SQL Query Engine with LlamaIndex + DuckDB#
This guide showcases the core LlamaIndex SQL capabilities with DuckDB.
We go through some core LlamaIndex data structures, including the NLSQLTableQueryEngine
and SQLTableRetrieverQueryEngine
.
If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
!pip install llama-index
!pip install duckdb duckdb-engine
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index import (
SQLDatabase,
SimpleDirectoryReader,
WikipediaReader,
Document,
)
from llama_index.indices.struct_store import (
NLSQLTableQueryEngine,
SQLTableRetrieverQueryEngine,
)
from IPython.display import Markdown, display
Basic Text-to-SQL with our NLSQLTableQueryEngine
#
In this initial example, we walk through populating a SQL database with some test datapoints, and querying it with our text-to-SQL capabilities.
Create Database Schema + Test Data#
We use sqlalchemy, a popular SQL database toolkit, to connect to DuckDB and create an empty city_stats
Table. We then populate it with some test data.
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("duckdb:///:memory:")
# uncomment to make this work with MotherDuck
# engine = create_engine("duckdb:///md:llama-index")
metadata_obj = MetaData()
# create city SQL table
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
# print tables
metadata_obj.tables.keys()
dict_keys(['city_stats'])
We introduce some test data into the city_stats
table
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{
"city_name": "Chicago",
"population": 2679000,
"country": "United States",
},
{"city_name": "Seoul", "population": 9776000, "country": "South Korea"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
[('Toronto', 2930000, 'Canada'), ('Tokyo', 13960000, 'Japan'), ('Chicago', 2679000, 'United States'), ('Seoul', 9776000, 'South Korea')]
Create SQLDatabase Object#
We first define our SQLDatabase abstraction (a light wrapper around SQLAlchemy).
from llama_index import SQLDatabase
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
/Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/duckdb_engine/__init__.py:162: DuckDBEngineWarning: duckdb-engine doesn't yet support reflection on indices
warnings.warn(
Query Index#
Here we demonstrate the capabilities of NLSQLTableQueryEngine
, which performs text-to-SQL.
We construct a
NLSQLTableQueryEngine
and pass in our SQL database object.We run queries against the query engine.
query_engine = NLSQLTableQueryEngine(sql_database)
response = query_engine.query("Which city has the highest population?")
INFO:llama_index.indices.struct_store.sql_query:> Table desc str: Table 'city_stats' has columns: city_name (VARCHAR), population (INTEGER), country (VARCHAR) and foreign keys: .
> Table desc str: Table 'city_stats' has columns: city_name (VARCHAR), population (INTEGER), country (VARCHAR) and foreign keys: .
/Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/langchain/sql_database.py:238: UserWarning: This method is deprecated - please use `get_usable_table_names`.
warnings.warn(
INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 332 tokens
> [query] Total LLM token usage: 332 tokens
INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens
> [query] Total embedding token usage: 0 tokens
str(response)
' Tokyo has the highest population, with 13,960,000 people.'
response.metadata
{'result': [('Tokyo', 13960000)],
'sql_query': 'SELECT city_name, population \nFROM city_stats \nORDER BY population DESC \nLIMIT 1;'}
Advanced Text-to-SQL with our SQLTableRetrieverQueryEngine
#
In this guide, we tackle the setting where you have a large number of tables in your database, and putting all the table schemas into the prompt may overflow the text-to-SQL prompt.
We first index the schemas with our ObjectIndex
, and then use our SQLTableRetrieverQueryEngine
abstraction on top.
engine = create_engine("duckdb:///:memory:")
# uncomment to make this work with MotherDuck
# engine = create_engine("duckdb:///md:llama-index")
metadata_obj = MetaData()
# create city SQL table
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
all_table_names = ["city_stats"]
# create a ton of dummy tables
n = 100
for i in range(n):
tmp_table_name = f"tmp_table_{i}"
tmp_table = Table(
tmp_table_name,
metadata_obj,
Column(f"tmp_field_{i}_1", String(16), primary_key=True),
Column(f"tmp_field_{i}_2", Integer),
Column(f"tmp_field_{i}_3", String(16), nullable=False),
)
all_table_names.append(f"tmp_table_{i}")
metadata_obj.create_all(engine)
# insert dummy data
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{
"city_name": "Chicago",
"population": 2679000,
"country": "United States",
},
{"city_name": "Seoul", "population": 9776000, "country": "South Korea"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
Construct Object Index#
from llama_index.indices.struct_store import SQLTableRetrieverQueryEngine
from llama_index.objects import (
SQLTableNodeMapping,
ObjectIndex,
SQLTableSchema,
)
from llama_index import VectorStoreIndex
table_node_mapping = SQLTableNodeMapping(sql_database)
table_schema_objs = []
for table_name in all_table_names:
table_schema_objs.append(SQLTableSchema(table_name=table_name))
obj_index = ObjectIndex.from_objects(
table_schema_objs,
table_node_mapping,
VectorStoreIndex,
)
INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens
> [build_index_from_nodes] Total LLM token usage: 0 tokens
INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 6343 tokens
> [build_index_from_nodes] Total embedding token usage: 6343 tokens
Query Index with SQLTableRetrieverQueryEngine
#
query_engine = SQLTableRetrieverQueryEngine(
sql_database,
obj_index.as_retriever(similarity_top_k=1),
)
response = query_engine.query("Which city has the highest population?")
INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens
> [retrieve] Total LLM token usage: 0 tokens
INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 7 tokens
> [retrieve] Total embedding token usage: 7 tokens
INFO:llama_index.indices.struct_store.sql_query:> Table desc str: Table 'city_stats' has columns: city_name (VARCHAR), population (INTEGER), country (VARCHAR) and foreign keys: .
> Table desc str: Table 'city_stats' has columns: city_name (VARCHAR), population (INTEGER), country (VARCHAR) and foreign keys: .
INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 337 tokens
> [query] Total LLM token usage: 337 tokens
INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens
> [query] Total embedding token usage: 0 tokens
response
Response(response=' The city with the highest population is Tokyo, with a population of 13,960,000.', source_nodes=[], metadata={'result': [('Tokyo', 13960000)], 'sql_query': 'SELECT city_name, population \nFROM city_stats \nORDER BY population DESC \nLIMIT 1;'})