Optimize multimodal search utilizing the TwelveLabs Embed API and Amazon OpenSearch Service

This weblog is co-authored by James Le, Head of Developer Expertise – TwelveLabs

The exponential development of video content material has created each alternatives and challenges. Content material creators, entrepreneurs, and researchers at the moment are confronted with the daunting job of effectively looking out, analyzing, and extracting worthwhile insights from huge video libraries. Conventional search strategies reminiscent of keyword-based textual content search usually fall brief when coping with video content material to investigate the visible content material, spoken phrases, or contextual parts throughout the video itself, leaving organizations struggling to successfully search by way of and unlock the complete potential of their multimedia belongings.

With the mixing of TwelveLabs’ Embed API and Amazon OpenSearch Service, we will work together with and derive worth from video content material. By utilizing TwelveLabs‘ superior AI-powered video understanding expertise and OpenSearch Service’s search and analytics capabilities, we will now carry out superior video discovery and achieve deeper insights.

On this weblog submit, we present you the method of integrating TwelveLabs Embed API with OpenSearch Service to create a multimodal search resolution. You’ll learn to generate wealthy, contextual embeddings from video content material and use OpenSearch Service’s vector database capabilities to allow search functionalities. By the top of this submit, you’ll be outfitted with the data to implement a system that may rework the way in which your group handles and extracts worth from video content material.

TwelveLabs’ multimodal embeddings course of visible, audio, and textual content indicators collectively to create unified representations, capturing the direct relationships between these modalities. This unified strategy delivers exact, context-aware video search that matches human understanding of video content material. Whether or not you’re a developer seeking to improve your purposes with superior video search capabilities, or a enterprise chief looking for to optimize your content material administration methods, this submit will offer you the instruments and steps to implement multimodal seek for your organizational information.

About TwelveLabs

TwelveLabs is an Superior AWS Companion and AWS Market Vendor that provides video understanding options. Embed API is designed to revolutionize the way you work together with and extract worth from video content material.

At its core, the Embed API transforms uncooked video content material into significant, searchable information through the use of state-of-the-art machine studying fashions. These fashions extract and signify advanced video data within the type of dense vector embeddings, every a typical 1024-dimensional vector that captures the essence of the video content material throughout a number of modalities (picture, textual content, and audio).

Key options of TwelveLabs Embed API

Under are the important thing options of TwelveLabs Embed API:

• Multimodal understanding: The API generates embeddings that encapsulate varied facets of the video, together with visible expressions, physique language, spoken phrases, and general context.
• Temporal coherence: In contrast to static image-based fashions, TwelveLabs’ embeddings seize the interrelations between totally different modalities over time, offering a extra correct illustration of video content material.
• Flexibility: The API helps native processing of all modalities current in movies, eliminating the necessity for separate text-only or image-only fashions.
• Excessive efficiency: By utilizing a video-native strategy, the Embed API offers extra correct and temporally coherent interpretation of video content material in comparison with conventional CLIP-like fashions.

Advantages and use instances

The Embed API presents quite a few benefits for builders and companies working with video content material:

• Enhanced Search Capabilities: Allow highly effective multimodal search throughout video libraries, permitting customers to search out related content material primarily based on visible, audio, or textual queries.
• Content material Suggestion: Enhance content material suggestion programs by understanding the deep contextual similarities between movies.
• Scene Detection and Segmentation: Mechanically detect and section totally different scenes inside movies for simpler navigation and evaluation.
• Content material Moderation: Effectively establish and flag inappropriate content material throughout massive video datasets.

Use instances embody:

• Anomaly detection
• Variety sorting
• Sentiment evaluation
• Suggestions

Structure overview

The structure for utilizing TwelveLabs Embed API and OpenSearch Service for superior video search consists of the next elements:

• TwelveLabs Embed API: This API generates 1024-dimensional vector embeddings from video content material, capturing visible, audio, and textual parts.
• OpenSearch Vector Database: Shops and indexes the video embeddings generated by TwelveLabs.
• Secrets and techniques Supervisor to retailer secrets and techniques reminiscent of API entry keys, and the Amazon OpenSearch Service username and password.
• Integration of TwelveLabs SDK and the OpenSearch Service consumer to course of movies, generate embeddings, and index them in OpenSearch Service.

The next diagram illustrates:

1. A video file is saved in Amazon Easy Storage Service (Amazon S3). Embeddings of the video file are created utilizing TwelveLabs Embed API.
2. Embeddings generated from the TwelveLabs Embed API at the moment are ingested to Amazon OpenSearch Service.
3. Customers can search the video embeddings utilizing textual content, audio, or picture. The consumer makes use of TwelveLabs Embed API to create the corresponding embeddings.
4. The consumer searches video embeddings in Amazon OpenSearch Service and retrieves the corresponding vector.

The use case

For the demo, you’ll work on these movies: Robin fowl forest Video by Federico Maderno from Pixabay and Island Video by Bellergy RC from Pixabay.

Nonetheless, the use case might be expanded to varied different segments. For instance, the information group struggles with:

1. Needle-in-haystack searches by way of 1000’s of hours of archival footage
2. Guide metadata tagging that misses nuanced visible and audio context
3. Cross-modal queries reminiscent of querying a video assortment utilizing textual content or audio descriptions
4. Fast content material retrieval for breaking information tie-ins

By integrating TwelveLabs Embed API with OpenSearch Service, you possibly can:

• Generate 1024-dimensional embeddings capturing every video’s visible ideas. The embeddings are additionally able to extracting spoken narration, on-screen textual content, and audio cues.
• Allow multimodal search capabilities permitting customers to:
  • Discover particular demonstrations utilizing text-based queries.
  • Find actions by way of image-based queries.
  • Establish segments utilizing audio sample matching.
• Scale back search time from hours to seconds for advanced queries.

Resolution walkthrough

GitHub repository accommodates a pocket book with detailed walkthrough directions for implementing superior video search capabilities by combining TwelveLabs’ Embed API with Amazon OpenSearch Service.

Conditions

Earlier than you proceed additional, confirm that the next stipulations are met:

• Verify that you’ve got an AWS account. Check in to the AWS account.
• Create a TwelveLabs account as a result of it will likely be required to get the API Key. TwelveLabs provide free tier pricing however you possibly can improve if obligatory to fulfill your requirement.
• Have an Amazon OpenSearch Service area. In the event you don’t have an current area, you possibly can create one utilizing the steps outlined in our public documentation for Creating and Managing Amazon OpenSearch Service Area. Make it possible for the OpenSearch Service area is accessible out of your Python atmosphere. You may also use Amazon OpenSearch Serverless for this use case and replace the interactions to OpenSearch Serverless utilizing AWS SDKs.

Step 1: Arrange the TwelveLabs SDK

Begin by organising the TwelveLabs SDK in your Python atmosphere:

1. Acquire your API key from TwelveLabs Dashboard.
2. Observe steps right here to create a secret in AWS Secrets and techniques Supervisor. For instance, identify the key as TL_API_Key.Word down the ARN or identify of the key (TL_API_Key) to retrieve. To retrieve a secret from one other account, you could use an ARN.For an ARN, we suggest that you just specify an entire ARN fairly than a partial ARN. See Discovering a secret from a partial ARN.Use this worth for the SecretId within the code block under.

import boto3
import json
secrets_manager_client=boto3.consumer("secretsmanager")
API_secret=secrets_manager_client.get_secret_value(
SecretId="TL_API_KEY"
)
TL_API_KEY=json.masses(API_secret["SecretString"])["TL_API_Key"]

Step 2: Generate video embeddings

Use the Embed API to create multimodal embeddings which can be contextual vector representations on your movies and texts. TwelveLabs video embeddings seize all of the delicate cues and interactions between totally different modalities, together with the visible expressions, physique language, spoken phrases, and the general context of the video, encapsulating the essence of all these modalities and their interrelations over time.

To create video embeddings, you could first add your movies, and the platform should end processing them. Importing and processing movies require a while. Consequently, creating embeddings is an asynchronous course of comprised of three steps:

1. Add and course of a video: While you begin importing a video, the platform creates a video embedding job and returns its distinctive job identifier.
2. Monitor the standing of your video embedding job: Use the distinctive identifier of your job to examine its standing periodically till it’s accomplished.
3. Retrieve the embeddings: After the video embedding job is accomplished, retrieve the video embeddings by offering the duty identifier. Be taught extra within the docs.

Video processing implementation

This demo relies upon upon some video information. To make use of this, you’ll obtain two mp4 information and add it to an Amazon S3 bucket.

1. Click on on the hyperlinks containing the Robin fowl forest Video by Federico Maderno from Pixabay and Island Video by Bellergy RC from Pixabay movies.
2. Obtain the 21723-320725678_small.mp4 and 2946-164933125_small.mp4 information.
3. Create an S3 bucket if you happen to don’t have one already. Observe the steps within the Making a bucket doc. Word down the identify of the bucket and substitute it the code block under (Eg., MYS3BUCKET).
4. Add the 21723-320725678_small.mp4 and 2946-164933125_small.mp4 video information to the S3 bucket created within the step above by following the steps within the Importing objects doc. Word down the identify of the objects and substitute it the code block under (Eg., 21723-320725678_small.mp4 and 2946-164933125_small.mp4)

s3_client=boto3.consumer("s3")
bird_video_data=s3_client.download_file(Bucket="MYS3BUCKET",  Key='21723-320725678_small.mp4', Filename="robin-bird.mp4")
island_video_data=s3_client.download_file(Bucket="MYS3BUCKET",  Key='2946-164933125_small.mp4', Filename="island.mp4")

def print_segments(segments: Record[SegmentEmbedding], max_elements: int = 1024):
    for section in segments:
        print(
            f"  embedding_scope={section.embedding_scope} start_offset_sec={section.start_offset_sec} end_offset_sec={section.end_offset_sec}"
        )
        print(f"  embeddings: {section.embeddings_float[:max_elements]}")

# Initialize consumer with API key
twelvelabs_client = TwelveLabs(api_key=TL_API_KEY)

video_files=["robin-bird.mp4", "island.mp4"]
duties=[]

Embedding technology course of

With the SDK configured, generate embeddings on your video and monitor job completion with real-time updates. Right here you employ the Marengo 2.7 mannequin to generate the embeddings:

for video in video_files:
    # Create embedding job
    job = twelvelabs_client.embed.job.create(
        model_name="Marengo-retrieval-2.7",
        video_file=video
    )
    print(
        f"Created job: id={job.id} engine_name={job.model_name} standing={job.standing}"
    )
    
    def on_task_update(job: EmbeddingsTask):
        print(f"  Standing={job.standing}")
    
    standing = job.wait_for_done(
        sleep_interval=2,
        callback=on_task_update
    )
    print(f"Embedding completed: {standing}")
    
    # Retrieve and examine outcomes
    job = job.retrieve()
    if job.video_embedding shouldn't be None and job.video_embedding.segments shouldn't be None:
        print_segments(job.video_embedding.segments)
    duties.append(job)

Key options demonstrated embody:

• Multimodal seize: 1024-dimensional vectors encoding visible, audio, and textual options
• Mannequin specificity: Utilizing Marengo-retrieval-2.7 optimized for scientific content material
• Progress monitoring: Actual-time standing updates throughout embedding technology

Anticipated output

Created job: id=67ca93a989d8a564e80dc3ba engine_name=Marengo-retrieval-2.7 standing=processing
  Standing=processing
  Standing=processing
  Standing=processing
  Standing=processing
  Standing=processing
  Standing=processing
  Standing=processing
  Standing=processing
  Standing=processing
  Standing=prepared
Embedding completed: prepared
  embedding_scope=clip start_offset_sec=0.0 end_offset_sec=6.0
  embeddings: [0.022429451, 0.00040668788, -0.01825908, -0.005862708, -0.03371106, 
-6.357456e-05, -0.015320076, -0.042556215, -0.02782445, -0.00019097517, 0.03258314, 
-0.0061399476, -0.00049206393, 0.035632476, 0.028209884, 0.02875258, -0.035486065, 
-0.11288028, -0.040782217, -0.0359422, 0.015908664, -0.021092793, 0.016303983, 
0.06351931,…………………

Step 3: Set up OpenSearch

To enable vector search capabilities, you first need to set up an OpenSearch client and test the connection. Follow these steps:

Install the required libraries

Install the necessary Python packages for working with OpenSearch:

!pip install opensearch-py
!pip install botocore
!pip install requests-aws4auth

Configure the OpenSearch client

Set up the OpenSearch client with your host details and authentication credentials:

from opensearchpy import OpenSearch, RequestsHttpConnection, helpers
from requests_aws4auth import AWS4Auth
from requests.auth import HTTPBasicAuth

# OpenSearch connection configuration
# host="your-host.aos.us-east-1.on.aws"
host="search-new-domain-mbgs7wth6r5w6hwmjofntiqcge.aos.us-east-1.on.aws"
port = 443  # Default HTTPS port

# Get OpenSearch username secret from Secrets Manager
opensearch_username=secrets_manager_client.get_secret_value(
    SecretId="AOS_username"
)
opensearch_username_string=json.loads(opensearch_username["SecretString"])["AOS_username"]

# Get OpenSearch password secret from Secrets and techniques Supervisor
opensearch_password = secrets_manager_client.get_secret_value(
    SecretId="AOS_password"
)
opensearch_password_string=json.masses(opensearch_password["SecretString"])["AOS_password"]

auth=(opensearch_username_string, opensearch_password_string)

# Create the consumer configuration
client_aos = OpenSearch(
    hosts=[{'host': host, 'port': port}],
    http_auth=auth,
    use_ssl=True,
    verify_certs=True,
    connection_class=RequestsHttpConnection
)

# Take a look at the connection
attempt:
    # Get cluster data
    cluster_info = client_aos.data()
    print("Efficiently linked to OpenSearch")
    print(f"Cluster data: {cluster_info}")
besides Exception as e:
    print(f"Connection failed: {str(e)}")

Anticipated output

If the connection is profitable, you need to see a message like the next:

Efficiently linked to OpenSearch
Cluster data: {'identify': 'bb36e8d98ee7bd517891ecd714bfb9d7', ...}

This confirms that your OpenSearch consumer is correctly configured and prepared to be used.

Step 4: Create an index in OpenSearch Service

Subsequent, you create an index optimized for vector search to retailer the embeddings generated by the TwelveLabs Embed API.

Outline the index configuration

The index is configured to help k-nearest neighbor (kNN) search with a 1024-dimensional vector discipline. You’ll these values for this demo however observe these greatest practices to search out acceptable values on your software. Right here’s the code:

# Outline the improved index configuration
index_name="twelvelabs_index"
new_vector_index_definition = {
    "settings": {
        "index": {
            "knn": "true",
            "number_of_shards": 1,
            "number_of_replicas": 0
        }
    },
    "mappings": {
        "properties": {
            "embedding_field": {
                "sort": "knn_vector",
                "dimension": 1024
            },
            "video_title": {
                "sort": "textual content",
                "fields": {
                    "key phrase": {
                        "sort": "key phrase"
                    }
                }
            },
            "segment_start": {
                "sort": "date"
            },
            "segment_end": {
                "sort": "date"
            },
            "segment_id": {
                "sort": "textual content"
            }
        }
    }
}

Create the Index

Use the next code to create the index in OpenSearch Service:

# Create the index in OpenSearch
response = client_aos.indices.create(index=index_name, physique=new_vector_index_definition, ignore=400)

# Retrieve and show index particulars to substantiate creation
index_info = client_aos.indices.get(index=index_name)
print(index_info)

Anticipated output

After working this code, you need to see particulars of the newly created index. For instance:

{'twelvelabs_index': {'aliases': {}, 'mappings': {'properties': {'embedding_field': {'sort': 'knn_vector', 'dimension': 1024}}}, 'settings': {...}}}

The next screenshot confirms that an index named twelvelabs_index has been efficiently created with a knn_vector discipline of dimension 1024 and different specified settings. With these steps accomplished, you now have an operational OpenSearch Service area configured for vector search. This index will function the repository for storing embeddings generated from video content material, enabling superior multimodal search capabilities.

Step 5: Ingest embeddings to the created index in OpenSearch Service

With the TwelveLabs Embed API efficiently producing video embeddings and the OpenSearch Service index configured, the following step is to ingest these embeddings into the index. This course of helps be certain that the embeddings are saved in OpenSearch Service and made searchable for multimodal queries.

Embedding ingestion course of

The next code demonstrates find out how to course of and index the embeddings into OpenSearch Service:

from opensearchpy.helpers import bulk

def generate_actions(duties, video_files):
    depend = 0
    for job in duties:
        # Verify if video embeddings can be found
        if job.video_embedding shouldn't be None and job.video_embedding.segments shouldn't be None:
            embeddings_doc = job.video_embedding.segments
            
            # Generate actions for bulk indexing
            for doc_id, elt in enumerate(embeddings_doc):
                yield {
                    '_index': index_name,
                    '_id': doc_id,
                    '_source': {
                        'embedding_field': elt.embeddings_float,
                        'video_title': video_files[count],
                        'segment_start': elt.start_offset_sec,
                        'segment_end': elt.end_offset_sec,
                        'segment_id': doc_id
                    }
                }
        print(f"Ready bulk indexing information for job {depend}")
        depend += 1

# Carry out bulk indexing
attempt:
    success, failed = bulk(client_aos, generate_actions(duties, video_files))
    print(f"Efficiently listed {success} paperwork")
    if failed:
        print(f"Didn't index {len(failed)} paperwork")
besides Exception as e:
    print(f"Error throughout bulk indexing: {e}")

Clarification of the code

1. Embedding extraction: The video_embedding.segments object accommodates a listing of section embeddings generated by the TwelveLabs Embed API. Every section represents a particular portion of the video.
2. Doc creation: For every section, a doc is created with a key (embedding_field) that shops its 1024-dimensional vector, video_title with the title of the video, segment_start and segment_end indicating the timestamp of the video section, and a segment_id.
3. Indexing in OpenSearch: The index() methodology uploads every doc to the twelvelabs_index created earlier. Every doc is assigned a singular ID (doc_id) primarily based on its place within the listing.

Anticipated output

After the script runs efficiently, you will notice:

• A printed listing of embeddings being listed.
• A affirmation message:

Ready bulk indexing information for job 0
Ready bulk indexing information for job 1
Efficiently listed 6 paperwork

Outcome

At this stage, all video section embeddings at the moment are saved in OpenSearch and prepared for superior multimodal search operations, reminiscent of text-to-video or image-to-video queries. This units up the muse for performing environment friendly and scalable searches throughout your video content material.

Step 6: Carry out vector search in OpenSearch Service

After embeddings are generated, you employ it as a question vector to carry out a kNN search within the OpenSearch Service index. Under are the features to carry out vector search and format the search outcomes:

# Operate to carry out vector search
def search_similar_segments(query_vector, okay=5):
    question = {
        "dimension": okay,
        "_source": ["video_title", "segment_start", "segment_end", "segment_id"],
        "question": {
            "knn": {
                "embedding_field": {
                    "vector": query_vector,
                    "okay": okay
                }
            }
        }
    }
    
    response = client_aos.search(
        index=index_name,
        physique=question
    )

    outcomes = []
    for hit in response['hits']['hits']:
        end result = {
            'rating': hit['_score'],
            'title': hit['_source']['video_title'],
            'start_time': hit['_source']['segment_start'],
            'end_time': hit['_source']['segment_end'],
            'segment_id': hit['_source']['segment_id']
        }
        outcomes.append(end result)

    return (outcomes)

# Operate to format search outcomes
def print_search_results(outcomes):
    print("nSearch Outcomes:")
    print("-" * 50)
    for i, end in enumerate(outcomes, 1):
        print(f"nResult {i}:")
        print(f"Video: {end result['title']}")
        print(f"Time Vary: {end result['start_time']} - {end result['end_time']}")
        print(f"Similarity Rating: {end result['score']:.4f}")

Key factors:

• The _source discipline accommodates the video title, section begin, section finish, and section id equivalent to the video embeddings.
• The embedding_field within the question corresponds to the sector the place video embeddings are saved.
• The okay parameter specifies what number of prime outcomes to retrieve primarily based on similarity.

Step 7:Performing text-to-video search

You need to use text-to-video search to retrieve video segments which can be most related to a given textual question. On this resolution, you’ll do that through the use of TwelveLabs’ textual content embedding capabilities and OpenSearch’s vector search performance. Right here’s how one can implement this step:

Generate textual content embeddings

To carry out a search, you first have to convert the textual content question right into a vector illustration utilizing the TwelveLabs Embed API:

from typing import Record
from twelvelabs.fashions.embed import SegmentEmbedding

def print_segments(segments: Record[SegmentEmbedding], max_elements: int = 1024):
    for section in segments:
        print(
            f"  embedding_scope={section.embedding_scope} start_offset_sec={section.start_offset_sec} end_offset_sec={section.end_offset_sec}"
        )
        print(f"  embeddings: {section.embeddings_float[:max_elements]}")

# Create textual content embeddings for the question
text_res = twelvelabs_client.embed.create(
    model_name="Marengo-retrieval-2.7",
    textual content="Fowl consuming meals",  # Substitute together with your desired question
)

print("Created a textual content embedding")
print(f" Mannequin: {text_res.model_name}")

# Extract and examine the generated textual content embeddings
if text_res.text_embedding shouldn't be None and text_res.text_embedding.segments shouldn't be None:
    print_segments(text_res.text_embedding.segments)

vector_search = text_res.text_embedding.segments[0].embeddings_float
print("Generated Textual content Embedding Vector:", vector_search)

Key factors:

• The Marengo-retrieval-2.7 mannequin is used to generate a dense vector embedding for the question.
• The embedding captures the semantic that means of the enter textual content, enabling efficient matching with video embeddings.

Carry out vector search in OpenSearch Service

After the textual content embedding is generated, you employ it as a question vector to carry out a kNN search within the OpenSearch index:

# Outline the vector search question
query_vector = vector_search
text_to_video_search = search_similar_segments(query_vector)
# print(text_video_search)
print_search_results(text_to_video_search)

Anticipated output

The next illustrates related outcomes retrieved from OpenSearch.

Search Outcomes:
--------------------------------------------------

Outcome 1:
Video: robin-bird.mp4
Time Vary: 18.0 - 21.087732
Similarity Rating: 0.4409

Outcome 2:
Video: robin-bird.mp4
Time Vary: 12.0 - 18.0
Similarity Rating: 0.4300

Outcome 3:
Video: island.mp4
Time Vary: 0.0 - 6.0
Similarity Rating: 0.3624

Insights from outcomes

• Every end result features a similarity rating indicating how carefully it matches the question, a time vary indicating the beginning and finish offset in seconds, and the video title.
• Observe that the highest 2 outcomes correspond to the robin fowl video segments matching the Fowl consuming meals question.

This course of demonstrates how textual queries reminiscent of Fowl consuming meals can successfully retrieve related video segments from an listed library utilizing TwelveLabs’ multimodal embeddings and OpenSearch’s highly effective vector search capabilities.

Step 8: Carry out audio-to-video search

You need to use audio-to-video search to retrieve video segments which can be most related to a given audio enter. By utilizing TwelveLabs’ audio embedding capabilities and OpenSearch’s vector search performance, you possibly can match audio options with video embeddings within the index. Right here’s find out how to implement this step:

Generate audio embeddings

To carry out the search, you first convert the audio enter right into a vector illustration utilizing the TwelveLabs Embed API:

# Create audio embeddings for the enter audio file
audio_res = twelvelabs_client.embed.create(
    model_name="Marengo-retrieval-2.7",
    audio_file="audio-data.mp3",  # Substitute together with your desired audio file
)

# Print particulars of the generated embedding
print(f"Created audio embedding: model_name={audio_res.model_name}")
print(f" Mannequin: {audio_res.model_name}")

# Extract and examine the generated audio embeddings
if audio_res.audio_embedding shouldn't be None and audio_res.audio_embedding.segments shouldn't be None:
    print_segments(audio_res.audio_embedding.segments)

# Retailer the embedding vector for search
vector_search = audio_res.audio_embedding.segments[0].embeddings_float
print("Generated Audio Embedding Vector:", vector_search)

Key factors:

• The Marengo-retrieval-2.7 mannequin is used to generate a dense vector embedding for the enter audio.
• The embedding captures the semantic options of the audio, reminiscent of rhythm, tone, and patterns, enabling efficient matching with video embeddings

Carry out vector search in OpenSearch Service

After the audio embedding is generated, you employ it as a question vector to carry out a k-nearest neighbor (kNN) search in OpenSearch:

# Carry out vector search
query_vector = vector_search
audio_to_video_search = search_similar_segments(query_vector)
# print(text_video_search)
    
print_search_results(audio_to_video_search)

Anticipated output

The next exhibits video segments retrieved from OpenSearch Service primarily based on their similarity to the enter audio.

Search Outcomes:
--------------------------------------------------

Outcome 1:
Video: island.mp4
Time Vary: 6.0 - 12.0
Similarity Rating: 0.2855

Outcome 2:
Video: robin-bird.mp4
Time Vary: 18.0 - 21.087732
Similarity Rating: 0.2841

Outcome 3:
Video: robin-bird.mp4
Time Vary: 12.0 - 18.0
Similarity Rating: 0.2837

Outcome 4:
Video: island.mp4
Time Vary: 0.0 - 6.0
Similarity Rating: 0.2835

Right here discover that segments from each movies are returned with a low similarity rating.

Step 9: Performing images-to-video search

You need to use image-to-video search to retrieve video segments which can be visually just like a given picture. By utilizing TwelveLabs’ picture embedding capabilities and OpenSearch Service’s vector search performance, you possibly can match visible options from a picture with video embeddings within the index. Right here’s find out how to implement this step:

Generate Picture Embeddings

To carry out the search, you first convert the enter picture right into a vector illustration utilizing the TwelveLabs Embed API:

# Create picture embeddings for the enter picture file
image_res = twelvelabs_client.embed.create(
    model_name="Marengo-retrieval-2.7",
    image_file="image-data.jpg",  # Substitute together with your desired picture file
)

# Print particulars of the generated embedding
print(f"Created picture embedding: model_name={image_res.model_name}")
print(f" Mannequin: {image_res.model_name}")

# Extract and examine the generated picture embeddings
if image_res.image_embedding shouldn't be None and image_res.image_embedding.segments shouldn't be None:
    print_segments(image_res.image_embedding.segments)

# Retailer the embedding vector for search
vector_search = image_res.image_embedding.segments[0].embeddings_float
print("Generated Picture Embedding Vector:", vector_search)

Key factors:

• The Marengo-retrieval-2.7 mannequin is used to generate a dense vector embedding for the enter picture.
• The embedding captures visible options reminiscent of shapes, colours, and patterns, enabling efficient matching with video embeddings

Carry out vector search in OpenSearch

After the picture embedding is generated, you employ it as a question vector to carry out a k-nearest neighbor (kNN) search in OpenSearch:

# Carry out vector search
query_vector = vector_search
image_to_video_search = search_similar_segments(query_vector)
# print(text_video_search)
    
print_search_results(image_to_video_search)

Anticipated output

The next exhibits video segments retrieved from OpenSearch primarily based on their similarity to the enter picture.

Search Outcomes:
--------------------------------------------------

Outcome 1:
Video: island.mp4
Time Vary: 6.0 - 12.0
Similarity Rating: 0.5616

Outcome 2:
Video: island.mp4
Time Vary: 0.0 - 6.0
Similarity Rating: 0.5576

Outcome 3:
Video: robin-bird.mp4
Time Vary: 12.0 - 18.0
Similarity Rating: 0.4592

Outcome 4:
Video: robin-bird.mp4
Time Vary: 18.0 - 21.087732
Similarity Rating: 0.4540

Observe that picture of an ocean was used to go looking the movies. Video clips from the island video are retrieved with a better similarity rating within the first 2 outcomes.

Clear up

To keep away from expenses, delete assets created whereas following this submit. For Amazon OpenSearch Service domains, navigate to the AWS Administration Console for Amazon OpenSearch Service dashboard and delete the area.

Conclusion

The mixing of TwelveLabs Embed API with OpenSearch Service offers a cutting-edge resolution for superior video search and evaluation, unlocking new potentialities for content material discovery and insights. By utilizing TwelveLabs’ multimodal embeddings, which seize the intricate interaction of visible, audio, and textual parts in movies, and mixing them with OpenSearch Service’s sturdy vector search capabilities, this resolution permits extremely nuanced and contextually related video search.

As industries more and more depend on video content material for communication, schooling, advertising, and analysis, this superior search resolution turns into indispensable. It empowers companies to extract hidden insights from their video content material, improve consumer experiences in video-centric purposes and make data-driven selections primarily based on complete video evaluation

This integration not solely addresses present challenges in managing video content material but additionally lays the muse for future improvements in how we work together with and derive worth from video information.

Get began

Able to discover the facility of TwelveLabs Embed API? Begin your free trial right now by visiting TwelveLabs Playground to enroll and obtain your API key.

For builders seeking to implement this resolution, observe our detailed step-by-step information on GitHub to combine TwelveLabs Embed API with OpenSearch Service and construct your personal superior video search software.

Unlock the complete potential of your video content material right now!

Concerning the Authors

James Le runs the Developer Expertise perform at TwelveLabs. He works with companions, builders, and researchers to deliver state-of-the-art video basis fashions to varied multimodal video understanding use instances.

Gitika is an Senior WW Knowledge & AI Companion Options Architect at Amazon Internet Providers (AWS). She works with companions on technical initiatives, offering architectural steering and enablement to construct their analytics follow.

Kruthi is a Senior Companion Options Architect specializing in AI and ML. She offers technical steering to AWS Companions in following greatest practices to construct safe, resilient, and extremely obtainable options within the AWS Cloud.