Handling Tabular Data in LlamaIndex: Best Practices

Why LlamaIndex for Tabular Data?

Key Features of LlamaIndex for Tabular Data Handling

1. Text-to-SQL Capabilities: With text-to-SQL functionality, users can convert natural language queries into SQL commands seamlessly.
2. Database Integration: It supports various SQL databases, such as SQLite, Postgres, and MySQL, making it easy to manage and retrieve table data across different systems.
3. Evidence-based Query Execution: LlamaIndex ensures all queries are executed safely under the recommended precautions, such as using restricted roles and read-only databases to mitigate security risks.

Setting Up LlamaIndex for Tabular Data

Step 1: Creating Your Database

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from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer

# Initialize SQLite in-memory database
engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()

Step 2: Defining a Table Schema

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# Create city stats 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)

Step 3: Inserting Data into the Table

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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:
        connection.execute(stmt)

Crafting Effective Natural Language SQL Queries

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from llama_index.core.query_engine 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)

Designing Effective Queries

• Simplicity: Keep your questions simple and clear. Complex queries can lead to ambiguous results.
• Contextual Keywords: Keywords like "highest population" give a clearer direction to the engine for retrieving relevant data.

Building a Table Index

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from llama_index.core.objects import SQLTableNodeMapping, ObjectIndex, SQLTableSchema

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

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

Optimizing Chunk Sizes in LlamaIndex

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from llama_index.core import Settings

Settings.chunk_size = 512
Settings.chunk_overlap = 20

Best Practices for Handling Tabular Data with LlamaIndex

1. Use Structured Data Types: Avoid using raw string or unsupported data formats. Utilize structured types, enabling better querying results.
2. Upgrade Chunk Sizes Based on Use Cases: Depending on the complexity of your queries, adjust the chunk sizes accordingly to optimize speed without compromising data accuracy.
3. Implement Metadata for Clarity: Adding metadata to your documents can be a game-changing tactic for information tracking during retrievals.
4. Hybrid Search Techniques: Combine keyword search with embedding similarity for richer query results. LlamaIndex supports hybrid search functionalities, enhancing retrieval performance.
5. Regular Data Audits: Regularly check on your data to maintain cleanliness and eliminate outdated or incorrect information to ensure accurate querying.

Utilize Arsturn for Optimized Engagement

Conclusion