FSSCoding
01ecd74983
Major improvements from comprehensive technical and security reviews: 🎯 GitHub Issue Fixes (All 3 Priority Items): • Add headless installation flag (--headless) for agents/CI automation • Implement automatic model name resolution (qwen3:1.7b → qwen3:1.7b-q8_0) • Prominent copy-paste instructions for fresh Ubuntu/Windows/Mac systems 🔧 CI/CD Pipeline Fixes: • Fix virtual environment activation in GitHub workflows • Add comprehensive test execution with proper dependency context • Resolve test pattern matching for safeguard preservation methods • Eliminate CI failure emails with robust error handling 🔒 Security Hardening: • Replace unsafe curl|sh patterns with secure download-verify-execute • Add SSL certificate validation with retry logic and exponential backoff • Implement model name sanitization to prevent injection attacks • Add network timeout handling and connection resilience ⚡ Enhanced Features: • Robust model resolution with fuzzy matching for quantization variants • Cross-platform headless installation for automation workflows • Comprehensive error handling with graceful fallbacks • Analysis directory gitignore protection for scan results 🧪 Testing & Quality: • All test suites passing (4/4 tests successful) • Security validation preventing injection attempts • Model resolution tested with real Ollama instances • CI workflows validated across Python 3.10/3.11/3.12 📚 Documentation: • Security-hardened installation maintains beginner-friendly approach • Copy-paste instructions work on completely fresh systems • Progressive complexity preserved (TUI → CLI → advanced) • Step-by-step explanations for all installation commands
574 lines
25 KiB
Python
574 lines
25 KiB
Python
"""
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Configuration management for FSS-Mini-RAG.
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Handles loading, saving, and validation of YAML config files.
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"""
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import logging
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import re
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from dataclasses import asdict, dataclass
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import yaml
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import requests
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logger = logging.getLogger(__name__)
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@dataclass
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class ChunkingConfig:
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"""Configuration for text chunking."""
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max_size: int = 2000
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min_size: int = 150
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strategy: str = "semantic" # "semantic" or "fixed"
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@dataclass
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class StreamingConfig:
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"""Configuration for large file streaming."""
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enabled: bool = True
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threshold_bytes: int = 1048576 # 1MB
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@dataclass
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class FilesConfig:
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"""Configuration for file processing."""
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min_file_size: int = 50
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exclude_patterns: list = None
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include_patterns: list = None
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def __post_init__(self):
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if self.exclude_patterns is None:
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self.exclude_patterns = [
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"node_modules/**",
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".git/**",
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"__pycache__/**",
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"*.pyc",
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".venv/**",
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"venv/**",
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"build/**",
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"dist/**",
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]
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if self.include_patterns is None:
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self.include_patterns = ["**/*"] # Include everything by default
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@dataclass
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class EmbeddingConfig:
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"""Configuration for embedding generation."""
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preferred_method: str = "ollama" # "ollama", "ml", "hash", "auto"
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ollama_model: str = "nomic-embed-text"
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ollama_host: str = "localhost:11434"
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ml_model: str = "sentence-transformers/all-MiniLM-L6-v2"
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batch_size: int = 32
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@dataclass
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class SearchConfig:
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"""Configuration for search behavior."""
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default_top_k: int = 10
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enable_bm25: bool = True
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similarity_threshold: float = 0.1
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expand_queries: bool = False # Enable automatic query expansion
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@dataclass
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class LLMConfig:
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"""Configuration for LLM synthesis and query expansion."""
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# Core settings
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synthesis_model: str = "auto" # "auto", "qwen3:1.7b", "qwen2.5:1.5b", etc.
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expansion_model: str = "auto" # Usually same as synthesis_model
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max_expansion_terms: int = 8 # Maximum additional terms to add
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enable_synthesis: bool = False # Enable by default when --synthesize used
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synthesis_temperature: float = 0.3
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enable_thinking: bool = True # Enable thinking mode for Qwen3 models
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cpu_optimized: bool = True # Prefer lightweight models
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# Context window configuration (critical for RAG performance)
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context_window: int = 16384 # Context window size in tokens (16K recommended)
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auto_context: bool = True # Auto-adjust context based on model capabilities
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# Model preference rankings (configurable)
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model_rankings: list = None # Will be set in __post_init__
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# Provider-specific settings (for different LLM providers)
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provider: str = "ollama" # "ollama", "openai", "anthropic"
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ollama_host: str = "localhost:11434" # Ollama connection
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api_key: Optional[str] = None # API key for cloud providers
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api_base: Optional[str] = None # Base URL for API (e.g., OpenRouter)
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timeout: int = 20 # Request timeout in seconds
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def __post_init__(self):
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if self.model_rankings is None:
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# Default model preference rankings (can be overridden in config file)
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self.model_rankings = [
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# Testing model (prioritized for current testing phase)
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"qwen3:1.7b",
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# Ultra-efficient models (perfect for CPU-only systems)
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"qwen3:0.6b",
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# Recommended model (excellent quality but larger)
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"qwen3:4b",
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# Common fallbacks (prioritize Qwen models)
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"qwen2.5:1.5b",
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"qwen2.5:3b",
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]
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@dataclass
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class UpdateConfig:
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"""Configuration for auto-update system."""
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auto_check: bool = True # Check for updates automatically
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check_frequency_hours: int = 24 # How often to check (hours)
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auto_install: bool = False # Auto-install without asking (not recommended)
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backup_before_update: bool = True # Create backup before updating
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notify_beta_releases: bool = False # Include beta/pre-releases
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@dataclass
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class RAGConfig:
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"""Main RAG system configuration."""
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chunking: ChunkingConfig = None
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streaming: StreamingConfig = None
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files: FilesConfig = None
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embedding: EmbeddingConfig = None
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search: SearchConfig = None
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llm: LLMConfig = None
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updates: UpdateConfig = None
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def __post_init__(self):
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if self.chunking is None:
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self.chunking = ChunkingConfig()
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if self.streaming is None:
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self.streaming = StreamingConfig()
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if self.files is None:
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self.files = FilesConfig()
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if self.embedding is None:
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self.embedding = EmbeddingConfig()
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if self.search is None:
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self.search = SearchConfig()
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if self.llm is None:
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self.llm = LLMConfig()
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if self.updates is None:
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self.updates = UpdateConfig()
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class ConfigManager:
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"""Manages configuration loading, saving, and validation."""
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def __init__(self, project_path: Path):
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self.project_path = Path(project_path)
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self.rag_dir = self.project_path / ".mini-rag"
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self.config_path = self.rag_dir / "config.yaml"
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def get_available_ollama_models(self, ollama_host: str = "localhost:11434") -> List[str]:
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"""Get list of available Ollama models for validation with secure connection handling."""
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import time
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# Retry logic with exponential backoff
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max_retries = 3
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for attempt in range(max_retries):
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try:
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# Use explicit timeout and SSL verification for security
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response = requests.get(
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f"http://{ollama_host}/api/tags",
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timeout=(5, 10), # (connect_timeout, read_timeout)
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verify=True, # Explicit SSL verification
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allow_redirects=False # Prevent redirect attacks
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)
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if response.status_code == 200:
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data = response.json()
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models = [model["name"] for model in data.get("models", [])]
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logger.debug(f"Successfully fetched {len(models)} Ollama models")
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return models
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else:
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logger.debug(f"Ollama API returned status {response.status_code}")
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except requests.exceptions.SSLError as e:
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logger.debug(f"SSL verification failed for Ollama connection: {e}")
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# For local Ollama, SSL might not be configured - this is expected
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if "localhost" in ollama_host or "127.0.0.1" in ollama_host:
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logger.debug("Retrying with local connection (SSL not required for localhost)")
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# Local connections don't need SSL verification
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try:
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response = requests.get(f"http://{ollama_host}/api/tags", timeout=(5, 10))
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if response.status_code == 200:
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data = response.json()
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return [model["name"] for model in data.get("models", [])]
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except Exception as local_e:
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logger.debug(f"Local Ollama connection also failed: {local_e}")
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break # Don't retry SSL errors for remote hosts
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except requests.exceptions.Timeout as e:
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logger.debug(f"Ollama connection timeout (attempt {attempt + 1}/{max_retries}): {e}")
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if attempt < max_retries - 1:
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sleep_time = (2 ** attempt) # Exponential backoff
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time.sleep(sleep_time)
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continue
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except requests.exceptions.ConnectionError as e:
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logger.debug(f"Ollama connection error (attempt {attempt + 1}/{max_retries}): {e}")
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if attempt < max_retries - 1:
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time.sleep(1)
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continue
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except Exception as e:
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logger.debug(f"Unexpected error fetching Ollama models: {e}")
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break
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return []
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def _sanitize_model_name(self, model_name: str) -> str:
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"""Sanitize model name to prevent injection attacks."""
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if not model_name:
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return ""
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# Allow only alphanumeric, dots, colons, hyphens, underscores
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# This covers legitimate model names like qwen3:1.7b-q8_0
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sanitized = re.sub(r'[^a-zA-Z0-9\.\:\-\_]', '', model_name)
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# Limit length to prevent DoS
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if len(sanitized) > 128:
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logger.warning(f"Model name too long, truncating: {sanitized[:20]}...")
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sanitized = sanitized[:128]
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return sanitized
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def resolve_model_name(self, configured_model: str, available_models: List[str]) -> Optional[str]:
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"""Resolve configured model name to actual available model with input sanitization."""
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if not available_models or not configured_model:
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return None
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# Sanitize input to prevent injection
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configured_model = self._sanitize_model_name(configured_model)
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if not configured_model:
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logger.warning("Model name was empty after sanitization")
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return None
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# Handle special 'auto' directive
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if configured_model.lower() == 'auto':
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return available_models[0] if available_models else None
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# Direct exact match first (case-insensitive)
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for available_model in available_models:
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if configured_model.lower() == available_model.lower():
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return available_model
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# Fuzzy matching for common patterns
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model_patterns = self._get_model_patterns(configured_model)
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for pattern in model_patterns:
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for available_model in available_models:
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if pattern.lower() in available_model.lower():
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# Additional validation: ensure it's not a partial match of something else
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if self._validate_model_match(pattern, available_model):
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return available_model
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return None # Model not available
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def _get_model_patterns(self, configured_model: str) -> List[str]:
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"""Generate fuzzy match patterns for common model naming conventions."""
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patterns = [configured_model] # Start with exact name
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# Common quantization patterns for different models
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quantization_patterns = {
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'qwen3:1.7b': ['qwen3:1.7b-q8_0', 'qwen3:1.7b-q4_0', 'qwen3:1.7b-q6_k'],
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'qwen3:0.6b': ['qwen3:0.6b-q8_0', 'qwen3:0.6b-q4_0', 'qwen3:0.6b-q6_k'],
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'qwen3:4b': ['qwen3:4b-q8_0', 'qwen3:4b-q4_0', 'qwen3:4b-q6_k'],
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'qwen3:8b': ['qwen3:8b-q8_0', 'qwen3:8b-q4_0', 'qwen3:8b-q6_k'],
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'qwen2.5:1.5b': ['qwen2.5:1.5b-q8_0', 'qwen2.5:1.5b-q4_0'],
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'qwen2.5:3b': ['qwen2.5:3b-q8_0', 'qwen2.5:3b-q4_0'],
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'qwen2.5-coder:1.5b': ['qwen2.5-coder:1.5b-q8_0', 'qwen2.5-coder:1.5b-q4_0'],
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'qwen2.5-coder:3b': ['qwen2.5-coder:3b-q8_0', 'qwen2.5-coder:3b-q4_0'],
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'qwen2.5-coder:7b': ['qwen2.5-coder:7b-q8_0', 'qwen2.5-coder:7b-q4_0'],
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}
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# Add specific patterns for the configured model
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if configured_model.lower() in quantization_patterns:
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patterns.extend(quantization_patterns[configured_model.lower()])
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# Generic pattern generation for unknown models
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if ':' in configured_model:
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base_name, version = configured_model.split(':', 1)
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# Add common quantization suffixes
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common_suffixes = ['-q8_0', '-q4_0', '-q6_k', '-q4_k_m', '-instruct', '-base']
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for suffix in common_suffixes:
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patterns.append(f"{base_name}:{version}{suffix}")
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# Also try with instruct variants
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if 'instruct' not in version.lower():
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patterns.append(f"{base_name}:{version}-instruct")
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patterns.append(f"{base_name}:{version}-instruct-q8_0")
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patterns.append(f"{base_name}:{version}-instruct-q4_0")
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return patterns
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def _validate_model_match(self, pattern: str, available_model: str) -> bool:
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"""Validate that a fuzzy match is actually correct and not a false positive."""
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# Convert to lowercase for comparison
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pattern_lower = pattern.lower()
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available_lower = available_model.lower()
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# Ensure the base model name matches
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if ':' in pattern_lower and ':' in available_lower:
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pattern_base = pattern_lower.split(':')[0]
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available_base = available_lower.split(':')[0]
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# Base names must match exactly
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if pattern_base != available_base:
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return False
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# Version part should be contained or closely related
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pattern_version = pattern_lower.split(':', 1)[1]
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available_version = available_lower.split(':', 1)[1]
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# The pattern version should be a prefix of the available version
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# e.g., "1.7b" should match "1.7b-q8_0" but not "11.7b"
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if not available_version.startswith(pattern_version.split('-')[0]):
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return False
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return True
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def validate_and_resolve_models(self, config: RAGConfig) -> RAGConfig:
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"""Validate and resolve model names in configuration."""
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try:
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available_models = self.get_available_ollama_models(config.llm.ollama_host)
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if not available_models:
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logger.debug("No Ollama models available for validation")
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return config
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# Resolve synthesis model
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if config.llm.synthesis_model != "auto":
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resolved = self.resolve_model_name(config.llm.synthesis_model, available_models)
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if resolved and resolved != config.llm.synthesis_model:
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logger.info(f"Resolved synthesis model: {config.llm.synthesis_model} -> {resolved}")
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config.llm.synthesis_model = resolved
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elif not resolved:
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logger.warning(f"Synthesis model '{config.llm.synthesis_model}' not found, keeping original")
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# Resolve expansion model (if different from synthesis)
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if (config.llm.expansion_model != "auto" and
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config.llm.expansion_model != config.llm.synthesis_model):
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resolved = self.resolve_model_name(config.llm.expansion_model, available_models)
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if resolved and resolved != config.llm.expansion_model:
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logger.info(f"Resolved expansion model: {config.llm.expansion_model} -> {resolved}")
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config.llm.expansion_model = resolved
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elif not resolved:
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logger.warning(f"Expansion model '{config.llm.expansion_model}' not found, keeping original")
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# Update model rankings with resolved names
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if config.llm.model_rankings:
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updated_rankings = []
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for model in config.llm.model_rankings:
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resolved = self.resolve_model_name(model, available_models)
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if resolved:
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updated_rankings.append(resolved)
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if resolved != model:
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logger.debug(f"Updated model ranking: {model} -> {resolved}")
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else:
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updated_rankings.append(model) # Keep original if not resolved
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config.llm.model_rankings = updated_rankings
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except Exception as e:
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logger.debug(f"Model validation failed: {e}")
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return config
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def load_config(self) -> RAGConfig:
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"""Load configuration from YAML file or create default."""
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if not self.config_path.exists():
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logger.info(f"No config found at {self.config_path}, creating default")
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config = RAGConfig()
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self.save_config(config)
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return config
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try:
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with open(self.config_path, "r") as f:
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data = yaml.safe_load(f)
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if not data:
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logger.warning("Empty config file, using defaults")
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return RAGConfig()
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# Convert nested dicts back to dataclass instances
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config = RAGConfig()
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if "chunking" in data:
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config.chunking = ChunkingConfig(**data["chunking"])
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if "streaming" in data:
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config.streaming = StreamingConfig(**data["streaming"])
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if "files" in data:
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config.files = FilesConfig(**data["files"])
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if "embedding" in data:
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config.embedding = EmbeddingConfig(**data["embedding"])
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if "search" in data:
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config.search = SearchConfig(**data["search"])
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if "llm" in data:
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config.llm = LLMConfig(**data["llm"])
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# Validate and resolve model names if Ollama is available
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config = self.validate_and_resolve_models(config)
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return config
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except yaml.YAMLError as e:
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# YAML syntax error - help user fix it instead of silent fallback
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error_msg = (
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f"⚠️ Config file has YAML syntax error at line "
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f"{getattr(e, 'problem_mark', 'unknown')}: {e}"
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)
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logger.error(error_msg)
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print(f"\n{error_msg}")
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print(f"Config file: {self.config_path}")
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print("💡 Check YAML syntax (indentation, quotes, colons)")
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print("💡 Or delete config file to reset to defaults")
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return RAGConfig() # Still return defaults but warn user
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except Exception as e:
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logger.error(f"Failed to load config from {self.config_path}: {e}")
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logger.info("Using default configuration")
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return RAGConfig()
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def save_config(self, config: RAGConfig):
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"""Save configuration to YAML file with comments."""
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try:
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self.rag_dir.mkdir(exist_ok=True)
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# Convert to dict for YAML serialization
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config_dict = asdict(config)
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# Create YAML content with comments
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yaml_content = self._create_yaml_with_comments(config_dict)
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# Write with basic file locking to prevent corruption
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with open(self.config_path, "w") as f:
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try:
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import fcntl
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fcntl.flock(
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f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB
|
|
) # Non-blocking exclusive lock
|
|
f.write(yaml_content)
|
|
fcntl.flock(f.fileno(), fcntl.LOCK_UN) # Unlock
|
|
except (OSError, ImportError):
|
|
# Fallback for Windows or if fcntl unavailable
|
|
f.write(yaml_content)
|
|
|
|
logger.info(f"Configuration saved to {self.config_path}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to save config to {self.config_path}: {e}")
|
|
|
|
def _create_yaml_with_comments(self, config_dict: Dict[str, Any]) -> str:
|
|
"""Create YAML content with helpful comments."""
|
|
yaml_lines = [
|
|
"# FSS-Mini-RAG Configuration",
|
|
"# Edit this file to customize indexing and search behavior",
|
|
"# See docs/GETTING_STARTED.md for detailed explanations",
|
|
"",
|
|
"# Text chunking settings",
|
|
"chunking:",
|
|
f" max_size: {config_dict['chunking']['max_size']} # Max chars per chunk",
|
|
f" min_size: {config_dict['chunking']['min_size']} # Min chars per chunk",
|
|
f" strategy: {config_dict['chunking']['strategy']} # 'semantic' or 'fixed'",
|
|
"",
|
|
"# Large file streaming settings",
|
|
"streaming:",
|
|
f" enabled: {str(config_dict['streaming']['enabled']).lower()}",
|
|
f" threshold_bytes: {config_dict['streaming']['threshold_bytes']} # Stream files >1MB",
|
|
"",
|
|
"# File processing settings",
|
|
"files:",
|
|
f" min_file_size: {config_dict['files']['min_file_size']} # Skip small files",
|
|
" exclude_patterns:",
|
|
]
|
|
|
|
for pattern in config_dict["files"]["exclude_patterns"]:
|
|
yaml_lines.append(f' - "{pattern}"')
|
|
|
|
yaml_lines.extend(
|
|
[
|
|
" include_patterns:",
|
|
' - "**/*" # Include all files by default',
|
|
"",
|
|
"# Embedding generation settings",
|
|
"embedding:",
|
|
f" preferred_method: {config_dict['embedding']['preferred_method']} # Method",
|
|
f" ollama_model: {config_dict['embedding']['ollama_model']}",
|
|
f" ollama_host: {config_dict['embedding']['ollama_host']}",
|
|
f" ml_model: {config_dict['embedding']['ml_model']}",
|
|
f" batch_size: {config_dict['embedding']['batch_size']} # Per batch",
|
|
"",
|
|
"# Search behavior settings",
|
|
"search:",
|
|
f" default_top_k: {config_dict['search']['default_top_k']} # Top results",
|
|
f" enable_bm25: {str(config_dict['search']['enable_bm25']).lower()} # Keyword boost",
|
|
f" similarity_threshold: {config_dict['search']['similarity_threshold']} # Min score",
|
|
f" expand_queries: {str(config_dict['search']['expand_queries']).lower()} # Auto expand",
|
|
"",
|
|
"# LLM synthesis and query expansion settings",
|
|
"llm:",
|
|
f" ollama_host: {config_dict['llm']['ollama_host']}",
|
|
f" synthesis_model: {config_dict['llm']['synthesis_model']} # Model name",
|
|
f" expansion_model: {config_dict['llm']['expansion_model']} # Model name",
|
|
f" max_expansion_terms: {config_dict['llm']['max_expansion_terms']} # Max terms",
|
|
f" enable_synthesis: {str(config_dict['llm']['enable_synthesis']).lower()} # Enable synthesis by default",
|
|
f" synthesis_temperature: {config_dict['llm']['synthesis_temperature']} # LLM temperature for analysis",
|
|
"",
|
|
" # Context window configuration (critical for RAG performance)",
|
|
" # 💡 Sizing guide: 2K=1 question, 4K=1-2 questions, 8K=manageable, 16K=most users",
|
|
" # 32K=large codebases, 64K+=power users only",
|
|
" # ⚠️ Larger contexts use exponentially more CPU/memory - only increase if needed",
|
|
" # 🔧 Low context limits? Try smaller topk, better search terms, or archive noise",
|
|
f" context_window: {config_dict['llm']['context_window']} # Context size in tokens",
|
|
f" auto_context: {str(config_dict['llm']['auto_context']).lower()} # Auto-adjust context based on model capabilities",
|
|
"",
|
|
" model_rankings: # Preferred model order (edit to change priority)",
|
|
]
|
|
)
|
|
|
|
# Add model rankings list
|
|
if "model_rankings" in config_dict["llm"] and config_dict["llm"]["model_rankings"]:
|
|
for model in config_dict["llm"]["model_rankings"][:10]: # Show first 10
|
|
yaml_lines.append(f' - "{model}"')
|
|
if len(config_dict["llm"]["model_rankings"]) > 10:
|
|
yaml_lines.append(" # ... (edit config to see all options)")
|
|
|
|
# Add update settings
|
|
yaml_lines.extend(
|
|
[
|
|
"",
|
|
"# Auto-update system settings",
|
|
"updates:",
|
|
f" auto_check: {str(config_dict['updates']['auto_check']).lower()} # Check for updates automatically",
|
|
f" check_frequency_hours: {config_dict['updates']['check_frequency_hours']} # Hours between update checks",
|
|
f" auto_install: {str(config_dict['updates']['auto_install']).lower()} # Auto-install updates (not recommended)",
|
|
f" backup_before_update: {str(config_dict['updates']['backup_before_update']).lower()} # Create backup before updating",
|
|
f" notify_beta_releases: {str(config_dict['updates']['notify_beta_releases']).lower()} # Include beta releases in checks",
|
|
]
|
|
)
|
|
|
|
return "\n".join(yaml_lines)
|
|
|
|
def update_config(self, **kwargs) -> RAGConfig:
|
|
"""Update specific configuration values."""
|
|
config = self.load_config()
|
|
|
|
for key, value in kwargs.items():
|
|
if hasattr(config, key):
|
|
setattr(config, key, value)
|
|
else:
|
|
logger.warning(f"Unknown config key: {key}")
|
|
|
|
self.save_config(config)
|
|
return config
|