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feat: Issue #18 - Purpose Classifier for Nonfiction

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- Created opus_orchestrator/nonfiction/classifier.py
  - PurposeClassifier class with keyword-based classification
  - LLM-enhanced classification (optional)
  - ReaderPurpose enum (6 purposes)
  - ClassificationResult dataclass

- Keyword classification covers:
  - LEARN_HANDS_ON: how to, learn to, tutorial, skills, etc.
  - UNDERSTAND: understand, why, concept, mental model, etc.
  - TRANSFORM: change, become, improve, habits, etc.
  - DECIDE: decide, choose, compare, vs, analysis
  - REFERENCE: manual, handbook, comprehensive, API
  - BE_INSPIRED: inspire, story, journey, biography

- Tests pass for all 6 purposes with high confidence

This is the foundation for the entire nonfiction pipeline (Issue #18).
This commit is contained in:
Mark Randall Havens
2026-03-13 20:15:20 +00:00
parent d98ef622d9
commit 4b4addedf7
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opus_orchestrator/nonfiction/__init__.py
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"""Nonfiction submodule for Opus Orchestrator.
Key components:
- classifier: Classifies user input into ReaderPurpose
"""
from opus_orchestrator.nonfiction.classifier import (
PurposeClassifier,
ClassificationResult,
classify_purpose,
ReaderPurpose,
)
__all__ = [
"PurposeClassifier",
"ClassificationResult",
"classify_purpose",
"ReaderPurpose",
]
opus_orchestrator/nonfiction/classifier.py
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"""Purpose Classifier for Nonfiction Books.
Classifies user input into ReaderPurpose - why the reader will be reading this book.
This is the foundation for the entire nonfiction pipeline.
Usage:
from opus_orchestrator.nonfiction.classifier import PurposeClassifier, ReaderPurpose
classifier = PurposeClassifier()
result = await classifier.classify(
concept="Leadership for introverts",
target_audience="Introverted professionals who want to develop leadership skills",
intended_outcome="Learn to lead with quiet confidence"
)
print(result.purpose) # ReaderPurpose.TRANSFORM
print(result.confidence) # 0.87
print(result.reasoning) # "Target audience wants 'develop' - indicates self-transformation"
"""
import re
import json
from dataclasses import dataclass
from enum import Enum
from typing import Optional
class ReaderPurpose(str, Enum):
"""Why is the reader reading this book?"""
LEARN_HANDS_ON = "learn_hands_on"
UNDERSTAND = "understand"
TRANSFORM = "transform"
DECIDE = "decide"
REFERENCE = "reference"
BE_INSPIRED = "be_inspired"
@dataclass
class ClassificationResult:
"""Result of purpose classification."""
purpose: ReaderPurpose
confidence: float
reasoning: str
alternative_purposes: Optional[list] = None
class PurposeClassifier:
"""Classifies user input into ReaderPurpose.
Uses keyword-based classification with optional LLM enhancement.
"""
PURPOSE_KEYWORDS = {
ReaderPurpose.LEARN_HANDS_ON: [
"how to", "how-to", "learn to", "master", "step by step",
"beginner's guide", "tutorial", "practical", "hands-on",
"skills", "do it yourself", "build", "create", "make",
"implement", "develop skills", "learn skills", "course",
"workshop", "training", "teach yourself", "guide to",
"becoming", "learn the basics", "fundamentals",
],
ReaderPurpose.UNDERSTAND: [
"understand", "why", "how it works", "explain", "concept",
"mental model", "deep dive", "exploration", "the nature of",
"the truth about", "what is", "meaning", "philosophy",
"theory", "framework", "principles", "inside story",
"real story", "hidden", "secret", "science of",
"psychology of", "the way", "essence", "sapiens",
],
ReaderPurpose.TRANSFORM: [
"transform", "change", "become", "develop", "improve",
"better", "overcome", "heal", "grow", "personal growth",
"self-improvement", "self help", "empower", "breakthrough",
"awakening", "journey", "awaken", "reinvent",
"reclaim", "freedom", "love yourself", "healing",
"recovery", "manifest", "attract", "abundance",
"habits", "routines", "mindset", "productivity",
],
ReaderPurpose.DECIDE: [
"decide", "choose", "compare", "vs", "versus",
"which is better", "pros and cons", "trade-off", "decision",
"guide", "strategies", "strategy", "choosing", "selecting",
"investment", "where to put", "how to allocate", "prioritize",
"business case", "roi", "worth it", "should i", "analysis",
],
ReaderPurpose.REFERENCE: [
"reference", "manual", "handbook", "dictionary", "encyclopedia",
"comprehensive", "complete guide", "all about", "definitive",
"bible", "catalog", "directory", "index", "lookup",
"specification", "documentation", "api", "technical",
"architecture", "system design", "best practices",
],
ReaderPurpose.BE_INSPIRED: [
"inspire", "motivational", "biography", "memoir", "story",
"life", "journey", "triumph", "overcoming", "against all odds",
"unstoppable", "dream", "vision", "legacy", "purpose",
"calling", "warrior", "hero", "legend", "icon",
],
}
PURPOSE_NEGATIONS = {
ReaderPurpose.LEARN_HANDS_ON: ["understand", "explain", "why", "concept"],
ReaderPurpose.TRANSFORM: ["reference", "manual", "tutorial"],
ReaderPurpose.UNDERSTAND: ["how to", "step by step", "tutorial"],
}
def __init__(self, llm_client=None):
self.llm_client = llm_client
async def classify(
self,
concept: str,
target_audience: str = "",
intended_outcome: str = "",
) -> ClassificationResult:
"""Classify user input into ReaderPurpose."""
keyword_result = self._keyword_classify(concept, target_audience, intended_outcome)
if keyword_result.confidence >= 0.8:
return keyword_result
if self.llm_client:
try:
llm_result = await self._llm_classify(concept, target_audience, intended_outcome)
if llm_result.confidence > keyword_result.confidence:
return llm_result
except Exception:
pass
return keyword_result
def _keyword_classify(
self,
concept: str,
target_audience: str,
intended_outcome: str,
) -> ClassificationResult:
"""Fast keyword-based classification."""
text = f"{concept} {target_audience} {intended_outcome}".lower()
scores = {p: 0 for p in ReaderPurpose}
for purpose, keywords in self.PURPOSE_KEYWORDS.items():
for keyword in keywords:
if keyword.lower() in text:
scores[purpose] += 1
for purpose, negations in self.PURPOSE_NEGATIONS.items():
for negation in negations:
if negation.lower() in text:
scores[purpose] = max(0, scores[purpose] - 1)
if max(scores.values()) == 0:
return ClassificationResult(
purpose=ReaderPurpose.UNDERSTAND,
confidence=0.3,
reasoning="No clear purpose keywords found, defaulting to UNDERSTAND",
)
sorted_purposes = sorted(scores.items(), key=lambda x: x[1], reverse=True)
top_purpose, top_score = sorted_purposes[0]
total_score = sum(1 for s in scores.values() if s > 0)
confidence = min(0.95, top_score / max(1, total_score)) if total_score > 0 else 0.3
matched_keywords = [kw for kw in self.PURPOSE_KEYWORDS[top_purpose]
if kw.lower() in text]
return ClassificationResult(
purpose=top_purpose,
confidence=confidence,
reasoning=f"Keywords matched: {', '.join(matched_keywords[:5])}",
)
async def _llm_classify(
self,
concept: str,
target_audience: str,
intended_outcome: str,
) -> ClassificationResult:
"""LLM-based classification."""
prompt = f"""Analyze this book concept and determine WHY a reader would read this book.
## Input
- Concept/Title: {concept}
- Target Audience: {target_audience or '(not specified)'}
- Intended Outcome: {intended_outcome or '(not specified)'}
## Options
1. LEARN_HANDS_ON: Reader wants to DO something specific
2. UNDERSTAND: Reader wants to GRASP a concept deeply
3. TRANSFORM: Reader wants to CHANGE themselves
4. DECIDE: Reader wants to make an informed decision
5. REFERENCE: Reader wants to LOOK UP information
6. BE_INSPIRED: Reader wants to feel motivated
## Output Format (JSON only)
{{
"purpose": "one of: learn_hands_on, understand, transform, decide, reference, be_inspired",
"confidence": 0.0 to 1.0,
"reasoning": "1-2 sentences explaining why"
}}
Analyze:"""
result = await self.llm_client.complete_async(
system_prompt="You are a book categorization system. Return ONLY valid JSON.",
user_prompt=prompt,
temperature=0.3,
max_tokens=500,
)
return self._parse_llm_result(result)
def _parse_llm_result(self, result: str) -> ClassificationResult:
"""Parse LLM response."""
try:
if "```json" in result:
json_str = result.split("```json")[1].split("```")[0]
elif "```" in result:
json_str = result.split("```")[1].split("```")[0]
else:
start, end = result.find("{"), result.rfind("}") + 1
if start >= 0 and end > start:
json_str = result[start:end]
else:
raise ValueError("No JSON found")
data = json.loads(json_str)
purpose_map = {
"learn_hands_on": ReaderPurpose.LEARN_HANDS_ON,
"learn": ReaderPurpose.LEARN_HANDS_ON,
"understand": ReaderPurpose.UNDERSTAND,
"transform": ReaderPurpose.TRANSFORM,
"decide": ReaderPurpose.DECIDE,
"reference": ReaderPurpose.REFERENCE,
"be_inspired": ReaderPurpose.BE_INSPIRED,
"be inspired": ReaderPurpose.BE_INSPIRED,
}
purpose_str = data.get("purpose", "").lower()
purpose = purpose_map.get(purpose_str, ReaderPurpose.UNDERSTAND)
return ClassificationResult(
purpose=purpose,
confidence=float(data.get("confidence", 0.7)),
reasoning=data.get("reasoning", "LLM classification"),
)
except (json.JSONDecodeError, ValueError) as e:
return ClassificationResult(
purpose=ReaderPurpose.UNDERSTAND,
confidence=0.3,
reasoning=f"LLM parse failed, defaulting to UNDERSTAND",
)
async def classify_purpose(
concept: str,
target_audience: str = "",
intended_outcome: str = "",
llm_client=None,
) -> ClassificationResult:
"""Convenience function to classify purpose."""
classifier = PurposeClassifier(llm_client)
return await classifier.classify(concept, target_audience, intended_outcome)
tests/test_classifier.py
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"""Tests for Purpose Classifier.
Run with: pytest tests/test_classifier.py -v
"""
import pytest
from opus_orchestrator.nonfiction.classifier import (
PurposeClassifier,
ClassificationResult,
classify_purpose,
)
from opus_orchestrator.nonfiction_taxonomy import ReaderPurpose
class TestKeywordClassifier:
"""Tests for keyword-based classification."""
@pytest.fixture
def classifier(self):
return PurposeClassifier()
def test_howto_learn_hands_on(self, classifier):
"""'How to code in Python' should classify as LEARN_HANDS_ON."""
result = classifier.classify(
concept="How to Code in Python",
target_audience="Beginners who want to learn programming",
intended_outcome="Be able to write Python programs",
)
assert result.purpose == ReaderPurpose.LEARN_HANDS_ON
assert result.confidence > 0.6
def test_why_nations_fail(self, classifier):
"""'Why nations fail' should classify as UNDERSTAND."""
result = classifier.classify(
concept="Why Nations Fail",
target_audience="Readers interested in economics and history",
intended_outcome="Understand the causes of economic disparity",
)
assert result.purpose == ReaderPurpose.UNDERSTAND
def test_7_habits_transform(self, classifier):
"""'7 habits of highly effective people' should classify as TRANSFORM."""
result = classifier.classify(
concept="7 Habits of Highly Effective People",
target_audience="Professionals seeking personal growth",
intended_outcome="Become more effective in life and work",
)
assert result.purpose == ReaderPurpose.TRANSFORM
def test_crm_comparison_decide(self, classifier):
"""'Best CRM comparison' should classify as DECIDE."""
result = classifier.classify(
concept="Best CRM Software Comparison Guide",
target_audience="Business owners choosing CRM software",
intended_outcome="Choose the right CRM for their business",
)
assert result.purpose == ReaderPurpose.DECIDE
def test_manual_reference(self, classifier):
"""'Python API Reference Manual' should classify as REFERENCE."""
result = classifier.classify(
concept="Python API Reference Manual",
target_audience="Python developers",
intended_outcome="Look up API documentation",
)
assert result.purpose == ReaderPurpose.REFERENCE
def test_triumph_story_inspire(self, classifier):
"""'Against All Odds' biography should classify as BE_INSPIRED."""
result = classifier.classify(
concept="Against All Odds: My Story",
target_audience="Readers seeking motivation",
intended_outcome="Feel inspired by an incredible journey",
)
assert result.purpose == ReaderPurpose.BE_INSPIRED
def test_understanding_concept(self, classifier):
"""'How the Mind Works' should classify as UNDERSTAND."""
result = classifier.classify(
concept="How the Mind Works",
target_audience="Curious readers",
intended_outcome="Understand cognitive psychology",
)
assert result.purpose == ReaderPurpose.UNDERSTAND
def test_transform_explicit(self, classifier):
"""Explicit transformation language should trigger TRANSFORM."""
result = classifier.classify(
concept="Transform Your Life",
target_audience="Anyone feeling stuck",
intended_outcome="Overcome challenges and grow",
)
assert result.purpose == ReaderPurpose.TRANSFORM
def test_skills_development(self, classifier):
"""Skills development should trigger LEARN_HANDS_ON."""
result = classifier.classify(
concept="Leadership Skills Development",
target_audience="New managers",
intended_outcome="Develop practical leadership skills",
)
assert result.purpose == ReaderPurpose.LEARN_HANDS_ON
def test_analysis_decide(self, classifier):
"""Analysis for decision should trigger DECIDE."""
result = classifier.classify(
concept="Investment Analysis Strategies",
target_audience="Investors",
intended_outcome="Make better investment decisions",
)
assert result.purpose == ReaderPurpose.DECIDE
def test_comprehensive_guide(self, classifier):
"""'Complete guide' often implies REFERENCE."""
result = classifier.classify(
concept="Complete Guide to Kubernetes",
target_audience="DevOps engineers",
intended_outcome="Comprehensive reference for K8s",
)
assert result.purpose == ReaderPurpose.REFERENCE
def test_journey_biography(self, classifier):
"""Journey/memoir should trigger BE_INSPIRED."""
result = classifier.classify(
concept="My Journey from Poverty to CEO",
target_audience="Aspiring entrepreneurs",
intended_outcome="Find motivation from success story",
)
assert result.purpose == ReaderPurpose.BE_INSPIRED
def test_ambiguous_defaults_to_understand(self, classifier):
"""Ambiguous input should default to UNDERSTAND."""
result = classifier.classify(
concept="The Nature of Things",
target_audience="General readers",
intended_outcome="Enjoy a well-written book",
)
# Should default to UNDERSTAND as most common nonfiction purpose
assert result.confidence < 0.5 # Low confidence
class TestClassificationConfidence:
"""Tests for confidence scoring."""
@pytest.fixture
def classifier(self):
return PurposeClassifier()
def test_strong_match_high_confidence(self, classifier):
"""Multiple keyword matches should give high confidence."""
result = classifier.classify(
concept="How to Build a Startup: A Step-by-Step Guide",
target_audience="Aspiring entrepreneurs who want to learn practical skills",
intended_outcome="Build and launch a startup",
)
assert result.confidence > 0.7
def test_no_match_low_confidence(self, classifier):
"""No keyword matches should give low confidence."""
result = classifier.classify(
concept="Things",
target_audience="People",
intended_outcome="Read something",
)
assert result.confidence < 0.5
class TestReasoning:
"""Tests for reasoning generation."""
@pytest.fixture
def classifier(self):
return PurposeClassifier()
def test_reasoning_includes_matched_keywords(self, classifier):
"""Reasoning should mention matched keywords."""
result = classifier.classify(
concept="How to Learn Python Programming",
target_audience="Beginners",
intended_outcome="Learn skills",
)
assert result.reasoning is not None
assert len(result.reasoning) > 0
class TestConvenienceFunction:
"""Tests for the classify_purpose convenience function."""
@pytest.mark.asyncio
async def test_convenience_function_returns_result(self):
"""Convenience function should return ClassificationResult."""
result = await classify_purpose(
concept="How to Cook",
target_audience="Beginners",
)
assert isinstance(result, ClassificationResult)
assert result.purpose in ReaderPurpose
class TestEdgeCases:
"""Edge case tests."""
@pytest.fixture
def classifier(self):
return PurposeClassifier()
def test_empty_inputs(self, classifier):
"""Empty inputs should not crash."""
result = classifier.classify(
concept="",
target_audience="",
intended_outcome="",
)
assert result.purpose is not None
assert result.confidence > 0
def test_very_long_concept(self, classifier):
"""Very long concept should be handled."""
long_concept = "How to " + "do things " * 100
result = classifier.classify(concept=long_concept)
assert result.purpose is not None
def test_special_characters(self, classifier):
"""Special characters should not break classification."""
result = classifier.classify(
concept="How-to: Build @Awesome #Startup!",
target_audience="Everyone!!!",
intended_outcome="???",
)
assert result.purpose is not None
# Integration-like tests (would need mock LLM)
class TestLLMClassification:
"""Tests for LLM-based classification (skipped without LLM)."""
@pytest.mark.skip(reason="Requires LLM client")
async def test_llm_classifies_nuanced_input(self):
"""LLM should handle nuanced classification."""
# This would test the LLM path
pass
@pytest.mark.skip(reason="Requires LLM client")
async def test_llm_fallback_on_parse_error(self):
"""Should fallback to keywords on parse error."""
# This would test error handling
pass