Public keys will not work. Keys starting with
pk_ are meant for browser widgets and are rejected by the reader. Read more in
Private Keys.
---
## Using EnConvertReader
The reader supports two modes.
### Perceive a few URLs
Turn individual URLs into markdown `Document`s:
```python
from llama_index.readers.enconvert import EnConvertReader
reader = EnConvertReader(api_key="sk_...")
docs = reader.load_data(urls=["https://example.com", "https://example.com/pricing"])
```
Each Document's metadata carries:
- `url`: the source URL
- `render_quality`: a number from 0.0 (blocked/empty) to 1.0 (clean render)
### Crawl a site into chunks
Crawl an entire site and return one Document per content chunk, ready for semantic search:
```python
from llama_index.readers.enconvert import EnConvertReader
reader = EnConvertReader(api_key="sk_...")
docs = reader.load_data(
ingest_url="https://docs.example.com",
mode="sitemap", # or "crawl" or "hybrid"
max_pages=100
)
```
Each Document's metadata carries the chunk's own source URL, title, and section context. The reader polls the ingestion job to completion, so `load_data()` is synchronous.
---
## Building a vector index
Chain the reader into a vector store and query it:
```python
from llama_index.readers.enconvert import EnConvertReader
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.vector_stores import SimpleVectorStore
# Load documents
reader = EnConvertReader(api_key="sk_...")
docs = reader.load_data(ingest_url="https://docs.example.com", mode="sitemap")
# Build index
index = VectorStoreIndex.from_documents(docs)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("How do I install this?")
print(response)
```
---
## Troubleshooting
**`raise ValueError("Provide either urls or ingest_url, but not both")`**
You passed both `urls=` and `ingest_url=` to `load_data()`. Pick one: perceive individual URLs, or crawl a site. Passing neither raises an error too.
**`AuthenticationError: 401 Unauthorized`**
The API key is wrong or missing. Verify you passed `api_key="sk_..."` or set `$ENCONVERT_API_KEY` to a **private** key (starts with `sk_`). Public keys are rejected immediately.
**`Load time seems very long.`**
Site crawls are asynchronous. The reader polls until the job finishes, which can take several minutes for a large site. For a responsive UX, poll the job status directly via `/v2/ingest/{job_id}` or use the async variant (if available).
**`render_quality is very low.`**
A low score (< 0.5) means the page is blocked, empty, or heavy JavaScript that didn't render in time. Check the source URL in a browser to see what the page actually serves. If it's a single-page app, a second render might succeed; if it's blocked, there's no retry.
---
## Source and links
- **Source**: [enconvert/llama-index-readers-enconvert](https://github.com/enconvert/llama-index-readers-enconvert)
- **Package**: [PyPI](https://pypi.org/project/llama-index-readers-enconvert)
- **Licence**: MIT
- **LlamaIndex documentation**: [docs.llamaindex.ai](https://docs.llamaindex.ai)
- **Underlying API**: [Introduction](/docs/introduction.md)
---
## Frequently asked questions
### How do I load a single URL into a Document?
```python
from llama_index.readers.enconvert import EnConvertReader
reader = EnConvertReader(api_key="sk_...")
docs = reader.load_data(urls=["https://example.com"])
```
Each Document's content is the page's markdown, and metadata carries the `url` and `render_quality` score.
### How do I crawl and index a whole site?
```python
from llama_index.readers.enconvert import EnConvertReader
from llama_index.core import VectorStoreIndex
reader = EnConvertReader(api_key="sk_...")
docs = reader.load_data(ingest_url="https://docs.example.com", mode="sitemap", max_pages=100)
index = VectorStoreIndex.from_documents(docs)
# Query
engine = index.as_query_engine()
response = engine.query("What is this?")
```
### How do I check a page's render quality before indexing?
Every Document's metadata includes `render_quality`, a score from 0.0 to 1.0. Filter documents before indexing:
```python
high_quality = [doc for doc in docs if doc.metadata.get('render_quality', 1.0) > 0.5]
index = VectorStoreIndex.from_documents(high_quality)
```
### Can I crawl multiple sites?
Yes. Create one reader per site and call `load_data()` sequentially for each, or parallelize the calls in your own async code.
### Why is my crawl taking so long?
Site crawls are asynchronous and polled. Large sites (100+ pages) can take several minutes. The reader blocks until done, returning one Document per indexed chunk.