Understanding RAG: How to integrate generative AI LLMs with your business knowledge

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In the rapidly evolving landscape of generative artificial intelligence (Gen AI), large language models (LLMs) such as OpenAI’s GPT-4, Google’s Gemma, Meta’s LLaMA 3.1, Mistral.AI, Falcon, and other AI tools are becoming indispensable business assets.

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One of the most promising advancements in this domain is Retrieval Augmented Generation (RAG). But what exactly is RAG, and how can it be integrated with your business documents and knowledge?

Understanding RAG

RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information repositories, enhancing their ability to generate accurate and contextually relevant responses.

As Maxime Vermeir, senior director of AI strategy at ABBYY, a leading company in document processing and AI solutions, explained: “RAG enables you to combine your vector store with the LLM itself. This combination allows the LLM to reason not just on its own pre-existing knowledge but also on the actual knowledge you provide through specific prompts. This process results in more accurate and contextually relevant answers.”

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This capability is especially crucial for businesses that need to extract and utilize specific knowledge from vast, unstructured data sources, such as PDFs, Word documents, and other file formats. As Vermeir details in his blog, RAG empowers organizations to harness the full potential of their data, providing a more efficient and accurate way to interact with AI-driven solutions.

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Why RAG is important for your organization

Traditional LLMs are trained on vast datasets, often called “world knowledge”. However, this generic training data is not always applicable to specific business contexts. For instance, if your business operates in a niche industry, your internal documents and proprietary knowledge are far more valuable than generalized information.

Maxime noted: “When creating an LLM for your business, especially one designed to enhance customer experiences, it’s crucial that the model has deep knowledge of your specific business environment. This is where RAG comes into play, as it allows the LLM to access and reason with the knowledge that truly matters to your organization, resulting in accurate and highly relevant responses to your business needs.”

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By integrating RAG into your AI strategy, you ensure that your LLM is not just a generic tool but a specialized assistant that understands the nuances of your business operations, products, and services.

How RAG works with vector databases

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Depiction of how a typical RAG data pipeline works.

Intel/LFAI & Data Foundation

At the heart of RAG is the concept of vector databases. A vector database stores data in vectors, which are numerical data representations. These vectors are created through a process known as embedding, where chunks of data (for example, text from documents) are transformed into mathematical representations that the LLM can understand and retrieve when needed.

Maxime elaborated: “Using a vector database begins with ingesting and structuring your data. This involves taking your structured data, documents, and other information and transforming it into numerical embeddings. These embeddings represent the data, allowing the LLM to retrieve relevant information when processing a query accurately.”

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This process allows the LLM to access specific data relevant to a query rather than relying solely on its general training data. As a result, the responses generated by the LLM are more accurate and contextually relevant, reducing the likelihood of “hallucinations” – a term used to describe AI-generated content that is factually incorrect or misleading.

Practical steps to integrate RAG into your organization

Assess your data landscape: Evaluate the documents and data your organization generates and stores. Identify the key sources of knowledge that are most critical for your business operations.
Choose the right tools: Depending on your existing infrastructure, you may opt for cloud-based RAG solutions offered by providers like AWS, Google, Azure, or Oracle. Alternatively, you can explore open-source tools and frameworks that allow for more customized implementations.
Data preparation and structuring: Before feeding your data into a vector database, ensure it is properly formatted and structured. This might involve converting PDFs, images, and other unstructured data into an easily embedded format.
Implement vector databases: Set up a vector database to store your data’s embedded representations. This database will serve as the backbone of your RAG system, enabling efficient and accurate information retrieval.
Integrate with LLMs: Connect your vector database to an LLM that supports RAG. Depending on your security and performance requirements, this could be a cloud-based LLM service or an on-premises solution.
Test and optimize: Once your RAG system is in place, conduct thorough testing to ensure it meets your business needs. Monitor performance, accuracy, and the occurrence of any hallucinations, and make adjustments as needed.
Continuous learning and improvement: RAG systems are dynamic and should be continually updated as your business evolves. Regularly update your vector database with new data and re-train your LLM to ensure it remains relevant and effective.

Implementing RAG with open-source tools

Several open-source tools can help you implement RAG effectively within your organization:

LangChain is a versatile tool that enhances LLMs by integrating retrieval steps into conversational models. LangChain supports dynamic information retrieval from databases and document collections, making LLM responses more accurate and contextually relevant.
LlamaIndex is an advanced toolkit that allows developers to query and retrieve information from various data sources, enabling LLMs to access, understand, and synthesize information effectively. LlamaIndex supports complex queries and integrates seamlessly with other AI components.
Haystack is a comprehensive framework for building customizable, production-ready RAG applications. Haystack connects models, vector databases, and file converters into pipelines that can interact with your data, supporting use cases like question-answering, semantic search, and conversational agents.
Verba is an open-source RAG chatbot that simplifies exploring datasets and extracting insights. It supports local deployments and integration with LLM providers like OpenAI, Cohere, and HuggingFace. Verba’s core features include seamless data import, advanced query resolution, and accelerated queries through semantic caching, making it ideal for creating sophisticated RAG applications.
Phoenix focuses on AI observability and evaluation. It offers tools like LLM Traces for understanding and troubleshooting LLM applications and LLM Evals for assessing applications’ relevance and toxicity. Phoenix supports embedding, RAG, and structured data analysis for A/B testing and drift analysis, making it a robust tool for improving RAG pipelines.
MongoDB is a powerful NoSQL database designed for scalability and performance. Its document-oriented approach supports data structures similar to JSON, making it a popular choice for managing large volumes of dynamic data. MongoDB is well-suited for web applications and real-time analytics, and it integrates with RAG models to provide robust, scalable solutions.
NVIDIA offers a range of tools that support RAG implementations, including the NeMo framework for building and fine-tuning AI models and NeMo Guardrails for adding programmable controls to conversational AI systems. NVIDIA Merlin enhances data processing and recommendation systems, which can be adapted for RAG, while Triton Inference Server provides scalable model deployment capabilities. NVIDIA’s DGX platform and Rapids software libraries also offer the necessary computational power and acceleration for handling large datasets and embedding operations, making them valuable components in a robust RAG setup.
Open Platform for Enterprise AI (OPEA): Contributed as a sandbox project by Intel, the LF AI & Data Foundation’s new initiative aims to standardize and develop open-source RAG pipelines for enterprises. The OPEA platform includes interchangeable building blocks for generative AI systems, architectural blueprints, and a four-step assessment for grading performance and readiness to accelerate AI integration and address critical RAG adoption pain points.