KoalaTrapLord
aefd0cace6
* feat(rag): Phase 1 — rewrite build_card_text to natural-language format * feat(rag): Phase 2 — Forge-enriched ChromaDB RAG store (2a–2e) Phase 2a: Forge lazy import guard (_try_import_forge_lookup) Phase 2b: _build_document appends Forge trigger/replacement/static/keyword tags Phase 2c: _build_metadata adds has_trigger, has_replacement, has_static, trigger_modes fields Phase 2d: query_cards extended with require_trigger, require_replacement, require_static params + ChromaDB where clause support Phase 2e: get_rag_stats reports forge_enriched and forge_coverage; _write_build_meta tracks coverage; build loop counts enriched cards * feat(rag): Phase 3 — add mtgrules second ChromaDB collection Adds build_rules_index() and query_rules() operating on a separate 'mtgrules' ChromaDB collection. Chunks MagicCompRules.txt by rule-number boundaries, embeds each chunk, and exposes semantic search. Completely independent from the mtg_oracle card collection. * feat(coach): Phase 4 — behavior-aware RAG + rules context in coach_service Phase 4a: run_coaching_session uses dual RAG query - Primary: commander synergy (n=10) - Secondary: require_replacement=True for graveyard/protection cards (n=5) - Deduplicates by name before merging Phase 4b: query_rules() fetches 3 Commander rules chunks to ground the prompt - Passed as rules_context kwarg to build_user_prompt() * feat(rag): Phase 5 — add /api/rag/build-rules and /api/rag/query-rules endpoints
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
"""
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services/card_text.py
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=====================
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Shared utility for building card text representations.
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Both services/rag_store.py and coach/embeddings.py need to produce
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identical text strings for embedding so that vector similarity is
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consistent across both systems. This module provides a single
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canonical implementation.
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Format: Natural-language sentences.
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(fields that are empty/missing are omitted)
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"""
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from __future__ import annotations
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def build_card_text(card: dict) -> str:
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"""Build canonical natural-language card text for embedding.
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Produces '{Name} is a {Type}. It costs {ManaCost}. {OracleText}'
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Fields that are empty/None are omitted.
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Args:
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card: Dict with keys 'name', 'mana_cost', 'type_line', 'oracle_text'.
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Missing keys are treated as empty strings.
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Returns:
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Natural language string suitable for nomic-embed-text or GTE models.
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"""
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name = (card.get("name", "") or "").strip()
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mana = (card.get("mana_cost", "") or "").strip()
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types = (card.get("type_line", "") or "").strip()
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oracle = (card.get("oracle_text", "") or "").strip()
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parts: list[str] = []
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if name:
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base = f"{name} is a {types}." if types else f"{name}."
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parts.append(base)
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if mana:
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parts.append(f"It costs {mana}.")
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if oracle:
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parts.append(oracle)
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return " ".join(parts) if parts else name
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