An AI-powered hybrid search engine combining Keyword Search, Vector Similarity Search, and LLM-based contextual answers using Retrieval Augmented Generation (RAG) via AI21, OpenAI or any other LLM.
QueryBaseAI lets users create organisations, upload documents, automatically chunk & index them using Elasticsearch and Milvus, and ask natural language queries. The system generates smart, contextual answers using RAG powered by AI21 or OpenAI (configurable).
| Layer | Stack |
|---|---|
| Backend | Django, Django REST Framework (DRF) |
| LLMs | AI21 & OpenAI (via user config) |
| Vector Store | Milvus |
| Text Search | Elasticsearch |
| Logging | Logstash, Kibana (ELK Stack) |
| Deployment | Docker, Docker Compose |
- 📄 Upload multi-format docs (PDF, DOCX, TXT, MD)
- 🔗 Chunking, indexing & hybrid retrieval (keyword + vector)
- 🧠 Answer generation using RAG with OpenAI or AI21
- 📦 Dockerized microservices (Milvus, Elastic, Kibana)
- 📊 Real-time centralized logging (ELK stack)
QueryBaseAI/
├── Backend/ # Django logic (views, serializers, APIs)
├── Core/ # Core database logics
├── LLM/ # LLM providers (OpenAI & AI21 logic)
├── ExternalTools/ # Elastic, Milvus connectors
├── .env # Environment variables
└── README.md # Project documentation
- Clone the repository from GitHub to your local machine
git clone https://github.com/GoodGuyAdy/QueryBaseAI.git- Change the current directory to the cloned project folder
cd QueryBaseAI- Install the Python dependencies listed in the requirements.txt file
pip install -r requirements.txt- Build and starts the Docker containers defined in the docker-compose.yml file
docker-compose up --build- Run the Django development server for the project
python manage.py runserver- Ensure that your .env file contains a valid OPENAI_API_KEY or AI21_API_KEY.
- Make sure you have an active internet connection.