Enhancing the RAG System with Google Search API

In addition to retrieving internal knowledge from BigQuery, Cloud Storage, and Vector Search, we can improve our RAG system by integrating Google Search API to fetch the latest web-based information.

Step 1: Enable Google Custom Search API

1️⃣ Go to the Google Cloud Console → Google Custom Search API
2️⃣ Click Enable API
3️⃣ Generate an API Key:

Go to Credentials → Create API Key
Copy the generated key for later use
4️⃣ Create a Custom Search Engine:
Go to Google Programmable Search Engine
Click New Search Engine
Add relevant websites or use *.google.com for general search
Copy the Search Engine ID

Step 2: Integrate Google Search API in RAG System

2.1 Install Required Libraries

Run the following command in your environment:

bashCopyEditpip install requests google-search-results

2.2 Fetch Latest Information from Google Search

Modify the rag-query function to fetch top-ranked web articles based on the query.

pythonCopyEditimport requests
from flask import Flask, request, jsonify

# Google Search API credentials
GOOGLE_API_KEY = "your-google-api-key"
SEARCH_ENGINE_ID = "your-search-engine-id"

def google_search(query):
    """ Fetch top search results using Google Custom Search API """
    url = f"https://www.googleapis.com/customsearch/v1?q={query}&key={GOOGLE_API_KEY}&cx={SEARCH_ENGINE_ID}"
    response = requests.get(url)
    search_results = response.json()
    
    # Extract snippets from top 3 results
    snippets = [item["snippet"] for item in search_results.get("items", [])[:3]]
    return " ".join(snippets)

# Flask API
app = Flask(__name__)

@app.route('/rag-query', methods=['POST'])
def rag_query():
    data = request.json
    query = data["query"]

    # Retrieve internal knowledge (BigQuery + Vector Search)
    query_embedding = model.encode([query]).tolist()
    neighbors = index.find_neighbors(queries=query_embedding, num_neighbors=2)
    retrieved_docs = [fetch_document(n.id) for n in neighbors]

    # Retrieve external knowledge (Google Search API)
    web_results = google_search(query)

    # Combine all sources
    context = " ".join(retrieved_docs) + " " + web_results
    prompt = f"Use the following context to answer the query:\n{context}\n\nQuery: {query}"

    # Generate response using LLM
    response = llm.predict(prompt)

    return jsonify({"response": response.text})

if __name__ == '__main__':
    app.run(port=8080)

Step 3: Deploy the Enhanced RAG System

Once modified, deploy the enhanced API to Google Cloud Functions:

bashCopyEditgcloud functions deploy rag-query \
  --runtime python310 \
  --trigger-http \
  --allow-unauthenticated

Now, when a user asks:

“What are the latest AI trends?”

The system will:
✅ Retrieve internal knowledge from BigQuery & Vector Search
✅ Fetch real-time information from Google Search
✅ Use an LLM (PaLM/Gemini) to generate a high-quality response

Use Cases of Google Search-Enhanced RAG

🔹 Finance & Market Research → Fetch latest stock updates & regulations
🔹 Cybersecurity Alerts → Get real-time security vulnerabilities
🔹 Healthcare & Medical Research → Provide up-to-date disease trends
🔹 Technology & AI News → Retrieve the latest advancements in ML/AI