#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
#OP/USDT analysis :
#OP is currently forming a continuation pattern within a downtrend. The price has broken previous lows, and it is anticipated that it will continue its bearish momentum. It is advisable to wait for a pullback to the identified zone for a short entry, as the previous swing low is expected to be tested.
TF : 1D
Entry : $1.493
Target : $1.076
SL : $1.773
#OP/USDT analysis :
#OP has broken out above the 200 EMA and the previous resistance zone with strong bullish momentum. It is now retesting the breakout zone. The price is expected to continue its bullish bias and test previous highs. Wait for a break of the $1.517 level to go long.
TF : 2H
Entry : $1.517
Target : $1.643
SL : $1.444