A dataset can store documents to be searched, retrieved, filtered and aggregated (similar to Collections in MongoDB, Tables in SQL, Indexes in ElasticSearch).
A powerful and core feature of VecDB is that you can store both your metadata and vectors in the same document.
When specifying the schema of a dataset and inserting your own vector use the suffix (ends with) "_vector_" for the field name, and specify the length of the vector in dataset_schema.
You don't have to specify the schema of every single field when creating a dataset, as VecDB will automatically detect the appropriate data type for each field (vectors will be automatically identified by its "_vector_" suffix). Infact you also don't always have to use this endpoint to create a dataset as /datasets/bulk_insert will infer and create the dataset and schema as you insert new documents.
A dataset name/id can only contain undercase letters, dash, underscore and numbers.
"_id" is reserved as the key and id of a document.
Once a schema is set for a dataset it cannot be altered. If it has to be altered, utlise the copy dataset endpoint.
For more information about vectors check out the 'Vectorizing' section, /services/search/vector or out blog at https://relevance.ai/blog.
For more information about chunks and chunk vectors check out /services/search/chunk.
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