Key takeaways:

• Retrieval Augmented Generation (RAG) connects Large Language Models (LLMs) to external data sources, providing LLMs with access to custom knowledge without fine-tuning.
• Vectorized databases organize information using embeddings based on meaning, enabling semantic search to improve search results.
• Graph databases capture relationships between entries; with graph RAG, users can uncover connections and insights in their documents using natural language.
• OneReach.ai GSX platform provides tools like Lookup and Graphs to quickly build advanced RAG systems that allow AI agents to store unstructured data, make decisions, and do real work with your organization’s knowledge.

Enhancing knowledge management with Retrieval Augmented Generation (RAG) 

LLMs are great at answering questions, but only if the answers exist somewhere in their knowledge base. This limitation is a problem for organizations that want to use LLMs as a conversational bridge to link employees to internal documents, data, and research. One solution is to train LLMs on your data, but this takes time and costs money. There’s also evidence that, after a certain point, simply increasing the size of LLMs may actually make them less reliable.

RAG was created to navigate these challenges. By connecting LLMs with graph and vector data sources, RAG makes it easy for organizations to create their own custom models that replace traditional knowledge management software. Turning unstructured information into organized data that can then be used for retrieval and automation by an LLM, puts orgs in a position to create radically dynamic and personalized experiences for customers and employees. 

RAG makes our data more accessible

RAG plays a redefining role in information management. It pairs LLMs with external data sources to give the model knowledge that doesn’t exist in the foundation model. 

Here’s how it works:

• User query: A user asks a question (e.g., “How many vacation days do I have left?”)
• Database search: The system performs a database search to retrieve relevant information from the connected databases.
• LLM integration: The retrieved information is then added to the initial prompt, enhancing the response generated by the LLM. 
• Answer: The user receives a precise response, e.g., “You have 10 vacation days remaining for this year.”

The ability of a RAG system to return accurate responses and make links within and between documents depends on how the database is organized and searched. RAG allows organizations to give LLMs new knowledge without the hassle and expense of fine-tuning the language model. 

Conventional RAG systems pair LLMs with documents that have been vectorized. Using a machine learning process known as embedding, text is transformed into vectors that capture how the text looks (its orthography) and what the text means (its semantics). 

Vector databases enable semantic search

If you’re not familiar with the term “vector”, picture an arrow in three-dimensional space. As shown in the image below, text with similar meaning is stored close together in this vector space because it has a similar vector representation. 

Vectorized databases enable semantic search—or a search for information based on the meaning of the search term, which is far superior to a simple keyword search. For example, a semantic search for the word “color” returns documents with this exact term and close matches like “colorful” and “colors”—exactly what you would get with a keyword search. But it also returns documents with related vector representations like “blue” and “rainbow”. Compared to a keyword search, semantic search does a better job of returning results that capture the searcher’s intent. 

Graph RAG unlocks relationships across data

Semantic search is a great tool for RAG systems, enabling users to interact with text-based document collections through LLMs. But if your database contains more than just text—including other forms of unstructured information like images, audio, and video—and you want to reveal hidden connections between items, organizing information in a graph allows you to establish relationships between different types of information, which might be a better approach. 

In a graph database, information is represented by nodes connected by edges. Nodes can be things like documents, people, or products; the edges that connect the nodes represent the relationship between entries in the database. 

Unlike a traditional filing system, graph databases allow users to quickly find complex relationships between items. Take, for example, a social network of four people: Alice, Bob, Jane, and Dan. Alice and Bob are friends, Bob and Jane are friends, Jane and Alice are friends, and Dan and Alice are friends. Although the connections between these friends may seem confusing when first read, the network is easily visualized in the graph below. And by looking at the graph you know exactly who connects Bob and Dan (Alice, of course). 

In graph databases, we can also attach additional details to the nodes and edges. In the simple social network above, each node could store the person’s age and profession, in addition to their name. The edges connecting nodes can store the dates when friendships were established and indicate the direction in which relationships were formed. This organization allows users to track changes in the relationship between database entries as they occur in time, and also go back in time to see how relationships evolved.

When graph databases are paired with LLMs, users can use natural language to quickly find connections between items in databases that might have remained hidden with a more traditional filing system. Graph RAG is a powerful tool for using natural language to both retrieve your information, but also to discover hidden relationships within your information. This presents a radically new approach to knowledge management software that connects data and makes it far more useful.

RAG in the OneReach.ai GSX platform

GSX makes it easy to develop advanced RAG solutions that power voice and text-based interactions with AI agents. Using a drag and drop tool called Lookup, users can build collections of documents (PDFs, text files, websites, and spreadsheets) to power the knowledge-base of agents via semantic search. Graph databases are also easily built and connected to agents using the Graphs tool. 

With Lookup and Graphs, RAG-powered AI agents can do real work in your organization with the most up-to-date information.   

Learn more about designing and orchestrating AI agents in our paper, “Using AI Agents to Establish Organizational Artificial General Intelligence (OAGI).”