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A RocketRide text-processing node that asks a connected LLM to turn text or table content into a glossary of company-specific terms. Choose it when downstream users or agents need concise definitions of internal vocabulary rather than a general structured-data extraction.

What it does

The node accepts text or table content and sends each incoming chunk to its required LLM connection with a structured prompt (expectJson: true) and instructions to return a single JSON array of definitions. It writes one document for every returned definition; each document contains the JSON-serialized definition and is marked as non-table content. Use it instead of extract_data when the output should be a reusable vocabulary of terms and descriptions, not rows with a predefined schema.

Connections

ConnectionRequiredDescription
llmyesLLM used to extract and define terms.

Lanes

Lane inLane outDescription
textdocumentsEmits one definition document for every item returned by the LLM.

Configuration

This node has no local configuration fields. Connect an LLM and supply text on its text lane; the same extraction path is also used when the runtime provides table content.

Notes

Definition output

The built-in prompt asks the LLM to include company-specific language, acronyms, and terms whose in-company meaning differs from common usage — including terms that are ambiguous. It always requests one JSON array: if the LLM response is valid JSON but is not an array, the node raises a descriptive ValueError before emitting any documents, and invalid JSON continues to surface the response parsing error. The example shape used by the prompt is {\"term\": \"...\", \"description\": \"...\"}, but the node serializes each returned object without imposing additional fields.

Dependencies

The node has no Python package requirements of its own: it relies entirely on the separately installed AI module.

Chunk metadata

The node resets its output chunkId for every input object, then increments it for each emitted definition. It writes isTable: false and tableId: 0 even when the input arrived through the table-processing path, so downstream nodes receive glossary entries as ordinary documents.

Schema

No configuration fields.