Mixpeek Recipes
Composable ML patterns for unstructured data retrieval & analysis
28 recipes available
Feature Extraction
Turn raw media into structured intelligence
Semantic Multimodal Search
Find anything across video, image, audio, and documents
Hierarchical Classification
Auto-label content into structured taxonomies
Multimodal RAG
LLMs that cite real clips, frames, and documents
Clustering & Theme Discovery
Reveal structure you didn't know existed
Semantic Join
Link extracted content to business context
Anomaly Detection
Spot unusual content automatically
Dataset Versioning
Rebuild any dataset state deterministically
Semantic Drift Detection
Track how your data changes over time
Multimodal RAG Pipeline
Feed multimodal context to LLMs for grounded answers
Metadata Enrichment Pipeline
Transform raw content into structured, queryable data
Image Similarity Search Pipeline
Find visually similar images with state-of-the-art models
What are Mixpeek Recipes?
Mixpeek recipes are practical blueprints for multimodal retrieval pipelines. They demonstrate how to combine feature extractors, retriever stages, and enrichment resources to solve real ML problems.
Composable Patterns
Each recipe shows how to combine extractors, stages, and enrichment resources. Copy the pattern and customize for your use case.
ML-Native Workflows
Semantic search, anomaly detection, dataset engineering—recipes are organized by the ML patterns you're trying to implement.
Production Ready
Clone templates directly into Mixpeek Studio. Each recipe includes Python snippets, retriever stages, and enrichment configurations.