[Feature]: creating an intelligent entity through multiple AI brain

[wangshuai-wuhan]

The Feature

Andrew Ng introduced the concept of creating an intelligent entity through multiple AI agents. Similar instances include CrewAI or Chatdev. Is it possible to coordinate multiple brains to form one intelligent entity? In this way, we have a second brain, a third brain, a fourth brain. These brains work together on a task according to different divisions of labor.

Motivation, pitch

Andrew Ng introduced the concept of creating an intelligent entity through multiple AI agents. Similar instances include CrewAI or Chatdev. Is it possible to coordinate multiple brains to form one intelligent entity? In this way, we have a second brain, a third brain, a fourth brain. These brains work together on a task according to different divisions of labor.

Twitter / LinkedIn details

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[dosubot]

Hey there @wangshuai-wuhan! 🚀 Long time no chat. I see you're diving into the collective brain pool this time. Fascinating stuff! 🧠✨

To integrate and coordinate multiple AI agents within the Quivr framework to create a single, more intelligent entity, you can follow a multi-step process that leverages the existing structure of the Quivr framework. The process involves the use of a conversational retrieval chain, embeddings for document retrieval, and a conversational language model for generating responses. Here's a breakdown of how this can be achieved:

1. Document Retrieval with Embeddings: Use embeddings to retrieve relevant documents or information from a database or memory. This is where multiple AI agents can contribute by fetching information from diverse sources or databases. The vector_store in the Quivr framework, possibly backed by OllamaEmbeddings or OpenAIEmbeddings, can be used for this purpose. Each AI agent can be responsible for a different aspect or type of information, ensuring a comprehensive search.

2. Conversational Memory: Utilize ConversationBufferMemory to maintain a conversation history. This allows the system to understand the context of the conversation and make decisions based on previous interactions. Multiple AI agents can input into this memory, providing a richer context for decision-making.

3. Question Rephrasing and Standalone Question Generation: Before fetching documents, rephrase the follow-up question into a standalone question using the CONDENSE_QUESTION_PROMPT. This step ensures that each query to the system is self-contained and can be understood without needing the entire conversation history. This rephrasing can be handled by a specialized AI agent trained for understanding and generating natural language queries.

4. Retrieving and Combining Documents: Once the standalone question is generated, use the document retrieval mechanism (get_retriever) to fetch relevant documents. The _combine_documents method can then combine these documents into a single context. This step can involve coordination between multiple AI agents, each retrieving documents based on their specialized knowledge domains.

5. Generating Answers: With the combined context and the standalone question, use a conversational language model (ChatLiteLLM) to generate an answer. This model can be seen as the coordinator or integrator of the inputs from various AI agents, synthesizing the information into a coherent and concise answer.

6. Custom Instructions and Prompts: The system allows for custom instructions (prompt_to_use) to be used when generating answers. This feature can be leveraged to adjust the behavior of the AI based on the specific needs of the conversation or the information retrieved by different agents.

7. Feedback Loop for Learning: Incorporate a feedback mechanism where the responses and the effectiveness of the integration are evaluated. This feedback can be used to adjust the strategies of the individual AI agents and the overall coordination mechanism.

By following these steps and leveraging the modular design of the Quivr framework, you can effectively integrate and coordinate multiple AI agents to create a single, more intelligent entity. This approach allows for the specialization of AI agents in different domains or tasks while ensuring that their contributions are synthesized into coherent and contextually relevant responses.

For more details, you can refer to the following sources:

• backend/modules/brain/rags/quivr_rag.py
• backend/packages/embeddings/vectors.py
• frontend/app/chat/[chatId]/hooks/useHandleStream.ts

Please note that while this approach outlines a possible way to integrate multiple AI agents within the Quivr framework, it may require significant development effort and a deep understanding of the Quivr codebase and its dependencies. It's also important to consider the computational resources required for running multiple AI agents and the potential complexity of coordinating their outputs.

Sources

• backend/modules/brain/rags/quivr_rag.py
• backend/packages/embeddings/vectors.py
• frontend/app/chat/[chatId]/hooks/useHandleStream.ts

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