298 lines
11 KiB
Python
298 lines
11 KiB
Python
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import yaml
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import os
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from typing import Dict, Any, Optional
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from pydantic import BaseModel, Field
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from pydantic_settings import BaseSettings
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class OpenAIConfig(BaseModel):
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base_url: str = "https://api.openai.com/v1"
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api_key: str
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model: str = "gpt-4o"
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class AzureConfig(BaseModel):
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base_url: str
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api_key: str
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deployment: str
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api_version: str = "2024-02-01"
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class EmbeddingConfig(BaseModel):
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base_url: str
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api_key: str
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model: str
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dimension: int
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api_version: Optional[str]
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class IndexConfig(BaseModel):
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standard_regulation_index: str
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chunk_index: str
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chunk_user_manual_index: str
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class RetrievalConfig(BaseModel):
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endpoint: str
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api_key: str
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api_version: str
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semantic_configuration: str
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embedding: EmbeddingConfig
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index: IndexConfig
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class PostgreSQLConfig(BaseModel):
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host: str
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port: int = 5432
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database: str
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username: str
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password: str
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ttl_days: int = 7
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class RedisConfig(BaseModel):
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host: str
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port: int = 6379
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password: str
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use_ssl: bool = True
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db: int = 0
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ttl_days: int = 7
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class AppLoggingConfig(BaseModel):
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level: str = "INFO"
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class AppConfig(BaseModel):
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name: str = "agentic-rag"
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memory_ttl_days: int = 7
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max_tool_rounds: int = 3 # Maximum allowed tool calling rounds
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max_tool_rounds_user_manual: int = 3 # Maximum allowed tool calling rounds for user manual agent
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cors_origins: list[str] = Field(default_factory=lambda: ["*"])
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logging: AppLoggingConfig = Field(default_factory=AppLoggingConfig)
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# Service configuration
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host: str = "0.0.0.0"
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port: int = 8000
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class SearchConfig(BaseModel):
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"""Search index configuration"""
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standard_regulation_index: str = ""
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chunk_index: str = ""
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chunk_user_manual_index: str = ""
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class CitationConfig(BaseModel):
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"""Citation link configuration"""
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base_url: str = "" # Default empty string
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class LLMParametersConfig(BaseModel):
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"""LLM parameters configuration"""
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temperature: Optional[float] = None
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max_context_length: int = 96000 # Maximum context length for conversation history (in tokens)
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max_output_tokens: Optional[int] = None # Optional limit for LLM output tokens (None = no limit)
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class LLMPromptsConfig(BaseModel):
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"""LLM prompts configuration"""
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agent_system_prompt: str
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synthesis_system_prompt: Optional[str] = None
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synthesis_user_prompt: Optional[str] = None
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intent_recognition_prompt: Optional[str] = None
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user_manual_prompt: Optional[str] = None
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class LLMPromptConfig(BaseModel):
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"""LLM prompt configuration from llm_prompt.yaml"""
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parameters: LLMParametersConfig = Field(default_factory=LLMParametersConfig)
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prompts: LLMPromptsConfig
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class LLMRagConfig(BaseModel):
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"""Legacy LLM RAG configuration for backward compatibility"""
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temperature: Optional[float] = None
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max_context_length: int = 96000 # Maximum context length for conversation history (in tokens)
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max_output_tokens: Optional[int] = None # Optional limit for LLM output tokens (None = no limit)
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# Legacy prompts for backward compatibility
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system_prompt: Optional[str] = None
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user_prompt: Optional[str] = None
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# New autonomous agent prompts
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agent_system_prompt: Optional[str] = None
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synthesis_system_prompt: Optional[str] = None
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synthesis_user_prompt: Optional[str] = None
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class LLMConfig(BaseModel):
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rag: LLMRagConfig
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class LoggingConfig(BaseModel):
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level: str = "INFO"
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format: str = "json"
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class Config(BaseSettings):
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provider: str = "openai"
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openai: Optional[OpenAIConfig] = None
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azure: Optional[AzureConfig] = None
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retrieval: RetrievalConfig
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postgresql: PostgreSQLConfig
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redis: Optional[RedisConfig] = None
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app: AppConfig = Field(default_factory=AppConfig)
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search: SearchConfig = Field(default_factory=SearchConfig)
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citation: CitationConfig = Field(default_factory=CitationConfig)
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llm: Optional[LLMConfig] = None
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logging: LoggingConfig = Field(default_factory=LoggingConfig)
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# New LLM prompt configuration
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llm_prompt: Optional[LLMPromptConfig] = None
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@classmethod
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def from_yaml(cls, config_path: str = "config.yaml", llm_prompt_path: str = "llm_prompt.yaml") -> "Config":
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"""Load configuration from YAML files with environment variable substitution"""
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# Load main config
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with open(config_path, 'r', encoding='utf-8') as f:
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yaml_data = yaml.safe_load(f)
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# Substitute environment variables
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yaml_data = cls._substitute_env_vars(yaml_data)
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# Load LLM prompt config if exists
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llm_prompt_data = None
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if os.path.exists(llm_prompt_path):
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with open(llm_prompt_path, 'r', encoding='utf-8') as f:
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llm_prompt_data = yaml.safe_load(f)
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llm_prompt_data = cls._substitute_env_vars(llm_prompt_data)
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yaml_data['llm_prompt'] = llm_prompt_data
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return cls(**yaml_data)
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@classmethod
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def _substitute_env_vars(cls, data: Any) -> Any:
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"""Recursively substitute ${VAR} and ${VAR:-default} patterns with environment variables"""
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if isinstance(data, dict):
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return {k: cls._substitute_env_vars(v) for k, v in data.items()}
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elif isinstance(data, list):
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return [cls._substitute_env_vars(item) for item in data]
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elif isinstance(data, str):
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# Handle ${VAR:-default} pattern
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if data.startswith("${") and data.endswith("}"):
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env_spec = data[2:-1]
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if ":-" in env_spec:
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var_name, default_value = env_spec.split(":-", 1)
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return os.getenv(var_name, default_value)
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else:
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return os.getenv(env_spec, data) # Return original if env var not found
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return data
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else:
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return data
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def get_llm_config(self) -> Dict[str, Any]:
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"""Get LLM configuration based on provider"""
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base_config = {}
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# Get temperature and max_output_tokens from llm_prompt config first, fallback to legacy llm.rag config
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if self.llm_prompt and self.llm_prompt.parameters:
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# Only add temperature if explicitly set (not None)
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if self.llm_prompt.parameters.temperature is not None:
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base_config["temperature"] = self.llm_prompt.parameters.temperature
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# Only add max_output_tokens if explicitly set (not None)
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if self.llm_prompt.parameters.max_output_tokens is not None:
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base_config["max_tokens"] = self.llm_prompt.parameters.max_output_tokens
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elif self.llm and self.llm.rag:
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# Only add temperature if explicitly set (not None)
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if hasattr(self.llm.rag, 'temperature') and self.llm.rag.temperature is not None:
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base_config["temperature"] = self.llm.rag.temperature
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# Only add max_output_tokens if explicitly set (not None)
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if self.llm.rag.max_output_tokens is not None:
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base_config["max_tokens"] = self.llm.rag.max_output_tokens
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if self.provider == "openai" and self.openai:
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return {
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**base_config,
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"provider": "openai",
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"base_url": self.openai.base_url,
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"api_key": self.openai.api_key,
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"model": self.openai.model,
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}
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elif self.provider == "azure" and self.azure:
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return {
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**base_config,
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"provider": "azure",
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"base_url": self.azure.base_url,
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"api_key": self.azure.api_key,
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"deployment": self.azure.deployment,
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"api_version": self.azure.api_version,
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}
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else:
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raise ValueError(f"Invalid provider '{self.provider}' or missing configuration")
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def get_rag_prompts(self) -> Dict[str, str]:
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"""Get RAG prompts configuration - prioritize llm_prompt.yaml over legacy config"""
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# Use new llm_prompt config if available
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if self.llm_prompt and self.llm_prompt.prompts:
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return {
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"system_prompt": self.llm_prompt.prompts.agent_system_prompt,
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"user_prompt": "{{user_query}}", # Default template
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"agent_system_prompt": self.llm_prompt.prompts.agent_system_prompt,
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"synthesis_system_prompt": self.llm_prompt.prompts.synthesis_system_prompt or "You are a helpful assistant.",
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"synthesis_user_prompt": self.llm_prompt.prompts.synthesis_user_prompt or "{{user_query}}",
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"intent_recognition_prompt": self.llm_prompt.prompts.intent_recognition_prompt or "",
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"user_manual_prompt": self.llm_prompt.prompts.user_manual_prompt or "",
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}
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# Fallback to legacy llm.rag config
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if self.llm and self.llm.rag:
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return {
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"system_prompt": self.llm.rag.system_prompt or "You are a helpful assistant.",
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"user_prompt": self.llm.rag.user_prompt or "{{user_query}}",
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"agent_system_prompt": self.llm.rag.agent_system_prompt or "You are a helpful assistant.",
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"synthesis_system_prompt": self.llm.rag.synthesis_system_prompt or "You are a helpful assistant.",
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"synthesis_user_prompt": self.llm.rag.synthesis_user_prompt or "{{user_query}}",
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"intent_recognition_prompt": "",
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"user_manual_prompt": "",
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}
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# Default fallback
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return {
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"system_prompt": "You are a helpful assistant.",
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"user_prompt": "{{user_query}}",
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"agent_system_prompt": "You are a helpful assistant.",
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"synthesis_system_prompt": "You are a helpful assistant.",
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"synthesis_user_prompt": "{{user_query}}",
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"intent_recognition_prompt": "",
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"user_manual_prompt": "",
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}
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def get_max_context_length(self) -> int:
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"""Get maximum context length for conversation history"""
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# Use new llm_prompt config if available
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if self.llm_prompt and self.llm_prompt.parameters:
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return self.llm_prompt.parameters.max_context_length
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# Fallback to legacy llm.rag config
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if self.llm and self.llm.rag:
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return self.llm.rag.max_context_length
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# Default fallback
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return 96000
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# Global config instance
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config: Optional[Config] = None
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def load_config(config_path: str = "config.yaml", llm_prompt_path: str = "llm_prompt.yaml") -> Config:
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"""Load and return the global configuration"""
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global config
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config = Config.from_yaml(config_path, llm_prompt_path)
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return config
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def get_config() -> Config:
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"""Get the current configuration instance"""
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if config is None:
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raise RuntimeError("Configuration not loaded. Call load_config() first.")
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return config
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