Data Pipelines for RAG

Our last post discussed data privacy in AI and large language models, which prepared us to examine the technologies behind the Retrieval-Augmented Generation (RAG). A robust data pipeline is essential for RAG. It helps gather, process, and retrieve data, improving AI outputs' accuracy and speed. In this blog, we will explain the main parts of data pipelines for RAG and highlight how they effectively support real-time AI solutions.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI approach that improves how an AI answers questions using information stored outside its central system. Instead of relying on what it already knows, RAG allows the language model (LLM) to access extra data or information sources when responding to queries. It uses “vector databases” or “embeddings,” which link the stored information and the LLM's processing ability. These vector databases help the AI find words or phrases similar in meaning to the query, allowing it to locate and show relevant data from your documents quickly.

The RAG process has two main steps. First, it finds relevant information from a specific source based on a query. Then, it uses that information to create a clear response. This method helps to provide more accurate answers. It reduces the chance of giving outdated or incorrect facts, which is important because relying only on static training data can lead to mistakes. The ability to retrieve current information makes RAG particularly useful for customer support and knowledge management, where having up-to-date and precise information is crucial.

Data Pipelines - What they are and how to create them

Data pipelines are crucial for a Retrieval-Augmented Generation (RAG) system. They help large language models (LLMs) access external knowledge or data sources to provide accurate, detailed answers. For enterprises, a well-thought-out data pipeline is important because this helps ensure that the answers relate to the company's specific domain of information. It also improves the accuracy of the output and helps reduce incorrect or hallucinated responses by basing them on factual, up-to-date information.

Data sovereignty in pipelines is essential for Enterprises, mainly when they handle proprietary or sensitive information. Keeping data within specific geographic areas or under strict control helps ensure compliance with regulations and manage risks. Don’t miss out on our previous blog post, AI and LLM Data Privacy and Data Sovereignty: Navigating the Challenges, where we unravel the complexities for you!

There are various methods for finding and storing information in databases. However, a critical component is the data pipeline, which is necessary before embedding content in a vector database to make a Retrieval-Augmented Generation (RAG) system work effectively. 

A data pipeline can help proactively turn noise into useful information (signal). We achieve this by designing the pipeline to sift through irrelevant data to extract relevant signals, transforming "noise" into meaningful input for the language model (LLM).

As illustrated in the visual below, data pipelines for RAG consist of several essential steps:

1. Ingestion Stage

• Augmentation Source is the source of external knowledge, such as documents, databases, websites, or other information repositories.
• Data Cleaning is the process of preparing the data by removing unnecessary information and ensuring it is structured for efficient processing.
• Chunking is the process of breaking down large volumes of data into manageable pieces, making it easier for the system to retrieve and process relevant segments.
• Embedding converts each chunk into a matrix of numbers that represent the semantic meaning of the chunk of text
• Vector Database stores the embeddings in a way that enables efficient retrieval based on semantic similarity. Some of the most common vector databases are QDrant, ChromaDB, PGVector, Pinecone, etc.

1. Inference Stage

• Query Embedding converts the query using an embedding model, which converts the query into vector embeddings representing the semantic meaning of the query.
• Prompt Template is structuring how we will use retrieval information to generate the final response.

Each step feeds into the LLM, which processes the input and generates a response tailored to the query. With this setup, enterprises can harness RAG to create AI systems that deliver precise, contextually aware answers drawn directly from their proprietary information sources.

Let's take a deeper look at these two stages individually. 

Ingestion Stage

The Ingestion Stage is the first step in building an effective data pipeline for Retrieval-Augmented Generation (RAG). This stage focuses on sourcing, preparing, and organizing external knowledge you will feed into the system.

External Knowledge Source & Data Cleaning

To improve the accuracy and reliability of your LLM responses, you need to include specific external data that supports the model. This extra data provides helpful context, making the model’s answers more accurate and relevant to your questions.

Whether taken from a webpage or a document, raw data often contains unnecessary information, such as metadata, code, and non-informational content. For example, when you scrape a webpage, you might end up with code, headers, or footers, while documents may include tables of contents or sections irrelevant to your needs.

Data cleaning is crucial at this stage. It means removing extra elements like headers, footers, and other irrelevant information, leaving you with only the necessary content. This step is not just a formality; it is essential. The cleaner your data is, the better your retrieval system and language model will work, leading to more accurate and helpful information retrieval.

Chunking

Chunking is an essential step in your RAG data pipeline, breaking your cleaned data into smaller, manageable pieces. After preparing the data, you can store it in chunks of different sizes. Smaller chunks can help you find relevant information quickly. However, it's essential to choose the right chunk size. If the chunks are too small, you might overwhelm the system with too many queries, slowing things down. If they're too large, getting the specific information you need may be difficult, and you might lose the context necessary for accurate answers. Finding the right balance is vital to building an efficient pipeline that delivers helpful information smoothly.

You can improve each piece further after breaking the text into smaller pieces (chunking). Using a language model like GPT4o-mini, you can create keywords or categories describing each chunk. For example, if you’re looking at a WebMD page about aspirin, the model might suggest keywords like "pain relief" or "reducing blood pressure," even if those terms aren’t directly in the text. This step creates extra information around each chunk, adding helpful tags that make searching and finding relevant content more manageable.

With these keywords stored as metadata, finding information becomes quicker and more precise. When someone searches for something like, “I have a massive headache and need something for pain relief,” the system can use these keywords to find relevant chunks, even if the exact words weren't in the original text. This method helps your retrieval system provide accurate and appropriate answers by using this extra information and the main content.

Embedding Model

With RAG, an embedding model changes text into numbers, creating a dense numerical representation that captures its meaning. This process allows the system to find and retrieve information based on similarity, even if the search terms aren’t exact matches. 

Adding extra data, like keywords, tags, or content summaries, can improve these embeddings. Combining this additional context with the main text increases the chances of retrieving relevant information when users ask questions. 

For example, if someone searches for “pain relief,” an embedding that includes related keywords—even if the original text doesn’t mention them—can help them find the correct information. This method ensures that the embeddings reflect a broader context, improving the RAG system's ability to provide accurate and relevant answers.

Vector Database

A vector database works like a traditional database but has a significant difference. In a conventional database, if you search for a specific word, like "aspirin," and that word isn’t in any entry, you will not get any results. However, a vector database uses embeddings to represent words and concepts based on their meanings, allowing for "fuzzy" searching, so you’re not limited to exact matches.

For example, a vector database can recognize that "aspirin" relates to phrases like "pain relief" and "Tylenol." Therefore, even if "aspirin" doesn’t appear, a search for pain relief can still pull up relevant information. This ability comes from a vector database representing words and phrases in a multidimensional space based on their meanings. By understanding these meanings, a vector database can effectively find information that connects to your query, making it useful for RAG systems.

Inference Stage

The Inference Stage is where the processed data begins to interact directly with the RAG system, transforming raw information into actionable insights. 

Prompt Template

A prompt template is like a recipe. It gives you a basic structure to customize with specific instructions to guide the model’s response. Usually, prompts include the main question, clear instructions, and background information to set limits on what the model should say. This template helps translate your request into a format the generative model can understand.

When you enter a query, the RAG pipeline finds relevant data and adds it to the prompt template. This organized input goes to the LLM, providing the context for a specific response. Using a prompt template ensures the model’s answers match your question and the information retrieved, improving its output's accuracy and relevance.

LLM (Large Language Model)

The large language model (LLM) is the last part of the RAG process, where we generate the response to a question. The LLM drives the "G" (Generation) in RAG. At this stage, the LLM uses its internal knowledge and the extra information pulled from outside sources to create a response that fits the question.

A common inexperienced approach when building RAG systems is grabbing raw data from internal knowledge management systems or websites, breaking it into chunks, and putting it into the vector database. While this may seem like it would work, it often leads to poor results. The RAG system will only give accurate answers if the initial data is well-prepared. Therefore, ensuring that the data in every process step is high-quality and relevant is just as important, if not more important, than the RAG system itself.

In Summary

A robust data pipeline is essential for building a successful RAG system. This includes necessary steps like data cleaning and enhancement. The success of RAG systems relies on the quality and organization of the data used. Simply using raw data is insufficient; you must prepare it by adding context, keywords, and summaries to make it worthwhile.

Data enrichment may sound complicated, but it can be simple if you take a systematic approach. Break down the information into smaller parts, add the correct tags, and identify the main themes. This process is similar to using ChatGPT to summarize a long text.

The message for organizations that want to get the most from RAG is clear: “Invest in your data pipeline.” A well-prepared dataset that is enhanced and cleaned can make a big difference. It helps create a reliable RAG system that meets your needs.

Watch out for our final blog in this series, where we will pull all the pieces together and cover the challenges of implementing a RAG while navigating the challenges of Data privacy and sovereignty.

Frequently Asked Questions

💬 What are the challenges of evaluating a RAG system's performance? 

Evaluating RAG systems can be challenging due to the subjective nature of language and the difficulty of creating comprehensive benchmarks. Standard evaluation metrics include accuracy, precision, recall, and F1-score (the harmonic mean of precision and recall, providing a single metric that balances both the precision and the completeness of the model). However, human evaluation is often the best way to assess the quality and relevance of the generated responses.

💬 What are the challenges in scaling RAG systems to handle large datasets and high query loads? 

Scaling RAG systems can be challenging due to the computational resources required for embedding generation and search. Some strategies for scaling include:

• Distributed vector databases: Distributing the vector database across multiple nodes.
• Approximate nearest neighbor search (ANN): Using approximate search algorithms to speed up query processing.
• Hardware acceleration: Utilizing GPUs or specialized hardware for faster computations.

💬 How can I ensure the privacy and security of data used in a RAG system?  

To ensure data privacy and security, consider implementing the following measures:

• Data anonymization and encryption: Protect sensitive information by anonymizing or encrypting it.
• Access controls: Limit access to the RAG system and its underlying data.
• Regular security audits: Conduct regular security audits to identify and address vulnerabilities.
• Compliance with regulations: Adhere to relevant data privacy regulations such as GDPR and CCPA.