Building RAG Pipelines with Real-Time Web Data

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLM responses in factual data. But most RAG pipelines rely on static document stores that go stale within hours. Here's how to build pipelines that stay current with real-time web data.

Why Static RAG Falls Short

Traditional RAG pipelines follow a familiar pattern:

1. Ingest documents into a vector store
2. Embed user queries
3. Retrieve relevant chunks
4. Feed context to an LLM for generation

The problem? Those documents were ingested at a point in time. Stock prices change. Products launch. News breaks. Your RAG pipeline doesn't know about any of it.

Real-Time RAG Architecture

A real-time RAG pipeline augments the retrieval step with live web data:

User Query
    ├── Vector Store Retrieval (existing knowledge)
    ├── Web Search (real-time context)
    │   └── link.sc Search API
    └── Page Fetch (deep content)
        └── link.sc Fetch API
            ↓
    Context Assembly
            ↓
    LLM Generation

Implementation with link.sc

Step 1: Search for Relevant Sources

import linksc

client = linksc.Client(api_key="lsc_...")

# Real-time web search
search_results = client.search(
    q="NVIDIA Q4 2025 earnings report",
    format="markdown",
    num_results=5
)

Step 2: Fetch Full Page Content

# Fetch full content from top results
pages = []
for result in search_results.results[:3]:
    page = client.fetch(
        url=result.url,
        format="markdown"
    )
    pages.append(page.content)

Step 3: Combine with Vector Store Results

# Retrieve from your existing knowledge base
vector_results = vector_store.query(
    query_embedding=embed(user_query),
    top_k=5
)

# Assemble context from both sources
context = assemble_context(
    static=vector_results,
    realtime=pages
)

Step 4: Generate with Full Context

response = llm.generate(
    prompt=f"""Answer the user's question using the provided context.

Context:
{context}

Question: {user_query}"""
)

Optimizing Token Usage

Web pages can be verbose. Here are strategies to maximize context quality:

• Use Markdown format: link.sc's Markdown output is 60-80% smaller than raw HTML
• Chunk intelligently: Split on heading boundaries to preserve semantic meaning
• Rank by relevance: Score retrieved chunks and keep only the most relevant
• Set content limits: Use link.sc's token limit parameter to cap content size

Production Considerations

Caching

Not every query needs fresh data. Implement a tiered caching strategy:

• Hot cache (5 min TTL): For trending topics and breaking news
• Warm cache (1 hour TTL): For general knowledge queries
• Cold cache (24 hour TTL): For stable reference content

Fallback Strategy

Always have a fallback path if the web search fails:

1. Try real-time web search
2. Fall back to cached web results
3. Fall back to vector store only
4. Return a transparent "I don't have current data" response

Cost Management

Monitor your token usage across both the web data API and LLM calls. link.sc's usage dashboard helps you track fetch and search volume per pipeline.

Results

Teams using real-time RAG with link.sc typically see:

• 40-60% reduction in hallucinations on time-sensitive queries
• 3x improvement in answer freshness scores
• Sub-second additional latency from web data integration

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Build your first real-time RAG pipeline today. Get started with link.sc — 500 free requests per month.