Treesequence_LLM_Viz

Query based Code Generation and Analysis of Tree-Sequence using LLM.

Goal

The goal is to leverage Large-language Models(LLM) to generate code and analyze tree-sequences using tskit by simply asking questions in plain English. With Retrieval-Augmented Generation (RAG), users can input questions in plain English, and the system will generate executable tskit code to answer these queries.

Current Version:

In this initial proof-of-concept, the tskit source code is used as a knowledge base for the Large Language Model (LLM). When users input queries in natural language, the LLM generates the appropriate code based on the knowledge and returns a python function as a response.

Current version is a naive prompt:answer approach which does not evaluate the accuracy of the generated code.

Next things to do.

• Code generation can be improved using Flow Engineering Approach. Use LangGraph and openai Function Calling to setup the workflow.
• Code execution with error checking.
• Multiple Iterations.
• Terminal chat interface / UI interface (flask-reactjs)
• human-in-the-loop. (human intervention to review the code or correct it.)
• Additional node(tool) to ask general tree-sequence question that are not related to code-generation.
• Accuracy/reliability of the generated answer.

Exploration

• How to enhance treesequence analysis. one way is MemoRAG. Memory-based knowledge discovery for long contexts.