#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
#FET/USDT Analysis-
The price has formed a symmetrical triangle pattern. Given the current bullish market scenario, there is a high probability of an upward breakout.
If the breakout occurs, the price is likely to rally toward the target zone from the breakout point.
T.F.- 1-D
ENTRY- as soon as it gives breakout
SL- 1.2
TARGET- 2.04
Note: If the stop-loss is triggered before entry, disregard the trade as the price action may develop differently.
#FET/USDT analysis :
#FET has broken down the 200 EMA and previous support levels. It is now undergoing a pullback and retesting the resistance zone. The price is expected to reject from there and bounce back to continue its bearish momentum.
TF : 1H
Entry : $1.170
Target : $1.057
SL : $1.252