#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
#POLYX/USDT analysis :
#POLYX has rebounded from the previously respected support zone and is currently trading above a minor support level. The price is expected to move upward from this level and test the swing high resistance.
TF : 2h
Entry : $0.1790
Target : $0.2036
SL : $0.1688
#POLYX/USDT analysis :
#POLYX has broken out and retested the support zone above the 200 EMA. The price is expected to continue its bullish momentum and test previous highs.
TF : 15min
Entry : $0.2353
Target : $0.2593
SL : $0.2232
#POLYX/USDT analysis :
#POLYX is presently establishing a bullish channel. The price has recently rebounded from the support zone and is now progressing to test the previous swing high.
TF : 4H
Entry : $0.2231
Target : $0.2538
SL : $0.2029