Understanding RAG: Its Relation to Knowledge Bases, Vector Databases, and Knowledge Graphs

1. What is RAG? – A Super Assistant That Can Retrieve and Generate

Have you ever encountered this problem: when asking a large model, it can answer many questions, but sometimes it also “makes things up” or only provides information based on its training knowledge base. In simple terms, it can tell you about events from ten years ago but knows nothing about events that just happened today. This “lack of updated knowledge” and “wild guessing” make the responses from large models less than perfect. Therefore, RAG was born.

RAG (Retrieval-Augmented Generation) technology essentially supplements the knowledge of large models through external databases to help solve these problems. Just like when you’re taking an exam, although you have memorized a lot of knowledge, if you encounter a question you can’t answer, you would look up some information to ensure you get it right. RAG allows large models to “look up information,” making them smarter and more precise in answering questions.

2. How Does RAG Work? – Three Steps to Smart Responses

So, how does RAG actually work? In fact, RAG has three simple steps:

1. 1. Create a Database: Imagine you have an “Encyclopedia” filled with various materials (text, images, videos, etc.) that can help the large model find answers.

2. 2. Look Up Information: When faced with a problem, the large model behaves like a smart student, checking these databases for content that can help solve the problem.

3. 3. Ask Questions with the Information: After finding relevant materials, the large model combines this information to answer the question.

If you are a student and encounter a difficult problem, instead of directly looking at the book, you would first ask the college students next door. If they also don’t understand, you would take your textbook and ask them. This is similar to how RAG works: first find the materials, then ask questions with the materials.

3. What are Knowledge Bases, Vector Databases, and Knowledge Graphs?

Now that we understand the workflow of RAG, you might wonder: What is the relationship between local knowledge bases, vector databases, knowledge graphs, and RAG?

Local Knowledge Base – Your “Database”

A local knowledge base is a collection of all the materials within your company, such as sales records, technical documents, employee manuals, etc. It is like a filing cabinet at home, organizing different types of information neatly by category.

Vector Database – The “Safe” for Storing Data

Imagine you want to store a business card; you need to save not only the name but also other detailed information. If you save all business cards as images, it would be inconvenient to search for specific information. Thus, a vector database is used to store data, converting each piece of information into numbers (vectors) for smarter “semantic” searches. In simple terms, a vector database is a place for storing the “digital form of information.”

Knowledge Graph – Clarifying “Data Relationships”

A knowledge graph visually represents knowledge points and their relationships. It is like a “knowledge map” that helps you see connections between different pieces of knowledge. For example, Google’s search engine uses knowledge graph technology to organize global information, clearly presenting data and their relationships to you.

4. Their Relationship with RAG – RAG’s “Friends”

Local knowledge bases, vector databases, knowledge graphs, and RAG may seem like four different concepts, but they are closely related.

• • Local Knowledge Base: It stores the materials of a company or organization and can certainly help RAG find relevant content.

• • Vector Database: When you need to retrieve information semantically, RAG will utilize it. Through the vector database, RAG can find relevant information.

• • Knowledge Graph: If you need more structured and hierarchical information, the knowledge graph can assist RAG in better understanding relationships between knowledge.

In summary, RAG is like a super-smart student, it doesn’t care where the materials come from; it only cares about how to use these materials to answer your questions. These “databases” act as its tools and assistants, helping it find answers more efficiently.

5. Why Does RAG Not Have a Direct Relationship with These Tools?

You might ask: “So what is the relationship between RAG and local knowledge bases, vector databases, and knowledge graphs?” Strictly speaking, they do not have a direct relationship. RAG merely compensates for the shortcomings of large models through external retrieval techniques, which are inherently independent.

RAG can rely on traditional databases, knowledge graphs, or even web search engines to obtain data. In other words, they are all ways for RAG to achieve its goals, but RAG itself does not specifically depend on any one of them.

6. Conclusion – Don’t Treat RAG as a “Magic Key”

The core purpose of RAG technology is to help large models avoid “making things up” when answering questions, enhancing their accuracy through external materials. As for how to obtain these materials—whether from local knowledge bases, vector databases, or knowledge graphs—depends on the specific application scenario.

Therefore, RAG does not have any mysterious relationship with these tools; their collaboration is merely to help RAG better accomplish its tasks. As technical tools in our daily work, these concepts are not difficult to understand; as long as we grasp their core functions, we can apply them flexibly in daily life.

7. Final Thoughts – When Technology Meets Life

Just like in our lives, we often have many tools to help us complete our work—email, phones, calendars, folders—they each have different purposes, but their common goal is to make our lives more efficient. RAG technology is similar; it collaborates with knowledge bases, vector databases, and knowledge graphs to help us extract useful knowledge from vast amounts of information to support decision-making.

Imagine if everything in life could efficiently retrieve and utilize information like RAG; wouldn’t our work and learning be much easier?