Embeddings are used in Generative AI to represent high-dimensional data in a lower-dimensional space. They are numerical representations, or vectors, of data that capture the relationships between data points. AI models can use these mathematical representations to generate new data points similar to the original data. Embeddings are crucial for implementing a Retrieval-Augmented Generation (RAG) model, which combines generative AI models with retrieval models to improve the quality of generated data.
LangChain is a framework for developing Large Language Model (LLM) applications. The LangChain framework provides various integrations for embedding models, including Amazon Bedrock.
In this lab, you will learn how to create a PDF document embedding application using the LangChain framework and deploy it using AWS Serverless Application Model (SAM).
Upon completion of this intermediate-level lab, you will be able to:
Familiarity with the following will be beneficial but is not required:
The following content can be used to fulfill the prerequisites:
December 16th, 2024 - Resolved an issue preventing the lab from deploying