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LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization , chatbots , and code analysis .
Since Lady Gaga's "Bad Romance" in 2009, every video that has reached the top of the "most-viewed YouTube videos" list has been a music video. In November 2005, a Nike advertisement featuring Brazilian football player Ronaldinho became the first video to reach 1,000,000 views. [1] The billion-view mark was first passed by Gangnam Style in ...
In-context learning, refers to a model's ability to temporarily learn from prompts.For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning.
"The Vatican Rag" takes musical inspiration from ragtime pieces such as "Spaghetti Rag" (1910) and "The Varsity Drag" (1927).[1] [2] A spoken introduction describes the song as a response to the "Vatican II" council—which, among other things, broadened the range of music that could be used in services—and humorously proposes this rag as a more accessible alternative to traditional ...
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Ready those dance moves now, now, now, now. Beyoncé's new country song "Texas Hold 'Em" has fans line dancing all over social media. "I wanna learn country dance now,” one fan posted on X. The ...
Music critic Thom Owen, writing for Allmusic, wrote of the album "Occasionally the sleek professional production and studied performances make these traditional songs sound lifeless, which prevents Cadillac Rag from being a truly engagining listen. However, it is an interesting one, particularly if you're interested in studying traditional ...
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.