> For the complete documentation index, see [llms.txt](https://docs.lilypad.tech/lilypad/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lilypad.tech/lilypad/use-cases-agents-and-projects/agents/ai-oncologist-agent.md).

# AI Oncologist Agent

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

{% embed url="<https://www.youtube.com/watch?v=5Hq3lUobrN4>" %}
Altruistic AI Agents - Lilypad Oncologist
{% endembed %}

## Agent Details

The AI Oncologist serves as a Research agent template that can be used to conduct a wide range of research. The Agent is an intelligent system for analyzing research papers using a multi-agent approach.

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

A guide to launch the Research agent locally and run inference on the Lilypad Network with the [Lilypad Inference API](https://github.com/Lilypad-Tech/HubSpot-LinkedIn-AgentSync).

### Prerequisites

* Python 3.8+
* OpenAI API key or compatible API (e.g., DeepSeek)
* PDF files

### Installation

1. Clone the repository:

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

2. Install required packages:

```bash
pip install -r requirements.txt
```

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

```env
# 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](https://api-docs.deepseek.com/api/deepseek-api))
* `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](https://docs.lilypad.tech/lilypad/developer-resources/inference-api))
* `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:

```bash
python AIOncologist.py
```

## Resources

* [GitHub](https://github.com/mavericb/ai-oncologist)


---

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