What's New

Announcements

Classification Chain

We now support enabling a pre-processing step to the conversational chain, called "classify", which is very flexible and powerful way to provide additional functionality and control over your chain. The classify sends the user question as input to LLM and returns JSON data which is parsed and provided to the following prompts. This can be used to detect original language, to perform translation, to categorize the question based on your own categorize, etc. This is very powerful when joined with the handlebar templates which can dynamically modify your prompts based on the output from classify step. Think prompt template selector but in a single handlebars template. Read more

Multiple embedding models support

We now support multiple embedding models! This enhancement allows you to configure multiple embedding models in a single deployment, providing greater flexibility and customization options. With this new feature, you can now: Set up multiple embedding models, each backed by a separate table in the Aurora PostgreSQL database. Specify the desired embedding model during document upload, indexing, and corpus API calls. Select the preferred embedding model for semantic search in the web UI's chat settings. Explore your embedding models using the updated developer tools. [Read more] (/aws-genai-conversational-rag-reference/development/vector-store/)

Disclaimer: Use of Third-Party Models

By using this sample, you agree that you may be deploying third-party models (“Third-Party Model”) into your specified user account. AWS does not own and does not exercise any control over these Third-Party Models. You should perform your own independent assessment, and take measures to ensure that you comply with your own specific quality control practices and standards, and the local rules, laws, regulations, licenses and terms of use that apply to you, your content, and the Third-Party Models, and any outputs from the Third-Party Models. AWS does not make any representations or warranties regarding the Third-Party Models.

Disclaimer: Use of Prompt Engineering Templates

Any prompt engineering template is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this prompt engineering template in your production accounts, or on production, or other critical data. You are responsible for testing, securing, and optimizing the prompt engineering as appropriate for production grade use based on your specific quality control practices and standards.

Alternative Reference

If you looking to benchmark multiple LLMs and RAG engines in a simple way, you should checkout aws-samples/aws-genai-llm-chatbot. That project focuses more on experimentation with models and vector stores, while this project focuses more on building an extendable 3-tier application.