Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA

With the emergence of intelligent Q&A bots like ChatGPT, the demand for large‑model applications across industries has exploded. Large models have become an indispensable part of enterprise data systems, offering opportunities for digital and intelligent development. This article introduces four approaches to applying large models in the data domain.

1. Embedding‑based Semantic Search

Traditional search built on ElasticSearch relies on tokenization and inverted indexes, which can miss semantically similar terms. By configuring synonym tables and, more powerfully, using embedding vectors, search relevance can be greatly improved.

The embedding‑based retrieval process includes:

Generate semantic vectors (embeddings) for stored metric information and store them in a vector database.

Encode the user's query into an embedding and search the vector database.

Compute vector distances (e.g., cosine similarity) to find the nearest vectors, which represent semantically similar results even if the keywords differ.

2. Building a Domain Knowledge Base for Private Q&A

Enterprises often need a private knowledge base when:

The required industry knowledge is highly specialized and generic large models cannot guarantee accuracy.

Data and environment must remain fully controlled to avoid privacy leaks and security risks.

The typical solution combines Embedding + Vector Retrieval Engine + LLM . The workflow is:

Extract all textual content from original documents.

Perform semantic chunking to split the text into meaningful chunks, optionally extracting metadata and detecting sensitive information.

Pass each chunk to an embedding model to obtain its vector representation.

Store the embeddings together with the original chunks in a vector database.

(Optional) Refine user questions that depend on context.

Retrieve the most relevant chunks via vector similarity.

Let the LLM reason over the retrieved knowledge and the user’s question to produce an answer.

3. Text2SQL Code Generation and Result Visualization

Large models can quickly generate SQL snippets from natural‑language queries and display the results visually, helping data professionals focus on business insight rather than query syntax.

Example: “Show the average revenue for each month in 2022.” The model produces the following SQL:

<code>SELECT AVG(revenue) AS average_revenue, MONTH(date) AS month
FROM sales
WHERE YEAR(date) = 2022
GROUP BY MONTH(date);</code>

4. Exploratory Data Analysis (EDA) with LLMs

Data analysts spend considerable time on data preparation. LLMs can assist with preprocessing tasks such as handling missing values, detecting outliers, analyzing variable correlations, and offering suggestions to improve data quality, thereby streamlining the analysis workflow.

Conclusion

The article briefly outlines how LLMs can be applied in the data domain, covering semantic search, private knowledge bases, Text2SQL generation, and EDA. As large models continue to evolve, they create new opportunities for enterprise data governance, security, integration, analysis, and business applications, boosting productivity across industries.