A conversational question answering tool built using LangChain, Pinecone, Flask, React and AWS.
Towards Data Science 11:25 pm on May 21, 2024
- The text details the process of creating a chatbot application using AWS services and Large Language Models (LLMs) for retrieval augmented generation with conversation memory, including setup and deployment steps.
- Explains integrating LLMs like ChatGPT into an AWS environment, utilizing APIs, AWS Amplify, and IAM roles for seamless functionality.
- Describes the use of chat transcript data from YouTube to train models, with acknowledgment given to a specific source for permission.
- Outlines steps including defining API resources, deploying React app via AWS Amplify, and setting up continuous deployment using branch monitoring.
- Summarizes the broader application potential of these methodologies in various professional and customer-facing scenarios.
- Large Language Models Integration:
- Utilizing AWS for setting up chatbot services with LLMs like ChatGPT.
- Deploying React app through Amplify, continuous deployment via branch resources.
- Chat transcript data utilization from YouTube video playlist acknowledgment to source.
- Providing insights into applying these techniques for practical, professional use-cases.
https://towardsdatascience.com/using-llms-to-learn-from-youtube-4454934ff3e0
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