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What you're looking at — how AI reads this
An embedding is a numerical fingerprint of meaning. When a chunk of your text is ingested, the AI model converts it into a long list of numbers (typically 384, 768, 1,536, or 3,072 dimensions). Each number is a coordinate in “meaning space” — texts about similar topics end up at similar coordinates.
At query time the AI converts your question into the same kind of vector and uses cosine similarity to find the chunks whose vectors point in the closest direction. The heatmap below shows the first dimensions of this chunk’s vector — blue cells are negative values, red are positive. An L2 Norm near 1.0 is the sanity check that the embedding is real (and not zeros).
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AI Vector Index — Table Schemas
The three system tables that comprise the Centralpoint AI Vector Index. Together they store the configuration, the chunked text, and the embedding vectors for every record made retrievable by the AI.
cpsys_VectorIndexConfig15 fields
| ConfigId | uniqueidentifier |
| SourceTable | nvarchar |
| ModuleId | uniqueidentifier |
| SourceAttributes | nvarchar |
| EmbeddingProvider | nvarchar |
| EmbeddingModel | nvarchar |
| ChunkSize | int |
| ChunkOverlap | int |
| MaxChunksPerRecord | int |
| IsEnabled | bit |
| TotalRecords | int |
| IndexedRecords | int |
| LastFullRebuild | datetime |
| LastIncrementalUpdate | datetime |
| CreateDate | datetime |
cpsys_VectorChunks6 fields
| ChunkId | uniqueidentifier |
| ModuleId | uniqueidentifier |
| DataId | uniqueidentifier |
| ChunkText | nvarchar(max) |
| ChunkIndex | int |
| CreateDate | datetime |
cpsys_VectorElements3 fields
| ChunkId | uniqueidentifier |
| DimIndex | int |
| Value | float |
How the three tables relate
cpsys_VectorIndexConfig registers which modules get indexed and how (chunk size, embedding model). For each record in those modules, cpsys_VectorChunks stores one row per text chunk (linked back by ModuleId + DataId). For each chunk, cpsys_VectorElements stores one row per dimension of the embedding vector. A typical record produces 2–10 chunks; each chunk produces 384–3,072 element rows depending on the embedding model.
Data Sources