Remove Document Remove K-nearest Neighbors Remove Python
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How Neighborly is K-Nearest Neighbors to GIS Pros?

Towards AI

Now, in the realm of geographic information systems (GIS), professionals often experience a complex interplay of emotions akin to the love-hate relationship one might have with neighbors. Enter K Nearest Neighbor (k-NN), a technique that personifies the very essence of propinquity and Neighborly dynamics.

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Optimize RAG in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service

Flipboard

For businesses, RAG offers a powerful way to use internal knowledge by connecting company documentation to a generative AI model. When an employee asks a question, the RAG system retrieves relevant information from the company’s internal documents and uses this context to generate an accurate, company-specific response.

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Build a Search Engine: Semantic Search System Using OpenSearch

PyImageSearch

In this tutorial, well explore how OpenSearch performs k-NN (k-Nearest Neighbor) search on embeddings. Beyond Keyword Matching) Traditional keyword-based search works by matching exact words in a query to those present in indexed documents. Implement and analyze search results using Python scripts.

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How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning Blog

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. Generate and run data transformation Python code. A custom Python function runs the Python code and returns the answer in tabular format.

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GIS Machine Learning With R-An Overview.

Towards AI

We shall look at various types of machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can 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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From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning Blog

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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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

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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