- Return relevant info for a prompt using the OP stack
- Vector queryable datebase with semantic search
- Use the sentence transformer library to generate embeddings
- Use pinecone database library to store and search embeddings
- Insert top k responses from query as context for gpt4 model
- Extract relevant information using chat completion
- return top result and gpt response
- Fine-tune GPT davinci model on policy manuels
- Standard prompting to ask questions on fine-tuned models
- React native application
- Backend Flask server
- Integrate React and Flask
- Use render for web hosting
