Can structured fields and vector search be combined?

Yes. Teams can use structured constraints alongside semantic retrieval so production search workflows do not have to choose between precision and recall.

What is the difference between SQL search and vector search?

Vector search finds semantically relevant media moments. SQL search is for structured analysis, aggregations, selected columns, and saved-query workflows over completed extraction execution data.

Does multimodal search work only on raw media?

No. VideoVector can search across indexed media context and the structured outputs generated during extraction workflows.

Solutions

Multimodal media search for large libraries

Name: VideoVector
Brand: VideoVector

Find the moment by describing what was shown, said, heard, or extracted. VideoVector combines semantic search, visual lookup, video vector retrieval, filters, SQL, and scoped follow-up so users can move from query to source timestamp.

On this pageWhy media search breaks down Search workflow Search modes Technical indicators Operational fit Why this is stronger than transcript search Agentic search workflows Example use cases Implementation path

Try now SDK docs

Why media search breaks down

Large media libraries fail search when filenames are incomplete, transcripts miss visual context, legacy tags drift, and different teams use different vocabulary.

VideoVector combines text, image, multimodal embeddings, metadata_text, structured fields, selected indexes, SQL media search, and conversational refinement so users can move from broad discovery to exact moments faster.

Search workflow

One search foundation can serve direct search, hybrid filters, SQL analysis, and agentic retrieval.

Prepare the searchable foundation

Media is uploaded or imported into indexes, then enriched through transcripts, image embeddings, metadata_text, and structured extraction outputs.

Search the way teams think

Users can ask for concepts, scenes, people, visual references, policy moments, or structured field combinations without knowing exact filenames or legacy tag names.

Narrow and act

Results can be constrained by execution, index, field, timestamp, SQL query, or conversational scope, then passed into review, reporting, export, or downstream systems.

Search modes

Hybrid search lets operators combine semantic recall with structured precision without splitting the retrieval layer across separate tools.

Semantic text search

Search by concept, event, or intent instead of memorizing brittle exact-match strings.

Image and multimodal retrieval

Use images and cross-modal retrieval patterns to locate visually similar content alongside text-driven workflows.

Vector and metadata filter search

Combine video vector embedding search with exact constraints such as nested field paths, run IDs, index IDs, timestamps, and selected fields.

SQL and agentic media search

Use SQL for structured analysis and agentic chat sessions for scoped multi-turn retrieval over the same searchable media substrate.

Technical indicators

Use video vector embedding search when retrieval has to work across speech, visuals, schema outputs, and metadata_text.
Use hybrid vector and metadata search when production users need both high recall and exact constraints.
Use agentic media search when users need follow-up questions, scoped turn history, streaming answers, and tool trace visibility.

Operational fit

Editorial and archive teams can surface relevant footage faster.
Security and public-sector operators can narrow broad video sets before deeper inspection.
Streaming operations can connect retrieval to downstream tagging, audit, and delivery tasks.

Why this is stronger than transcript search

Transcript-only search is useful when the answer is spoken clearly. It is much weaker when the important signal is visual, implied by scene context, stored in structured metadata, or spread across many moments in a long asset.

VideoVector searches across the media context teams actually use: transcripts, visual embeddings, extraction outputs, metadata_text, and structured fields. That gives operators better recall without losing the precision needed for production review.

For engineering leaders, the advantage is that one retrieval foundation can serve multiple interfaces. A search box, SQL workflow, agentic chat assistant, and downstream API can all work from the same indexed media substrate instead of creating separate transcript, vector, visual, and SQL tools.

Agentic search workflows

Use agentic chat-session based retrieval when analysts need follow-up questions instead of one fixed query.
Scope conversational search to the right indexes and extraction executions so assistants stay grounded in the intended evidence set.
Expose streaming answers and tool traces for review copilots, analyst workbenches, and operator-facing assistants.

Example use cases

Teams adopt multimodal search when operators need faster retrieval across both indexed media context and structured extraction output.

Broadcast archive retrieval

Surface interviews, clips, and program segments faster across large historical catalogs using semantic and structured search together.

Streaming catalog discovery

Support discovery, QA, and operations teams that need to find the right moments quickly when legacy tags are incomplete.

Security incident narrowing

Reduce broad surveillance footage into smaller, defensible sets by combining concept search, visual similarity, and exact metadata filters.

Implementation path

Start with the Search model page to choose direct, multimodal, SQL, filter, multi-run, or agentic retrieval.
Use schema-aware extraction and video embeddings first when search quality depends on domain-specific fields and generated media context.
Track query scopes deliberately by index ID, run ID, and field paths so results stay grounded in the intended corpus.