LOT 1 - Optimizare script build -Instalare mono comanda

ai_platform/modules/extractors/API.md

@@ -114,6 +114,98 @@ Each extractor returns a uniform `FeatureResult`:
 | `confidence` | number\|null | 0..1 (extraction confidence, not a verdict) |
 | `error` | string\|null | Set when `ok` is false |
 
+### POST /v1/sentiment
+
+Classify the sentiment of a text via the LLM gateway (Romanian-aware prompt,
+detects irony/sarcasm). **Delegated to the LLM gateway** — returns `503` if no
+gateway is configured.
+
+**Request** — `application/json`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `text` | string | Text to analyze (min length 1) |
+
+```bash
+curl -X POST http://localhost:54400/v1/sentiment \
+  -H "Content-Type: application/json" \
+  -d '{"text": "Ce zi frumoasă!"}'
+```
+
+**Response** `200 OK` — a `FeatureResult` (same schema as above), e.g.:
+
+```json
+{
+  "tool_id": "sentiment",
+  "name": "Sentiment",
+  "ok": true,
+  "results": { "label": "positive", "score": 0.92 },
+  "evidence": ["Sentiment: positive (0.92)."],
+  "anomalies": [],
+  "confidence": 0.92
+}
+```
+
+### POST /v1/ner
+
+Extract named entities via GLiNER multilingual (`urchade/gliner_multi-v2.1`).
+**Requires the optional `ml` extra** — returns `503` if the model is unavailable.
+
+**Request** — `application/json`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `text` | string | Text to analyze (min length 1) |
+| `labels` | string[] \| null | Entity types to extract (defaults to the RO set) |
+
+```bash
+curl -X POST http://localhost:54400/v1/ner \
+  -H "Content-Type: application/json" \
+  -d '{"text": "Klaus Iohannis și Guvernul României."}'
+```
+
+**Response** `200 OK` — a `FeatureResult`; `results` carries the extracted
+entities (text, label, span, score).
+
+### POST /v1/ocr
+
+Extract visible text verbatim from an image via the LLM vision model.
+**Delegated to the LLM gateway** — returns `503` if no gateway is configured.
+
+**Request** — `multipart/form-data`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `file` | file | Image file |
+
+```bash
+curl -X POST http://localhost:54400/v1/ocr \
+  -F "file=@scan.png"
+```
+
+**Response** `200 OK` — a `FeatureResult`; `results` carries the recognized text.
+
+### POST /v1/detect
+
+Detect objects (COCO classes) via YOLO (`yolov8n`), returning boxes + confidence.
+**Requires the optional `ml` extra** — returns `503` if the model is unavailable.
+
+**Request** — `multipart/form-data`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `file` | file | Image file |
+
+Optional query param: `threshold` (float, 0..1) — confidence threshold.
+
+```bash
+curl -X POST "http://localhost:54400/v1/detect?threshold=0.4" \
+  -F "file=@street.jpg"
+```
+
+**Response** `200 OK` — a `FeatureResult`; `results` carries detected objects
+(label, box, confidence).
+
 ## Error Responses
 
 | Status | Meaning |
@@ -121,6 +213,8 @@ Each extractor returns a uniform `FeatureResult`:
 | 400 | Empty file |
 | 413 | File exceeds `EXTRACTORS_MAX_UPLOAD_MB` |
 | 422 | Missing `file` field |
+| 502 | `LLMError` — the LLM gateway returned an error (`/v1/sentiment`, `/v1/ocr`) |
+| 503 | `LLMNotConfigured` (no gateway for `/v1/sentiment`, `/v1/ocr`) or the `ml` extra/model is unavailable (`/v1/ner`, `/v1/detect`) |
 
 ```json
 { "detail": "empty file" }