準備

# %%
!pip install python-dotenv
!pip install ibm_watsonx_ai
!pip install ibm_watson_openscale setuptools

import os
from dotenv import load_dotenv

load_dotenv(override=True)

# %%
WML_CREDENTIALS = {
	"url": os.environ.get("URL"),
	"apikey": os.environ.get("CLOUD_API_KEY")
}
WML_CREDENTIALS

# %%
project_id = os.environ.get("PROJECT_ID")
project_id

Prompt templateの作成 ← GUIでも可能

from ibm_watsonx_ai.foundation_models.prompts import PromptTemplateManager
from ibm_watsonx_ai.foundation_models.prompts import PromptTemplate
from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes

prompt_mgr = PromptTemplateManager(
	credentials=WML_CREDENTIALS,
	project_id=project_id
)

prompt_template = PromptTemplate(
    name="MLS_Car_Insurance_Summarization_Ja_Demo",
    model_id=ModelTypes.GRANITE_13B_INSTRUCT_V2,
    task_ids=["summarization"],
    instruction="以下の保険請求を最大 3 文で要約してください",
    input_text="{input_text}",
    input_variables=['input_text'],
    examples = [
        [
            "２０２３年１１月１日午前１１時、私の車両「日産　マキシマ　１９９８年式」は、ニューヨークで重大事故に遭いました。私の運転手は制限速度を守って走行していましたが、前の車が急停止したために前方から衝突しました。激しい衝撃により双方の車が大破し、私の運転手はすぐに救急に連絡をして、その場で応急処置を受けました。私の車両は前のバンパー、ボンネット、フロントガラスに損傷を受けました。私の運転手は重傷を負い、現在治療を受けています。私はあなたの代理人にただちに連絡し、事故の詳細と警察の報告書、医療診察書を添えて請求書を提出しました。また目撃者の供述書と破損した車両の写真も提出します。",
            "私の車両はニューヨークで重大事故に遭いました。私の運転手は重傷を負い、現在治療を受けています。"
        ]
    ]
)

stored_prompt_template = prompt_mgr.store_prompt(prompt_template=prompt_template)
stored_prompt_template

prompt_template_asset_id = stored_prompt_template.prompt_id
prompt_template_asset_id

Prompt templateの設定 ← 日本語でやるなら現状PythonSDK

# %%
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from ibm_watson_openscale import *
from ibm_watson_openscale.supporting_classes.enums import *
from ibm_watson_openscale.supporting_classes import *

service_instance_id = os.environ.get("SERVICE_INSTANCE_ID")
authenticator = IAMAuthenticator(apikey=os.environ.get("CLOUD_API_KEY"))
wos_client = APIClient(authenticator=authenticator, service_instance_id=service_instance_id)
data_mart_id = wos_client.service_instance_id
print(data_mart_id)
print(wos_client.version)

wos_client.data_marts.show()

language_code = "ja"

supporting_monitors = {
    "generative_ai_quality": {
        "parameters": {
            "min_sample_size": 10,
            "metrics_configuration": {
#                "rouge_score": {},
#                "normalized_precision": {},
#                "normalized_f1": {},
#                "normalized_recall": {},
#                "cosine_similarity": {},
#                "jaccard_similarity": {},
#                "sari": {},
#                "meteor": {},
#                "hap_score": {},
#                "pii": {
#                    "language_code" : language_code
#                }
            }
        }
    }
}

label_column = "reference_summary"
operational_space_id = "development"
problem_type= "summarization"
input_data_type= "unstructured_text"

response = wos_client.wos.execute_prompt_setup(
    prompt_template_asset_id=prompt_template_asset_id,
	project_id=project_id,
	label_column=label_column,
	operational_space_id=operational_space_id,
	problem_type=problem_type,
	input_data_type=input_data_type,
	supporting_monitors=supporting_monitors,
    data_input_locale=[language_code],
    generated_output_locale=[language_code],
	background_mode=True)
response.result.to_dict()

response = wos_client.wos.get_prompt_setup(
    prompt_template_asset_id=prompt_template_asset_id,
    project_id=project_id)
response.result.to_dict()

subscription_id = response.result.to_dict()['subscription_id']
subscription_id

wos_client.monitor_instances.show(target_target_id=subscription_id)

検証

filename = "llm_content_summarization_ja.csv"
!rm -fr "llm_content_summarization_ja.csv"
!wget "https://raw.githubusercontent.com/IBM/watson-openscale-samples/refs/heads/main/IBM%20Cloud/WML/assets/data/watsonx/Multi_Lingual_Support/llm_content_summarization_ja.csv"


# %%
import pandas as pd

df = pd.read_csv(filename)
df

# %%
df = df.rename(columns={'generated_predictions': 'generated_text'})
df

# %%
new_summarization_filename = "new_summarization_data_ja.csv"
df.to_csv(new_summarization_filename, index=False)
test_data_path = new_summarization_filename

# %%
import pandas as pd

df = pd.read_csv(test_data_path)
df.head()

# %% Read the MRM monitor instance id
monitor_definition_id = "mrm"
target_target_id = subscription_id
response = wos_client.monitor_instances.list(
    data_mart_id=data_mart_id,
    monitor_definition_id=monitor_definition_id,
    target_target_id=target_target_id,
    project_id=project_id)
response.result.to_dict()

# %%
mrm_monitor_instance_id = response.result.to_dict()["monitor_instances"][0]["metadata"]["id"]
mrm_monitor_instance_id

# %%
test_data_set_name = "data"
content_type = "multipart/form-data"

response = wos_client.monitor_instances.mrm.evaluate_risk(
    monitor_instance_id=mrm_monitor_instance_id, 
    test_data_set_name=test_data_set_name, 
    test_data_path=test_data_path,
    content_type=content_type,
    body=None,
    includes_model_output=True,
    project_id=project_id,
    background_mode=True)
response.result.to_dict()

# %%
response = wos_client.monitor_instances.mrm.get_risk_evaluation(
    monitor_instance_id=mrm_monitor_instance_id, project_id=project_id)
response.result.to_dict()

生成AI性能指標の可視化

# %% 生成AI性能指標の可視化
monitor_definition_id = "generative_ai_quality"
response = wos_client.monitor_instances.list(
    data_mart_id=data_mart_id,
    monitor_definition_id=monitor_definition_id,
    target_target_id=target_target_id,
    project_id=project_id)
response.result.to_dict()

# %%
gaiquality_monitor_instance_id = response.result.to_dict()["monitor_instances"][0]["metadata"]["id"]
gaiquality_monitor_instance_id

# %%
wos_client.monitor_instances.show_metrics(monitor_instance_id=gaiquality_monitor_instance_id, project_id=project_id)

Display record level metrics for Generative AI Quality

# %% Display record level metrics for Generative AI Quality
response = wos_client.data_sets.list(
    target_target_id=subscription_id,
    target_target_type="subscription",
    type="gen_ai_quality_metrics")
response.result.to_dict()

# %%
gaiquality_dataset_id = response.result.to_dict()['data_sets'][0]['metadata']['id']
gaiquality_dataset_id

# %%
wos_client.data_sets.show_records(data_set_id=gaiquality_dataset_id)