# Readwise Reader
```bash
pip install llama-index-readers-readwise
```
Use Readwise's export API to fetch your highlights from web articles, epubs, pdfs, Kindle, YouTube, and load the resulting text into LLMs.
## Setup
1. Get your Readwise API key from [readwise.io/access_token](https://readwise.io/access_token).
## Usage
Here is an example usage of the Readwise Reader:
```python
import os
from llama_index.core import VectorStoreIndex, download_loader
from llama_index.readers.readwise import ReadwiseReader
token = os.getenv("READWISE_API_KEY")
loader = ReadwiseReader(api_key=token)
documents = loader.load_data()
index = VectorStoreIndex.from_documents(documents)
index.query("What was the paper 'Attention is all you need' about?")
```
You can also query for highlights that have been created after a certain time:
```python
import os
import datetime
from llama_index.core import VectorStoreIndex, download_loader
from llama_index.readers.readwise import ReadwiseReader
token = os.getenv("READWISE_API_KEY")
loader = ReadwiseReader(api_key=token)
seven_days_ago = datetime.datetime.now() - datetime.timedelta(days=7)
documents = loader.load_data(updated_after=seven_days_ago)
index = VectorStoreIndex.from_documents(documents)
index.query("What has Elon Musk done this time?")
```
This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).
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"description": "# Readwise Reader\n\n```bash\npip install llama-index-readers-readwise\n```\n\nUse Readwise's export API to fetch your highlights from web articles, epubs, pdfs, Kindle, YouTube, and load the resulting text into LLMs.\n\n## Setup\n\n1. Get your Readwise API key from [readwise.io/access_token](https://readwise.io/access_token).\n\n## Usage\n\nHere is an example usage of the Readwise Reader:\n\n```python\nimport os\nfrom llama_index.core import VectorStoreIndex, download_loader\n\nfrom llama_index.readers.readwise import ReadwiseReader\n\ntoken = os.getenv(\"READWISE_API_KEY\")\nloader = ReadwiseReader(api_key=token)\ndocuments = loader.load_data()\nindex = VectorStoreIndex.from_documents(documents)\n\nindex.query(\"What was the paper 'Attention is all you need' about?\")\n```\n\nYou can also query for highlights that have been created after a certain time:\n\n```python\nimport os\nimport datetime\nfrom llama_index.core import VectorStoreIndex, download_loader\n\nfrom llama_index.readers.readwise import ReadwiseReader\n\ntoken = os.getenv(\"READWISE_API_KEY\")\nloader = ReadwiseReader(api_key=token)\nseven_days_ago = datetime.datetime.now() - datetime.timedelta(days=7)\ndocuments = loader.load_data(updated_after=seven_days_ago)\nindex = VectorStoreIndex.from_documents(documents)\n\nindex.query(\"What has Elon Musk done this time?\")\n```\n\nThis loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).\n",
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