AI Academic Research Assistant

Welcome to the AI Academic Research Assistant, an application designed to enhance academic research workflows. This tool enables users to manage, search, and analyze research papers using advanced AI models and vector search technologies. Below is a comprehensive guide to set up, configure, and use the application.

Features

• User and Paper Management: RESTful APIs to manage users and papers.
• PDF Parsing: Extract text from PDF files and save them to a database.
• Semantic Search: Search for research papers using natural language queries.
• Text Summarization: Summarize research papers for quick insights.
• Customizable Storage: Support for in-memory or Pinecone-based vector storage.
• Agents for Enhanced Functionality:
  • SummarizerAgent: Provides concise summaries of academic papers.
  • ResearchAgent: Performs semantic search and retrieval of relevant papers.
  • LitReviewAgent: Facilitates comprehensive literature reviews by recommending local and external papers based on user-provided topics or existing papers.

Table of Contents

1. Installation
2. Configuration
3. Usage
4. API Endpoints
5. License

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Installation

Prerequisites

• Python 3.8+
• MySQL or compatible database
• pip

Steps

1. Clone the repository:

   git clone https://github.com/Helixan/AI-Academic-Research-Assistant.git
   cd ai-academic-research-assistant

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure the database and API keys (see Configuration).
5. Run the application:

   uvicorn src.main:app --reload

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Configuration

config/config.yaml

Define environment variables and configurations in config/config.yaml.

environment: development

openai_api_key: YOUR_KEY

database:
  url: YOUR_DB_URL

test_database:
  url: YOUR_TEST_DB_URL

vector_store:
  type: pinecone
  api_key: YOUR_PINECONE_KEY
  environment: "us-east-1"
  index_name: "my-index"

Logging

Logging is configured via config/logging.yaml:

version: 1
formatters:
  simple:
    format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

handlers:
  console:
    class: logging.StreamHandler
    formatter: simple
    level: DEBUG
    stream: ext://sys.stdout

root:
  level: DEBUG
  handlers: [console]

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Usage

Endpoints

Run the application and navigate to http://127.0.0.1:8000/docs to explore the API documentation.

• GET /users: Retrieve all users.
• POST /users: Create a new user.
• GET /papers: Retrieve all papers.
• POST /papers: Upload a paper.
• GET /papers/search: Search for papers.
• POST /papers/summarize/{id}: Summarize a specific paper.
• POST /papers/literature_review/local: Perform a local literature review by recommending top locally stored papers relevant to a user's topic.
• POST /papers/literature_review/external: Perform an external literature review by fetching references from external sources (e.g., Arxiv) related to a user's topic.
• POST /papers/literature_review/full: Perform a comprehensive literature review by combining both local and external paper recommendations.

Adding Papers

Upload PDFs using the /papers endpoint. Extracted content is stored in the database, and embeddings are generated for semantic search.

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License

This project is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International license. See the LICENSE file for details.