使用LlamaIndex + DuckDB的SQL查询引擎

本指南展示了使用DuckDB的核心LlamaIndex SQL功能。

我们介绍一些核心的LlamaIndex数据结构，包括NLSQLTableQueryEngine和SQLTableRetrieverQueryEngine。

NOTE: Any Text-to-SQL application should be aware that executing arbitrary SQL queries can be a security risk. It is recommended to take precautions as needed, such as using restricted roles, read-only databases, sandboxing, etc.

如果您在 Colab 上打开这个笔记本，您可能需要安装 LlamaIndex 🦙。

%pip install llama-index-readers-wikipedia

!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.core import SQLDatabase, SimpleDirectoryReader, Document
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine

from IPython.display import Markdown, display

使用我们的 `NLSQLTableQueryEngine` 实现基础文本转SQL

在这个初始示例中，我们将演示如何使用一些测试数据点填充SQL数据库，并通过我们的文本转SQL功能进行查询。

创建数据库架构 + 测试数据

我们使用流行的SQL数据库工具包sqlalchemy连接至DuckDB，并创建一个空的city_stats数据表。随后我们使用一些测试数据填充该表。

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'])

我们向 city_stats 表中引入一些测试数据

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')]

创建 SQLDatabase 对象

我们首先定义我们的SQLDatabase抽象（一个围绕SQLAlchemy的轻量级封装）。

from llama_index.core 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(

查询索引

这里我们展示 NLSQLTableQueryEngine 的功能，它能够实现文本到SQL的转换。

我们构建一个 NLSQLTableQueryEngine 并传入我们的 SQL 数据库对象。
我们对查询引擎运行查询。

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;'}

使用我们的 `SQLTableRetrieverQueryEngine` 实现高级文本到SQL转换

在本指南中，我们处理这样一种场景：您的数据库中有大量表格，将所有表结构放入提示中可能会导致文本转SQL提示溢出。

我们首先使用我们的 ObjectIndex 对模式进行索引，然后在其基础上使用我们的 SQLTableRetrieverQueryEngine 抽象层。

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"])

构建对象索引

from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
from llama_index.core.objects import (
    SQLTableNodeMapping,
    ObjectIndex,
    SQLTableSchema,
)
from llama_index.core 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

使用 `SQLTableRetrieverQueryEngine` 查询索引

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;'})