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

How it works 6 steps

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

Process (6 steps)

◆ embed recued-core.groq.embedding.create

Create embedding on groq

■ vectors default

Apply default

■ vector_count count

Count items in vectors

✦ summarize core.ai.summarize

AI generates a concise summary

■ vector_table to_table

Format results as a data table

■ card to_summary

Format results as a summary card

▼

Output

summary card

table vector_table

Settings 5 configurable

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

focus setting = Summarize whether the selected Groq embedding setup is ready for semantic search or RAG. Cover model, encoding, returned vector rows, token usage, and practical next steps.

texts setting = [object Object]

max length setting = 800

embedding model setting = nomic-embed-text-v1_5

encoding format setting = float