The inspiration for this project came from the desire to create a smarter and more efficient search engine that can deliver more accurate and relevant results to users.
This AI-powered search engine uses natural language processing and machine learning algorithms to understand the user's query and provide personalized results based on their search history, preferences, and context.
This project was built using Python, Flask, and a variety of open-source libraries, including TensorFlow, Keras, NumPy, Pandas, and spaCy. The application uses Pinecone vector databases and OpenAI's GPT-3 API for semantic search and natural language processing.
One of the main challenges was integrating the Pinecone vector databases and OpenAI's GPT-3 API into the application and ensuring that they work seamlessly together. There were also challenges around optimizing the machine learning models and ensuring that the search engine is scalable and can handle a large volume of queries.
One of the biggest accomplishments of this project is the ability to deliver highly personalized and accurate search results to users. Another accomplishment is the integration of cutting-edge AI technologies into the application, which makes it more intelligent and efficient.
Through building this project, I learned a lot about natural language processing, machine learning algorithms, and how to integrate AI technologies into web applications. I also learned about the importance of data quality and the role it plays in the accuracy and relevance of search results.
Python
Flask
TensorFlow
Keras
NumPy
Pandas
spaCy
Pinecone vector databases
OpenAI's GPT-3 API
Create a virtual environment and activate it
python3 -m venv ai
source ai/bin/activate
Clone the repository
git clone /~https://github.com/digitalsimboja/boja-ai.git
cd boja-ai
Install the dependencies:
pip install -r requirements.txt
streamlit run src/app.py
Deployed app available at: https://digitalsimboja-boja-ai-srcapp-w4t22k.streamlit.app/