The article further discusses the use of Timescale Cloud, a PostgreSQL-based managed service, as a vector database in the Unstract platform. It demonstrates how Timescale Cloud can significantly reduce the cost of data extraction by reducing the number of LLM tokens consumed. The article also explores different retrieval strategies, such as simple and sub-question retrieval, and their impact on the cost and quality of data extraction.
Key takeaways:
- Large language models (LLMs) are transforming intelligent document processing by automating the extraction of structured data from unstructured documents, reducing the need for manual annotations.
- Vector databases, such as Timescale Cloud, play a crucial role in this process, especially for lengthy documents, by reducing the cost and improving the efficiency of data extraction.
- The retrieval strategy used can significantly impact the quality of data extraction. While a simple retrieval strategy may work for straightforward prompts, more complex prompts may require a sub-question retrieval strategy for better results.
- Timescale Cloud offers extensions like pgvector, pgvectorscale, pgai, and pgai Vectorizer, which turn PostgreSQL into a high-performance vector database, making it an ideal choice for AI applications that require structured data extraction.