#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
#CHR/USDT analysis :
#CHR is currently retracing towards a previously tested support zone. The price is expected to bounce from this level and resume its bullish momentum, aiming to test previous highs. For a long entry, it is advisable to wait for a breakout above the $0.2750 level.
TF : 4H
Entry : $0.2750
Target : $0.3440
SL : $0.2370
#CHR/USDT analysis :
#CHR has broken and retested the support zone, which is now acting as resistance for the price. The price is anticipated to continue its bearish momentum and test lower levels.
TF : 2H
Entry : $0.1780
Target : $0.1701
SL : $0.1838
#CHR/USDT analysis :
#CHR is currently consolidating above the support zone and the 200 EMA. The price is expected to sustain its bullish momentum and establish new highs. It is advisable to await a pullback for a long entry.
TF : 1H
Entry : $0.1862
Target : $0.2163
SL : $0.1755