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Weaviate: artificial intelligence features and setup
A vector database for AI applications, with an emphasis on reducing hallucinations and avoiding vendor lock-in.
Tool by the Otomatzia editorial team · source content checked on
Overview
Weaviate is used when an AI system needs a vector database that can be self-hosted or used as a service, with models and a data schema connected to it. It searches by similarity and filters by fields so the assistant gets relevant context, not just the closest matching chunk. Its stated focus is reducing hallucinations and avoiding vendor lock-in. Before production, check backups, permissions, and separation between customer databases.
Features
- A vector database for AI applications with a data schema
- Similarity search alongside field filtering
- Deployment as a managed service or a self-maintained installation
Business uses
- An internal knowledge database for a service assistant with department-based filtering
- Retrieving similar contract clauses before an initial legal response
Getting started
- Set up a test collection with a few documents that contain no identifying information.
- Run a known query and check which fields are returned with the chunk.
- Test a query without a source and check what the system returns in that case.
What to check
- Similarity search does not replace source citations and human review in sensitive cases.
- Check the backup process and customer separation before moving to production.
Sources to check next
Vendor features and prices change. Confirm details on the linked official pages before you rely on them.