docs: add async batch annotation for table extraction from PDFs

examples/mistral/jobs/async_jobs_ocr_batch_annotation.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+import asyncio
+import json
+import os
+from typing import List
+
+import httpx
+from pydantic import BaseModel, Field
+
+from mistralai import Mistral
+from mistralai.extra import response_format_from_pydantic_model
+from mistralai.models import File
+
+SAMPLE_PDF_URL = "https://arxiv.org/pdf/2401.04088"
+
+
+class Table(BaseModel):
+    name: str = Field(description="The name or title of the table")
+
+
+class TableExtraction(BaseModel):
+    tables: List[Table] = Field(description="List of tables found in the document")
+
+
+def create_ocr_batch_request(custom_id: str, document_url: str) -> dict:
+    """Batch requests require custom_id and body wrapper."""
+    response_format = response_format_from_pydantic_model(TableExtraction)
+    return {
+        "custom_id": custom_id,
+        "body": {
+            "document": {"type": "document_url", "document_url": document_url},
+            "document_annotation_format": response_format.model_dump(
+                by_alias=True, exclude_none=True
+            ),
+            "pages": [0, 1, 2, 3, 4, 5, 6, 7],
+            "include_image_base64": False,
+        },
+    }
+
+
+async def main():
+    client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
+
+    document_urls = [SAMPLE_PDF_URL]
+
+    batch_requests = [
+        json.dumps(create_ocr_batch_request(custom_id=str(i), document_url=url))
+        for i, url in enumerate(document_urls)
+    ]
+    batch_content = "\n".join(batch_requests)
+
+    print("Uploading batch file...")
+    batch_file = await client.files.upload_async(
+        file=File(file_name="ocr_batch.jsonl", content=batch_content.encode()),
+        purpose="batch",
+    )
+    print(f"Batch file uploaded: {batch_file.id}")
+
+    print("Creating batch job...")
+    created_job = await client.batch.jobs.create_async(
+        model="mistral-ocr-latest",
+        input_files=[batch_file.id],
+        endpoint="/v1/ocr",
+    )
+    print(f"Batch job created: {created_job.id}")
+
+    print("Waiting for job completion...")
+    job = await client.batch.jobs.get_async(job_id=created_job.id)
+    while job.status not in ["SUCCESS", "FAILED", "CANCELLED"]:
+        print(f"Status: {job.status}")
+        await asyncio.sleep(5)
+        job = await client.batch.jobs.get_async(job_id=created_job.id)
+
+    print(f"Job status: {job.status}")
+
+    async with httpx.AsyncClient() as http_client:
+        if job.output_file:
+            signed_url = await client.files.get_signed_url_async(
+                file_id=job.output_file
+            )
+            response = await http_client.get(signed_url.url)
+            for line in response.content.decode().strip().split("\n"):
+                result = json.loads(line)
+                annotation = result["response"]["body"].get("document_annotation")
+                if annotation:
+                    tables = TableExtraction.model_validate_json(annotation)
+                    for table in tables.tables:
+                        print(table.name)
+
+        if job.error_file:
+            signed_url = await client.files.get_signed_url_async(file_id=job.error_file)
+            response = await http_client.get(signed_url.url)
+            print("Errors:", response.content.decode())
+
+    print("\nCleaning up...")
+    await client.files.delete_async(file_id=batch_file.id)
+    print("Done!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())