| Name | llama-index-readers-papers JSON |
| Version |
0.1.6
JSON |
| download |
| home_page | None |
| Summary | llama-index readers papers integration |
| upload_time | 2024-05-19 03:57:43 |
| maintainer | thejessezhang |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Papers Loaders
```bash
pip install llama-index-readers-papers
```
## Arxiv Papers Loader
This loader fetches the text from the most relevant scientific papers on Arxiv specified by a search query (e.g. "Artificial Intelligence"). For each paper, the abstract is extracted and put in a separate document. The search query may be any string, Arxiv paper id, or a general Arxiv query string (see the full list of capabilities [here](https://info.arxiv.org/help/api/user-manual.html#query_details)).
## Usage
To use this loader, you need to pass in the search query. You may also optionally specify a local directory to temporarily store the paper PDFs (they are deleted automatically) and the maximum number of papers you want to parse for your search query (default is 10).
```python
from llama_index.readers.papers import ArxivReader
loader = ArxivReader()
documents = loader.load_data(search_query="au:Karpathy")
```
Alternatively, if you would like to load papers and abstracts separately:
```python
from llama_index.readers.papers import ArxivReader
loader = ArxivReader()
documents, abstracts = loader.load_papers_and_abstracts(
search_query="au:Karpathy"
)
```
This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).
## Pubmed Papers Loader
This loader fetches the text from the most relevant scientific papers on Pubmed specified by a search query (e.g. "Alzheimers"). For each paper, the abstract is included in the `Document`. The search query may be any string.
## Usage
To use this loader, you need to pass in the search query. You may also optionally specify the maximum number of papers you want to parse for your search query (default is 10).
```python
from llama_index.readers.papers import PubmedReader
loader = PubmedReader()
documents = loader.load_data(search_query="amyloidosis")
```
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": "# Papers Loaders\n\n```bash\npip install llama-index-readers-papers\n```\n\n## Arxiv Papers Loader\n\nThis loader fetches the text from the most relevant scientific papers on Arxiv specified by a search query (e.g. \"Artificial Intelligence\"). For each paper, the abstract is extracted and put in a separate document. The search query may be any string, Arxiv paper id, or a general Arxiv query string (see the full list of capabilities [here](https://info.arxiv.org/help/api/user-manual.html#query_details)).\n\n## Usage\n\nTo use this loader, you need to pass in the search query. You may also optionally specify a local directory to temporarily store the paper PDFs (they are deleted automatically) and the maximum number of papers you want to parse for your search query (default is 10).\n\n```python\nfrom llama_index.readers.papers import ArxivReader\n\nloader = ArxivReader()\ndocuments = loader.load_data(search_query=\"au:Karpathy\")\n```\n\nAlternatively, if you would like to load papers and abstracts separately:\n\n```python\nfrom llama_index.readers.papers import ArxivReader\n\nloader = ArxivReader()\ndocuments, abstracts = loader.load_papers_and_abstracts(\n search_query=\"au:Karpathy\"\n)\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\n## Pubmed Papers Loader\n\nThis loader fetches the text from the most relevant scientific papers on Pubmed specified by a search query (e.g. \"Alzheimers\"). For each paper, the abstract is included in the `Document`. The search query may be any string.\n\n## Usage\n\nTo use this loader, you need to pass in the search query. You may also optionally specify the maximum number of papers you want to parse for your search query (default is 10).\n\n```python\nfrom llama_index.readers.papers import PubmedReader\n\nloader = PubmedReader()\ndocuments = loader.load_data(search_query=\"amyloidosis\")\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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