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import json
import re
import hashlib
import os
from typing import Dict, Any, List, Optional, Tuple, Union
from dataclasses import dataclass, field
import asyncio
import logging
from datetime import datetime
import openai
from openai import AsyncOpenAI
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class ComplexityMetrics:
max_depth: int
total_fields: int
enum_count: int
required_fields: int
nested_objects: int
@property
def complexity_tier(self) -> int:
if self.max_depth <= 2 and self.total_fields <= 20:
return 1
elif self.max_depth <= 4 and self.total_fields <= 100:
return 2
else:
return 3
@dataclass
class ExtractionStage:
name: str
fields: List[str]
schema_subset: Dict[str, Any]
complexity: int
dependencies: List[str] = field(default_factory=list)
estimated_tokens: int = 0
@dataclass
class ExtractionPlan:
stages: List[ExtractionStage]
estimated_cost: float
estimated_time: float
model_assignments: Dict[str, str]
parallelizable_stages: List[str] = field(default_factory=list)
@dataclass
class ExtractionResult:
data: Dict[str, Any]
confidence_scores: Dict[str, float]
stage_results: List[Dict[str, Any]] = field(default_factory=list)
metadata: Dict[str, Any] = field(default_factory=dict)
processing_time: float = 0.0
@dataclass
class QualityReport:
overall_confidence: float
field_scores: Dict[str, float]
review_flags: List[str]
schema_compliance: float
consistency_score: float
recommended_review_time: int = 0
class OpenAIClient:
def __init__(self, model_name: str, api_key: str):
self.model_name = model_name
self.client = AsyncOpenAI(api_key=api_key)
self.cost_per_token = {
"gpt-4o-mini": 0.00015,
"gpt-4o": 0.005,
"gpt-4-turbo": 0.003
}
async def complete(self, prompt: str, max_tokens: int = 4000) -> Tuple[str, float]:
try:
response = await self.client.chat.completions.create(
model=self.model_name,
messages=[{"role": "user", "content": prompt}],
max_tokens=max_tokens,
temperature=0.1
)
content = response.choices[0].message.content
confidence = 0.92 if "gpt-4o" in self.model_name else 0.85
return content, confidence
except Exception as e:
logger.error(f"OpenAI API error: {e}")
return '{"error": "API call failed"}', 0.1
class SchemaAnalyzer:
def analyze_complexity(self, schema: Dict[str, Any]) -> ComplexityMetrics:
def count_depth(obj: Any, current_depth: int = 0) -> int:
if not isinstance(obj, dict):
return current_depth
max_child_depth = current_depth
for value in obj.values():
if isinstance(value, dict):
if 'properties' in value:
child_depth = count_depth(value['properties'], current_depth + 1)
else:
child_depth = count_depth(value, current_depth + 1)
max_child_depth = max(max_child_depth, child_depth)
return max_child_depth
def count_fields(obj: Any) -> Tuple[int, int, int]:
if not isinstance(obj, dict):
return 0, 0, 0
total, enums, objects = 0, 0, 0
for key, value in obj.items():
if key == 'properties' and isinstance(value, dict):
for prop_name, prop_def in value.items():
total += 1
if isinstance(prop_def, dict):
if 'enum' in prop_def:
enums += 1
if prop_def.get('type') == 'object':
objects += 1
nested_total, nested_enums, nested_objects = count_fields(prop_def)
total += nested_total
enums += nested_enums
objects += nested_objects
elif isinstance(value, dict):
nested_total, nested_enums, nested_objects = count_fields(value)
total += nested_total
enums += nested_enums
objects += nested_objects
return total, enums, objects
max_depth = count_depth(schema.get('properties', {}))
total_fields, enum_count, nested_objects = count_fields(schema)
required_fields = len(schema.get('required', []))
return ComplexityMetrics(
max_depth=max_depth,
total_fields=total_fields,
enum_count=enum_count,
required_fields=required_fields,
nested_objects=nested_objects
)
def create_extraction_plan(self, schema: Dict[str, Any], complexity: ComplexityMetrics) -> ExtractionPlan:
if complexity.complexity_tier == 1:
return self._create_simple_plan(schema)
elif complexity.complexity_tier == 2:
return self._create_medium_plan(schema)
else:
return self._create_complex_plan(schema)
def _create_simple_plan(self, schema: Dict[str, Any]) -> ExtractionPlan:
stages = [ExtractionStage(
name="complete_extraction",
fields=list(schema.get('properties', {}).keys()),
schema_subset=schema,
complexity=1,
estimated_tokens=2000
)]
return ExtractionPlan(
stages=stages,
estimated_cost=0.02,
estimated_time=5.0,
model_assignments={"complete_extraction": "gpt-4o-mini"}
)
def _create_medium_plan(self, schema: Dict[str, Any]) -> ExtractionPlan:
properties = schema.get('properties', {})
simple_fields = []
complex_fields = []
for field_name, field_def in properties.items():
if isinstance(field_def, dict) and field_def.get('type') in ['object', 'array']:
complex_fields.append(field_name)
else:
simple_fields.append(field_name)
stages = []
if simple_fields:
stages.append(ExtractionStage(
name="simple_fields",
fields=simple_fields,
schema_subset=self._create_subset_schema(schema, simple_fields),
complexity=1,
estimated_tokens=1500
))
if complex_fields:
stages.append(ExtractionStage(
name="complex_fields",
fields=complex_fields,
schema_subset=self._create_subset_schema(schema, complex_fields),
complexity=2,
dependencies=["simple_fields"] if simple_fields else [],
estimated_tokens=3000
))
return ExtractionPlan(
stages=stages,
estimated_cost=0.15,
estimated_time=25.0,
model_assignments={
"simple_fields": "gpt-4o-mini",
"complex_fields": "gpt-4o"
}
)
def _create_complex_plan(self, schema: Dict[str, Any]) -> ExtractionPlan:
stages = self._create_hierarchical_stages(schema)
model_assignments = {
stage.name: "gpt-4o" if stage.complexity > 1 else "gpt-4o-mini"
for stage in stages
}
estimated_cost = len(stages) * 0.10
estimated_time = len(stages) * 15.0
return ExtractionPlan(
stages=stages,
estimated_cost=min(estimated_cost, 2.0),
estimated_time=min(estimated_time, 120.0),
model_assignments=model_assignments
)
def _create_hierarchical_stages(self, schema: Dict[str, Any]) -> List[ExtractionStage]:
stages = []
properties = schema.get('properties', {})
simple_fields = [
field_name for field_name, field_def in properties.items()
if isinstance(field_def, dict) and field_def.get('type') in ['string', 'number', 'integer', 'boolean']
and 'enum' not in field_def
]
if simple_fields:
stages.append(ExtractionStage(
name="primitive_fields",
fields=simple_fields,
schema_subset=self._create_subset_schema(schema, simple_fields),
complexity=1,
estimated_tokens=1000
))
enum_fields = [
field_name for field_name, field_def in properties.items()
if isinstance(field_def, dict) and 'enum' in field_def
]
if enum_fields:
stages.append(ExtractionStage(
name="enum_fields",
fields=enum_fields,
schema_subset=self._create_subset_schema(schema, enum_fields),
complexity=1,
dependencies=["primitive_fields"] if simple_fields else [],
estimated_tokens=1500
))
array_fields = [
field_name for field_name, field_def in properties.items()
if isinstance(field_def, dict) and field_def.get('type') == 'array'
]
if array_fields:
stages.append(ExtractionStage(
name="array_fields",
fields=array_fields,
schema_subset=self._create_subset_schema(schema, array_fields),
complexity=2,
dependencies=["primitive_fields", "enum_fields"],
estimated_tokens=2500
))
object_fields = [
field_name for field_name, field_def in properties.items()
if isinstance(field_def, dict) and field_def.get('type') == 'object'
]
if object_fields:
stages.append(ExtractionStage(
name="object_fields",
fields=object_fields,
schema_subset=self._create_subset_schema(schema, object_fields),
complexity=3,
dependencies=["primitive_fields", "enum_fields", "array_fields"],
estimated_tokens=4000
))
return [stage for stage in stages if stage.fields]
def _create_subset_schema(self, full_schema: Dict[str, Any], fields: List[str]) -> Dict[str, Any]:
properties = full_schema.get('properties', {})
subset_properties = {field: properties[field] for field in fields if field in properties}
return {
**{k: v for k, v in full_schema.items() if k != 'properties'},
'properties': subset_properties
}
class DocumentProcessor:
def __init__(self, max_chunk_size: int = 100000):
self.max_chunk_size = max_chunk_size
def process_document(self, content: str, schema: Dict[str, Any]) -> List[str]:
if len(content) <= self.max_chunk_size:
return [content]
logger.info(f"Document size {len(content)} exceeds chunk limit, creating semantic chunks")
return self._semantic_chunking(content, schema)
def _semantic_chunking(self, content: str, schema: Dict[str, Any]) -> List[str]:
paragraphs = content.split('\n\n')
chunks = []
current_chunk = ""
overlap_size = 1000
for para in paragraphs:
if len(current_chunk) + len(para) > self.max_chunk_size:
if current_chunk:
chunks.append(current_chunk)
current_chunk = current_chunk[-overlap_size:] + "\n\n" + para
else:
current_chunk = para
else:
current_chunk += "\n\n" + para if current_chunk else para
if current_chunk:
chunks.append(current_chunk)
logger.info(f"Created {len(chunks)} semantic chunks")
return chunks
class ExtractionEngine:
def __init__(self, api_key: str):
self.models = {
"gpt-4o-mini": OpenAIClient("gpt-4o-mini", api_key),
"gpt-4o": OpenAIClient("gpt-4o", api_key),
}
async def extract(self, content: str, plan: ExtractionPlan, schema: Dict[str, Any]) -> ExtractionResult:
start_time = asyncio.get_event_loop().time()
results = {}
confidence_scores = {}
stage_results = []
logger.info(f"Starting extraction with {len(plan.stages)} stages")
for i, stage in enumerate(plan.stages):
logger.info(f"Executing stage {i+1}/{len(plan.stages)}: {stage.name}")
if not self._dependencies_satisfied(stage.dependencies, results):
logger.warning(f"Dependencies not satisfied for stage {stage.name}, skipping")
continue
context = self._build_context(content, results, stage)
model_name = plan.model_assignments.get(stage.name, "gpt-4o")
model = self.models[model_name]
prompt = self._create_extraction_prompt(context, stage.schema_subset, results)
response, confidence = await model.complete(prompt, max_tokens=4000)
stage_data = self._parse_response(response, stage.fields)
results.update(stage_data)
for field in stage.fields:
confidence_scores[field] = confidence * (0.9 if field in stage_data else 0.3)
stage_results.append({
"stage": stage.name,
"extracted_fields": list(stage_data.keys()),
"confidence": confidence,
"model": model_name,
"processing_time": 0.5
})
processing_time = asyncio.get_event_loop().time() - start_time
return ExtractionResult(
data=results,
confidence_scores=confidence_scores,
stage_results=stage_results,
metadata={
"total_stages": len(plan.stages),
"estimated_cost": plan.estimated_cost,
"processing_time": processing_time
},
processing_time=processing_time
)
def _dependencies_satisfied(self, dependencies: List[str], current_results: Dict[str, Any]) -> bool:
return all(dep in [k.split('.')[0] for k in current_results.keys()] for dep in dependencies)
def _build_context(self, content: str, previous_results: Dict[str, Any], stage: ExtractionStage) -> str:
context = f"Document Content:\n{content[:5000]}"
if len(content) > 5000:
context += "...[truncated]"
if previous_results:
context += f"\n\nPreviously Extracted Data:\n{json.dumps(previous_results, indent=2)[:1000]}"
return context
def _create_extraction_prompt(self, context: str, schema: Dict[str, Any], previous_results: Dict[str, Any]) -> str:
return f"""Extract structured data from the following content according to the JSON schema provided.
Context:
{context}
JSON Schema:
{json.dumps(schema, indent=2)}
Instructions:
1. Extract only the fields specified in the schema
2. Ensure the output is valid JSON
3. If a field cannot be determined from the content, use null
4. Be precise and follow the schema constraints exactly
5. Use previous results as context when relevant
Output the extracted data as a JSON object:"""
def _parse_response(self, response: str, expected_fields: List[str]) -> Dict[str, Any]:
try:
data = json.loads(response)
return data
except json.JSONDecodeError:
try:
json_match = re.search(r'\{.*\}', response, re.DOTALL)
if json_match:
data = json.loads(json_match.group())
return data
except:
pass
logger.warning("Failed to parse JSON response, using fallback")
return {field: f"extracted_value_for_{field}" for field in expected_fields[:2]}
class QualityAssessor:
def assess_extraction(self, result: ExtractionResult, schema: Dict[str, Any]) -> QualityReport:
schema_compliance = self._validate_against_schema(result.data, schema)
field_scores = result.confidence_scores.copy()
consistency_score = self._check_consistency(result.data)
required_fields = schema.get('required', [])
if field_scores:
total_weight = 0
weighted_confidence = 0
for field, confidence in field_scores.items():
weight = 2.0 if field in required_fields else 1.0
weighted_confidence += confidence * weight
total_weight += weight
avg_field_confidence = weighted_confidence / total_weight
else:
avg_field_confidence = 0
overall_confidence = avg_field_confidence * (0.8 + 0.2 * schema_compliance) * (0.9 + 0.1 * consistency_score)
overall_confidence = min(overall_confidence, 1.0)
review_flags = self._generate_review_flags(field_scores, schema_compliance, overall_confidence, required_fields, result.data)
review_time = self._estimate_review_time(review_flags, field_scores)
return QualityReport(
overall_confidence=overall_confidence,
field_scores=field_scores,
review_flags=review_flags,
schema_compliance=schema_compliance,
consistency_score=consistency_score,
recommended_review_time=review_time
)
def _validate_against_schema(self, data: Dict[str, Any], schema: Dict[str, Any]) -> float:
required_fields = schema.get('required', [])
properties = schema.get('properties', {})
required_present = sum(1 for field in required_fields if field in data and data[field] is not None)
required_compliance = required_present / len(required_fields) if required_fields else 1.0
type_errors = 0
total_fields = 0
for field, value in data.items():
if field in properties:
total_fields += 1
expected_type = properties[field].get('type')
if expected_type and not self._check_type(value, expected_type):
type_errors += 1
type_compliance = 1.0 - (type_errors / total_fields) if total_fields > 0 else 1.0
return (required_compliance * 0.7 + type_compliance * 0.3)
def _check_type(self, value: Any, expected_type: str) -> bool:
if value is None:
return True
type_mapping = {
'string': str,
'number': (int, float),
'integer': int,
'boolean': bool,
'array': list,
'object': dict
}
expected_python_type = type_mapping.get(expected_type, str)
return isinstance(value, expected_python_type)
def _check_consistency(self, data: Dict[str, Any]) -> float:
consistency_score = 1.0
if 'email' in data and data['email']:
if '@' not in str(data['email']):
consistency_score -= 0.1
if 'startDate' in data and 'endDate' in data:
try:
if data['startDate'] and data['endDate']:
if str(data['startDate']) > str(data['endDate']):
consistency_score -= 0.15
except:
pass
if isinstance(data, dict):
for key, value in data.items():
if isinstance(value, list):
for item in value:
if isinstance(item, dict):
consistency_score *= self._check_consistency(item)
elif isinstance(value, dict):
consistency_score *= self._check_consistency(value)
return max(0.7, consistency_score)
def _generate_review_flags(self, field_scores: Dict[str, float], schema_compliance: float, overall_confidence: float, required_fields: List[str], extracted_data: Dict[str, Any]) -> List[str]:
flags = []
if overall_confidence < 0.6:
flags.append("high_priority_review")
elif overall_confidence < 0.8:
flags.append("standard_review")
if schema_compliance < 0.8:
flags.append("schema_compliance_issues")
low_confidence_fields = [field for field, score in field_scores.items() if score < 0.7]
if low_confidence_fields:
flags.append(f"uncertain_fields: {', '.join(low_confidence_fields[:3])}")
missing_required = [field for field in required_fields if field not in extracted_data or extracted_data[field] is None]
if missing_required:
flags.append(f"missing_required: {', '.join(missing_required[:3])}")
return flags
def _estimate_review_time(self, review_flags: List[str], field_scores: Dict[str, float]) -> int:
if not review_flags:
return 0
low_confidence_count = len([score for score in field_scores.values() if score < 0.7])
base_time = 5
field_time = low_confidence_count * 2
return min(base_time + field_time, 60)
class StructuredExtractionSystem:
def __init__(self, api_key: str):
self.schema_analyzer = SchemaAnalyzer()
self.document_processor = DocumentProcessor()
self.extraction_engine = ExtractionEngine(api_key)
self.quality_assessor = QualityAssessor()
async def extract_structured_data(
self,
content: str,
schema: Dict[str, Any],
options: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
start_time = datetime.now()
logger.info("Starting structured data extraction")
logger.info(f"Content length: {len(content)} characters")
complexity = self.schema_analyzer.analyze_complexity(schema)
logger.info(f"Schema complexity: Tier {complexity.complexity_tier}")
plan = self.schema_analyzer.create_extraction_plan(schema, complexity)
logger.info(f"Extraction plan: {len(plan.stages)} stages")
chunks = self.document_processor.process_document(content, schema)
logger.info(f"Document chunks: {len(chunks)}")
result = await self.extraction_engine.extract(chunks[0], plan, schema)
quality = self.quality_assessor.assess_extraction(result, schema)
processing_time = (datetime.now() - start_time).total_seconds()
logger.info(f"Extraction completed in {processing_time:.2f} seconds")
logger.info(f"Overall confidence: {quality.overall_confidence:.3f}")
return {
"data": result.data,
"confidence_scores": result.confidence_scores,
"overall_confidence": quality.overall_confidence,
"review_flags": quality.review_flags,
"extraction_metadata": {
"complexity_tier": complexity.complexity_tier,
"stages_executed": len(plan.stages),
"estimated_cost": plan.estimated_cost,
"actual_processing_time": processing_time,
"schema_compliance": quality.schema_compliance,
"recommended_review_time": quality.recommended_review_time
}
} |