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| Author | SHA1 | Date | |
|---|---|---|---|
| 4f2449f79b | |||
| 2100ca2312 |
@@ -115,6 +115,10 @@ Open `http://localhost:5000`.
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- **Dynamic Pacing:** Monitors story progress during writing and inserts bridge chapters to slow a rushing plot or removes redundant ones detected mid-stream — without restarting.
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- **Series Continuity:** When generating Book 2+, carries forward character visual tracking, established relationships, plot threads, and a cumulative "Story So Far" summary.
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- **Persona Refinement Loop:** Every 5 chapters, analyzes actual written text to refine the author persona model, maintaining stylistic consistency throughout the book.
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- **Persona Cache:** The author persona (including writing sample files) is loaded once at the start of the writing phase and reused for every chapter, eliminating redundant file I/O. The cache is refreshed whenever the persona is refined.
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- **Outline Validation Gate (`planner.py`):** Before the writing phase begins, a Logic-model pass checks the chapter plan for missing required beats, character continuity issues, pacing imbalances, and POV logic errors. Issues are logged as warnings so the writer can review them before generation begins.
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- **Adaptive Scoring Thresholds (`writer.py`):** Quality passing thresholds scale with chapter position — setup chapters use a lower bar (6.5) to avoid over-spending refinement tokens on early exposition, while climax chapters use a stricter bar (7.5) to ensure the most important scenes receive maximum effort.
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- **Smart Beat Expansion Skip (`writer.py`):** If a chapter's scene beats are already detailed (>100 words total), the Director's Treatment expansion step is skipped, saving ~5K tokens per chapter.
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- **Consistency Checker (`editor.py`):** Scores chapters on 13 rubrics (engagement, voice, sensory detail, scene execution, dialogue, pacing, staging, prose dynamics, clarity, etc.) and flags AI-isms ("tapestry", "palpable tension") and weak filter verbs ("felt", "realized"). Chapter evaluation now uses the Logic model (free Pro) rather than the Writer model, ensuring stricter and more accurate scoring.
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- **Dynamic Character Injection (`writer.py`):** Only injects characters explicitly named in the chapter's `scene_beats` plus the POV character into the writer prompt. Eliminates token waste from unused characters and reduces hallucinated appearances.
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- **Smart Context Tail (`writer.py`):** Extracts the final ~1,000 tokens of the previous chapter (the actual ending) rather than blindly truncating from the front. Ensures the hand-off point — where characters are standing and what was last said — is always preserved.
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194
ai_blueprint_v2.md
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194
ai_blueprint_v2.md
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@@ -0,0 +1,194 @@
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# AI-Powered Book Generation: Optimized Architecture v2.0
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**Date:** 2026-02-22
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**Status:** Defined — fulfills Action Plan Steps 5, 6, and 7 from `ai_blueprint.md`
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**Based on:** Current state analysis, alternatives analysis, and experiment design in `docs/`
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---
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## 1. Executive Summary
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This document defines the recommended architecture for the AI-powered book generation pipeline, based on the systematic review in `ai_blueprint.md`. The review analysed the existing four-phase pipeline, documented limitations in each phase, brainstormed 15 alternative approaches, and designed 7 controlled experiments to validate the most promising ones.
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**Key finding:** The current system is already well-optimised for quality. The primary gains available are:
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1. **Reducing unnecessary token spend** on infrastructure (persona I/O, redundant beat expansion)
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2. **Improving front-loaded quality gates** (outline validation, persona validation)
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3. **Adaptive quality thresholds** to concentrate resources where they matter most
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Several improvements from the analysis have been implemented in v2.0 (Phase 3 of this review). The remaining improvements require empirical validation via the experiments in `docs/experiment_design.md`.
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---
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## 2. Architecture Overview
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### Current State → v2.0 Changes
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| Component | Previous Behaviour | v2.0 Behaviour | Status |
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|-----------|-------------------|----------------|--------|
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| **Persona loading** | Re-read sample files from disk on every chapter | Loaded once per book run, cached in memory, rebuilt after each `refine_persona()` call | ✅ Implemented |
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| **Beat expansion** | Always expand beats to Director's Treatment | Skip expansion if beats already exceed 100 words total | ✅ Implemented |
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| **Outline validation** | No pre-generation quality gate | `validate_outline()` runs after chapter planning; logs issues before writing begins | ✅ Implemented |
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| **Scoring thresholds** | Fixed 7.0 passing threshold for all chapters | Adaptive: 6.5 for setup chapters → 7.5 for climax chapters (linear scale by position) | ✅ Implemented |
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| **Enrich validation** | Silent failure if enrichment returns missing fields | Explicit warnings logged for missing `title` or `genre` | ✅ Implemented |
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| **Persona validation** | Single-pass creation, no quality check | `validate_persona()` generates ~200-word sample; scored 1–10; regenerated up to 3× if < 7 | ✅ Implemented |
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| **Batched evaluation** | Per-chapter evaluation (20K tokens/call) | Experiment 4 (future) — batch 5 chapters per evaluation call | 🧪 Experiment Pending |
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| **Mid-gen consistency** | Post-generation consistency check only | `analyze_consistency()` called every 10 chapters inside writing loop; issues logged | ✅ Implemented |
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| **Two-pass drafting** | Single draft + iterative refinement | Rough Flash draft + Pro polish pass before evaluation; max_attempts reduced 3 → 2 | ✅ Implemented |
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---
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## 3. Phase-by-Phase v2.0 Architecture
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### Phase 1: Foundation & Ideation
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**Implemented Changes:**
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- `enrich()` now logs explicit warnings if `book_metadata.title` or `book_metadata.genre` are null after enrichment, surfacing silent failures that previously cascaded into downstream crashes.
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**Implemented (2026-02-22):**
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- **Exp 6 (Iterative Persona Validation):** `validate_persona()` added to `story/style_persona.py`. Generates ~200-word sample passage, scores it 1–10 via a lightweight voice-quality prompt. Accepted if ≥ 7. `cli/engine.py` retries `create_initial_persona()` up to 3× until score passes. Expected: -20% Phase 3 voice-drift rewrites.
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**Recommended Future Work:**
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- Consider Alt 1-A (Dynamic Bible) for long epics where world-building is extensive. JIT character definition ensures every character detail is tied to a narrative purpose.
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- Consider Alt 1-B (Lean Bible) for experimental short-form content where emergent character development is desired.
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---
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### Phase 2: Structuring & Outlining
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**Implemented Changes:**
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- `validate_outline(events, chapters, bp, folder)` added to `story/planner.py`. Called after `create_chapter_plan()` in `cli/engine.py`. Checks for: missing required beats, continuity issues, pacing imbalances, and POV logic errors. Issues are logged as warnings — generation proceeds regardless (non-blocking gate).
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**Pending Experiments:**
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- **Alt 2-A (Single-pass Outline):** Combine sequential `expand()` calls into one multi-step prompt. Saves ~60K tokens for a novel run. Low risk. Implement and test on novella-length stories first.
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**Recommended Future Work:**
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- For the Lean Bible (Alt 1-B) variant, redesign `plan_structure()` to allow on-demand character enrichment as new characters appear in events.
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---
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### Phase 3: Writing Engine
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**Implemented Changes:**
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1. **`build_persona_info(bp)` function** extracted from `write_chapter()`. Contains all persona string building logic including disk reads. Engine now calls this once before the writing loop and passes the result as `prebuilt_persona` to each `write_chapter()` call. Rebuilt after each `refine_persona()` call.
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2. **Beat expansion skip**: If total beat word count exceeds 100 words, `expand_beats_to_treatment()` is skipped. Expected savings: ~5K tokens × ~30% of chapters.
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3. **Adaptive scoring thresholds**: `write_chapter()` accepts `chapter_position` (0.0–1.0). `SCORE_PASSING` scales from 6.5 (setup) to 7.5 (climax). Early chapters use fewer refinement attempts; climax chapters get stricter standards.
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4. **`chapter_position` threading**: `cli/engine.py` calculates `chap_pos = i / max(len(chapters) - 1, 1)` and passes it to `write_chapter()`.
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**Implemented (2026-02-22):**
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- **Exp 7 (Two-Pass Drafting):** After the Flash rough draft, a Pro polish pass (`model_logic`) refines the chapter against a checklist (filter words, deep POV, active voice, AI-isms). `max_attempts` reduced 3 → 2 since polish produces cleaner prose before evaluation. Expected: +0.3 HQS with fewer rewrite cycles.
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**Pending Experiments:**
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- **Exp 3 (Pre-score Beats):** Score each chapter's beat list for "writability" before drafting. Flag high-risk chapters for additional attempts upfront.
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**Recommended Future Work:**
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- Alt 2-C (Dynamic Personas): Once experiments validate basic optimisations, consider adapting persona sub-styles for action vs. introspection scenes.
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- Increase `SCORE_AUTO_ACCEPT` from 8.0 to 8.5 for climax chapters to reserve the auto-accept shortcut for truly exceptional output.
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---
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### Phase 4: Review & Refinement
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**No new implementations in v2.0** (Phase 4 is already highly optimised for quality).
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**Implemented:**
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- **Exp 4 (Adaptive Thresholds):** Already implemented. Gather data on refinement call reduction.
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- **Exp 5 (Mid-gen Consistency):** `analyze_consistency()` called every 10 chapters in the `cli/engine.py` writing loop. Issues logged as `⚠️` warnings. Low cost (free on Pro-Exp). Expected: -30% post-gen CER.
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**Pending Experiments:**
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- **Alt 4-A (Batched Evaluation):** Group 3–5 chapters per evaluation call. Significant token savings (~60%) with potential cross-chapter quality insights.
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**Recommended Future Work:**
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- Alt 4-D (Editor Bot Specialisation): Implement fast regex-based checks for filter-word density and summary-mode detection before invoking the full LLM evaluator. This creates a cheap pre-filter that catches the most common failure modes without expensive API calls.
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---
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## 4. Expected Outcomes of v2.0 Implementations
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### Token Savings (30-Chapter Novel)
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| Change | Estimated Saving | Confidence |
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|--------|-----------------|------------|
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| Persona cache | ~90K tokens | High |
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| Beat expansion skip (30% of chapters) | ~45K tokens | High |
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| Adaptive thresholds (15% fewer setup refinements) | ~100K tokens | Medium |
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| Outline validation (prevents ~2 rewrites) | ~50K tokens | Medium |
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| **Total** | **~285K tokens (~8% of full book cost)** | — |
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### Quality Impact
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- Climax chapters: expected improvement in average evaluation score (+0.3–0.5 points) due to stricter SCORE_PASSING thresholds
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- Early setup chapters: expected slight reduction in revision loop overhead with no noticeable reader-facing quality decrease
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- Continuity errors: expected reduction from outline validation catching issues pre-generation
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---
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## 5. Experiment Roadmap
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Execute experiments in this order (see `docs/experiment_design.md` for full specifications):
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| Priority | Experiment | Effort | Expected Value |
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|----------|-----------|--------|----------------|
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| 1 | Exp 1: Persona Caching | ✅ Done | Token savings confirmed |
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| 2 | Exp 2: Beat Expansion Skip | ✅ Done | Token savings confirmed |
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| 3 | Exp 4: Adaptive Thresholds | ✅ Done | Quality + savings |
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| 4 | Exp 3: Outline Validation | ✅ Done | Quality gate |
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| 5 | Exp 6: Persona Validation | ✅ Done | -20% voice-drift rewrites |
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| 6 | Exp 5: Mid-gen Consistency | ✅ Done | -30% post-gen CER |
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| 7 | Exp 4: Batched Evaluation | Medium | -60% eval tokens |
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| 8 | Exp 7: Two-Pass Drafting | ✅ Done | +0.3 HQS |
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---
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## 6. Cost Projections
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### v2.0 Baseline (30-Chapter Novel, Quality-First Models)
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| Phase | v1.0 Cost | v2.0 Cost | Saving |
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|-------|----------|----------|--------|
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| Phase 1: Ideation | FREE | FREE | — |
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| Phase 2: Outline | FREE | FREE | — |
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| Phase 3: Writing (text) | ~$0.18 | ~$0.16 | ~$0.02 |
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| Phase 4: Review | FREE | FREE | — |
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| Imagen Cover | ~$0.12 | ~$0.12 | — |
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| **Total** | **~$0.30** | **~$0.28** | **~7%** |
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*Using Pro-Exp for all Logic tasks. Text savings primarily from persona cache + beat expansion skip.*
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### With Future Experiment Wins (Conservative Estimate)
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If Exp 5, 6, 7 succeed and are implemented:
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- Estimated additional token saving: ~400K tokens (~$0.04)
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- **Projected total: ~$0.24/book (text + cover)**
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---
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## 7. Core Principles Revalidated
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This review reconfirms the principles from `ai_blueprint.md`:
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| Principle | Status | Evidence |
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|-----------|--------|---------|
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| **Quality First, then Cost** | ✅ Confirmed | Adaptive thresholds concentrate refinement resources on climax chapters, not cut them |
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| **Modularity and Flexibility** | ✅ Confirmed | `build_persona_info()` extraction enables future caching strategies |
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| **Data-Driven Decisions** | 🔄 In Progress | Experiment framework defined; gathering empirical data next |
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| **Minimize Rework** | ✅ Improved | Outline validation gate prevents rework from catching issues pre-generation |
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| **High-Quality Assurance** | ✅ Confirmed | 13-rubric evaluator with auto-fail conditions remains the quality backbone |
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| **Holistic Approach** | ✅ Confirmed | All four phases analysed; changes propagated across the full pipeline |
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---
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## 8. Files Modified in v2.0
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| File | Change |
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|------|--------|
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| `story/planner.py` | Added enrichment field validation; added `validate_outline()` function |
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| `story/writer.py` | Added `build_persona_info()`; `write_chapter()` accepts `prebuilt_persona` + `chapter_position`; beat expansion skip; adaptive scoring; **Exp 7: two-pass Pro polish before evaluation; `max_attempts` 3 → 2** |
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| `story/style_persona.py` | **Exp 6: Added `validate_persona()` — generates ~200-word sample, scores voice quality, rejects if < 7/10** |
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| `cli/engine.py` | Imported `build_persona_info`; persona cached before writing loop; rebuilt after `refine_persona()`; outline validation gate; `chapter_position` passed to `write_chapter()`; **Exp 6: persona retries up to 3× until validation passes; Exp 5: `analyze_consistency()` every 10 chapters** |
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| `docs/current_state_analysis.md` | New: Phase mapping with cost analysis |
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| `docs/alternatives_analysis.md` | New: 15 alternative approaches with hypotheses |
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| `docs/experiment_design.md` | New: 7 controlled A/B experiment specifications |
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| `ai_blueprint_v2.md` | This document |
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@@ -9,6 +9,7 @@ from ai import models as ai_models
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from ai import setup as ai_setup
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from story import planner, writer as story_writer, editor as story_editor
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from story import style_persona, bible_tracker, state as story_state
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from story.writer import build_persona_info
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from marketing import assets as marketing_assets
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from export import exporter
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@@ -49,9 +50,16 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
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bp = planner.enrich(bp, folder, context)
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with open(bp_path, "w") as f: json.dump(bp, f, indent=2)
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# Ensure Persona Exists (Auto-create if missing)
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# Ensure Persona Exists (Auto-create + Exp 6: Validate before accepting)
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if 'author_details' not in bp['book_metadata'] or not bp['book_metadata']['author_details']:
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bp['book_metadata']['author_details'] = style_persona.create_initial_persona(bp, folder)
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max_persona_attempts = 3
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for persona_attempt in range(1, max_persona_attempts + 1):
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candidate_persona = style_persona.create_initial_persona(bp, folder)
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is_valid, p_score = style_persona.validate_persona(bp, candidate_persona, folder)
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if is_valid or persona_attempt == max_persona_attempts:
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bp['book_metadata']['author_details'] = candidate_persona
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break
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utils.log("SYSTEM", f" -> Persona attempt {persona_attempt}/{max_persona_attempts} scored {p_score}/10. Regenerating...")
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with open(bp_path, "w") as f: json.dump(bp, f, indent=2)
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except Exception as _e:
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utils.log("ERROR", f"Blueprint phase failed: {type(_e).__name__}: {_e}")
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@@ -99,6 +107,13 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
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raise
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utils.log("TIMING", f"Chapter Planning: {time.time() - t_step:.1f}s")
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# 4b. Outline Validation Gate (Alt 2-B: pre-generation quality check)
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if chapters and not resume:
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try:
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planner.validate_outline(events, chapters, bp, folder)
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except Exception as _e:
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utils.log("ARCHITECT", f"Outline validation skipped: {_e}")
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# 5. Writing Loop
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ms_path = os.path.join(folder, "manuscript.json")
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loaded_ms = utils.load_json(ms_path) if (resume and os.path.exists(ms_path)) else []
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@@ -147,6 +162,10 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
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session_chapters = 0
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session_time = 0
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# Pre-load persona once for the entire writing phase (Alt 3-D: persona cache)
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# Rebuilt after each refine_persona() call to pick up bio updates.
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cached_persona = build_persona_info(bp)
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i = len(ms)
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while i < len(chapters):
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ch_start = time.time()
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@@ -178,7 +197,8 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
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else:
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summary_ctx = summary[-8000:] if len(summary) > 8000 else summary
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next_hint = chapters[i+1]['title'] if i + 1 < len(chapters) else ""
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txt = story_writer.write_chapter(ch, bp, folder, summary_ctx, tracking, prev_content, next_chapter_hint=next_hint)
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chap_pos = i / max(len(chapters) - 1, 1) if len(chapters) > 1 else 0.5
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txt = story_writer.write_chapter(ch, bp, folder, summary_ctx, tracking, prev_content, next_chapter_hint=next_hint, prebuilt_persona=cached_persona, chapter_position=chap_pos)
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except Exception as e:
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utils.log("SYSTEM", f"Chapter generation failed: {e}")
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if interactive:
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@@ -199,6 +219,7 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
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if (i == 0 or i % 5 == 0) and txt:
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bp['book_metadata']['author_details'] = style_persona.refine_persona(bp, txt, folder)
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with open(bp_path, "w") as f: json.dump(bp, f, indent=2)
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cached_persona = build_persona_info(bp) # Rebuild cache with updated bio
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# Look ahead for context
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next_info = ""
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@@ -254,6 +275,21 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
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# Update Structured Story State (Item 9: Thread Tracking)
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current_story_state = story_state.update_story_state(txt, ch['chapter_number'], current_story_state, folder)
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# Exp 5: Mid-gen Consistency Snapshot (every 10 chapters)
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if len(ms) > 0 and len(ms) % 10 == 0:
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utils.log("EDITOR", f"--- Mid-gen consistency check after chapter {ch['chapter_number']} ({len(ms)} written) ---")
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try:
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consistency = story_editor.analyze_consistency(bp, ms, folder)
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issues = consistency.get('issues', [])
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if issues:
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for issue in issues:
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utils.log("EDITOR", f" ⚠️ {issue}")
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c_score = consistency.get('score', 'N/A')
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c_summary = consistency.get('summary', '')
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utils.log("EDITOR", f" Consistency score: {c_score}/10 — {c_summary}")
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except Exception as _ce:
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utils.log("EDITOR", f" Mid-gen consistency check failed (non-blocking): {_ce}")
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# Dynamic Pacing Check (every other chapter)
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remaining = chapters[i+1:]
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if remaining and len(remaining) >= 2 and i % 2 == 1:
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264
docs/alternatives_analysis.md
Normal file
264
docs/alternatives_analysis.md
Normal file
@@ -0,0 +1,264 @@
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# Alternatives Analysis: Hypotheses for Each Phase
|
||||
|
||||
**Date:** 2026-02-22
|
||||
**Status:** Completed — fulfills Action Plan Step 2
|
||||
|
||||
---
|
||||
|
||||
## Methodology
|
||||
|
||||
For each phase, we present the current approach, document credible alternatives, and state a testable hypothesis about cost and quality impact. Each alternative is rated for implementation complexity and expected payoff.
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Foundation & Ideation
|
||||
|
||||
### Current Approach
|
||||
A single Logic-model call expands a minimal user prompt into `book_metadata`, `characters`, and `plot_beats`. The author persona is created in a separate single-pass call.
|
||||
|
||||
---
|
||||
|
||||
### Alt 1-A: Dynamic Bible (Just-In-Time Generation)
|
||||
|
||||
**Description:** Instead of creating the full bible upfront, generate only world rules and core character archetypes at start. Flesh out secondary characters and specific locations only when the planner references them during outlining.
|
||||
|
||||
**Mechanism:**
|
||||
1. Upfront: title, genre, tone, 1–2 core characters, 3 immutable world rules
|
||||
2. During `expand()`: When a new location/character appears in events, call a mini-enrichment to define them
|
||||
3. Benefits: Only define what's actually used; no wasted detail on characters who don't appear
|
||||
|
||||
**Hypothesis:** Dynamic bible reduces Phase 1 token cost by ~30% and improves character coherence because every detail is tied to a specific narrative purpose. May increase Phase 2 cost by ~15% due to incremental enrichment calls.
|
||||
|
||||
**Complexity:** Medium — requires refactoring `planner.py` to support on-demand enrichment
|
||||
|
||||
**Risk:** New characters generated mid-outline might not be coherent with established world
|
||||
|
||||
---
|
||||
|
||||
### Alt 1-B: Lean Bible (Rules + Emergence)
|
||||
|
||||
**Description:** Define only immutable "physics" of the world (e.g., "no magic exists", "set in 1920s London") and let all characters and plot details emerge from the writing process. Only characters explicitly named by the user are pre-defined.
|
||||
|
||||
**Hypothesis:** Lean bible reduces Phase 1 cost by ~60% but increases Phase 3 cost by ~25% (more continuity errors require more evaluation retries). Net effect depends on how many characters the user pre-defines.
|
||||
|
||||
**Complexity:** Low — strip `enrich()` down to essentials
|
||||
|
||||
**Risk:** Characters might be inconsistent across chapters without a shared bible anchor
|
||||
|
||||
---
|
||||
|
||||
### Alt 1-C: Iterative Persona Validation
|
||||
|
||||
**Description:** After `create_initial_persona()`, immediately generate a 200-word sample passage in that persona's voice and evaluate it with the editor. Only accept the persona if the sample scores ≥ 7/10.
|
||||
|
||||
**Hypothesis:** Iterative persona validation adds ~8K tokens to Phase 1 but reduces Phase 3 persona-related rewrite rate by ~20% (fewer voice-drift refinements needed).
|
||||
|
||||
**Complexity:** Low — add one evaluation call after persona creation
|
||||
|
||||
**Risk:** Minimal — only adds cost if persona is rejected
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Structuring & Outlining
|
||||
|
||||
### Current Approach
|
||||
Sequential depth-expansion passes convert plot beats into a chapter plan. Each `expand()` call is unaware of the final desired state, so multiple passes are needed.
|
||||
|
||||
---
|
||||
|
||||
### Alt 2-A: Single-Pass Hierarchical Outline
|
||||
|
||||
**Description:** Replace sequential `expand()` calls with a single multi-step prompt that builds the outline in one shot — specifying the desired depth level in the instructions. The model produces both high-level events and chapter-level detail simultaneously.
|
||||
|
||||
**Hypothesis:** Single-pass outline reduces Phase 2 Logic calls from 6 to 2 (one `plan_structure`, one combined `expand+chapter_plan`), saving ~60K tokens (~45% Phase 2 cost). Quality may drop slightly if the model can't maintain coherence across 50 chapters in one response.
|
||||
|
||||
**Complexity:** Low — prompt rewrite; no code structure change
|
||||
|
||||
**Risk:** Large single-response JSON might fail or be truncated by model. Novel (30 chapters) is manageable; Epic (50 chapters) is borderline.
|
||||
|
||||
---
|
||||
|
||||
### Alt 2-B: Outline Validation Gate
|
||||
|
||||
**Description:** After `create_chapter_plan()`, run a validation call that checks the outline for: (a) missing required plot beats, (b) character deaths/revivals, (c) pacing imbalances, (d) POV distribution. Block writing phase until outline passes validation.
|
||||
|
||||
**Hypothesis:** Pre-generation outline validation (1 Logic call, ~15K tokens, FREE on Pro-Exp) prevents ~3–5 expensive rewrite cycles during Phase 3, saving 75K–125K Writer tokens (~$0.05–$0.10 per book).
|
||||
|
||||
**Complexity:** Low — add `validate_outline()` function, call it before Phase 3 begins
|
||||
|
||||
**Risk:** Validation might be overly strict and reject valid creative choices
|
||||
|
||||
---
|
||||
|
||||
### Alt 2-C: Dynamic Personas (Mood/POV Adaptation)
|
||||
|
||||
**Description:** Instead of a single author persona, create sub-personas for different scene types: (a) action sequences, (b) introspection/emotion, (c) dialogue-heavy scenes. The writer prompt selects the appropriate sub-persona based on chapter pacing.
|
||||
|
||||
**Hypothesis:** Dynamic personas reduce "voice drift" across different scene types, improving average chapter evaluation score by ~0.3 points. Cost increases by ~12K tokens/book for the additional persona generation calls.
|
||||
|
||||
**Complexity:** Medium — requires sub-persona generation, storage, and selection logic in `write_chapter()`
|
||||
|
||||
**Risk:** Sub-personas might be inconsistent with each other if not carefully designed
|
||||
|
||||
---
|
||||
|
||||
### Alt 2-D: Specialized Chapter Templates
|
||||
|
||||
**Description:** Create genre-specific "chapter templates" for common patterns: opening chapters, mid-point reversals, climax chapters, denouements. The planner selects the appropriate template when assigning structure, reducing the amount of creative work needed per chapter.
|
||||
|
||||
**Hypothesis:** Chapter templates reduce Phase 3 beat expansion cost by ~40% (pre-structured templates need less expansion) and reduce rewrite rate by ~15% (templates encode known-good patterns).
|
||||
|
||||
**Complexity:** Medium — requires template library and selection logic
|
||||
|
||||
**Risk:** Templates might make books feel formulaic
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: The Writing Engine
|
||||
|
||||
### Current Approach
|
||||
Single-model drafting with up to 3 attempts. Low-scoring drafts trigger full rewrites using the Pro model. Evaluation happens after each draft.
|
||||
|
||||
---
|
||||
|
||||
### Alt 3-A: Two-Pass Drafting (Cheap Draft + Expensive Polish)
|
||||
|
||||
**Description:** Use the cheapest available Flash model for a rough first draft (focused on getting beats covered and word count right), then use the Pro model to polish prose quality. Skip the evaluation + rewrite loop entirely.
|
||||
|
||||
**Hypothesis:** Two-pass drafting reduces average chapter evaluation score variance (fewer very-low scores), but might be slower because every chapter gets polished regardless of quality. Net cost impact uncertain — depends on Flash vs Pro price differential. At current pricing (Flash free on Pro-Exp), this is equivalent to the current approach.
|
||||
|
||||
**Complexity:** Low — add a "polish" pass after initial draft in `write_chapter()`
|
||||
|
||||
**Risk:** Polish pass might not improve chapters that have structural problems (wrong beats covered)
|
||||
|
||||
---
|
||||
|
||||
### Alt 3-B: Adaptive Scoring Thresholds
|
||||
|
||||
**Description:** Use different scoring thresholds based on chapter position and importance:
|
||||
- Setup chapters (1–20% of book): SCORE_PASSING = 6.5 (accept imperfect early work)
|
||||
- Midpoint + rising action (20–70%): SCORE_PASSING = 7.0 (current standard)
|
||||
- Climax + resolution (70–100%): SCORE_PASSING = 7.5 (stricter standards for crucial chapters)
|
||||
|
||||
**Hypothesis:** Adaptive thresholds reduce refinement calls on setup chapters by ~25% while improving quality of climax chapters. Net token saving ~100K per book (~$0.02) with no quality loss on high-stakes scenes.
|
||||
|
||||
**Complexity:** Very low — change 2 constants in `write_chapter()` to be position-aware
|
||||
|
||||
**Risk:** Lower-quality setup chapters might affect reader engagement in early pages
|
||||
|
||||
---
|
||||
|
||||
### Alt 3-C: Pre-Scoring Outline Beats
|
||||
|
||||
**Description:** Before writing any chapter, use the Logic model to score each chapter's beat list for "writability" — the likelihood that the beats will produce a high-quality first draft. Flag chapters scoring below 6/10 as "high-risk" and assign them extra write attempts upfront.
|
||||
|
||||
**Hypothesis:** Pre-scoring beats adds ~5K tokens per book but reduces full-rewrite incidents by ~30% (the most expensive outcome). Expected saving: 30% × 15 rewrites × 50K tokens = ~225K tokens (~$0.05).
|
||||
|
||||
**Complexity:** Low — add `score_beats_writability()` call before Phase 3 loop
|
||||
|
||||
**Risk:** Pre-scoring accuracy might be low; Logic model can't fully predict quality from beats alone
|
||||
|
||||
---
|
||||
|
||||
### Alt 3-D: Persona Caching (Immediate Win)
|
||||
|
||||
**Description:** Load the author persona (bio, sample text, sample files) once per book run rather than re-reading from disk for each chapter. Store in memory and pass to `write_chapter()` as a pre-built string.
|
||||
|
||||
**Hypothesis:** Persona caching reduces per-chapter I/O overhead and eliminates redundant file reads. No quality impact. Saves ~90K tokens per book (3K tokens × 30 chapters from persona sample files).
|
||||
|
||||
**Complexity:** Very low — refactor engine.py to load persona once and pass it
|
||||
|
||||
**Risk:** None
|
||||
|
||||
---
|
||||
|
||||
### Alt 3-E: Skip Beat Expansion for Detailed Beats
|
||||
|
||||
**Description:** If a chapter's beats already exceed 100 words each, skip `expand_beats_to_treatment()`. The existing beats are detailed enough to guide the writer.
|
||||
|
||||
**Hypothesis:** ~30% of chapters have detailed beats. Skipping expansion saves 5K tokens × 30% × 30 chapters = ~45K tokens. Quality impact negligible for already-detailed beats.
|
||||
|
||||
**Complexity:** Very low — add word-count check before calling `expand_beats_to_treatment()`
|
||||
|
||||
**Risk:** None for already-detailed beats; risk only if threshold is set too low
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Review & Refinement
|
||||
|
||||
### Current Approach
|
||||
Per-chapter evaluation with 13 rubrics. Post-generation consistency check. Dynamic pacing interventions. User-triggered ripple propagation.
|
||||
|
||||
---
|
||||
|
||||
### Alt 4-A: Batched Chapter Evaluation
|
||||
|
||||
**Description:** Instead of evaluating each chapter individually (~20K tokens/eval), batch 3–5 chapters per evaluation call. The evaluator assesses them together and can identify cross-chapter issues (pacing, voice consistency) that per-chapter evaluation misses.
|
||||
|
||||
**Hypothesis:** Batched evaluation reduces evaluation token cost by ~60% (from 600K to 240K tokens) while improving cross-chapter quality detection. Risk: individual chapter scores may be less granular.
|
||||
|
||||
**Complexity:** Medium — refactor `evaluate_chapter_quality()` to accept chapter arrays
|
||||
|
||||
**Risk:** Batched scoring might be less precise per-chapter; harder to pinpoint which chapter needs rewriting
|
||||
|
||||
---
|
||||
|
||||
### Alt 4-B: Mid-Generation Consistency Snapshots
|
||||
|
||||
**Description:** Run `analyze_consistency()` every 10 chapters (not just post-generation). If contradictions are found, pause writing and resolve them before proceeding.
|
||||
|
||||
**Hypothesis:** Mid-generation consistency checks add ~3 Logic calls per 30-chapter book (~75K tokens, FREE) but reduce post-generation ripple propagation cost by ~50% by catching issues early.
|
||||
|
||||
**Complexity:** Low — add consistency snapshot call to engine.py loop
|
||||
|
||||
**Risk:** Consistency check might generate false positives that stall generation
|
||||
|
||||
---
|
||||
|
||||
### Alt 4-C: Semantic Ripple Detection
|
||||
|
||||
**Description:** Replace LLM-based ripple detection in `check_and_propagate()` with an embedding-similarity approach. When Chapter N is edited, compute semantic similarity between Chapter N's content and all downstream chapters. Only rewrite chapters above a similarity threshold.
|
||||
|
||||
**Hypothesis:** Semantic ripple detection reduces per-ripple token cost from ~15K (LLM scan) to ~2K (embedding query) — 87% reduction. Accuracy comparable to LLM for direct references; may miss indirect narrative impacts.
|
||||
|
||||
**Complexity:** High — requires adding `sentence-transformers` or Gemini embedding API dependency
|
||||
|
||||
**Risk:** Embedding similarity doesn't capture narrative causality (e.g., a character dying affects later chapters even if the death isn't mentioned verbatim)
|
||||
|
||||
---
|
||||
|
||||
### Alt 4-D: Editor Bot Specialization
|
||||
|
||||
**Description:** Create specialized sub-evaluators for specific failure modes:
|
||||
- `check_filter_words()` — fast regex-based scan (no LLM needed)
|
||||
- `check_summary_mode()` — detect scene-skipping patterns
|
||||
- `check_voice_consistency()` — compare chapter voice against persona sample
|
||||
- `check_plot_adherence()` — verify beats were covered
|
||||
|
||||
Run cheap checks first; only invoke full 13-rubric LLM evaluation if fast checks pass.
|
||||
|
||||
**Hypothesis:** Specialized editor bots reduce evaluation cost by ~40% (many chapters fail fast checks and don't need full LLM eval). Quality detection equal or better because fast checks are more precise for rule violations.
|
||||
|
||||
**Complexity:** Medium — implement regex-based fast checks; modify evaluation pipeline
|
||||
|
||||
**Risk:** Fast checks might have false positives that reject good chapters prematurely
|
||||
|
||||
---
|
||||
|
||||
## Summary: Hypotheses Ranked by Expected Value
|
||||
|
||||
| Alt | Phase | Expected Token Saving | Quality Impact | Complexity |
|
||||
|-----|-------|----------------------|----------------|------------|
|
||||
| 3-D (Persona Cache) | 3 | ~90K | None | Very Low |
|
||||
| 3-E (Skip Beat Expansion) | 3 | ~45K | None | Very Low |
|
||||
| 2-B (Outline Validation) | 2 | Prevents ~100K rewrites | Positive | Low |
|
||||
| 3-B (Adaptive Thresholds) | 3 | ~100K | Positive | Very Low |
|
||||
| 1-C (Persona Validation) | 1 | ~60K (prevented rewrites) | Positive | Low |
|
||||
| 4-B (Mid-gen Consistency) | 4 | ~75K (prevented rewrites) | Positive | Low |
|
||||
| 3-C (Pre-score Beats) | 3 | ~225K | Positive | Low |
|
||||
| 4-A (Batch Evaluation) | 4 | ~360K | Neutral/Positive | Medium |
|
||||
| 2-A (Single-pass Outline) | 2 | ~60K | Neutral | Low |
|
||||
| 3-B (Two-Pass Drafting) | 3 | Neutral | Potentially Positive | Low |
|
||||
| 4-D (Editor Bots) | 4 | ~240K | Positive | Medium |
|
||||
| 2-C (Dynamic Personas) | 2 | -12K (slight increase) | Positive | Medium |
|
||||
| 4-C (Semantic Ripple) | 4 | ~200K | Neutral | High |
|
||||
238
docs/current_state_analysis.md
Normal file
238
docs/current_state_analysis.md
Normal file
@@ -0,0 +1,238 @@
|
||||
# Current State Analysis: BookApp AI Pipeline
|
||||
|
||||
**Date:** 2026-02-22
|
||||
**Scope:** Mapping existing codebase to the four phases defined in `ai_blueprint.md`
|
||||
**Status:** Completed — fulfills Action Plan Step 1
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
BookApp is an AI-powered novel generation engine using Google Gemini. The pipeline is structured into four phases that map directly to the review framework in `ai_blueprint.md`. This document catalogues the current implementation, identifies efficiency metrics, and surfaces limitations in each phase.
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Foundation & Ideation ("The Seed")
|
||||
|
||||
**Primary File:** `story/planner.py` (lines 1–86)
|
||||
**Supporting:** `story/style_persona.py` (lines 81–104), `core/config.py`
|
||||
|
||||
### What Happens
|
||||
|
||||
1. User provides a minimal `manual_instruction` (can be a single sentence).
|
||||
2. `enrich(bp, folder, context)` calls the Logic model to expand this into:
|
||||
- `book_metadata`: title, genre, tone, time period, structure type, formatting rules, content warnings
|
||||
- `characters`: 2–8 named characters with roles and descriptions
|
||||
- `plot_beats`: 5–7 concrete narrative beats
|
||||
3. If the project is part of a series, context from previous books is injected.
|
||||
4. `create_initial_persona()` generates a fictional author persona (name, bio, age, gender).
|
||||
|
||||
### Costs (Per Book)
|
||||
|
||||
| Task | Model | Input Tokens | Output Tokens | Cost (Pro-Exp) |
|
||||
|------|-------|-------------|---------------|----------------|
|
||||
| `enrich()` | Logic | ~10K | ~3K | FREE |
|
||||
| `create_initial_persona()` | Logic | ~5.5K | ~1.5K | FREE |
|
||||
| **Phase 1 Total** | — | ~15.5K | ~4.5K | **FREE** |
|
||||
|
||||
### Known Limitations
|
||||
|
||||
| ID | Issue | Impact |
|
||||
|----|-------|--------|
|
||||
| P1-L1 | `enrich()` silently returns original BP on exception (line 84) | Invalid enrichment passes downstream without warning |
|
||||
| P1-L2 | `filter_characters()` blacklists keywords like "TBD", "protagonist" — can cull valid names | Characters named "The Protagonist" are silently dropped |
|
||||
| P1-L3 | Single-pass persona creation — no quality check on output | Generic personas produce poor voice throughout book |
|
||||
| P1-L4 | No validation that required `book_metadata` fields are non-null | Downstream crashes when title/genre are missing |
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Structuring & Outlining
|
||||
|
||||
**Primary File:** `story/planner.py` (lines 89–290)
|
||||
**Supporting:** `story/style_persona.py`
|
||||
|
||||
### What Happens
|
||||
|
||||
1. `plan_structure(bp, folder)` maps plot beats to a structural framework (Hero's Journey, Three-Act, etc.) and produces ~10–15 events.
|
||||
2. `expand(events, pass_num, ...)` iteratively enriches the outline. Called `depth` times (1–4 based on length preset). Each pass targets chapter count × 1.5 events as ceiling.
|
||||
3. `create_chapter_plan(events, bp, folder)` converts events into concrete chapter objects with POV, pacing, and estimated word count.
|
||||
4. `get_style_guidelines()` loads or refreshes the AI-ism blacklist and filter-word list.
|
||||
|
||||
### Depth Strategy
|
||||
|
||||
| Preset | Depth | Expand Calls | Approx Events |
|
||||
|--------|-------|-------------|---------------|
|
||||
| Flash Fiction | 1 | 1 | 1 |
|
||||
| Short Story | 1 | 1 | 5 |
|
||||
| Novella | 2 | 2 | 15 |
|
||||
| Novel | 3 | 3 | 30 |
|
||||
| Epic | 4 | 4 | 50 |
|
||||
|
||||
### Costs (30-Chapter Novel)
|
||||
|
||||
| Task | Calls | Input Tokens | Cost (Pro-Exp) |
|
||||
|------|-------|-------------|----------------|
|
||||
| `plan_structure` | 1 | ~15K | FREE |
|
||||
| `expand` × 3 | 3 | ~12K each | FREE |
|
||||
| `create_chapter_plan` | 1 | ~14K | FREE |
|
||||
| `get_style_guidelines` | 1 | ~8K | FREE |
|
||||
| **Phase 2 Total** | 6 | ~73K | **FREE** |
|
||||
|
||||
### Known Limitations
|
||||
|
||||
| ID | Issue | Impact |
|
||||
|----|-------|--------|
|
||||
| P2-L1 | Sequential `expand()` calls — each call unaware of final state | Redundant inter-call work; could be one multi-step prompt |
|
||||
| P2-L2 | No continuity validation on outline — character deaths/revivals not detected | Plot holes remain until expensive Phase 3 rewrite |
|
||||
| P2-L3 | Static chapter plan — cannot adapt if early chapters reveal pacing problem | Dynamic interventions in Phase 4 are costly workarounds |
|
||||
| P2-L4 | POV assignment is AI-generated, not validated against narrative logic | Wrong POV on key scenes; caught only during editing |
|
||||
| P2-L5 | Word count estimates are rough (~±30% actual variance) | Writer overshoots/undershoots target; word count normalization fails |
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: The Writing Engine (Drafting)
|
||||
|
||||
**Primary File:** `story/writer.py`
|
||||
**Orchestrated by:** `cli/engine.py`
|
||||
|
||||
### What Happens
|
||||
|
||||
For each chapter:
|
||||
1. `expand_beats_to_treatment()` — Logic model expands sparse beats into a "Director's Treatment" (staging, sensory anchors, emotional arc, subtext).
|
||||
2. `write_chapter()` constructs a ~310-line prompt injecting:
|
||||
- Author persona (bio, sample text, sample files from disk)
|
||||
- Filtered characters (only those named in beats + POV character)
|
||||
- Character tracking state (location, clothing, held items)
|
||||
- Lore context (relevant locations/items from tracking)
|
||||
- Style guidelines + genre-specific mandates
|
||||
- Smart context tail: last ~1000 tokens of previous chapter
|
||||
- Director's Treatment
|
||||
3. Writer model generates first draft.
|
||||
4. Logic model evaluates on 13 rubrics (1–10 scale). Automatic fail conditions apply for filter-word density, summary mode, and labeled emotions.
|
||||
5. Iterative quality loop (up to 3 attempts):
|
||||
- Score ≥ 8.0 → Auto-accept
|
||||
- Score ≥ 7.0 → Accept after max attempts
|
||||
- Score < 7.0 → Refinement pass (Writer model)
|
||||
- Score < 6.0 → Full rewrite (Pro model)
|
||||
6. Every 5 chapters: `refine_persona()` updates author bio based on actual written text.
|
||||
|
||||
### Key Innovations
|
||||
|
||||
- **Dynamic Character Injection:** Only injects characters named in chapter beats (saves ~5K tokens/chapter).
|
||||
- **Smart Context Tail:** Takes last ~1000 tokens of previous chapter (not first 1000) — preserves handoff point.
|
||||
- **Auto Model Escalation:** Low-scoring drafts trigger switch to Pro model for full rewrite.
|
||||
|
||||
### Costs (30-Chapter Novel, Mixed Model Strategy)
|
||||
|
||||
| Task | Calls | Input Tokens | Output Tokens | Cost Estimate |
|
||||
|------|-------|-------------|---------------|---------------|
|
||||
| `expand_beats_to_treatment` × 30 | 30 | ~5K | ~2K | FREE (Logic) |
|
||||
| `write_chapter` draft × 30 | 30 | ~25K | ~3.5K | ~$0.087 (Writer) |
|
||||
| Evaluation × 30 | 30 | ~20K | ~1.5K | FREE (Logic) |
|
||||
| Refinement passes × 15 (est.) | 15 | ~20K | ~3K | ~$0.090 (Writer) |
|
||||
| `refine_persona` × 6 | 6 | ~6K | ~1.5K | FREE (Logic) |
|
||||
| **Phase 3 Total** | ~111 | ~1.9M | ~310K | **~$0.18** |
|
||||
|
||||
### Known Limitations
|
||||
|
||||
| ID | Issue | Impact |
|
||||
|----|-------|--------|
|
||||
| P3-L1 | Persona files re-read from disk on every chapter | I/O overhead; persona doesn't change between reads |
|
||||
| P3-L2 | Beat expansion called even when beats are already detailed (>100 words) | Wastes ~5K tokens/chapter on ~30% of chapters |
|
||||
| P3-L3 | Full rewrite triggered at score < 6.0 — discards entire draft | If draft scores 5.9, all 25K output tokens wasted |
|
||||
| P3-L4 | No priority weighting for climax chapters | Ch 28 (climax) uses same resources/attempts as Ch 3 (setup) |
|
||||
| P3-L5 | Previous chapter context hard-capped at 1000 tokens | For long chapters, might miss setup context from earlier pages |
|
||||
| P3-L6 | Scoring thresholds fixed regardless of book position | Strict standards in early chapters = expensive refinement for setup scenes |
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Review & Refinement (Editing)
|
||||
|
||||
**Primary Files:** `story/editor.py`, `story/bible_tracker.py`
|
||||
**Orchestrated by:** `cli/engine.py`
|
||||
|
||||
### What Happens
|
||||
|
||||
**During writing loop (every chapter):**
|
||||
- `update_tracking()` refreshes character state (location, clothing, held items, speech style, events).
|
||||
- `update_lore_index()` extracts canonical descriptions of locations and items.
|
||||
|
||||
**Every 2 chapters:**
|
||||
- `check_pacing()` detects if story is rushing or repeating beats; triggers ADD_BRIDGE or CUT_NEXT interventions.
|
||||
|
||||
**After writing completes:**
|
||||
- `analyze_consistency()` scans entire manuscript for plot holes and contradictions.
|
||||
- `harvest_metadata()` extracts newly invented characters not in the original bible.
|
||||
- `check_and_propagate()` cascades chapter edits forward through the manuscript.
|
||||
|
||||
### 13 Evaluation Rubrics
|
||||
|
||||
1. Engagement & tension
|
||||
2. Scene execution (no summaries)
|
||||
3. Voice & tone
|
||||
4. Sensory immersion
|
||||
5. Show, Don't Tell / Deep POV (**auto-fail trigger**)
|
||||
6. Character agency
|
||||
7. Pacing
|
||||
8. Genre appropriateness
|
||||
9. Dialogue authenticity
|
||||
10. Plot relevance
|
||||
11. Staging & flow
|
||||
12. Prose dynamics (sentence variety)
|
||||
13. Clarity & readability
|
||||
|
||||
**Automatic fail conditions:** filter-word density > 1/120 words → cap at 5; summary mode detected → cap at 6; >3 labeled emotions → cap at 5.
|
||||
|
||||
### Costs (30-Chapter Novel)
|
||||
|
||||
| Task | Calls | Input Tokens | Cost (Pro-Exp) |
|
||||
|------|-------|-------------|----------------|
|
||||
| `update_tracking` × 30 | 30 | ~18K | FREE |
|
||||
| `update_lore_index` × 30 | 30 | ~15K | FREE |
|
||||
| `check_pacing` × 15 | 15 | ~18K | FREE |
|
||||
| `analyze_consistency` | 1 | ~25K | FREE |
|
||||
| `harvest_metadata` | 1 | ~25K | FREE |
|
||||
| **Phase 4 Total** | 77 | ~1.34M | **FREE** |
|
||||
|
||||
### Known Limitations
|
||||
|
||||
| ID | Issue | Impact |
|
||||
|----|-------|--------|
|
||||
| P4-L1 | Consistency check is post-generation only | Plot holes caught too late to cheaply fix |
|
||||
| P4-L2 | Ripple propagation (`check_and_propagate`) has no cost ceiling | A single user edit in Ch 5 can trigger 100K+ tokens of cascading rewrites |
|
||||
| P4-L3 | `rewrite_chapter_content()` uses Logic model instead of Writer model | Less creative rewrite output — Logic model optimizes reasoning, not prose |
|
||||
| P4-L4 | `check_pacing()` sampling only looks at recent chapters, not cumulative arc | Slow-building issues across 10+ chapters not detected until critical |
|
||||
| P4-L5 | No quality metric for the evaluator itself | Can't confirm if 13-rubric scores are calibrated correctly |
|
||||
|
||||
---
|
||||
|
||||
## Cross-Phase Summary
|
||||
|
||||
### Total Costs (30-Chapter Novel)
|
||||
|
||||
| Phase | Token Budget | Cost Estimate |
|
||||
|-------|-------------|---------------|
|
||||
| Phase 1: Ideation | ~20K | FREE |
|
||||
| Phase 2: Outline | ~73K | FREE |
|
||||
| Phase 3: Writing | ~2.2M | ~$0.18 |
|
||||
| Phase 4: Review | ~1.34M | FREE |
|
||||
| Imagen Cover (3 images) | — | ~$0.12 |
|
||||
| **Total** | **~3.63M** | **~$0.30** |
|
||||
|
||||
*Assumes quality-first model selection (Pro-Exp for Logic, Flash for Writer)*
|
||||
|
||||
### Efficiency Frontier
|
||||
|
||||
- **Best case** (all chapters pass first attempt): ~$0.18 text + $0.04 cover = ~$0.22
|
||||
- **Worst case** (30% rewrite rate with Pro escalations): ~$0.45 text + $0.12 cover = ~$0.57
|
||||
- **Budget per blueprint goal:** $2.00 total — current system is 15–29% of budget
|
||||
|
||||
### Top 5 Immediate Optimization Opportunities
|
||||
|
||||
| Priority | ID | Change | Savings |
|
||||
|----------|----|--------|---------|
|
||||
| 1 | P3-L1 | Cache persona per book (not per chapter) | ~90K tokens |
|
||||
| 2 | P3-L2 | Skip beat expansion for detailed beats | ~45K tokens |
|
||||
| 3 | P2-L2 | Add pre-generation outline validation | Prevent expensive rewrites |
|
||||
| 4 | P1-L1 | Fix silent failure in `enrich()` | Prevent silent corrupt state |
|
||||
| 5 | P3-L6 | Adaptive scoring thresholds by chapter position | ~15% fewer refinement passes |
|
||||
290
docs/experiment_design.md
Normal file
290
docs/experiment_design.md
Normal file
@@ -0,0 +1,290 @@
|
||||
# Experiment Design: A/B Tests for BookApp Optimization
|
||||
|
||||
**Date:** 2026-02-22
|
||||
**Status:** Completed — fulfills Action Plan Step 3
|
||||
|
||||
---
|
||||
|
||||
## Methodology
|
||||
|
||||
All experiments follow a controlled A/B design. We hold all variables constant except the single variable under test. Success is measured against three primary metrics:
|
||||
|
||||
- **Cost per chapter (CPC):** Total token cost / number of chapters written
|
||||
- **Human Quality Score (HQS):** 1–10 score from a human reviewer blind to which variant generated the chapter
|
||||
- **Continuity Error Rate (CER):** Number of plot/character contradictions per 10 chapters (lower is better)
|
||||
|
||||
Each experiment runs on the same 3 prompts (one each of short story, novella, and novel length). Results are averaged across all 3.
|
||||
|
||||
**Baseline:** Current production configuration as of 2026-02-22.
|
||||
|
||||
---
|
||||
|
||||
## Experiment 1: Persona Caching
|
||||
|
||||
**Alt Reference:** Alt 3-D
|
||||
**Hypothesis:** Caching persona per book reduces I/O overhead with no quality impact.
|
||||
|
||||
### Setup
|
||||
|
||||
| Parameter | Control (A) | Treatment (B) |
|
||||
|-----------|-------------|---------------|
|
||||
| Persona loading | Re-read from disk each chapter | Load once per book run, pass as argument |
|
||||
| Everything else | Identical | Identical |
|
||||
|
||||
### Metrics to Measure
|
||||
|
||||
- Token count per chapter (to verify savings)
|
||||
- Wall-clock generation time per book
|
||||
- Chapter quality scores (should be identical)
|
||||
|
||||
### Success Criterion
|
||||
|
||||
- Token reduction ≥ 2,000 tokens/chapter on books with sample files
|
||||
- HQS difference < 0.1 between A and B (no quality impact)
|
||||
- Zero new errors introduced
|
||||
|
||||
### Implementation Notes
|
||||
|
||||
- Modify `cli/engine.py`: call `style_persona.load_persona_data()` once before chapter loop
|
||||
- Modify `story/writer.py`: accept optional `persona_info` parameter, skip disk reads if provided
|
||||
- Estimated implementation: 30 minutes
|
||||
|
||||
---
|
||||
|
||||
## Experiment 2: Skip Beat Expansion for Detailed Beats
|
||||
|
||||
**Alt Reference:** Alt 3-E
|
||||
**Hypothesis:** Skipping `expand_beats_to_treatment()` when beats exceed 100 words saves tokens with no quality loss.
|
||||
|
||||
### Setup
|
||||
|
||||
| Parameter | Control (A) | Treatment (B) |
|
||||
|-----------|-------------|---------------|
|
||||
| Beat expansion | Always called | Skipped if total beats > 100 words |
|
||||
| Everything else | Identical | Identical |
|
||||
|
||||
### Metrics to Measure
|
||||
|
||||
- Percentage of chapters that skip expansion (expected: ~30%)
|
||||
- Token savings per book
|
||||
- HQS for chapters that skip vs. chapters that don't skip
|
||||
- Rate of beat-coverage failures (chapters that miss a required beat)
|
||||
|
||||
### Success Criterion
|
||||
|
||||
- ≥ 25% of chapters skip expansion (validating hypothesis)
|
||||
- HQS difference < 0.2 between chapters that skip and those that don't
|
||||
- Beat-coverage failure rate unchanged
|
||||
|
||||
### Implementation Notes
|
||||
|
||||
- Modify `story/writer.py` `write_chapter()`: add `if sum(len(b) for b in beats) > 100` guard before calling expansion
|
||||
- Estimated implementation: 15 minutes
|
||||
|
||||
---
|
||||
|
||||
## Experiment 3: Outline Validation Gate
|
||||
|
||||
**Alt Reference:** Alt 2-B
|
||||
**Hypothesis:** Pre-generation outline validation prevents costly Phase 3 rewrites by catching plot holes at the outline stage.
|
||||
|
||||
### Setup
|
||||
|
||||
| Parameter | Control (A) | Treatment (B) |
|
||||
|-----------|-------------|---------------|
|
||||
| Outline validation | None | Run `validate_outline()` after `create_chapter_plan()`; block if critical issues found |
|
||||
| Everything else | Identical | Identical |
|
||||
|
||||
### Metrics to Measure
|
||||
|
||||
- Number of critical outline issues flagged per run
|
||||
- Rewrite rate during Phase 3 (did validation prevent rewrites?)
|
||||
- Phase 3 token cost difference (A vs B)
|
||||
- CER difference (did validation reduce continuity errors?)
|
||||
|
||||
### Success Criterion
|
||||
|
||||
- Validation blocks at least 1 critical issue per 3 runs
|
||||
- Phase 3 rewrite rate drops ≥ 15% when validation is active
|
||||
- CER improves ≥ 0.5 per 10 chapters
|
||||
|
||||
### Implementation Notes
|
||||
|
||||
- Add `validate_outline(events, chapters, bp, folder)` to `story/planner.py`
|
||||
- Prompt: "Review this chapter plan for: (1) missing required plot beats, (2) character deaths/revivals without explanation, (3) severe pacing imbalances, (4) POV character inconsistency. Return: {issues: [...], severity: 'critical'|'warning'|'ok'}"
|
||||
- Modify `cli/engine.py`: call `validate_outline()` and log issues before Phase 3 begins
|
||||
- Estimated implementation: 2 hours
|
||||
|
||||
---
|
||||
|
||||
## Experiment 4: Adaptive Scoring Thresholds
|
||||
|
||||
**Alt Reference:** Alt 3-B
|
||||
**Hypothesis:** Lowering SCORE_PASSING for early setup chapters reduces refinement cost while maintaining quality on high-stakes scenes.
|
||||
|
||||
### Setup
|
||||
|
||||
| Parameter | Control (A) | Treatment (B) |
|
||||
|-----------|-------------|---------------|
|
||||
| SCORE_AUTO_ACCEPT | 8.0 (all chapters) | 8.0 (all chapters) |
|
||||
| SCORE_PASSING | 7.0 (all chapters) | 6.5 (ch 1–20%), 7.0 (ch 20–70%), 7.5 (ch 70–100%) |
|
||||
| Everything else | Identical | Identical |
|
||||
|
||||
### Metrics to Measure
|
||||
|
||||
- Refinement pass count per chapter position bucket
|
||||
- HQS per chapter position bucket (A vs B)
|
||||
- CPC for each bucket
|
||||
- Overall HQS for full book (A vs B)
|
||||
|
||||
### Success Criterion
|
||||
|
||||
- Setup chapters (1–20%): ≥ 20% fewer refinement passes in B
|
||||
- Climax chapters (70–100%): HQS improvement ≥ 0.3 in B
|
||||
- Full book HQS unchanged or improved
|
||||
|
||||
### Implementation Notes
|
||||
|
||||
- Modify `story/writer.py` `write_chapter()`: accept `chapter_position` (0.0–1.0 float)
|
||||
- Compute adaptive threshold: `passing = 6.5 + position * 1.0` (linear scaling)
|
||||
- Modify `cli/engine.py`: pass `chapter_num / total_chapters` to `write_chapter()`
|
||||
- Estimated implementation: 1 hour
|
||||
|
||||
---
|
||||
|
||||
## Experiment 5: Mid-Generation Consistency Snapshots
|
||||
|
||||
**Alt Reference:** Alt 4-B
|
||||
**Hypothesis:** Running `analyze_consistency()` every 10 chapters reduces post-generation CER without significant cost increase.
|
||||
|
||||
### Setup
|
||||
|
||||
| Parameter | Control (A) | Treatment (B) |
|
||||
|-----------|-------------|---------------|
|
||||
| Consistency check | Post-generation only | Every 10 chapters + post-generation |
|
||||
| Everything else | Identical | Identical |
|
||||
|
||||
### Metrics to Measure
|
||||
|
||||
- CER post-generation (A vs B)
|
||||
- Number of issues caught mid-generation vs post-generation
|
||||
- Token cost difference (mid-gen checks add ~25K × N/10 tokens)
|
||||
- Generation time difference
|
||||
|
||||
### Success Criterion
|
||||
|
||||
- Post-generation CER drops ≥ 30% in B
|
||||
- Issues caught mid-generation prevent at least 1 expensive post-gen ripple propagation per run
|
||||
- Additional cost ≤ $0.01 per book (all free on Pro-Exp)
|
||||
|
||||
### Implementation Notes
|
||||
|
||||
- Modify `cli/engine.py`: every 10 chapters, call `analyze_consistency()` on written chapters so far
|
||||
- If issues found: log warning and optionally pause for user review
|
||||
- Estimated implementation: 1 hour
|
||||
|
||||
---
|
||||
|
||||
## Experiment 6: Iterative Persona Validation
|
||||
|
||||
**Alt Reference:** Alt 1-C
|
||||
**Hypothesis:** Validating the initial persona with a sample passage reduces voice-drift rewrites in Phase 3.
|
||||
|
||||
### Setup
|
||||
|
||||
| Parameter | Control (A) | Treatment (B) |
|
||||
|-----------|-------------|---------------|
|
||||
| Persona creation | Single-pass, no validation | Generate persona → generate 200-word sample → evaluate → accept if ≥ 7/10, else regenerate (max 3 attempts) |
|
||||
| Everything else | Identical | Identical |
|
||||
|
||||
### Metrics to Measure
|
||||
|
||||
- Initial persona acceptance rate (how often does first-pass persona pass the check?)
|
||||
- Phase 3 persona-related rewrite rate (rewrites where critique mentions "voice inconsistency" or "doesn't match persona")
|
||||
- HQS for first 5 chapters (voice is most important early on)
|
||||
|
||||
### Success Criterion
|
||||
|
||||
- Phase 3 persona-related rewrite rate drops ≥ 20% in B
|
||||
- HQS for first 5 chapters improves ≥ 0.2
|
||||
|
||||
### Implementation Notes
|
||||
|
||||
- Modify `story/style_persona.py`: after `create_initial_persona()`, call a new `validate_persona()` function
|
||||
- `validate_persona()` generates 200-word sample, evaluates with `evaluate_chapter_quality()` (light version)
|
||||
- Estimated implementation: 2 hours
|
||||
|
||||
---
|
||||
|
||||
## Experiment 7: Two-Pass Drafting (Draft + Polish)
|
||||
|
||||
**Alt Reference:** Alt 3-A
|
||||
**Hypothesis:** A cheap rough draft followed by a polished revision produces better quality than iterative retrying.
|
||||
|
||||
### Setup
|
||||
|
||||
| Parameter | Control (A) | Treatment (B) |
|
||||
|-----------|-------------|---------------|
|
||||
| Drafting strategy | Single draft → evaluate → retry | Rough draft (Flash) → polish (Pro) → evaluate → accept if ≥ 7.0 (max 1 retry) |
|
||||
| Max retry attempts | 3 | 1 (after polish) |
|
||||
| Everything else | Identical | Identical |
|
||||
|
||||
### Metrics to Measure
|
||||
|
||||
- CPC (A vs B)
|
||||
- HQS (A vs B)
|
||||
- Rate of chapters needing retry (A vs B)
|
||||
- Total generation time per book
|
||||
|
||||
### Success Criterion
|
||||
|
||||
- HQS improvement ≥ 0.3 in B with no cost increase
|
||||
- OR: CPC reduction ≥ 20% in B with no HQS decrease
|
||||
|
||||
### Implementation Notes
|
||||
|
||||
- Modify `story/writer.py` `write_chapter()`: add polish pass using Pro model after initial draft
|
||||
- Reduce max_attempts to 1 for final retry (after polish)
|
||||
- This requires Pro model to be available (handled by auto-selection)
|
||||
|
||||
---
|
||||
|
||||
## Experiment Execution Order
|
||||
|
||||
Run experiments in this order to minimize dependency conflicts:
|
||||
|
||||
1. **Exp 1** (Persona Caching) — independent, 30 min, no risk
|
||||
2. **Exp 2** (Skip Beat Expansion) — independent, 15 min, no risk
|
||||
3. **Exp 4** (Adaptive Thresholds) — independent, 1 hr, low risk
|
||||
4. **Exp 3** (Outline Validation) — independent, 2 hrs, low risk
|
||||
5. **Exp 6** (Persona Validation) — independent, 2 hrs, low risk
|
||||
6. **Exp 5** (Mid-gen Consistency) — requires stable Phase 3, 1 hr, low risk
|
||||
7. **Exp 7** (Two-Pass Drafting) — highest risk, run last; 3 hrs, medium risk
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics Definitions
|
||||
|
||||
### Cost per Chapter (CPC)
|
||||
|
||||
```
|
||||
CPC = (total_input_tokens × input_price + total_output_tokens × output_price) / num_chapters
|
||||
```
|
||||
|
||||
Measure in both USD and token-count to separate model-price effects from efficiency effects.
|
||||
|
||||
### Human Quality Score (HQS)
|
||||
|
||||
Blind evaluation by a human reviewer:
|
||||
1. Read 3 chapters from treatment A and 3 from treatment B (same book premise)
|
||||
2. Score each on: prose quality (1–5), pacing (1–5), character consistency (1–5)
|
||||
3. HQS = average across all dimensions, normalized to 1–10
|
||||
|
||||
### Continuity Error Rate (CER)
|
||||
|
||||
After generation, manually review character states and key plot facts across chapters. Count:
|
||||
- Character location contradictions
|
||||
- Continuity breaks (held items, injuries, time-of-day)
|
||||
- Plot event contradictions (character alive vs. dead)
|
||||
|
||||
Report as errors per 10 chapters.
|
||||
@@ -80,6 +80,14 @@ def enrich(bp, folder, context=""):
|
||||
if 'plot_beats' not in bp or not bp['plot_beats']:
|
||||
bp['plot_beats'] = ai_data.get('plot_beats', [])
|
||||
|
||||
# Validate critical fields after enrichment
|
||||
title = bp.get('book_metadata', {}).get('title')
|
||||
genre = bp.get('book_metadata', {}).get('genre')
|
||||
if not title:
|
||||
utils.log("ENRICHER", "⚠️ Warning: book_metadata.title is missing after enrichment.")
|
||||
if not genre:
|
||||
utils.log("ENRICHER", "⚠️ Warning: book_metadata.genre is missing after enrichment.")
|
||||
|
||||
return bp
|
||||
except Exception as e:
|
||||
utils.log("ENRICHER", f"Enrichment failed: {e}")
|
||||
@@ -288,3 +296,66 @@ def create_chapter_plan(events, bp, folder):
|
||||
except Exception as e:
|
||||
utils.log("ARCHITECT", f"Failed to create chapter plan: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def validate_outline(events, chapters, bp, folder):
|
||||
"""Pre-generation outline validation gate (Action Plan Step 3: Alt 2-B).
|
||||
|
||||
Checks for: missing required beats, character continuity issues, severe pacing
|
||||
imbalances, and POV logic errors. Returns findings but never blocks generation —
|
||||
issues are logged as warnings so the writer can proceed.
|
||||
"""
|
||||
utils.log("ARCHITECT", "Validating outline before writing phase...")
|
||||
|
||||
beats_context = bp.get('plot_beats', [])
|
||||
chars_summary = [{"name": c.get("name"), "role": c.get("role")} for c in bp.get('characters', [])]
|
||||
|
||||
# Sample chapter data to keep prompt size manageable
|
||||
chapters_sample = chapters[:5] + chapters[-5:] if len(chapters) > 10 else chapters
|
||||
|
||||
prompt = f"""
|
||||
ROLE: Continuity Editor
|
||||
TASK: Review this chapter outline for issues that could cause expensive rewrites later.
|
||||
|
||||
REQUIRED_BEATS (must all appear somewhere in the chapter plan):
|
||||
{json.dumps(beats_context)}
|
||||
|
||||
CHARACTERS:
|
||||
{json.dumps(chars_summary)}
|
||||
|
||||
CHAPTER_PLAN (sample — first 5 and last 5 chapters):
|
||||
{json.dumps(chapters_sample)}
|
||||
|
||||
CHECK FOR:
|
||||
1. MISSING_BEATS: Are all required plot beats present? List any absent beats by name.
|
||||
2. CONTINUITY: Are there character deaths/revivals, unacknowledged time jumps, or contradictions visible in the outline?
|
||||
3. PACING: Are there 3+ consecutive chapters with identical pacing that would create reader fatigue?
|
||||
4. POV_LOGIC: Are key emotional scenes assigned to the most appropriate POV character?
|
||||
|
||||
OUTPUT_FORMAT (JSON):
|
||||
{{
|
||||
"issues": [
|
||||
{{"type": "missing_beat|continuity|pacing|pov", "description": "...", "severity": "critical|warning"}}
|
||||
],
|
||||
"overall_severity": "ok|warning|critical",
|
||||
"summary": "One-sentence summary of findings."
|
||||
}}
|
||||
"""
|
||||
try:
|
||||
response = ai_models.model_logic.generate_content(prompt)
|
||||
utils.log_usage(folder, ai_models.model_logic.name, response.usage_metadata)
|
||||
result = json.loads(utils.clean_json(response.text))
|
||||
|
||||
severity = result.get('overall_severity', 'ok')
|
||||
issues = result.get('issues', [])
|
||||
summary = result.get('summary', 'No issues found.')
|
||||
|
||||
for issue in issues:
|
||||
prefix = "⚠️" if issue.get('severity') == 'warning' else "🚨"
|
||||
utils.log("ARCHITECT", f" {prefix} Outline {issue.get('type', 'issue')}: {issue.get('description', '')}")
|
||||
|
||||
utils.log("ARCHITECT", f"Outline validation complete: {severity.upper()} — {summary}")
|
||||
return result
|
||||
except Exception as e:
|
||||
utils.log("ARCHITECT", f"Outline validation failed (non-blocking): {e}")
|
||||
return {"issues": [], "overall_severity": "ok", "summary": "Validation skipped."}
|
||||
|
||||
@@ -104,6 +104,86 @@ def create_initial_persona(bp, folder):
|
||||
return {"name": "AI Author", "bio": "Standard, balanced writing style."}
|
||||
|
||||
|
||||
def validate_persona(bp, persona_details, folder):
|
||||
"""Validate a newly created persona by generating a 200-word sample and scoring it.
|
||||
|
||||
Experiment 6 (Iterative Persona Validation): generates a test passage in the
|
||||
persona's voice and evaluates voice quality before accepting it. This front-loads
|
||||
quality assurance so Phase 3 starts with a well-calibrated author voice.
|
||||
|
||||
Returns (is_valid: bool, score: int). Threshold: score >= 7 → accepted.
|
||||
"""
|
||||
meta = bp.get('book_metadata', {})
|
||||
genre = meta.get('genre', 'Fiction')
|
||||
tone = meta.get('style', {}).get('tone', 'balanced')
|
||||
name = persona_details.get('name', 'Unknown Author')
|
||||
bio = persona_details.get('bio', 'Standard style.')
|
||||
|
||||
sample_prompt = f"""
|
||||
ROLE: Fiction Writer
|
||||
TASK: Write a 200-word opening scene that perfectly demonstrates this author's voice.
|
||||
|
||||
AUTHOR_PERSONA:
|
||||
Name: {name}
|
||||
Style/Bio: {bio}
|
||||
|
||||
GENRE: {genre}
|
||||
TONE: {tone}
|
||||
|
||||
RULES:
|
||||
- Exactly ~200 words of prose (no chapter header, no commentary)
|
||||
- Must reflect the persona's stated sentence structure, vocabulary, and voice
|
||||
- Show, don't tell — no filter words (felt, saw, heard, realized, noticed)
|
||||
- Deep POV: immerse the reader in a character's immediate experience
|
||||
|
||||
OUTPUT: Prose only.
|
||||
"""
|
||||
try:
|
||||
resp = ai_models.model_logic.generate_content(sample_prompt)
|
||||
utils.log_usage(folder, ai_models.model_logic.name, resp.usage_metadata)
|
||||
sample_text = resp.text
|
||||
except Exception as e:
|
||||
utils.log("SYSTEM", f" -> Persona validation sample failed: {e}. Accepting persona.")
|
||||
return True, 7
|
||||
|
||||
# Lightweight scoring: focused on voice quality (not full 13-rubric)
|
||||
score_prompt = f"""
|
||||
ROLE: Literary Editor
|
||||
TASK: Score this prose sample for author voice quality.
|
||||
|
||||
EXPECTED_PERSONA:
|
||||
{bio}
|
||||
|
||||
SAMPLE:
|
||||
{sample_text}
|
||||
|
||||
CRITERIA:
|
||||
1. Does the prose reflect the stated author persona? (voice, register, sentence style)
|
||||
2. Is the prose free of filter words (felt, saw, heard, noticed, realized)?
|
||||
3. Is it deep POV — immediate, immersive, not distant narration?
|
||||
4. Is there genuine sentence variety and strong verb choice?
|
||||
|
||||
SCORING (1-10):
|
||||
- 8-10: Voice is distinct, matches persona, clean deep POV
|
||||
- 6-7: Reasonable voice, minor filter word issues
|
||||
- 1-5: Generic AI prose, heavy filter words, or persona not reflected
|
||||
|
||||
OUTPUT_FORMAT (JSON): {{"score": int, "reason": "One sentence."}}
|
||||
"""
|
||||
try:
|
||||
resp2 = ai_models.model_logic.generate_content(score_prompt)
|
||||
utils.log_usage(folder, ai_models.model_logic.name, resp2.usage_metadata)
|
||||
data = json.loads(utils.clean_json(resp2.text))
|
||||
score = int(data.get('score', 7))
|
||||
reason = data.get('reason', '')
|
||||
is_valid = score >= 7
|
||||
utils.log("SYSTEM", f" -> Persona validation: {score}/10 {'✅ Accepted' if is_valid else '❌ Rejected'} — {reason}")
|
||||
return is_valid, score
|
||||
except Exception as e:
|
||||
utils.log("SYSTEM", f" -> Persona scoring failed: {e}. Accepting persona.")
|
||||
return True, 7
|
||||
|
||||
|
||||
def refine_persona(bp, text, folder):
|
||||
utils.log("SYSTEM", "Refining Author Persona based on recent chapters...")
|
||||
ad = bp.get('book_metadata', {}).get('author_details', {})
|
||||
|
||||
149
story/writer.py
149
story/writer.py
@@ -74,6 +74,49 @@ def get_genre_instructions(genre):
|
||||
)
|
||||
|
||||
|
||||
def build_persona_info(bp):
|
||||
"""Build the author persona string from bp['book_metadata']['author_details'].
|
||||
|
||||
Extracted as a standalone function so engine.py can pre-load the persona once
|
||||
for the entire writing phase instead of re-reading sample files for every chapter.
|
||||
Returns the assembled persona string, or None if no author_details are present.
|
||||
"""
|
||||
meta = bp.get('book_metadata', {})
|
||||
ad = meta.get('author_details', {})
|
||||
if not ad and 'author_bio' in meta:
|
||||
return meta['author_bio']
|
||||
if not ad:
|
||||
return None
|
||||
|
||||
info = f"Name: {ad.get('name', meta.get('author', 'Unknown'))}\n"
|
||||
if ad.get('age'): info += f"Age: {ad['age']}\n"
|
||||
if ad.get('gender'): info += f"Gender: {ad['gender']}\n"
|
||||
if ad.get('race'): info += f"Race: {ad['race']}\n"
|
||||
if ad.get('nationality'): info += f"Nationality: {ad['nationality']}\n"
|
||||
if ad.get('language'): info += f"Language: {ad['language']}\n"
|
||||
if ad.get('bio'): info += f"Style/Bio: {ad['bio']}\n"
|
||||
|
||||
samples = []
|
||||
if ad.get('sample_text'):
|
||||
samples.append(f"--- SAMPLE PARAGRAPH ---\n{ad['sample_text']}")
|
||||
|
||||
if ad.get('sample_files'):
|
||||
for fname in ad['sample_files']:
|
||||
fpath = os.path.join(config.PERSONAS_DIR, fname)
|
||||
if os.path.exists(fpath):
|
||||
try:
|
||||
with open(fpath, 'r', encoding='utf-8', errors='ignore') as f:
|
||||
content = f.read(3000)
|
||||
samples.append(f"--- SAMPLE FROM {fname} ---\n{content}...")
|
||||
except:
|
||||
pass
|
||||
|
||||
if samples:
|
||||
info += "\nWRITING STYLE SAMPLES:\n" + "\n".join(samples)
|
||||
|
||||
return info
|
||||
|
||||
|
||||
def expand_beats_to_treatment(beats, pov_char, genre, folder):
|
||||
"""Expand sparse scene beats into a Director's Treatment using a fast model.
|
||||
This pre-flight step gives the writer detailed staging and emotional direction,
|
||||
@@ -106,7 +149,15 @@ def expand_beats_to_treatment(beats, pov_char, genre, folder):
|
||||
return None
|
||||
|
||||
|
||||
def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None, next_chapter_hint=""):
|
||||
def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None, next_chapter_hint="", prebuilt_persona=None, chapter_position=None):
|
||||
"""Write a single chapter with iterative quality evaluation.
|
||||
|
||||
Args:
|
||||
prebuilt_persona: Pre-loaded persona string from build_persona_info(bp).
|
||||
When provided, skips per-chapter file reads (persona cache optimisation).
|
||||
chapter_position: Float 0.0–1.0 indicating position in book. Used for
|
||||
adaptive scoring thresholds (setup = lenient, climax = strict).
|
||||
"""
|
||||
pacing = chap.get('pacing', 'Standard')
|
||||
est_words = chap.get('estimated_words', 'Flexible')
|
||||
utils.log("WRITER", f"Drafting Ch {chap['chapter_number']} ({pacing} | ~{est_words} words): {chap['title']}")
|
||||
@@ -117,34 +168,11 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
|
||||
|
||||
pov_char = chap.get('pov_character', '')
|
||||
|
||||
ad = meta.get('author_details', {})
|
||||
if not ad and 'author_bio' in meta:
|
||||
persona_info = meta['author_bio']
|
||||
# Use pre-loaded persona if provided (avoids re-reading sample files every chapter)
|
||||
if prebuilt_persona is not None:
|
||||
persona_info = prebuilt_persona
|
||||
else:
|
||||
persona_info = f"Name: {ad.get('name', meta.get('author', 'Unknown'))}\n"
|
||||
if ad.get('age'): persona_info += f"Age: {ad['age']}\n"
|
||||
if ad.get('gender'): persona_info += f"Gender: {ad['gender']}\n"
|
||||
if ad.get('race'): persona_info += f"Race: {ad['race']}\n"
|
||||
if ad.get('nationality'): persona_info += f"Nationality: {ad['nationality']}\n"
|
||||
if ad.get('language'): persona_info += f"Language: {ad['language']}\n"
|
||||
if ad.get('bio'): persona_info += f"Style/Bio: {ad['bio']}\n"
|
||||
|
||||
samples = []
|
||||
if ad.get('sample_text'):
|
||||
samples.append(f"--- SAMPLE PARAGRAPH ---\n{ad['sample_text']}")
|
||||
|
||||
if ad.get('sample_files'):
|
||||
for fname in ad['sample_files']:
|
||||
fpath = os.path.join(config.PERSONAS_DIR, fname)
|
||||
if os.path.exists(fpath):
|
||||
try:
|
||||
with open(fpath, 'r', encoding='utf-8', errors='ignore') as f:
|
||||
content = f.read(3000)
|
||||
samples.append(f"--- SAMPLE FROM {fname} ---\n{content}...")
|
||||
except: pass
|
||||
|
||||
if samples:
|
||||
persona_info += "\nWRITING STYLE SAMPLES:\n" + "\n".join(samples)
|
||||
persona_info = build_persona_info(bp) or "Standard, balanced writing style."
|
||||
|
||||
# Only inject characters named in the chapter beats + the POV character
|
||||
beats_text = " ".join(str(b) for b in chap.get('beats', []))
|
||||
@@ -217,8 +245,15 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
|
||||
trunc_content = utils.truncate_to_tokens(prev_content, 1000)
|
||||
prev_context_block = f"\nPREVIOUS CHAPTER TEXT (Last ~1000 Tokens — For Immediate Continuity):\n{trunc_content}\n"
|
||||
|
||||
utils.log("WRITER", f" -> Expanding beats to Director's Treatment...")
|
||||
treatment = expand_beats_to_treatment(chap.get('beats', []), pov_char, genre, folder)
|
||||
# Skip beat expansion if beats are already detailed (saves ~5K tokens per chapter)
|
||||
beats_list = chap.get('beats', [])
|
||||
total_beat_words = sum(len(str(b).split()) for b in beats_list)
|
||||
if total_beat_words > 100:
|
||||
utils.log("WRITER", f" -> Beats already detailed ({total_beat_words} words). Skipping expansion.")
|
||||
treatment = None
|
||||
else:
|
||||
utils.log("WRITER", f" -> Expanding beats to Director's Treatment...")
|
||||
treatment = expand_beats_to_treatment(beats_list, pov_char, genre, folder)
|
||||
treatment_block = f"\n DIRECTORS_TREATMENT (Staged expansion of the beats — use this as your scene blueprint; DRAMATIZE every moment, do NOT summarize):\n{treatment}\n" if treatment else ""
|
||||
|
||||
genre_mandates = get_genre_instructions(genre)
|
||||
@@ -327,9 +362,59 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
|
||||
utils.log("WRITER", f"⚠️ Failed Ch {chap['chapter_number']}: {e}")
|
||||
return f"## Chapter {chap['chapter_number']} Failed\n\nError: {e}"
|
||||
|
||||
max_attempts = 3
|
||||
# Exp 7: Two-Pass Drafting — Polish the rough draft with the logic (Pro) model
|
||||
# before evaluation. Produces cleaner prose with fewer rewrite cycles.
|
||||
if current_text:
|
||||
utils.log("WRITER", f" -> Two-pass polish (Pro model)...")
|
||||
guidelines = get_style_guidelines()
|
||||
fw_list = '", "'.join(guidelines['filter_words'])
|
||||
polish_prompt = f"""
|
||||
ROLE: Senior Fiction Editor
|
||||
TASK: Polish this rough draft into publication-ready prose.
|
||||
|
||||
AUTHOR_VOICE:
|
||||
{persona_info}
|
||||
|
||||
GENRE: {genre}
|
||||
TARGET_WORDS: ~{est_words}
|
||||
BEATS (must all be covered): {json.dumps(chap.get('beats', []))}
|
||||
|
||||
POLISH_CHECKLIST:
|
||||
1. FILTER_REMOVAL: Remove all filter words [{fw_list}] — rewrite each to show the sensation directly.
|
||||
2. DEEP_POV: Ensure the reader is inside the POV character's experience at all times — no external narration.
|
||||
3. ACTIVE_VOICE: Replace all 'was/were + -ing' constructions with active alternatives.
|
||||
4. SENTENCE_VARIETY: No two consecutive sentences starting with the same word. Vary length for rhythm.
|
||||
5. STRONG_VERBS: Delete adverbs; replace with precise verbs.
|
||||
6. NO_AI_ISMS: Remove: 'testament to', 'tapestry', 'palpable tension', 'azure', 'cerulean', 'bustling', 'a sense of'.
|
||||
7. CHAPTER_HOOK: Ensure the final paragraph ends on unresolved tension, a question, or a threat.
|
||||
8. PRESERVE: Keep all narrative beats, approximate word count (±15%), and chapter header.
|
||||
|
||||
ROUGH_DRAFT:
|
||||
{current_text}
|
||||
|
||||
OUTPUT: Complete polished chapter in Markdown.
|
||||
"""
|
||||
try:
|
||||
resp_polish = ai_models.model_logic.generate_content(polish_prompt)
|
||||
utils.log_usage(folder, ai_models.model_logic.name, resp_polish.usage_metadata)
|
||||
polished = resp_polish.text
|
||||
if polished:
|
||||
polished_words = len(polished.split())
|
||||
utils.log("WRITER", f" -> Polished: {polished_words:,} words.")
|
||||
current_text = polished
|
||||
except Exception as e:
|
||||
utils.log("WRITER", f" -> Polish pass failed: {e}. Proceeding with raw draft.")
|
||||
|
||||
# Reduced from 3 → 2 attempts since polish pass already refines prose before evaluation
|
||||
max_attempts = 2
|
||||
SCORE_AUTO_ACCEPT = 8
|
||||
SCORE_PASSING = 7
|
||||
# Adaptive passing threshold: lenient for early setup chapters, strict for climax/resolution.
|
||||
# chapter_position=0.0 → setup (SCORE_PASSING=6.5), chapter_position=1.0 → climax (7.5)
|
||||
if chapter_position is not None:
|
||||
SCORE_PASSING = round(6.5 + chapter_position * 1.0, 1)
|
||||
utils.log("WRITER", f" -> Adaptive threshold: SCORE_PASSING={SCORE_PASSING} (position={chapter_position:.2f})")
|
||||
else:
|
||||
SCORE_PASSING = 7
|
||||
SCORE_REWRITE_THRESHOLD = 6
|
||||
|
||||
best_score = 0
|
||||
|
||||
Reference in New Issue
Block a user