The article provides a detailed guide on creating embeddings for data in PostgreSQL and keeping them up-to-date with tables. It explains the goals of any system that creates embeddings, such as no modifications to the original table or applications that interact with the table, automatic updating of embeddings when rows in the source table change, and resilience against network and service failures. The article also provides examples of how to implement these principles using the Timescale Vector Python library and LangChain. Finally, it discusses how to search through your embeddings and concludes by outlining the benefits of using PostgreSQL for both data storage and background embedding generation.
Key takeaways:
- Vector embeddings provide a mathematical representation of data, encapsulating its semantic essence in a form that machines can readily process. They can be used for semantic search, recommendation systems, generative AI, and data clustering.
- PgVectorizer is a library developed to create and manage embeddings for data residing in PostgreSQL. It creates embedding from your data and keeps your relational and embedding data in sync as your data changes.
- The Timescale Vector Python library can be used to easily manage embedding PostgreSQL data. It allows users to define how to embed their data and provides a robust framework for embedding creation.
- Embeddings can be used for various applications, such as hybrid search on metadata and time, integrations with chat and Retrieval Augmented Generation (RAG), and more. They can be generated using different frameworks like LangChain, LlamaIndex, or OpenAI’s text-embedding-ada-002 model.