Use “OpenAI embedding readiness brief” in Recued. It provides a read-only OpenAI embedding workflow.

How it works 12 steps

Inspect the data fetches, transforms, gates, and output this recipe runs.

Process (12 steps)

◆ models_raw recued-core.openai.model.search

Search model on openai

◆ embedding recued-core.openai.embedding.create

Create embedding on openai

■ models default

Apply default

■ model_rows slice

Take a subset of

■ embedding_rows default

Apply default

■ embedding_count count

Count items in embedding rows

■ model_count count

Count items in models

✦ summarize core.ai.summarize

AI generates a concise summary

■ model_table_rows map

Extract from each item

■ model_table to_table

Format results as a data table

■ embedding_table to_table

Format results as a data table

■ card to_summary

Format results as a summary card

▼

Output

summary card

table embedding_table

table model_table

Settings 7 configurable

Configurable at install. Defaults shown — change them anytime in Recued.

focus setting = Summarize whether the selected OpenAI embedding setup is ready for semantic search or RAG. Cover model visibility, embedding count, dimensions or format implied by the response, token usage, and practical follow-ups.

inputs setting = [object Object]

dimensions setting = 1536

max length setting = 900

embedding model setting = text-embedding-3-small

encoding format setting = float

model row limit setting = 20