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Mixpeek makes the world’s unstructured data programmable. Your AI agent can read text. It cannot watch a video, scan a photo for faces, or search audio by what was said. Mixpeek turns video, images, audio and documents into named semantic features, then serves them through retrievers your agent calls by name.

Start building on Mixpeek

Create a workspace and run your first multimodal search — index your own video, images, audio, or documents in minutes.

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How It Works

1

Index

Upload video, images, audio, and documents to Buckets. Mixpeek runs feature extraction automatically — faces, objects, transcripts, embeddings, and structured metadata all get indexed into searchable Collections.
Indexing multimodal content into semantic layers

Breaking down a video into semantic layers

See this running on real content — 7 PDFs that fan out into 59 searchable knowledge-graph nodes — in the sample data.
2

Search

Build retrieval pipelines that your agent calls. Semantic search, face search, object search, transcript search — chain them together into multi-stage Retrievers and expose them as a single endpoint.
Multi-stage retrieval pipeline

Chaining search stages into a retrieval pipeline

Run this exact pipeline against the sample data right now — no account or API key needed.
3

Integrate

Wire Mixpeek into your agent as a LangChain tool, an MCP server, or a direct REST call. Your agent sends a query, gets structured results back, and acts on them.
Already have embeddings? Skip extraction entirely — bring your own vectors and search instantly with the Mixpeek Vector Store. Sixty seconds to first query.

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Quickstart

Get Started

Index multimodal content and search it in under 10 minutes — LangChain, MCP, or REST

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What Can You Build?

Pick an outcome and follow the guide — each one is end-to-end and copy-pasteable.

Search video

Find moments by what’s shown or said — visual + speech embeddings

Reverse image & video search

Use an image or clip as the query to find visually similar media

Hybrid search

Combine dense vectors with keyword/BM25 and attribute filters

Rerank for precision

Push the most relevant results to the top with a cross-encoder

Cluster content

Group, label, and visualize a collection automatically

Build a taxonomy

Classify content against your own hierarchy with a multimodal join

Secure per-user retrieval

Enforce access control with built-in ACLs or external OpenFGA

Bring your own vectors

Use Mixpeek as a standalone vector store — upsert and query instantly

Auto-tune relevance

Improve ranking automatically from click and reward signals

Migrate embedding models

Re-extract, validate, and cut over to a new model with no downtime

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What Gets Extracted

Each extracted feature becomes an independently searchable document. A single video can produce hundreds of documents — one per face, one per transcript segment, one per scene.

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Key Concepts

  • Namespaces isolate data between tenants, environments, or projects. Every API request includes a namespace header.
  • Buckets hold your raw files. Upload once, process many ways.
  • Collections define what gets extracted. Each collection runs a feature extractor (CLIP, Whisper, LayoutLM, etc.) against objects in a bucket.
  • Retrievers are search pipelines you configure in JSON. Chain stages together — vector search, face matching, filters, re-ranking — and expose the result as one endpoint your agent calls.

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Next Steps

Core Concepts

Understand namespaces, buckets, collections, and retrievers in depth

Data Model

See how namespaces, buckets, collections, objects, and features fit together

Feature Extractors

Learn what each extractor does and how to configure it

Tutorials

Step-by-step guides for common use cases