Handling Partial and Missing Data
Design schemas with Optional fields and confidence scores, implement fallback extraction strategies for ambiguous documents, and log low-confidence extractions for human review.
Handling Partial and Missing Data is a free AI Engineering Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Engineering Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The Reality of Incomplete Documents
Real-world documents rarely contain every field your schema expects. An invoice might be missing a PO number, a resume might omit dates, and a news article might not mention a location. Designing your extraction schema to handle partial and missing data gracefully is as important as extracting what is present.
Optional Fields in Pydantic
Mark fields that might not appear in every document as Optional[type] and give them a None default. Pydantic v2 treats these fields as nullable, and the model is instructed not to hallucinate values when information is absent. Always prefer None over an empty string for missing data — it is easier to filter downstream.
from pydantic import BaseModel, Field
from typing import Optional
class JobPosting(BaseModel):
title: str
company: str
salary_min: Optional[float] = Field(None, description='Minimum salary if stated')
salary_max: Optional[float] = Field(None, description='Maximum salary if stated')
remote: Optional[bool] = Field(None, description='True if remote, False if on-site, None if unspecified')Adding Confidence Scores
Ask the model to rate its own confidence in each extracted field by adding a confidence score alongside the value. Wrap both in a generic FieldExtract helper. Low-confidence extractions can be routed to human reviewers rather than passed directly to downstream systems, reducing the risk of silently bad data.
from pydantic import BaseModel
from typing import Optional
class Confident(BaseModel):
value: Optional[str]
confidence: float # 0.0 to 1.0
class ContractExtract(BaseModel):
party_a: Confident
party_b: Confident
effective_date: Confident
termination_clause: ConfidentSentinel Values vs. None
Sometimes the absence of data is itself meaningful. Use Python's Optional plus a Literal enum to distinguish between not mentioned, explicitly stated as none, and unknown. This three-way distinction prevents downstream code from treating an explicitly stated absence the same as a missing mention, which can cause subtle business logic bugs.
from pydantic import BaseModel
from typing import Optional, Literal
class Discount(BaseModel):
# 'none' = explicitly no discount; None = not mentioned
discount_type: Optional[Literal['percentage', 'fixed', 'none']] = None
discount_value: Optional[float] = NoneFallback Extraction Strategies
When a primary extraction call returns too many None fields, try a targeted follow-up prompt that focuses specifically on the missing information. Send just the relevant paragraph alongside the partially extracted model and ask the model to fill only the empty fields. This two-pass approach significantly improves recall on ambiguous documents.
def fill_missing(partial: JobPosting, raw_text: str) -> JobPosting:
missing = [k for k, v in partial.model_dump().items() if v is None]
if not missing:
return partial
prompt = f'From this text, extract ONLY these fields: {missing}.\n\n{raw_text}'
supplement = client.chat.completions.create(
model='gpt-4o-mini',
response_model=JobPosting,
messages=[{'role': 'user', 'content': prompt}]
)
merged = partial.model_dump()
for field in missing:
if getattr(supplement, field) is not None:
merged[field] = getattr(supplement, field)
return JobPosting(**merged)Using Default Values and Factories
For fields that have a sensible default when missing, use Pydantic's default or default_factory. For example, a list of tags should default to an empty list rather than None so downstream code can always iterate over it. Reserve None for fields where the absence must be flagged and handled explicitly.
from pydantic import BaseModel, Field
from typing import List, Optional
class Article(BaseModel):
title: str
author: Optional[str] = None
tags: List[str] = Field(default_factory=list)
word_count: Optional[int] = None
published_date: Optional[str] = NoneRouting Low-Confidence Extractions
Build a review queue for extractions that have confidence below a threshold. Store low-confidence results in a separate database table with a needs_review flag, expose them in an internal review UI, and allow human annotators to correct them. Feed the corrections back as few-shot examples to improve future extractions.
CONFIDENCE_THRESHOLD = 0.75
def process_extraction(result: ContractExtract, doc_id: str):
needs_review = any(
field.confidence < CONFIDENCE_THRESHOLD
for field in [result.party_a, result.party_b, result.effective_date]
)
if needs_review:
queue_for_human_review(doc_id, result)
else:
store_in_production_table(doc_id, result)Handling Ambiguous Spans of Text
Some fields can be extracted in multiple valid ways from the same text. For example, a date written as 'next Monday' is ambiguous without a reference date. Use a raw_span field to capture the exact text the model used, alongside the normalized value. This preserves the original evidence and makes debugging extractions much easier.
from pydantic import BaseModel
from typing import Optional
class DateField(BaseModel):
raw_span: Optional[str] = None # exact text from document
iso_date: Optional[str] = None # normalized YYYY-MM-DD
confidence: float = 1.0
class Contract(BaseModel):
effective_date: DateField
expiration_date: DateFieldLogging Missing Field Patterns
Track which fields are most frequently None across your document corpus. High missing rates on a required field suggest either the field is genuinely absent in most documents, or your schema description is confusing the model. Logging missing patterns per document type helps you triage schema improvements that will have the biggest impact on data quality.
from collections import Counter
missing_counter = Counter()
def log_missing(result):
for field, value in result.model_dump().items():
if value is None:
missing_counter[field] += 1
# After processing 1000 documents:
for field, count in missing_counter.most_common(5):
print(f'{field}: {count} missing ({100*count//1000}%)')Combining Extraction Across Multiple Passes
For complex documents like annual reports or lengthy contracts, a multi-pass extraction strategy works best. In the first pass, extract high-confidence fields that are always present. In subsequent passes, focus on specific sections or paragraphs to extract the harder fields. Merge all passes into a single final record, letting later passes override earlier None values.
def multi_pass_extract(pages: list) -> Invoice:
# Pass 1: header info from first page
header = extract_header(pages[0])
# Pass 2: line items from middle pages
items = []
for page in pages[1:-1]:
items.extend(extract_line_items(page))
# Pass 3: totals from last page
totals = extract_totals(pages[-1])
return Invoice(
vendor=header.vendor,
invoice_number=header.invoice_number,
line_items=items,
total_amount=totals.total_amount
)Schema Design Best Practices
Well-designed schemas reduce missing data naturally. Use narrow, specific field descriptions so the model knows exactly what to look for. Avoid combining two concepts in one field. Add examples in the Field description to guide extraction. A schema that makes the model's job easy will have far fewer missing fields than one that relies on vague labels.
from pydantic import BaseModel, Field
class Address(BaseModel):
street: str = Field(description='Street number and name, e.g. 123 Main St')
city: str = Field(description='City name only, no state')
state: str = Field(description='Two-letter US state code, e.g. CA')
zip_code: str = Field(description='5-digit ZIP code, e.g. 94105')
country: str = Field(default='US', description='ISO 3166-1 alpha-2 country code')Quick Check
Test your understanding of handling partial and missing data in extraction pipelines.
Lesson Recap
In this lesson you learned: Optional fields with None defaults prevent hallucination of missing data, confidence scores and review queues create a safety net for uncertain extractions, and multi-pass extraction improves recall on complex documents by focusing each pass on specific sections. Next up we scale extraction with async processing and queues.
Frequently asked questions
Is the “Handling Partial and Missing Data” lesson free?
Yes — the full text of “Handling Partial and Missing Data” is free to read here on the web, and the AI Engineering Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Engineering Academy course, upgrade to CoddyKit PRO.
What will I learn in “Handling Partial and Missing Data”?
Design schemas with Optional fields and confidence scores, implement fallback extraction strategies for ambiguous documents, and log low-confidence extractions for human review. You practise AI Engineering Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AI Engineering Academy?
No prior experience is required. AI Engineering Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Handling Partial and Missing Data” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AI Engineering Academy lesson?
Yes. Every AI Engineering Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Instructor: Typed Extraction with Pydantic
- Handling Partial and Missing Data
- Batch Processing with Async and Queues
- Schema Evolution and Backward Compatibility