Knowledge Graph RAG Query Engine: LlamaIndex

Introduction

In the era of big data, extracting meaningful insights from vast amounts of textual data is a challenging task. This is where LlamaIndex, a powerful tool that combines SQL with Retrieval Augmented Generation (RAG), comes into play. In this article, we will delve into the details of the RAG Query Engine in LlamaIndex, its working, and its applications in Knowledge Graph systems.

What is LlamaIndex?

LlamaIndex is a powerful tool designed to analyse and interpret large amounts of textual data. It seamlessly integrates SQL with RAG, offering a streamlined approach to transform user queries into precise and informative answers. By breaking down user queries into two phases (data retrieval and final answer generation), LlamaIndex ensures accurate data retrieval and interpretation.

The overall goal of LlamaIndex is to enable businesses to extract the essence of user sentiments, track the evolution of data over time, and make informed decisions based on the insights gained. It aims to provide businesses with actionable insights from vast amounts of textual data.

How does LlamaIndex work?

The working of LlamaIndex can be broken down into two main phases: data retrieval and final answer generation.

Data Retrieval

In the data retrieval phase, LlamaIndex's NLSQLTableQueryEngine converts natural language queries into SQL queries. The SQL query is then executed against the database using the engine's query() method, resulting in a list of rows representing the data.

Here's a code snippet at a very basic level

Final Answer Generation

In the final answer generation phase, LlamaIndex utilizes ListIndex to refine and interpret the obtained data. ListIndex allows the execution of secondary questions on the text data to obtain a refined answer. This step helps in further analysing and summarising the data.

Considering the previously discussed general approach, LlamaIndex released Graph Query Engine with :

Applications in Knowledge Graph Systems

The RAG Query Engine in LlamaIndex can be applied in Knowledge Graph systems majorly in two scenarios:

1. Building a Knowledge Graph from documents: Llama Index can be used with LLM (Language Model) or local models to build a Knowledge Graph from documents. This is done by using the KnowledgeGraphIndex.
2. Leveraging an existing Knowledge Graph: If there is already an existing Knowledge Graph, Llama Index can be used with the KnowledgeGraphRAGQueryEngine to retrieve information from the Knowledge Graph.

The KnowledgeGraphRAGQueryEngine performs the following steps:

1. Search for related entities based on the question/task.
2. Retrieve the SubGraph (default 2-depth) of those entities from the Knowledge Graph.
3. Build context based on the SubGraph.

The retrieval of related entities can be done using either keyword extraction or embedding-based methods, depending on the configuration of the KnowledgeGraphRAGRetriever.

Additionally, the RAG Query Engine can be combined with nl2graphquery, which generates a Knowledge Graph Query based on the query and the schema of the Knowledge Graph. This allows for the synthesis of answers from both the retrieved context using Graph RAG and the generated context using NL2GraphQuery.

In summary, the RAG Query Engine in Llama Index provides a way to retrieve information from Knowledge Graphs by searching for related entities, retrieving SubGraphs, and building context based on the retrieved information. It can be used to build Knowledge Graphs from documents or leverage existing Knowledge Graphs, making it a powerful tool for businesses to derive actionable insights from vast amounts of textual data.