Retrieval Augmented Generation in SQLite
This two-part series explores using SQLite for machine learning. The previous article discussed SQLite's growing role in production-ready web applications. This article focuses on implementing retrieval-augmented generation (RAG) using SQLite.
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The code is available here.
Traditional RAG implementation often involves:
- Searching for tutorials on RAG.
- Selecting a popular framework (LangChain, LlamaIndex).
- Choosing a cloud vector database (Pinecone, Weaviate).
- Integrating these components.
While effective, this approach can be overly complex, especially for beginners. This article demonstrates a simpler method using SQLite with the sqlite-vec
extension and the OpenAI API. Part 1 of this series provides a detailed overview of SQLite's capabilities. For this article, it suffices to understand SQLite's simplicity as a single-file database.
This approach eliminates the need for cloud vector databases and bulky frameworks.
SQLite-Vec: Extending SQLite's Power
SQLite's strength lies in its extensibility. Extensions, similar to Python libraries, add functionality written in C. A prime example is the Full-Text Search (FTS) extension. sqlite-vec
adds vector search capabilities, enabling semantic understanding beyond keyword matching. Searching for "horses" might return "equestrian" or "pony."
sqlite-vec
uses virtual tables, offering:
- Custom Data Sources: Data can reside outside the database file (e.g., CSV, API).
- Flexible Functionality: Supports specialized indexing and complex data types.
- Seamless Integration: Integrates with standard SQLite query syntax.
- Modules: Backend logic is implemented in a separate module.
Virtual tables are created using:
CREATE VIRTUAL TABLE my_table USING my_extension_module();
my_extension_module()
specifies the module (here, vec0
from sqlite-vec
).
Code Walkthrough
The code (repo link) uses .txt
files as sample data (mostly physics-related). my_docs.db
is the SQLite database file.
-
Installation:
requirements.txt
lists the necessary libraries (sqlite-vec
,openai
,python-dotenv
). Create a virtual environment and runpip install -r requirements.txt
. -
OpenAI API Key: Obtain an OpenAI API key.
-
Loading the Extension: The Python code loads the
sqlite-vec
extension and creates a virtual table:
CREATE VIRTUAL TABLE my_table USING my_extension_module();
The documents
table stores embeddings (embedding
), filenames (file_name
), and content (content
).
denotes auxiliary fields.
- Embedding and Insertion: The code iterates through
.txt
files, generates embeddings using the OpenAI API, and inserts them into the database:
db.enable_load_extension(True) sqlite_vec.load(db) db.enable_load_extension(False) db.execute(''' CREATE VIRTUAL TABLE documents USING vec0( embedding float[1536], +file_name TEXT, +content TEXT ) ''')
- RAG Query: A KNN query retrieves similar documents based on embedding similarity:
# ... (OpenAI embedding function) ... for file_name in os.listdir("data"): # ... (Open file, get content, get embedding) ... db.execute( 'INSERT INTO documents (embedding, file_name, content) VALUES (?, ?, ?)', (serialize_float32(embedding), file_name, content) ) db.commit()
The results are then used as context for an OpenAI chat completion to answer the query.
Conclusion
sqlite-vec
simplifies RAG significantly. It eliminates the need for complex frameworks and cloud services, making it cost-effective and easy to iterate upon. While scaling might eventually require a more robust database, sqlite-vec
offers a compelling solution for smaller projects. The extension supports multiple programming languages.
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