Upload Your First Document

Upload a file (PDF, DOCX, images, etc.) and extract its content. Extracted text becomes available for knowledge base indexing.

Uploading a document creates a Source — the platform’s representation of your file. After upload, an asynchronous extraction pipeline converts the file into structured page texts. You’ll poll for status and then retrieve the extracted content.

Prerequisites:

• Authentication configured (see Authentication guide)

1. Upload a file

   Send a multipart form-data request with your file. The server starts extraction automatically and returns the source metadata.

   Endpoint: POST /api/sources/upload

   Python

   with open("document.pdf", "rb") as f:
       response = requests.post(
           f"{BASE_URL}/api/sources/upload",
           headers={"apikey": API_KEY, "Authorization": f"Bearer {API_KEY}"},
           files={"file": ("document.pdf", f, "application/pdf")},
       )
   source = response.json()
   source_id = source["id"]
   print(f"Source created: {source_id}")
   

   Response:

   {
     "id": "source-uuid",
     "filename": "document.pdf",
     "content_type": "application/pdf",
     "extraction_status": "pending",
     "created_at": "2026-01-01T00:00:00Z"
   }
   
2. Check extraction status

   Poll the source until extraction_status reaches a terminal state: extracted (success), attention_required (partial — some pages failed but the source is still indexable), failed, or cancelled. Typically takes a few seconds for small documents.

   Endpoint: GET /api/sources/{id}

   Python

   import time
   
   TERMINAL = {"extracted", "attention_required", "failed", "cancelled"}
   while True:
       response = requests.get(
           f"{BASE_URL}/api/sources/{source_id}",
           headers=headers,
       )
       body = response.json()
       status = body["extraction_status"]
       if status == "extracted":
           print("Extraction complete!")
           break
       elif status == "attention_required":
           print("Extraction partial:", body.get("error_message"))
           break
       elif status in ("failed", "cancelled"):
           print(f"Extraction {status}:", body.get("error_message"))
           break
       time.sleep(2)
   
3. View extracted content

   Retrieve the extracted text, organized by page. Each page includes its text content and page number.

   Endpoint: GET /api/sources/{id}/page-texts

   Python

   response = requests.get(
       f"{BASE_URL}/api/sources/{source_id}/page-texts",
       headers=headers,
   )
   pages = response.json()
   for page in pages:
       print(f"Page {page['page']}: {page['text'][:100]}...")
   

What’s Next

Create a Knowledge Base

Index your extracted content for semantic search.

Sources & Extraction

Understand the extraction pipeline in depth.

Sources API Reference

Full endpoint documentation.