YouTube Video: What are text embeddings?

This repository explores various techniques and use-cases for embedding in Machine Learning, with a particular focus on text embeddings and their applications.

• vector-search-quickstart.ipynb: Offers a quickstart guide to setting up and using vector search for finding semantically similar items.
• intro-textemb-vectorsearch.ipynb: Provides an introduction to text embeddings and their application in building vector search engines.
• hybrid-search.ipynb: Demonstrates building a hybrid search system leveraging both keyword-based search and semantic similarity search with embeddings.
• embedding-similarity-visualization.ipynb: Visualizes similarity relationships between embeddings using dimensionality reduction techniques like PCA and t-SNE.
• intro_multimodal_embeddings.ipynb: Introduces the concept of multimodal embeddings, which combine information from different modalities like text and images.
• intro_embeddings_tuning.ipynb: Explores techniques for fine-tuning pre-trained embedding models to specific domains and tasks.
• task-type-embedding.ipynb: Explores techniques for creating embeddings specialized for different tasks.
• large-embs-generation-for-vvs.ipynb: Demonstrates large-scale embeddings generation for Vertex AI Vector Search.

• bq-vector-search-outlier-detection-audit-logs.ipynb: Shows how to detect and investigate anomalies in audit logs using BigQuery vector search and Cloud Audit logs as an example dataset.
• bq-vector-search-outlier-detection-infra-logs.ipynb: Demonstrates building a real-world outlier detection using Gemini and BigQuery vector search. Also shows how to tune hyperparameters and evaluate performance using a public HDFS logs dataset.