# paper-qa-nemotron

[![GitHub](https://img.shields.io/badge/GitHub-black?logo=github\&logoColor=white)](https://github.com/Future-House/paper-qa/tree/main/packages/paper-qa-nemotron) [![tests](https://github.com/Future-House/paper-qa/actions/workflows/tests.yml/badge.svg)](https://github.com/Future-House/paper-qa) ![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg) ![PyPI Python Versions](https://img.shields.io/pypi/pyversions/paper-qa-nemotron)

PDF reading code backed by [Nvidia's nemotron-parse VLM](https://build.nvidia.com/nvidia/nemotron-parse).

For more info on nemotron-parse, check out:

* Technical blog: <https://developer.nvidia.com/blog/turn-complex-documents-into-usable-data-with-vlm-nvidia-nemotron-parse-1-1/>
* Hugging Face weights: <https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1>
* NIM and model card: <https://build.nvidia.com/nvidia/nemotron-parse>
  * Support matrix: <https://docs.nvidia.com/nim/vision-language-models/1.5.0/support-matrix.html#nemotron-parse>
* API docs: <https://docs.nvidia.com/nim/vision-language-models/1.5.0/examples/nemotron-parse/overview.html#nemotron-parse-overview>
* Cookbook: <https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-Parse-v1.1/build_general_usage_cookbook.ipynb>
* NGC catalog: <https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/nemotron-parse>
* AWS Marketplace: <https://aws.amazon.com/marketplace/pp/prodview-ny2ngku2i4ge6>

## Installation

```bash
pip install paper-qa[nemotron]
# Or
pip install paper-qa-nemotron
```

If you want to prompt nemotron-parse hosted on AWS SageMaker:

```bash
pip install paper-qa-nemotron[sagemaker]
```

## Getting Started

To use nemotron-parse via the Nvidia API, set the `NVIDIA_API_KEY` environment variable.

Then to directly access the reader:

```python
from paperqa.types import ParsedText
from paperqa_nemotron import parse_pdf_to_pages

async def main(pdf_path) -> ParsedText:
    return await parse_pdf_to_pages(pdf_path)
```

Or use the reader within PaperQA:

```python
from paperqa import Docs, PQASession, Settings

from paperqa_nemotron import parse_pdf_to_pages


async def main(pdf_path, question: str | PQASession) -> PQASession:
    settings = Settings(parsing={"parse_pdf": parse_pdf_to_pages})
    docs = Docs()
    await docs.aadd(pdf_path, settings=settings)
    return await docs.aquery(question, settings=settings)
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.edisonscientific.com/paperqa/packages/paper-qa-nemotron.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.