v0.8.4-beta

What's New in v0.8.4-beta

LLM Cost Optimization Suite

This release introduces a comprehensive set of 16 cost optimization techniques that reduce LLM API spending by up to 80-95% without sacrificing analysis quality. All optimizations are configurable through the web UI with sensible defaults.

New optimizations:

• O1: Zero-Score Window Skip -- when all events in an analysis window scored 0, the meta-analysis LLM call is skipped entirely
• O2: Zero-Score Event Filter -- events that scored 0 are excluded from the meta-analysis prompt, reducing input tokens
• O3: Per-Task Model Selection -- use different (potentially cheaper) models for scoring, meta-analysis, and RAG queries
• O5: Extended Score Cache -- default cache TTL increased from 60 to 360 minutes, reducing repeat scoring calls
• O6: RAG Usage Tracking -- Ask AI queries now tracked in the LLM usage dashboard with per-model cost estimates
• O7: Configurable Context Window -- control how many previous analysis summaries are included as LLM context

Full UI Configuration

All AI pipeline parameters are now configurable through the Settings page:

• Pipeline Settings -- pipeline interval, analysis window size, scoring limit per run, meta-analysis weight
• Per-Task Model Overrides -- independent model selection for scoring, meta-analysis, and RAG
• O1/O2 Toggles -- enable/disable zero-score optimizations in Token Optimization section
• Context Window & Recurring Lookback -- tune meta-analysis context depth in Meta-Analysis Tuning

Bug Fixes

• Fixed high-score prioritization using wrong event indices after O2 filtering
• Fixed LLM usage tracking reporting wrong model name when per-task override is active
• Aligned meta-analysis config defaults between API and runtime (max_new_findings_per_window, max_open_findings_per_system)

Documentation

• Added comprehensive LLM Cost Optimization section to README with flow diagram, 16-technique comparison table, cost tracking overview, and typical monthly cost estimates per model