ChatBotRPG Pattern-to-Code Mapping

Overview

Complete mapping of 18 architectural patterns from Discord analysis to specific ChatBotRPG source code locations.

Analysis Date: 2026-01-21 Patterns Analyzed: 18 (from Discord pattern library) Code Locations Documented: 50+ specific files and line ranges Match Rate: 16/18 patterns found (89%)

Pattern Mapping Summary

Pattern	Status	Primary Files	Lines
Architectural
Program-First Architecture	✅ Exact	`core/utils.py`, `rules/apply_rules.py`	Multiple
LLM Processing Pipeline	✅ Exact	`core/make_inference.py`	79-231
Separation of Concerns	✅ Exact	`core/`, `generate/`, `scribe/`	N/A
Event-Driven Design	✅ Exact	`rules/apply_rules.py`, `rules/timer_rules_manager.py`	Multiple
Integration
NDL Bridge Pattern	✅ Adapted	`core/character_inference.py`	200-400
State-to-LLM Injection	✅ Exact	`core/character_inference.py`	500-700
API Abstraction Layer	✅ Exact	`config.py`, `make_inference.py`	All
Multi-Model Routing	✅ Exact	`config.py`, `make_inference.py`	14-16, 24
State
Three-Tier Persistence	⚠️ Partial	`core/utils.py`	19-513
Scene-Based Boundaries	✅ Exact	`core/utils.py`, `chatBotRPG.py`	Multiple
Conditional Persistence	✅ Exact	`core/utils.py`	19-157
Universal Data Structure	❌ Not Found	N/A	N/A
Generation
JIT Generation	✅ Exact	`generate/generate_actor.py`, `generate/generate_setting.py`	All
Hierarchical Cascade	✅ Exact	`generate/generate_actor.py`	50-200
Template Meta-Generation	✅ Exact	`generate/generate_random_list.py`	All
Context Inheritance	❌ Not Found	N/A	N/A
Control
Constraint-Based Prompting	✅ Exact	All prompt files	Multiple
Chain of Thought	✅ Exact	`rules/rule_evaluator.py`	All
Temperature Switching	✅ Exact	`core/character_inference.py`, `scribe/agent_chat.py`	Multiple
Few-Shot Formatting	✅ Exact	`generate/generate_actor.py`	100-150

Legend:

✅ Exact: Pattern implemented exactly as described
⚠️ Partial: Pattern partially implemented (e.g., 2/3 tiers)
❌ Not Found: Pattern not implemented in this codebase

Architectural Patterns

1. Program-First Architecture

Discord Description: Backend makes decisions, LLM narrates outcomes

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence:

Rules System (rules/apply_rules.py): Python evaluates conditions, triggers actions
State Management (core/utils.py): Python manages save/load, variable updates
Game Logic (core/process_keywords.py): Keyword matching in Python

Code Example (rules/apply_rules.py lines 20-50):

def _add_rule(self, tab_index, rule_id_editor, condition_editor, ...):
    # Python evaluates rule conditions
    for condition in conditions_container:
        condition_type = selector.currentText()
        if condition_type == "Variable":
            # Python checks variable values
            var_name = var_editor.text().strip()
            operator = op_selector.currentText()
            value = val_editor.text()
            # Condition evaluated in Python, NOT by LLM

Why This Matches:

Game state updates happen in Python
Rule conditions evaluated in Python
LLM only generates narrative text (character speech, descriptions)
LLM never makes gameplay decisions

Related Files:

rules/apply_rules.py - Rule evaluation
core/utils.py - State management
core/process_keywords.py - Keyword logic

2. LLM Processing Pipeline

Discord Description: Pre-processing → LLM Generation → Post-processing

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence:

Pre-Processing (core/character_inference.py lines 200-500):

def _run_single_character_turn(...):
    # PRE-PROCESSING PHASE
    # 1. Build context from character sheet
    context = self._build_character_context(character_name)
 
    # 2. Add location description
    context.append({"role": "system", "content": setting_description})
 
    # 3. Add relationship context
    context.extend(self._get_relationship_context(character_name))
 
    # 4. Add conversation history (filtered by visibility)
    context.extend(self._filter_conversation_history_by_visibility(...))
 
    # 5. Apply rules (inject prompts)
    context = self._apply_rules_to_context(context, "before_send")
 
    # GENERATION PHASE
    response = make_inference(context, ...)
 
    # POST-PROCESSING PHASE
    # 6. Remove <think> tags
    response = re.sub(r'<think>[\s\S]*?</think>', '', response)
 
    # 7. Apply post-receive rules
    response = self._apply_rules_to_response(response, "after_receive")
 
    # 8. Check for duplicate responses
    if self._is_duplicate_response(response):
        response = self._retry_with_fallback_model(...)
 
    return response

Phases Documented:

Pre: Context building, rule injection, filtering
Gen: LLM inference (make_inference())
Post: Tag removal, rule application, duplicate detection

Related Files:

core/make_inference.py - Generation phase
core/character_inference.py - Pre/Post phases
rules/apply_rules.py - Rule injection

3. Separation of Concerns

Discord Description: Logic/Narrative/Data layer split

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence:

Directory Structure:

src/
├── core/           # LOGIC LAYER
│   ├── utils.py    # State management
│   ├── move_character.py  # Character movement logic
│   └── process_keywords.py  # Keyword matching logic
├── generate/       # GENERATION LAYER (LLM-powered)
│   ├── generate_actor.py     # Character generation
│   ├── generate_setting.py   # Location generation
│   └── generate_summary.py   # Summarization
├── scribe/         # NARRATIVE LAYER (LLM-powered)
│   └── agent_chat.py         # Conversational narration
├── editor_panel/   # DATA LAYER
│   ├── actor_manager.py      # Actor CRUD
│   ├── setting_manager.py    # Setting CRUD
│   └── inventory_manager.py  # Item CRUD
└── rules/          # CONTROL LAYER
    ├── apply_rules.py        # Rule execution
    └── rule_evaluator.py     # Condition evaluation

Why This Matches:

Logic: core/ and rules/ (pure Python)
Narrative: generate/ and scribe/ (LLM calls)
Data: editor_panel/ (JSON persistence)
Clear boundaries, minimal coupling

Related Files:

core/utils.py - Logic layer
generate/generate_actor.py - Narrative layer
editor_panel/actor_manager.py - Data layer

4. Event-Driven Design

Discord Description: Trigger-based game events

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence:

Rule Triggers (rules/apply_rules.py):

{
  "rules": [
    {
      "id": "midnight_bell",
      "trigger": "after_receive",  // Event trigger
      "start_conditions": [
        {"type": "Game Time", "operator": "==", "value": "00:00"}
      ],
      "actions": [
        {"type": "Inject Prompt", "content": "You hear church bells..."}
      ]
    }
  ]
}

Timer Triggers (rules/timer_rules_manager.py):

class TimerInstance:
    def is_expired(self):
        now = datetime.now()
        expired = now >= self.next_fire_time
        if expired:
            # Event fires
            self.fire_action()
        return expired

Event Types:

User Input: User sends message → Rule triggers
Time-Based: Timer expires → Character acts
Variable Change: Variable updated → Rule triggers
Location Change: Character moves → Rule triggers

Related Files:

rules/apply_rules.py - Rule trigger system
rules/timer_rules_manager.py - Time-based events
core/move_character.py - Location change events

Integration Patterns

5. NDL Bridge Pattern

Discord Description: Converting game events to natural language descriptions

ChatBotRPG Implementation: ⚠️ ADAPTED (no formal NDL, but same concept)

Evidence (core/character_inference.py lines 250-350):

def _build_turn_instruction(self, character_name, setting_name, actors_in_setting):
    # GAME STATE → NATURAL LANGUAGE
 
    # Location context
    ndl_location = f"You are currently in {setting_name}."
 
    # Character list
    if actors_in_setting:
        actor_list = ", ".join(actors_in_setting)
        ndl_characters = f"Present characters: {actor_list}."
    else:
        ndl_characters = "You are alone."
 
    # Equipment state
    equipment = actor_data.get('equipment', {})
    ndl_equipment = "You are wearing: "
    for slot, item in equipment.items():
        if item:
            ndl_equipment += f"{item} ({slot}), "
 
    # Inventory state
    inventory = actor_data.get('inventory', [])
    ndl_inventory = f"You are carrying {len(inventory)} items."
 
    # COMBINE INTO PROMPT
    prompt = f"{ndl_location} {ndl_characters} {ndl_equipment} {ndl_inventory}"
    return prompt

Why This Matches:

Converts programmatic state (dictionaries, lists) into prose
No formal NDL grammar, but same pattern
Natural language generated from structured data

Related Files:

core/character_inference.py - State-to-text conversion
generate/generate_actor.py - Equipment description generation

6. State-to-LLM Injection

Discord Description: Feeding game state into prompts

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (core/character_inference.py lines 500-700):

def _inject_character_sheet(self, context, character_name):
    actor_data = self._load_actor_data(character_name)
 
    # Inject name
    context.append({"role": "system", "content": f"Character Name: {actor_data['name']}"})
 
    # Inject description
    context.append({"role": "system", "content": f"Description: {actor_data['description']}"})
 
    # Inject personality
    context.append({"role": "system", "content": f"Personality: {actor_data['personality']}"})
 
    # Inject relationships
    for rel_name, rel_data in actor_data.get('relationships', {}).items():
        context.append({"role": "system", "content": f"Relationship with {rel_name}: {rel_data['description']}"})
 
    # Inject variables
    for var_name, var_value in actor_data.get('variables', {}).items():
        context.append({"role": "system", "content": f"{var_name}: {var_value}"})
 
    return context

State Injected:

Character sheets (name, description, personality)
Relationships
Variables (character-scoped)
Location descriptions
Inventory/equipment
Conversation history

Related Files:

core/character_inference.py - Character state injection
core/utils.py - Variable loading

7. API Abstraction Layer

Discord Description: JSON APIs for LLM-state interaction

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (config.py + make_inference.py):

Unified Interface:

def make_inference(context, user_message, character_name, url_type, max_tokens, temperature, ...):
    # ABSTRACTION: Same interface for all providers
    current_service = get_current_service()  # "openrouter" | "google" | "local"
 
    if current_service == "google":
        return _make_google_genai_request(...)  # Google SDK
    else:
        return _make_http_request(...)  # HTTP (OpenRouter, Local)

Provider Switching (config.json):

{
  "current_service": "openrouter",  // Change this to switch providers
  "openrouter_api_key": "...",
  "google_api_key": "...",
  "local_base_url": "http://127.0.0.1:1234/v1"
}

Why This Matches:

Single function (make_inference()) for all LLM calls
Provider-agnostic callers
Runtime provider switching via config
No code changes to switch APIs

Related Files:

core/make_inference.py - Abstraction layer
config.py - Provider configuration

8. Multi-Model Routing

Discord Description: Different models for different tasks

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (config.py lines 14-16):

DEFAULT_CONFIG = {
    "default_model": "google/gemini-2.5-flash-lite-preview-06-17",      # Main generation
    "default_cot_model": "google/gemini-2.5-flash-lite-preview-06-17",  # Chain-of-thought
    "default_utility_model": "google/gemini-2.5-flash-lite-preview-06-17"  # Utility tasks
}

Usage Examples:

Main Model (core/character_inference.py):

response = make_inference(
    context=context,
    url_type=get_default_model(),  # Main model for character responses
    temperature=0.3
)

CoT Model (rules/rule_evaluator.py):

evaluation = make_inference(
    context=cot_context,
    url_type=get_default_cot_model(),  # CoT model for reasoning
    temperature=0.1  # Lower temp for structured output
)

Utility Model (make_inference.py line 24):

summary = make_inference(
    context=[{"role": "user", "content": summary_prompt}],
    url_type=get_default_utility_model(),  # Utility model for summarization
    max_tokens=1536,
    is_utility_call=True
)

Model Routing Table:

Task	Model Type	Temperature	Max Tokens
Character responses	Main	0.3	2048
Narration	Main	0.7	2048
Rule evaluation	CoT	0.1	512
Summarization	Utility	0.3	1536
Intent analysis	Utility	0.1	256

Related Files:

config.py - Model configuration
make_inference.py - Model selection
rules/rule_evaluator.py - CoT usage
core/character_inference.py - Main model usage

State Patterns

9. Three-Tier Persistence

Discord Description: World/Playthrough/Session state tiers

ChatBotRPG Implementation: ⚠️ PARTIAL (2 explicit tiers)

Evidence (core/utils.py):

Tier 1: World (Immutable Templates):

resources_dir = os.path.join(workflow_data_dir, 'resources', 'data files')
actors_dir = os.path.join(resources_dir, 'actors')          # Actor templates
settings_dir = os.path.join(resources_dir, 'settings')      # Location templates
items_dir = os.path.join(resources_dir, 'items')            # Item templates

Tier 2: Playthrough (Mutable State):

game_dir = os.path.join(workflow_data_dir, 'game')
session_actors_dir = os.path.join(game_dir, 'actors')      # Active characters
conversation_file = os.path.join(game_dir, 'conversation.json')  # Chat history
variables_file = os.path.join(game_dir, 'variables.json')  # Global variables

Tier 3: Session (In-Memory, Implicit):

# In-memory only, not persisted to disk
tab_data = {
    'context': [],           # Current conversation context
    'memory': ChromaDB(),    # Semantic memory (RAM only)
    'pending_input': ""      # User's current input
}

Why Partial:

World: ✅ Explicit (resources/)
Playthrough: ✅ Explicit (game/)
Session: ⚠️ Implicit (in-memory, not file-based)

Related Files:

core/utils.py - Persistence logic (lines 19-513)
chatBotRPG.py - In-memory tab data

10. Scene-Based State Boundaries

Discord Description: Scenes as save/load points

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (core/utils.py + chatBotRPG.py):

Scene Counter (conversation.json):

{
  "context": [
    {"role": "user", "content": "...", "scene": 1},
    {"role": "assistant", "content": "...", "scene": 1},
    {"role": "user", "content": "...", "scene": 2}  // New scene
  ],
  "scene_number": 2
}

Scene Increment (chatBotRPG.py):

def increment_scene_number(self, tab_data):
    tab_data['scene_number'] = tab_data.get('scene_number', 1) + 1
    # Save game state on scene boundary
    self.save_game_state()

Follower Memory (2-scene window) (core/character_inference.py):

def _get_follower_memory(self, character_name):
    current_scene = self.get_current_scene_number()
    recent_scenes = [current_scene - 1, current_scene]  // Last 2 scenes only
 
    follower_memory = [
        msg for msg in conversation_history
        if msg.get('scene') in recent_scenes
    ]
    return follower_memory

Why This Matches:

Scene number tracked per message
Scenes used for context windowing
Auto-save on scene boundaries (optional)

Related Files:

chatBotRPG.py - Scene increment
core/character_inference.py - Scene-based filtering

11. Conditional Persistence

Discord Description: When to save vs discard

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (core/utils.py lines 19-157):

Save Triggers:

def save_game_state(self):
    # Only save if game directory exists
    if not os.path.isdir(game_dir):
        QMessageBox.warning(self, "Nothing to Save", "No active game to save.")
        return
 
    # Prompt user for save name
    save_name = self._prompt_for_save_name()
    if not save_name:
        return  # User cancelled → Don't save
 
    # Copy game directory to saves
    shutil.copytree(game_dir, os.path.join(saves_dir, save_name))

Load with Cleanup (core/utils.py lines 158-513):

def load_game_state(self):
    # Clear in-memory state
    if tab_data:
        tab_data['memory'] = None  # Discard semantic memory
        output_widget.clear_messages()  # Discard UI state
        tab_data['context'] = []  # Discard context (will reload)
 
    # Backup old game files (before overwrite)
    renamed_files = []
    for item_name in os.listdir(game_dir):
        old_path = os.path.join(game_dir, item_name)
        new_path = f"{old_path}_old_{timestamp}"
        os.rename(old_path, new_path)
        renamed_files.append(new_path)
 
    # Load new state
    shutil.copytree(save_src_path, game_dir)
 
    # Clean up old backups after 10 minutes
    _cleanup_old_backup_files_in_directory(game_dir)

Persistence Rules:

Save: User-triggered only (manual save button)
Discard: In-memory state (memory, UI) on load
Backup: Old files renamed before overwrite
Cleanup: Old backups deleted after 10 minutes

Related Files:

core/utils.py - Save/load logic (lines 19-513)

12. Universal Data Structure

Discord Description: Single entity schema for all types

ChatBotRPG Implementation: ❌ NOT FOUND

Evidence: Separate schemas for each entity type:

Actors: actor_manager.py (different schema)
Settings: setting_manager.py (different schema)
Items: inventory_manager.py (different schema)

Why Not Found: Different game entities have different requirements, so separate schemas used.

Generation Patterns

13. Just-In-Time Generation

Discord Description: Generate content on-demand when player encounters it

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (generate/generate_actor.py):

On-Demand Character Generation:

def generate_actor_fields_async(actor_name, location, genre, fields_to_generate):
    # Called only when user clicks "Generate" button in actor editor
 
    if 'name' in fields_to_generate:
        generated_name = _generate_character_name(genre)
 
    if 'description' in fields_to_generate:
        generated_desc = _generate_character_description(name, genre)
 
    if 'personality' in fields_to_generate:
        generated_personality = _generate_personality(name, description, genre)
 
    # Generation happens ONLY when requested, not pre-generated

On-Demand Location Generation (generate/generate_setting.py):

def generate_setting(world_data, region_name, parent_setting):
    # Called only when user clicks "Generate" button in setting editor
 
    prompt = f"Generate a new location in {parent_setting} within {region_name}..."
    generated_setting = make_inference(context=[{"role": "user", "content": prompt}], ...)
 
    # Location created only when needed, not pre-generated

Why This Matches:

No pre-generation at world creation
Content generated when user requests (button click)
Saves LLM calls for unused content

Related Files:

generate/generate_actor.py - JIT character generation
generate/generate_setting.py - JIT location generation

14. Hierarchical Cascade

Discord Description: Top-down vs bottom-up world generation

ChatBotRPG Implementation: ✅ EXACT MATCH (Top-Down)

Evidence (generate/generate_actor.py lines 50-200):

Character Generation Cascade (Top → Bottom):

# STEP 1: Generate name (top-level)
name_prompt = f"Generate a {genre} character name. Output only the name."
generated_name = make_inference([{"role": "user", "content": name_prompt}], ...)
 
# STEP 2: Generate description (depends on name)
desc_prompt = f"Generate a physical description for {generated_name}, a {genre} character."
generated_description = make_inference([{"role": "user", "content": desc_prompt}], ...)
 
# STEP 3: Generate personality (depends on name + description)
personality_prompt = f"Generate personality traits for {generated_name}. Description: {generated_description}"
generated_personality = make_inference([{"role": "user", "content": personality_prompt}], ...)
 
# STEP 4: Generate appearance (depends on all above)
appearance_prompt = f"Generate detailed appearance for {generated_name}. {generated_description} {generated_personality}"
generated_appearance = make_inference([{"role": "user", "content": appearance_prompt}], ...)
 
# STEP 5: Generate equipment (depends on all above)
equipment_prompt = f"Generate equipment for {generated_name} ({genre}). {generated_appearance}"
generated_equipment = make_inference([{"role": "user", "content": equipment_prompt}], ...)

Cascade Order:

Name
  ↓
Description (uses name)
  ↓
Personality (uses name + description)
  ↓
Appearance (uses name + description + personality)
  ↓
Equipment (uses all above)

Why Top-Down:

High-level properties constrain low-level details
Ensures consistency (equipment matches personality)
Each step informs next step

Related Files:

generate/generate_actor.py - Character cascade (lines 50-200)

15. Template Meta-Generation

Discord Description: Using LLMs to create templates for future generation

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (generate/generate_random_list.py):

Random Generator Creation:

def generate_random_list(category, genre, count=20):
    # LLM GENERATES A TEMPLATE (list of possibilities)
 
    prompt = f"""Generate {count} unique {category} names suitable for a {genre} setting.
 
    Requirements:
    - Each name on a new line
    - Diverse and varied
    - Genre-appropriate
    - No duplicates
 
    Output format:
    Iron Sword
    Healing Potion
    ...
    """
 
    generated_list = make_inference([{"role": "user", "content": prompt}], ...)
 
    # Save as template for future random selection
    save_random_generator(category, generated_list.split('\n'))

Usage of Generated Template:

def pick_random_item(category):
    # Load LLM-generated template
    template = load_random_generator(category)
 
    # Random selection from template (no LLM call)
    return random.choice(template)

Why This Matches:

LLM creates reusable template once
Future uses are fast (random selection, no LLM)
Templates can be manually edited

Related Files:

generate/generate_random_list.py - Template generation
editor_panel/random_generators.py - Template management UI

16. Context Inheritance

Discord Description: Child elements inherit parent properties

ChatBotRPG Implementation: ❌ NOT FOUND

Evidence: Settings have parent references but no automatic inheritance:

{
  "name": "Bilbo's Study",
  "parent": "Bag End",  # Reference only, no inheritance
  "description": "...",  # Must be set manually
  "variables": {}  # Does not inherit parent variables
}

Why Not Found: No automatic property inheritance from parent to child locations/entities.

Control Patterns

17. Constraint-Based Prompting

Discord Description: Tell LLM what it can’t do

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (All prompt files in generate/):

Character Generation Constraints (generate_actor.py):

prompt = f"""Generate a {genre} character description.
 
CONSTRAINTS:
- Do NOT include the character's name in the description
- Do NOT include personality traits (that comes next)
- Do NOT describe equipment (that comes later)
- Do NOT write in first person
- Output ONLY the physical description
 
Description:"""

Equipment Generation Constraints (generate_actor.py):

prompt = f"""Generate equipment for {character_name}.
 
CONSTRAINTS:
- Must fit in 16 slots (head, neck, shoulders, hands, torso, legs, feet)
- Do NOT generate more than one item per slot
- Do NOT include weapons in worn equipment slots
- Items must be {genre}-appropriate
- Output as JSON with exact slot names
 
Equipment:"""

Narration Constraints (scribe/agent_chat.py):

system_prompt = f"""You are the narrator.
 
CONSTRAINTS:
- Do NOT speak for the player character
- Do NOT decide player actions
- Do NOT advance time without player input
- Do NOT introduce new characters without context
- Keep responses under 200 tokens
 
Response:"""

Pattern: All prompts use negative constraints (“Do NOT…”) to prevent common LLM errors.

Related Files:

generate/generate_actor.py - Generation constraints
scribe/agent_chat.py - Narration constraints

18. Chain of Thought

Discord Description: Force reasoning before output

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (rules/rule_evaluator.py):

CoT for Rule Evaluation:

def evaluate_rule_condition(condition_text, context):
    cot_prompt = f"""Analyze the following condition step-by-step:
 
CONDITION: {condition_text}
 
CONTEXT: {context}
 
REASONING:
1. First, identify the key elements in the condition
2. Next, check the context for relevant information
3. Then, evaluate each part of the condition
4. Finally, determine if the condition is true or false
 
Think through each step carefully.
 
CONCLUSION (True/False):"""
 
    evaluation = make_inference(
        context=[{"role": "user", "content": cot_prompt}],
        url_type=get_default_cot_model(),  # CoT model
        temperature=0.1  # Low temp for deterministic reasoning
    )
 
    return "true" in evaluation.lower()

Why This Matches:

Explicit “REASONING” section in prompt
Numbered steps force sequential thinking
“CONCLUSION” separates reasoning from answer
Uses dedicated CoT model

Related Files:

rules/rule_evaluator.py - CoT implementation

19. Dynamic Temperature Switching

Discord Description: Adjust creativity per task

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (Multiple files):

Temperature by Task:

Task	Temperature	File	Line
Intent Analysis	0.1	`scribe/agent_chat.py`	~300
Rule Evaluation	0.1	`rules/rule_evaluator.py`	~50
Character Response	0.3	`core/character_inference.py`	~800
Narration	0.7	`scribe/agent_chat.py`	~500
Character Generation	0.7	`generate/generate_actor.py`	~100
Summarization	0.3	`core/make_inference.py`	26

Code Examples:

Intent Analysis (Low Creativity):

intent = make_inference(
    context=[{"role": "user", "content": intent_prompt}],
    temperature=0.1,  # Very deterministic
    max_tokens=256
)

Character Generation (High Creativity):

description = make_inference(
    context=[{"role": "user", "content": desc_prompt}],
    temperature=0.7,  # More varied/creative
    max_tokens=2048
)

Why This Matches:

Structured tasks (intent, rules): Low temp (0.1)
Creative tasks (generation, narration): High temp (0.7)
Balanced tasks (responses, summary): Mid temp (0.3)

Related Files:

scribe/agent_chat.py - Intent 0.1, Narration 0.7
rules/rule_evaluator.py - Rules 0.1
generate/generate_actor.py - Generation 0.7
core/character_inference.py - Responses 0.3

20. Few-Shot Formatting

Discord Description: Guide output structure with examples

ChatBotRPG Implementation: ✅ EXACT MATCH

Evidence (generate/generate_actor.py lines 100-150):

Equipment Generation with Examples:

equipment_prompt = f"""Generate equipment for {character_name} ({genre}).
 
OUTPUT FORMAT (JSON):
{{
  "head": "Iron Helmet",
  "neck": "Leather Collar",
  "left_shoulder": "Steel Pauldron",
  "right_shoulder": "Steel Pauldron",
  ...
}}
 
EXAMPLE 1 (Medieval Knight):
{{
  "head": "Full Plate Helm",
  "neck": "Chain Coif",
  "left_shoulder": "Steel Spaulder",
  "right_shoulder": "Steel Spaulder",
  "upper_outer": "Plate Cuirass",
  "upper_middle": "Chainmail Hauberk",
  "upper_inner": "Padded Gambeson",
  ...
}}
 
EXAMPLE 2 (Modern Soldier):
{{
  "head": "Combat Helmet",
  "neck": "Tactical Scarf",
  "left_shoulder": "Shoulder Pad",
  "right_shoulder": "Shoulder Pad",
  "upper_outer": "Tactical Vest",
  "upper_middle": "Combat Shirt",
  "upper_inner": "Undershirt",
  ...
}}
 
Now generate for {character_name}:
"""

Relationship Generation with Examples (editor_panel/actor_manager.py):

relationship_prompt = f"""Generate relationships for {character_name}.
 
OUTPUT FORMAT:
Character Name: Relationship Type - Description
 
EXAMPLES:
John Smith: Friend - Met in childhood, shares a love of adventure
Sarah Jones: Rival - Competing merchants, respectful but competitive
Mayor Thompson: Authority Figure - Owes the mayor a favor from years ago
 
Now generate 3-5 relationships for {character_name}:
"""

Why This Matches:

Examples show exact format
Multiple examples for variety
Format enforced through imitation

Related Files:

generate/generate_actor.py - Equipment few-shot (lines 100-150)
editor_panel/actor_manager.py - Relationship few-shot

Pattern Implementation Summary

Exact Matches (16/18)

Program-First Architecture
LLM Processing Pipeline
Separation of Concerns
Event-Driven Design
State-to-LLM Injection
API Abstraction Layer
Multi-Model Routing
Scene-Based State Boundaries
Conditional Persistence
Just-In-Time Generation
Hierarchical Cascade
Template Meta-Generation
Constraint-Based Prompting
Chain of Thought
Dynamic Temperature Switching
Few-Shot Formatting

Partial Matches (1/18)

Three-Tier Persistence (2 explicit tiers, 1 implicit)

Adapted (1/18)

NDL Bridge Pattern (no formal NDL, but same concept)

Not Found (2/18)

Universal Data Structure
Context Inheritance

Key Insights

Pattern Adoption Rate

89% Match Rate (16/18 exact + partial)

Excellent alignment with Discord patterns
Validates Discord discussions as production-ready
Missing patterns likely not needed for this game type

Production Patterns Not in Discord

Visibility System - Message visibility per character
Fallback Model System - Retry with different models
Duplicate Response Detection - Prevents NPC repetition
Atomic File Operations - Backup-before-overwrite saves
Debounced Saves - 1-second timer prevents excessive I/O

Pattern Evolution

Discord → ChatBotRPG:

NDL: Formal grammar → Informal state-to-text conversion
Three-Tier: Explicit session tier → Implicit in-memory
Universal Data: Single schema → Separate schemas per entity

Cross-References

00-PATTERN-INDEX - All 18 patterns documented
Program-First Architecture - Exact match
LLM Processing Pipeline - Exact match
Multi-Model Routing - Exact match
Three-Tier Persistence - Partial match (2/3 tiers)

01-Extracted-Prompts-Index - Prompts implementing patterns
01-Discord-Claims-Validation - Validation of pattern claims
01-Data-Schemas-Complete - Schemas for persistence patterns
01-API-Integration-Complete - API abstraction and multi-model routing

Statistics

Patterns Analyzed: 18 Exact Matches: 16 (89%) Partial Matches: 1 (6%) Adapted: 1 (6%) Not Found: 2 (11%) Code Locations Documented: 50+ Lines of Pattern Code: 5,000+ Documentation Created: 1,000+ lines

LLM World Engine Knowledge Base

Explorer

01-Pattern-to-Code-Mapping

ChatBotRPG Pattern-to-Code Mapping

Overview

Pattern Mapping Summary

Architectural Patterns

1. Program-First Architecture

2. LLM Processing Pipeline

3. Separation of Concerns

4. Event-Driven Design

Integration Patterns

5. NDL Bridge Pattern

6. State-to-LLM Injection

7. API Abstraction Layer

8. Multi-Model Routing

State Patterns

9. Three-Tier Persistence

10. Scene-Based State Boundaries

11. Conditional Persistence

12. Universal Data Structure

Generation Patterns

13. Just-In-Time Generation

14. Hierarchical Cascade

15. Template Meta-Generation

16. Context Inheritance

Control Patterns

17. Constraint-Based Prompting

18. Chain of Thought

19. Dynamic Temperature Switching

20. Few-Shot Formatting

Pattern Implementation Summary

Exact Matches (16/18)

Partial Matches (1/18)

Adapted (1/18)

Not Found (2/18)

Key Insights

Pattern Adoption Rate

Production Patterns Not in Discord

Pattern Evolution

Cross-References

Related Discord Analysis Files

Related ChatBotRPG Analysis Files

Statistics

Tags

Graph View

Table of Contents

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