judges.is_context_sufficient()预定义的法官评估通过 RAG 系统检索到的上下文或通过工具调用生成的上下文是否包含足够的信息，从而根据提供的expected_facts或expected_response基础真相标签充分回答用户的请求。
此评判工具可通过预定义的 RetrievalSufficiency 评分器来评估 RAG 系统，您需要确保检索过程能够提供所有必要的信息。
API 签名
from mlflow.genai.judges import is_context_sufficient
def is_context_sufficient(
*,
request: str, # User's question or query
context: Any, # Context to evaluate for relevance, can be any Python primitive or a JSON-seralizable dict
expected_facts: Optional[list[str]], # List of expected facts (provide either expected_response or expected_facts)
expected_response: Optional[str] = None, # Ground truth response (provide either expected_response or expected_facts)
name: Optional[str] = None # Optional custom name for display in the MLflow UIs
) -> mlflow.entities.Feedback:
"""Returns Feedback with 'yes' or 'no' value and a rationale"""
运行示例的先决条件
安装 MLflow 和所需包
pip install --upgrade "mlflow[databricks]>=3.1.0"请按照 设置环境快速指南 创建 MLflow 试验。
直接使用 SDK
from mlflow.genai.judges import is_context_sufficient
# Example 1: Context contains sufficient information
feedback = is_context_sufficient(
request="What is the capital of France?",
context=[
{"content": "Paris is the capital of France."},
{"content": "Paris is known for its Eiffel Tower."}
],
expected_facts=["Paris is the capital of France."]
)
print(feedback.value) # "yes"
print(feedback.rationale) # Explanation of sufficiency
# Example 2: Context lacks necessary information
feedback = is_context_sufficient(
request="What are MLflow's components?",
context=[
{"content": "MLflow is an open-source platform."}
],
expected_facts=[
"MLflow has four main components",
"Components include Tracking",
"Components include Projects"
]
)
print(feedback.value) # "no"
print(feedback.rationale) # Explanation of what's missing
使用预生成的记分器
is_context_sufficient 法官可通过 RetrievalSufficiency 预设的记分器获得。
要求：
-
跟踪要求：
- MLflow 跟踪必须至少包含一个 span，其中
span_type设置为RETRIEVER -
inputs和outputs必须位于跟踪的根节点上
- MLflow 跟踪必须至少包含一个 span，其中
-
真实标签：必需 - 必须在
expected_facts字典中提供expected_response或expectations之一
import os
import mlflow
from openai import OpenAI
from mlflow.genai.scorers import RetrievalSufficiency
from mlflow.entities import Document
from typing import List
mlflow.openai.autolog()
# Connect to a Databricks LLM via OpenAI using the same credentials as MLflow
# Alternatively, you can use your own OpenAI credentials here
mlflow_creds = mlflow.utils.databricks_utils.get_databricks_host_creds()
client = OpenAI(
api_key=cred.token,
base_url=f"{cred.host}/serving-endpoints"
)
# Define a retriever function with proper span type
@mlflow.trace(span_type="RETRIEVER")
def retrieve_docs(query: str) -> List[Document]:
# Simulated retrieval - some queries return insufficient context
if "capital of france" in query.lower():
return [
Document(
id="doc_1",
page_content="Paris is the capital of France.",
metadata={"source": "geography.txt"}
),
Document(
id="doc_2",
page_content="France is a country in Western Europe.",
metadata={"source": "countries.txt"}
)
]
elif "mlflow components" in query.lower():
# Incomplete retrieval - missing some components
return [
Document(
id="doc_3",
page_content="MLflow has multiple components including Tracking and Projects.",
metadata={"source": "mlflow_intro.txt"}
)
]
else:
return [
Document(
id="doc_4",
page_content="General information about data science.",
metadata={"source": "ds_basics.txt"}
)
]
# Define your RAG app
@mlflow.trace
def rag_app(query: str):
# Retrieve documents
docs = retrieve_docs(query)
context = "\n".join([doc.page_content for doc in docs])
# Generate response
messages = [
{"role": "system", "content": f"Answer based on this context: {context}"},
{"role": "user", "content": query}
]
response = client.chat.completions.create(
# This example uses Databricks hosted Claude. If you provide your own OpenAI credentials, replace with a valid OpenAI model e.g., gpt-4o, etc.
model="databricks-claude-3-7-sonnet",
messages=messages
)
return {"response": response.choices[0].message.content}
# Create evaluation dataset with ground truth
eval_dataset = [
{
"inputs": {"query": "What is the capital of France?"},
"expectations": {
"expected_facts": ["Paris is the capital of France."]
}
},
{
"inputs": {"query": "What are all the MLflow components?"},
"expectations": {
"expected_facts": [
"MLflow has four main components",
"Components include Tracking",
"Components include Projects",
"Components include Models",
"Components include Registry"
]
}
}
]
# Run evaluation with RetrievalSufficiency scorer
eval_results = mlflow.genai.evaluate(
data=eval_dataset,
predict_fn=rag_app,
scorers=[RetrievalSufficiency()]
)
了解结果
评分器会单独评估每个检索模块的范围。 它将：
- 如果检索的文档包含生成预期事实所需的所有信息，则返回“yes”
- 如果检索的文档缺少关键信息，则返回“否”，并说明缺少的内容的理由
这有助于确定检索系统何时无法提取所有必要的信息，这是 RAG 应用程序中不完整或不正确的响应的常见原因。
在自定义评分器中使用
在评估具有与预定义评分程序 要求 不同的数据结构的应用程序时，请将法官包装在自定义记分器中：
from mlflow.genai.judges import is_context_sufficient
from mlflow.genai.scorers import scorer
from typing import Dict, Any
eval_dataset = [
{
"inputs": {"query": "What are the benefits of MLflow?"},
"outputs": {
"retrieved_context": [
{"content": "MLflow simplifies ML lifecycle management."},
{"content": "MLflow provides experiment tracking and model versioning."},
{"content": "MLflow enables easy model deployment."}
]
},
"expectations": {
"expected_facts": [
"MLflow simplifies ML lifecycle management",
"MLflow provides experiment tracking",
"MLflow enables model deployment"
]
}
},
{
"inputs": {"query": "How does MLflow handle model versioning?"},
"outputs": {
"retrieved_context": [
{"content": "MLflow is an open-source platform."}
]
},
"expectations": {
"expected_facts": [
"MLflow Model Registry handles versioning",
"Models can have multiple versions",
"Versions can be promoted through stages"
]
}
}
]
@scorer
def context_sufficiency_scorer(inputs: Dict[Any, Any], outputs: Dict[Any, Any], expectations: Dict[Any, Any]):
return is_context_sufficient(
request=inputs["query"],
context=outputs["retrieved_context"],
expected_facts=expectations["expected_facts"]
)
# Run evaluation
eval_results = mlflow.genai.evaluate(
data=eval_dataset,
scorers=[context_sufficiency_scorer]
)
解释结果
法官返回一个 Feedback 对象，其中包含：
-
value：如果上下文足够，则为“是”，如果不足，则为“否” -
rationale：上下文中涵盖或缺少哪些预期事实的说明