Weaviate Invalid Data Type
The data type specified for a property is not supported.
Stuck? Let AI directly find root cause
AI that integrates with your stack & debugs automatically | Runs locally and privately
What is Weaviate Invalid Data Type
Understanding Weaviate: A Brief Overview
Weaviate is an open-source vector search engine that allows developers to build applications with semantic search capabilities. It is designed to handle unstructured data and offers features like data indexing, vectorization, and contextual search. Weaviate is particularly useful for applications that require natural language processing and machine learning integration.
Identifying the Symptom: Invalid Data Type
When working with Weaviate, you might encounter an error message indicating an 'Invalid Data Type'. This typically occurs when you attempt to define a property in your schema with a data type that Weaviate does not support. The error message might look something like this:
{ "error": [ { "message": "Invalid data type specified for property 'exampleProperty'." } ]}
Exploring the Issue: Unsupported Data Types
Weaviate supports a specific set of data types for properties within its schema. Commonly supported data types include string, int, float, boolean, and date. If you attempt to use a data type outside of this list, Weaviate will not recognize it, leading to the 'Invalid Data Type' error.
Common Mistakes
Developers often encounter this issue when they mistakenly use complex or custom data types that are not natively supported by Weaviate. For example, using a data type like array or object directly in the schema without proper configuration can trigger this error.
Steps to Fix the Issue: Correcting Data Types
To resolve the 'Invalid Data Type' error, follow these steps:
Step 1: Review the Schema
First, review your schema to identify the property with the unsupported data type. You can do this by accessing your schema configuration in Weaviate's dashboard or using the API.
GET /v1/schema
Step 2: Update the Data Type
Once you have identified the incorrect data type, update it to a supported type. For example, if you mistakenly used array, consider using string or int depending on your data needs.
{ "class": "ExampleClass", "properties": [ { "name": "exampleProperty", "dataType": ["string"] } ]}
Step 3: Apply the Changes
After updating the schema, apply the changes by sending a request to update the schema in Weaviate:
PUT /v1/schema
Additional Resources
For more information on Weaviate's supported data types and schema configuration, refer to the official Weaviate Schema Documentation. Additionally, you can explore the Weaviate Developer Guide for more insights on building applications with Weaviate.
By following these steps, you should be able to resolve the 'Invalid Data Type' error and ensure your Weaviate schema is correctly configured.
Weaviate Invalid Data Type
TensorFlow
- 80+ monitoring tool integrations
- Long term memory about your stack
- Locally run Mac App available
Time to stop copy pasting your errors onto Google!