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Video Analysis API Documentation

REST API for video deepfake detection using semantic analysis via vLLM backends. Note: This API depends on an external vLLM server configured via VIDEO_ANALYSIS_VLLM_BASE_URL.

Base URL

{BASE_URL}

Common configurations:

Local development: http://localhost:54600
Docker (internal): http://didiAI-video-api:54600
Production: Use your configured hostname

Authentication

No authentication required.

Vision & Aggregation Models

Both endpoints are backed by a single vision model — BusterX (l8cv/BusterX_plusplus, served as busterx) @ port 54500:

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`

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.

Endpoints

Health Check

Check if the service is running.

GET /health

Response

{
  "status": "ok"
}

Example

curl http://localhost:54600/health

Analyze Video

Upload a video for deepfake analysis.

POST /analyze/video

Request

Content-Type: multipart/form-data

Field	Type	Required	Description
`file`	file	Yes	Video file to analyze

Example

curl -X POST http://localhost:54600/analyze/video \
  -F "file=@/path/to/video.mp4"

Response

{
  "request_id": "550e8400-e29b-41d4-a716-446655440000",
  "run_dir": "runs/550e8400-e29b-41d4-a716-446655440000",
  "verdict": "FAKE",
  "explanation": "The video shows clear signs of manipulation...",
  "frames_analyzed": 16,
  "evidence": [
    {"frame_index": 0, "timestamp_s": 0.0},
    {"frame_index": 30, "timestamp_s": 1.0}
  ],
  "usage": {
    "prompt_tokens": 1250,
    "completion_tokens": 150,
    "total_tokens": 1400
  },
  "latency_s": {
    "sampling_time_s": 0.234,
    "encode_time_s": 0.567,
    "model_inference_time_s": 12.345
  },
  "meta": {
    "fps": 30.0,
    "total_frames": 450,
    "duration_s": 15.0,
    "sampled": 16,
    "indices": [0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 420, 449],
    "timestamps_s": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 14.97]
  }
}

Response Fields

Field	Description
`request_id`	UUID identifying the analysis request
`run_dir`	Directory where analysis artifacts are stored
`verdict`	Classification result: `REAL`, `FAKE`, or `UNCERTAIN`
`explanation`	Model explanation for the verdict
`usage`	Token usage statistics from the vLLM backend
`latency_s`	Timing breakdown in seconds
`meta`	Video metadata and frame sampling information

Semantic Video Analysis

Upload a video for deep semantic analysis with temporal chunking.

POST /analyze/video/semantic

This endpoint performs comprehensive content understanding by:

Dividing the video into temporal chunks (default: 10s each)
Densely sampling each chunk (default: 24 frames per chunk)
Analyzing each chunk with the vision LLM
Optionally aggregating chunk descriptions into a coherent narrative

Use cases:

Content description and understanding
Action recognition and tracking
Scene analysis
Narrative extraction from video

Request

Content-Type: multipart/form-data

Field	Type	Required	Default	Description
`file`	file	Yes	-	Video file to analyze
`chunk_duration_s`	float	No	10.0	Duration of each chunk in seconds (1-60)
`frames_per_chunk`	int	No	24	Number of frames to sample per chunk (4-64)
`enable_aggregation`	bool	No	true	Whether to aggregate chunks into final summary

Examples

Basic semantic analysis (default settings):

curl -X POST http://localhost:54600/analyze/video/semantic \
  -F "file=@meeting.mp4"

Custom chunk settings:

curl -X POST http://localhost:54600/analyze/video/semantic \
  -F "file=@meeting.mp4" \
  -F "chunk_duration_s=5.0" \
  -F "frames_per_chunk=32"

Without aggregation (get only chunk descriptions):

curl -X POST http://localhost:54600/analyze/video/semantic \
  -F "file=@meeting.mp4" \
  -F "enable_aggregation=false"

Response

{
  "request_id": "abc-123-def-456",
  "run_dir": "/app/runs/abc-123-def-456",
  "analysis_type": "semantic",
  "video_duration_s": 60.0,
  "num_chunks": 6,
  "chunk_results": [
    {
      "chunk_idx": 0,
      "time_range": "0.0s - 10.0s",
      "description": "A person in business attire enters a conference room, walks to the head of the table, and places a laptop down. The room has white walls and a large window showing daylight outside.",
      "frames_analyzed": 24,
      "inference_time_s": 12.3,
      "usage": {
        "prompt_tokens": 5234,
        "completion_tokens": 87,
        "total_tokens": 5321
      }
    },
    {
      "chunk_idx": 1,
      "time_range": "10.0s - 20.0s",
      "description": "The person opens the laptop and begins gesturing while speaking. Two other people enter the room and take seats at the conference table. One person carries a notebook.",
      "frames_analyzed": 24,
      "inference_time_s": 12.1,
      "usage": {
        "prompt_tokens": 5198,
        "completion_tokens": 92,
        "total_tokens": 5290
      }
    },
    {
      "chunk_idx": 2,
      "time_range": "20.0s - 30.0s",
      "description": "The presenter is now showing content on the laptop screen to the group. All three people are focused on the screen. One person is taking notes.",
      "frames_analyzed": 24,
      "inference_time_s": 11.8,
      "usage": {
        "prompt_tokens": 5201,
        "completion_tokens": 78,
        "total_tokens": 5279
      }
    },
    {
      "chunk_idx": 3,
      "time_range": "30.0s - 40.0s",
      "description": "Discussion is ongoing. The presenter is gesturing toward the screen. One attendee raises their hand and appears to ask a question.",
      "frames_analyzed": 24,
      "inference_time_s": 12.0,
      "usage": {
        "prompt_tokens": 5187,
        "completion_tokens": 71,
        "total_tokens": 5258
      }
    },
    {
      "chunk_idx": 4,
      "time_range": "40.0s - 50.0s",
      "description": "The presenter responds to the question with gestures. All participants are engaged in the discussion. Papers are visible on the table.",
      "frames_analyzed": 24,
      "inference_time_s": 11.9,
      "usage": {
        "prompt_tokens": 5209,
        "completion_tokens": 68,
        "total_tokens": 5277
      }
    },
    {
      "chunk_idx": 5,
      "time_range": "50.0s - 60.0s",
      "description": "The meeting appears to be concluding. Participants are gathering their belongings. The presenter closes the laptop and people begin standing up.",
      "frames_analyzed": 24,
      "inference_time_s": 12.2,
      "usage": {
        "prompt_tokens": 5223,
        "completion_tokens": 75,
        "total_tokens": 5298
      }
    }
  ],
  "final_summary": "The video captures a business meeting in a conference room. It begins with a presenter setting up and two colleagues joining. The presenter delivers a presentation using a laptop, with the group discussing the content. One attendee asks questions and takes notes throughout. The meeting concludes with participants gathering their items and preparing to leave. The entire sequence lasts approximately 60 seconds.",
  "aggregation_time_s": 4.5,
  "total_latency_s": 76.8,
  "meta": {
    "fps": 30.0,
    "total_frames": 1800,
    "duration_s": 60.0,
    "chunk_duration_s": 10.0,
    "frames_per_chunk": 24,
    "total_frames_sampled": 144
  }
}

Response Fields

Field	Description
`request_id`	UUID identifying the analysis request
`run_dir`	Directory where analysis artifacts are stored
`analysis_type`	Always "semantic" for this endpoint
`video_duration_s`	Total video duration in seconds
`num_chunks`	Number of temporal chunks processed
`chunk_results`	Array of results for each chunk (see below)
`final_summary`	Aggregated narrative (null if aggregation disabled)
`aggregation_time_s`	Time spent on aggregation (null if disabled)
`total_latency_s`	Total processing time in seconds
`meta`	Video metadata and sampling configuration

ChunkResult Fields:

Field	Description
`chunk_idx`	Zero-based chunk index
`time_range`	Temporal range of this chunk (e.g., "0.0s - 10.0s")
`description`	Semantic description of what happens in this chunk
`frames_analyzed`	Number of frames analyzed for this chunk
`inference_time_s`	Time spent on VLM inference for this chunk
`usage`	Token usage statistics for this chunk

Performance Characteristics

For a 60-second video with default settings:

Chunks: 6 (10s each)
Total frames analyzed: 144 (24 per chunk)
Coverage: ~8% of all frames (vs 0.89% for deepfake detection)
Latency: ~77s total
- Chunk processing: ~72s (6 × 12s per chunk)
- Aggregation: ~5s
- Overhead (sampling, encoding): <1s

Optimization options:

Setting	Fast	Balanced	Detailed
`chunk_duration_s`	20.0	10.0	5.0
`frames_per_chunk`	16	24	32
Coverage (60s video)	~3%	~8%	~21%
Latency estimate	~40s	~77s	~150s

Service Info

Return service catalog metadata (resources, models, functions). Consumed by the DIDI catalog-api.

GET /v1/info

Example

curl http://localhost:54600/v1/info

Error Responses

Status	Description
`400`	Bad Request (missing or invalid file)
`500`	Internal Server Error

Possible 500 Error Causes

vLLM backend unreachable
VIDEO_ANALYSIS_VLLM_BASE_URL not set
Model inference failed

Example

{
  "detail": "VIDEO_ANALYSIS_VLLM_BASE_URL is not set"
}

Minimal Python Client Example

import httpx

def analyze_video(video_path: str) -> dict:
    with open(video_path, "rb") as f:
        response = httpx.post(
            "http://localhost:54600/analyze/video",
            files={"file": f},
            timeout=180.0,
        )
    response.raise_for_status()
    return response.json()

result = analyze_video("suspect_video.mp4")
print(f"Verdict: {result['verdict']}")
print(f"Explanation: {result['explanation']}")

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Video Analysis API Documentation

Base URL

Authentication

Vision & Aggregation Models

Endpoints

Health Check

Analyze Video

Request

Response

Response Fields

Semantic Video Analysis

Request

Response

Response Fields

Performance Characteristics

Service Info

Error Responses

Possible 500 Error Causes

Minimal Python Client Example

11 KiB

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