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"""
Graph retrieval configuration for Epic 2 Week 2.
This module provides configuration classes for graph-based retrieval components,
following the established configuration patterns used throughout the system.
"""
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field
import logging
logger = logging.getLogger(__name__)
@dataclass
class GraphBuilderConfig:
"""Configuration for DocumentGraphBuilder."""
implementation: str = "networkx"
node_types: List[str] = field(default_factory=lambda: ["concept", "protocol", "architecture", "extension"])
relationship_types: List[str] = field(default_factory=lambda: ["implements", "extends", "requires", "conflicts"])
max_graph_size: int = 10000
update_strategy: str = "incremental"
enable_pruning: bool = True
pruning_threshold: float = 0.1
@dataclass
class EntityExtractionConfig:
"""Configuration for EntityExtractor."""
implementation: str = "spacy"
model: str = "en_core_web_sm"
entity_types: List[str] = field(default_factory=lambda: ["TECH", "PROTOCOL", "ARCH"])
confidence_threshold: float = 0.8
batch_size: int = 32
custom_patterns: Dict[str, List[str]] = field(default_factory=dict)
enable_custom_entities: bool = True
@dataclass
class RelationshipDetectionConfig:
"""Configuration for RelationshipMapper."""
implementation: str = "semantic"
similarity_threshold: float = 0.7
relationship_model: str = "sentence_transformer"
max_relationships_per_node: int = 20
enable_bidirectional: bool = True
weight_decay_factor: float = 0.9
@dataclass
class GraphRetrievalConfig:
"""Configuration for GraphRetriever."""
algorithms: List[str] = field(default_factory=lambda: ["shortest_path", "random_walk", "subgraph_expansion"])
max_graph_results: int = 10
max_path_length: int = 3
random_walk_steps: int = 10
subgraph_radius: int = 2
score_aggregation: str = "weighted_average"
enable_path_scoring: bool = True
@dataclass
class GraphAnalyticsConfig:
"""Configuration for GraphAnalytics."""
enabled: bool = True
collect_graph_metrics: bool = True
collect_retrieval_metrics: bool = True
enable_visualization: bool = False # Disabled by default for performance
visualization_max_nodes: int = 100
metrics_retention_hours: int = 24
@dataclass
class GraphConfig:
"""
Main configuration class for graph-based retrieval.
This class aggregates all graph-related configuration and provides
methods for creating configurations from dictionaries and YAML files.
"""
enabled: bool = True
builder: GraphBuilderConfig = field(default_factory=GraphBuilderConfig)
entity_extraction: EntityExtractionConfig = field(default_factory=EntityExtractionConfig)
relationship_detection: RelationshipDetectionConfig = field(default_factory=RelationshipDetectionConfig)
retrieval: GraphRetrievalConfig = field(default_factory=GraphRetrievalConfig)
analytics: GraphAnalyticsConfig = field(default_factory=GraphAnalyticsConfig)
# Performance settings
max_memory_mb: int = 500
enable_caching: bool = True
cache_size: int = 1000
@classmethod
def from_dict(cls, config_dict: Dict[str, Any]) -> "GraphConfig":
"""
Create GraphConfig from dictionary.
Args:
config_dict: Configuration dictionary
Returns:
GraphConfig instance
"""
try:
# Extract main config
enabled = config_dict.get("enabled", True)
max_memory_mb = config_dict.get("max_memory_mb", 500)
enable_caching = config_dict.get("enable_caching", True)
cache_size = config_dict.get("cache_size", 1000)
# Create sub-configurations
builder_config = GraphBuilderConfig()
if "builder" in config_dict:
builder_dict = config_dict["builder"].get("config", {})
builder_config = GraphBuilderConfig(
implementation=config_dict["builder"].get("implementation", "networkx"),
node_types=builder_dict.get("node_types", builder_config.node_types),
relationship_types=builder_dict.get("relationship_types", builder_config.relationship_types),
max_graph_size=builder_dict.get("max_graph_size", 10000),
update_strategy=builder_dict.get("update_strategy", "incremental"),
enable_pruning=builder_dict.get("enable_pruning", True),
pruning_threshold=builder_dict.get("pruning_threshold", 0.1)
)
entity_config = EntityExtractionConfig()
if "entity_extraction" in config_dict:
entity_dict = config_dict["entity_extraction"].get("config", {})
entity_config = EntityExtractionConfig(
implementation=config_dict["entity_extraction"].get("implementation", "spacy"),
model=entity_dict.get("model", "en_core_web_sm"),
entity_types=entity_dict.get("entity_types", entity_config.entity_types),
confidence_threshold=entity_dict.get("confidence_threshold", 0.8),
batch_size=entity_dict.get("batch_size", 32),
custom_patterns=entity_dict.get("custom_patterns", {}),
enable_custom_entities=entity_dict.get("enable_custom_entities", True)
)
relationship_config = RelationshipDetectionConfig()
if "relationship_detection" in config_dict:
rel_dict = config_dict["relationship_detection"].get("config", {})
relationship_config = RelationshipDetectionConfig(
implementation=config_dict["relationship_detection"].get("implementation", "semantic"),
similarity_threshold=rel_dict.get("similarity_threshold", 0.7),
relationship_model=rel_dict.get("relationship_model", "sentence_transformer"),
max_relationships_per_node=rel_dict.get("max_relationships_per_node", 20),
enable_bidirectional=rel_dict.get("enable_bidirectional", True),
weight_decay_factor=rel_dict.get("weight_decay_factor", 0.9)
)
retrieval_config = GraphRetrievalConfig()
if "retrieval" in config_dict:
ret_dict = config_dict["retrieval"]
retrieval_config = GraphRetrievalConfig(
algorithms=ret_dict.get("algorithms", retrieval_config.algorithms),
max_graph_results=ret_dict.get("max_graph_results", 10),
max_path_length=ret_dict.get("max_path_length", 3),
random_walk_steps=ret_dict.get("random_walk_steps", 10),
subgraph_radius=ret_dict.get("subgraph_radius", 2),
score_aggregation=ret_dict.get("score_aggregation", "weighted_average"),
enable_path_scoring=ret_dict.get("enable_path_scoring", True)
)
analytics_config = GraphAnalyticsConfig()
if "analytics" in config_dict:
analytics_dict = config_dict["analytics"]
analytics_config = GraphAnalyticsConfig(
enabled=analytics_dict.get("enabled", True),
collect_graph_metrics=analytics_dict.get("collect_graph_metrics", True),
collect_retrieval_metrics=analytics_dict.get("collect_retrieval_metrics", True),
enable_visualization=analytics_dict.get("enable_visualization", False),
visualization_max_nodes=analytics_dict.get("visualization_max_nodes", 100),
metrics_retention_hours=analytics_dict.get("metrics_retention_hours", 24)
)
return cls(
enabled=enabled,
builder=builder_config,
entity_extraction=entity_config,
relationship_detection=relationship_config,
retrieval=retrieval_config,
analytics=analytics_config,
max_memory_mb=max_memory_mb,
enable_caching=enable_caching,
cache_size=cache_size
)
except Exception as e:
logger.error(f"Failed to create GraphConfig from dict: {str(e)}")
# Return default configuration on error
return cls()
def to_dict(self) -> Dict[str, Any]:
"""
Convert GraphConfig to dictionary.
Returns:
Configuration dictionary
"""
return {
"enabled": self.enabled,
"builder": {
"implementation": self.builder.implementation,
"config": {
"node_types": self.builder.node_types,
"relationship_types": self.builder.relationship_types,
"max_graph_size": self.builder.max_graph_size,
"update_strategy": self.builder.update_strategy,
"enable_pruning": self.builder.enable_pruning,
"pruning_threshold": self.builder.pruning_threshold
}
},
"entity_extraction": {
"implementation": self.entity_extraction.implementation,
"config": {
"model": self.entity_extraction.model,
"entity_types": self.entity_extraction.entity_types,
"confidence_threshold": self.entity_extraction.confidence_threshold,
"batch_size": self.entity_extraction.batch_size,
"custom_patterns": self.entity_extraction.custom_patterns,
"enable_custom_entities": self.entity_extraction.enable_custom_entities
}
},
"relationship_detection": {
"implementation": self.relationship_detection.implementation,
"config": {
"similarity_threshold": self.relationship_detection.similarity_threshold,
"relationship_model": self.relationship_detection.relationship_model,
"max_relationships_per_node": self.relationship_detection.max_relationships_per_node,
"enable_bidirectional": self.relationship_detection.enable_bidirectional,
"weight_decay_factor": self.relationship_detection.weight_decay_factor
}
},
"retrieval": {
"algorithms": self.retrieval.algorithms,
"max_graph_results": self.retrieval.max_graph_results,
"max_path_length": self.retrieval.max_path_length,
"random_walk_steps": self.retrieval.random_walk_steps,
"subgraph_radius": self.retrieval.subgraph_radius,
"score_aggregation": self.retrieval.score_aggregation,
"enable_path_scoring": self.retrieval.enable_path_scoring
},
"analytics": {
"enabled": self.analytics.enabled,
"collect_graph_metrics": self.analytics.collect_graph_metrics,
"collect_retrieval_metrics": self.analytics.collect_retrieval_metrics,
"enable_visualization": self.analytics.enable_visualization,
"visualization_max_nodes": self.analytics.visualization_max_nodes,
"metrics_retention_hours": self.analytics.metrics_retention_hours
},
"max_memory_mb": self.max_memory_mb,
"enable_caching": self.enable_caching,
"cache_size": self.cache_size
}
def validate(self) -> List[str]:
"""
Validate configuration and return list of issues.
Returns:
List of validation error messages
"""
issues = []
# Validate builder config
if self.builder.max_graph_size <= 0:
issues.append("builder.max_graph_size must be positive")
if not self.builder.node_types:
issues.append("builder.node_types cannot be empty")
if not self.builder.relationship_types:
issues.append("builder.relationship_types cannot be empty")
# Validate entity extraction config
if self.entity_extraction.confidence_threshold < 0 or self.entity_extraction.confidence_threshold > 1:
issues.append("entity_extraction.confidence_threshold must be between 0 and 1")
if self.entity_extraction.batch_size <= 0:
issues.append("entity_extraction.batch_size must be positive")
# Validate relationship detection config
if self.relationship_detection.similarity_threshold < 0 or self.relationship_detection.similarity_threshold > 1:
issues.append("relationship_detection.similarity_threshold must be between 0 and 1")
if self.relationship_detection.max_relationships_per_node <= 0:
issues.append("relationship_detection.max_relationships_per_node must be positive")
# Validate retrieval config
if not self.retrieval.algorithms:
issues.append("retrieval.algorithms cannot be empty")
if self.retrieval.max_graph_results <= 0:
issues.append("retrieval.max_graph_results must be positive")
# Validate performance settings
if self.max_memory_mb <= 0:
issues.append("max_memory_mb must be positive")
if self.cache_size <= 0:
issues.append("cache_size must be positive")
return issues |