Here’s the translation into American English:
The implementation of artificial intelligence technologies is rapidly evolving, and one of the most notable methods in this field is Retrieval Augmented Generation (RAG). This approach focuses on improving both the accuracy and transparency of the responses provided by generative AI applications. Thanks to RAG, base models can access additional relevant data, which eliminates the costs and complexities associated with training or fine-tuning models.
A significant number of companies have decided to use Amazon Bedrock Knowledge Bases to implement RAG-based workflows. The initial setup of a knowledge base in Bedrock can be carried out quickly and easily through the AWS management console, allowing connection to data sources in just a few clicks. However, for those seeking a more stable production environment, migrating to an infrastructure as code (IaC) template is highly recommended. Many experts suggest using Terraform as their IaC management framework.
Recently, a Terraform-based IaC solution has been introduced for deploying a knowledge base in Amazon Bedrock, which also establishes connections to various data sources. This solution automates the creation and configuration of key AWS service components, such as the AWS Identity and Access Management (IAM) role that defines secure access policies, and Amazon OpenSearch Serverless, which allows for the efficient management and querying of large volumes of data. This approach not only optimizes the execution of RAG-based applications but also facilitates a more agile and sustainable process.
Interested customers are required to meet certain prerequisites, such as having an active AWS account and installing necessary tools like Terraform and the AWS CLI. Additionally, it is essential to configure access to a base model within Amazon Bedrock that generates embeddings, using the Titan Text Embeddings V2 model by default. This simplifies interaction with the system and enhances the quality of the responses provided.
For those looking to customize their implementation, the Terraform solution offers the flexibility to modify the content partitioning strategy and the dimensions of the OpenSearch vectors. This allows each organization to tailor the system to its specific needs, thereby improving the user experience when querying information.
Finally, it is crucial for users to clean their environment after testing resources to avoid unnecessary costs. This involves deleting the infrastructure created and clearing the contents of the Amazon S3 bucket used during the deployment. The advanced options for customizing the knowledge base enhance RAG capabilities, establishing Amazon Bedrock and Terraform as key tools in creating innovative and efficient solutions in the dynamic field of artificial intelligence.
Source: MiMub in Spanish
