1
import time
2
from twelvelabs import (
3
    TwelveLabs,
4
    VideoInputRequest,
5
    MediaSource,
6
    # For dynamic segmentation uncomment the next two lines:
7
    # VideoSegmentation_Dynamic,
8
    # VideoSegmentationDynamicDynamic,
9
    # For fixed segmentation uncomment the next two lines:
10
    # VideoSegmentation_Fixed,
11
    # VideoSegmentationFixedFixed,
12
)
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# 1. Initialize the client
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client = TwelveLabs(api_key="<YOUR_API_KEY>")
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# 2. Upload a video
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asset = client.assets.create(
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    method="url",
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    url="<YOUR_VIDEO_URL>" # Use direct links to raw media files. Video hosting platforms and cloud storage sharing links are not supported
21
    # Or use method="direct" and file=open("<PATH_TO_VIDEO_FILE>", "rb") to upload a local file up to 200 MB
22
)
23
print(f"Created asset: id={asset.id}")
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# 3. Check the status of the asset
26
print("Waiting for asset to be ready...")
27
while True:
28
    asset = client.assets.retrieve(asset.id)
29
    if asset.status == "ready":
30
        print("Asset is ready")
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        break
32
    if asset.status == "failed":
33
        raise RuntimeError(f"Asset processing failed: id={asset.id}")
34
    time.sleep(5)
35
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# 4. Create video embeddings
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task = client.embed.v_2.tasks.create(
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    input_type="video",
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    model_name="marengo3.0",
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    video=VideoInputRequest(
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        media_source=MediaSource(
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            asset_id=asset.id,
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            # url="<YOUR_VIDEO_URL>", # Use direct links to raw media files. Video hosting platforms and cloud storage sharing links are not supported
44
            # base_64_string="<BASE_64_ENCODED_DATA>",
45
        ),
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        # start_sec=0,
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        # end_sec=10,
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        # embedding_option=["visual", "audio", "transcription"],
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        # embedding_scope=["clip", "asset"],
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        # embedding_type=["separate_embedding", "fused_embedding"],
51
        # For dynamic segmentation:
52
        # segmentation=VideoSegmentation_Dynamic(
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        #     dynamic=VideoSegmentationDynamicDynamic(
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        #         min_duration_sec=3  # Minimum segment duration in seconds
55
        #     )
56
        # ),
57
        # For fixed segmentation:
58
        # segmentation=VideoSegmentation_Fixed(
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        #     fixed=VideoSegmentationFixedFixed(
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        #         duration_sec=5  # Exact segment duration in seconds
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        #     )
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        # ),
63
    ),
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)
65
print(f"Task ID: {task.id}")
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# 5. Monitor the status
68
while True:
69
    task = client.embed.v_2.tasks.retrieve(task_id=task.id)
70
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    if task.status == "ready":
72
        print(f"Task completed")
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        break
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    elif task.status == "failed":
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        print("Task failed")
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        break
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    else:
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        print("Task still processing...")
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        time.sleep(5)
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# 6. Process the results
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print(f"\n{'='*80}")
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print(f"EMBEDDINGS SUMMARY: {len(task.data)} total embeddings")
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print(f"{'='*80}\n")
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for idx, embedding_data in enumerate(task.data, 1):
87
    print(f"[{idx}/{len(task.data)}] {embedding_data.embedding_option.upper()} | {embedding_data.embedding_scope.upper()}")
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    print(f"├─ Time range: {embedding_data.start_sec}s - {embedding_data.end_sec}s")
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    print(f"├─ Dimensions: {len(embedding_data.embedding)}")
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    print(f"└─ First 10 values: {embedding_data.embedding[:10]}")
91
    print()

1
import time
2
from twelvelabs import (
3
    TwelveLabs,
4
    VideoInputRequest,
5
    MediaSource,
6
    # For dynamic segmentation uncomment the next two lines:
7
    # VideoSegmentation_Dynamic,
8
    # VideoSegmentationDynamicDynamic,
9
    # For fixed segmentation uncomment the next two lines:
10
    # VideoSegmentation_Fixed,
11
    # VideoSegmentationFixedFixed,
12
)
13
14
# 1. Initialize the client
15
client = TwelveLabs(api_key="<YOUR_API_KEY>")
16
17
# 2. Upload a file
18
asset = client.assets.create(
19
    method="url",
20
    url="<YOUR_VIDEO_URL>" # Use direct links to raw media files. Video hosting platforms and cloud storage sharing links are not supported
21
    # Or use method="direct" and file=open("<PATH_TO_VIDEO_FILE>", "rb") to upload a local file up to 200 MB
22
)
23
print(f"Created asset: id={asset.id}")
24
25
# 3. Check the status of the asset
26
print("Waiting for asset to be ready...")
27
while True:
28
    asset = client.assets.retrieve(asset.id)
29
    if asset.status == "ready":
30
        print("Asset is ready")
31
        break
32
    if asset.status == "failed":
33
        raise RuntimeError(f"Asset processing failed: id={asset.id}")
34
    time.sleep(5)
35
36
# 4. Create video embeddings
37
response = client.embed.v_2.create(
38
    input_type="video",
39
    model_name="marengo3.0",
40
    video=VideoInputRequest(
41
        media_source=MediaSource(
42
            asset_id=asset.id,
43
            # url="<YOUR_VIDEO_URL>", # Use direct links to raw media files. Video hosting platforms and cloud storage sharing links are not supported
44
            # base_64_string="<BASE_64_ENCODED_DATA>",
45
        ),
46
        # start_sec=0,
47
        # end_sec=10,
48
        # embedding_option=["visual", "audio", "transcription"],
49
        # embedding_scope=["clip", "asset"],
50
        # embedding_type=["separate_embedding", "fused_embedding"],
51
        # For dynamic segmentation:
52
        # segmentation=VideoSegmentation_Dynamic(
53
        #     dynamic=VideoSegmentationDynamicDynamic(
54
        #         min_duration_sec=3  # Minimum segment duration in seconds
55
        #     )
56
        # ),
57
        # For fixed segmentation:
58
        # segmentation=VideoSegmentation_Fixed(
59
        #     fixed=VideoSegmentationFixedFixed(
60
        #         duration_sec=5  # Exact segment duration in seconds
61
        #     )
62
        # ),
63
    ),
64
)
65
66
# 5. Process the results
67
print(f"\n{'='*80}")
68
print(f"EMBEDDINGS SUMMARY: {len(response.data)} total embeddings")
69
print(f"{'='*80}\n")
70
71
for idx, embedding_data in enumerate(response.data, 1):
72
    print(f"[{idx}/{len(response.data)}] {embedding_data.embedding_option.upper()} | {embedding_data.embedding_scope.upper()}")
73
    print(f"├─ Time range: {embedding_data.start_sec}s - {embedding_data.end_sec}s")
74
    print(f"├─ Dimensions: {len(embedding_data.embedding)}")
75
    print(f"└─ First 10 values: {embedding_data.embedding[:10]}")
76
    print()