A curated list of retrieval-augmented generation (RAG) in large language models
Explore cutting-edge Redis capabilities for Vector Similarity Search, Hybrid Search (Vector Similarity + Meta Search), Semantic Caching, and an advanced RAG model integrated with a Language Model (LLM) Chatbot. Unlock the full potential of Redis as a vector database with this comprehensive showcase of powerful features.
Learn how to build agents that can reason over their own documents
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
MariHacks winning project. Linky leverages a RAG AI & a Vector DB to convert your inputted URLs into tokenized inputs allowing you to then treat Linky as a living form of your URL.
End-to-End solution that harnesses the power of documents to provide insightful answers and valuable knowledge to user's query.
๐๐๐ญ๐ซ๐ข๐๐ฏ๐๐ฅ-๐๐ฎ๐ ๐ฆ๐๐ง๐ญ๐๐ ๐ ๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง (๐๐๐) is an innovative approach in the field of ๐ง๐๐ญ๐ฎ๐ซ๐๐ฅ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐ ๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐ง๐ (๐๐๐) that combines the strengths of retrieval-based and ๐ ๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง-๐๐๐ฌ๐๐ ๐ฆ๐จ๐๐๐ฅ๐ฌ to enhance the quality of generated text.
The LARGE LANGUAGE MODEL FOR HYDROGEN STORAGE project uses advanced natural language processing to improve research efficiency.
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