semantic.ai_dimensions_mismatch
Vector type an embedding is asked for as has a length other than embedding_dimensions:.
Area 21, AI; not declinable; the next step is usually the agent's.
ai.embed's as: names a vector type with as many dimensions: as
embedding_dimensions: says the model makes, so a model change that
alters the length is caught before a column holds two kinds of vector.
application { connections: { local_model: { driver: Driver.openai_compatible url: config.model_url } } ai: { connection: local_model models: { default: config.drafting_model embedding: config.search_model } embedding_dimensions: 1536 limits: { per_day: { tokens: 2000000 } } }}
/// A help article's meaning, as the older embedding model made it.type ArticleMeaning = Vector { dimensions: 768}
use support/types { ArticleMeaning }use support/failures { EmbeddingDelayed }/// A help article's meaning, from its text.action meaning_of_article(body: Markdown) fails EmbeddingDelayed -> ArticleMeaning { return attempt ai.embed(body, as: ArticleMeaning) { timed_out: EmbeddingDelayed over_budget: EmbeddingDelayed too_long: EmbeddingDelayed }}
error ArticleMeaning has 768 dimensions, and the model makes 1,536
--> domains/support/actions/meaning_of_article.jadpo:6:39
|6 | return attempt ai.embed(body, as: ArticleMeaning) { | ^^^^^^^^^^^^^^ 768 dimensions, not 1,536
- reason
- Each embedding the model makes has the 1,536 numbers
ai:'sembedding_dimensions:says, andArticleMeaningholds vectors of 768, so a column of it would hold two kinds of vector. - next
- Give
ArticleMeaningdimensions: 1536, or setembedding_dimensions: 768inai:if the model makes vectors that long (agent) - rule
- semantic.
ai_ dimensions_ mismatch - help
- https://jadpo.
dev/docs/errors/semantic. ai_ dimensions_ mismatch
Markdown for agents: /docs/errors/semantic.ai_dimensions_mismatch.md.Built from Jadpo main at aee2e8f.