---
title: "Pixeltable January 2026 Release Highlights"
date: "2026-01-29"
author: "Pixeltable Team"
tags:
  - Release
  - Pixeltable
  - Changelog
  - RunwayML
  - Gemini
  - Embeddings
description: "We kicked off 2026 by hardening Pixeltable, squashing 20 edge-case bugs across 6 releases. Plus new integrations with RunwayML and Gemini text embeddings, FP16 embedding indices, uuid7() for time-ordered UUIDs, and export_sql() for exporting to any SQL database."
url: "https://pixeltable.com/blog/pixeltable-january-2026-release-highlights"
---

# Pixeltable January 2026 Release Highlights

We kicked off 2026 by hardening Pixeltable, squashing 20 edge-case bugs across 6 releases to make the core more robust. We addressed corner cases in video frame extraction, view synchronization, index serialization, aggregate functions, and cross-instance data sharing. You can find the details in the [full changelog](https://docs.pixeltable.com/changelog/changelog).

 
Beyond stability work, we shipped several new capabilities this month.

 
## RunwayML Integration

 
Pixeltable now integrates with [RunwayML](https://runwayml.com/) for AI image and video generation. You can generate images from text prompts and reference images directly within your Pixeltable workflows:

 
```python
import pixeltable as pxt
import pixeltable.functions as pxtf

t = pxt.create_table('creative', {'prompt': pxt.String, 'ref_image': pxt.Image})
t.add_computed_column(
 response=pxtf.runwayml.text_to_image(
 t.prompt, [t.ref_image], model='gen4_image', ratio='16:9'
 )
)
t.insert(prompt='A colorful abstract painting', ref_image='image.jpg')
```

 
## Gemini Text Embeddings

 
We added support for Google's Gemini embedding models. These 3072-dimensional embeddings work seamlessly with Pixeltable's embedding index:

 
```python
import pixeltable as pxt
import pixeltable.functions as pxtf

t = pxt.create_table('docs', {'text': pxt.String})
t.add_embedding_index(
 'text',
 embedding=pxtf.gemini.generate_embedding.using(model='gemini-embedding-001'),
 precision='fp16' # default; use 'fp32' for full precision (max 2000 dims)
)
t.insert([{'text': 'First document'}, {'text': 'Second document'}])
```

 
## FP16 Embedding Indices

 
Embedding indices now default to half-precision (FP16) using pgvector's `halfvec` type. This doubles the maximum supported dimensions from 2000 to 4000 while cutting memory usage in half-a good tradeoff for large embedding models where you need scale. If you need full precision, set `precision='fp32'`.

 
## New uuid7() Function

 
We added a `uuid7()` function that generates time-ordered UUIDs. Unlike `uuid4()`, which produces randomly-ordered values, UUID v7 values sort chronologically. This means new rows cluster together in database indexes, improving insert and range-query performance:

 
```python
import pixeltable as pxt
import pixeltable.functions as pxtf

t = pxt.create_table('my_table', {'data': pxt.String})
t.add_computed_column(id=pxtf.uuid.uuid7())
t.insert([{'data': 'row1'}, {'data': 'row2'}])
```

 
## New export_sql() Function

 
The new `export_sql()` function lets you export any Pixeltable table or query result to an external SQL database. It automatically maps Pixeltable types to the appropriate SQL types for each database dialect:

 
```python
from pixeltable.io.sql import export_sql

# Works with PostgreSQL, SQLite, MySQL, Snowflake, etc.
export_sql(t, 'exported_table', db_connect_str='postgresql://...')
```

 
## In the News

 
We hosted a workshop on Vanishing Gradients about building multimodal AI systems!

 
 
 [Image: Vanishing Gradients Workshop: Building multimodal AI workflows with Pixeltable]
 
 *[Watch the workshop recording](https://www.youtube.com/live/UwdpNxHZDwI)*
 

 
---

 
## What's Next?

 
For the complete version-by-version breakdown, check out the [docs changelog](https://docs.pixeltable.com/changelog/changelog) or [GitHub compare view](https://github.com/pixeltable/pixeltable/compare/v0.5.9...v0.5.15).

 
You can also:

 

 - Browse the [full changelog](https://docs.pixeltable.com/changelog/changelog)

 - Explore [public datasets](https://www.pixeltable.com/data-products)

 - Join our [Discord community](https://discord.com/invite/QPyqFYx2UN)

 - Watch tutorials on [YouTube](https://www.youtube.com/@PixeltableHQ)

 

 
*Happy building!*

 
- The Pixeltable Team