Implementing EcoAssistant: Leveraging AutoGen for Enhanced Code-driven Question Answering

Introduction

EcoAssistant, built on the principles outlined in the paper "EcoAssistant: Using LLM Assistant More Affordably and Accurately", showcases an advanced application of AutoGen in AI-driven question answering. The system's implementation hinges on three pivotal features:

1. Iterative Code Refinement: Allowing conversational Large Language Models (LLMs) to interact with an automatic code executor, enabling iterative development and execution of code.
2. Assistant Hierarchy: A structured system starting with more cost-effective, less powerful LLMs, and seamlessly escalating to more powerful, expensive models for complex queries.
3. Solution Demonstration: Utilizing past successful queries as examples to guide LLMs in efficiently handling new, similar queries.

These features are brought to life through a series of Python scripts, namely run.py, retrieval_agent.py, assistant_hierarchy.py, and evaluation.py.

Core Components of EcoAssistant

1. Run Script (run.py)

run.py acts as the central orchestrator. It initializes key components like API tokens and model configurations, and manages the flow of interaction among different agents.

• Initialization and Configuration: It loads API keys and sets up configurations for various models.
• Assistant Hierarchy: Implements the assistant hierarchy, dynamically selecting models based on task complexity.
• Evaluation Functions: Sets up various evaluation methods, including human and model evaluations, to gauge response effectiveness.

2. Retrieval Agent (retrieval_agent.py)

retrieval_agent.py introduces the RetrievalAgent class, essential for sourcing relevant information and examples to assist in solving queries.

• API and Solution Demonstration: Manages API interactions and provides solution demonstrations to aid the AI model in generating accurate responses.
• Retrieval and Evaluation: Retrieves relevant past examples and evaluates responses using predefined functions.

3. Assistant Hierarchy (assistant_hierarchy.py)

assistant_hierarchy.py outlines a hierarchy of assistants, enabling the system to escalate queries from simpler models to more advanced ones as necessary.

• Sub-Assistants and HyperAssistantAgent: Manages a layered approach to query handling with sub-assistants and a hyper-assistant.
• Model Integration: Integrates different models, switching between them based on the query's complexity.

4. Evaluation Mechanism (evaluation.py)

evaluation.py offers mechanisms for assessing the responses generated by the assistant.

• Human and Model Evaluations: Incorporates both human and model evaluations for a comprehensive assessment of the assistant's performance.
• Mixed Evaluation Function: Combines human and AI judgments to validate the assistant's response effectiveness.

Conclusion

EcoAssistant, through its sophisticated integration of various Python scripts, demonstrates an effective approach to leveraging Large Language Models for complex, code-driven question answering. By intelligently combining different models, evaluation strategies, and retrieval techniques, EcoAssistant stands as a testament to the potential of AI in enhancing question-answering systems.

Reference

• Implementing EcoAssistant: Leveraging AutoGen for Enhanced Code-driven Question Answering.EcoAssistant on GitHub.

• EcoAssistant: Using LLM Assistant More Affordably And Accuracy.Link to Paper

Related

• paper Unraveling EcoAssistant: Autogen's Advancement in Economical and Precise Code-Driven Question Answering - 2023-11-13
• paper AutoGen: Unleashing the Power of Multi-Agent Conversations in LLM Applications - 2023-11-12
• paper Cost-Effective Hyperparameter Tuning for LLMs on a Budget - 2023-10-18
• paper A Comprehensive Overview of LLM-Based Autonomous Agents - 2023-11-10
• llm Optimizing Llama 2: Harnessing the Power of Prompt, RAG, and Fine-Tuning - 2023-11-04
• paper Cognitive Architectures for Language Agents - 2024-04-01
• paper Genie: Generative Interactive Environments - 2024-02-28
• paper Multi-Agent Reasoning with Large Language Models for Effective Corporate Planning - 2024-01-03
• paper Training Language Model Agents without Modifying Language Models - 2024-03-19
• paper A Survey on Language Models for Code: from Statistical Models to AI-driven Code Mastery - 2023-11-28

Created 2023-11-13T16:37:06-08:00, updated 2023-11-16T19:08:14-08:00 · History · Edit