AI: Add schema/README.md as labels schema reference #5322 #5123 #5232

Signed-off-by: Michael Mayer <michael@photoprism.app>
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Michael Mayer 2025-11-14 14:22:48 +01:00
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## PhotoPrism — Vision Schema Reference
**Last Updated:** November 14, 2025
### Overview
This package contains the canonical label response specifications used by PhotoPrisms external vision engines. It exposes two helpers:
- `LabelsJsonSchema(nsfw bool)` — returns a JSON **Schema** document tailored for OpenAI Responses requests, enabling strict validation of structured outputs.
- `LabelsJson(nsfw bool)` — returns a literal JSON **sample** that Ollama-style models can mirror when they only support prompt-enforced structures.
Both helpers build on the same field set (`name`, `confidence`, `topicality`, and optional NSFW flags) so downstream parsing logic (`LabelResult`) can remain engine-agnostic.
### Schema Types & Differences
| Helper | Target Engine | Format | Validation Style | When To Use |
|---------------------------|--------------------------|--------------------------------------------------------|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|
| `LabelsJsonSchema(false)` | OpenAI (standard labels) | JSON Schema Draft | Strong: OpenAI enforces field types/ranges server-side before returning a response. | When calling GPTvision models via `ApiFormatOpenAI` to ensure PhotoPrism receives well-formed label arrays. |
| `LabelsJsonSchema(true)` | OpenAI (labels + NSFW) | JSON Schema Draft with additional boolean/float fields | Strong: same enforcement plus required NSFW fields. | When `DetectNSFWLabels` or NSFW-specific prompts are active and the model must emit `nsfw` + `nsfw_confidence`. |
| `LabelsJson(false)` | Ollama (standard labels) | Plain JSON example | Soft: model is nudged to mimic the structure through prompt instructions. | When running self-hosted Ollama models that support “JSON mode” but do not consume JSON Schema definitions. |
| `LabelsJson(true)` | Ollama (labels + NSFW) | Plain JSON example with NSFW keys | Soft: prompts describe the required keys; the adapter validates after parsing. | When Ollama prompts mention NSFW scoring or PhotoPrism sets `DetectNSFWLabels=true`. |
**Key technical distinction:** OpenAIs Responses API accepts a JSON Schema (see `LabelsJsonSchema*`) and guarantees compliance by rejecting invalid responses, while Ollama currently relies on prompt-directed output. For Ollama integrations we provide a representative JSON document (`LabelsJson*`) that models can imitate; PhotoPrism then normalizes and validates the results in Go.
### Field Definitions
- `name` — single-word noun describing the subject (string, required).
- `confidence` — normalized score between `0` and `1` (float, required).
- `topicality` — relative relevance score between `0` and `1` (float, required; defaults to `confidence` if omitted after parsing).
- `nsfw` — boolean flag indicating sensitive content (required only in NSFW variants).
- `nsfw_confidence` — normalized probability for the NSFW assessment (required only in NSFW variants).
OpenAI schemas enforce these ranges/types, while Ollama prompts remind the model to emit matching keys. After parsing, PhotoPrism applies `LabelConfidenceDefault` and `normalizeLabelResult` to fill gaps and enforce naming rules.
### Usage Guidance
1. **OpenAI models** (`Engine: openai`, `RequestFormat: openai`):
- Leave `Schema` unset in `vision.yml`; the engine defaults call `LabelsJsonSchema(model.PromptContains("nsfw"))`.
- Optionally override the schema via `Schema`/`SchemaFile` if you extend fields, but keep required keys so `LabelResult` parsing succeeds.
2. **Ollama models** (`Engine: ollama`, `RequestFormat: ollama`):
- Rely on the built-in samples from `LabelsJson` or include them directly in prompts via `model.SchemaInstructions()`.
- Because enforcement happens after the response arrives, keep `Format: json` (default) and `Options.ForceJson=true` for label models to make parsing stricter.
3. **Custom engines**:
- Reuse these helpers to stay compatible with PhotoPrisms label DTOs.
- When adding new fields, update both schema/sample versions so OpenAI and Ollama adapters remain aligned.
### References
- JSON Schema primer: https://json-schema.org/learn/miscellaneous-examples
- OpenAI structured outputs: https://platform.openai.com/docs/guides/structured-outputs
- JSON mode background (Ollama-style prompts): https://www.alibabacloud.com/help/en/model-studio/json-mode
- JSON syntax refresher: https://www.json.org/json-en.html