


Create the fastest and precise invoice data extractor for structural output using AI
Using LlamaExtract with Pydantic Models for Shop Receipts Extraction
In this article, we'll explore how to use LlamaExtract incorporated with schemas from Pydantic models inorder to extract structured data from shop receipts. This approach helps in organizing receipt information systematically, making it easier to analyze and manage.
Setup
First, ensure you have the llama-extract client library installed. Use the following command:
pip install llama-extract pydantic
Note: If you see a notice about updating pip, you may update it using the command provided.
First, login and get an api-key for free from Llama Index Cloud
Set up the environment variable for your LlamaExtract API key:
import os os.environ["LLAMA_CLOUD_API_KEY"] = "YOUR LLAMA INDEX CLOUD API HERE"
Load Data
For this example, let's assume we have a dataset of shop receipts in PDF format. Place these files in a directory named receipts.
DATA_DIR = "data/receipts" fnames = os.listdir(DATA_DIR) fnames = [fname for fname in fnames if fname.endswith(".pdf")] fpaths = [os.path.join(DATA_DIR, fname) for fname in fnames] fpaths
The output should list the file paths of the receipts:
['data/receipts/receipt.pdf']
Define a Pydantic Model
We'll define our data model using Pydantic, this would tell the API which fields/data we are expecting or want to extract from the PDF. For shop receipts, we might be interested in extracting the store name, date, total amount, and list of items purchased.
from pydantic import BaseModel from typing import List class Item(BaseModel): name: str quantity: int price: float class Receipt(BaseModel): store_name: str date: str total_amount: float items: List[Item]
Create Schema
Now, we can use the Pydantic model to define an extraction schema in LlamaExtract.
from llama_extract import LlamaExtract extractor = LlamaExtract(verbose=True) schema_response = await extractor.acreate_schema("Receipt Schema", data_schema=Receipt) schema_response.data_schema
The output schema should resemble the following:
{ 'type': 'object', '$defs': { 'Item': { 'type': 'object', 'title': 'Item', 'required': ['name', 'quantity', 'price'], 'properties': { 'name': {'type': 'string', 'title': 'Name'}, 'quantity': {'type': 'integer', 'title': 'Quantity'}, 'price': {'type': 'number', 'title': 'Price'} } } }, 'title': 'Receipt', 'required': ['store_name', 'date', 'total_amount', 'items'], 'properties': { 'store_name': {'type': 'string', 'title': 'Store Name'}, 'date': {'type': 'string', 'title': 'Date'}, 'total_amount': {'type': 'number', 'title': 'Total Amount'}, 'items': { 'type': 'array', 'title': 'Items', 'items': {'$ref': '#/$defs/Item'} } } }
Run Extraction
With the schema defined, we can now extract structured data from our receipt files. By specifying Receipt as the response model, we ensure the extracted data is validated and structured.
responses = await extractor.aextract( schema_response.id, fpaths, response_model=Receipt )
You can access the raw JSON output if needed:
data = responses[0].data print(data)
Example JSON output:
{ 'store_name': 'ABC Electronics', 'date': '2024-08-05', 'total_amount': 123.45, 'items': [ {'name': 'Laptop', 'quantity': 1, 'price': 999.99}, {'name': 'Mouse', 'quantity': 1, 'price': 25.00}, {'name': 'Keyboard', 'quantity': 1, 'price': 50.00} ] }
Conclusion
In this article, we demonstrated how to use LlamaExtract with Pydantic models for defining data schemas and extract structured data from shop receipts. This approach ensures that the extracted information is well-organized and validated, making it easier to handle and analyze.
This can also be use for many cases, invoices, receipts, reports etc.
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