Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.particle.pro/llms.txt

Use this file to discover all available pages before exploring further.

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). Each result is one segment of one episode with bounded transcript windows pinpointing the highest-relevance lines and any highlight clips that overlap the segment. Use this for “find dialogue about a topic”. For “every line naming a person or company” use podcasts/get_entity_mentions instead — entity_slug and company_slug here narrow ranked results, they don’t drive the ranking.

​
Inputs

At least one of semantic_search or keyword_search is required.
FieldTypeRequiredDefaultDescription
semantic_searchstringone 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.
keyword_searchstringone of—BM25 lexical search over dialogue. Use when an exact token must appear (tickers, drug names, model numbers).
entity_slugstringno—Entity slug or ID. Filters results to episodes featuring this entity.
company_slugstringno—Company slug, domain, or ID. Resolves to the company’s linked entity and applies as a filter.
podcast_slugstringno—Podcast slug or ID.
episode_slugstringno—Filter to a specific episode by slug or ID.
segment_typestringno—Segment type filter. One of: INTRO, PERSONAL_BANTER, TOPIC_DISCUSSION, INTERVIEW, TRANSITION, AD, OUTRO.
rolestringno—Speaker/mention role filter on the entity_slug or company_slug filter. One of: guest, host, panelist, correspondent, mention.
sincestringno—Only segments from episodes published on or after this ISO 8601 date.
untilstringno—Only segments from episodes published on or before this ISO 8601 date.
sortstringnorelevanceSort order. One of: relevance, recency.
limitinteger (1–50)no10Results per page.
cursorstringno—Opaque pagination cursor.

​
Output

A markdown document with ## Dialogue matches (N) and one ### Segment title section per match. Each section carries **Episode:**, **Podcast:**, **Published:**, **Episode slug:**, **Segment type:**, **Match:** (the source: semantic, keyword, or hybrid) lines, then one or more bullets formatted Window @ start–end (or Preview @ … for truncated windows) followed by indented dialogue lines. The line that ranked highest for the query is bolded; surrounding context is plain. When highlight clips overlap the segment, they appear under an Overlapping clips: line as bullets. Older results paginate with cursor=<value>. The bolded line means “this is the highest-ranked line for the current query”. This is a different signal from the bolded line in podcasts/get_entity_mentions, which 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
**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: We estimate that automation or augmentation will impact ninety percent of occupations…**
    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
**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…**
---

More results available — pass cursor=r.4gfFC6 to paginate.
The string after **Episode slug:** is what you can feed into podcasts/get_episode for the full transcript or into podcasts/get_entity_mentions (format=detail, episode_slug) for mention windows around a specific person.

​
Example

Agent calls: podcasts/search_dialogue {
  "semantic_search": "how AI affects the labor market",
  "podcast_slug": "all-in",
  "since": "2024-01-01",
  "limit": 5
}
For hybrid ranking, populate both semantic_search and keyword_search: 
Agent calls: podcasts/search_dialogue {
  "semantic_search": "what investors think of GPT-5",
  "keyword_search": "GPT-5",
  "limit": 10
}