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

Connects to large language models via the Ollama server.

• Setup Ollama
• 🔗 Demo

We built Wiz Search using the following components:

• LLM: Open source models like llama3, mistral, LLaVA, etc using Ollama for natural language understanding and generation.
• Embeddings: BAAI/bge-small-en-v1.5 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.

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 

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.

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