Merge pull request #253 from svnlto/fix/consensus-temperature-handling

fix: resolve temperature handling issues for O3/custom models (#245)
This commit is contained in:
Beehive Innovations
2025-08-24 21:29:14 +04:00
committed by GitHub
3 changed files with 77 additions and 5 deletions

1
.gitignore vendored
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@@ -187,3 +187,4 @@ logs/
/worktrees/
test_simulation_files/
.mcp.json

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@@ -5,6 +5,7 @@ import os
from typing import Optional
from .base import (
FixedTemperatureConstraint,
ModelCapabilities,
ModelResponse,
ProviderType,
@@ -13,6 +14,20 @@ from .base import (
from .openai_compatible import OpenAICompatibleProvider
from .openrouter_registry import OpenRouterModelRegistry
# Temperature inference patterns
_TEMP_UNSUPPORTED_PATTERNS = [
"o1",
"o3",
"o4", # OpenAI O-series models
"deepseek-reasoner",
"deepseek-r1",
"r1", # DeepSeek reasoner models
]
_TEMP_UNSUPPORTED_KEYWORDS = [
"reasoner", # DeepSeek reasoner variants
]
class CustomProvider(OpenAICompatibleProvider):
"""Custom API provider for local models.
@@ -152,7 +167,16 @@ class CustomProvider(OpenAICompatibleProvider):
"Consider adding to custom_models.json for specific capabilities."
)
# Create generic capabilities with conservative defaults
# Infer temperature support from model name for better defaults
supports_temperature, temperature_reason = self._infer_temperature_support(resolved_name)
logging.warning(
f"Model '{resolved_name}' not found in custom_models.json. Using generic capabilities with inferred settings. "
f"Temperature support: {supports_temperature} ({temperature_reason}). "
"For better accuracy, add this model to your custom_models.json configuration."
)
# Create generic capabilities with inferred defaults
capabilities = ModelCapabilities(
provider=ProviderType.CUSTOM,
model_name=resolved_name,
@@ -163,8 +187,12 @@ class CustomProvider(OpenAICompatibleProvider):
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=False, # Conservative default
supports_temperature=True, # Most custom models accept temperature parameter
temperature_constraint=RangeTemperatureConstraint(0.0, 2.0, 0.7),
supports_temperature=supports_temperature,
temperature_constraint=(
FixedTemperatureConstraint(1.0)
if not supports_temperature
else RangeTemperatureConstraint(0.0, 2.0, 0.7)
),
)
# Mark as generic for validation purposes
@@ -172,6 +200,36 @@ class CustomProvider(OpenAICompatibleProvider):
return capabilities
def _infer_temperature_support(self, model_name: str) -> tuple[bool, str]:
"""Infer temperature support from model name patterns.
Returns:
Tuple of (supports_temperature, reason_for_decision)
"""
model_lower = model_name.lower()
# Check for specific model patterns that don't support temperature
for pattern in _TEMP_UNSUPPORTED_PATTERNS:
conditions = (
pattern == model_lower,
model_lower.startswith(f"{pattern}-"),
model_lower.startswith(f"openai/{pattern}"),
model_lower.startswith(f"deepseek/{pattern}"),
model_lower.endswith(f"-{pattern}"),
f"/{pattern}" in model_lower,
f"-{pattern}-" in model_lower,
)
if any(conditions):
return False, f"detected non-temperature-supporting model pattern '{pattern}'"
# Check for specific keywords that indicate non-supporting variants
for keyword in _TEMP_UNSUPPORTED_KEYWORDS:
if keyword in model_lower:
return False, f"detected non-temperature-supporting keyword '{keyword}'"
# Default to supporting temperature for most models
return True, "default assumption for unknown custom models"
def get_provider_type(self) -> ProviderType:
"""Get the provider type."""
return ProviderType.CUSTOM

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@@ -29,6 +29,7 @@ from mcp.types import TextContent
from config import TEMPERATURE_ANALYTICAL
from systemprompts import CONSENSUS_PROMPT
from tools.shared.base_models import WorkflowRequest
from utils.model_context import ModelContext
from .workflow.base import WorkflowTool
@@ -546,12 +547,24 @@ of the evidence, even when it strongly points in one direction.""",
stance_prompt = model_config.get("stance_prompt")
system_prompt = self._get_stance_enhanced_prompt(stance, stance_prompt)
# Call the model
# Get model context for temperature validation
model_context = ModelContext(model_name=model_name)
# Validate temperature against model constraints (respects supports_temperature)
validated_temperature, temp_warnings = self.validate_and_correct_temperature(
self.get_default_temperature(), model_context
)
# Log any temperature corrections
for warning in temp_warnings:
logger.warning(warning)
# Call the model with validated temperature
response = provider.generate_content(
prompt=prompt,
model_name=model_name,
system_prompt=system_prompt,
temperature=0.2, # Low temperature for consistency
temperature=validated_temperature,
thinking_mode="medium",
images=request.images if request.images else None,
)