RAMKITCS/WizSearch

Your intelligent ally for effortless information retrieval and seamless browsing across documents and the web.

WizSearch 🌟

Your intelligent ally for effortless data retrieval across documents and seamless browsing the web.

How we built it 🛠️

We built Wiz Search using the following components:

• LLM: Snowflakes Arctic LLM for natural language understanding and generation.
• Embeddings: Snowflakes Arctic Embedding Model to enhance search relevance.
• Intelligent Search: Tavily for advanced search capabilities.
• Vector Databases: Qdrant for efficient data storage and retrieval.
• Observability: Langfuse for monitoring and observability.
• UI: Streamlit for creating an interactive and user-friendly interface.

We focused on creating a modular architecture without relying on LangChain, allowing for a more customizable and flexible design.

Run The Application ⚙️

1. Clone the repo

git clone https://github.com/SSK-14/WizSearch.git

2. Install required libraries

• Create virtual environment

pip3 install virtualenv
python3 -m venv {your-venvname}
source {your-venvname}/bin/activate

• Install required libraries

pip3 install -r requirements.txt

• Activate your virtual environment

source {your-venvname}/bin/activate

3. Set up your secrets.toml file

• Copy example.secrets.toml into secrets.toml and replace the keys

4. Running

streamlit run app.py 

Contributing 🤝

Contributions to this project are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request on the project's GitHub repository.

License 📝

This project is licensed under the MIT License. Feel free to use, modify, and distribute the code as per the terms of the license.