# AI Oncologist Agent

{% 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)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.lilypad.tech/lilypad/use-cases-agents-and-projects/agents/ai-oncologist-agent.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.