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How to Build an AI-Powered Database for Archival Media Assets

Turn archival images, video, and audio into searchable database records. Tim combines embedded metadata, web research, visual captions, Whisper transcripts, and fused embeddings so researchers can find assets by meaning, not only exact keywords.

How to Build an AI-Powered Database for Archival Media Assets

Source episode · 04:16

How I AI: Tim McAleer's AI Workflows for Documentary Filmmaking at Florentine Films

with Claire Vo

Tim builds from a one image Python script to a REST API that extracts file metadata, captions sampled video frames, transcribes audio, and creates fused visual and text embeddings.

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Before you start

What you need

  • A media collection with stable asset IDs
  • A database or asset system that can call a webhook or API
  • Vision, speech to text, and embedding models
  • Available embedded metadata and source URLs

What you’ll make

A media database with sourced descriptions, time aligned video summaries, and semantic similarity search across images, video, and audio.

Tools used

Step by step

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

5 steps

Step01

Create an Initial Image Description Script

Start with one image and one script. Send the file to a vision model and return a plain visual description plus explicit uncertainty. Keep credentials in environment variables rather than a text file in the workspace.

Example prompt
Create a Python script that loads the JPEG at the workspace root, sends it to the configured vision API, and returns a factual visual description. Separate directly visible details from uncertain inference. Read credentials from environment variables and include setup and run commands.

Start with a simple, single-purpose script to validate the API connection and basic functionality before adding complexity.

Step02

Enhance Prompts with Embedded Metadata

Extract EXIF, IPTC, XMP, filename, and source URL data before asking for a description. Pass those fields as verified context and tell the model not to contradict or embellish them. Preserve the raw metadata beside the generated text.

Example prompt
Update the script to extract all available embedded metadata and include it as a clearly labeled verified context block in the vision request. Preserve the raw fields in the output. The model must distinguish source facts from visual inference and leave unsupported claims unknown.
Step03

Expand to Video and Audio Processing

Expand the pipeline to video by sampling frames at a practical interval, captioning each frame with its timestamp, and transcribing audio with timestamps. Send the ordered event stream to a reasoning model for one coherent summary.

Example prompt
Build a video pipeline that samples one frame every five seconds, creates a short timestamped caption for each frame, transcribes the audio into timestamped segments, and merges both streams in time order. Generate a final summary that describes the visible action and spoken content without inventing details between samples.

Using a more cost-effective model for initial frame captioning can significantly reduce costs when processing large video files.

Step04

Implement Semantic Search with Vector Embeddings

Create two embeddings for each asset: one from the thumbnail or representative frame and one from the verified description. Normalize and combine them, then store the vector with the asset ID, media type, date, source, and searchable text.

Step05

Build a Similarity Search Feature

Add find similar and semantic query endpoints to the database. Return the matching assets, similarity scores, and useful filters. Evaluate the results with searches researchers already struggle to express as exact keywords.

What good looks like

  • Each asset keeps its stable ID and source information.
  • Descriptions distinguish verified metadata from visual inference.
  • Video summaries combine sampled frames with time aligned transcripts.
  • Similarity search returns thematically or visually related assets without exact keyword matches.

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After the steps

Runbook notes

How to recover when the loop fails and where human judgment helps.

Recover

If it goes sideways

The model invents a date, person, or location
Pass embedded metadata and source page facts separately, require the model to label visual inference, and leave unsupported identity fields blank.
Video processing is too slow or expensive
Sample frames at a fixed interval, use a lower cost model for captions, transcribe audio once, and reserve the reasoning model for the final synthesis.
Frame captions and transcript refer to different moments
Store timestamps for every sampled frame and transcript segment, then sort both event streams before the final summary.
Similarity results are visually close but editorially irrelevant
Tune the weight between image and text embeddings, add source and date filters, and test with real researcher queries.

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