#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
#DEXE/USDT analysis :
#DEXE is currently in an uptrend on the higher time frame (HTF). The price is in an accumulation phase, consolidating sideways. A breakout from this consolidation is anticipated soon, which would likely continue the bullish rally and test the swing high. We should look for potential pullbacks to establish long entries.
TF : 1W
Entry : $8.260
Target : $18.100
SL : $6.340
#DEXE/USDT analysis :
#DEXE is currently facing resistance from the 200 EMA on the daily time frame. On the 4-hour time frame, it has broken and retested the support zone. The price is now likely to continue its bearish momentum and test new lows.
TF : 4H
Entry : $8.461
Target : $7.953
SL : $8.783