How can we search the wanted key information from 10,000+ pages of PDFs within 2.5 hours? For fact check, how do we implement it so that answers are backed by page-level references, minimizing hallucinations?
RAG-Challenge-2 is a great open-source project by Ilya Rice that ranked 1st at the Enterprise RAG Challenge, which has 4500+ lines of code for implementing a high-performing RAG system. It might seem overwhelming to newcomers who are just beginning to learn this technology. Therefore, to help you get started quickly—and to motivate myself to learn its ins and outs—I’ve created a complete tutorial on this.
We have a complete graph to explain its workflow
where multiple tools are used: Docling for parsing PDFs, LangChain for chunking text, faiss for vectorization and similarity searching, and chatgpt for LLMs.
Besides, I also outline the codeflow, demonstrating the running logic involving multiple python files where starters can easily get lost. Different files are colored differently. The purpose of showing this is not letting you memorize all of these file relationships. It works better for you to check the source code yourself and use this as a reference if you find yourself lost in the code.
The original project of Ilya Rice design its RAG system for answering questions of annual reports from companies, so he only designed three types of question response format for that challenge: a name, a number, or a boolean. But to ask questions about technical stuff, we absolutely ask general questions like How does RoPE work? to know about some concepts and the like
Therefore, I further modify the system logic to fit this need by customizing an AnswerWithRAGContextExplanationPrompt class and automatically matching the most related chapter and corresponding pages via searching through all faiss databases (only retrieve the top-1)
The final performance is demonstrated below (not cherry-picked, only tested once).
- What's the steps to train a nanoGPT from scratch?
The answer is:
Training nanoGPT from scratch involves several clearly defined steps. First, set up the environment by installing necessary libraries, using either Anaconda or Google Colab, and then download the dataset (e.g., tinyShakespeare). Next, tokenize the text into numerical representations and split the data into training and validation sets. Define the model architecture including token/positional embeddings, transformer blocks with multi-head self-attention and feed-forward networks, and layer normalization. Configure training hyperparameters and set up an optimizer (such as AdamW). Proceed with a training loop that performs forward passes, computes loss, backpropagates, and updates parameters, while periodically evaluating performance on both training and validation data. Finally, use the trained model to generate new text from a given context.
All code are provided on Colab and the tutorial is referenced here. Hope this helps!
zljdanceholic•1y ago
RAG-Challenge-2 is a great open-source project by Ilya Rice that ranked 1st at the Enterprise RAG Challenge, which has 4500+ lines of code for implementing a high-performing RAG system. It might seem overwhelming to newcomers who are just beginning to learn this technology. Therefore, to help you get started quickly—and to motivate myself to learn its ins and outs—I’ve created a complete tutorial on this.
We have a complete graph to explain its workflow where multiple tools are used: Docling for parsing PDFs, LangChain for chunking text, faiss for vectorization and similarity searching, and chatgpt for LLMs.
Besides, I also outline the codeflow, demonstrating the running logic involving multiple python files where starters can easily get lost. Different files are colored differently. The purpose of showing this is not letting you memorize all of these file relationships. It works better for you to check the source code yourself and use this as a reference if you find yourself lost in the code.
The original project of Ilya Rice design its RAG system for answering questions of annual reports from companies, so he only designed three types of question response format for that challenge: a name, a number, or a boolean. But to ask questions about technical stuff, we absolutely ask general questions like How does RoPE work? to know about some concepts and the like Therefore, I further modify the system logic to fit this need by customizing an AnswerWithRAGContextExplanationPrompt class and automatically matching the most related chapter and corresponding pages via searching through all faiss databases (only retrieve the top-1) The final performance is demonstrated below (not cherry-picked, only tested once).
- What's the steps to train a nanoGPT from scratch? The answer is:
Training nanoGPT from scratch involves several clearly defined steps. First, set up the environment by installing necessary libraries, using either Anaconda or Google Colab, and then download the dataset (e.g., tinyShakespeare). Next, tokenize the text into numerical representations and split the data into training and validation sets. Define the model architecture including token/positional embeddings, transformer blocks with multi-head self-attention and feed-forward networks, and layer normalization. Configure training hyperparameters and set up an optimizer (such as AdamW). Proceed with a training loop that performs forward passes, computes loss, backpropagates, and updates parameters, while periodically evaluating performance on both training and validation data. Finally, use the trained model to generate new text from a given context.
All code are provided on Colab and the tutorial is referenced here. Hope this helps!