- Add BATCH_RETRY_LIMIT and BATCH_RETRY_DELAY constants after DEFAULT_BATCH_SIZE
- Add _insert_batch_with_retry() helper with exponential backoff
- Replace both bare try/except insert blocks in _build_collection_from_bulk
with calls to the retry helper so transient ChromaDB errors are retried
up to BATCH_RETRY_LIMIT times before a batch is counted as failed
Adds a buildStateVector(int seat) method to GameSession that produces
a float[29] global scalar vector matching STATE_DIMS layout in
ml/config/scope.py:
Per-player block (14 features × 2 players = 28):
[0] life_total / 40.0
[1] commander_damage / 21.0 (stub 0.0 until real Forge tracks it)
[2] mana_available / 20.0 (stub 0.0 until mana pool wired)
[3] commander_tax / 10.0
[4] cards_in_hand / 15.0
[5] cards_in_graveyard / 100.0
[6] creatures_on_battlefield / 30.0
[7] total_power_on_board / 100.0 (stub = creature count until P/T data)
[8] is_active_player (0 or 1)
[9] phase one-hot[0] main1
[10] phase one-hot[1] combat
[11] phase one-hot[2] main2
[12] phase one-hot[3] end
[13] (padding / reserved)
Global:
[28] turn_number / max_turns (25)
buildStateSnapshot() now includes "state_vector": float[] so that
PolicyClient.decide(JsonObject) sends a machine-readable vector the
Python /api/policy/decide endpoint can pass directly to the encoder
without re-parsing the nested human-readable fields.
Stub fields (mana_available, commander_damage, total_power_on_board)
are zero until real Forge board state is wired in Step 4.
Wires forge_episode_generator into the PPO training loop:
- New --data-source flag: synthetic (default) | forge | mixed
- New --forge-results-dir, --forge-batch-id, --forge-weight flags
- ForgeDataSource class: runs run_forge_pipeline() in background thread,
drains episodes into RolloutBuffer via episodes_to_buffer()
- Mixed mode: blends Forge episodes + synthetic self-play per iteration
using --forge-weight ratio (0.0–1.0)
- Synthetic path unchanged — no regression for existing training runs
- _get_cards_folder() now tries both forge-gui/res/cardsfolder and res/cardsfolder
so FORGE_DIR can point to either the repo root or the forge-gui subdirectory
- Add reset_forge_cache() to allow hot-reloading without server restart
The previous backfill operated on seen_names dict which silently
ignored basics already present (dict keys are unique). Now backfills
directly on card_entries list after Scryfall fetch, incrementing
quantity on existing basics or appending new CardEntry objects.
Distributes shortfall evenly across commander color identity basics.
* feat(rag): Phase 2 — ChromaDB vector index over bulk card DB
* feat(rag): add routes/rag.py with build, search, status endpoints
* Update lab_api.pyfeat(rag): register rag_router in lab_api.py, remove old inline rag_status
* Update requirements.txtfeat(rag): add chromadb>=0.5.0 to requirements.txtfeat(rag): add chromadb to requirements.txt
- Add SubstitutionMethod enum to track how each card was resolved
- smart_substitute() now always returns a valid card string (never None/drops)
fallback chain: exact -> fuzzy -> functional analog -> original kept -> basic land
- apply_substitutions() loop no longer silently drops cards on failure
- _pad_to_99() circuit breaker added before BuildResult validation
- Pre-validation count logging per category for fast regression diagnosis
- Startup assertion: sum(SLOT_BUDGET) == 99 raises on app boot if misconfigured
- Prompt injection: slot budget + exact card count requirement sent to LLM
- run_substitution() anthropic fallback failure now pads with basic land instead of leaving slot missing
- SubstitutionBatchResult gains padded_slots field (> 0 = circuit breaker fired)
- anthropic_client.py: all 4 methods (chat_structured, chat_plain, achat_structured, achat_plain) now use .messages.stream() context manager
- coach_service.py: inline aclient.messages.create() replaced with async streaming via aclient.messages.stream()
- _parse_response() updated to accept pre-extracted text string (since stream gives us text directly)
- check_status() left as non-streaming (max_tokens=10, safe from the 10min rule)
Completes the sim → weights → deck builder feedback loop.
- Add run_update_weights() to overnight-run.py
- Add auto_update_weights config key (default True)
- Add --no-update-weights, --weights-min-games, --weights-lr CLI flags
- Runs before neural net training so both pipelines get fresh data
Closes#112
- Add sim_insights.py to translate learned_weights.json into LLM prompt text
- Add sim_context param to suggest_cards() and enforce_deck_ratios() in ollama_client.py
- Wire get_sim_insights() into build_deck pipeline (Steps 4 & 6)
- Fully backwards compatible — no-op until real sim data exists
- distillation.html: tooltips on all 10 params + ELO Ratings header
- simulator.html: tooltips on Opponent, Games, Record logs, API Endpoint, Model, Temp, Timeout
- coach.html: tooltips on Power Level, Meta Focus, Budget, Focus Areas, Similar Cards Results
- deckgenerator.html: tooltips on Target Bracket, Use my collection, Smart Substitution, AI Model
- All pages now share tooltip styles from global.css (no per-page duplication needed)
- Move .tip-icon tooltip CSS from training.css into global.css so all pages share it
- distillation.html: 8 param tooltips (Max Generations, Convergence Window, Supervised Epochs, Supervised LR, PPO Iterations, PPO Episodes/Iter, PPO LR, Min Win Rate Gate, Opponent, Playstyle) + ELO Ratings header
- simulator.html: tooltips on Opponent, Games, Record turn logs, API Endpoint, Model, Temp, Timeout
- coach.html: tooltips on Target Power Level, Meta Focus, Budget, Focus Areas, Find Similar Cards Results
- deckgenerator.html: tooltips on Target Bracket, Use my collection, Smart Substitution, AI Model
KoalaTrapLord