Data format, schema, and validation issues

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Issues

• ValidationError: For example, S3ConnectionConfig, PluginResponse, or filedata_meta input type errors.
• ClientResponseError: For example, an HTTP 422 Unprocessable Entity error.
• ListConversionException: For example, for Pinecone, an Expected list, got None error.
• ServerOperationError: For example, for Databricks, an UNRESOLVED_COLUMN error.
• WeaviateDeleteManyError: For example, a no such prop with name 'record_id' error.
• CollectionInsertManyException: For example, for Astra DB, a vector dimension mismatch error.
• TypeError: For example, for Pinecone, a PineconeUploader missing argument error.
• UserError: For example, a wrappingschema validation failure.

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Possible causes

• The data being processed or transferred doesn’t match the expected structure, format, or validation rules.
• The data provided in configuration or during processing is not valid.
• The data being sent to a destination doesn’t match the destination’s schema—for example, the data has missing fields, wrong data types, or an incorrect number of vector dimensions.
• Incorrect configuration values were provided—for example, a non-string token was provided where a string was expected instead.
• There is a schema mismatch between the data generated by Unstructured and the destination schema—for example, missing columns or properties, or wrong data types.
• The specified embedding model generates vectors of a different dimension than the destination index or collection is configured for.
• The data was generated in an unexpected format.

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Possible solutions

• Verify the data configuration: Double-check configuration parameters against the relevant documentation for the correct data format and type.
• Verify the destination schema: Ensure that the schema—including columns, properties, types, and vector dimensions—in the destination’s system matches the data being sent. You might need to update or recreate the destination schema.
• Check the data fields: Check if fields such as record_id, element_id, text, embeddings, and metadata are expected and present.
• Verify the embedding dimensions: Confirm that the specified embedding model produces vectors of the dimension expected by the destination collection or index.
• Contact Unstructured Support: For HTTP 422 Unprocessable Entity errors where the cause isn’t clear, ListConversionException errors, TypeError errors, or persistent schema mismatch issues after verification, contact Unstructured Support.

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Additional resources