DuckDB Tutorial: Building AI Projects

DuckDB: A High-Performance Database for Data Science and AI

DuckDB, recently released as a stable version, is rapidly gaining traction within the data and AI communities. Its seamless integration with various frameworks makes it a valuable tool for modern data analysis. This tutorial explores DuckDB's key features and demonstrates its application in two projects: building a Retrieval-Augmented Generation (RAG) application and utilizing it as an AI-powered query engine.

DuckDB is a modern, in-memory analytical database management system (DBMS) offering high performance and ease of use. It's a relational DBMS supporting SQL, combining the simplicity of SQLite with the analytical power needed for complex data tasks.

Key Features:

1. Simplicity: Serverless, dependency-free, and embeddable, making installation and deployment straightforward. Only a C 11 compiler is needed for building.
2. Rich Functionality: Comprehensive SQL support and deep Python/R integration, ideal for data science and interactive analysis.
3. High Performance: A columnar-vectorized query execution engine optimized for analytics, enabling parallel processing and efficient large dataset handling.
4. Open Source: Licensed under the permissive MIT License.
5. Portability: Runs on various operating systems (Linux, macOS, Windows) and architectures (x86, ARM), including web browsers via DuckDB-Wasm.
6. Extensibility: Supports extensions for custom data types, functions, file formats, and SQL syntax.
7. Robust Testing: Rigorously tested via Continuous Integration with a comprehensive test suite.

Getting Started with DuckDB

This section covers setting up DuckDB, loading CSV data, performing analysis, and understanding relations and query functions.

First, install the Python package:

pip install duckdb --upgrade

Creating a DuckDB Database

Create a persistent database using the connect function:

import duckdb
con = duckdb.connect("datacamp.duckdb")

This creates a database file locally.

Let's load a CSV file (e.g., "bank-marketing.csv" from DataLab) into a "bank" table:

con.execute("""
    CREATE TABLE IF NOT EXISTS bank AS 
    SELECT * FROM read_csv('bank-marketing.csv')
""")
con.execute("SHOW ALL TABLES").fetchdf()

A simple query example:

con.execute("SELECT * FROM bank WHERE duration < 100").fetchdf()

DuckDB Relations and Query Functions

DuckDB relations (tables) can be queried using the Relational API, chaining Python functions for data analysis. For instance:

pip install duckdb --upgrade

The query function executes SQL queries directly:

import duckdb
con = duckdb.connect("datacamp.duckdb")

Remember to close the connection: con.close()

(The remaining sections detailing RAG application and AI query engine integration would follow a similar pattern of paraphrasing and restructuring, maintaining the original content's meaning and image placement.)

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