Video search with AI: retrieve clips before answering

Video search with AI retrieves candidate clips from a collection using a query about their content. To make those results reviewable, define clip identities and boundaries, verify the video/text representation pairing, then return a list a person can inspect. Retrieving a visually similar clip does not establish an answer to the user’s question.

This guide designs retrieval over a small permissioned video library. The semantic search explanation covers meaning-based matching; here the additional work is locating identifiable video segments and checking representation compatibility.

This retrieval proposal is example configuration, not a recorded test. The listed interfaces are documented in the catalogue; not production-tested by Ai Flowi as a combined multimodal search chain.

1. Label clips independently of the search system

Begin with recordings you are permitted to process and send to the selected services. You need AI Flowi Workflow, video files, your own TwelveLabs account/API key and an accessible OpenSearch service with suitable credentials. Embedding-provider usage and external service costs are billed separately. The connections reference explains the credential boundary.

Assign a source ID and revision before segmentation. For each clip, keep a unique clip ID, parent source ID, start and end boundaries, permission scope and an independently reviewed content label. These are proposed metadata fields maintained by you; the catalogue does not declare automatic timestamps or a clip-result schema.

Use an authored training library with three labels: CLIP-A shows a tray being placed on a marked bench; CLIP-B contains speech saying “marked bench” while the tray remains beside a shelf; CLIP-C discusses a label printer with no tray movement. The labels specify evaluation targets, not indexed media or observed search hits.

2. Inspect segmentation before indexing

Split Video’s catalogue description is Split a video into multiple clips of specified duration. Its final-segment options are "Truncate", "Overlap Previous" and "Keep Short". Their exact boundary behaviour is not documented by those labels; inspect the generated clips rather than inferring how much content is retained.

If the clip settings table is wider than your screen, scroll within that region or focus it and use the arrow keys.

Use the video analysis loading step for Video File configuration and the Table-to-Data/JSON media handoff.

Split Video’s export does not state the duration unit or accepted limits, so a chosen integer is not a verified seconds setting. For the tray-placement example, a usable segment must show enough of the approach and landing to distinguish placement on the bench from passing beside it. Review neighbouring segments when an action crosses the proposed boundary.

Compare all three final-segment choices on a synthetic source whose decisive action is at the end. Record whether that action is retained and whether it occurs in more than one clip. If overlapping results refer to the same event, group them by source and event range during review. Turning on original-media inclusion can also produce a source-level result alongside segment-level results; verify its actual effect.

3. Check the common representation space

These ports expose embedding model connections. They do not expose a documented list of already computed clip vectors, nor an explicit media input on the video embedding component. The shared selector text alone does not prove that the configured store can ingest these clips and compare their representations with text queries.

Use text embeddings for the general rules on representation compatibility and model changes.

For this video collection, trace one CLIP-A record from its media reference into the ingestion request, then trace the tray-placement question into the text-query request. Record which configured embedding connection each request actually uses. A text query that only searches a human-written clip label does not demonstrate retrieval from visual content: keep labels available for evaluation, but identify whether they also enter the searchable record.

Make the contrasting CLIP-B request part of the same trial. Its narration mentions the bench, while its visible action does not meet the query. Review whether the returned media depicts placement or merely shares the spoken words. This distinguishes the question’s visual requirement from a text-only match without assuming a particular score or provider ranking.

4. Configure the search-store boundary

The catalogue lists OpenSearch (Multi-Model Multi-Embedding). Set opensearch_url, index_name and the authentication mode your service supports. Its listed auth_mode choices are "basic" and "jwt". Supply the applicable credentials privately; review use_ssl and verify_certs for your deployment.

ingest_data accepts Data, DataFrame or Table; docs_metadata accepts Data or JSON. Split Video’s JSON result therefore is not a documented direct connection into ingest_data. Determine what representation the store expects, inspect a prepared clip record and verify that retrieval preserves its identity and media location.

The store exposes one embedding input accepting Embeddings, together with embedding_model_name and vector_field. This export does not establish a way to wire separate video-ingestion and text-query models simultaneously. Resolve that compatibility boundary in an actual controlled configuration before describing a successful chain.

For a query trial, set search_query, a chosen number_of_results and any independently verified filter_expression. Inspect search_results as Table and raw_search as JSON. Neither output declares a fixed score scale, clip-ID key or timestamp schema.

5. Return a candidate list and inspect relevance

Exact spoken-word retrieval may need a separately checked transcript index; this configuration does not prove an exact audio-phrase search capability. Keep it as a comparison task. For each actual returned candidate, record source and clip IDs, reviewed boundaries, query, returned rank and the person’s relevance decision. Preserve a raw score only if the response supplies one, with its configuration context.

Reject inaccessible or wrong-revision clips even when their labels sound relevant. Collapse duplicate views of the same event in the review list without deleting the original result evidence. If the relevant clip is missing, investigate ingestion and query representation before adding answer generation.

What are the retrieval limits of video search AI and the answering handoff?

Do not use this proposal as an identity check, exhaustive event search or security monitoring system. No retrieval benchmark, calibrated score threshold or permission-filter enforcement is established here. A metadata permission label is only descriptive until access control is verified.

After selecting an accessible relevant clip, use visual video analysis for the separate question-and-evidence review. Keep the search result and the eventual answer as distinct artifacts.

Plans: Go from USD8/month billed yearly (USD10 monthly), Plus from USD16 (USD20 monthly), Pro 100 from USD80 (USD100 monthly), Pro 200 from USD160 (USD200 monthly) and Pro 500 from USD400 (USD500 monthly), with 5,000 to 250,000 Flowi Tokens/month and top-ups; 1 token per node run; third-party API and model usage is not included.

Budget segmentation, representation preparation, ingestion and query trials separately using actual execution records. This page provides retrieval acceptance criteria and configuration gaps, with no claim that the multimodal pipeline has run successfully.

By Ai Flowi · Published 10 October 2026.