A blog cover on the topic: The Downside of Vector Search in Q&A Chatbot RAG Workflows
Touchapon Kraisingkorn
min read
June 17, 2024

The Downside of Vector Search in Q&A Chatbot RAG Workflows

Vector search has gained significant popularity in Retrieval-Augmented Generation (RAG) workflows, particularly for its ability to retrieve relevant context from documents. This method leverages the power of embeddings to find semantically similar content, making it a potent tool for various applications, including Q&A chatbots. 

However, while vector search is highly effective for simple and limited document sets, it begins to show its limitations in real enterprise production environments where the volume of documents can reach hundreds or thousands.

Vector Space Image Retrieval: Blue points represent Document Embeddings, green points represent Similar Documents, and red crosshairs represent the Query Vector
Vector Space Image Retrieval

Vector Search in Simple Use Cases

In scenarios involving small or straightforward document collections, vector search excels. It can quickly and accurately retrieve relevant information, making it ideal for applications with limited data. For instance, a Q&A chatbot designed to answer questions from a small knowledge base can benefit immensely from vector search. The embeddings can capture the semantic meaning of the text, ensuring that the chatbot retrieves the most relevant answers.

Challenges in Enterprise Production

As the scale of documents increases, the effectiveness of vector search diminishes. In enterprise environments, where the document count can be in the thousands, vector search struggles to maintain its accuracy and efficiency. The sheer volume of data introduces complexities that simple vector search methods are not equipped to handle, leading to several shortcomings.

Shortcomings of Vector Search

1. Document Chunking

Vector search often necessitates splitting documents into smaller chunks to manage the data effectively. However, this chunking process can result in a significant loss of context. For example, consider an internal company policy document that is split into smaller sections. The relationship between different sections, such as guidelines and exceptions, may be lost, making it difficult for the search algorithm to retrieve the most relevant information.

2. High Similarity Documents

Another challenge arises when dealing with documents that have high similarity. Vector search can struggle to distinguish between documents with very similar content. For instance, a company might have multiple versions of a product promotion with slight variations. Vector search may retrieve all versions, leading to redundant results and making it harder for users to find the most relevant document.

3. Text Similarity Search Use Cases

Vector search is not always suitable for text similarity search use cases. For example, when searching for specific product names in a large e-commerce catalog, vector search may not yield accurate results. The embeddings might capture the semantic meaning but fail to account for exact matches, leading to less precise search outcomes and potentially missing the exact product the user is looking for.

4. Niche Contexts or Special Brands/Names

Vector search also struggles with niche contexts or special brands/names that are not well-represented in the training data. For instance, an enterprise might have internal jargon, custom brand names, or specific terms unique to their industry. The search algorithm may not recognize these terms if they are missing from the semantic embeddings, resulting in poor performance and failing to retrieve relevant documents.

Enhanced RAG Search System

To address these limitations, a more robust RAG search system is necessary. This system should incorporate multiple components to enhance the accuracy and efficiency of the search process.

1. Document Cleansing and Chunking

Proper document cleansing and chunking are crucial. For example, ensuring that a technical manual is logically divided into sections based on topics rather than arbitrary chunks can help maintain context and improve the relevance of search results. This approach ensures that the search algorithm can understand and retrieve comprehensive information.

2. Search Query Transformer

Transforming search queries can also enhance the search process. For instance, a search query transformer can refine a vague query like "latest sales report" into a more specific query like "Q3 2023 sales performance report." This optimization helps the system better understand the user's intent and retrieve more accurate results.

A computer with a search box
Illustration of search method

3. Text-Based Search

Incorporating traditional text-based search methods can complement vector search. For example, using exact match search for product names in an e-commerce catalog can ensure that users find the specific items they are looking for. This method addresses some of the limitations of vector search by handling precise terms and exact matches.

4. Vector Search

While vector search has its shortcomings, it remains a valuable tool when used in conjunction with other methods. For instance, combining vector search with text-based search in a customer support system can leverage the strengths of each approach, ensuring that both semantically similar and exact matches are retrieved.

5. Reranker Model

A reranker model can further improve search results by reordering them based on relevance. For example, after retrieving documents related to a legal case, a reranker model can prioritize the most relevant case laws and precedents, ensuring that users get the most pertinent information first.

6. Item Search with Structured Filters

For item search, structured filters like SQL queries and Elasticsearch filter queries are highly useful. For instance, an inventory management system can use SQL queries to filter products based on attributes like category, price range, and availability. These filters can handle complex search criteria and provide precise results, making them an essential component of a comprehensive RAG search system.


While vector search is a powerful tool for retrieving relevant context from documents, it has notable limitations, especially in large-scale enterprise environments. To overcome these challenges, a range of search methods are combined together based on their applicability with the following benefits:

Table showing details about various search methods, including descriptions, benefits, and example use cases
Table with search method details, benefits, and example use cases

Other more complex techniques such as incorporating document cleansing and chunking, search query transformers are also available to help clean the incoming document and data. With these techniques, organizations can build a robust RAG search system that delivers accurate and relevant results.

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