Vertex AI OCR

Overview

Property	Details
Description	Vertex AI OCR provides document intelligence capabilities powered by Mistral, enabling text extraction from PDFs and images
Provider Route on LiteLLM	vertex_ai/
Supported Operations	/ocr
Link to Provider Doc	Vertex AI ↗

Extract text from documents and images using Vertex AI's OCR models, powered by Mistral.

Quick Start

LiteLLM SDK

SDK Usage

import litellm
import os

# Set environment variables
os.environ["VERTEXAI_PROJECT"] = "your-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"

# OCR with PDF URL
response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505",
    document={
        "type": "document_url",
        "document_url": "https://example.com/document.pdf"
    }
)

# Access extracted text
for page in response.pages:
    print(page.text)

LiteLLM PROXY

proxy_config.yaml

model_list:
  - model_name: vertex-ocr
    litellm_params:
      model: vertex_ai/mistral-ocr-2505
      vertex_project: os.environ/VERTEXAI_PROJECT
      vertex_location: os.environ/VERTEXAI_LOCATION
      vertex_credentials: path/to/service-account.json  # Optional
    model_info:
      mode: ocr

Start Proxy

litellm --config proxy_config.yaml

Call OCR via Proxy

cURL Request

curl -X POST http://localhost:4000/ocr \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "model": "vertex-ocr",
    "document": {
      "type": "document_url",
      "document_url": "https://arxiv.org/pdf/2201.04234"
    }
  }'

Authentication

Vertex AI OCR supports multiple authentication methods:

Service Account JSON

Service Account Auth

response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505",
    document={"type": "document_url", "document_url": "https://..."},
    vertex_project="your-project-id",
    vertex_location="us-central1",
    vertex_credentials="path/to/service-account.json"
)

Application Default Credentials

Default Credentials

# Relies on GOOGLE_APPLICATION_CREDENTIALS environment variable
response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505",
    document={"type": "document_url", "document_url": "https://..."},
    vertex_project="your-project-id",
    vertex_location="us-central1"
)

Document Types

Vertex AI OCR supports both PDFs and images.

PDF Documents

PDF OCR

response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505",
    document={
        "type": "document_url",
        "document_url": "https://example.com/document.pdf"
    },
    vertex_project="your-project-id",
    vertex_location="us-central1"
)

Image Documents

Image OCR

response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505",
    document={
        "type": "image_url",
        "image_url": "https://example.com/image.png"
    },
    vertex_project="your-project-id",
    vertex_location="us-central1"
)

Base64 Encoded Documents

Base64 PDF

import base64

# Read and encode PDF
with open("document.pdf", "rb") as f:
    pdf_base64 = base64.b64encode(f.read()).decode()

response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505", # This doesn't work for deepseek
    document={
        "type": "document_url",
        "document_url": f"data:application/pdf;base64,{pdf_base64}"
    },
    vertex_project="your-project-id",
    vertex_location="us-central1"
)

Supported Parameters

All Parameters

response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505",
    document={                           # Required: Document to process
        "type": "document_url",
        "document_url": "https://..."
    },
    vertex_project="your-project-id",   # Required: GCP project ID
    vertex_location="us-central1",       # Optional: Defaults to us-central1
    vertex_credentials="path/to/key.json", # Optional: Service account key
    include_image_base64=True,           # Optional: Include base64 images
    pages=[0, 1, 2],                     # Optional: Specific pages to process
    image_limit=10                       # Optional: Limit number of images
)

Response Format

Response Structure

# Response has the following structure
response.pages          # List of pages with extracted text
response.model          # Model used
response.object         # "ocr"
response.usage_info     # Token usage information

# Access page content
for page in response.pages:
    print(f"Page {page.page_number}:")
    print(page.text)

Async Support

Async Usage

import litellm

response = await litellm.aocr(
    model="vertex_ai/mistral-ocr-2505",
    document={
        "type": "document_url",
        "document_url": "https://example.com/document.pdf"
    },
    vertex_project="your-project-id",
    vertex_location="us-central1"
)

Cost Tracking

LiteLLM automatically tracks costs for Vertex AI OCR:

• Cost per page: $0.0005 (based on $1.50 per 1,000 pages)

View Cost

response = litellm.ocr(
    model="vertex_ai/mistral-ocr-2505",
    document={"type": "document_url", "document_url": "https://..."},
    vertex_project="your-project-id"
)

# Access cost information
print(f"Cost: ${response._hidden_params.get('response_cost', 0)}")

Important Notes

URL Conversion

Vertex AI Mistral OCR endpoints don't have internet access. LiteLLM automatically converts public URLs to base64 data URIs before sending requests to Vertex AI.

Regional Availability

Mistral OCR is available in multiple regions. Specify vertex_location to use a region closer to your data:

• us-central1 (default)
• europe-west1
• asia-southeast1

Deepseek OCR is only available in global region.

Supported Models

• mistral-ocr-2505 - Latest Mistral OCR model on Vertex AI
• deepseek-ocr-maas - Lates Deepseek OCR model on Vertex AI

Use the Vertex AI provider prefix: vertex_ai/<model-name>