Introducing Algoboost: Revolutionizing Embedding Model Inference and Vector Storage

In the ever-evolving landscape of artificial intelligence and machine learning, efficiency and scalability are paramount. Introducing Algoboost, a cutting-edge platform designed to revolutionize embedding model inference and vector storage. Whether you're a data scientist, machine learning engineer, or a business leveraging AI, Algoboost is here to elevate your capabilities and streamline your workflows.

What is Algoboost?

Algoboost is an advanced platform that offers two core functionalities: embedding model inference via API and robust vector storage. Let's break down what this means and how it can benefit your project

Embedding Model Inference API

Embeddings are crucial in transforming raw data into meaningful numerical representations that machine learning models can interpret. They are widely used in natural language processing (NLP), image recognition, recommendation systems, and more. However, deploying and managing embedding models can be complex and resource-intensive.

Algoboost simplifies this with its powerful Embedding Model Inference API. Here's what it offers:

• Ease of Integration: With Algoboost's API, you can easily integrate state-of-the-art embedding models into your applications without the need for extensive setup or deep technical expertise.

• Scalability: Whether you're processing a few queries or millions, Algoboost scales seamlessly to meet your demands, ensuring that your applications remain responsive and efficient.

• Performance: Designed for high performance, Algoboost's inference API provides fast and accurate embeddings, enabling real-time applications and enhancing user experience.

• Flexibility: Support for a wide range of embedding models ensures that you can choose the best model for your specific use case, be it text, images, or other data types.

Vector Storage Platform

Once you have your embeddings, managing and storing these high-dimensional vectors is the next challenge. Algoboost offers a robust Vector Storage Platform to handle this critical aspect.

• Efficient Storage: Algoboost provides optimized storage solutions for your vectors, ensuring minimal latency and high throughput.

• Search and Retrieval: With built-in capabilities for fast vector search and retrieval, Algoboost allows you to quickly find similar vectors, facilitating tasks like similarity searches, clustering, and nearest neighbor searches.

• Scalability: Just like its inference API, Algoboost's storage platform scales effortlessly with your needs, from small datasets to large-scale enterprise applications.

Why Choose Algoboost?

Algoboost is not just another tool; it's a comprehensive solution designed to integrate seamlessly into your AI workflow, offering several key benefits:

• Time and Cost Efficiency: By handling the complexities of embedding model deployment and vector management, Algoboost saves you time and reduces costs, allowing you to focus on building innovative solutions.

• Reliability: Built with robust infrastructure and industry-leading technologies, Algoboost ensures high availability and reliability for your applications.

• Expert Support: With a team of experts behind the platform, you have access to unparalleled support and guidance, helping you make the most of Algoboost's capabilities.

Getting Started with Algoboost

Starting with Algoboost is simple:

Login: Create an account on the Algoboost platform.

Once you log in to Algoboost, you will have access to a variety of state-of-the-art text and image embedding models, including those from OpenAI. To begin using the Algoboost API, you'll need to load credits into your account. Algoboost operates on a pay-as-you-go model, allowing you to purchase credits based on your specific usage needs and budget. This flexible pricing structure ensures that you only pay for what you use, making it cost-effective and scalable for projects of any size.

Create API KEY: Create an api key for you to use in your all api calls

Inference: With your credits loaded and api key is created you can create a vector store/ collection to store your embeddings.

Your embedding vectors are automatically stored in our vector storages for you to use for similarity searches.

How to use the inference API

We will be using text-embedding-3-large in this example to make inferences and similarity searches.

1. Create a collectoion/ Vector store for your embeddings

import requests

api_key = "your_api_key"
model = "text-embedding-3-large"
endpoint = "get_text_embeddings"
collection_name = "your_collection" # vector store name
similarity_metric =  "COSINE" # or L2 for Euclidean distance

url = f"https://app.algoboost.ai/api/model/create/{model}/{endpoint}"
headers = {
    "Authorization": "Bearer your_api_key"
}

data = {
    "collection_name": collection_name,
    "similarity_metric": similarity_metric
}

response = requests.post(url, headers=headers, data=data)

print(response.text)

1. Once a collection is created you can start vector inference

import requests

ALGOBOOST_API_KEY = ''
model = "text-embedding-3-large"
endpoint = "get_text_embeddings"
collection_name = ''
partition = ''
text = ''  

def text_inference(model, endpoint, collection_name, partition, text):
    if not all([model, endpoint, collection_name, partition, text]):
        print("Error: Missing required parameters.")
        return None

    form_data = {'collection_name': collection_name, 'partition': partition, 'text': text}
    headers = {"Authorization": f"Bearer {ALGOBOOST_API_KEY}"}
    url = f"https://app.algoboost.ai/api/model/inference/{model}/{endpoint}"

    try:
        response = requests.post(url, headers=headers, data=form_data)
        if response.status_code == 200:
            return response.json()
        else:
            print(f"API request failed with status code: {response.status_code}")
            return None

    except Exception as e:
        print(f"An error occurred: {str(e)}")
        return None

text_inference(model, endpoint, collection_name, partition, text)

• Output

{
 "Call_id": 1,
 "results": [
  0.344,
  0.445
 ]
}

1. Vector similarity search

import requests

collection_name = "******" # Replace with your collection name
partition = "******" # Replace with your chosen partition :: OPTIONAL
text = "******"     # Replace with input text your want to search
model = "{MODEL}"  # Replace {MODEL} with your model name
endpoint = "{ENDPOINT}"  # Replace {ENDPOINT} with your endpoint
api_key = "{API_KEY}"  # Replace {API_KEY} with your API key

def embedding_vector_similarity(collection_name, partition, text, model, endpoint, api_key):
    payload = {
        "collection_name": collection_name,
        "partition": partition,
        "text": text,
    }

    # Make the POST request
    response = requests.post(
        f"https://app.algoboost.ai/api/model/similarity/{model}/{endpoint}",
        headers={"Authorization": f"Bearer {api_key}"},
        data=payload
    )

    # Parse the JSON response
    result = response.json()
    return result


result = embedding_vector_similarity(collection_name, partition, text, model, endpoint, api_key)
print(result)

• Output will show the top search and the associated vector IDs.

{
    "results": {
        "distance": [
            31.89039421081543,
            31.89039421081543
        ],
        "ids": [
            448241087060100524,
            448241087060100526
        ]
    }
}

Conclusion

In the world of AI and machine learning, staying ahead means leveraging the best tools available. Algoboost offers a powerful, efficient, and scalable solution for embedding model inference and vector storage, empowering you to innovate and achieve more. Experience the future of AI infrastructure with Algoboost.
Ready to boost your AI capabilities? Sign up for Algoboost today.