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LOTUS: A Query Engine For Processing Data with LLMs

Colab Demo Arxiv Slack Documentation Status PyPI - Python Version PyPI GitHub license

LOTUS makes LLM-powered data processing fast and easy.

LOTUS (LLMs Over Tables of Unstructured and Structured Data) 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.

For trouble-shooting or feature requests, please raise an issue and we'll get to it promptly. To share feedback and applications you're working on, you can send us a message on our community slack, or send an email ([email protected]).

Installation

conda create -n lotus python=3.10 -y
conda activate lotus
pip install lotus-ai

Running on Mac

If you are running on mac, please install Faiss via conda:

CPU-only version

conda install -c pytorch faiss-cpu=1.8.0

GPU(+CPU) version

conda install -c pytorch -c nvidia faiss-gpu=1.8.0

For more details, see Installing FAISS via Conda.

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 parameterized 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 LM

# configure the LM, and remember to export your API key
lm = LM(model="gpt-4o-mini")
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} will help me learn {Skill}")
print(res)

# Print total LM usage
lm.print_total_usage()

Key Concept: The Semantic Operator Model

LOTUS' implements is the semantic operator programming model. Semantic operators as declarative transformations on one or more datasets, parameterized by a natural language expression, that can be implemented by a variety of AI-based algorithms. Semantic operators seamlessly extend the relational model, operating over tables that may contain traditional structured data as well as unstructured fields, such as free-form text. These composable, modular language- based operators allow you to write AI-based pipelines with high-level logic, leaving the rest of the work to the query engine! Each operator can be implemented and optimized in multiple ways, opening a rich space for execution plans, similar to relational operators. To learn more about the semantic operator model, read the full research paper.

LOTUS offers a number of semantic operators in a Pandas-like API, some of which are described below. To learn more about semantic operators provided in LOTUS, check out the full documentation, run the colab tutorial, or you can also refer to these examples.

Operator Description
sem_map Map each record using a natural language projection
sem_filter Keep records that match the natural language predicate
sem_extract Extract one or more attributes from each row
sem_agg Aggregate across all records (e.g. for summarization)
sem_topk Order the records by some natural langauge sorting criteria
sem_join Join two datasets based on a natural language predicate
sem_sim_join Join two DataFrames based on semantic similarity
sem_search Perform semantic search the over a text column

Supported Models

There are 3 main model classes in LOTUS:

  • LM: The language model class.
    • The LM class is built on top of the LiteLLM library, and supports any model that is supported by LiteLLM. See this page for examples of using models on OpenAI, Ollama, and vLLM. Any provider supported by LiteLLM should work. Check out litellm's documentation for more information.
  • RM: The retrieval model class.
    • Any model from SentenceTransformers can be used with the SentenceTransformersRM class, by passing the model name to the model parameter (see an example here). Additionally, LiteLLMRM can be used with any model supported by LiteLLM (see an example here).
  • Reranker: The reranker model class.
    • Any CrossEncoder from SentenceTransformers can be used with the CrossEncoderReranker class, by passing the model name to the model parameter (see an example here).

Feature Requests and Contributing

If you have a feature request, we're happy to hear from you! Please open an issue.

If you're interested in contributing, we'd be happy to coordinate on ongoing efforts! Please send an email to Liana ([email protected]) or reach out on our slack.

References

For recent updates related to LOTUS, follow @lianapatel_ on X.

If you find LOTUS or semantic operators useful, we'd appreciate if you can please cite this work as follows:

@misc{patel2024semanticoperators,
      title={Semantic Operators: A Declarative Model for Rich, AI-based Analytics Over Text Data},
      author={Liana Patel and Siddharth Jha and Parth Asawa and Melissa Pan and Carlos Guestrin and Matei Zaharia},
      year={2024},
      eprint={2407.11418},
      archivePrefix={arXiv},
      primaryClass={cs.DB},
      url={https://arxiv.org/abs/2407.11418},
}