Document Ingestion Quickstart

import json
import os
from typing import Optional

from pydantic import BaseModel, Field
from tensorlake.documentai import (
  DocumentAI,
  ParsingOptions,
  StructuredExtractionOptions,
  ChunkingStrategy,
)

doc_ai = DocumentAI(api_key=os.getenv("TENSORLAKE_API_KEY"))

# Use a publicly accessible URL or upload a file to Tensorlake and use the file ID.
file_url = "https://pub-226479de18b2493f96b64c6674705dd8.r2.dev/real-estate-purchase-all-signed.pdf"

# Define a JSON schema using Pydantic
# Our structured extraction model will identify the properties we want to extract from the document.
# In this case, we are extracting the names and signature dates of the buyer and seller.
class Signers(BaseModel):
    buyer_name: Optional[str] = Field(
        default=None, description="The name of the buyer, do not extract initials"
    )
    buyer_signature_date: Optional[str] = Field(
        default=None, description="Date and time that the buyer signed."
    )
    seller_name: Optional[str] = Field(
        default=None, description="The name of the seller, do not extract initials"
    )
    seller_signature_date: Optional[str] = Field(
        default=None, description="Date and time that the seller signed."
    )

# Create a structured extraction options object with the schema
#
# You can send as many schemas as you want, and the API will return structured data for each schema
# indexed by the schema name.
real_estate_agreement_extraction_options = StructuredExtractionOptions(
    schema_name="Signers",
    json_schema=Signers,
)

# Tune in the options for finding data in the document
#
# We provide sane defaults, but every document is different, so you can adjust
# every option to your needs.
#
# In this example, we are using the PAGE chunking strategy, which means that each page of the document will be a separate chunk.
parsing_options = ParsingOptions(
    chunking_strategy=ChunkingStrategy.PAGE,
)

# Submit the parse operation and wait for the job to complete
parse_id = doc_ai.parse(
    file=file_url,
    page_range="9-10",
    parsing_options=parsing_options,
    structured_extraction_options=[real_estate_agreement_extraction_options],
)

result = doc_ai.wait_for_completion(parse_id)

markdown_chunks = result.chunks
with open("markdown_chunks.md", "w") as f:
    for chunk_number, chunk in enumerate(markdown_chunks):
        f.write(f"## CHUNK NUMBER {chunk_number}\n\n")
        f.write(f"## Page {chunk.page_number}\n\n{chunk.content}\n\n")

serializable_data = result.model_dump()
with open("structured_data.json", "w") as f:
    json.dump(serializable_data["structured_data"], f, indent=2)