Document enrichment in Amazon Q Business
Important
This section assumes that you understand document attributes and metadata controls in Amazon Q Business.
Note
Before you configure document enrichment, you must create a Amazon Q Business retriever and index for your application.
The Amazon Q Business document enrichment feature helps you control both what documents and document attributes are ingested into your index and also how they're ingested. Using document enrichment, you can create, modify, or delete document attributes and document content when you ingest them into your Amazon Q Business index.
You can use document enrichment to provide more structure and context to the data that the large language model (LLM) powering Amazon Q Business uses to generate responses. This is done in the following ways:
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Extracting metadata – To make data easier for the LLM to understand, document enrichment extracts metadata like document title, type, and date and organizes these by mapping them to index fields.
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Categorization – Tagging documents with metadata categories helps the LLM recognize both content and context. This helps LLMs prioritize relevant sources when answering queries about specific categories.
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Adding custom metadata – By enriching documents with your own custom metadata, you can provide more relevant context for the LLM to work with.
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Improved Relevance – As a result of structured data and metadata enrichment, the LLM can better match user queries to the most relevant documents. This can lead to more accurate responses.
Document enrichment offers two kinds of methods that you can use for your solution:
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Configure basic operations – Use basic operations to add, update, or delete document attributes from your data. For example, you can scrub personally identifiable information (PII) by choosing to delete any document attributes related to PII.
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Configure Lambda functions – Use a preconfigured Lambda function to perform more customized, advanced document attribute manipulation logic to your data. For example, your enterprise data might be stored as scanned images. In that case, you can use a Lambda function to run Optical Character recognition (OCR) on the scanned documents to extract text from them. Then, each scanned document is treated as a text document during ingestion. Finally, during chat, Amazon Q Business will factor the textual data extracted from the scanned documents when it generates responses.
When you implement your solution, you can choose to use both document enrichment methods together. That is, you can use basic operations to do a first parse of your data and then use a Lambda function for more complex operations. For example, you could first use a basic function to remove all PII information from your documents using document attributes. Then, use a Lambda function to extract text from scanned documents.
Document enrichment is supported both on the AWS Management Console and by Amazon Q Business API actions. If you use the console, you can only enrich documents connected to your application environment using an Amazon Q Business data source.
Note
Document enrichment is only supported in an Amazon Q Business application environment if you use an Amazon Q Business native retriever. If you use an Amazon Kendra retriever, you should configure document enrichment in Amazon Kendra.
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