deep_search - Augent

Unlike search_audio which matches exact keywords, deep_search finds content by meaning. “challenges of raising money” will match segments about fundraising difficulties even if those exact words aren’t used.

Example

Request:

{
  "audio_path": "/Users/you/Downloads/podcast.webm",
  "query": "challenges of raising venture capital"
}

Response:

{
  "query": "challenges of raising venture capital",
  "results": [
    {
      "start": 342.1,
      "end": 348.5,
      "text": "Getting investors to take us seriously was the hardest part of the whole journey.",
      "timestamp": "5:42",
      "similarity": 0.7823
    },
    {
      "start": 891.0,
      "end": 897.2,
      "text": "We pitched over fifty firms before anyone wrote a check.",
      "timestamp": "14:51",
      "similarity": 0.7104
    }
  ],
  "total_segments": 245,
  "model_used": "tiny"
}

Parameters

Parameter	Required	Default	Description
`audio_path`	Yes	—	Path to the audio/video file
`query`	Yes	—	Natural language search query
`model_size`	No	`tiny`	Whisper model size for transcription
`top_k`	No	`5`	Number of results to return
`output`	No	—	File path to save results (`.csv` or `.xlsx`)
`context_words`	No	`25`	Words of context per result. Use `150` for full evidence blocks when answering questions
`dedup_seconds`	No	`0`	Merge matches within this many seconds of each other. Use `60` for Q&A to avoid redundant results
`clip`	No	`false`	Export video clips around each match. Requires the audio to have been downloaded from a URL
`clip_padding`	No	`15`	Seconds of padding before and after each match for clip export

Notes

Embeddings are stored in memory. The first search on a file computes embeddings for all segments. Subsequent searches on the same file are instant.

Results are ranked by cosine similarity (0 to 1). Higher similarity = closer match.