fix: convert dict entities to text strings for highlights field (Fixes #386)

[lb1192176991-lab]

What

The /ai/analyze endpoint crashes with a 500 error because the highlights field in TicketResponse is typed as list[str] but receives list[dict] objects.

Root cause: ner_service.extract_entities() returns list[dict] with keys {"text", "label", "confidence"}. When this list is passed to highlights=[e.text for e in entities], it crashes with AttributeError: dict object has no attribute text because the entities variable is still list[dict] at that point in the code (it is only converted to list[EntityInfo] in the separate entities= parameter).

Fix: Changed line 1855 from:

highlights=[e.text for e in entities] if entities else []

to:

highlights=[e.get("text", "") for e in entities] if entities else []

The analyze_stream() endpoint already had the correct form (e.get("text", "")) on line 2019 — it was only the analyze_only() function that had the wrong access pattern.

Why

Users hitting the /ai/analyze endpoint get a 500 error when NER entities are present, making the AI analysis feature completely broken whenever entities are detected. Pydantic v2 cannot coerce dict objects into str.

Testing

• When entities = [{"text": "Python", "label": "tech", "confidence": 0.95}], highlights becomes ["Python"] ✓
• When entities = [], highlights becomes [] ✓
• When entities = [{"label": "tech"}] (missing text key), highlights becomes [""] ✓ (graceful fallback)

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