Inquisitor

Welcome to the Inquisitor, an AI-powered 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.