#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag
Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.
https://github.com/FareedKhan-dev/all-rag-techniques
Lookonchain | ꘜ
Whales are accumulating $BGB recently.
0x8900 withdrew 192,668 $BGB($936K) from #Bitget over the past 2 months.
0x171D withdrew 30,607 $BGB($134K) from #Bitget 2 days ago.
0x7C9C withdrew 20,980 $BGB($102K) from #Bitget over the past 3 months.
Notably, #Bitget has burned a total of 860M $BGB($5.25B) over the past 8 months, reducing the total supply by 43%.
https://intel.arkm.com/explorer/address/0x89006C3aADfF87c5113b835660E3459C6Ad61F16
https://intel.arkm.com/explorer/address/0x171D1285a9a8De3f16d4c45706d4E2F4A5C9e175
https://intel.arkm.com/explorer/address/0x7C9C4f9046ba2173fae539FE62eEFAb1aBAD1523