OpenAI Embedding Generation for Future Vector Search of Banking Data
Description
The current design of a long term document for a banking partner consists of cloud blob storage which are registered into a database with metadata. A future phase will require full-text document search. We can OCR this information now, and implementing vector embeddings would dramatically help individuals find the specific documents. PNC bank will save documents for 7 years and wants to be able to find specific documents.
Detailed example
An API will be exposed to a secure VA-internal website where appropriate personelle will use the UI, which will make an API call which returns a list of semantic search matches.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Increased efficiency of document retrieval in long term document archive.
Controls / human review
ATO: Not reported; PIA: Not published