LOTUS is the framework that allows you to easily process your datasets, including unstructured and structured data, with LLMs. It provides an intuitive Pandas-like API, offers algorithms for optimizing your programs for up to 1000x speedups, and makes LLM-based data processing robust with accuracy guarantees with respect to high-quality reference algorithms.

LOTUS stands for LLMs Over Text, Unstructured and Structured Data, and it introduces semantic operators. Semantic operators extend the core philosophy of relational operators—designed for declarative and robust structured-data processing—to unstructured-data processing with AI. Semantic operators are expressive, allowing you to easily capture all of your data-intensive AI programs, from simple RAG, to document extraction, image classification, LLM-judge evals, unstructured data analysis, complex research-based synthesis and more.

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 discord, or send an email ([email protected]).

For the latest stable release:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create a new project or navigate to your existing project
uv add lotus-ai

For the latest features:

uv add git+https://github.com/lotus-data/lotus.git@main

For the latest stable release:

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

For the latest features, you can alternatively install as follows:

conda create -n lotus python=3.10 -y
conda activate lotus
pip install git+https://github.com/lotus-data/lotus.git@main

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

conda install -c pytorch faiss-cpu=1.8.0

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

If you're using uv, the faiss-cpu dependency will be handled automatically.

For more details, see Installing FAISS via Conda.

If you're already familiar with Pandas, getting started will be a breeze! Below we provide a simple example using sem_filter. Like all semantic operators, it is specified by a langex (natural language expression) parameterized by one or more column names in brackets — here {title}. The langex for a sem_filter is a NL predicate, which is a natural language expression that can be evaluated to a True/False value.

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

# Configure the LM — export your API key before running (e.g. OPENAI_API_KEY)
lm = LM(model="gpt-4.1-nano")
lotus.settings.configure(lm=lm)

# A sample of GitHub-style issue titles from an open source project
issues = pd.DataFrame({
    "title": [
        "Fix typo in README",
        "Add dark mode support to dashboard",
        "Refactor entire auth system to use OAuth2",
        "Update copyright year in LICENSE",
        "Implement distributed transaction support across microservices",
        "Change button color on settings page",
        "Migrate database from Postgres 13 to 16 with zero downtime",
        "Add missing comma in error message",
        "Build custom query planner to replace third-party dependency",
        "Bump lodash to fix known CVE",
        "Support multi-region active-active replication",
        "Remove unused import in utils.py",
    ]
})

# Use sem_filter to find issues approachable for a first-time contributor
good_first_issues = issues.sem_filter(
    "The {title} describes a small, self-contained task that a new open source contributor could tackle without deep knowledge of the codebase"
)

print("Good first issues for new contributors:\n")
print(good_first_issues.to_string(index=False))

# Uncomment to print the total LM usage
# lm.print_total_usage()

Below are some short tutorials in Google Colab, to help you get started. We recommend starting with [1] Introduction to Semantic Operators and LOTUS, which will provide a broad overview of useful functionality to help you get started.

Tutorial	Difficulty	Colab Link
1. Introduction to Semantic Operators and LOTUS
2. Failure Analysis Over Agent Traces
3. System Prompt Analysis with LOTUS
4. Processing Multimodal Datasets

LOTUS introduces the semantic operator programming model. Semantic operators are declarative transformations over 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 modular language-based operators allow you to write AI-based pipelines with high-level logic, leaving optimizations 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.

Semantic operators provide a unified API for both LLM-based primitives...

... and embedding-based primitives:

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).

We welcome contributions from the community! Whether you're reporting bugs, suggesting features, or contributing code, we have comprehensive templates and guidelines to help you get started.

Before contributing, please:

1. Read our Contributing Guide - Comprehensive guidelines for contributors
2. Check existing issues - Avoid duplicates by searching existing issues and pull requests
3. Join our community - Connect with us on Discord

• Discord: Join our community
• Email: [email protected]
• Discussions: GitHub Discussions

We're excited to see what you build with LOTUS! 🚀 If you would like to be listed as a user and have your project featured, please reach out to [email protected].

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:

@article{patel2025semanticoptimization,
    title = {Semantic Operators and Their Optimization: Enabling LLM-Based Data Processing with Accuracy Guarantees in LOTUS},
    author = {Patel, Liana and Jha, Siddharth and Pan, Melissa and Gupta, Harshit and Asawa, Parth and Guestrin, Carlos and Zaharia, Matei},
    year = {2025},
    journal = {Proc. VLDB Endow.},
    url = {https://doi.org/10.14778/3749646.3749685},
}
@article{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},
      url={https://arxiv.org/abs/2407.11418},
}