release-2026.06-rag-preview
Pre-release
Pre-release
·
6 commits
to main
since this release
Channel
Preview
Summary
This release introduces the first RAG preview layer for the DevOps Incident Triage Model.
The project remains classifier-first. The existing Transformer-based incident classifier is kept intact, while this release adds a preview retrieval layer that can retrieve evidence from domain-specific runbook documents.
LLM-assisted remediation generation is not included yet. That will be introduced in a later incident-assist beta release.
Included
POST /retrieveFastAPI endpoint- Domain-aware runbook retrieval flow
- Local TF-IDF sparse retrieval index for preview use
- Evidence-based retrieval response schema
- Section-level citations from runbook documents
- Retrieval metadata including index type, embedding model placeholder, and latency
- Prometheus-style retrieval metrics:
ditri_retrieval_requests_totalditri_retrieval_latency_seconds
- RAG roadmap documentation
- RAG evaluation plan
- Runbook placeholder document structure
- Release evidence document for
release-2026.06-rag-preview
Validation
GitHub Actions on main: Success
Local validation:
uv run --extra dev --extra api ruff check .
uv run --extra dev --extra api pytest -qResult:
ruff check: passed
pytest: 47 passed, 10 skipped
FastAPI smoke validation:
GET /health -> 200
POST /retrieve -> 200
GET /metrics -> 200
Known Limitations
- This is a preview release, not a production RAG backend.
- Retrieval currently uses a local TF-IDF sparse index, not a production Vector DB.
/assistand LLM-generated remediation guidance are not implemented yet.- Runbooks are portfolio-grade placeholders, not real production incident records.
- The current dataset remains synthetic.
- Retrieval quality still needs formal evaluation with retrieval hit rate, groundedness, citation coverage, and hallucination checks.
Next Release Direction
The next planned release is:
release-2026.07-incident-assist-beta
Planned focus:
- Classifier + RAG integration
POST /assistAPI design and implementation- LLM-generated remediation guidance
- Root cause candidate generation
- Evidence citations from retrieved runbooks