RCRAG Converter

What an online RAG converter is, and is not

An online RAG converter is a page that reads your documents and writes overlapping passages with embeddings - it is not a chatbot, not a hosted index, and not a form that takes the file away to do the same work.

Three products share the search term

Search 'online RAG converter' and you get chatbots that ingest a PDF, platforms that parse on a server, and a smaller set of pages that actually convert in the client. Only the last one matches the words: online, because you opened a URL; converter, because the output is a different representation of the same file. A chatbot answers. A platform stores. A converter produces an artifact you can take elsewhere.

The output is JSONL, not an answer

Each line is one chunk: an id, the passage, a vector, and metadata for the source. That file loads into any vector database that will take newline-delimited JSON, which is nearly all of them. If the site instead shows a chat box after you drop a file, it converted something for itself and offered you a session. Useful, and a different purchase. You cannot move that session into Qdrant on Friday.

The input should be a folder, not a single demo PDF

Real conversion is a pile. An online RAG converter that only accepts one file at a time is a demo. The useful unit is a folder of mixed types - PDF, Word, slides, a spreadsheet of notes - with a per-file yield so you can see which names produced nothing. RAG Converter takes a folder in the tab and lists skips next to successes, because a silent zero on one name is the information that matters.

Use it when you want the corpus, not the conversation

If the next step is your own app, your own SpaceDB, or a script that already talks to pgvector, convert here and stop. If the next step is asking questions inside someone else's UI, you wanted a hosted RAG product and this page will feel unfinished. That distinction is the whole of choosing an online RAG converter rather than a workspace.

More on converting for RAG

  • rusty_ragdocument ingest, chunking and retrieval
  • rusty_xmlWord, PowerPoint and Excel, without LibreOffice
  • rusty_jpeg / rusty_pngimage decode
  • rusty_flacaudio decode
  • rusty_zstdcompression
  • rusty_allocthe allocator, in wasm too
  • rusty_timeNTP timestamps on every chunk
  • spacedbthe storage seam, CRDT per entry
  • FFAIthe AI toolkit the three engines below come from
  • CarmentaOCR - reads scans and screenshots, in your browser
  • Mercuryspeech recognition - turns recordings into transcripts
  • Argusvision - describes images and video frames

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