Intelligence · Component · semantic-search-rag
Semantic intelligence search and RAG chat
What it does
Embed and match news/entities and answer questions using retrieved news plus entity knowledge context.
Core pattern
Document and entity context retrieval + grounded questions and answers
- Input
- Question or entity name
- Output
- Ranked evidence and contextual AI answer
Key parameters
| Name | Type | Default | Values |
|---|---|---|---|
| query | string | — | — |
| limit | integer | 20 | 1–1000 |
| similarity_threshold | — | — | — |
| entity_context | — | — | — |
| model | — | — | — |
Maturity and limits
Vector methods and tactical/strategic chat branches implemented; not a confirmed Qdrant deployment. Static source inspection only; no runtime, provider, deployment or test-pass claim.
composer.json declares MIT but retains Laravel scaffold metadata; confirm application rights. Source/site terms, personal data and LLM accuracy matter. Several schedules are commented out; tests are mainly scaffold auth tests.
Used in pipelines
Industry tags
- Media & contentSource-supported industry
For work involving editorial material, programs and reader services, use document and entity context retrieval followed by grounded questions and answers.
- Legal servicesTransferable use
For clause lookup in an authorized contract library, use document and entity context retrieval followed by grounded questions and answers.
- EnergyTransferable use
Support “Link change topics” with document and entity context retrieval followed by grounded questions and answers.
Skills pack
Contract scaffold and deterministic mock
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