PROMPTE TEMPLATEをproduction(実働)向けのSPACEにPROMOTE＆DEPLOY

Evaluate(検証)を有効化

SPACE_IDとSUBSCRIPTION_IDの取得

Payloadデータの送信の実装＆手動評価

PayloadデータはRAGへの入出力（context、Question、generated_text）のうち正解（reference）を含まないデータのこと。

payload.py

# %%
from dotenv import load_dotenv
import os

load_dotenv()

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

url = os.environ.get("CPD_URL")
username = os.environ.get("CPD_USERNAME")
password = os.environ.get("CPD_PASSWORD")

authenticator = CloudPakForDataAuthenticator(
    url=url,
    username=username,
    password=password,
    disable_ssl_verification=True
)

wos_client = APIClient(
    service_url=url,
    authenticator=authenticator,
)
data_mart_id = wos_client.service_instance_id
print(data_mart_id)
print(wos_client.version)

# %%
subscription_id = os.environ.get("SUBSCRIPTION_ID")
wos_client.monitor_instances.show(target_target_id=subscription_id)

# %%
monitor_definition_id = "mrm"
space_id = os.environ.get("SPACE_ID")
response = wos_client.monitor_instances.list(
   data_mart_id=data_mart_id,
   monitor_definition_id=monitor_definition_id,
   target_target_id=subscription_id,
   space_id=space_id)
response.result.to_dict()
# %%
monitor_instance_id = response.result.to_dict()["monitor_instances"][0]["metadata"]["id"]
print(monitor_instance_id)

# %%
import pandas as pd

filepath = "test.csv"
df = pd.read_csv(filepath_or_buffer=filepath)
df

# %%
request_body = []
for index, row in df.iterrows():
    print(index)
    tmp = {
        "request": {
            "parameters": {
                "template_variables": {
                    "context": row.context,
                    "question": row.question
                }
            }
        },
        "response": {
            "results": [
                {
                    "generated_text": row.generated_text
                }
            ]
        }
    }
    request_body.append(tmp)

# %%
import json

print(json.dumps(request_body, indent=2, ensure_ascii=False))

# %%
from ibm_watson_openscale.supporting_classes.enums import *

data_set_id = None
response = wos_client.data_sets.list(
    type=DataSetTypes.PAYLOAD_LOGGING,
    target_target_id=subscription_id,
    target_target_type=TargetTypes.SUBSCRIPTION)
response.result.to_dict()

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

# %%
response = wos_client.data_sets.store_records(
   data_set_id=data_set_id,
   request_body=request_body,
   background_mode=True)
response.result.to_dict()

# %%
wos_client.data_sets.get_records_count(data_set_id=data_set_id)

# %%

Feedbackデータの送信の実装＆手動評価

PayloadデータはRAGへの入出力（context、Question、generated_text）と正解（reference）を含むデータのこと。

wxg_feedback.py

# %%
CPD_URL = "<INPUT YOUR CPD_URL>"
CPD_USERNAME = "<INPUT YOUR CPD_USERNAME>"
CPD_PASSWORD = "<INPUT YOUR CPD_PASSWORD>"
CPD_API_KEY = "<INPUT YOUR CPD_API_KEY>"

# %%
SPACE_ID = "<INPUT YOUR SPACE_ID>"
SUBSCRIPTION_ID = "<INPUT YOUR SUBSCRIPTION_ID>"

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

authenticator = CloudPakForDataAuthenticator(
    url=CPD_URL,
    username=CPD_USERNAME,
    password=CPD_PASSWORD,
    disable_ssl_verification=True
)

wos_client = APIClient(
    service_url=CPD_URL,
    authenticator=authenticator,
)
data_mart_id = wos_client.service_instance_id
print(data_mart_id)
print(wos_client.version)

# %%
wos_client.monitor_instances.show(target_target_id=SUBSCRIPTION_ID)

# %%
monitor_definition_id = "mrm"
response = wos_client.monitor_instances.list(
   data_mart_id=data_mart_id,
   monitor_definition_id=monitor_definition_id,
   target_target_id=SUBSCRIPTION_ID,
   space_id=SPACE_ID)
response.result.to_dict()
# %%
monitor_instance_id = response.result.to_dict()["monitor_instances"][0]["metadata"]["id"]
print(monitor_instance_id)

# %%
from ibm_watson_openscale.supporting_classes.enums import *

data_set_id = None
response = wos_client.data_sets.list(
    type=DataSetTypes.FEEDBACK, 
    target_target_id=SUBSCRIPTION_ID, 
    target_target_type=TargetTypes.SUBSCRIPTION)
response.result.to_dict()

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

# %%
import pandas as pd

test_data_path = "test_generated_text.csv"
llm_data = pd.read_csv(test_data_path)
llm_data.head()

# %%
llm_data.shape

# %%
test_data_content = []
context_fields = ["context"]
question_field = "question"
feature_fields = context_fields + [question_field]   #For Alternative Dataset
prediction_list = llm_data["generated_text"].tolist()
label_column = "reference"

#for _, row in llm_data.iloc.iterrows(): 100件だとなぜかうまく行かないので…
for _, row in llm_data.iloc[:10].iterrows():
    # Read each row from the DataFrame and add label and prediction values
    result_row = [row[key] for key in feature_fields if key in row]
    result_row.append(row[label_column])
    result_row.append(row["generated_text"])
    test_data_content.append(result_row)

if len(test_data_content) == 10: # 10 records are there in the downloaded CSV
    print("generated feedback data from DataFrame")
else:
    print("Failed to generate feedback data from DataFrame, kindly verify the DataFrame content")

fields = feature_fields.copy()
fields.append(label_column)
fields.append("_original_prediction")
feedback_data = [
    {
        "fields": fields,
        "values": test_data_content
    }
]

feedback_data
# %%
import json

print(json.dumps(feedback_data, indent=2, ensure_ascii=False))

# %%
response = wos_client.data_sets.store_records(
    data_set_id=data_set_id,
    request_body=feedback_data,
    background_mode=True)
response.result.to_dict()

# %%
wos_client.data_sets.get_records_count(data_set_id=data_set_id)

# %%

RAGのWebアプリケーションに実装

app.py

# %%
#!pip install gradio

# %%
import gradio as gr
import os
from rag import vector_store, PROMPT_TEMPLATE, client, AZURE_OPENAI_DEPLOYMENT_NAME
from wxg_payload import wos_client, data_set_id

# %%

# %%
def test(question):
    results = vector_store.similarity_search(query=question)

    contexts = ""
    for result in results:
#        print(result)
        contexts += result.page_content
        contexts += "\n"
        contexts += "--------------------------------------------------------------------------------"
        contexts += "\n"
    content = PROMPT_TEMPLATE.format(context=contexts, question=question)

    max_tokens = 4096
    response = client.chat.completions.create(
        model=os.environ.get('AZURE_OPENAI_DEPLOYMENT_NAME', AZURE_OPENAI_DEPLOYMENT_NAME),
        messages=[{"role": "user", "content": content}],
        max_tokens=max_tokens
    )
    answer = response.choices[0].message.content

################################################################################
    payload_data = [{
        "request": {
            "parameters": {
                "template_variables": {
                    "context": contexts,
                    "question": question
                }
            }
        },
        "response": {
            "results": [
                {
                    "generated_text": answer
                }
            ]
        }
    }]
    import json
    print(json.dumps(payload_data, indent=2, ensure_ascii=False))

    response = wos_client.data_sets.store_records(
       data_set_id=data_set_id,
       request_body=payload_data,
       background_mode=True)
    print(response.result.to_dict())


################################################################################

    return answer, contexts

# %%
with gr.Blocks() as app:
    question = gr.Textbox(label="question")
    answer = gr.Textbox(label="answer")
    contexts = gr.Textbox(label="contexts")
    button = gr.Button()
    button.click(fn=test, inputs=[question], outputs=[answer, contexts])
# %%
app.launch(server_name="0.0.0.0")

# %%

Qustionをするたびに、入出力データをpayloadデータとして送信する。

↑では3回の質問をしたので100+3で103回のPayloadで蓄積されている

すう追加のQAをした後の評価結果。