- They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19).
- They make predictions by only looking up information stored in its parameters, leading to inferior interpretability.
- They’re mostly trained on general domain corpora, making them less effective on domain-specific tasks. There are scenarios when you want models to generate text based on specific data rather than generic data.
- For example, a health insurance company may want their question answering bot to answer questions using the latest information stored in their enterprise document repository or database, so the answers are accurate and reflect their unique business rules.
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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