Why does RAG technology require knowledge vectorization?

Have you ever wondered how ChatGPT finds relevant knowledge from a sea of ​​information when you ask it a question? One of the secrets behind this is "knowledge vectorization". Today, we will unveil this powerful technology in plain language, especially its key role in the RAG (retrieval-augmented generation) system.

What is knowledge vectorization?

Imagine if you need to organize a library filled with thousands of books. The traditional method is to arrange them alphabetically or in a fixed classification. But there is a problem with this method: when you want to find a book on "how to improve memory", the relevant content may be scattered in multiple categories such as "psychology", "self-improvement", "brain science", etc.

Knowledge vectorization is like assigning a "magic coordinate" to each book, which contains not only the surface information of the book, but also its deep content characteristics. In this way, all books that talk about "memory improvement", no matter what category they belong to on the surface, will be close to each other in this "magic coordinate system".

Why does RAG need knowledge vectorization?

 1. Understand "similar in meaning" rather than just "same in words"

Traditional search engines work by keyword matching, just like you can only find books by their exact titles. After vectorization, the system can understand semantic similarity.

For example: If you search for "how to make children fall in love with reading", the vectorization system can find relevant content such as "ways to cultivate children's reading habits", even if the two have no common keywords.

2. Super fast "find similarity" capability

The vectorized knowledge is like stars in the sky, each knowledge point has its own position. When you ask a question, the system only needs to find the "stars" closest to your question.

Example: In an enterprise knowledge base containing millions of documents, traditional searches may take several seconds, while vector searches can return results in milliseconds, as fast as finding a photo of a specific person in a cell phone album.

3. Capturing the rich dimensions of language

Human language is extremely complex. "I am happy" and "I am very happy" express similar emotions but use different words. Vectorization converts text into multi-dimensional data points, like creating a "DNA sequence" containing hundreds of features for each sentence.

For example, when you ask "what is Apple's latest product", the system can distinguish that you are referring to the technology company rather than fruit, because in the vector space, "Apple" is close to concepts such as "iPhone" and "Tim Cook", but is far away from "fruit" and "nutrition".

4. Make AI smarter, not bigger

Language models have memory limitations, just like humans cannot remember an entire encyclopedia at once. Vectorization allows AI to consult external knowledge when needed.

For example, when you ask for the latest information such as "Who will be the 2023 World Cup champion", the model does not need to be retrained, but only needs to find relevant news articles through vector retrieval.

5. Reduce AI "storytelling"

AI can sometimes "hallucinate" - generating information that looks plausible but doesn't actually exist. Finding accurate references through vector search can greatly reduce this.

Example: Without RAG, AI might say "Shakespeare wrote Dream of the Red Chamber". With vector-based RAG, it can find the correct information: Dream of the Red Chamber was written by Cao Xueqin in the Qing Dynasty.

6. Easily update the knowledge base

Example: Let’s say a tech company launches a new product. Traditional AI would require retraining the entire model, but a vectorized RAG system would only need to add this new information and instantly answer questions about the new product.

7. Break down information silos

Vectorization allows different types of information (text, images, videos) to "communicate" with each other.

For example: You can upload a product photo and ask "What functions does this product have?" The system will find relevant text descriptions; or enter a technical problem and the system will find video tutorials for solutions.

7. Overcoming LLM context window limitations

The context window of LLM is limited, while vector retrieval can dynamically obtain relevant information from massive plug-ins.

Actual application scenarios

Customer service robots: can accurately find the most relevant solutions to customer problems, not just answers containing the same keywords

Personalized learning assistant: find the most suitable learning materials according to students' problems

Legal document retrieval: Helping lawyers find precedents that are most similar to the current case from a large number of cases

Product recommendation: Recommend the most suitable products based on the needs described by the user, rather than relying solely on keywords

Conclusion

Knowledge vectorization is like equipping AI with a pair of "understanding glasses" so that it can see the meaning behind the words, not just the words themselves. In RAG technology, it is the bridge connecting massive knowledge with accurate answers, and is the key to making AI both knowledgeable and accurate.

As this technology continues to develop, we can expect AI assistants to continue to improve in accuracy, personalization, and usefulness. In the future, whether it is a corporate knowledge base, a personal learning assistant, or a professional consultant in complex fields, RAG-powered AI systems will become an important tool for us to acquire knowledge.