一起优化多个提示

在复杂的代理系统中，你可能已将多个提示链接在一起。 你可以一起提供所有这些提示，让 GEPA 考虑和优化每个提示。

安装依赖项

%pip install --upgrade mlflow databricks-sdk dspy openai
dbutils.library.restartPython()

使用基本提示来建立两个基本函数

import mlflow
from mlflow.genai.scorers import Correctness
from databricks.sdk import WorkspaceClient
from mlflow.genai.optimize import GepaPromptOptimizer

w = WorkspaceClient()
openai_client = w.serving_endpoints.get_open_ai_client()

catalog = ""
schema = ""

plan_prompt_name = f"{catalog}.{schema}.plan"
answer_prompt_name = f"{catalog}.{schema}.answer"

# Register multiple prompts
plan_prompt = mlflow.genai.register_prompt(
    name=plan_prompt_name,
    template="Make a plan to classify {{query}}.",
)
answer_prompt = mlflow.genai.register_prompt(
    name=answer_prompt_name,
    template="classify {{query}} following the plan: {{plan}}",
)

def predict_fn(query: str) -> str:
    plan_prompt = mlflow.genai.load_prompt(f"prompts:/{plan_prompt_name}/1")
    completion = openai_client.chat.completions.create(
        model="databricks-gpt-5",  # strong model
        messages=[{"role": "user", "content": plan_prompt.format(query=query)}],
    )
    plan = completion.choices[0].message.content

    answer_prompt = mlflow.genai.load_prompt(f"prompts:/{answer_prompt_name}/1")
    completion = openai_client.chat.completions.create(
        model="databricks-gpt-5-nano",  # cost efficient model
        messages=[
            {
                "role": "user",
                "content": answer_prompt.format(query=query, plan=plan),
            }
        ],
    )
    return completion.choices[0].message.content

按原样测试模型

from IPython.display import Markdown

output = predict_fn("The emergence of HIV as a chronic condition means that people living with HIV are required to take more responsibility for the self-management of their condition , including making physical , emotional and social adjustments .")

Markdown(output)

dataset = [
    {
        "inputs": {"query": "The emergence of HIV as a chronic condition means that people living with HIV are required to take more responsibility for the self-management of their condition , including making physical , emotional and social adjustments ."},
        "outputs": {"response": "BACKGROUND"},
        "expectations": {"expected_facts": ["Classification label must be 'CONCLUSIONS', 'RESULTS', 'METHODS', 'OBJECTIVE', 'BACKGROUND'"]}
    },
    {
        "inputs": {"query": "This paper describes the design and evaluation of Positive Outlook , an online program aiming to enhance the self-management skills of men living with HIV ."},
        "outputs": {"response": "BACKGROUND"},
        "expectations": {"expected_facts": ["Classification label must be 'CONCLUSIONS', 'RESULTS', 'METHODS', 'OBJECTIVE', 'BACKGROUND'"]}
    },
    {
        "inputs": {"query": "This study is designed as a randomised controlled trial in which men living with HIV in Australia will be assigned to either an intervention group or usual care control group ."},
        "outputs": {"response": "METHODS"},
        "expectations": {"expected_facts": ["Classification label must be 'CONCLUSIONS', 'RESULTS', 'METHODS', 'OBJECTIVE', 'BACKGROUND'"]}
    },
    {
        "inputs": {"query": "The intervention group will participate in the online group program ` Positive Outlook ' ."},
        "outputs": {"response": "METHODS"},
        "expectations": {"expected_facts": ["Classification label must be 'CONCLUSIONS', 'RESULTS', 'METHODS', 'OBJECTIVE', 'BACKGROUND'"]}
    },
    {
        "inputs": {"query": "The program is based on self-efficacy theory and uses a self-management approach to enhance skills , confidence and abilities to manage the psychosocial issues associated with HIV in daily life ."},
        "outputs": {"response": "METHODS"},
        "expectations": {"expected_facts": ["Classification label must be 'CONCLUSIONS', 'RESULTS', 'METHODS', 'OBJECTIVE', 'BACKGROUND'"]}
    },
    {
        "inputs": {"query": "Participants will access the program for a minimum of 90 minutes per week over seven weeks ."},
        "outputs": {"response": "METHODS"},
        "expectations": {"expected_facts": ["Classification label must be 'CONCLUSIONS', 'RESULTS', 'METHODS', 'OBJECTIVE', 'BACKGROUND'"]}
    }
]

# Optimize both
result = mlflow.genai.optimize_prompts(
    predict_fn=predict_fn,
    train_data=dataset,
    prompt_uris=[plan_prompt.uri, answer_prompt.uri],
    optimizer=GepaPromptOptimizer(reflection_model="databricks:/databricks-gemini-2-5-pro"),
    scorers=[Correctness(model="databricks:/databricks-claude-sonnet-4-5")],
)

# Access optimized prompts
optimized_plan = result.optimized_prompts[0]
optimized_answer = result.optimized_prompts[1]

加载新提示并再次测试

查看提示的外观，并将其加载到预测函数中，以查看模型的性能如何不同。

plan_prompt = mlflow.genai.load_prompt(f"prompts:/{plan_prompt_name}/4")
Markdown(plan_prompt.template)

answer_prompt = mlflow.genai.load_prompt(f"prompts:/{answer_prompt_name}/4")
Markdown(answer_prompt.template)

from IPython.display import Markdown

def predict_fn(query: str) -> str:
    plan_prompt = mlflow.genai.load_prompt(f"prompts:/{plan_prompt_name}/4")
    completion = openai_client.chat.completions.create(
        model="databricks-gpt-5",  # strong model
        messages=[{"role": "user", "content": plan_prompt.format(query=query)}],
    )
    plan = completion.choices[0].message.content

    answer_prompt = mlflow.genai.load_prompt(f"prompts:/{answer_prompt_name}/4")
    completion = openai_client.chat.completions.create(
        model="databricks-gpt-5-nano",  # cost efficient model
        messages=[
            {
                "role": "user",
                "content": answer_prompt.format(query=query, plan=plan),
            }
        ],
    )
    return completion.choices[0].message.content

output = predict_fn("Participants will access the program for a minimum of 90 minutes per week over seven weeks")

Markdown(output)

使用 GEPA 和 GPT-OSS 20B 提示优化

获取笔记本