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).
Learning objectives
Upon completion of this intermediate-level lab, you will be able to:
- Utilize the LangChain framework to create a PDF document embedding application
- Deploy the embedding application and its resources using AWS SAM
Intended audience
- Candidates for the AWS Certified Machine Learning Specialty certification
- Cloud Architects
- Software Engineers
Prerequisites
Familiarity with the following will be beneficial but is not required:
- Amazon Bedrock
- AWS Lambda
- Amazon Simple Storage Service (S3)
- Amazon Simple Queue Service (SQS)
- AWS Serverless Application Model (SAM)
The following content can be used to fulfill the prerequisites:
Environment before
Environment after
Jun is a Cloud Labs Developer with previous experience as a Software Engineer and Cloud Developer. He holds the AWS Certified Solutions Architect and DevOps Engineer Professional certifications. He also holds the AWS Certified Solutions Architect, Developer, and SysOps Administrator Associate certifications.
Jun is focused on giving back to the growing cloud community by sharing his knowledge and experience with students and creating engaging content.