A page load that goes from 1 to 3 seconds makes users 32% more likely to bounce, according to Google.

Now imagine waiting a full minute for a chatbot to reply.

This isn’t uncommon in chatbots that use LLMs to pull answers from internal SQL databases. When the user sends a prompt, the system has to build an SQL query, run it, fetch the data, and format a response.

The process is not only slow but presents a lot of challenges:

• Structured data poses a challenge to LLMs. They don’t see schemas the way people do. Column names and table relationships can confuse them when there's too little context of how the data is organized and collected. Because of that, LLMs require large prompts with a lot of background information to effectively read and interpret SQL, which reduces the response time.
• Certain questions may cause LLMs to obtain large result sets, which can be slow and costly.
• SQL databases may contain sensitive information. LLM-based chatbots need built-in access control to keep the information from the wrong hands. 
• Finally, even if the LLM manages to form a valid SQL query, it may take several tries to get it right.

This article shows how to improve both accuracy and speed by precomputing the conversion of SQL data to vector embeddings.

We focus on identifying frequent query types and preparing the responses ahead of time. In case the answer can't be found in the vector store, the system dynamically routes the request to a fallback SQL agent.

From SQL to vector store: building rapid-response chatbots with vector embeddings

How Chatbots Interact with SQL

Typically, when a user asks a chatbot something that SQL data can answer, an LLM agent follows a prompt template to (1) turn the user's question into an SQL query; (2) run the query; (3) rewrite the result in natural language; (4) evaluate the output; (5) return the answer or retry if needed (Figure 1).

Fig. 1: Chatbot interaction with SQL

This can work but is time-consuming and error-prone. If a result doesn’t pass internal checks, the agent keeps trying until satisfied. It might also generate an answer that sounds right, but isn’t, and pass it on to the unsuspecting user.

Why Do Chatbots Struggle with SQL Content?

SQL databases hold discrete units of information, like menu items or prices, without any context. That’s a problem for LLMs, because they need natural-language clues to operate.

For example, imagine a user asks about dairy-free meals under 500 calories. The SQL database may contain all the necessary information for an answer, but it's likely spread across multiple tables. A person could piece it together. An LLM might not.

Most LLM-based chatbots need help to work with SQL. That help comes from long prompt templates explaining what to do. Without these, LLMs wouldn’t be able to translate the user’s intent into correct SQL queries.

The problem is that these lengthy prompt templates slow down LLMs, and also fail to prevent mistakes.

What if, instead of interpreting queries on the fly, we could translate them ahead of time into a language LLMs can easily understand and generate?

This is the idea behind precomputation. The goal is to convert common responses to vector embeddings and place them in a vector store that the LLM can easily read when similar questions come up.

What Is Encoding and Retrieval?

LLMs are context-dependent, so it doesn’t make sense to copy the SQL database directly into a vector store. Instead, it's better to precompute answers into rich, narrative-like documents that provide the necessary context, and use vector embeddings to help LLMs find the relevant documents fast. This process is called encoding (Figure 2).

Fig. 2: Encoding

When a user asks a question, the chatbot searches the vector store for similar content using various metrics (e.g. cosine similarity, dot product, and others), and pulls out the most relevant documents. This process is called retrieval (Figure 3).

Fig. 3: Retrieval

Once the relevant documents have been retrieved, the LLM uses their content to generate the answer to the question. The documents are said to augment the generation - hence the term "Retrieval-Augmented Generation."

Retrieval-augmented generation is significantly faster and more cost-effective than executing a fresh SQL query each time, especially when queries are complex or require large result sets.

But it only works when the data is already encoded into vector embeddings.

How to Encode SQL Queries to Vectors Manually?

To encode SQL queries manually, you’ll need to run common queries, prepare summaries, and save them as documents in a vector database like Qdrant or Pinecone. This will work, but take a lot of time and won’t exactly scale. Automation can solve the problem.

How to Automate SQL-to-Vector Encoding with Ingestion Pipeline?

To speed up your chatbot responses, you can create an ingestion pipeline (Figure 4). The pipeline will automatically precompute answers to SQL-based questions and store them in a natural-language (read: LLM-friendly) format, so the chatbot will be able to generate replies a lot faster than when building them from scratch.

Think of it like a translation memory. Translators use specialized software to create and keep records of how they translated entire sentences and paragraphs. For every new project, the software combs the database for similar passages and suggests them to the translator. This saves them a lot of time they would otherwise have to spend translating the passages word after word, occasionally looking up words in the dictionary, doing research of things they don't remember researching, etc.

Fig. 4: Ingestion pipeline abstract flow

Here’s a detailed breakdown of the flow:

1. Get a Frequent Query with a Representative ID
   First, we look at chat logs and find common questions, like "What are your dairy-free options?". We then give the relevant SQL query a unique ID to keep track of it later. If at some point the query's underlying data changes, we want to find it quickly and update the related documents granularily instead of reprocessing whole tables.
2. Rewrite the Query to Include a Primary Key
   Next, the system checks the SQL query and makes sure that every result row - for example, each menu item or ingredient - can be clearly identified. If something is missing, the query gets updated to include a primary key.
3. Use the Rewritten Query to Fetch Data
   Once the query looks good, it’s run against the SQL database - like PostgreSQL. The database returns the data, which might include dish names, ingredients, calorie counts, or other pieces of information the chatbot might need to answer the question.
4. Transform the Data into a Format Suitable for the Vector Store
   Now, the system creates a universal ID for each row, adds metadata, and uses a prompt template to turn the raw data into readable summaries - like “The Vegan Buddha Bowl is dairy-free and contains 420 calories.” Interestingly, we found that, if given enough context around SQL query execution, the LLM can dynamically generate working prompt templates by itself.
5. Insert the Formatted Data into the Vector Store
   Finally, the generated summaries are turned into embeddings and stored in a vector database like Qdrant. If the original query was a ranked list (like “Top 5 low-calorie meals”), all the rows are stored together in one document. For simple queries (like “Tell me about the Chicken Caesar Wrap”), each row is saved as a separate document. This setup allows the chatbot to find the most relevant answers quickly, without having to re-run database queries every time.

What Are The Benefits of SQL-to-Vector Conversion?

In our experience, this method can cut chatbot response time from roughly one minute to a few seconds. Accuracy and consistency also improve because the chatbot doesn’t build queries on the fly but pulls the answer from precomputed summaries.

In addition, there's fewer mathematical errors involved because calculations can be performed offline before precomputation, without relying on LLMs' imperfect inference mechanism. This further improves the correctness and the reliability of the chatbot's responses.

Finally, the solution saves costs in multiple ways: by reducing the number of tokens processed by the chatbot and by minimizing the load on the SQL database - especially in scenarios where the LLM agent would otherwise need multiple attempts to generate a correct query.

What Are The Use Cases for SQL-to-Vector Conversion?

The solution is well-suited for interactive systems with underlying SQL databases, such as support bots, HR assistants, internal dashboards, and anywhere else that speed and accuracy matter because it uses vectorization to optimize performance.

Ideally, user questions in this scenario should involve similar SQL queries for easy templating, although they can use different filters. If the questions require complex calculations, the solutions will prevent chatbots from misinterpreting the math and producing incorrect results. 

Make Your Chatbot Faster and More Efficient with Janea Systems

Structured databases are convenient, but LLMs can’t manipulate them directly. Our ingestion pipeline solves that by turning SQL queries into vector embeddings that the chatbot uses to provide fast and reliable answers. If you're using a chatbot on top of structured data and want to boost speed, cut costs, and improve results – get in touch!

Frequently Asked Questions (FAQ)

What is a vector embedding in a chatbot context?

A vector embedding is a numerical representation of a text summary that allows a chatbot to quickly retrieve semantically similar answers using similarity search.

Why not just query the SQL database every time?

Running live SQL queries takes time and may involve complex logic that LLMs struggle with. Precomputed vector embeddings are faster and more reliable.

Can I manually create vector embeddings from SQL data?

Yes, but it’s time-consuming and hard to scale. Automation ensures consistency, speed, and the ability to update frequently.

What is an ingestion pipeline for LLMs?

It’s an automated system that fetches SQL data, transforms it into readable summaries, and stores those summaries as vector embeddings in a vector database.

Which vector databases work with this approach?

Popular choices include Qdrant, Pinecone, and Weaviate.