conflare 0.1.3

conformal retreival augmented generation with LLMs

CONFLARE: CONFormal LArge language model REtrieval

This is the repo for the CONFLARE paper and the related python package conflare.

Installation

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

Usage

Example:

# 1
import os
os.environ['OPENAI_API_KEY'] = 'your openai secret key'
# to use HuggingFace models w/o needing an openai key, look below.

import conflare
from conflare import initialize_pipeline
from conflare.conformal.calibration import create_calibration_records
from conflare.augmented_retrieval.rag import ConformalRetrievalQA

document_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: 2

A 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:

from conflare.conformal.calibration import QuestionEvaluation

q_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.