#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
RsS iS dEaD LOL: discover RSS Feeds of your follows on Mastodon
频道曾经提及过一个叫 FeedsMage 的服务,用于从你 fo 的推友的 Bio 里找链接,再从链接里找 Feed ,最后可生成一个 #OPML 文件。RsS iS dEaD LOL 则是长毛象版的 FeedsMage,从你 fo 的 Fediverse 用户的 Bio 里找链接,发现 RSS,然后可生成 #OPML:
https://rss-is-dead.lol/
例如我的:
https://rss-is-dead.lol/user?profileUrl=https%3A%2F%2Fmastodon.social%2Fusers%2FAboutRSS
发现于作者嘟文:
https://mastodon.social/@paulcuth/112178886374464145