rag-systems

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ksm26 / Embedding-Models-From-Architecture-to-Implementation

Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.

machine-learning word2vec word-embeddings embedding-models semantic-search bert model-training sentence-embeddings contrastive-loss dual-encoder bert-model bert-embeddings transformer-models bert-fine-tuning ai-applications ai-architecture rag-systems question-answer-retrieval

Updated Aug 21, 2024
Jupyter Notebook

feld-m / rag_blueprint

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A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.

machine-learning natural-language-processing ai customization production scalability data-privacy vector-database retrieval-augmented-generation rag-systems

Updated Feb 19, 2025
Python

ksm26 / Retrieval-Optimization-From-Tokenization-to-Vector-Quantization

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The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.