Can VideoVector support RAG over both raw media context and structured outputs?

Yes. Applications can retrieve across indexed media context, transcripts, image embeddings, metadata_text, schema-aware extraction outputs, and prompt-run results.

How do agent workflows stay grounded?

Teams can scope retrieval to approved indexes and runs, preserve segment timestamps, return evidence objects, and route outputs into human-review workflows before downstream action.

Developer solution

Video RAG and agentic workflows grounded in the footage

Name: VideoVector
Brand: VectorMethods

VideoVector gives product and engineering teams the retrieval, schema, and workflow layer needed for video RAG and MediaRAG: indexed media, multimodal embeddings, structured prompt outputs, scoped chat sessions, SQL, filters, and timestamped evidence that applications can cite.

On this pageWhy video RAG is different What teams can build Reference architecture Grounding contract Agentic workflow patterns Developer evaluation checklist Connected capabilities

Plan RAG architecture Read search docs

Why video RAG is different

Text RAG usually retrieves passages. Video RAG has to retrieve moments, visual context, spoken context, generated metadata, structured fields, and evidence boundaries from long media assets.

A useful assistant cannot simply say an answer. It needs to point back to the relevant segment, stay scoped to approved indexes, respect prompt-run context, and let humans inspect the source moment behind the response.

VideoVector provides that foundation by connecting ingestion, embeddings, schema-aware extraction, segment analysis, hybrid retrieval, SQL search, chat sessions, exports, webhooks, SDKs, and MCP-accessible tooling. The same foundation can support VideoRAG, AudioRAG, and ImageRAG patterns over approved media.

What teams can build

Internal media copilots

Let operators ask follow-up questions across approved archives, prompt runs, reports, incidents, shows, training libraries, or customer media collections.

Analyst and reviewer workbenches

Combine scoped search, structured filters, SQL, and timestamped evidence so analysts can investigate media without leaving the approved context.

Customer-facing media applications

Build retrieval experiences that let users find clips, scenes, answers, examples, and evidence from their own media collections.

Reference architecture

Ingest media into indexes that represent the collection, tenant, customer, case, archive, or workflow boundary.
Generate transcripts, image embeddings, metadata_text, schema-aware outputs, and segment-level evidence as the retrieval substrate.
Use hybrid retrieval to combine semantic relevance with exact metadata constraints, run IDs, index IDs, SQL queries, and field paths.
Expose scoped chat sessions or application flows that return answers with supporting media segments and structured result payloads.
Deliver results through API, SDK, MCP, exports, and webhooks so applications can automate follow-up work.

Grounding contract

A grounded media assistant should return the answer and the evidence path needed to inspect it.

agentic-video-answer.json

{
  "answer": "The safety issue appears during the loading bay sequence.",
  "evidence": [
    {
      "media_id": "facility_camera_north_2026_05_10",
      "segment_id": "seg_0042",
      "start_timestamp": "00:21:14.000",
      "end_timestamp": "00:21:46.000",
      "matched_fields": ["incident.type", "incident.location", "safety_signals[]"],
      "reason": "Forklift enters pedestrian lane while worker is inside marked crossing."
    }
  ],
  "follow_up_queries": [
    "Find adjacent camera angles for the same time window",
    "List prior incidents involving the north loading bay"
  ]
}

Agentic workflow patterns

Scoped discovery

Keep the assistant inside approved indexes, prompt runs, customers, cases, teams, or time windows so answers do not drift into unrelated media.

Tool-backed refinement

Let the assistant use search, filters, SQL, run inspection, segment retrieval, and export actions as tools instead of relying on one prompt.

Human-review handoff

Return timestamped evidence, uncertainty, and recommended next actions so a reviewer can validate the answer before it affects a workflow.

Developer evaluation checklist

Does every answer include media IDs, segment timestamps, and retrievable evidence?
Can search scope be constrained by tenant, index, run, field path, and workflow boundary?
Can the assistant combine semantic retrieval with exact business filters?
Can generated outputs be versioned through prompt schemas and reused in downstream systems?
Can the workflow continue after the answer through exports, webhooks, SDK calls, or operator tools?