feat: Add Oghma RAG Proxy for SkyrimNet lore injection
RAG proxy that intercepts SkyrimNet LLM requests and enriches them with relevant Tamrielic lore from CHIM's Oghma Infinium database. Features: - FastAPI proxy compatible with OpenAI API - ChromaDB semantic search for lore retrieval - NPC profile extraction from SkyrimNet prompts - Google Sheets ingestion for CHIM's Oghma data - Kubernetes deployment manifests - Debug endpoint for RAG operation monitoring Collections ingested to iris-dev ChromaDB: - oghma_lore: 1951 entries (scholar knowledge) - oghma_basic: 1949 entries (commoner knowledge) - oghma_visual: 1151 entries (Omnisight perception) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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dafit
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oghma-proxy/src/oghma_proxy/extractor.py
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147
oghma-proxy/src/oghma_proxy/extractor.py
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"""NPC Profile Extractor - Parses SkyrimNet prompts to extract NPC context."""
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from __future__ import annotations
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import re
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import structlog
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from .models import NPCProfile
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logger = structlog.get_logger()
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class NPCExtractor:
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"""Extracts NPC profile information from SkyrimNet prompts."""
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# Regex patterns for extraction
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PATTERNS = {
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# Character bio header
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"bio_header": re.compile(
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r"## (?P<name>[\w\s'-]+) Bio\s*\n"
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r"- Gender: (?P<gender>\w+)\s*\n"
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r"- Race: (?P<race>[\w\s]+)",
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re.MULTILINE,
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),
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# Alternative role description
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"role_intro": re.compile(
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r"You are (?P<name>[^,\n]+),?\s*(?:a |an )?(?P<descriptor>[^.\n]+)",
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re.IGNORECASE,
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),
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# Faction membership
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"faction": re.compile(
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r"(?:member of|belongs to|joined|part of) (?:the )?(?P<faction>[\w\s]+?)(?:\.|,|\n|$)",
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re.IGNORECASE,
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),
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# Location mentions
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"location": re.compile(
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r"(?:in|at|near|from) (?P<location>Whiterun|Windhelm|Solitude|Riften|"
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r"Markarth|Morthal|Dawnstar|Winterhold|Falkreath|Riverwood|Rorikstead|"
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r"Ivarstead|Solstheim|Raven Rock)",
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re.IGNORECASE,
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),
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# Profession/occupation
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"occupation": re.compile(
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r"(?:works as|profession:|occupation:|is a|as a) (?P<profession>[\w\s]+?)(?:\.|,|\n|$)",
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re.IGNORECASE,
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),
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}
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# Known professions for fuzzy matching
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KNOWN_PROFESSIONS = {
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"priest", "priestess", "mage", "wizard", "scholar", "blacksmith",
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"guard", "soldier", "warrior", "thief", "merchant", "innkeeper",
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"hunter", "farmer", "peasant", "noble", "jarl", "bard", "alchemist",
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"healer", "assassin", "spy", "courier", "carriage driver", "fisherman",
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"miller", "brewer", "smith", "armorer", "fletcher", "jeweler",
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}
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def extract(self, messages: list[dict]) -> NPCProfile:
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"""Extract NPC profile from chat messages."""
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# Combine all message content for analysis
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full_text = "\n".join(
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msg.get("content", "") for msg in messages if msg.get("content")
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)
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profile = NPCProfile()
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# Try bio header first (most reliable)
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if match := self.PATTERNS["bio_header"].search(full_text):
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profile.name = match.group("name").strip()
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profile.gender = match.group("gender").strip()
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profile.race = match.group("race").strip()
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logger.debug("Extracted from bio header", name=profile.name, race=profile.race)
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# Fallback to role intro
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elif match := self.PATTERNS["role_intro"].search(full_text):
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profile.name = match.group("name").strip()
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descriptor = match.group("descriptor")
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# Try to parse race from descriptor
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profile.race = self._extract_race_from_descriptor(descriptor)
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logger.debug("Extracted from role intro", name=profile.name)
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# Extract location
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if match := self.PATTERNS["location"].search(full_text):
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profile.location = match.group("location").strip()
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# Extract factions
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for match in self.PATTERNS["faction"].finditer(full_text):
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faction = match.group("faction").strip()
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if faction and faction not in profile.factions:
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profile.factions.append(faction)
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# Extract profession
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if match := self.PATTERNS["occupation"].search(full_text):
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profession = match.group("profession").strip().lower()
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# Validate against known professions
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for known in self.KNOWN_PROFESSIONS:
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if known in profession:
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profile.profession = known
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break
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# Compute knowledge classes
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profile.compute_knowledge_classes()
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logger.info(
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"Extracted NPC profile",
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name=profile.name,
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race=profile.race,
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profession=profile.profession,
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factions=profile.factions,
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location=profile.location,
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knowledge_classes=profile.knowledge_classes,
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education_level=profile.education_level.value,
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)
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return profile
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def _extract_race_from_descriptor(self, descriptor: str) -> str:
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"""Try to extract race from a descriptor string."""
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races = [
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"Nord", "Dunmer", "Dark Elf", "Altmer", "High Elf",
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"Bosmer", "Wood Elf", "Argonian", "Khajiit", "Breton",
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"Redguard", "Orsimer", "Orc", "Imperial",
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]
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descriptor_lower = descriptor.lower()
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for race in races:
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if race.lower() in descriptor_lower:
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# Normalize to single-word form
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return race.replace(" ", "")
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return "Unknown"
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def extract_conversation_context(self, messages: list[dict]) -> str:
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"""Extract the current conversation topic for RAG query."""
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# Get the last few user/assistant exchanges
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recent_content = []
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for msg in reversed(messages[-6:]):
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content = msg.get("content", "")
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if content and msg.get("role") in ("user", "assistant"):
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# Skip very long content (likely system prompts)
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if len(content) < 500:
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recent_content.append(content)
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if not recent_content:
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return ""
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# Combine recent conversation as the query context
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return " ".join(reversed(recent_content[-3:]))
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