Embed

NEW

すべてをベクトル化。

何でもできる。

画像・テキスト・音声を含む豊富な動画データをベクトル化し、新たな可能性へと変える。

Playgroundをお試しください

Products

Enterprise

Research

Developers

Company

Select Language

Playground

Talk to Sales

Embed 01

マルチモーダルだからといって、マルチモデルである必要はない。

画像・テキスト・音声・動画ごとに分断されたソリューションを、もはやつなぎ合わせる必要はありません。すべてのモダリティに対応し、豊富な動画データを同一空間内のベクトルへと変換します。

Embed 01

Multimodal doesn’t have to mean multi-model.

No more piecing together siloed solutions for image, text, audio and video. Support all modalities and turn rich video data into vectors in the same space.

Embed 02

シンプルだからといって、ありきたりである必要はない。

データが唯一無二であるように、モデルもそうあるべきです。独自のドメインに合わせてモデルを容易にファインチューニングし、圧倒的なパフォーマンスを実現します。

Embed 02

Simple doesn’t have to mean generic.

Your data is unique – your models should be, too. Fine-tune our models easily for your domain until they deliver unparalleled performance.

Embed 03

より高品質な結果を、より短い処理時間で。

動画のネイティブサポートにより、Embed APIは処理時間を短縮し、スループットを向上させ、これにより、時間とコストを削減します。

Embed 03

Better output with shorter processing times.

With native video support, Embed API reduces processing time, increasing throughput, and saving you time and money.

For everything your video can do.

RAG pairing

Pair our models with your RAG pipeline to retrieve relevant information and improve data output.

High-quality training data

Transform workflows with embeddings to create training data, improve data quality, and reduce manual labeling needs.

Training models

Use embeddings to improve data quality when training large language models.

Anomaly detection

Codentify anomalies – for example, detect and remove corrupt videos that only display a black background – to enhance data quality.

For everything your video can do.

RAG pairing

Pair our models with your RAG pipeline to retrieve relevant information and improve data output.

High-quality training data

Transform workflows with embeddings to create training data, improve data quality, and reduce manual labeling needs.

Customer Search

Let customers easily find   any video moment within   your platform.

Asset Management

Comb through petabytes   of data using natural   language queries.

For everything your video can do.

RAG pairing

Pair our models with your RAG pipeline to retrieve relevant information and improve data output.

High-quality training data

Transform workflows with embeddings to create training data, improve data quality, and reduce manual labeling needs.

Customer Search

Let customers easily find   any video moment within   your platform.

Asset Management

Comb through petabytes   of data using natural   language queries.

Sample Apps

node

PYTHON

Contextual and Personalized Ads

A tool for analyzing source footage, summarizing content, and recommending ads based on the footage's context and emotional tone.

Try this sample app

PYTHON

Recommendations using Multimodal Embeddings

Start exploring videos and discovering similar content powered by TwelveLabs Multimodal Embeddings.

Try this sample app

Python

Node

from twelvelabs import TwelveLabs
from twelvelabs.models.embed import EmbeddingsTask, SegmentEmbedding

client = TwelveLabs("<YOUR_API_KEY>")

# Create a video embedding task for your video
task = client.embed.task.create(
   model_name="Marengo-retrieval-2.7",
   video_url: "<YOUR_VIDEO_URL>"
)

print(f"Created task: id={task.id} model_name={task.model_name} status={task.status}")

# Wait for embedding task to finish
status = task.wait_for_done()
print(f"Embedding done: {status}")

# Retrieve the video embeddings
task = task.retrieve()

# Print the embeddings
if task.video_embedding is not None and task.video_embedding.segments is not None:
    for segment in task.video_embedding.segments:
            print(
                f"  embedding_scope={segment.embedding_scope} start_offset_sec={segment.start_offset_sec} end_offset_sec={segment.end_offset_sec}"
            )
            print(f"  embeddings: {", ".join(str(segment.embeddings_float))}")

Python

Node

from twelvelabs import TwelveLabs
from twelvelabs.models.embed import EmbeddingsTask, SegmentEmbedding

client = TwelveLabs("<YOUR_API_KEY>")

# Create a video embedding task for your video
task = client.embed.task.create(
   model_name="Marengo-retrieval-2.7",
   video_url: "<YOUR_VIDEO_URL>"
)

print(f"Created task: id={task.id} model_name={task.model_name} status={task.status}")

# Wait for embedding task to finish
status = task.wait_for_done()
print(f"Embedding done: {status}")

# Retrieve the video embeddings
task = task.retrieve()

# Print the embeddings
if task.video_embedding is not None and task.video_embedding.segments is not None:
    for segment in task.video_embedding.segments:
            print(
                f"  embedding_scope={segment.embedding_scope} start_offset_sec={segment.start_offset_sec} end_offset_sec={segment.end_offset_sec}"
            )
            print(f"  embeddings: {", ".join(str(segment.embeddings_float))}")

Python

Node

from twelvelabs import TwelveLabs
from twelvelabs.models.embed import EmbeddingsTask, SegmentEmbedding

client = TwelveLabs("<YOUR_API_KEY>")

# Create a video embedding task for your video
task = client.embed.task.create(
   model_name="Marengo-retrieval-2.7",
   video_url: "<YOUR_VIDEO_URL>"
)

print(f"Created task: id={task.id} model_name={task.model_name} status={task.status}")

# Wait for embedding task to finish
status = task.wait_for_done()
print(f"Embedding done: {status}")

# Retrieve the video embeddings
task = task.retrieve()

# Print the embeddings
if task.video_embedding is not None and task.video_embedding.segments is not None:
    for segment in task.video_embedding.segments:
            print(
                f"  embedding_scope={segment.embedding_scope} start_offset_sec={segment.start_offset_sec} end_offset_sec={segment.end_offset_sec}"
            )
            print(f"  embeddings: {", ".join(str(segment.embeddings_float))}")