处理不完整和缺失数据
使用 Optional 字段和置信度分数设计模式，为含糊文档实现备用提取策略，并记录低置信度提取结果以供人工审核。
处理不完整和缺失数据 是 CoddyKit 上的免费 AI Engineering Academy 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 AI Engineering Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 AI Engineering Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
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.
常见问题解答
「处理不完整和缺失数据」课时是免费的吗？
是的 — 「处理不完整和缺失数据」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 AI Engineering Academy 课程的其余内容，请升级到 CoddyKit PRO。 AI Engineering Academy 课程共包含 4 节课。
「处理不完整和缺失数据」这节课中我会学到什么？
使用 Optional 字段和置信度分数设计模式，为含糊文档实现备用提取策略，并记录低置信度提取结果以供人工审核。 你通过在浏览器中直接运行的动手代码来练习 AI Engineering Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 AI Engineering Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 AI Engineering Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「处理不完整和缺失数据」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 AI Engineering Academy 课中编写并运行代码吗？
能。每节 AI Engineering Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。