Searchable Video Library

Turn an unstructured video archive into a fully searchable library. Each video is decomposed into scenes with transcriptions, visual embeddings, and metadata. Users search by natural language and jump directly to the relevant moment in any video.

video

text

audio

Multi-Tier

2.9K runs

Run in Builder

from mixpeek import Mixpeek

client = Mixpeek(api_key="YOUR_API_KEY")

# Create video library collection
collection = client.collections.create(
    namespace_id="ns_your_namespace",
    name="video_library",
    extractors=["multimodal-extractor", "text-extractor"],
    params={"video_chunking": "scene-based"}
)

# Upload your video archive
client.buckets.upload(
    bucket_id="bkt_videos",
    url="s3://your-bucket/video-archive/"
)

# Build a search retriever
retriever = client.retrievers.create(
    namespace_id="ns_your_namespace",
    name="video_library_search",
    collection_ids=["col_video_library"],
    stages=[
        {"type": "feature_search", "top_k": 50},
        {"type": "rerank", "top_k": 10}
    ]
)

# Search for specific moments
results = client.retrievers.execute(
    retriever_id=retriever["retriever_id"],
    query={"text": "product roadmap presentation Q3 goals"}
)

for doc in results["documents"]:
    print(f"Video: {doc['root_object_id']}")
    print(f"  Timestamp: {doc['start_time']:.1f}s - {doc['end_time']:.1f}s")
    print(f"  Transcript: {doc['text'][:100]}...")

Feature Extractors

Retriever Stages

rerank

Rerank documents using cross-encoder models for accurate relevance

sort

Related Recipes & Resources

Explore these related resources to deepen your understanding and discover more powerful features

Recipe

Video Transcription & Indexing Pipeline

Automatically transcribe video content with speaker identification, timestamps, and full-text indexing for downstream search and analytics.

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Video Content Analytics Pipeline

Analyze video content at scale to extract insights: scene composition, speaker time, topic distribution, and sentiment across your video library.

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Video RAG Pipeline

Retrieval-augmented generation specifically designed for video content. Decomposes videos into scenes and transcripts, retrieves relevant segments for a given question, and passes them as context to an LLM with precise timestamp citations.

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Recipe

Video Scene Search

Find specific scenes within videos using natural language descriptions. The pipeline detects scene boundaries, generates embeddings for each scene, and enables precise timestamp-level search across an entire video library. Query for visual content, actions, or spoken dialogue.

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Recipe

Semantic Multimodal Search

Unified semantic search across all content types. Query by natural language and retrieve relevant video clips, images, audio segments, and documents based on meaning-not keywords or manual tags.

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Recipe

Feature Extraction

Multi-tier feature extraction that decomposes content into searchable components: embeddings, transcripts, detected objects, OCR text, scene boundaries, and more. The foundation for all downstream retrieval and analysis.

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