Mehrere Eingabeaufforderungen gleichzeitig optimieren

In komplexen Agentsystemen haben Sie möglicherweise mehrere Eingabeaufforderungen miteinander verkettet. Sie können alle diese Aufforderungen zusammen bereitstellen, damit GEPA jede Aufforderung in Betracht ziehen und optimieren kann.

Installieren von Abhängigkeiten

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

Einrichten von zwei grundlegenden Funktionen mit grundlegenden Eingabeaufforderungen

import mlflow
from mlflow.genai.scorers import Correctness
from databricks_openai import DatabricksOpenAI
from mlflow.genai.optimize import GepaPromptOptimizer

openai_client = DatabricksOpenAI()

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

Testen Sie die Modelle wie folgt

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 gay 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]

Laden Sie die neue Eingabeaufforderung, und testen Sie es erneut.

Sehen Sie sich an, wie die Eingabeaufforderung aussieht, und laden Sie sie in Ihre vorhergesagte Funktion, um zu sehen, wie unterschiedlich das Modell ausgeführt wird.

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)

Prompt-Optimierung mit GEPA und GPT-OSS 20B

Notebook abrufen