Rx Data Science and Artificial Intelligence
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GraphRAG: Graph-Enhanced Retrieval-Augmented Generation
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GraphRAG is an advanced question-answering system that combines the power of graph-based knowledge representation with retrieval-augmented ...

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Fusion Retrieval in Document Search
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This code implements a Fusion Retrieval system that combines vector-based similarity search with keyword-based BM25 retrieval. The approach aims to ...

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Fusion Retrieval in Document Search
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This code implements a Fusion Retrieval system that combines vector-based similarity search with keyword-based BM25 retrieval. The approach aims to ...

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Explainable Retrieval in Document Search
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This code implements an Explainable Retriever, a system that not only retrieves relevant documents based on a query but also provides explanations ...

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Document Augmentation through Question
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This implementation demonstrates a text augmentation technique that leverages additional question generation to improve document retrieval within a ...

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Corrective RAG Process: With Dynamic Correction
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The Corrective RAG (Retrieval-Augmented Generation) process is an advanced information retrieval and response generation system. It extends the ...

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Contextual Compression in Document Retrieval
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This code demonstrates the implementation of contextual compression in a document retrieval system using LangChain and OpenAI's language models. The ...

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Contextual Chunk Headers (CCH)
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Contextual chunk headers (CCH) is a method of creating chunk headers that contain higher-level context (such as document-level or section-level ...

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Context Enrichment Window for Document Retrieval
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This code implements a context enrichment window technique for document retrieval in a vector database. It enhances the standard retrieval process by ...

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Context Enrichment Window for Document Retrieval
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This code implements a context enrichment window technique for document retrieval in a vector database. It enhances the standard retrieval process by ...

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Adaptive Retrieval-Augmented Generation (RAG) System
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This system implements an advanced Retrieval-Augmented Generation (RAG) approach that adapts its retrieval strategy based on the type of query. By ...

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Microsoft GraphRAG: Enhancing Retrieval-Augmented Generation
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Microsoft GraphRAG is an advanced Retrieval-Augmented Generation (RAG) system that integrates knowledge graphs to improve the performance of large ...

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Hypothetical Document Embedding (HyDE) in Document Retrieval
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This code implements a Hypothetical Document Embedding (HyDE) system for document retrieval. HyDE is an innovative approach that transforms query ...

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Video Joint Embedding Predictive Architecture (V-JEPA)
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Video Joint Embedding Predictive Architecture (V-JEPA) model, a crucial step in advancing machine intelligence with a more grounded understanding of ...

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What are Different Data Science Technologies?
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Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and ...

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LangChain
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LangChain is an open source framework for building applications based on large language models (LLMs). LLMs are large deep-learning models ...

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AI in Data Augmentation
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Data augmentation is the process of artificially generating new data from existing data, primarily to train new machine learning (ML) models. ML ...

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What are the benefits of cloud containers?
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Cloud containers are software code packages that contain an application’s code, its libraries, and other dependencies that it needs to run in the ...

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PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
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Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts. ...

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Measure performance with the RAIL model
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RAIL is a user-centric performance model that provides a structure for thinking about performance. The model breaks down the user's experience into key actions ...

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