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https://github.com/Mayo-Radiology-Informatics-Lab/conflare
This is the repository for the CONFLARE (CONformal LArge language model REtrieval) Python package.
https://github.com/Mayo-Radiology-Informatics-Lab/conflare
Last synced: 3 months ago
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This is the repository for the CONFLARE (CONformal LArge language model REtrieval) Python package.
- Host: GitHub
- URL: https://github.com/Mayo-Radiology-Informatics-Lab/conflare
- Owner: Mayo-Radiology-Informatics-Lab
- License: mit
- Created: 2024-04-02T16:50:44.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-04-09T13:46:52.000Z (7 months ago)
- Last Synced: 2024-04-14T10:51:07.265Z (7 months ago)
- Language: Python
- Size: 1.11 MB
- Stars: 6
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CONFLARE: CONFormal LArge language model REtrieval
This is the official repo for the [CONFLARE paper](https://arxiv.org/abs/2404.04287) and the related python package: `conflare`.
## Installation
[![Downloads](https://static.pepy.tech/badge/conflare)](https://pepy.tech/project/conflare)```bash
pip install conflare
```Here are the 3 main tasks this package helps you with:
1. Loading the source documents (+ cleaning and chunking them)
2. Creating (or loading) a Calibration set
3. Retrieval Augmented Generation by applying conformal prediction## How to use
First, install the `conflare` package using `pip`. Then, use the following example as a starting point to use this package.Example:
```python
# 1
import os
os.environ['OPENAI_API_KEY'] = 'your openai secret key'
# to use HuggingFace models w/o needing an openai key, look at the arguments section below.import conflare
from conflare import initialize_pipeline
from conflare.conformal.calibration import create_calibration_records
from conflare.augmented_retrieval.rag import ConformalRetrievalQAdocument_dir = './data/documents'
docs, qa_pipeline, vector_db = initialize_pipeline(document_dir)# 2
calibration_records = create_calibration_records(
docs,
qa_pipeline=qa_pipeline,
vector_db=vector_db,
size=100,
topic_of_interest="Deep Learning"
)# 3
conformal_rag = ConformalRetrievalQA(
qa_pipeline=qa_pipeline,
vector_db=vector_db,
calibration_records=calibration_records,
error_rate=0.10,
verbose=True
)response, retrieved_docs = conformal_rag(
"How can a transformer model be used in detection of COVID?"
)
print(response)
```
```
>>>
Input Error Rate: 10.00%
Selected cosine distance thereshold: 0.456
Number of retrieved documents: 2A transformer model can be used in the detection of COVID-19 by analyzing medical images ...
```If you have run this script once before and saved the calibration records to disk, you can use the following to load the calibration records. We've provided example `.pkl` files of generated questions and calibration recordings in the `./data/calibration_set/` directory of this repo.
```python
from conflare.conformal.calibration import QuestionEvaluationq_evaluation = QuestionEvaluation.from_pickle(path_to_pickle)
calibration_records = q_evaluation.get_calibration_records()
```## Arguments
Here are some of the more important arguments that the functions and classes in this package use.
You can also take a look at the definition of `initialize_pipeline` function to see most of them.
Looking at the definition of `initialize_pipeline`, you can see the sequence of the functions called inside it and use them in your own custom way if neccessary.`model`: the model name used for QA and retreivals. If set to `gpt-*` models, it will use the OpenAI models and an OpenAI API Key will be required. It can also be set to models names on HuggingFace like `mistralai/Mistral-7B-Instruct-v0.1` to use HF models w/o needing a key.
`embedding_model`: the model from `sentence-transformers` library to be used to create embeddings for text chunks and user questions.
## Citation
If you use this code in your research, please cite the following paper:
```
@article{conflare,
title={CONFLARE: CONFormal LArge language model REtrieval},
author={Pouria Rouzrokh and Shahriar Faghani and Cooper U. Gamble and Moein Shariatnia and Bradley J. Erickson},
journal={arXiv preprint arXiv:2404.04287},
year={2024},
eprint={2404.04287},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```