Jeff Tang 69b2a0561f typo fix | 6 mēneši atpakaļ | |
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RAG | 6 mēneši atpakaļ | |
chatbots | 6 mēneši atpakaļ | |
text2sql | 6 mēneši atpakaļ | |
LiveData.ipynb | 6 mēneši atpakaļ | |
README.md | 6 mēneši atpakaļ | |
VideoSummary.ipynb | 6 mēneši atpakaļ |
This demo app uses Llama 3 to return a text summary of a YouTube video. It shows how to retrieve the caption of a YouTube video and how to ask Llama to summarize the content in different ways, from the simplest naive way that works for short text to more advanced methods of using LangChain's map_reduce and refine to overcome the 8K context length limit of Llama 3.
This demo app shows how to use LangChain and Llama 3 to let users ask questions about structured data stored in a SQL DB. As the 2023-24 NBA season is entering the playoff, we use the NBA roster info saved in a SQLite DB to show you how to ask Llama 3 questions about your favorite teams or players.
This demo app shows how to perform live data augmented generation tasks with Llama 3, LlamaIndex, another leading open-source framework for building LLM apps, and the Tavily live search API.
This step-by-step tutorial shows how to use the WhatsApp Business API to build a Llama 3 enabled WhatsApp chatbot.
This step-by-step tutorial shows how to use the Messenger Platform to build a Llama 3 enabled Messenger chatbot.
A complete example of how to build a Llama 3 chatbot hosted on your browser that can answer questions based on your own data using retrieval augmented generation (RAG). You can run Llama2 locally if you have a good enough GPU or on OctoAI if you follow the note here.