# RAG Web Browser (`bornoo/rag-web-browser`) Actor

RAG Web Browser is a lightweight Python Actor that fetches any URL or search query, strips ads and clutter, and extracts clean, readable text from web pages. It returns structured, LLM-ready content optimized for retrieval-augmented generation pipelines and AI-powered applications.

- **URL**: https://apify.com/bornoo/rag-web-browser.md
- **Developed by:** [Biddut Hossain](https://apify.com/bornoo) (community)
- **Categories:** AI, Agents, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## RAG Web Browser

RAG Web Browser is a lightweight Python Actor that fetches any URL or search
query, strips ads and clutter, and extracts clean, readable text from web
pages. It returns structured, LLM-ready content optimized for
retrieval-augmented pipelines and AI chat apps needing accurate real-time
context.

### ✨ Features

- Accepts a direct URL **or** a plain text search query
- Strips scripts, nav bars, footers, ads, and other noise
- Returns clean, structured JSON output (title + text + char count)
- Configurable character limit and CSS selectors to remove
- Built on `httpx` + `BeautifulSoup` for fast, reliable scraping

### 📥 Input

| Field | Type | Description | Default |
|---|---|---|---|
| `query` | string | A URL or search query | *(required)* |
| `maxChars` | integer | Max characters of text to return | `5000` |
| `removeSelectors` | array | CSS selectors to strip before extraction | `["script","style","nav","footer","header","aside","iframe","noscript"]` |

#### Example input

```json
{
  "query": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation",
  "maxChars": 3000
}
```

### 📤 Output

Each run pushes one item to the dataset:

```json
{
  "query": "https://example.com",
  "url": "https://example.com",
  "title": "Example Domain",
  "text": "This domain is for use in illustrative examples...",
  "charCount": 214
}
```

### 🚀 Usage

1. Set your `query` input (URL or search term).
2. Run the Actor.
3. Retrieve extracted text from the **Dataset** tab, or via the Apify API.

### 🧠 Use Cases

- Feeding live web content into LLM prompts (RAG pipelines)
- Building AI chatbots with up-to-date web knowledge
- Content extraction and summarization pipelines
- Lightweight alternative to full-page scrapers when only text is needed

### 🛠 Built With

- [Apify SDK for Python](https://docs.apify.com/sdk/python/)
- [httpx](https://www.python-httpx.org/)
- [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup/)

# Actor input Schema

## `query` (type: `string`):

Enter a full URL (e.g. https://example.com) to scrape directly, or a plain text search query to fetch results for.

## `maxChars` (type: `integer`):

Limits the length of extracted text returned per page.

## `removeSelectors` (type: `array`):

HTML elements to strip out before extracting text (ads, nav, footer, etc.)

## Actor input object example

```json
{
  "query": "https://example.com",
  "maxChars": 5000,
  "removeSelectors": [
    "script",
    "style",
    "nav",
    "footer",
    "header",
    "aside",
    "iframe",
    "noscript"
  ]
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "query": "https://example.com"
};

// Run the Actor and wait for it to finish
const run = await client.actor("bornoo/rag-web-browser").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "query": "https://example.com" }

# Run the Actor and wait for it to finish
run = client.actor("bornoo/rag-web-browser").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "query": "https://example.com"
}' |
apify call bornoo/rag-web-browser --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,bornoo/rag-web-browser"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/i7qyebMePRXF7pahF/builds/yyjp9U73JvBPl6WWa/openapi.json