I Built a RAG Pipeline From Scratch. Here’s What I Learned About Unstructured Data.

I built a Retrieval Augmented Generation (RAG) pipeline from scratch. It seemed complicated at first glance but turned out to be pretty easy. But, my journey was far from simple. Let’s talk about all the things I learned as I dappled into building my own pipeline.

The Task At Hand

The task at hand was pretty straightforward. I had a tonne of unstructured data that I needed to clean up, sift, and then turn into vector search indexes. On this, I thought, I probably need an expert understanding of data science. I also thought I might need a machine learning expert to help me fine tune my model. But, as it turns out I needed neither.

Ease is not the only revelation for me there. There’s more. I was able to discover so much about the nature of unstructured data, the promise it holds, how to leverage it, and its challenges. These revelations will dawn on anyone who tries to build their own RAG pipeline from scratch as well. However, I feel like if I knew these before I started my process would have been even better. So, perhaps use these insights to speed up your RAG building curve and simplify your journey.

Demystifying Unstructured Data

You will be dealing with a lot of unstructured data. There is no need to be overwhelmed. I was told that because there is no format, structure, or quantifiability I will have trouble cleaning up the data. That is not the case.

Just think of it as information that doesn’t fit into a predefined model or format. It comprises your usual text, images, videos, social media posts, emails, and other forms of data. The main characteristic there is that such data is not easily quantifiable. But, that changes quickly as you try to sift, normalize and clean it up.

So much of the data around us is unstructured. It is growing at an unprecedented rate. The IDC predicts that 80% of worldwide data will be unstructured by 2025. This gives us another reason to befriend this data type and learn what to do with it.

Sure, it can be complicated to decide what goes, what stays, which format or model to choose for the data unification process, and so on. The truth though is that it’s all part of the play. You will have to make some key decisions in your RAG journey, this is just one of those. Business as usual. Nothing crazy.

However, despite its prevalence, unstructured data poses significant challenges. It can be difficult to analyze and interpret. Nothing you can’t fix with a bunch of sophisticated tools and techniques. Befriend this rich data. Use the nuanced information. It is the pathway to building competitive products that users love.

The Power of a RAG Pipeline

RAG pipelines can retrieve, compute, and process unstructured data through search vector indexes. They can generate relevant information, and usable insights and answer questions. This pipeline contains two parts: a retriever and a generator.

So the retriever identifies relevant data from a large corpus of unstructured data. The generator uses the retrieved information to generate a response.

Building a RAG pipeline from scratch is no small feat. It does require an understanding of machine learning, natural language processing, and data engineering. However, that knowledge unlocks a door to smart algorithms. A well-built RAG pipeline can handle huge amounts of data. It can help you build truly remarkable systems that transform the lives of their users. That kind of power makes it all worth the salt.

Building the RAG Pipeline: A Step-by-Step Guide

Step 1: Preprocessing the Data

Preprocessing involves data cleanup. This is where we remove irrelevant information. Finetune the data into a usable format. This is a key step. Preprocessing determines the quality of output the pipeline produces.

During this stage, I learned the importance of thorough data cleaning. Unstructured data often contains noise. Sometimes, there can be a lot of noise. These include irrelevant information and contradictions, that can interfere with the pipeline’s performance. By focusing on the cleanup, I was able to improve the accuracy and reliability of my RAG pipeline.

Step 2: Building the Retriever

The next step is building the retriever. This one also determines the efficiency of your RAG pipeline’s output.

Building the retriever was a challenging but enlightening experience. It required a keen eye for detail. Through trial and error, I was able to fine-tune its performance. That improved its ability to identify relevant documents.

Step 3: Building the Generator

Then, the final step was building the generator. Again, this is a crucial step to determine the quality of the output. I had to train the generator and teach it what a valuable response contains.

Building the generator was a complex task in itself. A deep understanding of machine learning and natural language generation is needed. But, every time I improved it the results improved. The effect on the results made it super rewarding. Ultimately, the generator was able to produce accurate and relevant responses.

Lessons Learned

Building a RAG pipeline from scratch taught me a lot about the nature of unstructured data. I also saw the power of machine learning unfold before my eyes. I learned that unstructured data, despite its challenges, holds enormous potential. With the right tools and techniques, you can extract game-changing insights.I also learned that machine learning, natural language processing, and data engineering skills can learnt through practice.

Finally, I learned that building a RAG pipeline is a continuous process of learning and improvement. The more you optimize, the better and stronger it becomes. There’s always room for improvement. Each component can be consistently improved. You can fine-tune your retriever’s performance, the generator’s accuracy, and cleanliness of the data. The journey is challenging, keep working on it, you’ll get there in no time.