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- Added `optimization-plan.md` detailing strategies to reduce response latency and improve training throughput. - Enhanced performance analysis in `performance-analysis.md` with identified bottlenecks and completed optimizations. - Created `productContext.md` summarizing project goals, user scenarios, and implementation priorities. - Developed `markov-store.ts` for high-performance serialized chain storage with alias method sampling. - Implemented database performance indexes in `1704067200000-AddPerformanceIndexes.ts`. - Introduced `markov-worker.ts` for handling CPU-intensive operations in separate threads. - Established a worker pool in `worker-pool.ts` to manage multiple worker threads efficiently.
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[MEMORY BANK: ACTIVE] productContext - Markov Discord
Date: 2025-09-25
Project: Markov Discord — lightweight Markov-chain based Discord responder
Summary:
- This project builds and serves Markov chains derived from Discord message data to generate bot responses with low latency and high throughput.
Problem statement:
- Current response generation and training paths can be CPU- and I/O-bound, causing high latency and slow bulk imports.
Goals & success metrics:
- End-to-end response latency: target < 500ms (95th percentile).
- Training throughput: target 1,000,000 messages/hour on dev hardware.
- Memory during training: keep max heap < 2GB on 16GB host.
Primary users:
- Bot maintainers and operators who run training and rollouts.
- End-users in Discord guilds who interact with the bot.
Key usage scenarios:
- Real-time response generation for user messages in active channels.
- Bulk training/imports from historical message archives.
- Canary rollouts to validate performance before global enablement.
Constraints & assumptions:
- Runs primarily on single-node hosts with 16GB RAM (dev).
- Uses SQLite as primary storage unless replaced per optimization plan.
- Backwards compatibility required for serialization across releases.
Dependencies & related docs:
Implementation priorities (short):
- Persist precomputed chains, alias sampling, worker threads, LRU cache.
- See detailed tasks in the optimization plan linked above.
Operational notes:
- Feature flags and toggles live in
config/config.json. - Instrument metrics (latency histograms, cache hit ratio, DB durations).
Stakeholders & owners:
- Owner: repository maintainer (designate as needed).
Open questions:
- Confirm canary guild and traffic profile for 24h test.
Next actions:
- Create
src/markov-store.ts,src/workers/markov-worker.ts, bench scripts, and update config toggles (seememory-bank/optimization-plan.md).
End.