Academicagent

Academicagent is a Python package that integrates downloading papers from arXiv and evaluating them using a local large model (Ollama).

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Features

• Download arXiv Papers
  Search for and download a specified number of PDF papers from arXiv based on the keywords provided by the user.

• Large Model Q&A
  For each downloaded paper, extract the first page from the PDF and use the local large model to generate a Chinese summary along with an evaluation score for the paper.

Installation

1. Download Ollama and Start the Local Large Model Service

   Download Ollama from: https://ollama.com/

   For example, to download the deepseek-r1:1.5b model, run:

   ollama pull deepseek-r1:1.5b
   

2. Install paperagent

Install using pip:

pip install paperagent

Usage Example

from academicagent.agent import run_agent

run_agent(paper_keyword="object detection", total_count=1, save_path="papers", model_name="deepseek-r1:1.5b")

Input Parameters

• paper_keyword (string): The keyword used to search for papers on arXiv.
  Example: "object detection"

• total_count (integer): The total number of papers to download.
  Example: 5

• save_path (string, default "papers"): The folder path where the downloaded PDFs will be saved. If not provided, it defaults to "papers".
  Example: "papers"

• question (string, optional): The question to provide to the large model. If not specified, the default question is:
  "Please generate a Chinese summary of this paper and evaluate its value based on originality, effectiveness, and scope, on a scale of 0 to 10, then provide your score after the summary is generated."

• model_name (string, default "deepseek-r1:1.5b"):
  The name of the local large model to be used for invoking Ollama.

Output

• PDF Download: The PDFs of the papers are downloaded from arXiv into the specified save_path folder based on the provided keyword and count.

• Evaluation File: The title of each paper and the large model's response are written into a Markdown file.

Version History

• v0.1.0
  Initial release, implementing arXiv paper downloading and large model Q&A functionality.

Contact

If you have any questions or suggestions, please feel free to submit an issue or pull request.