Search Data node

The Search Data node embeds a query and finds the nearest stored vectors in the flow’s vector namespace, returning the top matches. Pair it with an Embed Data node that populated the same namespace.

Configuration

• Model — the embedding model for the query. Must match the model the data was embedded with.
• Query — the search query (may reference {…} templates); empty means the incoming payload.
• Top K — how many matches to return.
• Rerank model — optional. When set (default cohere/rerank-v3.5), the node fetches a larger candidate pool and re-scores it for better relevance than raw vector distance.

Output

The node returns a plain JSON array downstream nodes can read:

[{ "id": "…", "score": 0.87, "text": "…" }]

Each entry is the stored chunk with its vector id and similarity/rerank score.

Notes

• Embedding + rerank costs are booked separately through the gateway’s usage tracking.
• An Agent with vector search enabled gets a vector_search tool over the same namespace, so it can retrieve embedded documents mid-answer (text output only — not with structured output).

Related

• Embed Data — populating the vector namespace.
• Agent — vector search as a tool.