Structured Data#

A Guide to LlamaIndex + Structured Data#

A lot of modern data systems depend on structured data, such as a Postgres DB or a Snowflake data warehouse. LlamaIndex provides a lot of advanced features, powered by LLM’s, to both create structured data from unstructured data, as well as analyze this structured data through augmented text-to-SQL capabilities.

This guide helps walk through each of these capabilities. Specifically, we cover the following topics:

Setup: Defining up our example SQL Table.
Building our Table Index: How to go from sql database to a Table Schema Index
Using natural language SQL queries: How to query our SQL database using natural language.

We will walk through a toy example table which contains city/population/country information. A notebook for this tutorial is available here.

Setup#

First, we use SQLAlchemy to setup a simple sqlite db:

from sqlalchemy import (
    create_engine,
    MetaData,
    Table,
    Column,
    String,
    Integer,
    select,
    column,
)

engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()

We then create a toy city_stats table:

# 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)

Now it’s time to insert some datapoints!

If you want to look into filling into this table by inferring structured datapoints from unstructured data, take a look at the below section. Otherwise, you can choose to directly populate this table:

from sqlalchemy import insert

rows = [
    {"city_name": "Toronto", "population": 2731571, "country": "Canada"},
    {"city_name": "Tokyo", "population": 13929286, "country": "Japan"},
    {"city_name": "Berlin", "population": 600000, "country": "Germany"},
]
for row in rows:
    stmt = insert(city_stats_table).values(**row)
    with engine.begin() as connection:
        cursor = connection.execute(stmt)

Finally, we can wrap the SQLAlchemy engine with our SQLDatabase wrapper; this allows the db to be used within LlamaIndex:

from llama_index import SQLDatabase

sql_database = SQLDatabase(engine, include_tables=["city_stats"])

Natural language SQL#

Once we have constructed our SQL database, we can use the NLSQLTableQueryEngine to construct natural language queries that are synthesized into SQL queries.

Note that we need to specify the tables we want to use with this query engine. If we don’t the query engine will pull all the schema context, which could overflow the context window of the LLM.

from llama_index.indices.struct_store import NLSQLTableQueryEngine

query_engine = NLSQLTableQueryEngine(
    sql_database=sql_database,
    tables=["city_stats"],
)
query_str = "Which city has the highest population?"
response = query_engine.query(query_str)

This query engine should used in any case where you can specify the tables you want to query over beforehand, or the total size of all the table schema plus the rest of the prompt fits your context window.

Building our Table Index#

If we don’t know ahead of time which table we would like to use, and the total size of the table schema overflows your context window size, we should store the table schema in an index so that during query time we can retrieve the right schema.

The way we can do this is using the SQLTableNodeMapping object, which takes in a SQLDatabase and produces a Node object for each SQLTableSchema object passed into the ObjectIndex constructor.

from llama_index.objects import (
    SQLTableNodeMapping,
    ObjectIndex,
    SQLTableSchema,
)

table_node_mapping = SQLTableNodeMapping(sql_database)
table_schema_objs = [
    (SQLTableSchema(table_name="city_stats")),
    ...,
]  # one SQLTableSchema for each table

obj_index = ObjectIndex.from_objects(
    table_schema_objs,
    table_node_mapping,
    VectorStoreIndex,
)

Here you can see we define our table_node_mapping, and a single SQLTableSchema with the “city_stats” table name. We pass these into the ObjectIndex constructor, along with the VectorStoreIndex class definition we want to use. This will give us a VectorStoreIndex where each Node contains table schema and other context information. You can also add any additional context information you’d like.

# manually set extra context text
city_stats_text = (
    "This table gives information regarding the population and country of a given city.\n"
    "The user will query with codewords, where 'foo' corresponds to population and 'bar'"
    "corresponds to city."
)

table_node_mapping = SQLTableNodeMapping(sql_database)
table_schema_objs = [
    (SQLTableSchema(table_name="city_stats", context_str=city_stats_text))
]

Using natural language SQL queries#

Once we have defined our table schema index obj_index, we can construct a SQLTableRetrieverQueryEngine by passing in our SQLDatabase, and a retriever constructed from our object index.

from llama_index.indices.struct_store import SQLTableRetrieverQueryEngine

query_engine = SQLTableRetrieverQueryEngine(
    sql_database, obj_index.as_retriever(similarity_top_k=1)
)
response = query_engine.query("Which city has the highest population?")
print(response)

Now when we query the retriever query engine, it will retrieve the relevant table schema and synthesize a SQL query and a response from the results of that query.

Concluding Thoughts#

This is it for now! We’re constantly looking for ways to improve our structured data support. If you have any questions let us know in our Discord.

Relevant Resources:

Airbyte SQL Index Guide