RAG-Evaluation-Dataset-JAを取得

git clone https://huggingface.co/datasets/allganize/RAG-Evaluation-Dataset-JA

ドキュメントをダウンロード

downloads.py

# %%
#!pip install pandas
#!pip install requests
#!mkdir data

# %%
import pandas as pd

df = pd.read_csv("RAG-Evaluation-Dataset-JA/documents.csv")
df

# %%
import os
import requests

for index, row in df.iterrows():
    url = row['url']
    file_name = row['file_name']
    print(url)
    file = f"data/{file_name}"
    if os.path.exists(file):
        continue

    try:
        buffer = requests.get(url=url, timeout=10)
        with open(file=file, mode="wb") as f:
            f.write(buffer.content)
    except Exception as e:
        print(f"{e}")

# %%

ドキュメントをMilvusに登録

milvus.py

# %%
#!pip install langchain_huggingface
#!pip install langchain
#!pip install langchain_milvus
#!pip install langchain_community
#!pip install pypdf

# %%
import glob

file_paths = glob.glob(pathname="data/*.pdf")

# %%
from langchain_community.document_loaders import PyPDFLoader
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_milvus import Milvus

# %%
embedding = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
uri = "RAG-Evaluation-Dataset-JA.db"
connection_args = {"uri": uri}
vector_store = Milvus(embedding_function=embedding, connection_args=connection_args, drop_old=True, auto_id=True)

# %%
for file_path in file_paths:
    print(file_path)
    try:
        loader = PyPDFLoader(file_path=file_path)
        documents = loader.load_and_split()
        vector_store.add_documents(documents=documents)
        print(f"{file_path}:")
    except Exception as e:
        print(f"{file_path}: {e}")
        continue

documents
# %%

Azure OpenAI/GPT-35-TubroでRAGの構築

templates.py

# %%
template = """# 指示：
与えられた文脈にもとづいて質問に回答してください。

# 文脈：
{context}

# 質問：
{question}

# 回答：
"""

rag.py

# %%
#!pip install azure.identity
#!pip install langchain_openai

# %%
from dotenv import load_dotenv

load_dotenv(override=True)

# %%
import os

AZURE_TENANT_ID = os.environ.get("AZURE_TENANT_ID")
AZURE_CLIENT_ID = os.environ.get("AZURE_CLIENT_ID")
AZURE_CLIENT_SECRET = os.environ.get("AZURE_CLIENT_SECRET")
AZURE_OPENAI_ENDPOINT = os.environ.get('AZURE_OPENAI_ENDPOINT')
AZURE_OPENAI_DEPLOYMENT_NAME = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME")
API_VERSION = os.environ.get("API_VERSION")

# %%
from langchain_huggingface import HuggingFaceEmbeddings

embedding = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")

# %%
from langchain_milvus import Milvus

URI = "RAG-Evaluation-Dataset-JA.db"
connection_args = {"uri": URI}
vector_store = Milvus(embedding_function=embedding, connection_args=connection_args, auto_id=True)

# %%
from azure.identity import ClientSecretCredential, get_bearer_token_provider

credential = ClientSecretCredential(
    tenant_id=AZURE_TENANT_ID,
    client_id=AZURE_CLIENT_ID,
    client_secret=AZURE_CLIENT_SECRET
)

# %%
scopes = "https://cognitiveservices.azure.com/.default"
azure_ad_token_provider = get_bearer_token_provider(credential, scopes)

# %%
from langchain_openai import AzureChatOpenAI

temperature = 0
max_tokens = 4096
llm = AzureChatOpenAI(
    azure_endpoint=AZURE_OPENAI_ENDPOINT,
    api_version=API_VERSION,
    azure_deployment=AZURE_OPENAI_DEPLOYMENT_NAME,
    azure_ad_token_provider=azure_ad_token_provider,
    temperature=temperature,
    max_tokens=max_tokens,
)

# %%
from templates import template
from langchain_core.prompts import PromptTemplate

prompt_template = PromptTemplate.from_template(template=template)

回答の生成

rag_generated_text.py

# %%
from rag import vector_store, prompt_template, llm

# %%
import pandas as pd

filepath = "RAG-Evaluation-Dataset-JA/rag_evaluation_result.csv"
df = pd.read_csv(filepath_or_buffer=filepath).iloc[:20]
df

# %%
from unicodedata import normalize

generated_text = []
for index, row in df.iterrows():
    print(index)
    question = row.question
    reference_text = row.target_answer

    context = ""
    try:
        results = vector_store.similarity_search(query=question)
#        print(results)
        for index, result in enumerate(results):
            context += f"# {index+1}:"
            context += "\n"
            context += normalize("NFKD", result.page_content).replace("\n", "")
            context += "\n"
        response = llm.invoke(input=prompt_template.format(context=context, question=question))
        generated_text.append(response.content)
    except Exception as e:
        print(f"{e}")
        generated_text.append("")
        continue

# %%
df['generated_text'] = generated_text
df

# %%
df['context'] = context
df

detached prompt templateの作成

create_detached_prompte_template.py

# %%
#!pip install ibm_aigov_facts_client
#!pip install tabulate

# %%
from dotenv import load_dotenv

load_dotenv(override=True)

# %%
import os

CPD_URL = os.environ.get("CPD_URL")
CPD_USERNAME = os.environ.get("CPD_USERNAME")
CPD_API_KEY = os.environ.get("CPD_API_KEY")
PROJECT_ID = os.environ.get("PROJECT_ID")

# %%
from ibm_aigov_facts_client import AIGovFactsClient
from ibm_aigov_facts_client import CloudPakforDataConfig

creds=CloudPakforDataConfig(
    service_url=CPD_URL,
    username=CPD_USERNAME,
    api_key=CPD_API_KEY
)

facts_client = AIGovFactsClient(
    cloud_pak_for_data_configs=creds,
    container_id=PROJECT_ID,
    container_type="project",
    disable_tracing=True
)

# %%
from ibm_aigov_facts_client import DetachedPromptTemplate, PromptTemplate

AZURE_OPENAI_DEPLOYMENT_NAME = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME")
AZURE_OPENAI_ENDPOINT = os.environ.get("AZURE_OPENAI_ENDPOINT")

detached_information = DetachedPromptTemplate(
    prompt_id="rag-azure-openai-gpt-35-turbo",
    model_id=AZURE_OPENAI_DEPLOYMENT_NAME,
    model_provider="azure-ml-openai",
    model_name=AZURE_OPENAI_DEPLOYMENT_NAME,
    model_url=AZURE_OPENAI_ENDPOINT,
    prompt_url="prompt_url",
    prompt_additional_info={"model_owner": "Microsoft"}
)

# %%
from templates import template_rag

prompt_template = PromptTemplate(
    input=template_rag,
    prompt_variables={"context": "", "question": ""},
    input_prefix="",
    output_prefix="",
)

# %%
response = facts_client.assets.create_detached_prompt(
    name="rag-azure-openai-gpt-35-turbo",
    model_id=AZURE_OPENAI_DEPLOYMENT_NAME,
    task_id="retrieval_augmented_generation",
    detached_information=detached_information,
    description="RAG based on Azure OpenAI GPT-3.5-turbo",
    prompt_details=prompt_template)

# %%
response

# %%

detached prompt templateの設定

execute_detached_prompte_template.py

# %%
#!pip install ibm_watson_openscale
#!pip install "pandas<=2.1.9"

# %%
from dotenv import load_dotenv

load_dotenv(override=True)

# %%
import os

CPD_URL = os.environ.get("CPD_URL")
CPD_USERNAME = os.environ.get("CPD_USERNAME")
CPD_PASSWORD = os.environ.get("CPD_PASSWORD")
CPD_API_KEY = os.environ.get("CPD_API_KEY")

PROJECT_ID = os.environ.get("PROJECT_ID")
PROMPT_TEMPLATE_ASSET_ID = os.environ.get("PROMPT_TEMPLATE_ASSET_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)

# %%
from ibm_watson_openscale.base_classes import ApiRequestFailure

try:
  wos_client.wos.add_instance_mapping(                
    service_instance_id=data_mart_id,
    project_id=PROJECT_ID
  )
except ApiRequestFailure as arf:
    if arf.response.status_code == 409:
      # Instance mapping already exists
      pass
    else:
      raise arf

# %%
wos_client.data_marts.show()

# %%
from ibm_metrics_plugin.metrics.llm.utils.constants import LLMTextMetricGroup, LLMCommonMetrics, LLMRAGMetrics, RetrievalQualityMetric

supporting_monitors = {
    "generative_ai_quality": {
        "parameters": {
            "min_sample_size": 10,
            "metrics_configuration": {
            }
        }
    }
}
import json
print(json.dumps(supporting_monitors, indent=2, ensure_ascii=False))

# %%
language_code = "ja"

response = wos_client.wos.execute_prompt_setup(
    prompt_template_asset_id=PROMPT_TEMPLATE_ASSET_ID,
	label_column="reference_text",
	operational_space_id="development",
	problem_type="retrieval_augmented_generation",
	input_data_type="unstructured_text",
    data_input_locale=[language_code],
    generated_output_locale=[language_code],
	project_id=PROJECT_ID,
    context_fields=["context"],     
    question_field="question",
	supporting_monitors=supporting_monitors,
	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()

# %%

評価

evaluate_risk.py

# %%
from dotenv import load_dotenv

load_dotenv(override=True)

# %%
import os

CPD_URL = os.environ.get("CPD_URL")
CPD_USERNAME = os.environ.get("CPD_USERNAME")
CPD_PASSWORD = os.environ.get("CPD_PASSWORD")
CPD_API_KEY = os.environ.get("CPD_API_KEY")
PROJECT_ID = os.environ.get("PROJECT_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)

# %%
SUBSCRIPTION_ID = os.environ.get("SUBSCRIPTION_ID")
SUBSCRIPTION_ID

# %%
wos_client.monitor_instances.show(target_target_id=SUBSCRIPTION_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

# %%
import pandas as pd

df = pd.read_csv("rag_generated_text.csv")
df

# %%
df.rename(columns={
    "question": "question",
    "target_answer": "reference_text",
    "context": "context",
    "generated_text": "generated_text"
}, inplace=True)
df

# %%
import csv

df[["context", "question", "generated_text", "reference_text"]]

# %%
df[["context", "question", "generated_text", "reference_text"]].to_csv("test.csv", index=False, quoting=csv.QUOTE_ALL)

# %%
test_data_path = "test.csv"
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,
    project_id=PROJECT_ID,
    includes_model_output=True,
    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()

# %%
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()

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

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

# %%
response = wos_client.data_sets.list(
  target_target_id=SUBSCRIPTION_ID,
  target_target_type="subscription",
  type="gen_ai_quality_metrics"
)
response.result.to_dict()

# %%
gaiq_data_set_id = response.result.to_dict()["data_sets"][0]["metadata"]["id"]
gaiq_data_set_id
# %%
wos_client.data_sets.show_records(data_set_id = gaiq_data_set_id)

# %%

Detached prompt templateを本番環境へプロモート

プロモートしたDetached prompte templateを設定

execute_detached_prompte_template_production.py

# %%
from dotenv import load_dotenv

load_dotenv(override=True)

# %%
import os

CPD_URL = os.environ.get("CPD_URL")
CPD_USERNAME = os.environ.get("CPD_USERNAME")
CPD_PASSWORD = os.environ.get("CPD_PASSWORD")
CPD_API_KEY = os.environ.get("CPD_API_KEY")

PROJECT_ID = os.environ.get("PROJECT_ID")

SPACE_ID = os.environ.get("SPACE_ID")
PROMPTE_TEMPLATE_ASSET_ID = os.environ.get("PROMPT_TEMPLATE_ASSET_ID_PRODUCTION")
DEPLOYMENT_ID = os.environ.get("DEPLOYMENT_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)

# %%
from ibm_watson_openscale.base_classes import ApiRequestFailure

try:
  wos_client.wos.add_instance_mapping(                
    service_instance_id=data_mart_id,
    space_id=SPACE_ID     # <- ここが大切！
  )
except ApiRequestFailure as arf:
    if arf.response.status_code == 409:
      # Instance mapping already exists
      pass
    else:
      raise arf

# %%
supporting_monitors = {
    "generative_ai_quality": {
        "parameters": {
            "min_sample_size": 10,
            "metrics_configuration": {
            }
        }
    }
}

# %%
language_code = "ja"

response = wos_client.wos.execute_prompt_setup(
    prompt_template_asset_id=PROMPTE_TEMPLATE_ASSET_ID,
    space_id=SPACE_ID,
    deployment_id=DEPLOYMENT_ID,
	label_column="reference_text",
	operational_space_id="production",
	problem_type="retrieval_augmented_generation",
	input_data_type="unstructured_text",
    data_input_locale=[language_code],
    generated_output_locale=[language_code],
    context_fields=["context"],     
    question_field="question",
	supporting_monitors=supporting_monitors,
	background_mode=True)
response.result.to_dict()

# %%
response = wos_client.monitor_instances.mrm.get_prompt_setup(
    prompt_template_asset_id=PROMPTE_TEMPLATE_ASSET_ID,
    deployment_id=DEPLOYMENT_ID,
    space_id=SPACE_ID)
response.result.to_dict()

# %%

watsonx.governanceへのログ送付機能の実装

pyload.py

# %%
from dotenv import load_dotenv

load_dotenv(override=True)

# %%
import os

CPD_URL = os.environ.get("CPD_URL")
CPD_USERNAME = os.environ.get("CPD_USERNAME")
CPD_PASSWORD = os.environ.get("CPD_PASSWORD")
CPD_API_KEY = os.environ.get("CPD_API_KEY")

PROJECT_ID = os.environ.get("PROJECT_ID")

SPACE_ID = os.environ.get("SPACE_ID")
SUBSCRIPTION_ID = os.environ.get("SUBSCRIPTION_ID_PRODUCTION")

# %%
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
data_mart_id

# %%
wos_client.version

# %%
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'][1]['metadata']['id']
data_set_id

# %%
#import json
#import pandas as pd

#with open(file="hap_low_pii_low.json", mode="r", encoding="utf-8") as f:
#    data = json.load(f)
#dataset = pd.json_normalize(data=data)
#dataset

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

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

# %%
#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)

# %%

RAGの構築

app.py

# %%
#!pip install gradio

# %%
import gradio as gr
from rag import vector_store, prompt_template, llm
from unicodedata import normalize
from payload import wos_client, data_set_id

# %%
import pandas as pd

df = pd.read_csv("RAG-Evaluation-Dataset-JA/rag_evaluation_result.csv")
df

# %%
import os

existed_files = []
files = df['target_file_name'].unique()
for file in files:
    path = f"data/{file}"
    if os.path.exists(path=path):
        existed_files.append(file)

# %%
df = df[df["target_file_name"].isin(existed_files)].copy()

# %%
def rag(question):
    context = ""
    try:
        results = vector_store.similarity_search(query=question)
        for index, result in enumerate(results):
            source = result.metadata['source']
            page = result.metadata['page'] + 1
            context += f"# {index+1}/{source}/{page}:"
            context += "\n"
            context += normalize("NFKD", result.page_content).replace("\n", "")
            context += "\n"
        response = llm.invoke(input=prompt_template.format(context=context, question=question))
        generated_text = response.content

        request_body = [{
            "request": {
                "parameters": {
                    "template_variables": {
                        "context": context,
                        "question": question
                    }
                }
            },
            "response": {
                "results": [
                    {
                        "generated_text": generated_text
                    }
                ]
            }
        }]

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

    except Exception as e:
        generated_text = ""
        context = ""
        return generated_text, context

    return generated_text, context

# %%
def get_reference_text(selected_question):
    reference_text = df[df["question"] == selected_question]["target_answer"].iloc[0]
    target_file_name = df[df["question"] == selected_question]["target_file_name"].iloc[0]
    target_page_no = df[df["question"] == selected_question]["target_page_no"].iloc[0]
    return reference_text, target_file_name, target_page_no

# %%
with gr.Blocks() as app:
    question = gr.Dropdown(choices=df['question'].tolist(), label="question")
    reference_text = gr.Textbox(label="reference_text", value=df.iloc[0]["target_answer"])
    target_file_name = gr.Textbox(label="target_file_name", value=df.iloc[0]["target_file_name"])
    target_page_no = gr.Textbox(label="target_page_no", value=df.iloc[0]["target_page_no"])
    button = gr.Button()
    generated_text = gr.Textbox(label="generated_text")
    context = gr.Textbox(label="context")
    question.change(fn=get_reference_text, inputs=[question], outputs=[reference_text, target_file_name, target_page_no])
    button.click(fn=rag, inputs=[question], outputs=[generated_text, context])
    question.change(fn=rag, inputs=[question], outputs=[generated_text, context])

# %%
app.launch(server_name="0.0.0.0")
#app.launch(server_name="0.0.0.0", server_port=8080)

Webアプリケーションの実行

python app.py

評価@watsonx

監視@watsonx