#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
🛰️ Flags of the #Kaliningrad Region and the City of Zelenogradsk are back from #ISS🌍 to be later exhibited in #Kaliningrad Regional Museum of History and Arts.
📸 by the Museum
🚀🌍Le commandant de l'ISS et envoyé spécial de TASS à bord, Sergueï Koud-Svertchkov, a montré des images du vaisseau spatial Crew Dragon approchant de la station.
#iss#espace
LIVE: Farewells, hatch closing for Soyuz MS-18 crew on ISS
Farewells and hatch closing for the Soyuz MS-18 crew on the International Space Station.
#Reuters#Live#News#Space#ISS
➖@reutersworldchannel➖
🇷🇺🛰️ Le vaisseau cargo Progress MS‑32 s’est désamarré du module Zvezda du segment russe de la Station spatiale internationale (ISS) avant l’arrivée d’un nouveau cargo, montre la retransmission de Roscosmos.
#russie#vaisseau#iss