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Extraction Engines

Overview

Extraction Engines in Griptape facilitate the extraction of data from text formats such as CSV and JSON. These engines play a crucial role in the functionality of Extraction Tasks. As of now, Griptape supports two types of Extraction Engines: the CSV Extraction Engine and the JSON Extraction Engine.

CSV Extraction Engine

The CSV Extraction Engine is designed specifically for extracting data from CSV-formatted content.

Info

The CSV Extraction Engine requires the column_names parameter for specifying the columns to be extracted.

from griptape.engines import CsvExtractionEngine

# Initialize the CsvExtractionEngine instance
csv_engine = CsvExtractionEngine()

# Define some unstructured data
sample_text = """
Alice, 28, lives in New York.
Bob, 35 lives in California.
Charlie is 40 and lives in Texas.
"""

# Extract CSV rows using the engine
result = csv_engine.extract(sample_text, column_names=["name", "age", "location"])

for row in result.value:
    print(row.to_text())
name,age,location
Alice,28,New York
Bob,35,California
Charlie,40,Texas

JSON Extraction Engine

The JSON Extraction Engine is tailored for extracting data from JSON-formatted content.

Info

The JSON Extraction Engine requires the template_schema parameter for specifying the structure to be extracted.

from griptape.engines import JsonExtractionEngine
from schema import Schema

json_engine = JsonExtractionEngine()

# Define some unstructured data
sample_json_text = """
Alice (Age 28) lives in New York.
Bob (Age 35) lives in California.
"""

# Define a schema for extraction
user_schema = Schema(
    {"users": [{"name": str, "age": int, "location": str}]}
).json_schema("UserSchema")

# Extract data using the engine
result = json_engine.extract(sample_json_text, template_schema=user_schema)

for artifact in result.value:
    print(artifact.value)
{'name': 'Alice', 'age': 28, 'location': 'New York'}
{'name': 'Bob', 'age': 35, 'location': 'California'}