- Add POST /api/events/{id}/explain endpoint that fetches event + related events
and asks the LLM for a plain-language explanation with security context
- Add 'Copy' button to raw event modal (uses navigator.clipboard)
- Add 'Explain' button to raw event modal (only when AI_FEATURES_ENABLED)
- Show explanation in modal with markdown rendering
- Add CSS for modal actions and explanation panel
- Add tests for explain endpoint (404, no LLM key, mocked LLM success)
- Add keyword-based intent extraction: 'device' → Intune, 'user' → Directory, etc.
- Broad questions without intent auto-exclude noisy services (Exchange, SharePoint)
- Smart stratified sampling: failures always included, high-value services prioritised
- Fetch up to 1000 events from MongoDB, then curate best 200 for the LLM
- Excluded services noted in LLM prompt and query_info so the admin knows the scope
When a query matches >50 events, the LLM now receives:
- Aggregated counts by service, operation, result, and actor
- A list of failures (up to 10)
- The 50 most recent raw events as samples
This scales to thousands of events without blowing the token budget
or losing signal. The LLM gets a bird's-eye view plus concrete examples.
Also updates the system prompt to handle both individual event lists
and aggregated overviews correctly.
- Bump LLM_MAX_EVENTS default from 50 to 200
- Add total_matched count to /api/ask response
- Include 'Showing X of Y total' header in LLM prompt so the model
knows when its view is a subset and avoids false certainty
- Update system prompt to instruct acknowledging scale when truncated
- Update test mocks to accept new total parameter
- AskRequest now accepts optional filter fields: services, actor, operation,
result, start, end, include_tags, exclude_tags
- ask_question merges NL-extracted constraints with explicit UI filters
- Frontend sends active filter state with every ask request
- Show filter hint below ask input when filters are active
- Add tests for service+result filtering and actor filtering in /api/ask
Bump version to 1.2.0
- Replace max_tokens with max_completion_tokens (required by newer Azure models)
- Remove hardcoded temperature (not supported by all model types)
- Add response body logging on LLM API errors for easier debugging
- Add llm_error field to AskResponse so users know why AI summarisation was skipped
- Show orange warning banner in frontend when LLM is not configured or call fails
- Update AskEndpoint tests to assert llm_error presence
- Add LLM_API_VERSION config for Azure api-version query param
- Detect Azure endpoints and use api-key header instead of Bearer
- Handle base URLs that already include /chat/completions path
- Update .env.example with Azure OpenAI guidance
Features:
- Add /api/ask endpoint for plain-language audit log queries
- Regex-based time/entity extraction (no LLM required for parsing)
- LLM-powered narrative summarisation with OpenAI-compatible APIs
- Graceful fallback to structured bullet lists when LLM is unavailable
- Frontend ask panel with markdown rendering and cited events
Production:
- Harden Dockerfile: non-root user, gunicorn+uvicorn workers
- Add docker-compose.prod.yml with internal networks and health checks
- Add nginx reverse proxy with security headers
- MongoDB no longer exposed externally in production
Tests:
- 29 new tests for ask parsing, query building, and endpoint behaviour
- Fix conftest monkeypatch for routes.ask events collection
Bump version to 1.1.0
