instances based on the majority class among its k nearest neighbours in the training dataset. Example: Organising documents into a tree structure based on topic similarity for better information retrieval systems.

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PyImageSearch

MAY 12, 2025

Powering Neural Search : Enables advanced similarity-based retrieval using OpenSearchs k-NN (k-Nearest Neighbors) indexing. This pipeline will allow OpenSearch to automatically generate embeddings for plot summaries whenever a new document is indexed. The function in utils.py The function in utils.py

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

Embeddings for documents are generated using the text-to-embeddings model and these embeddings are indexed into OpenSearch Service. A k-Nearest Neighbor (k-NN) index is enabled to allow searching of embeddings from the OpenSearch Service.

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Flipboard

MAY 5, 2025

Full-Text and Structured Search: Powers fast, scalable, and accurate search for e-commerce, enterprise search, and document retrieval systems. Uploading sample text embeddings or documents. The response shows "result": "created" , indicating that the document was indexed correctly. This includes: Creating a sample index.

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PyImageSearch

JANUARY 27, 2025

Another example is in the field of text document similarity. Imagine you have a vast library of documents and want to identify near-duplicate documents or find documents similar to a query document. text documents, images, and other multimedia content).

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AWS Machine Learning Blog

DECEMBER 10, 2024

We performed a k-nearest neighbor (k-NN) search to retrieve the most relevant embedding matching the question. SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images. The metadata of the response from the OpenSearch index contains a path to the image corresponding to the most relevant slide.

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. K-means clustering is commonly used for market segmentation, document clustering, image segmentation and image compression.

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PyImageSearch

JUNE 19, 2023

For example, term frequency–inverse document frequency (TF-IDF) ( Figure 7 ) is a popular text-mining technique in content-based recommendations. Inverse document frequency (IDF) assigns weight inversely proportional to the times the keyword occurs in the whole corpus. Figure 6: Illustration of how text mining works (source: Ko et al.,

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AWS Machine Learning Blog

JANUARY 30, 2024

This event in the SQS queue acts as a trigger to run the OSI pipeline, which in turn ingests the data (JSON file) as documents into the OpenSearch Serverless index. We perform a k-nearest neighbor (k=1) search to retrieve the most relevant embedding matching the user query.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

This benefits enterprise software development and helps overcome the following challenges: Sparse documentation or information for internal libraries and APIs that forces developers to spend time examining previously written code to replicate usage. Semantic retrieval BM25 focuses on lexical matching.

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AWS Machine Learning Blog

APRIL 19, 2024

This solution includes the following components: Amazon Titan Text Embeddings is a text embeddings model that converts natural language text, including single words, phrases, or even large documents, into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity.

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AWS Machine Learning Blog

AUGUST 26, 2024

Numbers checking – Identifies numerical data in both the input and generated documents, determining their intersection and flagging potential hallucinations. This helps catch any fabricated or misrepresented quantitative information in the summaries.

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AWS Machine Learning Blog

JUNE 3, 2024

You store the embeddings of the video frame as a k-nearest neighbors (k-NN) vector in your OpenSearch Service index with the reference to the video clip and the frame in the S3 bucket itself (Step 3). You split the video files into frames and save them in a S3 bucket (Step 1).

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ODSC - Open Data Science

JANUARY 5, 2024

It is easy to use, with a well-documented API and a wide range of tutorials and examples available. First, it’s easy to use, the code is easy to learn and it has a well-documented API. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use. What really makes Django are a few things.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region. For documentation on Planet’s SDK for Python, see Planet SDK for Python.

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AWS Machine Learning Blog

JANUARY 6, 2023

OpenSearch Service offers kNN search, which can enhance search in use cases such as product recommendations, fraud detection, and image, video, and some specific semantic scenarios like document and query similarity. Solution overview.

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Flipboard

FEBRUARY 7, 2025

You will create a connector to SageMaker with Amazon Titan Text Embeddings V2 to create embeddings for a set of documents with population statistics. Alternately, you can follow the Boto 3 documentation to make sure you use the right credentials. When you build a RAG application, you choose a knowledge base and a retrieval mechanism.

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Mlearning.ai

MARCH 28, 2023

You can reach the documentation from here. For each sample in the minority class, it selects k nearest neighbors from the same class. It then selects one of these k neighbors at random and computes the difference between the feature vector of the original sample and the selected neighbor.

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AWS Machine Learning Blog

APRIL 5, 2023

Implementing this unified image and text search application consists of two phases: k-NN reference index – In this phase, you pass a set of corpus documents or product images through a CLIP model to encode them into embeddings. You save those embeddings into a k-NN index in OpenSearch Service.

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