Is vector database a good solution for developing a chatbot that helps users finding the right healthcare insurance plan? Where there are 1000s of plans and each plan has more than 100 attributes.
Hi @mir.majeed – your use case (healthcare chatbot) is a pretty good fit.
You’re dealing here with structured data (insurance plans with 100+ attributes) that has semantic meaning.
It’s feasible that your patients may know in their head what they’re seeking, but lack knowledge of industry specific terms or jargon, especially when it comes to medical coding. A vector database can help patients find relevant plans even when they’re asking questions in natural language.
(Your Guide to Vectorizing Structured Text | Pinecone).
The advantages:
- You can add metadata to your vectors for all those plan attributes, which lets you filter results efficiently. So when someone asks about specific plan features, you can quickly narrow down the options.
- It’s fast. Vector databases are built for real-time applications like chatbots. Users won’t be waiting a long time for responses.
- The semantic search capabilities are really powerful - they can understand the meaning behind user questions and match them to relevant plans, even if the exact words don’t match.
You can also have the best of both worlds by combining semantic search with keyword search (called hybrid search
) to get even better results