A full-stack application that collects relevant information from search APIs (e.g., PubMed), creates latent embedding vector using embedding models, and provides a user-friendly interface for users to search and explore the information, then create readable report utilizing LLMs.
- Extract expert knowledge and store it in a database.
- Retrieve expert knowlege efficiently.
- Generate a reliable summary of the expert knowledge.
- Generate a readable marketing brochure out of it.
- Provide a user-friendly interface for users to search and explore the information.
./services
./backend
./frontend
./tfconfig
./scripts
make env-ui
make env-api
A full-stack application with frontend and backend services.
- Svelte
- User interface to get input from users and display search results.
- FastAPI + Docker
- Collects information from search APIs (e.g., PubMed).
- Creates index using Llama-index and embedding models.
- RAG (Retrieval Augmented Generation) approach and llms (e.g., Llama2-7B) to generate expert-knowledge include summary.
- Provides a user-friendly interface for users to search and explore the information.
- Creates readable report out of it.
- Azure Static Web Apps
- Azure App Service
- Azure MongoDB (vector database)
- Azure ML studio (Language model and embedding model)
- Azure Container Registry
make run-ui
make run-api
You might need to set the following environment variable to avoid the error message OMP: Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized.
:
export KMP_DUPLICATE_LIB_OK=TRUE