EnConvert reader for LlamaIndex

llama-index-readers-enconvert turns web pages and whole sites into LlamaIndex Documents through EnConvert. Every perceived page carries a render_quality score (0.0-1.0) in its metadata, so a blocked or empty page comes back flagged rather than trusted.

Install

pip install llama-index-readers-enconvert

Use

from llama_index.readers.enconvert import EnConvertReader

reader = EnConvertReader(api_key="sk_...")  # or set $ENCONVERT_API_KEY

# A few URLs into clean-markdown Documents:
docs = reader.load_data(urls=["https://example.com", "https://example.com/pricing"])

# Or a whole site into RAG-ready chunk Documents:
docs = reader.load_data(ingest_url="https://docs.example.com", mode="sitemap", max_pages=100)

from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(docs)

• URLs are perceived into markdown; metadata carries url and render_quality.
• ingest_url crawls the site (async; the reader polls to completion), then returns one Document per chunk, each carrying the chunk's own metadata (source URL, title, etc.).

Auth: a private key (sk_...) from your dashboard. Public pk_ keys are rejected. The key is read from api_key= or $ENCONVERT_API_KEY and is excluded from the reader's serialized form.

Licence

MIT