Unlocking Better Text Retrieval with Late Chunking: A Practical Approach for RAG Applications
As we continue to advance the capabilities of natural language processing (NLP) systems, one key challenge remains at the forefront of text retrieval systems: how to efficiently split and embed long documents to retain contextual integrity while still enabling fast and effective retrieval. While transformer models have significantly improved the quality of text embeddings, they […]
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AI Security Audit Reveals Exploits in MCP-Enabled LLM Integrations
As AI agents grow more sophisticated and interconnected, the infrastructure supporting them must scale in complexity and security. A key innovation addressing this need is the Model Context Protocol (MCP) — a standardized framework from Anthropic that facilitates…
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Anthropic’s Model Context Protocol (MCP) for AI Applications and Agents
Artificial Intelligence (AI) is evolving at an unprecedented pace, and with it, the need for seamless integration between AI applications, tools, and data sources has become more critical than ever. Model Context Protocol (MCP), an open protocol developed by Anthropic…
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Cache-Augmented Generation (CAG): Faster AI Response
Cache-Augmented Generation (CAG) has emerged as an innovative approach for enhancing language model performance by leveraging preloaded knowledge. It provides a streamlined, efficient alternative to…
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Streamline Cybersecurity Compliance with OpenAI Swarm Framework
As the digital landscape evolves, organizations across industries are facing increasingly complex cybersecurity challenges. With governments, industries, and global organizations mandating compliance…
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Self-Extend in LLMs: Unlocking Longer Contexts for Enhanced Language Models
LLMs like GPT-3 or BERT are typically trained on fixed-length sequences due to practical constraints like managing computational resources and maintaining efficiency. These models, as a result, have a predetermined maximum sequence length…
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Traditional NER vs LLMs: Dual Approaches to Building Knowledge Graphs
Knowledge graphs are powerful tools for representing relationships between entities in a structured format. They are widely used in various industries like healthcare, finance, e-commerce, and more to organize vast amounts of data…
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Transforming Software Testing with AI
Artificial Intelligence (AI) has made a significant impact across various industries, and software testing is no exception. By integrating AI into software testing, organisations can enhance the efficiency, accuracy, and effectiveness of their testing processes…
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Small Language Models (SLMs): Benefits and Applications
Small Language Models (SLMs) are revolutionising the field of natural language processing (NLP) by offering efficient, cost-effective, and highly capable alternatives to larger language models…
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RAG to GraphRAG | Enhancing Info Retrieval with Knowledge Graphs
In the realm of artificial intelligence, the pursuit of more accurate and contextually relevant information retrieval has led to significant advancements. One such evolution is from Retrieval-Augmented Generation (RAG) to GraphRAG, a concept detailed in a recent Microsoft blog post…
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