MASTERMIND

MASTERMIND

MASTERMIND documentation on the blockchain

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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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The asyncio library in Python

The asyncio library in Python provides a framework for writing single-threaded concurrent code using coroutines, which are a type of asynchronous function. It allows you to manage asynchronous operations easily and is suitable for I/O-bound and high-level structured network code. Key Concepts Basic Usage Here’s a simple example of using asyncio to run a couple of coroutines: Creating Tasks You can use asyncio.create_task() to schedule a coroutine to run concurrently: Anticipate Futures Futures represent a […]

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RAGE for LLM as a Tool to Create Reasoning Agents as MASTERMIND

Introduction: article created as first test of GPT-RESEARCHER as a research tool The integration of Retrieval-Augmented Generative Engine (RAGE) with Large Language Models (LLMs) represents a significant advancement in the field of artificial intelligence, particularly in enhancing the reasoning capabilities of these models. This report delves into the application of RAGE in transforming LLMs into sophisticated reasoning agents, akin to a “MASTERMIND,” capable of strategic reasoning and intelligent decision-making. The focus is on how RAG […]

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