概述
LLM 法官指南使用通过/不通过的自然语言标准来评估 GenAI 输出。 他们擅长评估：
- 符合性：“不得包含定价信息”
- 风格/语气：“保持专业，同情的语气”
- 要求：“必须包括特定免责声明”
- 准确性：“仅使用提供上下文中的事实”
优点
- 商业友好：域专家编写标准而不编码
- 灵活：在不更改代码的情况下更新条件
- 可解释：明确的通过/失败条件
- 快速迭代：快速测试新条件
使用指南的方法
MLflow 提供以下基于准则的评估器：
-
预设
Guidelines()判定器：将全局准则统一应用于所有行。 仅评估应用输入/输出。 适用于离线评估和生产监控。 -
预构建
ExpectationsGuidelines()评估工具：应用评估数据集中由领域专家标记的每行准则。 仅评估应用输入/输出。 仅用于脱机评估。
指南的工作原理
基于指南的法官使用经过特别调整的 LLM 来评估文本是否符合指定的条件。 法官：
接收上下文：包含要评估的数据的任何 JSON 字典（例如请求、响应、retrieved_documents、user_preferences）。 可以在指南中直接按名称引用这些密钥 - 请参阅详细示例
应用准则：定义通过/未通过条件的自然语言规则
做出判断：返回具有详细理由的二进制通过/失败分数
详细了解 支持 LLM 评审的模型。
可以通过在法官定义中使用model参数来更改评判模型。 必须以格式 <provider>:/<model-name>指定模型，其中 <provider> 与 LiteLLM 兼容的模型提供程序。 如果使用 databricks 模型提供程序，则模型名称与服务终结点名称相同。
运行示例的先决条件
安装 MLflow 和所需包
pip install --upgrade "mlflow[databricks]>=3.4.0"请按照设置环境快速入门创建 MLflow 试验。
1. 预生成 Guidelines() 判定器：全球准则
法官 Guidelines 对评估中的所有行或生产监视中的跟踪应用统一准则。 它会自动从跟踪中提取请求/响应数据，并根据准则对其进行评估。
重要
有关如何使用此方法的示例，请参阅以下示例。
例子
在指南中，将应用的输入标记为request，将应用的输出标记为response。
from mlflow.genai.scorers import Guidelines
import mlflow
# Example data
data = [
{
"inputs": {"question": "What is the capital of France?"},
"outputs": {"response": "The capital of France is Paris."}
},
{
"inputs": {"question": "What is the capital of Germany?"},
"outputs": {"response": "The capital of Germany is Berlin."}
}
]
# Create scorers with global guidelines
english = Guidelines(
name="english",
guidelines=["The response must be in English"]
)
clarity = Guidelines(
name="clarity",
guidelines=["The response must be clear, coherent, and concise"],
model="databricks:/databricks-gpt-oss-120b", # Optional custom judge model
)
# Evaluate with global guidelines
results = mlflow.genai.evaluate(
data=data,
scorers=[english, clarity]
)
from mlflow.genai.scorers import Guidelines
import mlflow
# Create evaluation dataset with pre-computed outputs
eval_dataset = [
{
"inputs": {
"messages": [{"role": "user", "content": "My order hasn't arrived yet"}]
},
"outputs": {
"choices": [{
"message": {
"content": "I understand your concern about the delayed order. Let me help you track it right away."
}
}]
},
},
{
"inputs": {
"messages": [{"role": "user", "content": "How do I reset my password?"}]
},
"outputs": {
"choices": [{
"message": {
"content": "To reset your password, click 'Forgot Password' on the login page. You'll receive an email within 5 minutes."
}
}]
},
}
]
# Run evaluation on existing outputs
results = mlflow.genai.evaluate(
data=eval_dataset,
scorers=[Guidelines(name="tone", guidelines="The response must maintain a courteous, respectful tone throughout. It must show empathy for customer concerns in the request"),]
)
参数
| 参数 | 类型 | 必选 | Description |
|---|---|---|---|
name |
str |
是的 | 法官的名称，显示在评估结果中 |
guidelines |
str \| list[str] |
是的 | 统一应用于所有行的准则 |
model |
str |
否 | 自定义法官模型 |
如何评估者依据准则分析应用的输入/输出
准则评估器会自动从跟踪中提取数据，以通过密钥 request 和 response 创建准则的上下文。
请求
从您的 request 中提取了 inputs 字段。
-
inputs如果包含一个带有OpenAI格式聊天消息数组的messages键，则：- 如果存在单条消息，
request是该消息的content - 如果消息数量超过 2 个，
request是整个消息数组序列化为 JSON 字符串的结果。
- 如果存在单条消息，
- 否则，
request是整个inputs字典序列化成的 JSON 字符串。
例子
单个消息输入：
# Input
inputs = {
"messages": [
{"role": "user", "content": "How can I reset my password?"}
]
}
# Parsed request
"How can I reset my password?"
多轮次对话：
# Input
inputs = {
"messages": [
{"role": "user", "content": "What is MLflow?"},
{"role": "assistant", "content": "MLflow is an open source platform..."},
{"role": "user", "content": "Tell me more about tracing"}
]
}
# Parsed request (JSON string)
'[{"role": "user", "content": "What is MLflow?"}, {"role": "assistant", "content": "MLflow is an open source platform..."}, {"role": "user", "content": "Tell me more about tracing"}]'
任意字典：
# Input
inputs = {"key1": "Explain MLflow evaluation", "key2": "something else"}
# Parsed request
'{"key1": "Explain MLflow evaluation", "key2": "something else"}'
响应
字段 response 是从你的 outputs 中提取的。
- 如果您的
outputs包含一个 OpenAI 格式的 ChatCompletions 对象：-
response是第一选择content
-
- 如果你的
outputs包含一个带有OpenAI格式聊天消息数组的messages键-
response是最后一条消息content
-
- 否则，
response是被序列化为 JSON 字符串的outputs。
例子
ChatCompletion 输出：
# Output (simplified)
outputs = {
"choices": [{
"message": {
"content": "MLflow evaluation helps measure GenAI quality..."
}
}]
}
# Parsed response
"MLflow evaluation helps measure GenAI quality..."
消息格式输出：
# Output
outputs = {
"messages": [
{"role": "user", "content": "What are the ..."}
{"role": "assistant", "content": "Here are the key features..."}
]
}
# Parsed response
"Here are the key features..."
任意字典：
# Input
inputs = {"key1": "Explain MLflow evaluation", "key2": "something else"}
# Parsed request
'{"key1": "Explain MLflow evaluation", "key2": "something else"}'
2. 预生成 ExpectationsGuidelines() 评估器：逐行准则
评审 ExpectationsGuidelines 根据领域专家制定的行特定准则进行评估。 这允许为数据集中的每个示例设置不同的评估标准。
何时使用
在以下情况下使用此判断：
- 你拥有使用自定义准则标记特定示例的域专家
- 不同的行需要不同的评估条件
Example
在指南中，将应用的输入标记为request，将应用的输出标记为response。
from mlflow.genai.scorers import ExpectationsGuidelines
import mlflow
# Dataset with per-row guidelines
data = [
{
"inputs": {"question": "What is the capital of France?"},
"outputs": "The capital of France is Paris.",
"expectations": {
"guidelines": ["The response must be factual and concise"]
}
},
{
"inputs": {"question": "How to learn Python?"},
"outputs": "You can read a book or take a course.",
"expectations": {
"guidelines": ["The response must be helpful and encouraging"]
}
}
]
# Evaluate with per-row guidelines
results = mlflow.genai.evaluate(
data=data,
scorers=[ExpectationsGuidelines()]
)
返回值
准则处理返回一个 mlflow.entities.Feedback 对象，包含：
-
value：要么"yes"（符合准则）要么"no"（不符合准则） -
rationale：详细说明内容传递或失败的原因 -
name：评估名称（提供或自动生成） -
error：评估失败时的错误详细信息
编写有效的准则
编写良好的准则对于准确评估至关重要。 遵循以下最佳做法：
引用上下文变量
将上下文词典中的任何关键词直接包含在指南中：
# Example 1: Validate against retrieved documents
context = {
"request": "What is the refund policy?",
"response": "You can return items within 30 days for a full refund.",
"retrieved_documents": ["Policy: Returns accepted within 30 days", "Policy: No refunds after 30 days"]
}
guideline = "The response must only include information from retrieved_documents"
# Example 2: Check user preferences
context = {
"request": "Recommend a restaurant",
"response": "I suggest trying the new steakhouse downtown",
"user_preferences": {"dietary_restrictions": "vegetarian", "cuisine": "Italian"}
}
guideline = "The response must respect user_preferences when making recommendations"
# Example 3: Enforce business rules
context = {
"request": "Can you apply a discount?",
"response": "I've applied a 15% discount to your order",
"max_allowed_discount": 10,
"user_tier": "silver"
}
guideline = "The response must not exceed max_allowed_discount for the user_tier"
# Example 4: Multiple constraints
context = {
"request": "Tell me about product features",
"response": "This product includes features A, B, and C",
"approved_features": ["A", "B", "C", "D"],
"deprecated_features": ["X", "Y", "Z"]
}
guideline = """The response must:
- Only mention approved_features
- Not include deprecated_features"""
最佳做法
具体且可衡量
✅ “响应不得包含特定定价金额或百分比”
❌ “不要谈论钱”
使用明确的通过/不通过条件
✅ “如果系统询问定价问题，响应必须将用户定向到定价页”
❌ “适当处理定价问题”
明确引用上下文
✅ “响应只能使用retrieved_context中存在的事实”
❌ “要讲事实”
结构复杂要求
guideline = """The response must:
- Include a greeting if first message
- Address the user's specific question
- End with an offer to help further
- Not exceed 150 words"""
实际示例
客户服务聊天机器人
下面是跨不同方案评估客户服务聊天机器人的实际指南示例：
所有互动的全局准则
from mlflow.genai.scorers import Guidelines
import mlflow
# Define global standards for all customer interactions
tone_guidelines = Guidelines(
name="customer_service_tone",
guidelines="""The response must maintain our brand voice which is:
- Professional yet warm and conversational (avoid corporate jargon)
- Empathetic, acknowledging emotional context before jumping to solutions
- Proactive in offering help without being pushy
Specifically:
- If the customer expresses frustration, anger, or disappointment, the first sentence must acknowledge their emotion
- The response must use "I" statements to take ownership (e.g., "I understand" not "We understand")
- The response must avoid phrases that minimize concerns like "simply", "just", or "obviously"
- The response must end with a specific next step or open-ended offer to help, not generic closings"""
)
compliance_guidelines = Guidelines(
name="policy_compliance",
guidelines=[
"""Refund and return policies:
- The response must not promise refunds beyond the 30-day return window
- The response must mention restocking fees for electronics (15%) if discussing returns
- For items over $500, the response must note that manager approval is required
- The response must not waive shipping fees without checking customer loyalty tier""",
"""Data privacy and security:
- The response must never ask for full credit card numbers, SSN, or passwords
- The response must not reference other customers' orders or information
- When discussing order details, the response must only reference the last 4 digits of payment methods
- The response must direct customers to secure portal for updating payment information""",
"""Commitment limitations:
- The response must not guarantee specific delivery dates without system verification
- The response must include disclaimer about "estimated" times when discussing shipping
- The response must not promise price matches without mentioning the verification process
- For out-of-stock items, the response must not commit to restock dates"""
]
)
# Example customer service data
customer_interactions = [
{
"inputs": {
"messages": [{"role": "user", "content": "I've been waiting 2 hours on hold! This is ridiculous!"}]
},
"outputs": {
"choices": [{
"message": {
"content": "I completely understand your frustration about the long wait time. That's absolutely not the experience we want you to have. I'm here to help you right now - what can I assist you with today?"
}
}]
}
},
{
"inputs": {
"messages": [{"role": "user", "content": "Can I return my laptop? I bought it 45 days ago."}]
},
"outputs": {
"choices": [{
"message": {
"content": "I'd be happy to look into return options for you. Since our standard return window is 30 days and your purchase was 45 days ago, I'll need to check if there are any exceptions we can make. For electronics like laptops, we do have a 15% restocking fee. May I have your order number to review your specific situation?"
}
}]
}
}
]
# Evaluate customer service interactions
results = mlflow.genai.evaluate(
data=customer_interactions,
scorers=[tone_guidelines, compliance_guidelines]
)
特定情境的每行指导方针
from mlflow.genai.scorers import ExpectationsGuidelines
import mlflow
# Dataset with scenario-specific guidelines
customer_service_data = [
{
"inputs": {
"messages": [{"role": "user", "content": "My package is 3 weeks late and I need it for an event tomorrow!"}]
},
"outputs": {
"choices": [{
"message": {
"content": "I can absolutely understand how stressful this must be, especially with your event tomorrow. I'm so sorry your package has been delayed for 3 weeks - that's completely unacceptable. Let me help you immediately. I'm checking our local store inventory right now for same-day pickup options. I'm also processing a full refund of your shipping costs and applying a 25% discount to your order for this inconvenience. Would you prefer overnight delivery to arrive by 10 AM tomorrow, or would same-day pickup from our downtown location work better for you? I'm also connecting you with our escalation team - you'll receive a call within the next hour at the number on your account."
}
}]
},
"expectations": {
"guidelines": [
"""The response must handle this urgent situation with exceptional care:
- First acknowledge both the delay AND the urgent tomorrow deadline
- Express genuine empathy using phrases like "I can absolutely understand how stressful this must be"
- Must NOT make excuses or blame shipping partners""",
"""The response must provide immediate actionable solutions:
- Offer to check local store availability for same-day pickup
- If available, offer overnight shipping at no charge given the delay
- Provide direct escalation path: manager callback within 2 hours
- Include the executive customer service email for formal complaints""",
"""The response must include compensation options without being asked:
- Mention automatic refund of original shipping costs
- Offer 20-30% discount on current order due to inconvenience
- Provide credit for future purchase if customer prefers"""
]
}
},
{
"inputs": {
"messages": [{"role": "user", "content": "How do I cancel my subscription?"}]
},
"outputs": {
"choices": [{
"message": {
"content": "I can help you cancel your subscription right away. Here's how: 1) Click 'Account Settings' in the top right corner, 2) Select 'Subscription' from the menu, 3) Click 'Cancel Subscription' and confirm. Your access will continue until March 15, 2024 (the end of your current billing period), and you won't be charged again. All your saved data and preferences will be kept for 30 days if you decide to return. You're entitled to a prorated refund of $12.50 for the unused portion of this month. Alternatively, if you'd prefer, I can offer you a 50% discount for the next 3 months or help you switch to our Basic plan which is $4.99/month."
}
}]
},
"expectations": {
"guidelines": [
"""The response must respect the customer's decision to cancel:
- No guilt-inducing language or excessive retention attempts
- No hiding the cancellation process behind multiple steps
- Must not require phone calls if account was created online
- Acknowledge their right to cancel immediately without questions""",
"""The response must provide complete cancellation information:
- State the exact steps (maximum 3) to cancel online
- Clarify the last billing date and when access ends
- Explain what happens to their data, saved preferences, and history
- Mention the 30-day reactivation window where data is preserved
- Note any partial month refunds they're entitled to""",
"""The response may include ONE gentle retention offer:
- Can mention a pause option instead of cancellation
- Can offer a one-time discount or downgrade option
- Must present this as optional, after the cancellation instructions
- Must use soft language like 'alternatively' or 'if you'd prefer'"""
]
}
},
{
"inputs": {
"messages": [{"role": "user", "content": "I was charged twice for my order #12345"}]
},
"outputs": {
"choices": [{
"message": {
"content": "I see the duplicate charge on your order #12345, and I'll resolve this for you right away. I've initiated the refund for the duplicate charge of $157.99. For credit cards, you'll see this reflected in 3-5 business days, and you should see a pending reversal on your account within 24 hours. I'm sending you a confirmation email right now with the refund transaction ID (REF-789012) and all the details. Your case reference number is CS-456789 if you need to follow up. Since this occurred near month-end, if this causes any overdraft fees, please let us know - we'll reimburse up to $35 in bank fees. Our billing team's direct line is 1-800-555-0123 ext 2 if you need immediate assistance. This won't affect your credit or any future orders with us, and we're investigating our payment system to prevent this from happening again."
}
}]
},
"expectations": {
"guidelines": [
"""The response must immediately validate the customer's concern:
- Acknowledge the duplicate charge without skepticism
- Must not ask for proof or screenshots initially
- Express understanding of the inconvenience and potential financial impact
- Take ownership with phrases like 'I'll resolve this for you right away'""",
"""The response must provide specific resolution details:
- State exact refund timeline (e.g., '3-5 business days for credit cards, 5-7 for debit')
- Mention that they'll see a pending reversal within 24 hours
- Offer to send detailed confirmation email with transaction IDs
- Provide a reference number for this billing dispute
- Include the direct billing department contact for follow-up""",
"""The response must address potential concerns proactively:
- If near month-end, acknowledge potential impact on their budget
- Offer to provide a letter for their bank if overdraft fees occurred
- Mention our overdraft reimbursement policy (up to $35)
- Assure that this won't affect their credit or future orders
- Note that we're investigating to prevent future occurrences"""
]
}
}
]
results = mlflow.genai.evaluate(
data=customer_service_data,
scorers=[ExpectationsGuidelines()]
)
文档提取应用
下面是用于评估文档提取应用程序的实际指南示例：
提取质量的全球准则
from mlflow.genai.scorers import Guidelines
import mlflow
# Define extraction accuracy standards
extraction_accuracy = Guidelines(
name="extraction_accuracy",
guidelines=[
"""Field extraction completeness and accuracy:
- The response must extract ALL requested fields, using exact values from source
- For ambiguous data, the response must extract the most likely value and include a confidence score
- When multiple values exist for one field (e.g., multiple addresses), extract all and label them
- Preserve original formatting for IDs, reference numbers, and codes (including leading zeros)
- For missing fields, use null with reason: {"field": null, "reason": "not_found"} """,
"""Numerical and financial data handling:
- Currency values must preserve exact decimal places as shown in source
- Must differentiate between currencies if multiple are present (USD, EUR, etc.)
- Percentage values must clarify if they're decimals (0.15) or percentages (15%)
- For calculated fields (totals, tax), must match source exactly - no recalculation
- Negative values must be preserved with proper notation (-$100 or ($100))""",
"""Entity recognition and validation:
- Company names must be extracted exactly as written (including suffixes like Inc., LLC)
- Person names must preserve original order and formatting
- Must not merge similar entities (e.g., "John Smith" and "J. Smith" are kept separate)
- Email addresses and phone numbers must be validated for basic format
- Physical addresses must include all components present in source"""
]
)
format_compliance = Guidelines(
name="output_format",
guidelines="""Output structure must meet these enterprise data standards:
JSON Structure Requirements:
- Must be valid JSON that passes strict parsing
- All field names must use snake_case consistently
- Nested objects must maintain hierarchy from source document
- Arrays must be used for multiple values, never concatenated strings
Data Type Standards:
- Dates: ISO 8601 format (YYYY-MM-DD) with timezone if available
- Timestamps: ISO 8601 with time (YYYY-MM-DDTHH:MM:SSZ)
- Currency: {"amount": 1234.56, "currency": "USD", "formatted": "$1,234.56"}
- Phone: {"number": "+14155551234", "formatted": "(415) 555-1234", "type": "mobile"}
- Boolean: true/false (not "yes"/"no" or 1/0)
Metadata Requirements:
- Include extraction_timestamp in UTC
- Include source_page for multi-page documents
- Include confidence_score (0-1) for each ML-extracted field
- Include validation_flags array for any data quality issues detected"""
)
# Example document extraction data
extraction_tasks = [
{
"inputs": {
"document_text": "Invoice #INV-2024-001\nDate: 2024-01-15\nBill To: Acme Corp\n123 Main St, Suite 100\nAnytown, CA 94000\n\nItems:\n- Widget Pro (SKU: WP-100) - Qty: 10 x $50.00 = $500.00\n- Service Fee - $100.00\n\nSubtotal: $600.00\nTax (8.75%): $52.50\nTotal: $652.50\n\nDue Date: 2024-02-15\nPayment Terms: Net 30",
"fields_to_extract": ["invoice_number", "customer", "total_amount", "due_date", "line_items"]
},
"outputs": {
"invoice_number": "INV-2024-001",
"customer": {
"name": "Acme Corp",
"address": {
"street": "123 Main St, Suite 100",
"city": "Anytown",
"state": "CA",
"zip": "94000"
}
},
"total_amount": {
"amount": 652.50,
"currency": "USD",
"formatted": "$652.50"
},
"due_date": "2024-02-15",
"line_items": [
{
"description": "Widget Pro",
"sku": "WP-100",
"quantity": 10,
"unit_price": 50.00,
"total": 500.00
},
{
"description": "Service Fee",
"quantity": 1,
"unit_price": 100.00,
"total": 100.00
}
],
"extraction_timestamp": "2024-01-20T10:30:00Z",
"source_page": 1,
"confidence_score": 0.95
}
},
{
"inputs": {
"document_text": "Contract between TechStart Inc. and CloudProvider LLC\nEffective Date: January 1, 2024\nContract ID: C-2024-789\n\nThis agreement outlines cloud hosting services...\nMonthly Fee: €5,000\nContract Term: 24 months\nCancellation: 90 days written notice required",
"fields_to_extract": ["contract_id", "parties", "monthly_fee", "term_length"]
},
"outputs": {
"contract_id": "C-2024-789",
"parties": [
{"name": "TechStart Inc.", "role": "customer"},
{"name": "CloudProvider LLC", "role": "provider"}
],
"monthly_fee": {
"amount": 5000.00,
"currency": "EUR",
"formatted": "€5,000"
},
"term_length": {
"duration": 24,
"unit": "months"
},
"cancellation_notice": {
"days": 90,
"type": "written"
},
"extraction_timestamp": "2024-01-20T10:35:00Z",
"confidence_score": 0.92
}
}
]
# Evaluate document extractions
results = mlflow.genai.evaluate(
data=extraction_tasks,
scorers=[extraction_accuracy, format_compliance]
)
每行文档类型指南
from mlflow.genai.scorers import ExpectationsGuidelines
import mlflow
# Dataset with document-type specific guidelines
document_extraction_data = [
{
"inputs": {
"document_type": "invoice",
"document_text": "Invoice #INV-2024-001\nBill To: Acme Corp\nAmount: $1,234.56\nDue Date: 2024-03-15"
},
"outputs": {
"invoice_number": "INV-2024-001",
"customer": "Acme Corp",
"total_amount": 1234.56,
"due_date": "2024-03-15"
},
"expectations": {
"guidelines": [
"""Invoice identification and classification:
- Must extract invoice_number preserving exact format including prefixes/suffixes
- Must identify invoice type (standard, credit memo, proforma) if specified
- Must extract both invoice date and due date, calculating days until due
- Must identify if this is a partial, final, or supplementary invoice
- For recurring invoices, must extract frequency and period covered""",
"""Financial data extraction and validation:
- Line items must be extracted as array with: description, quantity, unit_price, total
- Must identify and separate: subtotal, tax amounts (with rates), shipping, discounts
- Currency must be identified explicitly, not assumed to be USD
- For discounts, must specify if percentage or fixed amount and what it applies to
- Payment terms must be extracted (e.g., "Net 30", "2/10 Net 30")
- Must flag any mathematical inconsistencies between line items and totals""",
"""Vendor and customer information:
- Must extract complete billing and shipping addresses as separate objects
- Company names must include any DBA ("doing business as") variations
- Must extract tax IDs, business registration numbers if present
- Contact information must be categorized (billing contact vs. delivery contact)
- Must preserve any customer account numbers or reference codes"""
]
}
},
{
"inputs": {
"document_type": "contract",
"document_text": "This agreement between Party A and Party B commences on January 1, 2024..."
},
"outputs": {
"parties": ["Party A", "Party B"],
"effective_date": "2024-01-01",
"term_length": "Not specified"
},
"expectations": {
"guidelines": [
"""Party identification and roles:
- Must extract all parties with their full legal names and entity types (Inc., LLC, etc.)
- Must identify party roles (buyer/seller, licensee/licensor, employer/employee)
- Must extract any parent company relationships or guarantors mentioned
- Must capture all representatives, their titles, and authority to sign
- Must identify jurisdiction for each party if specified""",
"""Critical dates and terms extraction:
- Must differentiate between: execution date, effective date, and expiration date
- Must extract notice periods for termination (e.g., "30 days written notice")
- Must identify any automatic renewal clauses and their conditions
- Must extract all milestone dates and deliverable deadlines
- For amendments, must note which version/date of original contract is modified""",
"""Obligations and risk analysis:
- Must extract all payment terms, amounts, and schedules
- Must identify liability caps, indemnification clauses, and insurance requirements
- Must flag any non-standard clauses that deviate from typical contracts
- Must extract all conditions precedent and subsequent
- Must identify dispute resolution mechanism (arbitration, litigation, jurisdiction)
- Must extract any non-compete, non-solicitation, or confidentiality periods"""
]
}
},
{
"inputs": {
"document_type": "medical_record",
"document_text": "Patient: John Doe\nDOB: 1985-06-15\nDiagnosis: Type 2 Diabetes\nMedications: Metformin 500mg"
},
"outputs": {
"patient_name": "John Doe",
"date_of_birth": "1985-06-15",
"diagnoses": ["Type 2 Diabetes"],
"medications": [{"name": "Metformin", "dosage": "500mg"}]
},
"expectations": {
"guidelines": [
"""HIPAA compliance and privacy protection:
- Must never extract full SSN (only last 4 digits if needed for matching)
- Must never include full insurance policy numbers or member IDs
- Must redact or generalize sensitive mental health or substance abuse information
- For minors, must flag records requiring additional consent for sharing
- Must not extract genetic testing results without explicit permission flag""",
"""Clinical data extraction standards:
- Diagnoses must use ICD-10 codes when available, with lay descriptions
- Medications must include: generic name, brand name, dosage, frequency, route, start date
- Must differentiate between active medications and discontinued/past medications
- Allergies must specify type (drug, food, environmental) and reaction severity
- Lab results must include: value, unit, reference range, abnormal flags
- Vital signs must include measurement date/time and measurement conditions""",
"""Data quality and medical accuracy:
- Must flag any potentially dangerous drug interactions if multiple meds listed
- Must identify if vaccination records are up-to-date based on CDC guidelines
- Must extract both chief complaint and final diagnosis separately
- For chronic conditions, must note date of first diagnosis vs. most recent visit
- Must preserve clinical abbreviations but also provide expansions
- Must extract provider name, credentials, and NPI number if available"""
]
}
}
]
results = mlflow.genai.evaluate(
data=document_extraction_data,
scorers=[ExpectationsGuidelines()]
)
后续步骤
- 使用内置的 LLM 评委 - 通过 MLflow 的其他经过研究验证的内置 LLM 评委评估质量
- 创建自定义 LLM 法官 - 根据特定需求构建自定义法官
- 评估概念概述 - 了解法官如何适应评估框架