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File Operations

Upload and manage documents that power your RAG Q&A system.

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Upload Files

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Upload from Local File

from cerevox import Hippo

hippo = Hippo(api_key="your-api-key")

# Upload a local file
file = hippo.upload_file(
    folder_id="folder_123",
    file_path="documents/user-guide.pdf"
)

print(f"Uploaded: {file.name}")
print(f"File ID: {file.id}")
print(f"Status: {file.status}")

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Upload from URL

# Upload directly from a URL
file = hippo.upload_file_from_url(
    folder_id="folder_123",
    file_url="https://example.com/whitepaper.pdf",
    file_name="whitepaper.pdf"  # Optional custom name
)

print(f"Uploaded from URL: {file.name}")

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Batch Upload

files = []

for file_path in ["doc1.pdf", "doc2.docx", "doc3.pptx"]:
    file = hippo.upload_file(folder_id, file_path)
    files.append(file)
    print(f"Uploaded: {file.name}")

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Supported File Formats

Documents

  • PDF (.pdf)
  • Word (.docx, .doc)
  • PowerPoint (.pptx, .ppt)
  • Text (.txt)
  • RTF (.rtf)

Spreadsheets

  • Excel (.xlsx, .xls)
  • CSV (.csv)
  • TSV (.tsv)

Web & Other

  • HTML (.html)
  • MHTML (.mhtml)
  • Markdown (.md)
File size limits:
  • Max file size: 100MB per file
  • Contact support for larger files or custom formats

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List Files

# Get all files in a folder
files = hippo.get_files(folder_id="folder_123")

for file in files:
    print(f"{file.name} - {file.status} - {file.size_bytes} bytes")
File status values:
  • uploading: File is being uploaded
  • processing: File is being indexed
  • completed: File is ready for Q&A
  • failed: Processing failed

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Get File Details

# Get specific file information
file = hippo.get_file(file_id="file_456")

print(f"Name: {file.name}")
print(f"Status: {file.status}")
print(f"Size: {file.size_bytes} bytes")
print(f"Pages: {file.page_count}")
print(f"Uploaded: {file.created_at}")

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Delete Files

# Delete a file
hippo.delete_file(file_id="file_456")

print("File deleted successfully")
Deleted files cannot be recovered. The file will be removed from all chats and answers that referenced it.

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File Processing

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Processing Time

Files are automatically processed after upload:
1

Upload

File is uploaded to Cerevox (a few seconds)
2

Parsing

Document is parsed for text and structure (10s - 2min)
3

Chunking

Content is split into semantic chunks (a few seconds)
4

Indexing

Chunks are indexed for search (10s - 1min)
5

Ready

File is ready for Q&A!
Total processing time:
  • Small files (< 10 pages): 10-30 seconds
  • Medium files (10-100 pages): 30-120 seconds
  • Large files (> 100 pages): 2-5 minutes

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Monitor Processing Status

import time

# Upload file
file = hippo.upload_file(folder_id, "large-document.pdf")

# Poll until processing completes
while file.status != "completed":
    time.sleep(5)
    file = hippo.get_file(file.id)
    print(f"Status: {file.status}")

print("File ready for Q&A!")

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Best Practices

Before uploading:
  • Ensure PDFs are text-based (not scanned images)
  • Check that documents aren’t password-protected
  • Verify file isn’t corrupted
Tip: OCR (scanned) PDFs work but may have lower accuracy. Use text-based PDFs when possible.
Reduce processing time:
  • Remove unnecessary pages (covers, blanks, ads)
  • Compress images in PDFs
  • Split very large documents (500+ pages)
Smaller, focused documents = faster processing + better search results
Good: product-api-authentication-guide.pdf Bad: doc1.pdf, untitled.pdfDescriptive names help with source citations and debugging.
# Async = 10x faster for multiple files
async with AsyncHippo() as hippo:
    tasks = [hippo.upload_file(folder_id, f) for f in files]
    results = await asyncio.gather(*tasks)
Async concurrent uploads are significantly faster than sequential.

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Complete Example: Batch Upload

import asyncio
from pathlib import Path
from cerevox import AsyncHippo

async def batch_upload_directory(folder_id, directory_path):
    """Upload all PDFs from a directory"""
    async with AsyncHippo(api_key="your-api-key") as hippo:
        # Get all PDF files
        pdf_files = list(Path(directory_path).glob("*.pdf"))

        print(f"Found {len(pdf_files)} PDF files")

        # Upload concurrently
        tasks = [
            hippo.upload_file(folder_id, str(pdf))
            for pdf in pdf_files
        ]

        files = await asyncio.gather(*tasks)

        # Report results
        print(f"\n✅ Uploaded {len(files)} files:")
        for file in files:
            print(f"  - {file.name} ({file.status})")

        return files

# Usage
folder = await hippo.create_folder("Uploaded Docs")
files = await batch_upload_directory(folder.id, "./documents")

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Error Handling

from cerevox import Hippo, HippoError

hippo = Hippo()

try:
    file = hippo.upload_file(folder_id, "document.pdf")
    print(f"Uploaded: {file.name}")

except FileNotFoundError:
    print("Error: File not found")
except HippoError as e:
    if "unsupported format" in str(e).lower():
        print("Error: File format not supported")
    elif "too large" in str(e).lower():
        print("Error: File exceeds size limit")
    else:
        print(f"Error: {e}")

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Next Steps

Chat Sessions

Create chats to ask questions

Q&A System

Ask questions over uploaded files

Best Practices

Optimize file preparation
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