rage

MASTERMIND

Innovative Approach: IA mode to AGI prompt template from Professor Codephreak

Professor-Codephreak is the first LLM that I developed. Professor-Codephreak is also a GPT4 agent designed to be a platform architect and software engineer. You know, the kind of solution oriented person you would gladly pay $1000 / hour to hang out with in the real world. The two parts of Professor-Codephreak have not “met” each other though the automindx engine in the GPT4 version uses automind to dynamically respond. automind was developed as codephreak’s first […]

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Hackathon Challenge:

OpenAI Assistants API Llama-Index/MongoDB In this hackathon, you will build and iterate on an LLM-based application using AI observability to validate the performance of your app. You can choose between two sets of tools for building your app: Tool set 1: The OpenAI Assistants API Tool set 2: Llama-Index, MongoDB and GPT-4. With either choice, you will use TruLens to validate and improve the performance of your application. By bringing together TruEra, OpenAI, Llama-Index, and […]

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Reliable fully local RAG agents with LLaMA3

https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_rag_agent_llama3_local.ipynb Building reliable local agents using LangGraph and LLaMA3-8b within the RAGE framework involves several key components and methodologies: Model Integration and Local Deployment: LLaMA3-8b: Utilize this robust language model for generating responses based on user queries. It serves as the core generative engine in the RAGE system. LangGraph: Enhance the responses of LLaMA3 by integrating structured knowledge graphs through LangGraph, boosting the model’s capability to deliver contextually relevant and accurate information. Advanced RAGE Techniques: […]

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