dpq: data. prompt. query.

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

installation

bash pip install dpq

quick start

```python import dpq

Initialize dpq agent with API configuration

dpqagent = dpq.Agent( url="ENDPOINTURL", apikey="YOURAPIKEY", model="MODELID", custommessagespath="OPTIONALPATHTOCUSTOMPROMPTS" )

Apply prompt to each item in list-like iterable such as pandas series

dpqagent.classifysentiment(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.

```python

Define messages

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

Add new function

dpqagent.returncountry = dpqagent.generatefunction(messages)

Apply to a list

dpqagent.returncountry(["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.

python 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)).

debugging

dpq logs detailed error information to help with debugging. You can view these logs by simply inspecting the errors variable of the class.