The Lang Graph Librarian

# [[Entity Name]] pattern extraction — Stage 3
import re

ENTITY_PATTERN = r'\[\[([^\]]+)\]\]+'

def extract_entities(text: str) -> List[dict]:
    # Find all [[Entity]] mentions
    raw_matches = re.findall(ENTITY_PATTERN, text)

    entities = []
    for name in raw_matches:
        entity = {
            "name": name.strip(),
            "type": infer_type(name),
            "confidence": 0.95,
            "aliases": []
        }
        entities.append(entity)

    return entities

# [[A]] VERB [[B]] relationship extraction — Stage 6
RELATION_PATTERN = r'\[\[([^\]]+)\]\]\s+(\w+)\s+\[\[([^\]]+)\]\]+'

def extract_relations(text: str) -> List[dict]:
    matches = re.finditer(RELATION_PATTERN, text)
    triplets = []
    for m in matches:
        triplets.append({
            "subject": m.group(1),
            "verb": m.group(2),
            "object": m.group(3),
            "loom": classify_verb(m.group(2))
        })
    return triplets

# PARA classification — Stage 5
from enum import Enum

class PARA(Enum):
    PROJECT = "P"
    AREA     = Enum.value("A")
    RESOURCE = "R"
    ARCHIVE  = "Archive"

def classify_para(doc: Document) -> PARA:
    score = { PARA.PROJECT: 0, PARA.AREA: 0, PARA.RESOURCE: 0 }

    # Heuristic scoring
    if doc.has_verbs("plan,build,launch,create,ship"):
        score[PARA.PROJECT] += 3
    if doc.has_verbs("manage,maintain,support,oversee"):
        score[PARA.AREA] += 3
    if doc.has_keywords("guide,reference,tutorial,docs"):
        score[PARA.RESOURCE] += 3
    if doc.is_stale(): # >90 days no update
        return PARA.ARCHIVE

    best = max(score, key=score.get)
    return best

# Collection routing — Stage 8
from dataclasses import dataclass

@dataclass
class Loom:
    name: str
    collection: str
    threshold: float

LOOMS = [
    Loom("facts",      "exocortex-facts",      0.95),
    Loom("decisions",  "exocortex-decisions",  0.90),
    Loom("reports",    "exocortex-reports",     0.85),
    Loom("predictions","exocortex-predictions", 0.50),
]

def route_collections(doc: Document) -> List[str]:
    scores = score_looms(doc)   # LLM classifies loom scores
    hits = []
    for loom in LOOMS:
        if scores.get(loom.name, 0) >= loom.threshold:
            hits.append(loom.collection)

    # Always store in PARA collection too
    hits.append(para_collection(doc.para))
    return list(set(hits))   # deduplicate

# Darwinism scoring — quality gate at end of pipeline
def darwinism_score(doc: Document) -> float:
    score = 1.0

    # Penalize missing components
    if not doc.summary:    score -= 0.2
    if not doc.entities:    score -= 0.1
    if not doc.edges:       score -= 0.15

    # Penalize short content
    if len(doc.text) < 100:    score -= 0.25
    if len(doc.text) < 50:     score -= 0.15

    # Bonus for high loom confidence
    if doc.loom_score and doc.loom_score >= 0.90:
        score += 0.1

    return max(0.0, min(1.0, score))