Documentation Index
Fetch the complete documentation index at: https://docs.vh3.ai/llms.txt
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Semantic Search Guide
VH3 AI’s search is hybrid — it combines semantic search (understanding meaning) with BM25 keyword search (matching exact terms). This means you get relevant results whether you describe a problem conceptually or use specific technical terminology.How it works
When you submit a search query, the platform:- Embeds your query into a vector representation using the same model that embedded all job text at ingestion time
- Runs BM25 keyword matching in parallel for exact term hits
- Fuses the results using reciprocal rank fusion to produce a single ranked list
Tips for effective queries
Describe the problem, not the solution
- Good
- Less effective
“air conditioning not cooling despite running”
Use natural language
The search engine understands context. Write queries the way you’d describe the problem to a colleague.Combine with filters
For precision, combine semantic queries with structured filters:Search types
| Endpoint | Searches across | Best for |
|---|---|---|
/search/outcomes | AI-enriched job outcomes | ”What happened?” / “How was it fixed?” |
/search/intake | Raw fault descriptions | ”What was reported?” |
/search/summary-sections | Generated report summaries | Finding previous analysis |
Use cases
- Pre-visit preparation — search for similar past faults at a site before attending
- Diagnostic support — find how similar problems were resolved historically
- Trend detection — search for a fault pattern and see if it’s emerging across sites
- Knowledge transfer — new engineers searching the collective experience of the team