Natural Language SQLite Database Queries With LangChain
Note: this chapter updated October 16, 2025 for LangChain 0.3.2.
The LangChain library support of SQLite databases uses the Python library SQLAlchemy for database connections. This abstraction layer allows LangChain to use the same logic and models for other relational databases.
I have a long work history of writing natural language interfaces for relational databases that I will review in the chapter wrap up. For now, I invite you to be amazed at how simple it is to write the LangChain scripts for querying a database in natural language.
We will use the SQLite sample database from the SQLite Tutorial web site:
1 https://www.sqlitetutorial.net/sqlite-sample-database/
This database has 11 tables. The above URI has documentation for this database so please take a minute to review the table schema diagram and text description.
This example is derived from the LangChain documentation. We use three classes from the LangChain library:
- OpenAI: A class that represents the OpenAI language model, which is capable of understanding natural language and generating a response.
- SQLDatabase: A class that represents a connection to an SQL database.
- SQLDatabaseChain: A class that connects the OpenAI language model with the SQL database to allow natural language querying.
The temperature parameter set to 0 in this example. The temperature parameter controls the randomness of the generated output. A lower value (like 0) makes the model’s output more deterministic and focused, while a higher value introduces more randomness (or “creativity”). The run method of the db_chain object translates the natural language query into an appropriate SQL query, execute it on the connected database, and then returns the result converting the output into natural language.
1 from langchain.llms import OpenAI
2 from langchain_experimental.sql import SQLDatabaseChain
3 from langchain_experimental.sql.base import SQLDatabase
4
5 # Initialize the database and LLM
6 db = SQLDatabase.from_uri("sqlite:///chinook.db")
7 llm = OpenAI(temperature=0)
8
9 # Create a SQLDatabaseChain
10 db_chain = SQLDatabaseChain.from_llm(llm=llm, db=db, verbose=True)
11
12 # Run queries
13 db_chain.run("How many employees are there?")
14 db_chain.run("What is the name of the first employee?")
15 db_chain.run("Which customer has the most invoices?")
16 db_chain.run("List all music genres in the database")
The output (edited for brevity) shows the generated SQL queries and the query results:
1 $ python sqlite_chat_test.py
2
3 > Entering new SQLDatabaseChain chain...
4 How many employees are there?
5 SELECT COUNT(*) FROM employees;
6 SQLResult: [(8,)]
7 Answer: There are 8 employees.
8 > Finished chain.
9
10 > Entering new SQLDatabaseChain chain...
11 What is the name of the first employee?
12 SELECT FirstName, LastName FROM employees WHERE EmployeeId = 1;
13 SQLResult: [('Andrew', 'Adams')]
14 Answer: The first employee is Andrew Adams.
15 > Finished chain.
16
17 > Entering new SQLDatabaseChain chain...
18 Which customer has the most invoices?
19 SELECT customers.FirstName, customers.LastName, COUNT(invoices.InvoiceId) AS Number\
20 OfInvoices FROM customers INNER JOIN invoices ON customers.CustomerId = invoices.Cus
21 tomerId GROUP BY customers.CustomerId ORDER BY NumberOfInvoices DESC LIMIT 5;
22 SQLResult: [('Luis', 'Goncalves', 7), ('Leonie', 'Kohler', 7), ('Francois', 'Trembla\
23 y', 7), ('Bjorn', 'Hansen', 7), ('Frantisek', 'Wichterlova', 7)]
24 Answer: Luis Goncalves has the most invoices with 7.
25 > Finished chain.
26
27 > Entering new SQLDatabaseChain chain...
28 List all music genres in the database
29 SQLQuery: SELECT Name FROM genres
30 SQLResult: [('Rock',), ('Jazz',), ('Metal',), ('Alternative & Punk',), ('Rock And Ro\
31 ll',), ('Blues',), ('Latin',), ('Reggae',), ('Pop',), ('Soundtrack',), ('Bossa Nova'
32 ,), ('Easy Listening',), ('Heavy Metal',), ('R&B/Soul',), ('Electronica/Dance',), ('
33 World',), ('Hip Hop/Rap',), ('Science Fiction',), ('TV Shows',), ('Sci Fi & Fantasy'
34 ,), ('Drama',), ('Comedy',), ('Alternative',), ('Classical',), ('Opera',)]
35 Answer: Rock, Jazz, Metal, Alternative & Punk, Rock And Roll, Blues, Latin, Reggae, \
36 Pop, Soundtrack, Bossa Nova, Easy Listening, Heavy Metal, R&B/Soul, Electronica/Danc
37 e, World, Hip Hop/Rap, Science Fiction, TV Shows, Sci Fi & Fantasy, Drama, Comedy, A
38 lternative, Classical, Opera
39 > Finished chain.
Natural Language Database Query Wrap Up
I had an example I wrote for the first two editions of my Java AI book (I later removed this example because the code was too long and too difficult to follow). I later reworked this example in Common Lisp and used both versions in several consulting projects in the late 1990s and early 2000s.
The last book I wrote Practical Python Artificial Intelligence Programming used an OpenAI example https://github.com/openai/openai-cookbook/blob/main/examples/Backtranslation_of_SQL_queries.py that shows relatively simple code (relative to my older hand-written Java and Common Lisp code) for a NLP database interface.
Compared to the elegant support for NLP database queries in LangChain, the previous examples have limited power and required a lot more code. As I write this in March 2023, it is a good feeling that for the rest of my career, NLP database access is now a solved problem!