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from $1.50 / 1,000 results

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Wikipedia RAG Search

Try for free

Generate structured, citation-ready Wikipedia evidence for RAG pipelines and AI agents. Includes canonical IDs, clean excerpts, attribution metadata, freshness, dedupe, and optional change detection.

Pricing

from $1.50 / 1,000 results

Rating

0.0

(0)

Developer

Simon Jessen

Actor stats

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Total users

Monthly active users

2 days ago

Last modified

Categories

Automation

You can access the Wikipedia RAG Search programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {
9    "query": "retrieval augmented generation",
10    "maxResults": 10,
11}
12
13# Run the Actor and wait for it to finish
14run = client.actor("primeselectai/wikipedia-evidence-pack-api").call(run_input=run_input)
15
16# Fetch and print Actor results from the run's dataset (if there are any)
17print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
18for item in client.dataset(run.default_dataset_id).iterate_items():
19    print(item)
20
21# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Wikipedia Evidence Pack API for RAG API in Python

The Apify API client for Python is the official library that allows you to use Wikipedia RAG Search API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Wikipedia RAG Search API in JavaScript

Wikipedia RAG Search API through CLI

Wikipedia RAG Search OpenAPI definition

Wikipedia RAG Search API

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Wikipedia Article Extractor

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Extract Wikipedia articles via MediaWiki API. Get full text, summaries, sections, categories, images, links. Multi-language. Perfect for AI/ML training data and RAG.