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from dataclasses import dataclass, field
from typing import List, Dict, Optional, Any
import json
import glob
from pathlib import Path
from pipeline_config import PipelineConfig
@dataclass
class SchemaGuidedDialogue:
"""
Structured representation of a Schema-Guided dialogue
"""
dialogue_id: str
service_name: str
service_description: Optional[str]
schema: Dict[str, Any]
turns: List[Dict[str, Any]]
original_metadata: Dict[str, Any] = field(default_factory=dict)
class SchemaGuidedProcessor:
"""
Handles processing and preparation of Schema-Guided dataset dialogues
"""
def __init__(self, config: PipelineConfig):
self.config = config
self.services = set()
self.domains = set()
self.schemas = {}
def load_dataset(self, base_dir, max_examples: Optional[int] = None) -> List[SchemaGuidedDialogue]:
"""
Load and parse Schema-Guided Dialogue dataset
Args:
dialogue_path: Path to the dialogue JSON file
schema_path: Path to the schema JSON file
"""
# Define schema and dialogue file patterns
schema_file = Path(base_dir, "schema.json")
dialogue_files_pattern = str(Path(base_dir, "dialogues_*.json"))
# Check for schema file
if not schema_file.exists():
raise FileNotFoundError(f"Schema file not found at {schema_file}")
# Load schema
self.schemas = self._load_schemas(schema_file)
# Find and validate dialogue files
dialogue_files = glob.glob(dialogue_files_pattern)
if not dialogue_files:
raise FileNotFoundError(f"No dialogue files found matching pattern {dialogue_files_pattern}")
print(f"Found {len(dialogue_files)} dialogue files to process.")
# Process all dialogues
processed_dialogues = []
for file_path in dialogue_files:
with open(file_path, 'r', encoding='utf-8') as f:
raw_dialogues = json.load(f)
for dialogue in raw_dialogues:
processed_dialogues.append(self._process_single_dialogue(dialogue))
if max_examples and len(processed_dialogues) >= max_examples:
break
return processed_dialogues
def _process_single_dialogue(self, dialogue: Dict[str, Any]) -> SchemaGuidedDialogue:
"""
Process a single dialogue JSON object into a SchemaGuidedDialogue object.
"""
dialogue_id = str(dialogue.get("dialogue_id", ""))
services = dialogue.get("services", [])
service_name = services[0] if services else None
schema = self.schemas.get(service_name, {})
service_description = schema.get("description", "")
# Process turns
turns = self._process_turns(dialogue.get("turns", []))
# Store metadata
metadata = {
"services": services,
"original_id": dialogue_id,
}
return SchemaGuidedDialogue(
dialogue_id=f"schema_guided_{dialogue_id}",
service_name=service_name,
service_description=service_description,
schema=schema,
turns=turns,
original_metadata=metadata,
)
def _validate_schema(self, schema: Dict[str, Any]) -> bool:
"""
Validate a schema
"""
required_keys = {"service_name", "description", "slots", "intents"}
missing_keys = required_keys - schema.keys()
if missing_keys:
print(f"Warning: Missing keys in schema {schema.get('service_name', 'unknown')}: {missing_keys}")
return False
return True
def _load_schemas(self, schema_path: str) -> Dict[str, Any]:
"""
Load and process service schemas
"""
with open(schema_path, 'r', encoding='utf-8') as f:
schemas = json.load(f)
# Validate and index schemas
return {
schema["service_name"]: schema for schema in schemas if self._validate_schema(schema)
}
def _process_turns(self, turns: List[Dict]) -> List[Dict]:
"""
Process dialogue turns into standardized format
"""
processed_turns = []
for turn in turns:
try:
# Map speakers to standard format
speaker = 'assistant' if turn.get('speaker') == 'SYSTEM' else 'user'
# Extract utterance and clean it
text = turn.get('utterance', '').strip()
# Extract frames and dialogue acts
frames = turn.get('frames', [])
acts = []
slots = []
for frame in frames:
if 'actions' in frame:
acts.extend(frame['actions'])
if 'slots' in frame:
slots.extend(frame['slots'])
# Create the processed turn
processed_turn = {
'speaker': speaker,
'text': text,
'original_speaker': turn.get('speaker', ''),
'dialogue_acts': acts,
'slots': slots,
'metadata': {k: v for k, v in turn.items()
if k not in {'speaker', 'utterance', 'frames'}}
}
processed_turns.append(processed_turn)
except Exception as e:
print(f"Error processing turn: {str(e)}")
continue
return processed_turns
def convert_to_pipeline_format(self, schema_dialogues: List[SchemaGuidedDialogue]) -> List[Dict]:
"""
Convert SchemaGuidedDialogues to the format expected by the ProcessingPipeline
"""
pipeline_dialogues = []
for dialogue in schema_dialogues:
# Convert turns to the expected format
processed_turns = [
{"speaker": turn["speaker"], "text": turn["text"]}
for turn in dialogue.turns if turn["text"].strip()
]
# Create dialogue in pipeline format
pipeline_dialogue = {
'dialogue_id': dialogue.dialogue_id,
'turns': processed_turns,
'metadata': {
'service_name': dialogue.service_name,
'service_description': dialogue.service_description,
'schema': dialogue.schema,
**dialogue.original_metadata
}
}
pipeline_dialogues.append(pipeline_dialogue)
return pipeline_dialogues
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