Getting Started

Install DELM and run your first extraction pipeline in minutes.

Installation

Install from PyPI:

pip install delm

Or with optional dependencies (pdf, excel, alternative caching, etc)

pip install delm[extras]

Environment Variables

DELM requires API keys for the LLM providers you use. You must set these environment variables before using DELM.

For a complete list of supported providers and their required environment variable names, see the Instructor documentation.

Quick Example: For OpenAI, you would set:

export OPENAI_API_KEY="sk-..."

Optional: If you prefer using .env files with python-dotenv:

from dotenv import load_dotenv
load_dotenv()

Define Your Schema

Import the necessary classes and define what you want to extract:

from delm import DELM, Schema, ExtractionVariable

# Define extraction schema
schema = Schema.nested(
    container_name="commodities",
    ExtractionVariable(
        name="commodity_type",
        description="Type of commodity mentioned",
        data_type="string",
        required=True,
    ),
    ExtractionVariable(
        name="price_value",
        description="Price value mentioned",
        data_type="number",
        required=False,
    ),
)

Run Extraction

Create a DELM pipeline and extract structured data from your text:

import pandas as pd

# Initialize pipeline
delm = DELM(
    schema=schema,
    provider="openai",
    model="gpt-4o-mini",
    temperature=0.0,
)

# Prepare input data
data = pd.DataFrame({
    "text": [
        "Oil prices rose to $75 per barrel while gold fell to $1,850 per ounce.",
    ]
})

# Run extraction
results = delm.extract(data)
print(results)

Understanding Results

The results DataFrame will contain your original data plus extracted information. For the example above, DELM would extract:

Input text: "Oil prices rose to $75 per barrel while gold fell to $1,850 per ounce."

Extracted data:

{
  "commodities": [
    {
      "commodity_type": "oil",
      "price_value": 75.0
    },
    {
      "commodity_type": "gold",
      "price_value": 1850.0
    }
  ]
}

The results DataFrame includes all your original columns plus extraction results:

text	delm_record_id	delm_chunk_id	delm_extracted_data_json
Oil prices rose to $75 per barrel...	0	0	{"commodities": [{"commodity_type": "oil", "price_value": 75.0}, ...]}
...	...	...	...