Building a Real-Time RAG Pipeline with SearchCans and LangChain

The biggest weakness of Large Language Models (LLMs) isn’t their reasoning capability—it’s their memory. GPT-4o and Claude 3.5 are frozen in time, limited by their training data cutoffs.

Retrieval-Augmented Generation (RAG) was supposed to fix this. But traditional RAG relies on static vector databases (like Pinecone or Milvus) that you have to scrape, chunk, embed, and update manually. If breaking news happens today, your RAG pipeline won’t know about it until you re-index.

To build truly autonomous AI agents, you need Real-Time RAG: a pipeline that can “Google” the answer on the fly.

The Architecture of Real-Time RAG

Unlike static RAG, which retrieves from a pre-built index, Real-Time RAG retrieves from the open web.

1. Query Analysis: The LLM decides it needs external information.
2. Discovery (SERP API): The agent searches Google/Bing for the latest URLs.
3. Extraction (Reader API): The agent visits the top URLs and extracts clean content.
4. Synthesis: The LLM answers the user using this fresh context.

This approach eliminates the “knowledge cutoff” problem entirely. But it introduces a new challenge: Data Cleanliness.

The Problem: HTML is Toxic to LLMs

Most developers try to implement this using basic scraping tools (like Beautiful Soup). The problem? Raw HTML is full of noise: navbars, ads, scripts, and tracking pixels.

Feeding raw HTML to an LLM:

Wastes Tokens

60% of HTML code is useless for context.

Confuses the Model

LLMs struggle to distinguish between main content and boilerplate.

Increases Latency

Processing 100kb of HTML takes time.

This is why SearchCans Reader API returns Markdown, not HTML. Markdown is the native language of LLMs—structured, semantic, and token-efficient.

Building the Pipeline (Python Example)

Let’s build a simple “Search Tool” compatible with LangChain or AutoGPT. We’ll use SearchCans to fetch search results and then “read” the top result in Markdown.

import requests

# Configuration
SEARCHCANS_KEY = "YOUR_API_KEY"
HEADERS = {"Authorization": f"Bearer {SEARCHCANS_KEY}"}

def get_realtime_context(query):
    # Step 1: Search for the latest info
    # We use 'google' engine for best coverage
    print(f"🔍 Searching for: {query}...")
    search_resp = requests.get(
        "https://www.searchcans.com/api/search",
        headers=HEADERS,
        params={"query": query, "engine": "google"}
    )
    results = search_resp.json().get('result', {}).get('data', [])
    
    if not results:
        return "No results found."

    # Step 2: Get the top URL
    top_url = results[0]['url']
    print(f"📄 Reading: {top_url}...")

    # Step 3: Convert to Markdown using Reader API
    # This strips ads/navbars and handles JS rendering
    # Note: 'use_browser' ensures we capture dynamic JS content
    reader_resp = requests.get(
        "https://www.searchcans.com/api/url",
        headers=HEADERS,
        params={"url": top_url, "use_browser": "true"}
    )
    
    return reader_resp.text

# Usage Example
context = get_realtime_context("latest spacex launch outcome")
print(f"--- RAG Context ---\n{context[:500]}...")

Integrating with LangChain

If you are using LangChain, you can wrap the function above into a Tool. While LangChain has a built-in GoogleSearchAPIWrapper, it relies on the official Google API, which is often expensive and rate-limited.

Switching to SearchCans gives you:

1. No Rate Limits: Scale to millions of requests without hitting quotas.
2. Full Content: We don’t just give you a snippet; we give you the whole page in Markdown.
3. Cost Efficiency: At $0.56 per 1,000 requests (Ultra Plan), you can afford to let your agent search on every turn of the conversation.

When to Use Real-Time vs. Static RAG

Feature	Static RAG (Vector DB)	Real-Time RAG (SearchCans)
Data Freshness	Low (Updates required)	Instant
Cost	Storage + Embedding costs	Per-search API cost
Latency	Milliseconds	Seconds (Web request time)
Use Case	Company wikis, Policies	News, Competitor analysis, Market trends

The Hybrid Approach: The best AI agents use both. They query their internal vector database first. If the similarity score is low (meaning they don’t know the answer), they fallback to SearchCans to search the web.

Conclusion

Hallucinations often aren’t a failure of reasoning—they are a failure of context. By giving your LLM eyes to browse the live web via high-fidelity Markdown extraction, you transform it from a static text generator into an intelligent research assistant.

Stop feeding your AI stale data. Give it the live internet.

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Resources

Related Topics:

• Build an AI News Monitor with n8n - No-code implementation of this concept.
• JSON to Markdown Data Cleaning Guide - Why format matters for AI.

Get Started:

• Free Trial - Get 100 free credits
• API Documentation - Technical reference
• Pricing - 10x cheaper than competitors
• Playground - Test RAG search queries

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SearchCans provides real-time data for AI agents. Start building now →