Explore the sample data

The fastest way to understand Mixpeek is to run it. sample-data is a live namespace loaded with real content — video, ad creatives, PDFs, and a product catalog, already extracted and searchable — and the retrievers below are published publicly, so every example on this page runs with no account and no API key. Copy any curl here into a terminal right now. It will return real results. It is also the working version of the two diagrams on the Introduction page: how content decomposes into layers, and how a retriever reassembles those layers at query time.

Run your first query

No Authorization header. No X-Namespace. Just a query.

curl -X POST "https://api.mixpeek.com/v1/public/retrievers/sample-document-knowledge/execute" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": { "query": "chevron quarterly revenue" },
    "pagination": { "method": "offset", "page_size": 10, "page_number": 1 }
  }'

import requests

resp = requests.post(
    "https://api.mixpeek.com/v1/public/retrievers/sample-document-knowledge/execute",
    json={
        "inputs": {"query": "chevron quarterly revenue"},
        "pagination": {"method": "offset", "page_size": 10, "page_number": 1},
    },
)
for hit in resp.json()["results"]:
    print(hit["score"], hit["title"], "—", hit["description"][:60])

const resp = await fetch(
  'https://api.mixpeek.com/v1/public/retrievers/sample-document-knowledge/execute',
  {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      inputs: { query: 'chevron quarterly revenue' },
      pagination: { method: 'offset', page_size: 10, page_number: 1 },
    }),
  }
);
const { results } = await resp.json();

On this public path, hits come back under results. This differs from the authenticated path — POST /v1/retrievers/{retriever_id}/execute returns hits under documents, where results is a deprecated field that is no longer populated. Read the field that matches the path you called, or you will parse an empty list from a working query.

Diagram 1 — how content decomposes

The first diagram on the Introduction page (source → temporal segments → detected entities) is a real two-tier pipeline here. Each bucket is extracted into a tier-0 collection, and tier-0 is extracted again into a tier-1 collection — the layer you actually search.

Bucket	Tier 0 — segments	Tier 1 — entities
`video-library`	`video-scenes` — scene segments with transcript + on-screen text	`scene-entities` — faces per scene (512-d ArcFace, with crops and bounding boxes)
`ad-creatives`	`ad-visual` — creative-level embeddings	`ad-entities` — faces per creative
`document-archive`	`document-text` — PDF text + embeddings	`document-graph` — a knowledge graph: 7 PDFs fan out into 59 nodes
`product-catalog`	`product-images` (SigLIP) and `product-text` (E5)	`product-attributes` — structured attributes per product

The document-archive row is the clearest illustration: decomposition is a real one-to-many fan-out, not a reformat. Seven PDFs become 59 independently searchable nodes — and each node still knows which PDF it came from, which is what makes the next section work.

Diagram 2 — how retrieval reassembles

The second diagram (query → vector search → entity filter → similarity rank → enrich) is exactly what Scene Moment Reassembly does. The other three apply the same reassembly idea to different content. Every one is runnable as-is.

Scene Moment Reassembly — the diagram, literally

sample-scene-reassembly — all four stages in the diagram’s order: feature_search (vector) → attribute_filter (genre) → rerank (cross-encoder) → document_enrich (join the tier-1 faces back in).

{ "inputs": { "query": "functions in C programming" } }

Returns 8 scene moments — title, description, start_time/end_time, genre — each carrying a face_crop_url for the person detected in that scene. That join is the whole point: the tier-0 scene and its tier-1 entities come back reassembled into one result, so you get “who is on screen, and when.”

Person Across Media — one face, across two ads

sample-person-across-media — feature_search over ArcFace embeddings spanning two buckets’ tier-1 collections (scene-entities + ad-entities) → document_enrich to join back to the source.

{
  "inputs": {
    "query_image": "https://mixpeek-public-demo.s3.us-east-2.amazonaws.com/demo-faces/mixpeek-brand-reel-face.jpg"
  }
}

Returns 2 hits with different titles — Pipeline is product reel and Vector DB scam reel. That is the lesson: the same person found across two different documents, not a single self-match.Swap in any face image URL. A face that does not appear in the sample content correctly returns nothing — that is reverse-face search working, not failing. This retriever needs an image; a text query has no face to embed.

Document Knowledge Explorer — search nodes, get the source

sample-document-knowledge — feature_search over the graph nodes → document_enrich to join back to the parent PDF.

{ "inputs": { "query": "chevron quarterly revenue" } }

Each hit’s description is the matched node text and object_type tells you what it is (e.g. paragraph) — but title is the parent document (Chevron Financial Filing). You search fine-grained pieces and still get the source back. Several hits often share one parent: that is the 7-PDFs-to-59-nodes fan-out from Diagram 1, seen from the query side.

Hybrid Catalog Search — one query, two modalities

sample-hybrid-catalog — a single feature_search running two modalities at once (SigLIP text-to-image over product-images + E5 text over product-text), fused with RRF.

{ "inputs": { "query": "adjustable LED lamp" } }

Returns products with product_name, price, category, and description. A product can appear from both its imagery and its text — the same item reached down two independent paths and merged into one ranking.

RRF scores are rank-based (all hits land near 0.017), so read the order, not the magnitude — the score is not a similarity. The sample catalog is small (8 products), so a broad query returns most of it; ranking is what changes.

Media fields (thumbnail_url, video_segment_url, face_crop_url) are signed URLs minted fresh on every request and expire after about a day. The fields are always present — just don’t expect a URL you copied yesterday to still resolve.

Make it yours

These pipelines use the same stages you would configure yourself. Fork the namespace in Studio for an editable copy, or read how the pieces work:

Retrievers

Compose search stages into a pipeline

Feature extraction

How content is decomposed into searchable layers

Get started

Connect your data

Extract features

Build retrievers

Enrich & organize

Integrate & operate

Resources

Explore the sample data

Run your first query

Diagram 1 — how content decomposes

Diagram 2 — how retrieval reassembles

Make it yours

Retrievers

Feature extraction

​Run your first query

​Diagram 1 — how content decomposes

​Diagram 2 — how retrieval reassembles

​Make it yours

Retrievers

Feature extraction

Run your first query

Diagram 1 — how content decomposes

Diagram 2 — how retrieval reassembles

Make it yours