> ## Documentation Index
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> Use this file to discover all available pages before exploring further.
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# Working with Twelve Labs in Pixeltable
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
Mountain Hiking. Hikers climbing a steep mountain trail with scenic
views.
0.539
Beach Sunset
Beach Sunset. A beautiful sunset over the ocean with palm
trees.
0.435
`, `
title
thumbnail
score
Beach Sunset
0.759
Mountain Hiking
0.742
`, `
title
thumbnail
score
City Street
0.135
Mountain Hiking
-0.003
`];
Twelve Labs provides multimodal embeddings that project text, images,
audio, and video into the **same semantic space**. This enables true
**cross-modal search** - the most powerful feature of this integration.
**What makes this special?** You can search a video index using *any*
modality:
This notebook demonstrates this cross-modal capability with video, then
shows how to apply the same embeddings to other modalities.
### Prerequisites
* A Twelve Labs account with an API key
([playground.twelvelabs.io](https://playground.twelvelabs.io/))
* Audio and video must be at least 4 seconds long
## Setup
```python theme={null}
%pip install -qU pixeltable twelvelabs
```
```python theme={null}
import getpass
import os
if 'TWELVELABS_API_KEY' not in os.environ:
os.environ['TWELVELABS_API_KEY'] = getpass.getpass(
'Enter your Twelve Labs API key: '
)
```
```python theme={null}
import pixeltable as pxt
import pixeltable.functions as pxtf
# Create a fresh directory for our demo
pxt.drop_dir('twelvelabs_demo', force=True)
pxt.create_dir('twelvelabs_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'twelvelabs\_demo'.
\
## Cross-Modal Video Search
Let’s index a video and search it using text, images, audio, and other
videos - all against the same index.
### Create Video Table and Index
```python theme={null}
# Create a table for videos
video_t = pxt.create_table('twelvelabs_demo/videos', {'video': pxt.Video})
# Insert a sample video
video_url = 'https://github.com/pixeltable/pixeltable/raw/main/docs/resources/The-Pursuit-of-Happiness.mp4'
video_t.insert([{'video': video_url}])
```
Created table 'videos'.
Inserted 1 row with 0 errors in 1.60 s (0.63 rows/s)
1 row inserted.
```python theme={null}
# Create a view that segments the video into searchable chunks
# Twelve Labs requires minimum 4 second segments
video_chunks = pxt.create_view(
'twelvelabs_demo/video_chunks',
video_t,
iterator=pxtf.video.video_splitter(
video=video_t.video, duration=5.0, min_segment_duration=4.0
),
)
# Add embedding index for cross-modal search
video_chunks.add_embedding_index(
'video_segment',
embedding=pxtf.twelvelabs.embed.using(model_name='marengo3.0'),
)
```
Let’s look at the index we just added in the table metadata:
```python theme={null}
video_chunks
```
The iterator created a larger table from our single video:
```python theme={null}
video_chunks.count()
```
51
### Text to Video Search
Find video segments matching a text description.
```python theme={null}
sim = video_chunks.video_segment.similarity(string='pink')
video_chunks.order_by(sim, asc=False).limit(3).select(
video_chunks.video_segment, score=sim
).collect()
```
### Image to Video Search
Find video segments similar to an image.
```python theme={null}
image_query = 'https://github.com/pixeltable/pixeltable/raw/main/docs/resources/The-Pursuit-of-Happiness-Screenshot.png'
sim = video_chunks.video_segment.similarity(image=image_query)
video_chunks.order_by(sim, asc=False).limit(2).select(
video_chunks.video_segment, score=sim
).collect()
```
### Video to Video Search
Find video segments similar to another video clip.
```python theme={null}
video_query = 'https://github.com/pixeltable/pixeltable/raw/main/docs/resources/The-Pursuit-of-Happiness-Video-Extract.mp4'
sim = video_chunks.video_segment.similarity(video=video_query)
video_chunks.order_by(sim, asc=False).limit(2).select(
video_chunks.video_segment, score=sim
).collect()
```
### Audio to Video Search
Find video segments with similar audio/speech content.
```python theme={null}
audio_query = 'https://github.com/pixeltable/pixeltable/raw/main/docs/resources/The-Pursuit-of-Happiness-Audio-Extract.m4a'
sim = video_chunks.video_segment.similarity(audio=audio_query)
video_chunks.order_by(sim, asc=False).limit(2).select(
video_chunks.video_segment, score=sim
).collect()
```
## Embedding Options
For video embeddings, you can focus on specific aspects:
* `'visual'` - Focus on what you see
* `'audio'` - Focus on what you hear
* `'transcription'` - Focus on what is said
```python theme={null}
# Add a visual-only embedding column
video_chunks.add_computed_column(
visual_embedding=pxtf.twelvelabs.embed(
video_chunks.video_segment,
model_name='marengo3.0',
embedding_option=['visual'],
)
)
video_chunks.select(
video_chunks.video_segment, video_chunks.visual_embedding
).limit(2).collect()
```
Added 51 column values with 0 errors in 19.81 s (2.57 rows/s)
## Other Modalities: Text, Images, and Documents
Twelve Labs embeddings also work for text, images, and documents. Here’s
a compact example showing **multiple embedding indexes on a single
table**.
```python theme={null}
# Create a multimodal content table
content_t = pxt.create_table(
'twelvelabs_demo/content',
{
'title': pxt.String,
'description': pxt.String,
'thumbnail': pxt.Image,
},
)
# Add computed column combining title and description
content_t.add_computed_column(
text_content=content_t.title + '. ' + content_t.description
)
# Add embedding index on combined text column
content_t.add_embedding_index(
'text_content',
embedding=pxtf.twelvelabs.embed.using(model_name='marengo3.0'),
)
# Add embedding index on image column
content_t.add_embedding_index(
'thumbnail',
embedding=pxtf.twelvelabs.embed.using(model_name='marengo3.0'),
)
```
Created table 'content'.
Added 0 column values with 0 errors in 0.01 s
```python theme={null}
# Insert sample content
content_t.insert(
[
{
'title': 'Beach Sunset',
'description': 'A beautiful sunset over the ocean with palm trees.',
'thumbnail': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000025.jpg',
},
{
'title': 'Mountain Hiking',
'description': 'Hikers climbing a steep mountain trail with scenic views.',
'thumbnail': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000139.jpg',
},
{
'title': 'City Street',
'description': 'Busy urban street with cars and pedestrians.',
'thumbnail': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000042.jpg',
},
{
'title': 'Wildlife Safari',
'description': 'Elephants and zebras on the African savanna.',
'thumbnail': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000061.jpg',
},
]
)
```
Inserted 4 rows with 0 errors in 1.97 s (2.03 rows/s)
4 rows inserted.
We can see the two indexes we added in the schema:
```python theme={null}
content_t
```
```python theme={null}
# Search by text description
sim = content_t.text_content.similarity(string='outdoor nature adventure')
content_t.order_by(sim, asc=False).limit(2).select(
content_t.title, content_t.text_content, score=sim
).collect()
```
```python theme={null}
# Search by image similarity
query_image = 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000001.jpg'
sim = content_t.thumbnail.similarity(image=query_image)
content_t.order_by(sim, asc=False).limit(2).select(
content_t.title, content_t.thumbnail, score=sim
).collect()
```
```python theme={null}
# Cross-modal: Search images using text!
sim = content_t.thumbnail.similarity(string='shoe rack')
content_t.order_by(sim, asc=False).limit(2).select(
content_t.title, content_t.thumbnail, score=sim
).collect()
```
## Summary
**Twelve Labs + Pixeltable enables:**
* **Cross-modal search**: Query video with text, images, audio, or
other videos
* **Multiple indexes per table**: Add embedding indexes on different
columns
* **Embedding options**: Focus on visual, audio, or transcription
aspects
* **All modalities**: Text, images, audio, video, and documents
### Learn More
* [Twelve Labs Documentation](https://docs.twelvelabs.io/)
* [Pixeltable Documentation](/)
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