Structured Data

Find structure in data

Article summaries: Extract key points from lengthy reports or articles to create concise summaries for decision-makers.
Entity recognition: Identify and extract entities such as names, dates, and locations from unstructured text to create structured datasets.
Sentiment analysis: Extract sentiment scores and associated entities from customer reviews or social media posts to gain insights into public opinion.
Classification: Classify text into predefined categories, such as spam detection or topic classification.
Image/PDF input: Extract data from images or PDFs, such as tables or forms, to automate data entry processes.

Components

chatlas: Chat.extract_data() method
pydantic: data model from BaseModel, with optional Field descriptions

Simple example

import chatlas as ctl
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

chat = ctl.ChatOpenAI()
chat.extract_data(
  "My name is Susan and I'm 13 years old",
  data_model=Person,
)

output:

{"name": "Susan", "age": 13}

Add descriptions

Field(): add a description to the model
Type hint with None: to allow optional

import chatlas as ctl
from pydantic import BaseModel, Field


class Person(BaseModel):
    """A person"""

    name: str = Field(description="Name")
    age: int = Field(description="Age, in years")
    hobbies: list[str] | None = Field(
        description="List of hobbies. Should be exclusive and brief."
    )

chat = ctl.ChatAnthropic() # changed to Anthropic
chat.extract_data(
  "My name is Susan and I'm 13 years old",
  data_model=Person,
)