Generative AI

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Advanced RAG Techniques
Advanced RAG Techniques
Retrieval-Augmented Generation (RAG) techniques represent a significant advancement in the capabilities of generative AI models. By…
Hierarchical Document Clustering
·medium.com·
Advanced RAG Techniques
Ragas
Ragas
Ragas is an open source framework for testing and evaluating LLM applications. Ragas provides metrics , synthetic test data generation and workflows for ensuring the quality of your application while development and also monitoring it's quality in production.
·ragas.io·
Ragas
ScrapeGraphAI
ScrapeGraphAI
Extract structured data from any website using our powerful AI-driven web scraping API.
·scrapegraphai.com·
ScrapeGraphAI
Space and Time | ZK Coprocessor
Space and Time | ZK Coprocessor
Sub-second ZK coprocessor. AI Studio for SQL and dashboards. Indexed blockchain data & decentralized database. Verifiable compute for smart contracts and AI.
·spaceandtime.io·
Space and Time | ZK Coprocessor
Sketch: A Toolkit for Streamlining LLM Operations
Sketch: A Toolkit for Streamlining LLM Operations
Large language models (LLMs) represented by GPT family have achieved remarkable success. The characteristics of LLMs lie in their ability to accommodate a wide range of tasks through a generative...
·arxiv.org·
Sketch: A Toolkit for Streamlining LLM Operations
TonghanWang/ROMA: Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039)
TonghanWang/ROMA: Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039)
Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039) - TonghanWang/ROMA
·github.com·
TonghanWang/ROMA: Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039)
microsoft/UniRec: UniRec is an easy-to-use, lightweight, and scalable implementation of recommender systems. Its primary objective is to enable users to swiftly construct a comprehensive ecosystem of recommenders using a minimal set of robust and practical recommendation models.
microsoft/UniRec: UniRec is an easy-to-use, lightweight, and scalable implementation of recommender systems. Its primary objective is to enable users to swiftly construct a comprehensive ecosystem of recommenders using a minimal set of robust and practical recommendation models.
UniRec is an easy-to-use, lightweight, and scalable implementation of recommender systems. Its primary objective is to enable users to swiftly construct a comprehensive ecosystem of recommenders us...
·github.com·
microsoft/UniRec: UniRec is an easy-to-use, lightweight, and scalable implementation of recommender systems. Its primary objective is to enable users to swiftly construct a comprehensive ecosystem of recommenders using a minimal set of robust and practical recommendation models.