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Vector Store (MVS)

Bring your own vectors. Dense, sparse, and BM25 search on object storage.

Managed Indexing

Connect a bucket and auto-extract scenes, faces, OCR, transcripts, and embeddings.

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Compose multi-stage search in <100ms: filter, join, rerank.

Feature Extractors

Typed pipelines for faces, scenes, transcripts, OCR, fingerprints.

S3, GCS, R2, Mux, LangChain, MCP, and more. Connect your stack.

Generate and store embeddings from 50+ models, then search them.

By Industry

Map, search, and reuse the moments that perform. Plugs into iconik & Mux.

Talent search, brand safety, creative analytics.

Scene search, recommendation, archive access.

Visual search, PDP enrichment, catalog QA.

Lecture search, transcript Q&A, content safety.

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By Use Case

Face & Person Search

Find anyone across video libraries in milliseconds.

IP & Copyright Detection

Logos, songs, faces: one pipeline, one report.

Visual Taste & Recs

Scene-similarity ranked recommendations with RL.

Brand & Ad Safety

Pre-publish content screening at bid-time speeds.

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API reference, SDKs, recipes, and architecture guides.

Launches, deep dives, and field notes from our engineers.

Browse supported HuggingFace models by task and modality.

See what teams are building with Mixpeek.

Education

Vendor-neutral deep dives on perception, retrieval, and embeddings.

Multimodal University

Fundamentals of multimodal retrieval, modules + certs.

Every term you need: embeddings to re-rankers.

Talks, demos, and customer sessions on demand.

Mixpeek vs. Pinecone, Weaviate, Twelve Labs, more.

Mission, team, and the multimodal vision.

We're hiring across research, infra, and design.

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White-glove 30-day production pilot for new customers.

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Back to Glossary

What is Latent Semantic Indexing (LSI)

Latent Semantic Indexing (LSI) - Concept-based retrieval

A technique that uses singular value decomposition to identify patterns in the relationships between terms and concepts in unstructured data.

How It Works

LSI analyzes relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. It reduces the dimensionality of the data, capturing the underlying structure in the data.

Technical Details

LSI uses singular value decomposition (SVD) to decompose the term-document matrix into three matrices, capturing the most important relationships. This process reduces noise and reveals the latent semantic structure.

Best Practices

Use LSI for concept-based retrieval
Combine with other retrieval techniques
Implement efficient computation pipelines
Regularly update document collections
Monitor LSI performance

Common Pitfalls

Ignoring document collection updates
Over-relying on LSI alone
Inefficient computation pipelines
Poor performance monitoring
Lack of comprehensive analysis

Advanced Tips

Use hybrid retrieval techniques
Implement LSI optimization
Consider domain-specific adjustments
Optimize for specific use cases
Regularly review LSI performance

Managed Mixpeek

Put multimodal search to work

Connect a bucket and Mixpeek runs the whole multimodal search pipeline for you: extraction, indexing, and search over your own objects. No models to wire up, nothing to host.

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MVS · bring your own

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Keep your embeddings on your own cloud and run dense, sparse, and BM25 search directly on object storage. From $25/mo.

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Related Terms

ACID API Blob Storage CLIP Embedding