DataLLM: prompt LLMs for Tabular Data 🔮
Welcome to DataLLM, your go-to open-source platform for Tabular Data Generation!
DataLLM allows you to efficiently tap into the vast power of LLMs to...
- create mock data that fits your needs, as well as
- enrich datasets with world knowledge.
MOSTLY AI is hosting a rate-limited DataLLM server instance at data.mostly.ai, with its default model being a fine-tuned Mistral-7b.
Note: For use cases, that relate to personal data, it is advised to enrich synthetic rather than real data. For one, to protect the privacy of the individuals when transmitting data. And for two, to preempt ethical concerns regarding the application of LLMs on personal data. This principle is referred to as Synthetic Data Augmented Generation (SAG), which taps into the knowledge of LLMs but grounded in a representative version of your data assets. MOSTLY AI is hosting a top-in-class Synthetic Data platform at https://app.mostly.ai, which allows you to easily transform your existing customer data into synthetic data.
Start using DataLLM
- Sign in and retrieve your API key here.
- Install the latest version of the DataLLM Python client.
pip install -U datallm
- Instantiate a client with your retrieved API key.
from datallm import DataLLM
datallm = DataLLM(api_key='INSERT_API_KEY', base_url='https://data.mostly.ai')
- Enrich an existing dataset with new columns, that are coherent with any of the already present columns.
import pandas as pd
df = pd.DataFrame({
"age in years": [5, 10, 13, 19, 30, 40, 50, 60, 70, 80],
"gender": ["m", "f", "m", "f", "m", "f", "m", "f", "m", "f"],
"country code": ["AT", "DE", "FR", "IT", "ES", "PT", "GR", "UK", "SE", "FI"],
})
# enrich the DataFrame with a new column containing the official country name
df["country"] = datallm.enrich(df, prompt="official name of the country")
# enrich the DataFrame with first name and last name
df["first name"] = datallm.enrich(df, prompt="the first name of that person")
df["last name"] = datallm.enrich(df, prompt="the last name of that person")
# enrich the DataFrame with a categorical
df["age group"] = datallm.enrich(
df, prompt="age group", categories=["kid", "teen", "adult", "elderly"]
)
# enrich with a boolean value and a integer value
df["speaks german"] = datallm.enrich(df, prompt="speaks german?", dtype="boolean")
df["body height"] = datallm.enrich(df, prompt="the body height in cm", dtype="integer")
print(df)
# age in years gender country code country first name last name age group speaks german body height
# 0 5 m AT Austria Julian Kittner kid True 106
# 1 10 f DE Germany Julia Buchner teen True 156
# 2 13 m FR France Benjamin Dumoulin teen False 174
# 3 19 f IT Italy Alessia Santamaria teen False 163
# 4 30 m ES Spain Paco Ruiz adult False 185
# 5 40 f PT Portugal Elisa Santos adult False 168
# 6 50 m GR Greece Dimitris Kleopas adult False 166
# 7 60 f UK United Kingdom Diane Huntley elderly False 162
# 8 70 m SE Sweden Stig Nordstrom elderly False 174
# 9 80 f FI Finland Aili Juhola elderly False 157
- Or create a completely new dataset from scratch.
df = datallm.mock(
n=100, # number of generated records
data_description="Guests of an Alpine ski hotel in Austria",
columns={
"full name": {"prompt": "first name and last name of the guest"},
"nationality": {"prompt": "the 2-letter code for the nationality"},
"date_of_birth": {"prompt": "the date of birth of that guest", "dtype": "date"},
"gender": {"categories": ["male", "female", "non-binary", "n/a"]},
"beds": {"prompt": "the number of beds within the hotel room; min: 2", "dtype": "integer"},
"email": {"prompt": "the customers email address", "regex": "([a-z|0-9|\\.]+)(@foo\\.bar)"},
},
temperature=0.7
)
print(df)
# full name nationality date_of_birth gender beds email
# 0 Melinda Baxter US 1986-07-09 female 2 [email protected]
# 1 Andy Rouse GB 1941-03-14 male 4 [email protected]
# 2 Andreas Kainz AT 2001-01-10 male 2 [email protected]
# 3 Lisa Nowak AT 1994-01-02 female 2 [email protected]
# .. ... ... ... ... ... ...
# 96 Mike Peterson US 1997-04-28 male 2 [email protected]
# 97 Susanne Hintze DE 1987-04-12 female 2 [email protected]
# 98 Ernst Wisniewski AT 1992-04-03 male 2 [email protected]
# 99 Tobias Schmitt AT 1987-06-24 male 2 [email protected]
Key Features
- Efficient Tabular Data Generation: Easily prompt LLMs for structured data at scale.
- Contextual Generation: Each data row is sampled independently, and considers the prompt, the existing row values, as well as the dataset descriptions as context.
- Data Type Adherence: Supported data types are
string,categorical,integer,floats,boolean,date, anddatetime. - Regular Expression Support: Further constrain the range of allowed values with regular expressions.
- Flexible Sampling Parameters: Tailor the diversity and realism of your generated data via
temperatureandtop_p. - Esay-to-use Python Client: Use
datallm.mock()anddatallm.enrich()directly from any Python environment. - Multi-model Support: Optionally host multiple models to cater for different speed / knowledge requirements of your users.
Use Case Examples
Mock PII fields
import pandas as pd
df = pd.read_csv('https://github.com/mostly-ai/public-demo-data/raw/dev/census/census.csv.gz', nrows=10)
df = df[['race', 'sex', 'native_country']]
df['mock name'] = datallm.enrich(df, prompt='full name, consisting of first name, last name but without any titles')
df['mock email'] = datallm.enrich(df, prompt='email')
df['mock SSN'] = datallm.enrich(df, prompt='social security number', regex='\\d{3}-\\d{2}-\\d{4}')
print(df)
# race sex native_country mock name mock email mock SSN
# 0 White Male United-States James Ridgway [email protected] 393-36-5291
# 1 White Male United-States Jacob Lopez [email protected] 467-64-7848
# 2 White Male United-States Robert Jansen [email protected] 963-13-6498
# 3 Black Male United-States Darnell Dixon [email protected] 125-59-9615
# 4 Black Female Cuba Alexis Ramirez [email protected] 881-46-9037
# 5 White Female United-States Kristen Miller [email protected] 098-69-6224
# 6 Black Female Jamaica Coleen Williams [email protected] 980-26-3724
# 7 White Male United-States Jay Stephenson [email protected] 464-05-4106
# 8 White Female United-States Lois Rodriguez [email protected] 332-10-6400
# 9 White Male United-States Eddie Watson [email protected] 645-47-1545
Summarize data records
import pandas as pd
df = pd.read_csv('https://github.com/mostly-ai/public-demo-data/raw/dev/census/census.csv.gz', nrows=10)
df['summary'] = datallm.enrich(df, prompt='summarize the data record in a single sentence')
print(df[['summary']])
# summary
# 0 Never married male employee
# 1 White male from United States, 50 years old, works as an executive
# 2 White male who is divorced and working as a Handlers-cleaners with
# 3 Male from United-States, aged 53, works as Handlers
# 4 Black female from Cuba with Bachelors degree who works as Prof-specialty
# 5 White married female with masters degree who works as exec-managerial
# 6 Jamaican immigrant who works in other-service and makes 18
# 7 52 year old US born male married with high school education working as an executive
# 8 Professional with a masters degree working 50 hours a week
# 9 White male from United-States with Bachelors degree who works as Exec
Augment your data
import pandas as pd
df = pd.DataFrame({'movie title': [
'A Fistful of Dollars', 'American Wedding', 'Ice Age', 'Liar Liar',
'March of the Penguins', 'Curly Sue', 'Braveheart', 'Bruce Almighty'
]})
df['genre'] = datallm.enrich(
df,
prompt='what is the genre of that movie?',
categories = ["action", "comedy", "drama", "horror", "sci-fi", "fantasy", "thriller", "documentary", "animation"],
temperature=0.0
)
print(df)
# movie title genre
# 0 A Fistful of Dollars action
# 1 American Wedding comedy
# 2 Ice Age animation
# 3 Liar Liar comedy
# 4 March of the Penguins documentary
# 5 Curly Sue comedy
# 6 Braveheart drama
# 7 Bruce Almighty comedy
Label your data
import pandas as pd
df = pd.read_csv('https://github.com/mostly-ai/public-demo-data/raw/dev/tweets/TheSocialDilemma.csv.gz', nrows=10)[['text']]
df['DataLLM sentiment'] = datallm.enrich(
df[['text']],
prompt='tweet sentiment',
categories=['Positive', 'Neutral', 'Negative'],
temperature=0.0,
)
print(df)
# text DataLLM sentiment
# 0 @musicmadmarc @SocialDilemma_ @netflix @Facebo... Positive
# 1 @musicmadmarc @SocialDilemma_ @netflix @Facebo... Neutral
# 2 Go watch “The Social Dilemma” on Netflix!\n\nI... Positive
# 3 I watched #TheSocialDilemma last night. I’m sc... Negative
# 4 The problem of me being on my phone most the t... Neutral
# 5 #TheSocialDilemma 😳 wow!! We need regulations ... Positive
# 6 @harari_yuval what do you think about #TheSoci... Positive
# 7 Erm #TheSocialDilemma makes me want to go off ... Negative
# 8 #TheSocialDilemma is not a documentary, it's h... Negative
# 9 Okay i’m watching #TheSocialDilemma now. Neutral
Data Harmonization
# let's assume we have an open-text column on gender
import pandas as pd
df = pd.DataFrame({'gender original': ['MAle', 'masculin', 'w', '♂️', 'M', 'Female', 'W', 'woman', '♀️', '👩']})
# we harmonize the data by mapping these onto fewer categories
df['gender unified'] = datallm.enrich(df, prompt='gender', categories=['male', 'female'], temperature=0.0)
print(df)
# gender original gender unified
# 0 MAle male
# 1 masculin male
# 2 w female
# 3 ♂️ male
# 4 M male
# 5 Female female
# 6 W female
# 7 woman female
# 8 ♀️ female
# 9 👩 female
Identify LLM biases
# construct a test data frame with 500 male and 500 female applicants
import pandas as pd
df = pd.DataFrame({'gender': ['Male', 'Female'] * 500})
# probe the LLM for a new attribute
df['success'] = datallm.enrich(
df,
prompt='Will this person be successful as a manager?',
dtype='boolean',
temperature=1.0,
)
# calculate whether we see systematic differences in answers given the gender
df.groupby('gender')['success'].mean()
# gender
# Female 0.552
# Male 0.598
In above example, the underlying LLM model is apparently biased towards believing that men are more likely to be successful as a manager.
Talk to synthetic customers
import pandas as pd
df = pd.read_csv('https://github.com/mostly-ai/public-demo-data/raw/dev/census/census.csv.gz', nrows=10)
df = df[['age', 'race', 'sex', 'income', 'occupation', 'education']]
df['shoe brand preference'] = datallm.enrich(
df,
prompt='Do you prefer Nike or Adidas shoes?',
categories=['Nike', 'Adidas'],
)
df['show brand why'] = datallm.enrich(
df,
prompt='Why do you prefer that show brand? Answer in your own voice.',
)
print(df)
# age race sex income occupation education shoe brand preference show brand why
# 0 39 White Male <=50K Adm-clerical Bachelors Nike Some of my friends wear Nike shoes.
# 1 50 White Male <=50K Exec-managerial Bachelors Nike I like the brand.
# 2 38 White Male <=50K Handlers-cleaners HS-grad Adidas They're super comfortable and they've never gi...
# 3 53 Black Male <=50K Handlers-cleaners 11th Nike I like the shoes they make.
# 4 28 Black Female <=50K Prof-specialty Bachelors Nike I like the way they look and feel.
# 5 37 White Female <=50K Exec-managerial Masters Adidas They make very comfortable shoes and I find th...
# 6 49 Black Female <=50K Other-service 9th Nike I dont
# 7 52 White Male >50K Exec-managerial HS-grad Adidas They make the best shoes in the world.
# 8 31 White Female >50K Prof-specialty Masters Nike I love the fit and style of their shoes. They ...
# 9 42 White Male >50K Exec-managerial Bachelors Nike I like how it looks on my feet.
Note: For this to work well, it is advised to use a powerful, yet well-balanced underlying LLM model.
More Use Cases
This is just the beginning. We are curious to learn more about your use cases and how DataLLM can help you.
Architecture
DataLLM is leveraging fine-tuned foundational models. These are served via vLLM to a Python-based server instance, exposing its service as a REST API. The Python client is a wrapper around this API, making it easy to interact with the service.
These are the core components, with all of these being open-sourced and available on GitHub:
- Server Component
datallm-server: Exposes the REST API for the service. - Engine Component
datallm-engineRuns on top of vLLM and handles the actual prompts. - Python Client
datallm-client: A python wrapper for the interacting with the service. - Utility Scripts
datallm-utils: A set of utility scripts for fine-tuning new DataLLM models.
A fine-tuned model, as well as its corresponding instruction dataset, can be found on HuggingFace.
Python API docs
datallm.enrich(data, prompt, ...)
Creates a new pd.Series given the context of a pd.DataFrame. This allows to easily enrich a DataFrame with new values generated by DataLLM.
datallm.enrich(
data: Union[pd.DataFrame, pd.Series],
prompt: str,
data_description: Optional[str] = None,
dtype: Union[str, DtypeEnum] = None,
regex: Optional[str] = None,
categories: Optional[list[str]] = None,
max_tokens: Optional[int] = 16,
temperature: Optional[float] = 0.7,
top_p: Optional[float] = 1.0,
model: Optional[str] = None,
progress_bar: bool = True,
) -> pd.Series:
- data. The existing values used as context for the newly generated values. The returned values will be of same length and in the same order as the provided list of values.
- prompt. The prompt for generating the returned values.
- data_description. Additional information regarding the context of the provided values.
- dtype. The dtype of the returned values. One of
string,category,integer,float,boolean,dateordatetime. - regex. A regex used to limit the generated values.
- categories. The allowed values to be sampled from. If provided, then the dtype is set to
category. - max_tokens. The maximum number of tokens to generate. Only applicable for string dtype.
- temperature. The temperature used for sampling.
- top_p. The top_p used for nucleus sampling.
- model. The model used for generating new values. Check available models with
datallm.models(). The default model is the first model in that list. - progress_bar. Whether to show a progress bar.
datallm.mock(n, columns, ...)
Create a pd.DataFrame from scratch using DataLLM. This will create one column after the other for as many rows as requested. Note, that rows are sampled independently of each other, and thus may contain duplicates.
datallm.mock(
n: int,
data_description: Optional[str] = None,
columns: Union[List[str], Dict[str, Any]] = None,
temperature: Optional[float] = 0.7,
top_p: Optional[float] = 1.0,
model: Optional[str] = None,
progress_bar: bool = True,
) -> pd.DataFrame:
- n. The number of generated rows.
- data_description. Additional information regarding the context of the provided values.
- columns. Either a list of column names. Or a dict, with column names as keys, and sampling parameters as values. These may contain
prompt,dtype,regex,categories,max_tokens,temperature,top_p. - temperature. The temperature used for sampling. Can be overridden at column level.
- top_p. The top_p used for nucleus sampling. Can be overridden at column level.
- model. The model used for generating new values. Check available models with
datallm.models(). The default model is the first model in that list. - progress_bar. Whether to show a progress bar.
Contribute
We're committed to making DataLLM better every day. Your feedback and contributions are not just welcome—they're essential. Join our community and help shape the future of tabular data generation!
About MOSTLY AI
MOSTLY AI is pioneer and leader for GenAI for Tabular Data. Our mission is to enable organizations to unlock the full potential of their data while preserving privacy and compliance. We are a team of data scientists, engineers, and privacy experts, dedicated to making data and thus information more accessible. We are proud to be a trusted partner for leading organizations across industries and geographies.
If you like DataLLM, then also check out app.mostly.ai for our Synthetic Data Platform, which allows you to easily train a Generative AI on top of your own original data. These models can then be used to generate synthetic data at any volume, that is statistically similar to the original, yet is free of any personal information.