Note
This section assumes that you’ve completed the section above Prerequisites for document querying.
Document querying is a feature that you can use while interacting with foundation models in Canvas. With document querying, you can access a corpus of documents stored in an Amazon Kendra index, which holds the contents of your documents and is structured in a way to make documents searchable. You can ask specific questions that are targeted to the data in your Amazon Kendra index, and the foundation model returns answers to your questions. For example, you can query an internal knowledge base of IT information and ask questions such as “How do I connect to my company’s network?” For more information about setting up an index, see the Amazon Kendra Developer Guide.
When using the document query feature, the foundation models restrict their responses
to the content of the documents in your index with a technique called Retrieval
Augmented Generation (RAG). This technique bundles the most relevant information from
the index along with the user's prompt and sends it to the foundation model to get a
response. Responses are limited to what can be found in your index, preventing the model
from giving you incorrect responses based on external data. For more information about
this process, see the blog post Quickly build high-accuracy Generative AI applications on enterprise
data
To get started, in a chat with a foundation model in Canvas, turn on the Document query toggle at the top of the page. From the dropdown, select the Amazon Kendra index that you want to query. Then, you can begin asking questions related to the documents in your index.
Important
Document querying supports the Compare model outputs feature. Any existing chat history is overwritten when you start a new chat to compare model outputs.
