In the realm of artificial intelligence, ensuring machine learning models access accurate, real-time data is paramount for reliable outcomes. This article elucidates the integration of IPFLY’s comprehensive proxy services into Google Vertex AI Pipelines to construct a robust data collection pipeline. By utilizing IPFLY’s extensive IP resource library—encompassing static residential proxies, dynamic residential proxies, and data center proxies—organizations can securely retrieve web data, grounding large language models (LLMs) against outdated or fabricated information. This tutorial, designed for a 25-minute engagement, explores Vertex AI Pipelines, the imperative for external data integration via Retrieval-Augmented Generation (RAG), and the advantages of IPFLY’s proxies over conventional methods, offering superior anonymity, global coverage across over 190 countries, and unlimited concurrency for enterprise-scale operations.
Key objectives include:
Comprehending the functionality of Vertex AI Pipelines.
Mastering the incorporation of IPFLY proxies for data retrieval.
Constructing a data collection pipeline tailored for fact-checking or market analysis.
To initiate, establish an IPFLY account to harness these advanced proxy capabilities, ensuring high success rates and compliance in cross-border data acquisition.
What Are Vertex AI Pipelines?
Vertex AI Pipelines constitutes a managed service within Google Cloud, facilitating the automation, orchestration, and replication of comprehensive machine learning workflows. It decomposes intricate processes into modular components that are traceable and version-controlled, operating in a serverless framework to streamline machine learning operations (MLOps). This architecture supports efficient scaling, particularly when integrated with external data sources like IPFLY’s proxy network, which provides over 90 million IPs for seamless, high-speed data flows in scenarios such as SEO optimization, market research, and ad verification.
Building a Data Collection Pipeline: Why and How
Large language models possess static knowledge bases, rendering them susceptible to inaccuracies without access to current web information. Retrieval-Augmented Generation (RAG) mitigates this by incorporating up-to-date external data prior to response formulation, enhancing precision in applications like fact-checking.
While certain built-in tools exist for data grounding, they often lack scalability, customization, and robust control over sources. IPFLY’s proxies present a superior solution, enabling programmatic web access with high anonymity and stability. Featuring rigorous IP selection from real end-user devices, IPFLY ensures non-reusable, secure connections that align with business needs, such as cross-border e-commerce or social media marketing.
The data collection pipeline outlined here comprises three phases:
1.Query extraction: An LLM identifies key claims and formulates searchable queries.
2.Web data retrieval: IPFLY proxies facilitate secure fetching of real-time content.
3.Data validation: An LLM processes the retrieved data to generate validated outputs.
This methodology extends to diverse applications, including trend analysis, content summarization, and automated testing, bolstered by IPFLY’s 99.9% uptime and millisecond-level responses.
How to Integrate IPFLY Proxies into a Vertex AI Pipeline
Prerequisites
An active Google Cloud Console account.
An IPFLY account with configured proxy credentials (recommend administrative access for optimal setup).
Step #1: Create and Configure a New Google Cloud Project
Establish a project entitled “IPFLY Data Collection Pipeline” with an identifier such as ipfly-pipeline. Record the project number and ID. Activate essential APIs, including:
Vertex AI API.
Notebooks API. Optionally enable supplementary APIs for enhanced functionality, such as Cloud Resource Manager or Cloud Storage.
Step #2: Set Up the Cloud Storage Bucket
Generate a uniquely named bucket, for instance, ipfly-pipeline-artifacts, opting for a multi-region configuration like “us” to ensure accessibility. Assign the Storage Admin role to the project’s Compute Engine service account ([project-number]@developer.gserviceaccount.com) to avert authorization issues during execution. The bucket URI might resemble gs://ipfly-pipeline-artifacts.
Step #3: Configure IAM Permissions
Proceed to the IAM & Admin section. Confer the following roles upon the Compute Engine default service account:
Service Account User.
Vertex AI User. This configuration authorizes the creation and operation of pipelines.
Step #4: Set Up Vertex AI Workbench
Navigate to Vertex AI Workbench in the Google Cloud Console. Instantiate a new environment with standard specifications (e.g., n1-standard-4 machine type, JupyterLab 3). Launch JupyterLab and initiate a Python 3 notebook. Development occurs entirely in the cloud, eliminating local requirements.
Step #5: Install and Initialize Required Python Libraries
Execute the installation of necessary packages:
!pip install kfp google-cloud-aiplatform google-generativeai requests --quiet --upgradeInitialize the Vertex AI SDK:
import kfpfrom kfp.dsl import component, pipeline, Input, Output, Artifactfrom kfp import compilerfrom google.cloud import aiplatformfrom typing import ListPROJECT_ID = "<YOUR_GC_PROJECT_ID>"REGION = "<YOUR_REGION>" # e.g., "us-central1"BUCKET_URI = "<YOUR_BUCKET_URI>" # e.g., "gs://ipfly-pipeline-artifacts"aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)Step #6: Define the Query Extraction Component
Employ Gemini to derive searchable queries from input text:
@component( base_image="python:3.10", packages_to_install=["google-generativeai"],)def extract_queries( input_text: str, project: str, location: str,) -> List[str]: import google.generativeai as genai import json genai.configure(api_key="<YOUR_GEMINI_API_KEY>") # Use secure key management in production model = genai.GenerativeModel('gemini-1.5-flash') # Updated model reference prompt = f""" As a data analyst, review the text and extract a list of precise search queries for verifying key claims. Output only a Python list of strings. Example: Input: "The Great Wall of China is visible from space and was built in the 7th century BC." Output: ["is the great wall of china visible from space", "when was the great wall of china built"] Text: "{input_text}" """ response = model.generate_content(prompt) query_list: List[str] = json.loads(response.text.strip()) return query_listThis leverages a fast model for efficiency.
Step #7: Create the IPFLY Proxy–Powered Web Data Retrieval Component
Utilize IPFLY proxies to fetch content:
@component( base_image="python:3.10", packages_to_install=["requests"],)def fetch_web_data( queries: List[str], ipfly_proxy_host: str, ipfly_proxy_port: str, ipfly_username: str, ipfly_password: str, output_file: Output[Artifact],): import requests import json proxies = { 'http': f'http://{ipfly_username}:{ipfly_password}@{ipfly_proxy_host}:{ipfly_proxy_port}', 'https': f'http://{ipfly_username}:{ipfly_password}@{ipfly_proxy_host}:{ipfly_proxy_port}' } results = [] for query in queries: url = f"https://www.google.com/search?q={query.replace(' ', '+')}" try: response = requests.get(url, proxies=proxies, timeout=10) results.append({"query": query, "content": response.text}) # Parse as needed for production except Exception as e: results.append({"query": query, "error": str(e)}) with open(output_file.path, "w") as f: json.dump(results, f)This component ensures anonymous, stable retrieval using IPFLY’s dynamic residential proxies for rotation and bypass of restrictions.
Step #8: Implement the Data Validation Component
Process the data for validation:
@component( base_image="python:3.10", packages_to_install=["google-generativeai"],)def validate_with_web_data( input_text: str, web_data_file: Input[Artifact], project: str, location: str,) -> str: import google.generativeai as genai import json with open(web_data_file.path, "r") as f: web_data = json.load(f) genai.configure(api_key="<YOUR_GEMINI_API_KEY>") model = genai.GenerativeModel('gemini-1.5-pro') prompt = f""" Validate the original text using the provided web data in JSON format. Produce a Markdown report highlighting accuracies and discrepancies. [Original Text] "{input_text}" [Web Data] "{json.dumps(web_data)}" """ response = model.generate_content(prompt) return response.textA more advanced model is selected for intricate analysis.
Step #9: Define and Compile the Pipeline
Interconnect components:
@pipeline( name="ipfly-data-collection-pipeline", description="Retrieves web data via IPFLY proxies for validation.")def data_collection_pipeline( input_text: str, ipfly_proxy_host: str, ipfly_proxy_port: str, ipfly_username: str, ipfly_password: str, project: str = PROJECT_ID, location: str = REGION,): step1 = extract_queries(input_text=input_text, project=project, location=location) step2 = fetch_web_data( queries=step1.output, ipfly_proxy_host=ipfly_proxy_host, ipfly_proxy_port=ipfly_proxy_port, ipfly_username=ipfly_username, ipfly_password=ipfly_password ) step3 = validate_with_web_data( input_text=input_text, web_data_file=step2.outputs["output_file"], project=project, location=location )compiler.Compiler().compile( pipeline_func=data_collection_pipeline, package_path="data_collection_pipeline.json")Step #10: Run the Pipeline
Sample input:
“Tokyo is the capital of Japan, which uses the euro as its currency.”
TEXT_TO_VALIDATE = """Tokyo is the capital of Japan, which uses the euro as its currency."""IPFLY_PROXY_HOST = "<YOUR_IPFLY_HOST>"IPFLY_PROXY_PORT = "<YOUR_IPFLY_PORT>"IPFLY_USERNAME = "<YOUR_IPFLY_USERNAME>"IPFLY_PASSWORD = "<YOUR_IPFLY_PASSWORD>"job = aiplatform.PipelineJob( display_name="data-collection-pipeline-run", template_path="data_collection_pipeline.json", pipeline_root=BUCKET_URI, parameter_values={ "input_text": TEXT_TO_VALIDATE, "ipfly_proxy_host": IPFLY_PROXY_HOST, "ipfly_proxy_port": IPFLY_PROXY_PORT, "ipfly_username": IPFLY_USERNAME, "ipfly_password": IPFLY_PASSWORD })job.run()For production, employ secure secret management rather than direct credential inclusion.
Step #11: Monitor the Pipeline Execution
Observe progress through:
https://console.cloud.google.com/vertex-ai/pipelines?project={PROJECT_ID}Inspect component statuses, logs, and artifacts.
Step #12: Explore the Output
Queries extracted: e.g., “what is the capital of Japan”, “what currency does Japan use”.
Retrieved data: JSON artifact in the bucket containing web content.
Validation report: Markdown output identifying correct (Tokyo as capital) and incorrect (yen, not euro) elements.
This exposition illustrates the efficacious integration of IPFLY proxies within Vertex AI to forge dependable data collection pipelines. By capitalizing on IPFLY’s secure, expansive proxy ecosystem, enterprises can attain unparalleled data integrity and efficiency in AI workflows. IPFLY’s support for high-concurrency, global IP rotation empowers advanced applications in data scraping, financial services, and beyond. We encourage exploration of IPFLY’s offerings to elevate your AI infrastructure with premium web data solutions.
