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 extraction execution 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 approval workflows before downstream action.

Can agents ask follow-up questions across different videos and media types?

Yes. Teams can scope retrieval to approved indexes, extraction executions, cases, and collections, then let agents search across video, audio, images, transcripts, metadata_text, and structured outputs while preserving source IDs and timestamps.

Developer solution

Video RAG and agentic MediaRAG grounded in the footage

Name: VideoVector
Brand: VideoVector

Video RAG works when every answer can point back to the source moment. VideoVector gives teams indexed media, multimodal embeddings, extraction outputs, scoped agentic chat sessions, SQL, filters, and evidence objects for assistants that can retrieve, answer, and cite across video, audio, and images.

On this pageWhy video RAG is different Application patterns RAG patterns for multimodal media Reference architecture Grounding contract Agentic workflow patterns What agentic MediaRAG should preserve 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 extraction execution context, and let authorized users inspect the source moment behind the response.

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

Application patterns

Internal media copilots

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

Analyst 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.

Dataset and evaluation workflows

Search for rare events, negatives, edge cases, examples, and similar sequences across training media with source timestamps and structured metadata.

RAG patterns for multimodal media

VideoRAG over scenes and segments

Retrieve exact video moments, scene context, actions, entities, transcripts, and extracted metadata so answers cite the relevant timestamp.

AudioRAG over speech and sound

Search dialogue, sound events, speaker context, summaries, and audio-derived fields for podcasts, field recordings, calls, broadcasts, and evidence media.

ImageRAG over frames and visual evidence

Use reference frames, visual similarity, object context, and image-derived metadata to find matching scenes and still-image evidence.

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 agentic 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, retrieval scope, source media, and evidence path needed to inspect it.

agentic-video-answer.json

{
  "assistant_session": "ops_media_rag_042",
  "query": "Find similar loading bay safety issues across approved media this month.",
  "retrieval_scope": {
    "indexes": ["warehouse_cameras_may_2026", "incident_uploads_may_2026"],
    "media_types": ["video", "audio", "image"],
    "allowed_tools": ["vector_search", "filter_search", "sql_search", "segment_lookup"]
  },
  "answer": "The safety issue appears during the loading bay sequence.",
  "evidence": [
    {
      "rag_mode": "VideoRAG",
      "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."
    },
    {
      "rag_mode": "AudioRAG",
      "media_id": "incident_radio_2026_05_12",
      "timestamp": "00:03:18.000",
      "reason": "Radio call mentions blocked visibility at the same loading bay."
    },
    {
      "rag_mode": "ImageRAG",
      "media_id": "safety_still_2026_05_14",
      "frame_timestamp": "00:00:00.000",
      "reason": "Reference image matches the obstructed pedestrian-lane layout."
    }
  ],
  "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, extraction executions, customers, cases, teams, or time windows so answers do not drift into unrelated media.

Tool-backed refinement

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

Controlled handoff

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

What agentic MediaRAG should preserve

The index, source, camera, asset, run, segment, timestamp, and workflow boundary behind each retrieved moment.
Visual context, spoken context, image context, metadata_text, embeddings, structured fields, notes, and uncertainty markers.
Prior search results, evidence sets, report outputs, labels, corrections, and follow-up actions that should inform future turns.
Scope controls so agents retrieve only from approved collections, cases, customers, teams, or time windows.
Export paths into data pipelines, operational dashboards, records systems, media applications, or internal copilots.

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 extraction schemas and reused in downstream systems?
Can the workflow continue after the answer through exports, webhooks, SDK calls, or operator tools?