AI Oncologist Agent
A research agent template implemented as an AI oncologist. Read papers, isolate important information, and produce actionable reports.
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A research agent template implemented as an AI oncologist. Read papers, isolate important information, and produce actionable reports.
Last updated
Was this helpful?
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:
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
Extracts text from identified papers
Splits content into manageable chunks
Scores paragraph relevance using LLM
Returns top-scoring paragraphs with relevance scores
Analyzes provided text passages
Generates focused answers to specific queries
Uses contextual understanding to provide accurate responses
Python 3.8+
OpenAI API key or compatible API (e.g., DeepSeek)
PDF files
Clone the repository:
Install required packages:
Create a .env
file in the project root with the following variables:
BASE_URL
: API endpoint for OpenAI Compatible LLM service (default: "https://api.deepseek.com")
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_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)
Place your PDF research papers in the documents/
directory.
Run the main script:
A guide to launch the Research agent locally and run inference on the Lilypad Network with the .
OPENAI_API_KEY
: Your OpenAI Compatible API key (for example: )
ANURA_API_KEY
: Your Anura API key (get it here: )