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 (lianapat@stanford.edu).
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, 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
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 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()LOTUS implements 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 |
There are 3 main model classes in LOTUS:
LM: The language model class.- The
LMclass is built on top of theLiteLLMlibrary, and supports any model that is supported byLiteLLM. See this page for examples of using models onOpenAI,Ollama, andvLLM. Any provider supported byLiteLLMshould work. Check out litellm's documentation for more information.
- The
RM: The retrieval model class.- Any model from
SentenceTransformerscan be used with theSentenceTransformersRMclass, by passing the model name to themodelparameter (see an example here). Additionally,LiteLLMRMcan be used with any model supported byLiteLLM(see an example here).
- Any model from
Reranker: The reranker model class.- Any
CrossEncoderfromSentenceTransformerscan be used with theCrossEncoderRerankerclass, by passing the model name to themodelparameter (see an example here).
- Any
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 (lianapat@stanford.edu) or reach out on our slack.
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},
}