dpq 0.1.3

dpq is an open-source python library that makes prompt-based data processing and feature engineering easy.

dpq: data. prompt. query.

dpq is a Python library that makes it easy to process data and engineer features using generative AI.

quick start

import dpq

# Initialize dpq agent with API configuration
dpq_agent = dpq.Agent(
    url="ENDPOINT_URL",
    api_key="YOUR_API_KEY",
    model="MODEL_ID",
    custom_messages_path="OPTIONAL_PATH_TO_CUSTOM_PROMPTS"
)

# Apply prompt to each item in list-like iterable such as pandas series
dpq_agent.classify_sentiment(df['some_column'])

adding functionalities

A function is defined by a JSON holding messages.

[
    {
        "role": "system",
        "content": "You are a sentiment classifier. You classify statements as having
         either a positive or negative sentiment. You return only one of two words:
         positive, negative."
    },
    {
        "role": "user",
        "content": "I like dpq. It makes prompt-based feature engineering a breeze."
    },
    {
        "role": "assistant",
        "content": "positive"
    }
]

To add a new function, simply add the JSON file to a prompts folder on your system and initialize the dpq agent with the respective custom_messages_path pointing to the folder. The function name is automatically set to the name of the JSON file.

Alternatively, you can pass the messages to generate a new function directly in your code.

# Define messages
messages = [
    {
        "role": "system",
        "content": "You return the country of a city."
    },
    {
        "role": "user",
        "content": "Berlin"
    },
    {
        "role": "assistant",
        "content": "Germany"
    },
]

# Add new function
dpq_agent.return_country = dpq_agent.generate_function(messages)

# Apply to a list
dpq_agent.return_country(["Berlin", "London", "Paris"])

examples

In addition to the prompts in the prompts directory, which are loaded by default when initializing the dpq.Agent(), we maintain a library of additional examples in the examples directory. These are typically slightly less general-purpose. Feel free to open a pull request and share prompts you have found useful with everyone!

features

• feature engineering using prompts
• library of standard functions
• parallelized by default

compatibility

dpq uses the requests library to send OpenAI-style Chat Completions API requests. For GPT-3.5 Turbo, the configuration is as follows.

dpq_agent = dpq.Agent(
    url="https://api.openai.com/v1/chat/completions",
    api_key="YOUR_API_KEY",
    model="gpt-3.5-turbo",
)

costs and speed

dpq currently comes as is without cost or speed guarantees. To still give a very rough estimate: on a test data set of 1000 product reviews, the classify_sentiment.json finishes in approx. 30 seconds (parallelized) on a standard Macbook and costs $0.05 using gpt-3.5-turbo.

is using LLMs a good idea?

Recent studies have shown promising results using general-purpose LLMs for text annotation and classification. For example, Gilardi, Alizadeh, and Kubli (2023) and Törnberg (2023) report better-than-human performance. This is an active research area and we are looking forward to seeing more results in this field. In general, we believe that LLMs can deliver consistent, high-quality output resulting in scalability, reduced time and costs (see also Aguda (2024)).