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A single video can have multiple indexes, each capturing a different aspect. Layer them together for precise, multi-dimensional retrieval.

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Quick Example

from videodb import IndexType

# Create two indexes with different focus
car_index = video.index_scenes(
    prompt="Identify the color and model of each vehicle",
    name="vehicle_identification"
)

motion_index = video.index_scenes(
    prompt="Describe the movement pattern - speeding, stopping, turning",
    name="motion_analysis"
)

# Search each index separately
car_results = video.search(
    query="red sedan",
    index_type=IndexType.scene,
    index_id=car_index
)

motion_results = video.search(
    query="driving recklessly",
    index_type=IndexType.scene,
    index_id=motion_index
)

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Why Multiple Indexes?

Different prompts extract different information. A single index optimized for one use case may miss details relevant to another.
Index FocusPrompt ExampleUse Case
Objects”Identify all objects and their colors”Inventory, cataloging
Actions”Describe activities and movements”Activity detection
People”Describe people, clothing, expressions”Person identification
Environment”Describe the setting and location”Scene classification
Text”Read any visible text or signage”OCR, document extraction

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Creating Focused Indexes

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Same Video, Different Perspectives

from videodb import SceneExtractionType

# Environment index
env_index = video.index_scenes(
    prompt="Describe the environment - indoor/outdoor, lighting, weather",
    name="environment"
)

# People index
people_index = video.index_scenes(
    prompt="Describe the people - count, clothing, actions",
    name="people"
)

# Object index
object_index = video.index_scenes(
    prompt="List all visible objects and their positions",
    name="objects"
)

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Different Extraction Configs

from videodb import SceneExtractionType

# Fast sampling for color detection (1 frame per second)
color_index = video.index_scenes(
    extraction_type=SceneExtractionType.time_based,
    extraction_config={"time": 1, "frame_count": 1},
    prompt="Identify vehicle colors",
    name="colors"
)

# Slower sampling for motion analysis (5 frames over 4 seconds)
motion_index = video.index_scenes(
    extraction_type=SceneExtractionType.time_based,
    extraction_config={"time": 4, "frame_count": 5},
    prompt="Describe vehicle movement patterns",
    name="motion"
)

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Combining Search Results

Search multiple indexes and combine results for precise retrieval.

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Intersection (AND)

Find segments that match criteria in both indexes: Intersection method showing segments that match criteria in both search indexes combined with AND logic 
def get_intersection(results1, results2):
    """Find overlapping timestamps from two search results"""
    shots1 = [(s.start, s.end) for s in results1.get_shots()]
    shots2 = [(s.start, s.end) for s in results2.get_shots()]

    intersection = []
    for s1 in shots1:
        for s2 in shots2:
            start = max(s1[0], s2[0])
            end = min(s1[1], s2[1])
            if start < end:
                intersection.append((start, end))

    return intersection

# Find "red sedan" that is "driving recklessly"
car_results = video.search("red sedan", index_type=IndexType.scene, index_id=car_index)
motion_results = video.search("reckless driving", index_type=IndexType.scene, index_id=motion_index)

combined = get_intersection(car_results, motion_results)
stream_url = video.generate_stream(combined)

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Union (OR)

Find segments that match criteria in either index: Union method showing segments that match criteria in either search index combined with OR logic 
def get_union(results1, results2):
    """Merge timestamps from two search results"""
    all_shots = [(s.start, s.end) for s in results1.get_shots()]
    all_shots += [(s.start, s.end) for s in results2.get_shots()]

    # Sort and merge overlapping intervals
    all_shots.sort()
    merged = []
    for start, end in all_shots:
        if merged and start <= merged[-1][1]:
            merged[-1] = (merged[-1][0], max(merged[-1][1], end))
        else:
            merged.append((start, end))

    return merged

# Find any scene with "red sedan" OR "motorcycle"
car_results = video.search("red sedan", index_type=IndexType.scene, index_id=car_index)
bike_results = video.search("motorcycle", index_type=IndexType.scene, index_id=car_index)

combined = get_union(car_results, bike_results)

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Custom Annotations

Bring your own scene descriptions from external pipelines or manual tagging. Custom scene annotation example showing manually created scene descriptions and timestamps 
from videodb.scene import Scene

# Create scenes with custom descriptions
scenes = [
    Scene(
        video_id=video.id,
        start=0,
        end=60,
        description="Opening credits with cityscape aerial shot"
    ),
    Scene(
        video_id=video.id,
        start=60,
        end=180,
        description="Interview with CEO discussing Q4 results"
    ),
    Scene(
        video_id=video.id,
        start=180,
        end=300,
        description="Product demo showing new features"
    )
]

# Index custom scenes
custom_index = video.index_scenes(scenes=scenes, name="manual_chapters")

# Search the custom index
results = video.search(
    query="product demo",
    index_type=IndexType.scene,
    index_id=custom_index
)

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External Vision Pipeline

Integrate descriptions from your own models: 
# Extract scenes
scene_collection = video.extract_scenes(
    extraction_type=SceneExtractionType.time_based,
    extraction_config={"time": 10, "select_frames": ["middle"]}
)

# Process with your own model
for scene in scene_collection.scenes:
    for frame in scene.frames:
        # Call your custom vision model
        frame.description = your_model.describe(frame.url)

    # Aggregate frame descriptions into scene description
    scene.description = aggregate_descriptions(scene.frames)

# Index the processed scenes
custom_index = video.index_scenes(
    scenes=scene_collection.scenes,
    name="custom_model_index"
)

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Use Cases

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Investigation Workflow

# Index 1: Vehicle identification
vehicle_index = video.index_scenes(prompt="Identify vehicle make, model, color")

# Index 2: Person detection
person_index = video.index_scenes(prompt="Describe people - clothing, actions")

# Index 3: Location context
location_index = video.index_scenes(prompt="Identify location markers, signs, landmarks")

# Query: "Person in red jacket near blue Toyota"
person_results = video.search("red jacket", index_id=person_index)
vehicle_results = video.search("blue Toyota", index_id=vehicle_index)
combined = get_intersection(person_results, vehicle_results)

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Sports Highlights

# Index 1: Player actions
player_index = video.index_scenes(prompt="Describe player actions and jersey numbers")

# Index 2: Game events
event_index = video.index_scenes(prompt="Identify goals, fouls, penalties, celebrations")

# Query: "Player #10 scoring"
player_results = video.search("player 10", index_id=player_index)
event_results = video.search("goal", index_id=event_index)
combined = get_intersection(player_results, event_results)

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Next Steps

Natural Language Query

Search your indexes with plain English

Accuracy Tips

Improve precision and recall