Find similar content

Source channel @githubtrending · Post #14993 · Jul 24

#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

Hashtags

#jupyter_notebook

Results

1 similar post found

Search: #thingsboard

当前筛选 #thingsboard清除筛选

GitHub Trends

@githubtrending · Post #15204 · 10/08/2025, 11:30 AM

Find similar View

#java#cloud#coap#dashboard#iot#iot_analytics#iot_platform#iot_solutions#java#kafka#lwm2m#microservices#middleware#mqtt#netty#platform#snmp#thingsboard#visualization#websockets#widgets ThingsBoard is an open-source IoT platform that helps manage and analyze data from connected devices. It allows users to collect data, create real-time dashboards, and automate tasks using a powerful rule engine. This platform supports various protocols like MQTT and HTTP, making it easy to connect devices. Users can also define relationships between devices and assets, and trigger alarms based on specific conditions. The benefit is that it simplifies IoT project development, making it scalable and efficient for applications like smart farming, smart offices, and more. https://github.com/thingsboard/thingsboard

Hashtags

#java #cloud #coap #dashboard #iot #iot_analytics #iot_platform #iot_solutions #kafka #lwm2m #microservices #middleware #mqtt #netty #platform #snmp #thingsboard #visualization #websockets #widgets