NEWMVS for embeddings. Managed for files. Both on object storage.Vectors or files. Pick a path.Start →

Where we lead

Find what makes your best ads work

The hooks and patterns in your top-performing video ads, ready for the next brief.

Brand marks, faces and policy risks flagged in every segment of every video.

Get answers from documents

The answer out of every PDF and scan, with the page it came from.

Most visited

Search game film

Describe the play and get timestamped clips back from every game.

Organize a media library

Search and license every film, episode and clip by what is seen and said.

See them running in the showcase →

All solutions →

Look it up against data we host

Screen a frame for IP risk

Every public figure and brand mark in one frame, scored.

Find trademarks in a frame

The registered brand marks it looks most like, ranked for review.

Check copy against trademarked slogans

The registered slogans a line resembles, and who owns them.

Identify public figures

Every public figure a photo matches, with a confidence score.

Classify content for ad targeting

IAB content categories for a page or transcript, across all four tiers.

All retrievers →

Deploy on your own files

Find your best ad creative

Every scene of every ad, and the ones that keep earning spend.

Segments, keyframes and brand-mark matches for a moderation policy.

Search footage archives

Millions of scenes, clustered so you can move through them.

Match video to articles

An article in, a ranked playlist of video out.

Search UX sessions

Transcripts joined to what was on screen, across every session.

Monitor live cameras

RTSP feeds you can search, with alerts from retriever results.

Curate training data

Turn model failures into training and evaluation sets.

All templates →

Run open models on your data

Text, image and video vectors for search.

Objects located in images and video frames.

Pixel masks for the things in each frame.

Video and image Q&A

Ask a question about a frame or a clip.

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Filter

Feature Search

Search and filter documents by vector similarity using feature embeddings

feature_search

Searches vectors fromintfloat/multilingual-e5-large-instruct jinaai/jina-embeddings-v2-base-code google/siglip-base-patch16-224 facebook/dinov2-base google/gemini-embedding-2 written by these extractors

Note: This playground provides simulated output to showcase functionality. No data is processed on our servers. Use this demo to explore the stage's configuration options before integrating it into your retriever pipeline.

Configuration

feature_urisarray

Required

REQUIRED. List of feature URIs to search with, each with input and weight.

collection_idsarray | null

OPTIONAL. Collection IDs to search in.

limitinteger

Maximum number of results to return.

fusion_strategystring

How to combine results from multiple features: 'weighted', 'rrf', 'max'.

filtersobject | null

OPTIONAL. Metadata filters to apply to search.

Output

No output yet

Configure the stage parameters and click "Run Stage" to see the simulated output.

Related to feature_search

Pages on the same stages, extractors and patterns.

Attribute Filter

Filter documents by metadata attribute values using boolean logic

Llm Filter

Filter documents using LLM-based semantic evaluation

Agent Search

LLM-driven multi-step retrieval with iterative reasoning and tool orchestration

Query Expand

Generate query variations with LLM and fuse search results via RRF

Score Threshold

Drop results below an absolute score and return no results when nothing qualifies