📄️ Chunk size and overlap
In the context of Retrieval-Augmented Generation (RAG), chunking refers to the division of a large input data, typically a large document or documents, into smaller, more manageable segments or "chunks" for efficient information retrieval. RAG techniques leverage these chunks to retrieve relevant context that enhances the generation of responses or text outputs from large language models or LLMs
📄️ Run Platform
Use the following available options of run-platform.sh launch script to run Unstract Open Source Edition in Docker containers.
📄️ Calculating extraction costs
As a customer planning to use Unstract to extract structured data from unstructured documents, you might want to be sure about total cost of ownership. While it's easy to understand how much the Unstract Cloud or Unstract On-Prem editions might cost for the kind of volume you expect to deal with, calculating how much you might expect to spend on Large Language Models is a little bit more involved, but fairly easy and straightforward to calculate.