🧬AI Oncologist Agent

A research agent template implemented as an AI oncologist. Read papers, isolate important information, and produce actionable reports.

An intelligent system for analyzing oncological research papers using a multi-agent approach. The system employs three specialized agents to search, extract, and analyze information from medical research papers.

Altruistic AI Agents - Lilypad Oncologist

Agent Details

The system consists of three main agents:

Paper Relevance Agent

• Searches through PDF documents in the documents directory

• Uses embeddings and cosine similarity for initial filtering

• Verifies relevance using LLM-based analysis

• Returns a list of most relevant paper filenames

Top Paragraphs Agent

• Extracts text from identified papers

• Splits content into manageable chunks

• Scores paragraph relevance using LLM

• Returns top-scoring paragraphs with relevance scores

Text Query Agent

• Analyzes provided text passages

• Generates focused answers to specific queries

• Uses contextual understanding to provide accurate responses

Getting Started

Prerequisites

• Python 3.8+

• OpenAI API key or compatible API (e.g., DeepSeek)

• PDF files

Installation

1. Clone the repository:

git clone https://github.com/mavericb/ai-oncologist.git
cd ai-oncologist

1. Install required packages:

pip install -r requirements.txt

1. Create a .env file in the project root with the following variables:

# OpenAI-Like API configuration
BASE_URL="https://api.deepseek.com"
OPENAI_API_KEY=your_deepseek_api_key
MODEL="deepseek-chat"

ANURA_BASE_URL=https://anura-testnet.lilypad.tech
ANURA_API_KEY=your_anura_api_key
ANURA_MODEL=phi4:14b

# Search configuration
MAX_RESULTS=3
SIMILARITY_THRESHOLD=0.3

• BASE_URL: API endpoint for OpenAI Compatible LLM service (default: "https://api.deepseek.com")

• OPENAI_API_KEY: Your OpenAI Compatible API key (for example: DeepSeek API Docs)

• MODEL: One OpenAI Compatible model to use (default: "deepseek-chat")

• ANURA_BASE_URL: API endpoint for the Anura LLM service (default: "https://anura-testnet.lilypad.tech")

• ANURA_API_KEY: Your Anura API key (get it here: Lilypad Inference API Docs)

• ANURA_MODEL: The Anura model to use (default: "phi4:14b")

• MAX_RESULTS: Maximum number of papers to return (default: 3)

• SIMILARITY_THRESHOLD: Minimum similarity score for document selection (default: 0.3)

Usage

1. Place your PDF research papers in the documents/ directory.

2. Run the main script:

python AIOncologist.py

Resources

• GitHub