Artificial General Intelligence (AGI) is an evolving field that holds the promise of machines capable of understanding, learning, and applying intelligence to a broad range of tasks. In this guide, we will delve into a collection of personally developed projects that significantly contribute to the advancement of AGI. Each project reflects extensive research and design, aiming to provide maximum value to the AI community.
Project Overview
The following projects are categorized into three main domains: Complex QA, Knowledge Graphs, and Retrieval Augmented Generation (RAG). Let’s explore these projects in detail.
Complex QA
Knowledge Graphs
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Neo4j Agent Vector and Graph Chain
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Link Prediction Ultra
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Knowledge Graph Rebel vs LlamaIndex
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Retrieval Augmented Generation (RAG)
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Integrated QA Neo4j
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Integrated QA Neo4j Unstructured and Graph Knowledge
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Llama2-7B Medical QA
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Understanding Project Code With an Analogy
Imagine you’re preparing a meal in a kitchen. Each project represented in our list is like a unique recipe that requires specific ingredients, tools, and steps. Just as you would gather different kitchen gadgets and follow the cooking process to create a dish, each project needs particular libraries and programming functions to achieve its goal.
For instance, the “Top-k Retrieval Cross Encoder” project can be compared to a chef selecting the best ingredients based on taste preferences and availability from a pantry. Similarly, the project retrieves the most relevant data using various methods, ensuring that the question-answering process is smooth and efficient.
Likewise, “Knowledge Graphs” are akin to organizing your recipe book in a visually appealing manner, allowing for easy access to the right recipe based on what you want to eat. This categorization is essential for creating meaningful connections and insights from raw data.
Troubleshooting Tips
As you embark on your journey through these projects, you might encounter some hiccups. Here are some troubleshooting ideas to get you back on track:
- Make sure that all required libraries are installed. Missing libraries are akin to not having the right cooking tools—your dish just won’t turn out right.
- Check for compatibility issues with your coding environment. Just like ensuring your oven heats properly, make sure your framework and language versions align.
- If you encounter errors when running a notebook, look at the error message closely. It often points directly to the issue, similar to how burnt smells guide you to a charred roast.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
