AI Rerank

ai.rerank AI v0.1.0

Reorder retrieved documents by true relevance to the query — the RAG quality layer between vector-store retrieval and the prompt. llm = one JSON-scored call to any chat model (offline-capable); cohere = the hosted /v1/rerank API. Outputs the same matches, best first, each with a rerankScore.

The AI Rerank step on the Studio canvas — The **AI Rerank** step as it appears on the Studio canvas — input pins on the left, output ports on the right.

Finding it in the library

Search the builder's node library for AI Rerank (it lives under AI). A single click opens the in-editor docs panel shown here — description, ports, and every property, without leaving the canvas. Double-click (or drag) to add it to the workflow.

AI Rerank in the node library, with the in-editor docs panel open — The library entry and the in-editor docs panel for **AI Rerank** — the same reference this page is generated from.

Wired up in the builder

AI Rerank in a real, runnable flow — captured live from the Studio editor, exactly as it looks on your canvas. The ◈ ports carry the AI Model provider you wire in. This is the same workflow used for the example input & output below.

AI Rerank wired into a runnable workflow in the Studio builder — **AI Rerank** wired into a runnable flow — input on the left, output on the right, AI Model provider wired into the ◈ ports below.

How it’s configured

The node’s Configure panel as it opens in the builder when you select the step — every setting laid out with real values. Click any field to edit it.

The AI Rerank node's Configure panel in the Studio builder — The **Configure** panel for **AI Rerank**, showing the settings from the flow above.

Ports

Ports are the node’s contract with its neighbours. In the editor a port label renders bold when wired and italic when optional; ◈ ports accept attachment carriers rather than data wires.

Direction	Port	Label	What flows through it
Input	`input`	Input
Output	`output`	Reranked

How data flows through it

AI Rerank consumes the content of the incoming envelope — when it is fed directly by a trigger, the trigger’s wrapper is unwrapped at the node boundary so the node sees the actual data, not the metadata shell. Its output becomes the payload for the next node, while the envelope (trace ids, correlation, binary refs) rides along untouched. In the Runs view you always see the whole envelope for both sides of this node.

Expressions in the config

None of this node’s properties are string-typed, so {{ }} expressions don’t apply here — JSON- and code-typed fields are always taken literally.

Build it with AI

Every node in this reference is reachable through Flowdrome’s AI Copilot and the MCP tools — say what you want, and the graph surgery happens server-side. Node types resolve fuzzily, so the catalog label (AI Rerank) works as well as the exact type id (ai.rerank).

In the Copilot panel (or any connected AI):

add a ai rerank node after the trigger

As a step in a create_chain_workflow call:

{"type":"AI Rerank","config":{}}

Raw MCP call — add this node to a workflow with add_node

curl -s -X POST http://localhost:48170/mcp -H "content-type: application/json" -d '{ "jsonrpc": "2.0", "id": "1", "method": "tools/call", "params": { "name": "add_node", "arguments": { "workflowId": "<id>", "type": "AI Rerank" } } }'

Example input & output

Captured from a real test run of the workflow above — this is what you see in the run data panel after pressing Test workflow.

Input — what the node received

The AI Rerank node's input envelope in the run data viewer — The input envelope in the Runs view — Flowdrome always shows the whole envelope, with the payload inside `body`.

Output — what the node produced

The AI Rerank node's output envelope in the run data viewer — The output envelope after the step ran.

Property reference

Every setting, with its type and default — the same fields shown configured in the panel above.

Property	Type	Default	Description
Query field `queryField`	`field`	`"query"`	Dot-path to the query text on the input.
Documents field `documentsField`	`field`	`"matches"`	Dot-path to the documents — an array of strings or of { text, … } objects (ai.vector-store's matches shape).
Top K `topK`	`int`	`5`	How many reranked matches to keep.
Timeout (ms) `timeoutMs`	`int`	`60000`	Abort the scoring call after this many milliseconds.

Related nodes

The rest of the AI group — the same folder you’d scan in the editor’s library.

This page is generated from the node registry by gen-node-docs.mjs on every site build — ports, properties, defaults and visibility rules cannot drift from the code. The screenshots and example data are captured from a live Flowdrome by npm run shots:nodes and npm run gen:examples.