Configuration for indexing documents for semantic search in the store.
This governs how text is converted into embeddings and stored for vector-based lookups.
IndexConfig()Required. Dimensionality of the embedding vectors you will store.
Must match the output dimension of your selected embedding model or custom embed function. If mismatched, you will likely encounter shape/size errors when inserting or querying vectors.
Required. Identifier or reference to the embedding model or a custom embedding function.
Optional. List of JSON fields to extract before generating embeddings.
Defaults to ["$"], which means the entire JSON object is embedded as one piece of text. If you provide multiple fields (e.g. ["title", "content"]), each is extracted and embedded separately, often saving token usage if you only care about certain parts of the data.