#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
#MBL/USDT analysis :
#MBL is currently consolidating above the support zone. The price is expected to test this zone again before moving towards the swing high. It is recommended to wait for a pullback to enter a long position.
TF : 1D
Entry : $0.002672
Target : $0.003095
SL : $0.002527
#MBL/USDT analysis :
#MBL is in an uptrend, forming higher highs (HHs) and higher lows (HLs) above the 200 EMA. The price has recently bounced back from a support zone after retracement to it. It is expected that the price will sustain its bullish momentum and test the previous swing high.
TF : 1H
Entry : $0.00256
Target : $0.00265
SL : $0.00251
#MBL/USDT analysis :
#MBL is in a downtrend, trading below the 200 EMA. The price is expected to experience a pullback and will continue its downward momentum. Wait for the price to reject from the resistance zone for short entry.
TF : 15min
Entry : $0.00211
Target : $0.00190
SL : $0.00220
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