I need to convert files into a format that is easy to chunk and embed for retrieval-augmented generation.

This feature converts source files into a clean, structured text format that is optimized for chunking and generating embeddings for retrieval-augmented generation (RAG). It helps you prepare consistent, searchable content so downstream retrieval is more accurate and reliable.

This feature prepares documents for retrieval-augmented generation by converting files into a standardized representation that is easy to chunk and embed. It focuses on producing clean, extraction-friendly text that can be split into stable chunks for indexing in a vector database. By normalizing content across different input file types, it reduces inconsistencies that can harm embedding quality and retrieval precision. The output is intended to be fed into a chunking step where you define chunk sizes and boundaries suited to your application. It also supports building repeatable ingestion pipelines so the same file produces consistent output over time. This is useful when you need to process many documents and maintain uniform formatting for search and retrieval. It benefits teams building assistants, knowledge bases, and internal search tools that rely on embedding-based retrieval. It is especially valuable for turning complex or varied documents into predictable text suitable for RAG workflows. The result is faster ingestion, improved relevance during retrieval, and fewer downstream issues caused by messy or inconsistent text extraction.