Multiple Indexes

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
)

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"
)

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"
)

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)

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)

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
)

# 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"
)