lotus-ai 0.1.3

lotus

LOTUS: An Engine For Semantic Queries

Easily build knowledge-intensive LLM applications that reason over your data with LOTUS!

LOTUS provides a declarative programming model and an optimized query engine for serving powerful reasoning-based query pipelines over structured and unstructured data! We provide a simple and intuitive Pandas-like API, that implements semantic operators to extend the relational model with a set of modular language-based operators. Programmers can easily compose semantic operators along with traditional data operations to build state-of-the-art AI systems that reason over vast knowledge corpora.

Below are just a few semantic operators provided by LOTUS. For more details, check out the full documentation documentation, or colab tutorial

Operator	Description
sem_map	Map each row of the dataframe using a natural language projection
sem_filter	Keep rows that match the natural language predicate
sem_agg	Performs a natural language aggregation across all rows of a column (e.g. for summarization)
sem_topk	Order the dataframe by some natural langauge sorting criteria
sem_join	Join two dataframes based on a natural language predicate
sem_index	Create a semantic similarity index over a text column
sem_search	Perform top-k search the over a text column

Installation

conda create -n lotus python=3.9 -y
conda activate lotus
pip install lotus-ai

Quickstart

If you're already familiar with Pandas, getting started will be a breeze! Below we provide a simple example program using the semantic join operator. The join, like many semantic operators, are specified by langex (natural language expressions), which the programmer uses to specify the operation. Each langex is paramterized by one or more table columns, denoted in brackets. The join's langex serves as a predicate and is parameterized by a right and left join key.

import pandas as pd
import lotus
from lotus.models import OpenAIModel

# configure the LM, and remember to export your API key
lm = OpenAIModel()
lotus.settings.configure(lm=lm)

# create dataframes with course names and skills
courses_data = {
    "Course Name": [
        "History of the Atlantic World",
        "Riemannian Geometry",
        "Operating Systems",
        "Food Science",
        "Compilers",
        "Intro to computer science",
    ]
}
skills_data = {"Skill": ["Math", "Computer Science"]}
courses_df = pd.DataFrame(courses_data)
skills_df = pd.DataFrame(skills_data)

# lotus sem join 
res = courses_df.sem_join(skills_df, "Taking {Course Name:left} will help me learn {Skill:right}")
print(res)

Citation

If you use LOTUS in a research paper, please cite this work as follows:

@misc{patel2024lotusenablingsemanticqueries,
      title={LOTUS: Enabling Semantic Queries with LLMs Over Tables of Unstructured and Structured Data},
      author={Liana Patel and Siddharth Jha and Carlos Guestrin and Matei Zaharia},
      year={2024},
      eprint={2407.11418},
      archivePrefix={arXiv},
      primaryClass={cs.DB},
      url={https://arxiv.org/abs/2407.11418},
}