Building Semantic Search with Google Cloud Vertex AI Text Embeddings
Gio Peralto takes us on a deep dive into semantic search using Google Cloud's Vertex AI text-embedding-005 model. Through his AIMDB application (a clever play on IMDB), he demonstrates how vector embeddings can revolutionize content discovery by understanding meaning rather than just matching text. The episode explores the technical implementation with 768-dimensional vectors, compares semantic search to traditional regex approaches, and discusses how it fits into the broader RAG ecosystem. Gio shows both the power and limitations of semantic search through real movie recommendations, highlighting important considerations like embedding model consistency and search scope optimization.
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Key Takeaways
- Semantic search uses vector embeddings to find content based on meaning rather than exact text matches
- Google Cloud's text-embedding-005 model provides 768-dimensional embeddings for semantic search
- Semantic search can be a subset of RAG, enhancing the retrieval process with better intent understanding
- Vector dimensionality affects search quality - industry standard is moving towards 1536 dimensions
- Embedding models must match between data preparation and query time for accurate results
- Search scope affects results - including titles, characters, and plot summaries can improve relevance
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Google Cloud Vertex AI
Google's machine learning platform offering pre-trained and custom ML models