A developer building a LlamaIndex pipeline that reads Wikipedia pages needs clean page text before passing it into a vector index or query engine. Wikipedia pages mix article content with infoboxes, citation markers, edit links, hatnotes, contents tables, language selector, and “See also” footers, and scraping them by hand requires CSS selectors that break when Wikipedia updates its layout. EnConvert Perceive renders up to 10 Wikipedia pages in a single batch call and returns clean Markdown for each one, with navigation and sidebars stripped by default. The extracted Markdown is ready to load into a LlamaIndex VectorStoreIndex with no HTML parsing.
How to Scrape Wikipedia with LlamaIndex
Scrape Wikipedia pages into a LlamaIndex pipeline with EnConvert Perceive, returning clean Markdown with navigation and sidebars stripped in a single batch call
Get API keyExample
python
import os
import requests
from llama_index.core import VectorStoreIndex
from llama_index.core.schema import TextNode
# LlamaIndex's default embedding model and LLM read OPENAI_API_KEY from the environment
BASE_URL = "https://api.enconvert.com/v2"
headers = {
"X-API-Key": os.environ["ENCONVERT_API_KEY"],
"Content-Type": "application/json",
}
page_urls = [
"https://en.wikipedia.org/wiki/Retrieval-augmented_generation",
"https://en.wikipedia.org/wiki/Vector_database",
]
if len(page_urls) > 10:
raise ValueError("This example covers inline batches of 10 URLs or fewer")
response = requests.post(f"{BASE_URL}/perceive/batch", headers=headers,
json={"urls": page_urls, "options": {"outputs": ["markdown"]}}, timeout=300)
if response.status_code == 402:
raise RuntimeError("Monthly ops allowance exhausted")
response.raise_for_status()
nodes = []
for result in response.json()["items"]:
page_url = result["url"]
if result.get("status") != "completed":
print(f"Skipped {page_url}: {result.get('status')}")
continue
if result.get("is_blocked"):
print(f"Skipped {page_url}: blocked page")
continue
render_quality = result.get("render_quality", 1.0)
if render_quality < 0.40:
print(f"Skipped {page_url}: low render quality ({render_quality:.2f})")
continue
markdown_url = result["outputs"]["markdown"]["url"]
markdown = requests.get(markdown_url, timeout=60)
markdown.raise_for_status()
nodes.append(TextNode(
text=markdown.text,
id_=page_url,
metadata={"source_url": page_url, "render_quality": render_quality},
))
if not nodes:
raise RuntimeError("No Wikipedia pages passed the render checks")
index = VectorStoreIndex(nodes)
query_engine = index.as_query_engine(similarity_top_k=3)
print(query_engine.query("What is retrieval-augmented generation, and how do vector databases support it?"))What you get back
Perceive returns clean Markdown for each URL in the batch, with navigation and sidebars stripped by default, delivered as pre-signed URLs that you fetch once per page. Scraping Wikipedia by hand needs CSS selectors that break when the layout changes, and passing raw Wikipedia HTML into a LlamaIndex node fills the index with infobox rows, citation markers, and edit links that add noise to every similarity search result.