How to improve accuracy of RAG based search with SQL

Question

I have created a AI Search index based on SQL database with vectorization of data. I am using the index and trying to query data from it using natural language prompt with AI. (C# Code).

It is giving right answer for simple queries. But it does not work well with complex queries (like which involves with aggregate functions or greater/lesser than or date related ).

So how can i improve the accuracy of the index or is there better way to do RAG based search with SQL database

Answer 1

Hi Elangovan, Nandha,

Thanks for reaching out to Microsoft Q&A.

To improve the accuracy of RAG search based on your SQL db & vectorized data, particularly for complex queries involving aggregate functions, comparisons, or date-related queries, you can consider the following strategies:

1.Enhance Your Vector Search Pipeline:

• Semantic Index Refinement: Ensure that the text-based fields being vectorized capture meaningful data for complex queries. If necessary, break down large text blocks into smaller, more semantically relevant units.
• Use Hybrid Search (Vectors + Keywords): Combine vector search with traditional keyword-based search to ensure more accurate results for queries involving comparison operators, aggregates, or date filters. Many systems offer hybrid models where you can run keyword-based filtering after an initial vector similarity search.
• Fine Tune Embedding Models: If you're using a pre-trained model for vectorization, fine-tuning it on your domain-specific SQL data can help it better understand complex relational concepts and aggregates. You can train it with examples of questions similar to those you expect users to ask.

2.Incorporate Post-Processing for Complex Queries:

• SQL Query Generation: After the initial search (either keyword or vector-based), leverage a NLP model to translate complex user queries into SQL statements. These SQL queries can handle the aggregation and filtering functions that vector-based retrieval alone might not manage well.
• Prompt Engineering: Use carefully crafted prompts to guide the AI on how to interpret complex queries. For ex, give more explicit instruction in the prompt for aggregate functions or date comparisons.
• Chaining Queries: For more complex scenarios, consider multi-step or chain-of-thought reasoning. The AI model first retrieves relevant documents or records, and in a second step, performs the aggregation or filtering via SQL queries based on the retrieved results.

3.Improve Vector Representation with SQL Metadata:

• Contextual Embeddings: Enhance vectorization by adding SQL metadata (such as column names, types, and table relationships) into your data before vectorization.This will give the embeddings richer context to process complex operations.
• Schema-Based Retrieval: Use SQL schema knowledge for retrieval-based reasoning. For instance, add an intermediary layer in your system that maps natural language queries to relevant parts of the SQL schema before generating SQL queries.

4.Leverage Larger or Specialized Models for Query Understanding:

• Use a Model with More Understanding of SQL Operations: Leverage models like GPT-4 or Codex that have better understanding of SQL syntax and relational logic. These models can not only return natural language but also generate structured SQL queries for complex operations.
• Domain-Specific LLMs: If your queries are highly specialized, consider training or fine-tuning an LLM on a dataset consisting of SQL queries that involve aggregations, comparisons, and date manipulations.

5.Implement a Multi-Stage RAG Approach:

• Initial Search and Post Query Refinement: Use an initial RAG search to retrieve the most relevant documents or records from the SQL database. Afterward, apply more complex SQL-based logic to further refine the results (ex., applying filters or aggregations).
• Incorporate Feedback Loops: Allow the system to get feedback on incorrect or less accurate results and refine the query models over time.

6.Caching and Indexing Strategies:

• Precompute Common Queries: If some complex queries are common, you can precompute and store these in the search index, either in the vector or SQL-based formats.
• Use SQL-Specific Indexing: Depending on the queries you're dealing with, make sure your SQL database is well-indexed for the types of queries you're running, especially for large datasets.

By combining these techniques, improving vector search, refining query generation, leveraging SQL metadata, and tuning your models... you should be able to significantly improve the accuracy and reliability of complex RAG based search queries with your SQL db.

Note: This is a generic approach, you will have to trial multiple combinations or the one that fits the best based on your requirement.

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