Coupang’s current business model demands that it places the burden of quality assurance on its vendors. Although coupang has been able to leverage this to offer its users extremely fast and affordable delivery, the quality of such products could certainly be better.
We propose leveraging Coupang’s vast review data on product reviews in addition to recent AI technologies to implement a Vendor evaluation pipeline and tool.
- LLM model gpt-3.5-turbo
- SinglestoreDB vector database
- python3.8+
- Natural Language Processing (NLP)
- Responsive Design <Html, javascript, css>
In other to start you'd need to install the following packages.
- SinglestoreDB
- python3.8+
SingleStoreDB installation on Linux (Centos)
yum install -y yum-utils
sudo yum-config-manager --add-repo https://release.memsql.com/production/rpm/x86_64/repodata/memsql.repo
sudo yum install -y singlestore-client singlestoredb-toolbox singlestoredb-studio
sdb-deploy cluster-in-a-box --license <input-Licence-from-the-website> --password PASSW0RD
# Navigate into the project directory
cd text-summarization-LLM-RAG
# Install the dependencies
python -m pip install -r requirements.txt-
Review sentiment analysis This feature processes product reviews and determines the overall sentiment (positive, negative, or neutral) expressed by customers.
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LLM based on Review summarization This uses text to sql technique to query from the database and makes a summarization with RAG
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Vendor evaluation tool This tool aggregates the sentiment analysis and review summarisation across vendor reeviews to evaluate them