LOT 1 - Optimizare script build -Instalare mono comanda

ai_platform/modules/video-analysis/API.md

@@ -22,49 +22,17 @@ No authentication required.
 
 ---
 
-## ⚠️ TESTING REMINDER: Alternative Vision Models
+## Vision & Aggregation Models
 
-**Current Configuration:**
-- **Deepfake Detection:** Uses BusterX (Qwen2.5-VL-7B fine-tuned) @ port 54500
-- **Semantic Analysis:** Uses BusterX (7B parameters)
+Both endpoints are backed by a single vision model — **BusterX** (`l8cv/BusterX_plusplus`, served as `busterx`) @ port `54500`:
 
-**TODO - Test with Qwen3-VL-30B for Better Semantic Analysis:**
+| Stage | Model | Endpoint | Role |
+|-------|-------|----------|------|
+| Deepfake verdict | BusterX (Qwen2.5-VL-7B fine-tune) | `http://didiAI-video-vllm-buster:54500` | `REAL` / `FAKE` / `UNCERTAIN` + explanation |
+| Semantic chunk descriptions | BusterX (same endpoint) | `http://didiAI-video-vllm-buster:54500` | per-chunk `description` |
+| Semantic aggregation | DIDI text LLM (Qwen3.5) | `http://didiAI-llm-api:14011` | merges chunk descriptions into `final_summary` |
 
-The semantic analysis endpoint can be configured to use **Qwen3-VL-30B** (already running @ port 14002) instead of BusterX for potentially better results:
-
-| Model | Size | Port | Best For |
-|-------|------|------|----------|
-| **BusterX** | 7B | 54500 | Deepfake detection (specialized) |
-| **Qwen3-VL-30B** | 30B | 14002 | General semantic understanding |
-
-**To test with Qwen3-VL-30B:**
-
-1. Update `.env`:
-```bash
-VIDEO_ANALYSIS_VLLM_BASE_URL=http://didiAI-llm-vllm-vision:14002  # Use Qwen3-VL instead of BusterX
-VIDEO_ANALYSIS_VLLM_MODEL=qwen3-vl                     # Change from busterx
-```
-
-2. Rebuild container:
-```bash
-cd deploy/
-docker compose build video-analysis-api
-docker compose up -d video-analysis-api
-```
-
-3. Test semantic analysis:
-```bash
-curl -X POST http://localhost:54600/analyze/video/semantic \
-  -F "file=@test_video.mp4"
-```
-
-**Expected Benefits:**
-- More detailed scene descriptions (30B vs 7B parameters)
-- Better context understanding
-- More coherent narrative flow
-- Higher accuracy for complex scenes
-
-**Note:** Deepfake detection should continue using BusterX (specialized model).
+BusterX is self-contained — Qwen2.5-VL is bundled inside the fine-tune, so no separate vision base model is loaded. There is no separate Qwen3-VL vision backend in this deployment.
 
 ---
 
@@ -359,6 +327,20 @@ curl -X POST http://localhost:54600/analyze/video/semantic \
 
 ---
 
+### Service Info
+
+Return service catalog metadata (resources, models, functions). Consumed by the DIDI `catalog-api`.
+
+**GET** `/v1/info`
+
+**Example**
+
+```bash
+curl http://localhost:54600/v1/info
+```
+
+---
+
 ## Error Responses
 
 | Status | Description |