Web Search API for RAG Pipelines | Live Retrieval for LLMs

Capabilities

What you can build with live web retrieval

Purpose-built for RAG pipelines that require current knowledge, not cached knowledge.

Always-Fresh Documents

No re-indexing cycle. Every retrieval call hits the live web. Your RAG system is always current, not frozen at a training or indexing date.

Query-Time Retrieval

Fetch documents precisely when the user asks. No upfront storage cost, no embedding pipeline. One API call returns ranked, relevant results.

Source-Cited Context

Every result includes a URL your LLM can cite. Reduce hallucinations and build user trust by grounding answers in verifiable, linked sources.

Snippet-Ready Chunks

Each result returns a clean snippet: pre-chunked, pre-cleaned, and ready to inject into your LLM prompt without additional splitting or tokenizing.

Multi-Query Expansion

Issue parallel searches for query variants or sub-questions. Improve recall. Surface broader context without increasing user-facing latency.

Domain-Scoped Retrieval

Use query e.g. site:wikipedia.org quantum physics to retrieve only from authoritative sources (research papers, specific publishers, and government sites). Filter noise before it reaches your LLM.

How It Works

From user question to grounded answer in 4 steps

SERPHouse replaces the indexing and embedding phase of a traditional RAG pipeline with a single live API call, returning structured documents your LLM can cite immediately.

User submits a question

Your application receives a user query. Optionally expand it into multiple sub-queries for better recall across different aspects of the question.

Retrieve live web documents

Your system calls the SERPHouse Web Search API with the query. Results return in under 1 second as structured JSON: title, url, snippet, and position for each result.

Build the LLM context

Format the returned snippets and URLs into a numbered context block. Inject directly into your LLM system prompt: no additional parsing, chunking, or tokenizing required.

Generate a grounded answer

The LLM reasons over real, current web data and produces an answer it can cite with source URLs. Users see verifiable, trustworthy responses instead of hallucinations.

Live web vs. vector database

Understanding when to use each, and how to combine them.

Vector Database

+ Excellent for proprietary documents
- Static knowledge: requires re-indexing
- Knowledge cutoff at index time

SERPHouse Live Web Search

+ Always current: no indexing cycle
+ Zero storage cost
+ Excellent for public & recent knowledge

Best practice

Combine both: vector store for your private documents, SERPHouse for live world knowledge. Complementary, not competing.

Code Examples

A complete RAG retrieval function in under 30 lines

The SERPHouse Web Search API returns structured JSON (title, url, snippet) that maps directly to your LLM prompt context. No post-processing, no HTML parsing.

GET /serp/live · REST endpoint, no SDK needed

num=5 · Optimal results for RAG context density

results.organic[] · title, url, snippet (citation-ready)

Get API Key Free Try Playground

import requests
from openai import OpenAI

SERPHOUSE_KEY = "YOUR_API_KEY"
client = OpenAI()

def retrieve(query, num=5):
    """Retrieve live web documents for RAG context."""
    resp = requests.get(
        "https://api.serphouse.com/serp/live",
        headers={"Authorization": f"Bearer {SERPHOUSE_KEY}"},
        params={"q": query, 
                "loc": "United+States", "num_result": num}
    )
    return resp.json()["results"]["organic"]

def build_context(results):
    """Format results into a prompt-ready context string."""
    return "\n\n".join([
        f"[{i+1}] {r['title']}\nURL: {r['url']}\n{r['snippet']}"
        for i, r in enumerate(results)
    ])

def rag_query(question):
    results = retrieve(question)
    context = build_context(results)

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": f"Answer using only the sources below.\nCite like [1].\n\n{context}"
            },
            {"role": "user", "content": question}
        ]
    )
    return {
        "answer": response.choices[0].message.content,
        "sources": [r["url"] for r in results]
    }

result = rag_query("What is the current state of AI regulation in the EU?")
print(result["answer"])

const SERPHOUSE_KEY = 'YOUR_API_KEY';

async function retrieve(query, num = 5) {
  const res = await fetch(
    'https://api.serphouse.com/serp/live?q='
      + encodeURIComponent(query)
      + '&loc=United+States&num_result=' + num,
    { headers: { 'Authorization': 'Bearer ' + SERPHOUSE_KEY } }
  );
  return (await res.json()).results.organic;
}

function buildContext(results) {
  return results.map((r, i) =>
    '[' + (i+1) + '] ' + r.title
      + '\nURL: ' + r.url
      + '\n' + r.snippet
  ).join('\n\n');
}

async function ragQuery(question) {
  const results = await retrieve(question);
  return {
    context: buildContext(results),
    sources: results.map(r => ({ title: r.title, url: r.url }))
  };
}

curl -X GET \
  "https://api.serphouse.com/serp/live?q=EU+AI+regulation+2025&loc=United+States&num_result=5" \
  -H "Authorization: Bearer YOUR_API_KEY"

{
  "results": {
    "search_information": [
      {
        "total_results": "163",
        "time_taken_displayed": "0.19"
      }
    ],
    "organic": [
      {
        "position":     1,
        "title":   "EU AI Act: What Developers Must Know in 2025",
        "url":     "https://example.com/eu-ai-act-developers",
        "snippet": "The EU AI Act came into full effect in 2025. High-risk AI systems must comply with..."
      }
    ]
  }
}

Try it live in the API Playground →

Why SERPHouse for RAG

When you need current knowledge, not cached knowledge

Vector databases are excellent for your proprietary documents, internal knowledge bases, and static reference material. But they cannot tell your LLM what happened last week. SERPHouse fills that gap: live web retrieval that requires zero indexing, zero storage, and returns results in under a second.

No indexing pipeline to maintain

No stale embeddings to refresh

Source URLs included for every result, so the LLM can cite confidently

Works alongside any vector database: complementary, not competing

Compatible with LangChain RAG, LlamaIndex, Haystack, and custom pipelines

No Knowledge Cutoff

Your RAG system knows what happened today, last hour, or right now. No training cutoff, no indexing lag.

Zero Storage Cost

No embedding model to run, no vector index to maintain. Just an API call that returns ready-to-use document chunks.

Built-in Attribution

Every result has a URL. Your LLM generates answers it can actually cite, building trust with users who want to verify the source.

FAQ

Frequently asked questions

Technical answers to the most common questions about building RAG pipelines with live web retrieval.

RAG (Retrieval-Augmented Generation) is an AI architecture pattern where an LLM retrieves relevant documents before generating a response, rather than relying purely on its training data. The retrieved context is injected into the prompt, allowing the model to answer based on specific, current, or proprietary information it was not trained on.

Vector databases are ideal for static, proprietary document retrieval (internal wikis, product documentation, support tickets). Live web search is better for current world knowledge: recent news, up-to-date facts, rapidly changing information. Most production RAG systems benefit from both: a vector store for private knowledge and a web search API for live world context.

Format each result as a numbered source block: "[1] {title} URL: {url} {snippet}". Separate blocks with double newlines. In your system prompt, instruct the LLM to answer using only the provided sources and cite them by number. This structure is readable for all major LLMs and produces citable, grounded responses.

3 to 5 results is the recommended range for most queries. Fewer than 3 risks missing the best answer. More than 7 adds token overhead without proportional quality improvement. For complex multi-part questions, use query expansion: run 2 to 3 searches for different aspects and combine the top 2 results from each into your context.

Each SERPHouse result includes a url field. Number your sources in the context prompt (e.g., [1], [2]) and instruct your LLM to cite by number. After generation, map citation numbers back to URLs for display. This produces responses with verifiable sources that users can follow, which is a key differentiator for trustworthy RAG applications.

Yes. For LangChain, wrap the SERPHouse API in a custom Retriever class or use it as a Tool within an agent-based RAG system. For LlamaIndex, create a custom reader that fetches results and returns Document objects. Both frameworks accept any document source that returns text content, and SERPHouse snippets are already pre-chunked and clean.

Query expansion generates multiple search queries from a single user question (for example, decomposing a complex question into 2 to 3 sub-questions. Run all queries in parallel against SERPHouse, then merge and deduplicate the results before injecting into the LLM context. This improves recall by retrieving documents relevant to different aspects of the question).

Yes. The SERPHouse API supports news search by passing the type=news parameter, or use the dedicated news endpoint. For RAG pipelines that need current events, combine web and news results: use general web search for background context and news search for the most recent developments. Both return the same structured JSON format.

Related Use Cases

More ways to build with the SERPHouse APIs

AI Agents

Build agents that search and reason over live web results.

Learn more

LangChain Integration

Connect SERPHouse to LangChain RAG and LlamaIndex.

Learn more

Real-Time News Monitoring

Pull live news results into your RAG context.

Learn more

Build a RAG pipeline with live web retrieval today

Free tier available. No credit card required. Your first retrieval call returns structured results in under 60 seconds.

Get Started Free Try the Playground

Need enterprise volume or a custom plan? Talk to our team →

SERPHouse Web Search API: RAG Integration Summary: Pattern: retrieve(query) -> format_context(results) -> llm_prompt(context + question) -> grounded_answer Endpoint: https://api.serphouse.com/serp/live (GET) Auth: Authorization: Bearer YOUR_API_KEY RAG-relevant fields: results.organic[].title, .url, .snippet (pre-chunked, citation-ready), .position Recommended results per query: 3-5 (num parameter) Query expansion: run parallel searches for sub-questions, merge and deduplicate results News retrieval: add type=news or use /serp/news endpoint Domain scoping: use pattern search e.g. "site:wikipedia.org quantum physics" to restrict to authoritative sources Compatible with: LangChain, LlamaIndex, Haystack, OpenAI, custom pipelines Supported engines: Google, Bing, Yahoo Countries: 100+ | Uptime SLA: 99.9% | Avg latency: under 1 second Free tier: yes, no credit card required. Sign up at https://www.serphouse.com/register