Knowledge Graphs Supercharge Vector Search for RAG – Here’s How.

The latest generation of large language models (LLMs) are impressive wordsmiths. Trained on reams of internet-scraped content, they excel at analyzing, summarizing, and generating texts. Their abilities have already made them invaluable for organizations, enabling retrieval augmented generation (RAG) systems capable of quickly generating answers or summaries based on the most relevant information identified in internal data repositories. 

But when it comes to pinpointing precise facts, figures, or other entities within complex data sets, traditional RAG setups can fall short. While they are likely to give you a compellingly phrased output, they can struggle with more granular queries, like pulling a specific number in a document or differentiating between synonyms. Ask it to retrieve a figure buried in a dense report, and it will likely find the right document. It will return a figure, too, it just might not be the specific one you were after – especially when the source contains several similarly labeled entities.

For organizations in competitive or highly regulated industries, this can be a dealbreaker. Mistakes can be costly, and inaccuracies undermine trust in GenAI-driven systems. As a result, accuracy, reliability, and trustworthiness have become critical differentiators for enterprise GenAI technology. And recently, they have thrust an additional enabling technology into the spotlight to tackle these challenges: knowledge graphs.

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The Promise of Vector Search…

To understand how knowledge graphs elevate knowledge retrieval in RAG systems, let’s first look under the hood of a traditional setup – starting with vector search. 

So, what is vector search? Unlike keyword search, which matches exact words or phrases from a query with those in a dataset (like looking up a term in a book index), vector search represents data and the user query as vectors. Instead of matching keywords, a vector search engine identifies which vectors from the dataset align most closely with the query vector. 

A lot goes on behind the scenes for this to work seamlessly. First, the entire dataset is split into smaller, more manageable chunks. Each chunk is transformed into an embedding vector, typically with over one thousand dimensions. The “direction” of the vector captures the semantic meaning of the data it represents. The closer two vectors are aligned, the more similar they are in meaning.

When a user enters a query, that query is also first converted into a vector, which is then compared against the pre-stored embeddings in the vector database. The RAG system then retrieves the most similar vectors – those representing the most similar data – and uses them to augment the user query with additional context or information, enhancing the overall quality and relevance of the generated results

…And its Pitfalls

While vector search combines semantic understanding with high operational efficiency, it, too, has its limitations. Sure, theoretically, it would be possible to increase accuracy by parsing ever smaller chunks of data. But there’s a trade-off: as accuracy improves, efficiency decreases, and with it, the cost-effectiveness of the technology. At the end of the day, it’s simply impractical and unfeasible to tag every “thing” in every document in every corpus of information at the word level. 

And, even as it enhances a RAG system by providing relevant context, vector search alone cannot guarantee that the identified chunks and the data it pulls from them are the most relevant. Why? While vector search is deterministic, LLM text generation is probabilistic. That means that if you perform the same search several times, there’s a chance that you’ll get different results each time. Ask it to retrieve a specific number from a document full of data, and you might be left disappointed. 

Enhancing RAG with Enterprise Knowledge Graphs

In many enterprise use cases, especially in highly regulated sectors such as government, healthcare, and finance, precision is non-negotiable, all but disqualifying probabilistic methods such as vector search-enabled RAG. In such cases, knowledge graphs come to the rescue, bringing much needed determinism into the data retrieval process.

Enterprise knowledge graphs build on carefully defined enterprise taxonomies and ontologies that systematically define and disambiguate concepts and capture the relationships and hierarchies within the data. By classifying enterprise data according to these structures, the RAG system gains a deeper understanding of the context. This enables it to effectively navigate the graph of interconnected data points and guide the data retrieval process by leveraging the semantic relationships encoded in the ontology. 

The result of this semantic search, or contextual search, is dramatically increased accuracy and reliability. Consider, for example, a financial asset manager who uses the system to retrieve the expense ratio for a large fund. With traditional RAG, there’s a good chance that the system will successfully find the document containing the sought-after value; it might, however, generate a text about definition of expense ratios rather than outputting the values. 

Not so with GraphRAG. By parsing documents against the enterprise taxonomy, the system gains access to more granular information; for high-precision and high-compliance applications the taxonomy provides authoritative human-curated domain knowledge. This paves the way for otherwise impractical use cases. Users can, for example, ask the system to provide the expense ratios of large, mid, and small cap mutual funds, or structured data stored in tables or databases. In response, the system can return the answer, nicely summarized in three easy-to-read bullet points.

GraphRAG: A Game-Changer For RAG Accuracy – When Done Right

Over the past months, knowledge graphs have become a buzzword in the enterprise AI space. But while many providers leverage the technology in their AI solutions, the specifics of their implementation will make all the difference. 

Taxonomy and Ontology Management System (TOMS) like Graphite, centralize taxonomies and ontologies, enabling organizations to establish a Single Source of Truth that ensures metadata consistency across platforms. This provides a structured foundation that unifies content, allowing it to be classified by semantically unambiguous concepts rather than ambiguous keywords, improving both search precision and recall. 

The result is a semantic knowledge graph, in which concepts in the graph are interrelated to each other via semantically unambiguous logic-bearing relationships. Why does this matter? Because it completely changes the game by enabling machine inferencing across the graph to deliver powerful aboutness classification as well as associative recommendations.

Integrating such a Single Source of Truth with content classification workflows enables the system to automatically build a content-aware knowledge graph that can improve search, empower analytics, and control the flow of conversational AI. Squirro’s content-aware knowledge graph is self-learning and improves automatically with use. The more content it is exposed to, the more connections it builds between conceptual entities. The more connections in the knowledge graph, the greater is its ability to generate inferences and recommendations. 

To sum up: An enterprise taxonomy-ontology combined with a content-aware knowledge graph literally generates new knowledge out of existing knowledge. This comprehensive approach not only enhances the precision and depth of insights, it also ensures that users gain access to the right information faster while supporting more nuanced, accurate responses in conversational AI. 

At Squirro, we are currently in the process of implementing graphRAG to support customers in a variety of verticals. With promising data from initial deployments, we look forward to sharing interesting use cases and ROI metrics from these early adopters with you! 

We also look forward to learning about your projects at KMWorld 2024, where we'll showcase the Synaptica Graphite Taxonomy and Ontology Management Solution at booth #301 and our cutting-edge Enterprise GenAI Platform at booth #305.

We also look forward to discussing how these powerful technologies can transform your business, so be sure to stop by and learn how we can help you unlock the full potential of your enterprise data! Don’t want to wait until the event? Contact us today to start the conversation and explore how we can help you right now.