Simplifies the process of generating Embeddings for various types of data without requiring users to train their own models
- The API leverages pre-trained models that Google has already trained on large datasets.
- These models are fine-tuned and optimized for various tasks, ensuring that the Embeddings they generate are effective for a wide range of applications.
- Efficient due to vector search technology: Vertex AI Matching Engine
- Can use with text processing tasks:
- LLM-enabled semantic search
- LLM-enabled text classification
- LLM-enabled recommendation
- It has a large embedding space with 768 dimensions
How companies use this
- Companies provide their own data (e.g., text documents, images) as input to the API
- The API processes this input data and converts it into embeddings. These embeddings are dense vector representations that capture the semantic meaning of the data.
- The company might use text embeddings for tasks
- Document Similarity: Embeddings can be used to find similar documents based on their content, which is useful for search engines and recommendation systems.
- Search: Embeddings enable semantic search, where the search engine understands the intent behind the query and retrieves relevant results based on the meaning rather than just keywords.
- Sentiment Analysis: Embeddings can help analyze the sentiment of text data, identifying whether the sentiment is positive, negative, or neutral.
- Clustering and Classification: Embeddings can be used to cluster similar items together or classify data into predefined categories.