Tagged Prompt Snippet Library

Context

Consuming extensions — first nr_repurpose — assemble prompts from recurring building blocks: a persona, a tone of voice, a target audience, an image style, a layout instruction. Editors want to manage these fragments centrally, once, instead of re-typing them into every extension's own configuration.

The existing PromptTemplate entity does not fit this need. It is a heavyweight complete prompt: it binds a feature, carries model parameters (temperature, max tokens, top-p), supports versioning with parent/variant relations, and tracks usage performance. A persona like "You are Nova, a friendly expert." has none of these concerns — it is a fragment that only becomes a prompt when a consumer composes it with its own instructions. Forcing fragments into PromptTemplate would either bloat every fragment record with irrelevant model fields or fork the template semantics depending on a "fragment" flag.

A second question is how consumers select fragments. A fixed category enum (like Task categories) would require an nr-llm release every time a consuming extension introduces a new fragment kind, which contradicts the goal of nr-llm being a shared foundation that consumers extend without touching it.

Decision

Introduce a separate, lightweight PromptSnippet entity (table tx_nrllm_promptsnippet) next to — not on top of — PromptTemplate:

1. Fragments, not templates. A snippet is identifier + name + description + fragment text. No model parameters, no versioning, no performance tracking. PromptTemplate stays untouched.

2. Free-form CSV tags instead of a category enum. Snippets carry a comma-separated tags field. Consumers query PromptSnippetRepository::findActiveByTag(), which matches tags as exact, case-insensitive tokens — style never matches lifestyle. The tag vocabulary is a convention between editors and consumers (established so far: audience, tone_of_voice, persona, layout, style), documented in the TCA field description and the administration guide. New fragment kinds need no nr-llm release.

3. JSON metadata side-channel. An optional metadata JSON object carries consumer-specific settings (e.g. {"voice": "nova"} on persona snippets so speech features can pick a matching TTS voice). getMetadataArray() returns [] for empty or invalid JSON — bad editor input must never break a consumer.

4. Composition stays in nr-llm. PromptSnippetComposer renders an ordered label-to-snippet map into labeled prompt blocks (LABEL: + fragment text, blank-line separated), so all consumers produce uniformly structured prompt sections.

5. Editing via FormEngine. The backend module gets a "Snippets" list following the established Providers/Models/Tasks pattern; create/edit links into FormEngine, no custom forms.

Consequences

• Editors manage personas, tones, audiences, styles, and layouts once, centrally; every consuming extension reads the same library.

• The free-tag model keeps nr-llm release-independent from consumer vocabulary — at the cost of no referential integrity: a typo in a tag silently yields an empty query result. The documented convention and the tag badges in the list view mitigate this.

• Token matching is implemented over the CSV field in PHP, not SQL LIKE, guaranteeing exact-token semantics on every database platform. The snippet library is small (tens of records), so loading active snippets for tag filtering is not a performance concern.

• Two prompt-related entities now coexist. The split is intentional (template = complete prompt, snippet = fragment) and documented here, in the administration guide, and in both entities' PHPDoc.