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Announcing general availability of Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics

AWS Machine Learning Blog

This capability enhances responses from generative AI applications by automatically creating embeddings for semantic search and generating a graph of the entities and relationships extracted from ingested documents. This new capability integrates the power of graph data modeling with advanced natural language processing (NLP).

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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text.

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Top 25 AI Tools for Software Development in 2025

Marktechpost

It suggests code snippets and even completes entire functions based on natural language prompts. TabNine TabNine is an AI-powered code auto-completion tool developed by Codota, designed to enhance coding efficiency across a variety of Integrated Development Environments (IDEs).

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NLP News Cypher | 08.09.20

Towards AI

Photo by Kunal Shinde on Unsplash NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 08.09.20 What is the state of NLP? For an overview of some tasks, see NLP Progress or our XTREME benchmark. In the next post, I will outline interesting research directions and opportunities in multilingual NLP.”

NLP 98
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

The traditional approach of manually sifting through countless research documents, industry reports, and financial statements is not only time-consuming but can also lead to missed opportunities and incomplete analysis. This event-driven architecture provides immediate processing of new documents. Follow Octus on LinkedIn and X.

DevOps 90
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AI code-generation software: What it is and how it works

IBM Journey to AI blog

Auto-generated code suggestions can increase developers’ productivity and optimize their workflow by providing straightforward answers, handling routine coding tasks, reducing the need to context switch and conserving mental energy. It includes code formatting, language detection and documentation.

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Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation

Flipboard

A typical RAG solution for knowledge retrieval from documents uses an embeddings model to convert the data from the data sources to embeddings and stores these embeddings in a vector database. When a user asks a question, it searches the vector database and retrieves documents that are most similar to the user’s query.