🤖rag GPT is a chatbot that uses the retriever-generator (rag) architecture.
💡The chatbot can connect with a vector database, upload documents, and generate document summaries.
🔍Content retrieval is a key step in rag systems, where queries are matched to relevant document chunks.
💼The chatbot's functionalities include connecting with a vector database, uploading documents, and generating document summaries.
📚The chatbot uses gradio, OpenAI's embedding model, and GPT-3.5 for language processing.
What is the main architecture used in rag GPT chatbot?
—rag GPT utilizes the retriever-generator (rag) architecture.
What are the key functionalities of the chatbot?
—The chatbot can connect with a vector database, upload documents, and generate document summaries.
What is content retrieval in rag systems?
—Content retrieval is the process of matching queries with relevant document chunks.
What libraries are used in the chatbot?
—The chatbot utilizes gradio for the user interface, OpenAI's embedding model, and GPT-3.5 for language processing.
Can the chatbot generate document summaries?
—Yes, the chatbot has the capability to generate summaries from uploaded documents.
00:13Introduction to rag retrieval augmented generation and chatbot design.
00:37Overview of the libraries used in the chatbot: gradio, OpenAI's embedding model, and GPT-3.5.
01:32Explanation of content retrieval in rag systems, matching queries with relevant document chunks.
02:31Demo of the chatbot's functionalities, including connecting with a vector database and uploading documents.
05:14Demonstration of the chatbot's document summarization feature.
