Retrieval-Augmented Generation (RAG) agents combine the power of large language models (LLMs) with external web data to deliver accurate, context-rich responses—critical for enterprise use cases like market research, customer support, and competitive analysis. Building a RAG agent with Google ADK (Agent Development Kit) and Vertex AI simplifies workflow orchestration and LLM integration, but the biggest bottleneck is unrestricted access to high-quality web data.
IPFLY’s premium proxy solutions (90M+ global IPs across 190+ countries, static/dynamic residential, and data center proxies) solve this: multi-layer IP filtering bypasses anti-scraping measures, global coverage unlocks region-specific data, and 99.9% uptime ensures consistent data ingestion. This guide walks you through building a RAG agent step-by-step—from setting up Google Cloud tools to integrating IPFLY for web data collection, vectorizing data, and deploying a production-ready agent.
Introduction to RAG Agents, Google ADK, Vertex AI & IPFLY’s Role
RAG agents eliminate the “knowledge cutoff” problem of traditional LLMs by retrieving real-time, relevant web data to augment responses. For example:
A customer support RAG agent can pull the latest product specs from your website.
A market research agent can scrape competitor pricing and industry trends.
A sales agent can access regional market data to personalize pitches.
Google ADK and Vertex AI streamline RAG development:
Google ADK: Orchestrates workflows (web scraping, data retrieval, LLM prompting) with pre-built tools for agent logic.
Vertex AI: Hosts powerful LLMs (Gemini Pro/Ultra) and vector databases (Vertex AI Vector Search) for fast, scalable knowledge retrieval.
But here’s the catch: Web data collection for RAG often hits roadblocks—IP bans, geo-restrictions, and anti-scraping tools (e.g., CAPTCHAs, WAFs) that limit data quality and scope. This is where IPFLY becomes indispensable.
IPFLY’s proxy infrastructure is built for enterprise RAG needs:
Dynamic Residential Proxies: Rotate per request to mimic real user behavior, avoiding detection on scraping-sensitive sites (e.g., LinkedIn, industry blogs).
Static Residential Proxies: Permanent ISP-allocated IPs for consistent access to trusted sources (e.g., government datasets, company websites).
Data Center Proxies: High-speed, exclusive IPs for large-scale data processing (e.g., bulk industry reports).
Full protocol support (HTTP/HTTPS/SOCKS5) for seamless integration with Google ADK’s scraping tools.
In short, IPFLY is the “data pipeline backbone” of your RAG agent—ensuring you have the clean, diverse web data needed to train and power accurate responses.
Prerequisites
Before starting, ensure you have:
1.A Google Cloud Platform (GCP) account with Vertex AI enabled (sign up for a free trial here).
2.Google ADK installed (follow GCP’s official guide for setup).
3.An IPFLY account (with access to your preferred proxy type: static/dynamic residential or data center).
4.A vector database (we’ll use Vertex AI Vector Search, but you can also use Pinecone or Weaviate).
5.Basic Python knowledge (for configuring scrapers and agent workflows).
6.GCP service account key (with permissions for Vertex AI, Cloud Storage, and ADK).
💡 Pro Tip: Test IPFLY proxies with a small scraping script first to validate connectivity and avoid setup delays later.
Step-by-Step Guide to Building a RAG Agent with Google ADK, Vertex AI & IPFLY
We’ll build a market research RAG agent that scrapes industry trends, competitor data, and regional market insights—then uses Gemini Pro (via Vertex AI) to answer business questions. IPFLY will power all web data collection.
1.Configure IPFLY Proxies for Web Data Collection
First, set up IPFLY to handle web scraping for your RAG agent. We’ll use IPFLY’s dynamic residential proxies for high anonymity and rotation—ideal for scraping diverse market data sources.
Step 1.1: Get IPFLY Proxy Credentials
Log into your IPFLY account and retrieve:
Proxy endpoint (e.g., http://proxy.ipfly.com:8080).
Username/password (for authentication).
Proxy type (we’ll use dynamic_residential for this project).
Step 1.2: Create a Scraper with IPFLY Integration
Use Python’s requests library (compatible with Google ADK) to build a scraper that pulls data from target sites (e.g., industry blogs, competitor websites, market research portals). Integrate IPFLY’s proxies to bypass blocks.
import requests
from bs4 import BeautifulSoup
import json
# IPFLY Proxy Configuration
IPFLY_PROXY = {"http": "http://[IPFLY_USERNAME]:[IPFLY_PASSWORD]@proxy.ipfly.com:8080","https": "http://[IPFLY_USERNAME]:[IPFLY_PASSWORD]@proxy.ipfly.com:8080"}# Target Sites for Market Research Data (customize for your use case)
TARGET_SITES = ["https://www.forbes.com/industries/technology","https://techcrunch.com/startups/","https://www.statista.com/topics/3374/artificial-intelligence-ai/"]defscrape_with_ipfly(url):"""Scrape web data using IPFLY proxies to avoid blocks."""try:# Send request with IPFLY proxy
response = requests.get(
url,
proxies=IPFLY_PROXY,
timeout=30,
headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"})
response.raise_for_status() # Raise error for HTTP issues# Parse content (customize based on site structure)
soup = BeautifulSoup(response.text, "html.parser")
articles = soup.find_all("article") # Adjust selector for target site
scraped_data = []for article in articles[:5]: # Limit to top 5 articles for demo
title = article.find("h2").get_text(strip=True) if article.find("h2") elseNone
summary = article.find("p").get_text(strip=True) if article.find("p") elseNone
date = article.find("time")["datetime"] if article.find("time") elseNoneif title and summary:
scraped_data.append({"title": title,"summary": summary,"source_url": url,"scraped_date": json.dumps(datetime.utcnow(), default=str),"ipfly_proxy_used": "dynamic_residential"})return scraped_data
except Exception as e:print(f"Scraping failed for {url}: {str(e)}")return []# Scrape all target sites
all_scraped_data = []for site in TARGET_SITES:
data = scrape_with_ipfly(site)
all_scraped_data.extend(data)# Save data to JSON (for ingestion into vector database)withopen("ipfly_scraped_market_data.json", "w") as f:
json.dump(all_scraped_data, f, indent=2)print(f"Successfully scraped {len(all_scraped_data)} records with IPFLY proxies!")Key IPFLY Benefits Here:
Anti-Scraping Bypass: IPFLY’s multi-layer IP filtering ensures no blacklisted IPs are used, avoiding blocks on sites like Forbes or TechCrunch.
Global Coverage: If you need regional data (e.g., Asian tech trends), switch to IPFLY’s Asian IPs (190+ countries supported) by updating the proxy endpoint—no code rewrites.
Unlimited Concurrency: IPFLY’s dedicated servers handle high-volume scraping (scale to 100+ target sites without slowdowns), critical for enterprise RAG agents.
2.Set Up Vertex AI Vector Search (Knowledge Base)
RAG agents rely on vector databases to store and retrieve relevant web data. We’ll use Vertex AI Vector Search for seamless integration with Google ADK and Gemini.
Step 2.1: Create a Vector Index in Vertex AI
1.Go to the Vertex AI Console.
2.Navigate to Vector Search > Indexes and click Create Index.
3.Configure:
- Index name:
rag-market-research-index. - Embedding model: Use Vertex AI’s
text-embedding-004(1536-dimensional vectors). - Storage: Cloud Storage bucket (create a new one or use an existing).
Step 2.2: Embed & Ingest IPFLY-Scraped Data
Use Vertex AI’s embedding API to convert scraped text (titles, summaries) into vectors, then ingest them into the vector index.
from google.cloud import aiplatform
from google.oauth2 import service_account
# Authenticate with GCP
credentials = service_account.Credentials.from_service_account_file("gcp-service-account-key.json")
aiplatform.init(credentials=credentials, project="[YOUR_GCP_PROJECT_ID]", region="[YOUR_REGION]")# Load IPFLY-scraped datawithopen("ipfly_scraped_market_data.json", "r") as f:
scraped_data = json.load(f)# Embed data using Vertex AI Embedding APIdefembed_text(text):"""Generate embeddings for text using Vertex AI."""
embedding_client = aiplatform.EmbeddingClient(model_name="text-embedding-004")
response = embedding_client.embed_text([text])return response.embeddings[0].values
# Prepare data for vector index
vector_records = []for item in scraped_data:
combined_text = f"Title: {item['title']} | Summary: {item['summary']}"
embedding = embed_text(combined_text)
vector_records.append({"id": item["title"].replace(" ", "-").lower(),"embedding": embedding,"metadata": {"summary": item["summary"],"source_url": item["source_url"],"scraped_date": item["scraped_date"],"proxy_type": item["ipfly_proxy_used"]}})# Ingest into Vertex AI Vector Search
index = aiplatform.MatchingEngineIndex(index_name="[YOUR_VECTOR_INDEX_RESOURCE_NAME]")
index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name="[YOUR_INDEX_ENDPOINT_NAME]")# Batch ingest (supports up to 10k records per batch)
index_endpoint.upsert_embeddings(embeddings=vector_records)print(f"Ingested {len(vector_records)} vector records into Vertex AI Vector Search!")3.Build RAG Workflow with Google ADK
Google ADK orchestrates the RAG pipeline: user query → retrieve relevant vectors → augment LLM prompt → generate response. We’ll define the workflow using ADK’s Agent and Tool classes.
Step 3.1: Define a Retrieval Tool (Connect to Vector Index)
Create a tool that queries the Vertex AI Vector Search index to retrieve relevant web data for a user’s query.
from google.agent.builder import Agent, Tool
from google.agent.builder.tool import ToolResult
class VectorSearchRetrievalTool(Tool):"""Tool to retrieve relevant data from Vertex AI Vector Search."""def__init__(self, index_endpoint):super().__init__(
name="vector_search_retriever",
description="Retrieves relevant market research data from web sources (scraped with IPFLY proxies). Use this for questions about industry trends, competitor insights, or market statistics.",
input_schema={"query": "string"})
self.index_endpoint = index_endpoint
defrun(self, query: str) -> ToolResult:# Embed user query
query_embedding = embed_text(query)# Search vector index (top 3 relevant results)
response = self.index_endpoint.match(
query_embeddings=[query_embedding],
num_neighbors=3,
return_full_documents=True)# Format results
retrieved_context = ""formatchin response.matches[0].matches:
metadata = match.metadata
retrieved_context += f"Source: {metadata['source_url']}\nSummary: {metadata['summary']}\n\n"return ToolResult(output=retrieved_context)Step 3.2: Integrate IPFLY for On-Demand Scraping
Extend the workflow with an on-demand scraping tool—if the vector index lacks relevant data, the agent scrapes fresh data using IPFLY proxies.
class IPFlyOnDemandScraperTool(Tool):"""Tool to scrape fresh web data using IPFLY proxies for on-demand queries."""def__init__(self):super().__init__(
name="ipfly_on_demand_scraper",
description="Scrapes fresh market research data from web sources using IPFLY proxies. Use this if the vector search tool doesn't return relevant results.",
input_schema={"query": "string", "target_url": "string"})defrun(self, query: str, target_url: str) -> ToolResult:# Scrape fresh data with IPFLY
fresh_data = scrape_with_ipfly(target_url)# Format results
fresh_context = ""for item in fresh_data[:3]: # Top 3 fresh results
fresh_context += f"Source: {item['source_url']}\nTitle: {item['title']}\nSummary: {item['summary']}\n\n"# Optional: Ingest fresh data into vector index for future queries# (Add code to embed and upsert fresh_data here)return ToolResult(output=fresh_context)Step 3.3: Assemble the RAG Agent with Google ADK & Vertex AI LLM
Combine the tools with Gemini Pro (via Vertex AI) to build the full RAG agent.
defbuild_rag_agent(index_endpoint):"""Build RAG agent with Google ADK, Vertex AI LLM, and IPFLY tools."""# Initialize tools
retrieval_tool = VectorSearchRetrievalTool(index_endpoint)
scraping_tool = IPFlyOnDemandScraperTool()# Define agent prompt (augment with IPFLY-scraped context)
agent_prompt = """
You are a market research RAG agent powered by Google ADK, Vertex AI, and IPFLY proxies.
Use the following steps to answer user queries:
1. First, use the vector_search_retriever tool to find relevant existing web data (scraped with IPFLY).
2. If no relevant data is found, use the ipfly_on_demand_scraper tool to scrape fresh data.
3. Augment your response with the retrieved/scraped context—always cite sources.
4. Keep responses concise, data-driven, and focused on the user's query.
Do not make up information—only use data from IPFLY-scraped sources.
"""# Create agent with Gemini Pro LLM
agent = Agent(
prompt=agent_prompt,
tools=[retrieval_tool, scraping_tool],
llm=aiplatform.ChatModel(model_name="gemini-pro"),
temperature=0.3 # Lower for factual responses)return agent
# Initialize agent
rag_agent = build_rag_agent(index_endpoint)print("RAG agent built successfully with Google ADK, Vertex AI, and IPFLY!")4.Test the RAG Agent
Test the agent with a market research query to validate data retrieval and response quality.
# Test query: "What are the latest trends in AI startups?"
user_query = "What are the latest trends in AI startups?"
response = rag_agent.query(user_query)print("User Query:", user_query)print("\nAgent Response:", response.text)Sample Output:
User Query: What are the latest trends in AI startups?
Agent Response: Based on IPFLY-scraped data from industry sources:
1. Source: https://techcrunch.com/startups/
Summary: AI startups focused on vertical-specific solutions (e.g., healthcare diagnostics, industrial automation) are attracting record funding—up 40% YoY in Q1 2025.
2. Source: https://www.forbes.com/industries/technology
Summary: Generative AI for enterprise workflow automation (e.g., document processing, customer support) is the fastest-growing segment, with 60% of Fortune 500 companies piloting tools from startups like AutomationAI.
3. Source: https://www.statista.com/topics/3374/artificial-intelligence-ai/
Summary: AI startups integrating edge computing to reduce latency are gaining traction, especially in IoT and manufacturing use cases.
All data was collected via IPFLY dynamic residential proxies to ensure unrestricted access to web sources.5.Optimize the RAG Agent with IPFLY
To enhance performance, use these IPFLY-specific optimizations:
5.1: Choose the Right Proxy Type
High-Anonymity Needs (e.g., scraping competitor sites): Use IPFLY’s dynamic residential proxies (rotate per request).
Consistent Access (e.g., government datasets): Use static residential proxies (permanent ISP IPs).
Large-Scale Scraping (e.g., bulk industry reports): Use data center proxies (high speed, low latency).
5.2: Schedule Regular Data Refreshes
Use IPFLY’s proxies to automate daily/weekly scraping (via cron jobs or Google Cloud Scheduler) to keep the vector index updated with fresh data.
5.3: Leverage IPFLY’s 24/7 Support
If you encounter scraping blocks or proxy issues, IPFLY’s technical support resolves them fast—critical for production RAG agents that require 99.9% uptime.
Key Considerations for Enterprise RAG Agents
1.Compliance: Ensure web scraping aligns with target sites’ terms of service and regulations (GDPR, CCPA). IPFLY’s proxies are filtered to avoid blacklisted IPs, supporting lawful data collection.
2.Scalability: IPFLY’s 90M+ IP pool and unlimited concurrency scale with your agent’s data needs—from 10 to 10,000 target sites.
3.Cost Efficiency: IPFLY’s pay-as-you-go pricing (no hidden fees) keeps scraping costs low, even for large-scale RAG agents.
4.Data Quality: IPFLY’s multi-layer IP filtering eliminates low-quality or reused IPs, ensuring scraped data is clean and reliable.
Troubleshooting Common Issues
| Issue | Solution |
| Scraping blocks on target sites | Switch to IPFLY’s dynamic residential proxies; update user-agent headers to mimic real browsers. |
| Slow data ingestion | Use IPFLY’s data center proxies for high-speed scraping; batch ingest vectors into Vertex AI. |
| Irrelevant RAG responses | Refine the vector search tool to return more neighbors (e.g., 5 instead of 3); add metadata filters (e.g., scrape date). |
| Proxy connectivity errors | Verify IPFLY credentials; check GCP firewall rules to allow proxy traffic. |
Building a RAG agent with Google ADK and Vertex AI unlocks powerful, data-driven AI capabilities—but the agent’s accuracy depends entirely on access to high-quality web data. IPFLY’s premium proxies solve the biggest bottleneck: unrestricted, reliable data collection from global sources.
By integrating IPFLY into your RAG pipeline, you get:
190+ country coverage for region-specific data.
Anti-scraping bypass to access hard-to-reach sites.
99.9% uptime for consistent data ingestion.
Seamless compatibility with Google ADK and Vertex AI.
Whether you’re building a market research agent, customer support tool, or sales assistant, IPFLY’s proxies ensure your RAG agent has the context it needs to deliver accurate, valuable responses.
Ready to build your enterprise RAG agent? Pair Google ADK, Vertex AI, and IPFLY’s global proxy solutions—unlock the full potential of web data for AI.
