I need an easy way to turn uploaded files into plain text that is suitable for chunking and embeddings.

Convert uploaded documents into clean, plain text that is ready for downstream chunking and embedding workflows. This makes it easier to reliably index content for semantic search, retrieval, and other AI use cases without manual copy-paste or reformatting.

This feature transforms uploaded files into standardized plain text output optimized for chunking and embeddings. It focuses on producing consistent text that can be segmented into chunks and passed into embedding models with minimal additional preprocessing. Users upload one or more files and receive extracted text as the primary output for downstream pipelines. The resulting text can be used to build searchable knowledge bases, retrieval-augmented generation (RAG) systems, and document understanding workflows. By reducing formatting noise and normalizing content into plain text, it helps create more predictable chunk boundaries and embedding inputs. It also streamlines ingestion by removing the need to manually open documents and copy content into a processing tool. Teams can use it to quickly prepare internal documents, reports, manuals, and other knowledge sources for indexing. The output is suitable for storing alongside metadata and later reprocessing as chunking strategies evolve. Overall, it accelerates the path from raw uploads to embedding-ready text, improving iteration speed for search and AI assistants.