Building with Palantir AIP: Semantic Search

This post introduces the concept of building semantic search applications using Palantir AIP. It details the steps involved: connecting to data sources with Virtual Tables, generating text embeddings using models, building data pipelines with Pipeline Builder, storing embeddings in an Ontology-powered Vector Store, creating a semantic search function using the Ontology, orchestrating LLM logic with AIP Logic, and building a user-facing application with Workshop. It highlights how AIP enables rapid development of production-grade AI workflows by leveraging the Ontology for context and data access.