Build the Best RAG Pipeline for Your GenAI Apps (Step by Step)

Fast forward to 2024.

This is a time when retrieval-augmented generation (RAG) turns unstructured/unorganized data into real-time, upcycled masterpieces. RAG augments large language model (LLM) queries with knowledge that generates a more meaningful answer for the end user.

Adding relevancy to data is important because the world creates 328.77 million terabytes of data every day. Projections put global annual data generation at around 181 zettabytes by 2025.

Source

Video makes up half of that data (54%). Add in social (13%) and gaming (10%) and the top three data categories consume 77% of all internet data traffic—and large language models (LLMs) have a role to play in each of those categories. Then there’s ChatGPT on its own LLM island, which scores 637 million searches a month.

Safe to say, the demand for LLMs is extremely high:

• By 2025, half of all digital work will be automated using LLM apps—and there will be somewhere around 750 million of those apps in play.
• The global LLM market is expected to grow at a CAGR of 30% to reach $85.6 billion by 2034.

This is why the current generation of ML researchers and computer scientists are focused on optimizing LLM performance with retrieval-augmented generation (RAG): to provide better context that generates better responses.

But, you may ask, how do developers achieve that?

By building the best RAG pipelines possible (that would make even Mark Twain super proud).

Introducing RAG

In the fast-paced world of artificial intelligence, retrieval augmented generation (RAG) has emerged as a transformative approach for developing powerful, context-aware generative AI applications. By combining the vast language understanding of large language models (LLMs) with specific, up-to-date information from custom datasets, RAG offers a unique solution for organizations looking to leverage their proprietary data.

What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation (RAG) is a methodology that enhances traditional large language models. It incorporates a retrieval step that fetches relevant information from a curated knowledge base before generating responses. This adds contextual relevance to responses. RAG is particularly effective in applications like chatbots, question-answering systems, and research tools, where accurate and up-to-date information is crucial.

At its core, RAG first retrieves pertinent information from a designated dataset based on the user's query then feeds it into the generative model along with the original query. The AI produces more accurate, informed, and tailored responses this way.

This process grounds the output of the LLM in specific, relevant data, reducing the likelihood of inaccurate responses.

The power and challenges of unstructured data

The need for relevant information is not new in application development. However, the rise of generative AI has highlighted the challenges of integrating and leveraging unstructured data effectively. Unlike structured data, unstructured data includes an array of information types, such as text documents, emails, and social media posts, which hold immense potential for enriching AI applications.

Harnessing the power of unstructured data comes with challenges: it’s difficult to organize, search, and retrieve relevant information efficiently. A RAG pipeline addresses these challenges by

• passing data through an embedding model
• storing it in a vector database
• running a similarity search to find the most relevant documents.

This approach gives AI applications access to external knowledge, leading to more accurate and contextually relevant responses.

Benefits of RAG—why use it?

Retrieval augmented generation (RAG) enhances enterprise AI capabilities while maintaining data security and accuracy. Here are two key benefits that make RAG an attractive solution:

Empowering LLM solutions with real-time data access

Accessing current information has always been an advantage. A RAG pipeline plugs LLMs into custom data beyond their initial training data, making that data much more valuable. For sectors like finance and healthcare, where information changes rapidly, RAG makes query responses accurate and timely.

Preserving data privacy

Data privacy is a top concern for organizations, especially when handling sensitive information. A retrieval-augmented generation system adds security to enterprises that store data on-premises. Combined with a self-hosted LLM, RAG protects sensitive source data while still leveraging AI. This approach is particularly beneficial for industries with strict regulatory requirements, such as healthcare and finance, where data protection is non-negotiable.

With RAG pipelines, businesses use their AI applications to deliver accurate responses while maintaining the integrity and privacy of their data.

Understanding RAG pipelines

Retrieval augmented generation pipelines are essential for developing advanced AI applications that leverage both the power of LLMs and the specificity of custom datasets.

Primary objective of a pipeline

The main goal of a RAG pipeline is to create a reliable vector search index filled with relevant information, custom data, and context. This index enhances LLM capabilities by providing them with data specific to the user's query, ensuring accurate responses grounded in factual knowledge.

Step-by-step breakdown of a RAG pipeline

A typical RAG pipeline transforms unstructured data into an optimized vector database that retrieves and uses external knowledge.

Here's a simplified flow:

1. Document ingestion: The pipeline identifies and collects relevant data sources: knowledge bases, web pages, code repositories, or custom datasets from SaaS platforms.
2. Document pre-processing: Once ingested, the documents undergo preprocessing to extract useful text data. This step may involve techniques like text splitting to break down long documents into manageable chunks, or using vision models to convert PDFs into images.
3. Generating embeddings: The preprocessed text is then converted into high-dimensional vectors (embeddings) using a specialized embedding model, which can differ from the embeddings used by the end-LLM. These embeddings represent the semantic meaning of the text in a format that machines efficiently process.
4. Storing embeddings in a vector database: The generated embeddings, along with their associated metadata, are stored in vector databases optimized to handle vectorized data for rapid search and retrieval operations.
5. Querying: When a user submits a query, the system converts it into a vector and searches the vector database to identify the most relevant documents.

By following this structured approach, retrieval-augmented generation pipelines connect proprietary data to LLMs, leading to contextually relevant responses over the retrieved data.

Building a RAG pipeline: Step by step

Let's break down how to retrieve information effectively:

Source: LangChain: A Primer | Lakshya Agarwal

Document ingestion and pre-processing

A RAG system ingests and process diverse data sources effectively. There are three steps:

• Collect data: The RAG system ingests raw data from sources like databases, documents, and live feeds, building a comprehensive knowledge base.
• Load document: Frameworks like LangChain provide document loaders that handle numerous data types, from PDFs and text files to Confluence pages and CSV files.
• Split text: After loading data, long documents are broken down into smaller, manageable segments. This step is crucial for fitting text into embedding models, which typically have token length limitations.

Generating vector embeddings for efficient retrieval

Once the documents are ingested and pre-processed, the next step transforms them into an efficient retrieval format. The ingested data converts into high-dimensional vectors. Specialized models, such as OpenAI's text-embedding-3-large or Cohere's embed-english-v3.0, generate these vector representations. These models capture complex semantic relationships within the text, allowing for more nuanced understanding and retrieval.

Storing embeddings in vector databases

The final step stores the generated embeddings in specialized databases designed for fast search and retrieval operations that support real-time interactions. Specialized indexing techniques facilitate efficient similarity searches. Vector databases often use distributed architectures and optimized indexing methods to maintain high performance — even with large datasets and complex queries — so the RAG system scales effectively as the knowledge base grows.

Querying and retrieval in RAG

The querying and retrieval process efficiently accesses and uses external knowledge to answer questions. This forms the bridge between a user query and the contextually-appropriate response that it generates.

How user queries are processed in RAG

When a user submits a query, the RAG system leverages its indexed data and vector representations to perform efficient searches:

• Embed query: The user's query is converted into a vector representation using the same embedding model used for document indexing.
• Compare vectors: The system identifies relevant information by comparing the query vector with data in the vector store. This comparison often uses similarity measures such as cosine similarity or Euclidean distance.
• Rank relevance: The most similar document chunks or passages are retrieved and optionally reranked based on their relevance to the query. This reranking step ensures that only the relevant chunks that are close to the user's intent make it into the context.

The role of LLMs in RAG

Language models play a pivotal role in the pipeline: they generate human-like responses based on the retrieved information.

Generating contextually relevant responses with LLMs

LLMs synthesize raw data and generate coherent responses. Their role involves:

• Integrate context and understand natural language (NLU): LLMs combine the retrieved information with the original user query to create a comprehensive context. The extracted data is used as relevant context.
• Generate coherent response: LLMs use their language generation capabilities to produce well-formed, contextually appropriate responses that address the user's query while incorporating the retrieved information.
• Adapt domain-specific knowledge: LLMs use retrieved information to generate responses that are tailored to specific domains or organizational knowledge, even if such information wasn't part of their original training data.

By combining efficient retrieval techniques with powerful language models, RAG systems deliver more accurate, relevant, and trustworthy responses to user queries, making them invaluable tools for applications ranging from customer service to enterprise search.

Deploying and scaling RAG pipelines

Deploying and scaling retrieval-augmented generation (RAG) pipelines requires careful consideration of the underlying architecture and data processing mechanisms. As organizations look to implement retrieval-augmented generation applications at scale, they need robust solutions that can handle large volumes of data and provide real-time updates.

Event streaming as a solid foundation using Astra Vectorize

One approach that has proven effective for building scalable RAG pipelines is leveraging event streaming platforms.

Astra Vectorize, for example, uses Apache Pulsar as the foundation for its RAG pipeline. It turns unstructured data into efficient vector search indexes.

This has several advantages:

• Real-time data processing: Event streaming immediately handles new information so the RAG system always has access to the most up-to-date data.
• Automated optimization: Vectorize uses experimentation to identify the best-performing embedding models and chunking strategies for unique datasets, reducing the need for manual tuning.
• Scalability: Platforms like Apache Pulsar are designed to handle massive volumes of data and can scale horizontally to meet increasing demands.
• Fault tolerance: Event streaming systems are built with fault tolerance in mind, making them resilient to failures and ensuring data integrity.

A resilient system accepts that errors will happen and builds a strategy to deal with those errors.

Lessons learned from Vectorize and error handling

The experiences of teams working with platforms like Vectorize yield valuable insights for those looking to deploy RAG pipelines at scale.

Recognizing that errors are inevitable in complex systems, it's crucial to build robust error handling mechanisms into the pipeline. This includes strategies for

• retrying failed operations
• logging errors for analysis
• gracefully degrading functionality when necessary.

It helps to think like a data engineer: to maximize retrieval accuracy and accelerate application development, it's essential to approach RAG pipeline design with a data engineering mindset. Consider data quality, processing efficiency, and system architecture when dealing with vector data.

By learning from these experiences and leveraging platforms designed for scalability, organizations build RAG pipelines that are powerful, resilient, and capable of handling enterprise-scale demands.

Best practices and future directions

As RAG pipelines continue to evolve and gain prominence in enterprise AI applications, several best practices and future directions are emerging:

The future of RAG pipelines and their applications

RAG is rapidly becoming the standard framework to implement enterprise applications powered by large language models (LLMs). The future of RAG pipelines plays a transformative role in information retrieval and human-computer interaction. Ongoing advancements in retrieval algorithms will incorporate sophisticated retrieval methods to improve relevance, such as hybrid search that combines sparse and dense retrieval and contextual re-ranking.

As LLMs continue to improve, RAG pipelines will benefit from their increased comprehension and multimodality, broadening the range of application use cases. As these advancements unfold, RAG pipelines will continue to enhance the capabilities of AI-powered applications, enabling more accurate, contextually relevant, and trustworthy interactions between humans and machines across a wide range of domains and industries.