particle_podcast_search_transcripts

Search the podcast catalog by what is said in episodes — by meaning (semantic_search), by exact phrase (keyword_search), or both at once (hybrid ranking). This is THE way to retrieve relevant dialogue, segments, and clips: each result is one segment of one episode with bounded transcript windows pinpointing the highest-relevance lines, plus any highlight clips that overlap the segment, inline on the match.

This tool replaces the old search_dialogue and list_clips tools. There is no separate clip-search tool — relevant clips arrive on these matches. A known episode’s full clip list is particle_podcast_get_episode with include: ["clips"].

Segments vs clips

Segments tile an episode end-to-end — every minute belongs to exactly one segment. Search ranks segments and returns the matching ones.
Clips are sparse, engagement-ranked highlights that overlap some segments. When a clip overlaps a ranked segment, it appears under an Overlapping clips: line on that match.

Use this for “find dialogue about a topic”. For “every line naming a person or company” use particle_podcast_find_mentions instead — person_slug and company_slug here narrow ranked results, they don’t drive the ranking. What semantic_search is not good at:

Don’t ask it…	Use this instead
”Every line naming OpenAI” — a name, not a topic. Resolving the name and filtering finds the mentions; a vector query drifts to adjacent dialogue.	Resolve it, then `particle_podcast_find_mentions` with `company_slug`
An exact token that must appear verbatim — a ticker, model number, or drug name.	`keyword_search` (double-quote a phrase for an ordered match)

Inputs

At least one of semantic_search or keyword_search is required.

Field	Type	Required	Default	Description
`semantic_search`	string	one of	—	Vector-similarity search by meaning. Express the query the way you’d describe the topic to a colleague — paraphrase tolerant. Combine with `keyword_search` for hybrid ranking. Describe a topic, not a name — for a specific person/company/entity filter with `person_slug`/`company_slug`/`entity_slug` (or use `particle_podcast_find_mentions` for every line about them); for an exact token use `keyword_search`.
`keyword_search`	string	one of	—	BM25 lexical search over dialogue. Use when an exact token must appear (tickers, drug names, model numbers).
`person_slug`	string	no	—	Person slug from `particle_person_resolve` or `particle_entity_resolve` (e.g. `"sam-altman"`). Filters results to episodes featuring this person. Also accepts a bare entity slug for non-person entities like places. For “every line about X” use `particle_podcast_find_mentions` instead.
`company_slug`	string	no	—	Company slug, domain, or ID. Resolves to the company’s linked entity and applies as a filter.
`entity_slug`	string	no	—	Knowledge-graph entity slug from `particle_entity_resolve` for the long tail that isn’t a person or company — places, organizations, events, products, concepts (e.g. `"germany"`). Use `person_slug` for people and `company_slug` for companies.
`podcast_slug`	string	no	—	Podcast slug, internal ID, or numeric iTunes ID.
`episode_slug`	string	no	—	Filter to a specific episode by slug or ID.
`segment_type`	string	no	—	Segment type filter. One of: `INTRO`, `PERSONAL_BANTER`, `TOPIC_DISCUSSION`, `INTERVIEW`, `TRANSITION`, `AD`, `OUTRO`.
`role`	string	no	—	Speaker/mention role filter on the `person_slug` or `company_slug` filter. One of: `guest`, `host`, `panelist`, `correspondent`, `mention`.
`since`	string	no	—	Only segments from episodes published on or after this ISO 8601 date.
`until`	string	no	—	Only segments from episodes published on or before this ISO 8601 date.
`sort`	string	no	`relevance`	Sort order. One of: `relevance`, `recency`.
`context`	integer (1–15)	no	1	Lines of surrounding dialogue around each matched line. Widens each match window in place — raise it instead of fetching the full transcript when a match needs more context.
`limit`	integer (1–50)	no	10	Results per page.
`cursor`	string	no	—	Opaque pagination cursor.

Output

A markdown document with ## Dialogue matches (N) and one ### Segment title section per match. Each section carries bulleted KV rows: - **Episode:**, - **Podcast:**, - **Podcast slug:**, - **Published:**, - **Episode slug:**, - **Segment type:**, - **Match:** (the source: semantic, keyword, or hybrid), then one or more bullets formatted Window @ start–end (or Preview @ … for truncated windows) followed by indented dialogue lines. Every line that matched the query is rendered with the entire Speaker: text wrapped in bold (a window can contain several matched lines); surrounding context renders as **Speaker:** text. When highlight clips overlap the segment, they appear under an Overlapping clips: line as bullets formatted Title (TYPE, score N) @ start–end — clip ID: <id>. When more pages exist, a horizontal rule and a **Cursor:** <value> line are appended. Bolded lines mark the lines that ranked for the current query (a window can contain several). This is a different signal from the bolding in particle_podcast_find_mentions, where a bolded line means “this line names the resolved entity” — they answer different questions and can both be true within the same episode for different reasons. Sample (semantic_search="how AI affects the labor market", limit=2):

## Dialogue matches (2)

### Labor Market Transformation from AI

- **Episode:** Where Investment Themes Intersect and Beat Markets
- **Podcast:** Thoughts on the Market
- **Podcast slug:** thoughts-on-the-market
- **Published:** 2026-04-20
- **Episode slug:** where-investment-themes-intersect-and-beat-markets
- **Segment type:** TOPIC_DISCUSSION
- **Match:** semantic
- Window @ 108s–143s
    **Stephen Byrd: Now, at the same time, AI is reshaping the labor market.**
    **Stephen Byrd:** And on net, we see a four percent job loss driven by eleven percent of outright elimination…

### Goldman Sachs Report on AI and Job Displacement

- **Episode:** TNB Tech Minute: Thrive Capital and Andreesen Horowitz Co-lead …
- **Podcast:** WSJ Tech News Briefing
- **Podcast slug:** wsj-tech-news-briefing
- **Published:** 2026-03-03
- **Episode slug:** tnb-tech-minute-thrive-capital-and-andreesen-horowitz-co-lead-multibillion
- **Segment type:** TOPIC_DISCUSSION
- **Match:** semantic
- Window @ 43s–67s
    **Danny Lewis: Speaking of AI, a new research report by Goldman Sachs is predicting the technology will eventually displace eleven million jobs…**

Overlapping clips:
- AI to displace 11 million jobs (SHOCKING, score 84) @ 40s–72s — clip ID: e8342676-…
---

**Cursor:** r.4gfFC6

The string after - **Episode slug:** is what you can feed into particle_podcast_get_episode for the full transcript or clip list, or into particle_podcast_find_mentions (format=detail, episode_slug) for mention windows around a specific person.

Example

Agent calls: particle_podcast_search_transcripts {
  "semantic_search": "how AI affects the labor market",
  "podcast_slug": "all-in",
  "since": "2024-01-01",
  "context": 4,
  "limit": 5
}

For hybrid ranking, populate both semantic_search and keyword_search:

Agent calls: particle_podcast_search_transcripts {
  "semantic_search": "what investors think of GPT-5",
  "keyword_search": "GPT-5",
  "limit": 10
}

REST equivalent: GET /v1/podcasts/episodes/search.
For “every line naming X” use particle_podcast_find_mentions.
For a known episode’s full clip list, use particle_podcast_get_episode with include: ["clips"].
For metadata-only episode discovery (no dialogue), use particle_podcast_list_episodes.

Get started

Tools

particle_podcast_search_transcripts

Segments vs clips

Inputs

Output

Example

​Segments vs clips

​Inputs

​Output

​Example

​Related

Segments vs clips

Inputs

Output

Example

Related