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That is the whole RAG pipeline. No vector database to provision, no embedding service to call, no retriever to write. You uploaded a file and asked a question.
What "agentic" means here
You never call a retrieval endpoint. When you send a message to an assistant that has documents, Backboard decides what to fetch and pulls the relevant chunks with hybrid search (keyword and vector together), then answers. The response tells you what it used:
reply = await client.send_message(
"Summarize section 3.",
assistant_id=assistant.assistant_id,
)
print(reply.retrieved_files) # filenames used as context
print(reply.retrieved_files_count) # how many
Want deeper or shallower retrieval? Set tok_k on the assistant. It is the number of chunks pulled per query (default 10).
assistant = await client.create_assistant(
name="Docs Assistant",
system_prompt="Answer using the documents.",
tok_k=20, # retrieve more context per query
)
Two scopes
Where you upload decides who can see the document:
-
Assistant scope (
upload_document_to_assistant): shared across every thread under that assistant. Use it for a knowledge base, product docs, or policies that all users should query.
-
Thread scope (
upload_document_to_thread): visible only in that one conversation. Use it for a file a single user drops into a single chat.
# This file is only visible in one conversation
await client.upload_document_to_thread(thread_id, "meeting-notes.pdf")
Same upload, same query, different reach. No extra config.
Supported files
PDFs, Office files (.docx, .pptx, .xlsx), text and data (.txt, .csv, .md, .json, .xml), source code in most languages, and images. Upload it, and it is searchable once indexed.
The point
Agentic RAG is not a new trick. The win is that you do not build it. One upload, one status check, one message, and your assistant answers from your documents with retrieval handled inside the call. It is all in the same API, and that is the entire feature.
Grab a key and try it: app.backboard.io
Documents docs: docs.backboard.io/concepts/documents