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from typing import Optional, Dict, Any, List |
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from pydantic_settings import BaseSettings, SettingsConfigDict |
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from pydantic import Field |
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from pathlib import Path |
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import fnmatch |
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class PathPatterns(BaseSettings): |
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include: List[str] = Field( |
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default=["**/*"], |
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description="Glob patterns for paths to include" |
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) |
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exclude: List[str] = Field( |
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default=["**/venv/**", "**/.git/**", "**/__pycache__/**"], |
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description="Glob patterns for paths to exclude" |
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) |
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def should_process_path(self, path: str) -> bool: |
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"""Check if a path should be processed based on include/exclude patterns""" |
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path_str = str(path) |
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for pattern in self.exclude: |
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if fnmatch.fnmatch(path_str, pattern): |
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return False |
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for pattern in self.include: |
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if fnmatch.fnmatch(path_str, pattern): |
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return True |
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return False |
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class LanguageConfig(BaseSettings): |
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enabled: bool = True |
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file_extensions: List[str] |
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tree_sitter_language: str |
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chunk_types: List[str] |
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max_file_size: int = Field( |
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default=1_000_000, |
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description="Maximum file size to process in bytes" |
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) |
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class ParserConfig(BaseSettings): |
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languages: Dict[str, LanguageConfig] = Field( |
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default={ |
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"python": LanguageConfig( |
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file_extensions=[".py"], |
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tree_sitter_language="python", |
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chunk_types=["class_definition", "function_definition"] |
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) |
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} |
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) |
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path_patterns: PathPatterns = Field(default_factory=PathPatterns) |
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class LLMConfig(BaseSettings): |
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model_name: str = Field( |
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default="llama3.2", |
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description="Name of the LLM model to use" |
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) |
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model_provider: str = Field( |
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default="ollama", |
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description="Model provider (anthropic, openai, ollama, etc)" |
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) |
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api_key: Optional[str] = Field( |
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default=None, |
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description="API key for the model provider" |
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) |
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model_settings: Dict[str, Any] = Field( |
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default_factory=dict, |
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description="Additional model settings" |
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) |
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embedding_model: str = Field( |
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default="mxbai-embed-large", |
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description="Name of the embedding model to use" |
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) |
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embedding_provider: str = Field( |
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default="ollama", |
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description="Provider for embeddings (ollama, openai, etc)" |
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) |
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class DBConfig(BaseSettings): |
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persist_directory: Path = Field( |
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default=Path("./chroma_db"), |
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description="Directory to store ChromaDB files" |
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) |
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collection_name: str = Field( |
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default="code_chunks", |
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description="Name of the ChromaDB collection" |
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) |
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embedding_model: str = Field( |
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default="sentence-transformers/all-mpnet-base-v2", |
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description="Embedding model to use" |
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) |
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codebase_directory: Path = Field( |
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default=Path("./"), |
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description="Root directory of the codebase to analyze" |
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) |
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class AppConfig(BaseSettings): |
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model_config = SettingsConfigDict( |
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env_file='.env', |
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env_file_encoding='utf-8', |
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env_nested_delimiter='__' |
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) |
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llm: LLMConfig = Field(default_factory=LLMConfig) |
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db: DBConfig = Field(default_factory=DBConfig) |
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parser: ParserConfig = Field(default_factory=ParserConfig) |
