sentrev 0.0.0.post2

SenTrEv - Simple customizable evaluation for RAG performance of Sentence Transformers models on PDFs

SenTrEv

Simple evaluation for RAG on your PDFs

SenTrEv (Sentence Transformers Evaluator) is a python package that is aimed at running simple evaluation tests to help you choose the best embedding model for Retrieval Augmented Generation (RAG) with your PDF documents.

Applicability

SenTrEv works with:

• Text encoders/embedders loaded through the class SentenceTransformer in the python package sentence_transformers
• PDF documents (single and multiple uploads supported)
• Qdrant vector databases (both local and on cloud)

Installation

You can install the package using pip (easier but no customization):

python3 -m pip install sentrev

Or you can build it from the source code (more difficult but customizable):

# clone the repo
git clone https://github.com/AstraBert/SenTrEv.git
# access the repo
cd SenTrEv
# build the package
python3 -m build
# install the package locally with editability settings
python3 -m pip install -e .

Evaluation process

The evaluation process is simple:

• The PDFs are loaded and chunked (the size of the chunks is customizable, but default is 1000)
• Each chunk is then vectorized and uploaded to a Qdrant collection
• For each chunk, a percentage of the text is extracted (the percentage is customizable, but default is 25%) and is mapped to it's original chunk.
• Each piece of reduced chunk is then vectorized and semantic search with cosine distance (customizable) is performed inside the collection
• We evaluate the retrieval success rate (a reduced chunk is correctly linked to the original one) by correct/total retrieval attempts.
• We evaluate the retrieval average time and calculate the standard deviation for it
• Everything is reported into a CSV and can optionally be displayed with bar plots

Use cases

1. Local Qdrant

You can easily run Qdrant locally with Docker:

docker pull qdrant/Qdrant:latest
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest

Now your vector database is listening at http://localhost:6333

Let's say we have three PDFs (~/pdfs/instructions.pdf, ~/pdfs/history.pdf, ~/pdfs/info.pdf ) and we want to test retrieval with three different encoders sentence-transformers/all-MiniLM-L6-v2 , sentence-transformers/sentence-t5-base, sentence-transformers/all-mpnet-base-v2.

We can do it with this very simple code:

from sentrev.evaluator import evaluate_rag
from sentence_transformers import Sentence transformer
from qdrant_client import QdrantClient

# load all the embedding moedels
encoder1 = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
encoder2 = SentenceTransformer('sentence-transformers/sentence-t5-base')
encoder3 = SentenceTransformer('sentence-transformers/all-mpnet-base-v1')

# create a list of the embedders and a dictionary that map each one with its name for the stats report which will be output by SenTrEv
encoders = [encoder1, encoder2, encoder3]
encoder_to_names = {encoder1: 'all-MiniLM-L6-v2', encoder2: 'sentence-t5-base', encoder3: 'all-mpnet-base-v1'}

# set up a Qdrant client
client = QdrantClient("http://localhost:6333")

# create a list of your PDF paths
pdfs = ['~/pdfs/instructions.pdf', '~/pdfs/history.pdf', '~/pdfs/info.pdf']

# Choose a path for the CSV where the evaluation stats will be saved

csv_path = '~/eval/stats.csv'

# evaluate retrieval
evaluate_rag(pdfs=pdfs, encoders=encoders, client=client, csv_path=csv_path)

You can play around with the chunking of your PDF by setting the chunking_size argument or with the percentage of text used to test retrieval by setting text_percentage, or with the distance metric used for retrieval by setting the distance argument; you can also pass plot=True if you want also plots for the evaluation: plots will be saved under the same folder of the CSV file.

2. On-cloud Qdrant

You can also exploit Qdrant on-cloud database solutions (more about it here). You just need your Qdrant cluster URL and the API key to access it:

from qdrant_client import QdrantClient

client = QdrantClient(url="YOUR-QDRANT-URL", api_key="YOUR-API-KEY")

This is the only change you have to make to the code provided in the example before.

3. Upload PDFs to Qdrant

You can use SenTrEv also to chunk, vectorize and upload your PDFs to a Qdrant database.

from sentrev.evaluator import upload_pdfs

encoder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
pdfs = ['~/pdfs/instructions.pdf', '~/pdfs/history.pdf', '~/pdfs/info.pdf']
client = QdrantClient("http://localhost:6333")

upload_pdfs(pdfs=pdfs, encoder=encoder, client=client)

As for before, you can also play around with the chunking_size argument (default is 1000) and with the distance argument (default is cosine).

4. Implement semantic search on a Qdrant collection

You can also search already-existent collections in a Qdrant database with SenTrEv:

from sentrev.utils import NeuralSearcher

encoder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
collection_name = 'customer_help'
client = QdrantClient("http://localhost:6333")

searcher = NeuralSearcher(client=client, model=encoder, collection_name=collection_name)
res = searcher.search("Is it possible to pay online with my credit card?", limit=5)

The results will be returned as a list of payloads (the metadata you uploaded to the Qdrant collection along with the vector points).

If you used SenTrEv upload_pdfs function, you should be able to access the results in this way:

text = res[0]["text"]
source = res[0]["source"]
page = res[0]["page"]

Reference

Find a reference for all the functions and classes here

Contributing

Contributions are always welcome!

Find contribution guidelines at CONTRIBUTING.md

License, Citation and Funding

This project is open-source and is provided under an MIT License.

If you used SenTrEv to evaluate your retrieval models, please consider citing it:

If you found it useful, please consider funding it .