This task represents a valuable contribution to OPEA, focusing on how integrating AWS Bedrock as the LLM (Large Language Model) can provide a serverless option to ChatQnA. The task will focus on the ease with which OPEA can accommodate different LLMs, providing hands-on experience in customizing RAG setups to work with cloud-native solutions like AWS Bedrock, ensuring scalability and adaptability.
Learning Objectives
Understand OPEA’s Flexibility in Integrating Different LLMs: Explore how OPEA’s modular design allows for seamless integration of various LLMs, including AWS Bedrock.
Implement AWS Bedrock as LLM in OPEA: Gain practical experience replacing the default LLM with AWS Bedrock, adjusting the RAG pipeline to utilize the available models.
Optimize RAG Pipelines for Flexibility and Scalability: Learn how to leverage OPEA’s flexibility to integrate and customize LLMs like AWS Bedrock, ensuring scalable and adaptable RAG solutions.
This lab emphasizes OPEA’s ability to integrate AWS Bedrock, showcasing its flexibility in adapting to different technologies and use cases for enterprise-grade AI solutions.