Metadata-Version: 2.4
Name: lotus-ai
Version: 1.1.4
Summary: lotus
Author-email: Liana Patel <lianapat@stanford.edu>, Siddharth Jha <sidjha@berkeley.edu>
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# LOTUS: LLM-Powered Data Processing Made Fast, Easy, and Robust
<!--- BADGES: START --->
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[#slack]: https://join.slack.com/t/lotus-fnm8919/shared_invite/zt-319k232lx-nEcLF~5w274dcQLmw2Wqyg
<!--- BADGES: END --->

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 **L**LMs **O**ver **T**ext, **U**nstructured and **S**tructured Data, and it implements [**semantic operators**](https://arxiv.org/abs/2407.11418), which 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, 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 slack](https://join.slack.com/t/lotus-fnm8919/shared_invite/zt-319k232lx-nEcLF~5w274dcQLmw2Wqyg), or send an email (lianapat@stanford.edu).

# Installation
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
```


## 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](https://github.com/facebookresearch/faiss/blob/main/INSTALL.md#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.
```python
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-4.1-nano")
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()
```
### Tutorials

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.

<div align="center">

| Tutorial                                           | Difficulty                                                      | Colab Link                                                                                                                                                                                                    |
|----------------------------------------------------|-----------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1. Introduction to Semantic Operators and LOTUS             | ![](https://img.shields.io/badge/Level-Beginner-green.svg)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1mP65YHHdD6mnZmC5-Uqm2uCXJ4-Kbkhu?usp=sharing)              |
| 2. Failure Analysis Over Agent Traces                           | ![](https://img.shields.io/badge/Level-Intermediate-yellow.svg)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EJm9A8r_ShYxR0s218J70XhsopOgeT6k?usp=sharing)   |
| 3. System Prompt Analysis with LOTUS | ![](https://img.shields.io/badge/Level-Intermediate-yellow.svg)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NSVQYOMp2GCre5ZRgvgs6BPGOa20ySMc?usp=sharing) |
| 4. Processing Multimodal Datasets                             | ![](https://img.shields.io/badge/Level-Intermediate-yellow.svg) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/18oaa12T6PrhHIYGw-L01gw1bDmTYaE_e)   |
</div>

## Key Concept: The Semantic Operator Model
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](https://arxiv.org/abs/2407.11418).

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](https://lotus-ai.readthedocs.io/en/latest/), run the [colab tutorial](https://colab.research.google.com/drive/1mP65YHHdD6mnZmC5-Uqm2uCXJ4-Kbkhu?usp=sharing), or you can also refer to these [examples](https://github.com/TAG-Research/lotus/tree/main/examples/op_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](CONTRIBUTING.md) for examples of using models on `OpenAI`, `Ollama`, and `vLLM`. Any provider supported by `LiteLLM` should work. Check out [litellm's documentation](https://litellm.vercel.app) 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](examples/op_examples/dedup.py)). Additionally, `LiteLLMRM` can be used with any model supported by `LiteLLM` (see [an example here](examples/op_examples/sim_join.py)).
- `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](examples/op_examples/search.py)).

# Feature Requests and Contributing

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.

## Getting Started

Before contributing, please:

1. **Read our [Contributing Guide](CONTRIBUTING.md)** - 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 [Slack](https://join.slack.com/t/lotus-fnm8919/shared_invite/zt-319k232lx-nEcLF~5w274dcQLmw2Wqyg)


## Development Setup

For development setup and detailed contribution guidelines, see our [Contributing Guide](CONTRIBUTING.md).

## Community

- **Slack**: [Join our community](https://join.slack.com/t/lotus-fnm8919/shared_invite/zt-319k232lx-nEcLF~5w274dcQLmw2Wqyg) 
- **Email**: lianapat@stanford.edu
- **Discussions**: [GitHub Discussions](https://github.com/lotus-data/lotus/discussions)

We're excited to see what you build with LOTUS! 🚀

# References
For recent updates related to LOTUS, follow [@lianapatel_](https://x.com/lianapatel_) on X.

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