Technical Tutorials

In the rapidly evolving landscape of AI and machine learning, data is no longer static. As we retrain embedding models to capture more nuance or improve semantic search accuracy, the underlying vector representations change. Managing these shifts has traditionally been a nightmare for data engineers. How do you roll back to the "last good" model? How do you audit why a specific query result changed between last month and today?

Enter LanceDB, an open-source vector database built on top of the Lance file format. Unlike traditional SQL databases or even standard NoSQL stores, LanceDB is designed with a native support for versioned datasets. It treats your vector data not just as a collection of points, but as a time-series object, allowing you to implement a "Git-like" history for your embeddings with minimal overhead.

Understanding the Lance File Format

At the heart of this capability is the Lance format. It is a columnar storage format optimized for high-dimensional vectors, random access, and zero-copy updates. Crucially, Lance files support manifests. Each time you write data to a Lance dataset, a new manifest is created. This manifest acts as a snapshot of the dataset's state, referencing the specific data files that constitute that version.

Because of this architecture, you don't need to copy entire tables to create a backup or a version. Instead, you simply reference a specific manifest ID. This makes versioning lightweight, instantaneous, and atomic.

Implementing Version Control in LanceDB

Let's look at how to implement this in practice. We'll start by creating a LanceDB table and writing some initial data. Then, we'll simulate a model update by writing new vectors and check how we can query historical states.

import lancedb
import pandas as pd

# Connect to the database
db = lancedb.connect("./my_lancedb")

# Initial data: Vectors generated by Model v1.0
df_v1 = pd.DataFrame({
    "id": [1, 2, 3],
    "text": ["hello world", "lance db", "vector search"],
    "vector": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
})

# Create the table with the initial version
table = db.create_table("embeddings", data=df_v1)

# Simulate a model update: Vectors generated by Model v2.0
# Note: We update the existing table, creating a new version
df_v2 = pd.DataFrame({
    "id": [1, 2, 3],
    "text": ["hello world", "lance db", "vector search"],
    "vector": [[0.11, 0.21, 0.31], [0.41, 0.51, 0.61], [0.71, 0.81, 0.91]]
})

table.add(df_v2)

# Check the current version
print(f"Current version: {table.version}")

Querying Historical States

Here is where the "Git-like" feature shines. If we realize that Model v2.0 introduced bias or hallucinations into our search results, we can instantly revert our queries to the v1.0 state without altering any stored data. We simply specify the version in our query.

# Query the current version (v2.0)
current_results = table.search([0.1, 0.2, 0.3]).limit(1).to_pandas()
print("Current (v2.0) Result:", current_results)

# Query the previous version (v1.0)
# We can access specific versions using the version parameter
prev_results = table.search([0.1, 0.2, 0.3], version=table.version - 1).limit(1).to_pandas()
print("Previous (v1.0) Result:", prev_results)

Practical Use Cases

A/B Testing Models: You can run inference pipelines against different versions of your embedding dataset to compare performance metrics without maintaining separate physical databases.
Audit Trails: Regulated industries can log exactly which vector version was used to generate a specific search result or recommendation at a specific timestamp.
Incremental Updates: As new documents are ingested, LanceDB handles the incremental writes, preserving the history of the dataset while efficiently storing only the changes.

Conclusion

Versioned datasets in LanceDB solve a critical pain point in MLOps: the lack of observability and rollback capabilities for vector data. By leveraging the Lance file format's manifest-based architecture, developers can treat their vector databases with the same rigor as their code repositories. This not only enhances debugging and auditing but also provides a safety net for continuous model integration, ensuring that AI applications remain reliable, transparent, and easily maintainable.