AI Chat Applications with the Metacognition Approach: Tree of Thoughts (ToT)
Tags: tot, ai, rag, chatbot
Published: 2024-03-22 12:02:46

In the rapidly evolving field of artificial intelligence (AI), the quest for creating chat applications that can understand and respond with almost human-like accuracy and context-awareness has led to significant innovations. One such innovation is the application of metacognitive strategies, particularly the ToT approach, integrated with Retrieval-Augmented Generation (RAG) technology. This combination promises to revolutionize how chatbots process information and interact with users.

## **Understanding Metacognition in AI**

Metacognition, in the context of AI, refers to the ability of systems to understand and analyze their own thought processes. In human cognition, this involves self-reflection and awareness of one's own knowledge and the ability to adjust strategies accordingly. When applied to AI, metacognition enables chat applications to assess their response mechanisms, leading to more accurate and contextually relevant interactions.

[[2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models (arxiv.org)](https://arxiv.org/abs/2305.10601)

## **The Role of RAG in Chat Applications**

Retrieval-Augmented Generation (RAG) is a technique that enhances chatbot responses by retrieving relevant information from a dataset to support the generation of answers. This method allows chat applications to pull from a vast pool of data, ensuring that responses are not only relevant but also enriched with the context necessary for meaningful conversations.

[Retrieval-Augmented Generation for Large Language Models: A Survey  (arxiv.org)](https://arxiv.org/pdf/2312.10997.pdf)

## **The Tree of Thoughts Approach**

The ToT is a conceptual framework that organizes information in a hierarchical structure, mirroring how human thought processes branch out and interconnect. In chat applications, this approach can manage dependent and independent pieces of information, ensuring that all relevant context is considered in the response generation process.

For instance, when translating code from one file, a chatbot might need additional context from related files or documentation. The ToT approach ensures that these connections are made, enriching the chatbot's response with a comprehensive understanding of the query.

### **Implementing Tree of Thoughts in Chat Applications with RAG**

To address the challenge of integrating the ToT with RAG in a chat application, particularly for tasks like translating code, where I have most experience, for example from Perl to C#, it's essential to consider dependencies that are often overlooked by standard prompting methods. Here's an enhanced approach with concrete examples:

### **Step-by-Step Integration with Concrete Examples**

1. **Data Structuring and Dependency Mapping**:
    - Begin by organizing your dataset and code repositories in a hierarchical structure that reflects the dependencies among various pieces of code, libraries, and documentation.
        - abstract syntax trees (AST) by [Tree sitter](https://tree-sitter.github.io/tree-sitter/)
        - Dependency Analysis and Change May-Impact Analysis mention in [CodePlan](https://arxiv.org/abs/2309.12499) (These features help understand the complex interdependencies within the code repository and predict how specific updates might affect other areas of the codebase)
    - **Example**: For a Perl to C# translation task, create a dependency graph that maps out how different Perl scripts interact with each other and with external libraries. This graph will guide the retrieval process.
2. **Retrieval Mechanism with Dependency Awareness**:
    - Implement a retrieval system capable of understanding and navigating the dependency graph to fetch not just the target script for translation but also any dependent scripts and libraries.
    - **Example**: If a Perl script **`main.pl`** uses a module **`helper.pl`**, the retrieval system should fetch both **`main.pl`** and **`helper.pl`** when tasked with translating **`main.pl`**.
3. **Enhanced Prompting for Translation**:
    - Use the retrieved information to construct enhanced prompts for the RAG model. These prompts should include context about the dependencies to inform the translation process.
    - **Example**: When translating **`main.pl`** to C#, the prompt to the RAG model should not only include the content of **`main.pl`** but also a summary or key functions from **`helper.pl`** to ensure the translated C# code maintains functional integrity.
4. **Iterative Refinement with ToT**:
    - Apply the Tree of Thoughts framework to iteratively refine the translation. After an initial translation attempt, use ToT to explore alternative translations or adjustments based on the dependencies and the overall structure of the code. For better understanding you can read Cyril Sadovsky [Blog](https://medium.com/aimonks/metacognition-experiments-with-ai-8ba10f284e4c).
    - **Example**: If the initial translation of **`main.pl`** misses a crucial aspect handled in **`helper.pl`**, ToT can guide the model to reconsider this dependency and adjust the translation accordingly.


| ![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/zze74hksnh1au2rjk8ug.png) |
|:--:| 
| Fig.1 - Tree of thought method visualization. While GPT-4 with chain-of-thought prompting only solved 4% of tasks, ToT method achieved a success rate of 74%. Code repo with all prompts: [this https URL](https://github.com/princeton-nlp/tree-of-thought-llm). |


### Most common used strategy: Multi-Agent Approach

The multi-agent approach in chatbot development involves the coordination of multiple AI agents or models to handle different aspects of a conversation or to combine their strengths for complex tasks.

Implementation examples:

[Agentic RAG With LlamaIndex — LlamaIndex, Data Framework for LLM Applications](https://www.llamaindex.ai/blog/agentic-rag-with-llamaindex-2721b8a49ff6)

[LangGraph: Multi-Agent Workflows (langchain.dev)](https://blog.langchain.dev/langgraph-multi-agent-workflows/)

Product which you can try - https://github.com/Pythagora-io/pythagora, check also video - [Open-Source AI Agent Can Build FULL STACK Apps (FREE “Devin” Alternative) (youtube.com)](https://www.youtube.com/watch?v=xQlnqTMC9xA)

## Metacognition approach vs Multi agent approach

- **Complexity and Implementation:** The metacognition approach focuses on the internal processes of a single agent, which might be simpler to implement but challenging to perfect, whereas the multi-agent approach involves coordinating multiple components, which can be more complex but offers scalability and specialization.
- **Flexibility vs. Specialization:** Metacognition lends flexibility and adaptability to a single agent, making it better at self-improvement and handling a wide range of interactions with some depth. In contrast, the multi-agent approach leverages specialization, potentially offering depth in specific domains and a broader overall range of expertise.
- **Integration Potential:** These approaches are not mutually exclusive and could be integrated for enhanced performance. For instance, a multi-agent system could include a metacognitive agent responsible for assessing the system's performance and guiding the collaboration among agents.

## Why to consider metacognition approach?

The integration of the metacognition approach, particularly the ToT, with RAG technology represents a significant leap forward in the development of AI chat applications. By enabling chatbots to understand and utilize the full context of their interactions, we can create more engaging, accurate, and human-like conversational experiences. As we continue to explore these innovative approaches, the potential for AI to understand and interact with the world in a more nuanced and meaningful way seems increasingly within reach.

** comment: I really like generating picture with AI, so please do not hate me for my header picture.