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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/timepoint-ai/timepoint-pro/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

The LLMClient provides a unified interface for LLM operations with backward compatibility and centralized service architecture. Supports structured output, persona-based generation, and comprehensive error handling. Module: llm_v2.py Architecture: 
LLMClient
    ↓
LLMService (centralized)
    ↓
Provider (OpenRouter, OpenAI, etc.)
    ↓
API Client

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Initialization

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from_hydra_config()

Create client from Hydra configuration. Signature: 
@classmethod
def from_hydra_config(
    cls,
    cfg: Any,
    use_centralized_service: bool = True
) -> "LLMClient"
Parameters:
  • cfg: Hydra config object with llm section
  • use_centralized_service: Use new centralized service (recommended)
Example: 
from llm_v2 import LLMClient
import hydra

@hydra.main(config_path="conf", config_name="config")
def main(cfg):
    llm = LLMClient.from_hydra_config(cfg)
    # Use llm...

​
Direct Initialization

Signature: 
def __init__(
    self,
    api_key: str,
    base_url: str = "https://openrouter.ai/api/v1",
    default_model: str | None = None,
    model_cache_ttl_hours: int = 24,
    use_centralized_service: bool = True,
    service_config: LLMServiceConfig | None = None
)
Example: 
from llm_v2 import LLMClient
import os

llm = LLMClient(
    api_key=os.getenv("OPENROUTER_API_KEY"),
    default_model="meta-llama/llama-3.1-70b-instruct",
    use_centralized_service=True
)

​
Core Methods

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populate_entity()

Populate entity with structured LLM output. Signature: 
def populate_entity(
    self,
    entity_schema: dict,
    context: dict,
    previous_knowledge: list[str] = None,
    model: str | None = None
) -> EntityPopulation
Parameters:
  • entity_schema: Entity schema with entity_id
  • context: Context information for generation
  • previous_knowledge: Previous knowledge state (for evolution)
  • model: Model override
Returns: EntityPopulation with:
  • entity_id: Entity identifier
  • knowledge_state: List of knowledge items (3-8 items)
  • energy_budget: Cognitive resources (0-100)
  • personality_traits: 5 floats between -1 and 1
  • temporal_awareness: Time perception description
  • confidence: Generation confidence (0-1)
Example: 
entity_schema = {
    "entity_id": "alexander_hamilton"
}

context = {
    "timepoint": "1787-05-25",
    "location": "Philadelphia",
    "event": "Constitutional Convention"
}

population = llm.populate_entity(entity_schema, context)

print(f"Knowledge: {population.knowledge_state}")
print(f"Energy: {population.energy_budget}")
print(f"Traits: {population.personality_traits}")
Intelligent Retry Logic:
  • Attempts with multiple models (70B → 405B → Qwen 72B)
  • Temperature adjustment (0.5 → 0.3 → 0.8)
  • Fallback defaults if LLM fails

​
score_relevance()

Score how relevant a knowledge item is to a query. Signature: 
def score_relevance(
    self,
    query: str,
    knowledge_item: str,
    model: str | None = None
) -> float
Returns: Relevance score (0.0-1.0)
  • 1.0: Perfectly relevant, directly answers query
  • 0.5: Somewhat relevant but not central
  • 0.0: Completely irrelevant
Example: 
query = "What are Hamilton's views on central banking?"
knowledge = "Founded First Bank of United States"

score = llm.score_relevance(query, knowledge)
print(f"Relevance: {score:.2f}")

​
generate_structured()

Generate structured output using Pydantic schema. Signature: 
def generate_structured(
    self,
    prompt: str,
    response_model: type,
    model: str | None = None,
    **kwargs
)
Parameters:
  • prompt: Generation prompt
  • response_model: Pydantic model class for output
  • model: Model override
  • **kwargs: Additional parameters (temperature, max_tokens, etc.)
Example: 
from pydantic import BaseModel

class Analysis(BaseModel):
    summary: str
    key_points: list[str]
    confidence: float

prompt = "Analyze the Federalist Papers"
result = llm.generate_structured(
    prompt,
    Analysis,
    temperature=0.3,
    max_tokens=1000
)

print(result.summary)
print(result.key_points)

​
validate_consistency()

Validate temporal consistency of entities. Signature: 
def validate_consistency(
    self,
    entities: list[dict],
    timepoint: datetime,
    model: str | None = None
) -> ValidationResult
Returns: ValidationResult with:
  • is_valid: Boolean (true if no issues)
  • violations: List of problems found
  • confidence: Validation confidence (0-1)
  • reasoning: Explanation of result
Checks:
  • Anachronisms (knowledge before event)
  • Biological impossibilities (age, health)
  • Knowledge contradictions
Example: 
from datetime import datetime

entities = [
    {"entity_id": "hamilton", "age": 32, "knowledge": [...]},
    {"entity_id": "jefferson", "age": 44, "knowledge": [...]}
]

timepoint = datetime(1787, 5, 25)
result = llm.validate_consistency(entities, timepoint)

if not result.is_valid:
    for violation in result.violations:
        print(f"⚠️ {violation}")

​
Dialog Generation

​
generate_dialog()

Generate structured dialog with turns and metadata. Signature: 
def generate_dialog(
    self,
    prompt: str,
    max_tokens: int = 2000,
    model: str | None = None
) -> DialogData
Returns: DialogData with:
  • turns: List of DialogTurn objects
  • total_duration: Estimated duration (seconds)
  • information_exchanged: Knowledge items passed
  • relationship_impacts: How relationships changed
  • atmosphere_evolution: Atmosphere changes over time
Example: 
prompt = """
Generate dialog between Hamilton and Jefferson debating federal power.
Setting: 1791 Cabinet meeting
Tension: High (0.8)
Duration: 5 minutes
"""

dialog = llm.generate_dialog(prompt, max_tokens=3000)

for turn in dialog.turns:
    print(f"{turn.speaker}: {turn.content}")
    if turn.emotional_tone:
        print(f"  Tone: {turn.emotional_tone}")

​
generate_single_turn()

Generate one dialog turn with persona-derived parameters. Signature: 
def generate_single_turn(
    self,
    system_prompt: str,
    user_prompt: str,
    persona_params: PersonaParams,
    model: str | None = None
) -> dict[str, Any]
Returns: Dictionary with:
  • content: Dialog text
  • emotional_tone: Emotional state
  • knowledge_references: Referenced knowledge items
Example: 
from synth.params2persona import PersonaParams

persona = PersonaParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=500,
    frequency_penalty=0.2,
    presence_penalty=0.1
)

system = "You are Alexander Hamilton, debating federal power."
user = "Respond to Jefferson's critique of centralization."

turn = llm.generate_single_turn(system, user, persona)
print(turn['content'])

​
Advanced Methods

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generate_expectations()

Generate expectations for prospection mechanism (M15). Signature: 
def generate_expectations(
    self,
    entity_context: dict,
    timepoint_context: dict,
    model: str | None = None
) -> list[Expectation]
Example: 
entity_context = {
    "entity_id": "hamilton",
    "forecast_horizon_days": 30,
    "max_expectations": 5,
    "knowledge_sample": ["Bank plan ready", "Opposition growing"],
    "personality": {"risk_tolerance": 0.8}
}

timepoint_context = {
    "current_timepoint": "1791-01-15",
    "current_timestamp": "1791-01-15T10:00:00"
}

expectations = llm.generate_expectations(
    entity_context,
    timepoint_context
)

for exp in expectations:
    print(f"Event: {exp.predicted_event}")
    print(f"Probability: {exp.subjective_probability}")
    print(f"Actions: {exp.preparation_actions}")

​
enrich_animistic_entity()

Enrich animistic entity with LLM-generated background (M16). Signature: 
def enrich_animistic_entity(
    self,
    entity_id: str,
    entity_type: str,
    base_metadata: dict,
    context: dict,
    model: str | None = None
) -> dict
Supported Entity Types:
  • animal: Species, biological state, training
  • building: Structure, capacity, age, condition
  • abstract: Concepts, propagation, carriers
Example: 
base_metadata = {
    "species": "horse",
    "age": 5,
    "training_level": 0.7
}

context = {
    "timepoint_context": "1787 Philadelphia"
}

enriched = llm.enrich_animistic_entity(
    "washington_horse",
    "animal",
    base_metadata,
    context
)

print(enriched['llm_enrichment']['background_story'])
print(enriched['llm_enrichment']['notable_traits'])

​
generate_scene_atmosphere()

Generate rich narrative description of scene atmosphere. Signature: 
def generate_scene_atmosphere(
    self,
    timepoint: dict,
    entities: list,
    environment: dict,
    atmosphere_data: dict,
    model: str | None = None
) -> dict
Returns: Dictionary with:
  • atmospheric_narrative: Vivid 2-3 paragraph description
  • dominant_mood: Overall emotional tone
  • sensory_details: Key sensory observations
  • social_dynamics: Interaction patterns
  • historical_context: Period-appropriate cultural notes
Example: 
timepoint = {
    "event_description": "Constitutional debate",
    "timestamp": "1787-05-25T14:00:00"
}

environment = {
    "location": "Independence Hall",
    "ambient_temperature": 22,
    "lighting_level": 0.7,
    "weather": "overcast"
}

atmosphere_data = {
    "tension_level": 0.8,
    "formality_level": 0.9,
    "emotional_valence": -0.2,
    "energy_level": 0.7
}

atmosphere = llm.generate_scene_atmosphere(
    timepoint,
    entities,
    environment,
    atmosphere_data
)

print(atmosphere['atmospheric_narrative'])
print(f"Mood: {atmosphere['dominant_mood']}")

​
predict_counterfactual_outcome()

Predict outcomes of counterfactual interventions (M12). Signature: 
def predict_counterfactual_outcome(
    self,
    baseline_timeline: dict,
    intervention: dict,
    affected_entities: list,
    model: str | None = None
) -> dict
Returns: Dictionary with:
  • immediate_effects: Direct consequences
  • ripple_effects: Cascading changes
  • entity_state_changes: Predicted entity changes
  • divergence_significance: Timeline difference magnitude (0-1)
  • timeline_narrative: Divergent timeline description
  • probability_assessment: Prediction confidence (0-1)
  • key_turning_points: Critical moments of change
Example: 
baseline = {
    "timeline_id": "baseline",
    "event_summary": "Constitution ratified 1788"
}

intervention = {
    "type": "entity_removal",
    "target": "alexander_hamilton",
    "intervention_point": "tp_001",
    "description": "Hamilton absent from convention"
}

affected = [
    {"entity_id": "madison"},
    {"entity_id": "washington"}
]

prediction = llm.predict_counterfactual_outcome(
    baseline,
    intervention,
    affected
)

print(prediction['timeline_narrative'])
for effect in prediction['ripple_effects']:
    print(f"- {effect}")

​
Statistics and Monitoring

Token Tracking: 
llm.token_count  # Total tokens used
llm.cost         # Total cost (USD)
Service Statistics: 
stats = llm.service.get_statistics()
print(f"Total tokens: {stats['logger_stats']['total_tokens']}")
print(f"Total cost: ${stats['total_cost']:.4f}")

​
Model Management

Default Model:
  • meta-llama/llama-3.1-70b-instruct (70B parameters)
  • 327K context window (Llama 4 Scout)
Fallback Models:
  • meta-llama/llama-3.1-405b-instruct (405B, 100K output)
  • qwen/qwen-2.5-72b-instruct (72B, alternative provider)
Model Override: 
# Override via environment variable
import os
os.environ["TIMEPOINT_MODEL_OVERRIDE"] = "gpt-4"

# Override per-call
result = llm.populate_entity(
    entity_schema,
    context,
    model="meta-llama/llama-3.1-405b-instruct"
)

​
Error Handling

Automatic Retry:
  • Multiple model attempts
  • Temperature adjustment
  • Exponential backoff
Fallback Behavior:
  • Empty knowledge_state → Generate defaults
  • JSON parse failure → Heuristic scoring
  • API timeout → Return safe defaults
Error Messages: 
try:
    result = llm.populate_entity(schema, context)
except RuntimeError as e:
    print(f"LLM error: {e}")
    # Check logs/llm_calls/*.jsonl for details

​
Configuration

Hydra Config (config.yaml): 
llm:
  api_key: ${oc.env:OPENROUTER_API_KEY}
  base_url: https://openrouter.ai/api/v1
  model: meta-llama/llama-3.1-70b-instruct
  model_cache_ttl_hours: 24

llm_service:
  provider: openrouter
  mode: production
  defaults:
    temperature: 0.7
    max_tokens: 1500

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Best Practices

  1. Use centralized service (default, better monitoring)
  2. Provide context for better entity population
  3. Set max_tokens appropriately for task
  4. Use structured_call for Pydantic schemas
  5. Monitor token usage for cost control
  6. Handle errors gracefully with fallbacks
  7. Check confidence scores before using results
  8. Log all calls for debugging and analysis