#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
# The standard string repr for dicts is hard to read:
»> my_mapping = {'a': 23, 'b': 42, 'c': 0xc0ffee}
»> my_mapping
{'b': 42, 'c': 12648430. 'a': 23} # 😞
# The "#json" module can do a much better job:
»> import json
»> print(json.dumps(my_mapping, indent=4, sort_keys=True))
{
"a": 23,
"b": 42,
"c": 12648430
}
# Note this only works with dicts containing
# primitive types (check out the "pprint" module):
»> json.dumps({all: 'yup'})
TypeError: keys must be a string
История(12м) как в Альфа-Банке сокращали размер JSON файла, который передается на устройство для работы SDUI. Решением стала шаблонизация для отказа от одинаковых блоков UI с разными данными
#оптимизация#json
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