Why should the RAG system embrace vector search? Revealing the fatal weakness of keyword search!
In-depth analysis of the reasons why the RAG system chooses vector search, revealing the shortcomings of traditional keyword search.
Core content:
1. Keyword search cannot understand semantic information, resulting in inaccurate matching
2. Vector search effectively solves traditional problems by capturing semantic similarity
3. Application advantages and technical details of vector search in the RAG system
When building the RAG system, the main reason for using vector search is that it can effectively solve the limitations of traditional keyword search and improve the accuracy and efficiency of search.
So what are the limitations of keyword search ? How does vector search solve this problem? This article will help you understand the reasons behind this.
Limitations of keyword searches
Traditional keyword search relies on word frequency statistics, such as the inverted index based on BM25. This method cannot understand semantic information, so there are the following problems when processing complex queries:
Insufficient semantic understanding : Keyword search only matches words that are literally the same, and cannot capture semantic similarities . For example, if you query "What are Apple's latest products?", keyword search may return documents about the technology of growing the fruit "apple".
Ambiguous expressions and spelling errors: For queries with vague descriptions or spelling errors, keyword searches are prone to return irrelevant results.
Insufficient support for long-tail queries: When a query contains multiple keywords or a complex structure, the recall rate of keyword retrieval is low.
Advantages of vector search
Vector retrieval overcomes the limitations of keyword retrieval by converting text into multi-dimensional vector representations and using similarity calculations between vectors, such as cosine similarity , to match queries:
Strong semantic understanding ability : Vector retrieval can capture the semantic information of the text. Even if the query and document do not have exactly the same words or are in different languages , semantically related matches can be found.
For example, when querying "Why do ants line up to move before it rains?" , even if the document does not explicitly mention "ants moving", the model can still associate it with the popular science content "insects sense changes in air pressure" through vector similarity .
Handling ambiguous expressions and spelling errors : Vector retrieval has a certain tolerance for ambiguous expressions and spelling errors, and can match relevant content through semantic similarity.
Support for complex queries: Vector search can better handle queries containing multiple keywords or complex structures, improving recall and relevance.
Vector retrieval and RAG system
In the RAG system, vector retrieval is a core component responsible for quickly finding the content closest to the query semantics from a large-scale data set:
Efficiency: The vector database supports fast similarity search of high-dimensional data and can quickly find the most similar vectors in large-scale data sets.
Flexibility: Vector retrieval can be combined with different embedding models and vector database technologies, such as BGE-M3 and text-embedding-3-small , and vector databases such as Milvus and Faiss, to flexibly meet the needs of different scenarios.
Improve generation quality: Use relevant content obtained through vector retrieval as input and combine it with a large language model to generate more accurate and richer answers.
Class Representative Summary
Although vector search has advantages in semantic understanding, in some cases, such as searching for proper nouns, names, and abbreviations , traditional keyword search may be more effective.
Therefore, a hybrid search method combining vector retrieval and keyword retrieval is proposed to make up for their respective shortcomings and achieve a wider range of text searches.
