#!/usr/bin/env python3
# MIT License
#
# Copyright (c) 2025 Recursive Companion Contributors
# Based on work by Hank Besser (https://github.com/hankbesser/recursive-companion)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Configuration module for Recursive Companion MCP Server
Centralizes all configuration values and environment variables
"""
import os
from dataclasses import dataclass, field
from typing import Any
@dataclass
class ServerConfig:
"""Configuration for the MCP server"""
# AWS Configuration
aws_region: str = field(default_factory=lambda: os.getenv("AWS_REGION", "us-east-1"))
# Model Configuration
bedrock_model_id: str = field(
default_factory=lambda: os.getenv(
"BEDROCK_MODEL_ID", "anthropic.claude-3-sonnet-20240229-v1:0"
)
)
critique_model_id: str = field(
default_factory=lambda: os.getenv(
"CRITIQUE_MODEL_ID", "anthropic.claude-3-haiku-20240307-v1:0"
)
)
embedding_model_id: str = field(
default_factory=lambda: os.getenv("EMBEDDING_MODEL_ID", "amazon.titan-embed-text-v1")
)
# Refinement Configuration
max_iterations: int = field(default_factory=lambda: int(os.getenv("MAX_ITERATIONS", "10")))
convergence_threshold: float = field(
default_factory=lambda: float(os.getenv("CONVERGENCE_THRESHOLD", "0.98"))
)
parallel_critiques: int = field(
default_factory=lambda: int(os.getenv("PARALLEL_CRITIQUES", "3"))
)
quick_refine_timeout: int = field(
default_factory=lambda: int(os.getenv("QUICK_REFINE_TIMEOUT", "30"))
)
# Cache Configuration
embedding_cache_size: int = 1000
embedding_cache_trim_to: int = 900
cache_key_length: int = 16
# Display Configuration
prompt_preview_length: int = 50
draft_preview_length: int = 200
max_active_sessions_display: int = 3
# Thread Pool Configuration
executor_max_workers: int = 4
# API Limits
max_tokens: int = 4096
embedding_max_tokens: int = 8192
# Temperature Settings
default_temperature: float = 0.7
critique_temperature: float = 0.3
# Prompt Validation
max_prompt_length: int = field(
default_factory=lambda: int(os.getenv("MAX_PROMPT_LENGTH", "10000"))
)
min_prompt_length: int = field(
default_factory=lambda: int(os.getenv("MIN_PROMPT_LENGTH", "10"))
)
# Request Timeout
request_timeout: int = field(default_factory=lambda: int(os.getenv("REQUEST_TIMEOUT", "300")))
def to_dict(self) -> dict[str, Any]:
return {
"aws_region": self.aws_region,
"bedrock_model_id": self.bedrock_model_id,
"critique_model_id": self.critique_model_id,
"embedding_model_id": self.embedding_model_id,
"max_iterations": self.max_iterations,
"convergence_threshold": self.convergence_threshold,
"parallel_critiques": self.parallel_critiques,
"quick_refine_timeout": self.quick_refine_timeout,
"embedding_cache_size": self.embedding_cache_size,
"max_tokens": self.max_tokens,
"default_temperature": self.default_temperature,
}
# Global configuration instance
config = ServerConfig()
