You can create a data index and use the metadata and semantic content of objects as index conditions to quickly search for images, videos, documents, and audio files in Object Storage Service (OSS).
Benefits
Ease of use: You can use the data indexes created by using OSS without the need to migrate data or build a search system.
Multimodal search: Multiple types of data indexes, including object metadata, media metadata, custom metadata, and semantic content, are supported. Approximately 100 index conditions are provided.
High-performance search: You can index and aggregate data within seconds and build an index library that supports up to tens of billions of objects to meet large-scale data processing requirements.
Supported data indexing methods
OSS supports MetaSearch and AISearch. The following table describes the preceding data indexing methods.
Item | MetaSearch | AISearch |
Description | Search for specific objects based on metadata attributes, such as object metadata, ETags, and tags. | Search for specific objects based on the information about documents, images, videos, and audio files. You can specify semantic content as index conditions, and OSS compares the semantic content with objects in OSS. |
Scenario | Object query and statistics | Multimodal search and complex object search |
Sample index condition | Search for Standard objects whose access control list (ACL) is private and which are uploaded on September 14, 2024
| Search for images related to the semantic content "apple"
|
Sample result | Return Standard objects whose ACL is private and which are uploaded on September 14, 2024
| Return images related to the semantic content "apple"
|
Instructions on selecting a data indexing method
You can select a suitable data indexing method based on search conditions. The following table describes the search conditions supported by MetaSearch and AISearch.
Search condition | MetaSearch | AISearch |
OSS metadata | ✅ | ✅ |
Object tags and ETags | ✅ | ✅ |
User metadata | ❌ | ✅ |
Multimedia metadata | ❌ | ✅ |
Semantic content | ❌ | ✅ |
For more information about the fields and operators supported by MetaSearch, see Appendix: Fields and operators supported in scalar search.
For more information about the fields and operators supported by AISearch, see Appendix: Fields and operators supported by AISearch.
Process
The following figures show how MetaSearch and AISearch work.
How MetaSearch works
The following figure shows how to use MetaSearch to search for objects based on metadata attributes.
You upload files, such as images, videos, documents, and audio files, from an application to an OSS bucket.
You use a RAM user that has the permissions to manage OSS to enable data indexing for the bucket and select MetaSearch.
OSS uses the default index table structure to automatically create data indexes that contain OSS metadata, object ETags, and object tags.
The application calls the DoMetaQuery operation to search for objects based on metadata attributes.
OSS returns the objects that meet the search conditions.
How AISearch works
The following figure shows to use AISearch to search for objects based on metadata attributes and semantic content.
You upload files, such as images, videos, documents, and audio files, from an application to an OSS bucket.
You use a RAM user that has the permissions to manage OSS to enable data indexing for the bucket and select AISearch.
OSS uses the default index table structure and Embedding model to automatically create data indexes that contain OSS metadata, object ETags, object tags, user metadata, multimedia metadata, and semantic content.
The application calls the DoMetaQuery operation to search for objects based on metadata attributes and semantic content.
OSS returns the objects that meet the search conditions.
References
For more information about how to use MetaSearch and AISearch to search for objects, see the following topics: