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Query Construction in Retrieval-Augmented Generation (RAG)

Haziqa Sajid

Haziqa Sajid

— 8 min read
Query Construction in Retrieval-Augmented Generation (RAG)

Query construction forms the backbone of modern information retrieval systems, particularly in Retrieval-Augmented Generation (RAG). At its core, query construction transforms natural language inputs into structured queries that databases and retrieval systems understand. This transformation bridges the communication gap between humans and machines, enabling more accurate and relevant information retrieval.

RAG combines document retrieval with natural language generation to create contextually aware responses. The system retrieves relevant information from a knowledge base and uses it to generate accurate, well-supported answers. Query construction plays a vital role in this process by ensuring the retrieval component accurately captures user intent and finds the most relevant information.

Well-constructed queries lead to more precise document retrieval, reducing irrelevant results and improving the quality of generated responses. This precision becomes crucial when dealing with large-scale data systems where efficiency and accuracy determine the success of information retrieval.

This article explores query construction for RAG systems, fundamental concepts, implementation techniques, and practical challenges.

Query construction pipeline for retrieval | source

What is Query Construction?

Query construction converts natural language questions into formats that databases and retrieval systems can process. When users ask questions like "Find movies about aliens released in 1980," the system must translate this into a structured query that specifies both the semantic search component ("about aliens") and exact match criteria ("year = 1980").

This process matters because it creates a vital link between human communication patterns and machine-readable instructions. Practical query construction ensures database systems receive clear, actionable instructions matching user intent. Even the most sophisticated retrieval systems could only provide relevant results with proper query construction.

Database Query Construction Pipeline

Types of Data in Query Construction

Different data types require specific approaches to query construction. Understanding these types helps in developing effective query strategies:

1. Structured Data

Structured data lives in SQL and graph databases, following predefined schemas that organize information in tables or relationships. These databases excel at precise operations through standardized query languages like SQL or Cypher. For example, a movie database might store titles, release dates, and ratings in specific columns, enabling exact filtering and sorting.

2. Semi-Structured Data

Semi-structured data combines organized elements with free-form content, commonly found in JSON or XML files. These formats provide some organizational structure but also allow for flexibility. Document databases, where each item may hold structured metadata (author, date) and unstructured content (body text), are a common example.

3. Unstructured Data

Vector databases primarily handle unstructured data, relying on semantic indexing and metadata filtering for retrieval. These systems excel at finding similar content through vector similarity searches. Text documents, articles, and general content fall into this category, where meaning matters more than exact matches.

Techniques for Query Construction

Query construction techniques vary based on the target database type and data structure. Each approach addresses specific challenges in translating natural language into machine-readable queries.

1. Text-to-SQL Translation

Text-to-SQL translation converts natural language questions into SQL queries for structured database interactions. This process involves several key components:

Database Schema Understanding

  • The system analyzes the database structure, including tables, columns, and relationships.
  • Creates a context map of available data fields and their connections.
  • This information is used to generate valid SQL syntax.

Query Formation Steps

  • Extracts key entities and conditions from the natural language input.
  • Maps identified elements to corresponding database fields.
  • Constructs appropriate WHERE clauses and JOIN operations.
  • Validates query syntax before execution.

For example, the question "Find action movies released after 2020 with ratings above 8" transforms into:

SELECT title, release_year, rating
FROM movies
WHERE genre = 'action'
AND release_year > 2020
AND rating > 8;
SQL agent | source

2. Metadata-Based Filtering

Metadata filtering enhances retrieval from vector databases by combining semantic search with structured filters. This technique involves several key components and implementation steps:

Query Components

  • Separates the semantic search terms from filtering conditions.
  • Creates structured filters based on available metadata fields.
  • Combines both elements for precise document retrieval.

Implementation Approach

  • Analyzes natural language input for filter conditions.
  • Constructs metadata filters using comparison operators.
  • Applies filters alongside semantic similarity search.

For instance, "Find technical articles about machine learning published this year" generates semantic search parameters for "machine learning" and metadata filters for the publication date.

3. Text-to-Cypher Translation

Text-to-Cypher translation builds queries for graph databases, focusing on relationship-based data structures. This technique operates through two main processes:

Graph Pattern Matching

  • Identifies entities and their relationships in natural language.
  • Maps these to node and edge patterns in the graph.
  • Constructs path-based queries using Cypher syntax.

Query Construction Process

  • Analyzes relationship requirements in the user query.
  • Determines appropriate graph traversal patterns.
  • Builds Cypher queries that capture complex relationships.

For example, "Find all collaborators who worked with Christopher Nolan on science fiction movies" becomes a Cypher query that traverses director-movie-genre relationships.

Each technique requires careful consideration of the following:

  • Input validation to prevent errors
  • Query optimization for performance
  • Error handling for invalid inputs
  • Schema compatibility checks

These approaches work together in modern systems, often combining multiple techniques for comprehensive data retrieval across different storage types.

Implementation: Query Construction in RAG

The implementation demonstrates building a RAG system focused on movie search functionality. This implementation creates a pipeline to understand natural language queries about movies and translate them into structured SQL queries to retrieve relevant information from a PostgreSQL database. 

The system is particularly designed to handle queries about movie genres, release years, and ratings, with built-in support for pagination and sorting.

Step 1: Create Project Structure

The implementation begins by setting up a clear project structure with separate files for configuration, main RAG implementation, and testing. 

movie_search/
    ├── config.py
    ├── movie_rag.py
    └── test_queries.py

Step 2: Install Dependencies

Essential packages are installed, including langchain for the RAG pipeline, psycopg2 for PostgreSQL interaction, SQLAlchemy for database operations, and sentence-transformers for handling embeddings. These form the backbone of our RAG system.

python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install requirements
pip install langchain langchain-community psycopg2-binary sqlalchemy sentence-transformers

Step 3: Create Configuration (config.py)

A DatabaseConfig class is implemented to manage database connection details. This separation of configuration allows for easy modification of database parameters without touching the core logic.

class DatabaseConfig:
    HOST = "localhost"
    PORT = 5432
    DATABASE = "movies_db"
    USER = "your_username"
    PASSWORD = "your_password"


    @property
    def connection_string(self):
        return f"postgresql://{self.USER}:{self.PASSWORD}@{self.HOST}:{self.PORT}/{self.DATABASE}"

Step 4: Implement RAG Pipeline (movie_rag.py)

The MovieRAGPipeline class contains three main components:

  • parse_query(): Breaks down natural language queries into structured components (genre, year, limit, sort_by)
  • build_query(): Constructs SQL queries from the parsed components
  • process_query(): Orchestrates the entire process from parsing to query execution
from typing import Dict
from langchain.sql_database import SQLDatabase
from langchain_community.embeddings import HuggingFaceEmbeddings
import re
class MovieRAGPipeline:
    def __init__(self, connection_string: str):
        self.db = SQLDatabase.from_uri(connection_string)
        self.embeddings = HuggingFaceEmbeddings()


    def parse_query(self, query: str) -> Dict:
        components = {
            "genre": None,
            "year": None,
            "limit": 5,
            "sort_by": None
        }
        
        query = query.lower()
        
        genres = ["comedy", "action", "drama", "sci-fi"]
        for genre in genres:
            if genre in query:
                components["genre"] = genre
                break


        year_match = re.search(r'\b\d{4}\b', query)
        if year_match:
            components["year"] = int(year_match.group())
        
        limit_match = re.search(r'top (\d+)', query)
        if limit_match:
            components["limit"] = int(limit_match.group(1))
        
        return components


    def build_query(self, components: Dict) -> str:
        conditions = []
        
        if components["genre"]:
            conditions.append(f"genre = '{components['genre']}'")
        if components["year"]:
            conditions.append(f"year = {components['year']}")
        
        where_clause = " AND ".join(conditions) if conditions else "TRUE"
        
        return f"""
        SELECT title, genre, year, rating
        FROM movies
        WHERE {where_clause}
        ORDER BY rating DESC
        LIMIT {components['limit']}
        """
    def process_query(self, query: str) -> Dict:
        try:
            components = self.parse_query(query)
            sql_query = self.build_query(components)
            
            results = self.db.run(sql_query)
            
            return {
                'status': 'success',
                'components': components,
                'sql_query': sql_query,
                'results': results
            }
        except Exception as e:
            return {
                'status': 'error',
                'message': str(e)
            }

Step 5: Create Test Script (test_queries.py)

A comprehensive test script is created to validate the pipeline's functionality with various query patterns. This ensures the system can handle different types of natural language inputs consistently.

from config import DatabaseConfig
from movie_rag import MovieRAGPipeline


def test_pipeline():
    config = DatabaseConfig()
    pipeline = MovieRAGPipeline(config.connection_string)
    
    test_queries = [
        "List top 5 comedy movies from 2022",
        "Show me comedy films released in 2022",
        "What are the highest rated comedy movies from 2022",
    ]
    for query in test_queries:
        print(f"\nProcessing Query: {query}")
        print("-" * 50)
        
        result = pipeline.process_query(query)
        
        if result['status'] == 'success':
            print("\nExtracted Components:")
            for key, value in result['components'].items():
                print(f"{key}: {value}")
            
            print("\nGenerated SQL Query:")
            print(result['sql_query'])
            
            print("\nResults:")
            print(result['results'])
        else:
            print("Error:", result['message'])
        
        print("\n" + "="*70 + "\n")


if __name__ == "__main__":
    test_pipeline()

Step 6: Setup Database

The implementation includes creating a PostgreSQL database with a movies table with essential fields (title, genre, year, rating) and a vector column for embeddings. Sample data is provided to test the functionality.

  1. Create database and table:
CREATE DATABASE movies_db;


CREATE TABLE movies (
    id SERIAL PRIMARY KEY,
    title TEXT NOT NULL,
    genre TEXT,
    year INTEGER,
    rating FLOAT,
    embeddings vector(384)
);
  1. Insert sample data:
INSERT INTO movies (title, genre, year, rating) VALUES
('The Menu', 'comedy', 2022, 8.5),
('Glass Onion', 'comedy', 2022, 8.2),
('Everything Everywhere', 'comedy', 2022, 8.9);

Step 7: Run the Test

The final step involves running the test script to verify the entire pipeline works as expected, demonstrating how natural language queries are converted into SQL queries and executed against the database.

python test_queries.py

Output:

Challenges in Query Construction

Query construction faces several challenges in database operations and information retrieval. Here are the key challenges:

  • LLM Hallucination Issues: Models can generate fictitious elements during query construction, including schema hallucination with non-existent tables and invalid relationships. This requires implementing strict validation processes and using database metadata to ensure accuracy.
  • User Error Management: The system manages user input errors like name misspellings, inconsistent formatting, and ambiguous terms. This requires robust error handling via fuzzy matching algorithms and clarification prompts to ensure accurate query interpretation.
  • Complex Query Processing: Handling multi-condition queries requires sophisticated management of multiple WHERE clauses and JOIN operations, while coordinating queries across different data stores demands proper transaction management and consistency maintenance.

Advantages of Query Construction

Query construction provides several important benefits that enhance data retrieval and system functionality. Here are the main advantages:

  • Improved Accuracy Benefits: Query construction significantly enhances retrieval precision through structured query generation, effective pattern matching, and context preservation, leading to more relevant responses. 
  • Enhanced Flexibility Features: The approach provides adaptability across multiple database types and query languages, supporting hybrid search approaches that combine exact and semantic matching for comprehensive data retrieval.
  • Efficient Data Access: Query construction optimizes resource utilization through reduced query execution time, better cache utilization, and efficient memory usage while reducing network bandwidth and optimizing storage access patterns.
  • User Intent Understanding: The system captures user requirements by maintaining semantic meaning across translations, preserving user intent in structured formats, and effectively handling complex relationship queries.
  • Scalable Solution Benefits: Query construction systems grow effectively with the organizational needs, handling increasing data volumes and supporting growing user bases while providing easy integration with existing systems and compatibility with modern cloud architectures.

Conclusion

In today’s data-centric world, query construction is essential for successful RAG systems. It bridges the gap between human communication and complex database operations, enabling these systems to deliver accurate and relevant responses. The importance of query construction is evident in data retrieval, user interactions, and data relationship management. These features help RAG systems deliver relevant responses while maintaining error handling and optimization.

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