A Quick Guide to RAG Using Algoboost for Embedding Vector Inference

In the evolving landscape of artificial intelligence and machine learning, enhancing the quality of generated content has always been a paramount goal. One innovative approach that has gained significant traction is Retrieval Augmented Generation (RAG). RAG combines the strengths of retrieval-based systems and generation-based models to produce more accurate, contextually rich, and informative outputs. This blog will introduce you to RAG and demonstrate how Algoboost, an app specializing in embedding vector inference and vector embedding storage via API, plays a crucial role in this paradigm.

Understanding Retrieval Augmented Generation

The RAG process typically involves two main components:

Retriever: This component searches a database of embeddings to find the most relevant pieces of information related to the input query. These embeddings are precomputed vectors that represent the semantic content of various documents or data points.
Generator: After retrieving relevant information, the generator uses this context to produce a well-informed response. The additional context helps the generator to produce outputs that are not only contextually appropriate but also factually accurate.

The Role of Algoboost in RAG

Algoboost is a powerful tool designed to facilitate embedding vector inference and vector embedding storage via API. It seamlessly integrates with the RAG framework, providing essential functionalities that enhance the retrieval and generation process.

Embedding Vector Inference

Embedding vector inference is the process of converting text data into fixed-size numerical vectors that capture the semantic meaning of the data. Algoboost excels in this domain by providing robust API endpoints that allow users to infer embeddings from their text data efficiently. These embeddings serve as the foundation for the retrieval process in RAG.

Vector Embedding Storage

Once embeddings are generated, they need to be stored in a manner that allows for efficient retrieval. Algoboost offers a sophisticated storage solution for vector embeddings, ensuring that the retrieval process is both fast and scalable. By using Algoboost’s API, developers can store vast amounts of embeddings and retrieve them quickly when needed, facilitating the smooth operation of RAG systems.

Implementing RAG with Algoboost: A Step-by-Step Guide

Before you can start, checkout the how to get started with algoboost blog.

Here’s a simplified overview of how you can implement a Retrieval Augmented Generation system using Algoboost:

Generate Embeddings: Use Algoboost’s API to convert your text data into embedding vectors. This involves sending your text data to Algoboost’s embedding inference endpoint and receiving the corresponding vectors.

Lets first generate our test data to inference:

[
    "Artificial intelligence is transforming the way we interact with technology.",
    "Blockchain technology offers a secure and transparent method for conducting transactions.",
    "Regular exercise and a balanced diet are essential for maintaining good health.",
    "Mental health awareness is crucial for creating a supportive community.",
    "Online learning platforms provide access to quality education for students worldwide.",
    "STEM education encourages critical thinking and problem-solving skills.",
    "Exploring new cultures and destinations can broaden one's perspective.",
    "Sustainable travel practices help protect the environment and local communities.",
    "Investing in stocks requires a thorough understanding of the market.",
    "Cryptocurrencies have gained popularity as alternative investment options.",
    "Climate change is a pressing issue that requires global cooperation.",
    "Renewable energy sources, such as solar and wind power, are vital for a sustainable future.",
    "Electric vehicles are becoming more prevalent as technology advances.",
    "Machine learning algorithms are used to analyze large datasets efficiently.",
    "Telemedicine provides healthcare access to remote and underserved areas.",
    "Social media platforms influence public opinion and behavior.",
    "Data privacy is a significant concern in the digital age.",
    "Autonomous vehicles have the potential to reduce traffic accidents.",
    "The gig economy offers flexibility but lacks job security.",
    "Augmented reality enhances user experiences in various applications.",
    "3D printing technology allows for rapid prototyping and manufacturing.",
    "Artificial neural networks mimic the human brain's functionality.",
    "Genetic engineering can potentially eradicate hereditary diseases.",
    "Quantum computing promises to solve complex problems faster than classical computers.",
    "Cybersecurity measures are essential to protect sensitive information.",
    "Wearable technology can monitor and improve personal health.",
    "Cloud computing offers scalable and cost-effective IT solutions.",
    "The Internet of Things (IoT) connects everyday devices for smarter living.",
    "Big data analytics helps businesses make informed decisions.",
    "Virtual reality creates immersive experiences for users.",
    "E-commerce has revolutionized the way we shop.",
    "Renewable resources are crucial for a sustainable environment.",
    "Digital currencies could reshape global financial systems.",
    "Bioinformatics integrates biology and data science to understand genetic information.",
    "Smart cities leverage technology to improve urban living.",
    "Robotics is advancing automation in various industries.",
    "Personalized medicine tailors treatments to individual patients.",
    "5G technology enhances communication speed and reliability.",
    "Digital art is gaining recognition in the mainstream art world.",
    "Artificial intelligence can detect patterns in data that humans might miss.",
    "E-learning can be tailored to different learning styles.",
    "Remote work has become more common due to technological advancements.",
    "Space exploration expands our understanding of the universe.",
    "Renewable energy initiatives are critical for reducing carbon emissions.",
    "Biotechnology can lead to new medical breakthroughs.",
    "AI-driven chatbots improve customer service efficiency.",
    "Smart home devices can enhance convenience and security.",
    "Blockchain can improve supply chain transparency.",
    "Predictive analytics uses historical data to forecast future trends.",
    "Digital marketing strategies are essential for modern businesses.",
    "Edtech tools support interactive and engaging learning experiences.",
    "The sharing economy promotes resource efficiency.",
    "Nanotechnology enables advancements in medicine and materials.",
    "Artificial intelligence assists in automating repetitive tasks.",
    "Renewable energy technologies are becoming more affordable.",
    "Social media can amplify the reach of important social movements.",
    "Advanced robotics can perform tasks with high precision.",
    "Digital twins replicate physical assets for better management.",
    "Facial recognition technology has applications in security and convenience.",
    "The advancement of AI ethics is crucial for responsible development.",
    "Biohacking explores the potential to enhance human capabilities.",
    "Digital transformation is essential for staying competitive in today's market."
]

import requests
import json
# Replace 'YOUR_ALGOBOOST_API_KEY' with your actual AlgoBoost API key
ALGOBOOST_API_KEY = 'replace_with_api_key'
model = 'clip-vit-b-32-multilingual-v1'
endpoint = 'get_text_embeddings'
collection_name = 'ragtest'
partition = "test_partition"
data_path = "data.json"

# Load the JSON file
with open(data_path, "r") as f:
    sentences = json.load(f)


def batch_text_inference(model, endpoint, collection_name, partition, sentences):
    """
    Perform batch text inference using AlgoBoost API.

    Args:
        model (str): The name of the model.
        endpoint (str): The API endpoint for inference.
        collection_name (str): The name of the collection.
        partition (str): The partition for collection.
        sentences (list): List of text sentences to infer.

    Returns:
        dict: Dictionary containing the inference results.
    """
    # Check if required parameters are provided
    if not all([model, endpoint, collection_name, partition, sentences]):
        print("Error: Missing required parameters.")
        return None

    # Prepare the form data for the request
    form_data = {
        'collection_name': collection_name,
        'partition': partition,
        'sentences': sentences
    }

    # Set the request headers with the API key
    headers = {"Authorization": f"Bearer {ALGOBOOST_API_KEY}"}
    url = f"https://app.algoboost.ai/api/model/batch/inference/{model}/{endpoint}"

    try:
        # Make a POST request to the API with form data and files
        response = requests.post(
            url,
            headers=headers,
            data=form_data,
        )

        # Check the HTTP status code
        if response.status_code == 200:
            # Parse the JSON response
            results = response.json()
            return results
        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


# Call the function
result = batch_text_inference(model, endpoint, collection_name, partition, sentences)

print(result)

Retrieve Relevant Documents

The next step is to retrieve relevant documents based on a query. This involves converting the query into an embedding vector and then retrieving similar embeddings from the storage.


import requests
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Replace 'YOUR_ALGOBOOST_API_KEY' with your actual AlgoBoost API key
ALGOBOOST_API_KEY = 'replace_with_api_key'
model = 'clip-vit-b-32-multilingual-v1'
endpoint = 'get_text_embeddings'
collection_name = 'ragtest'
partition = 'test_partition'
query = 'What are the benefits of machine learning in data analysis?'

# Define a function for API model inference with local image files
def similarity():
    # Prepare the form data for the request
    form_data = {
        'collection_name': collection_name,
        'partition': partition,
        'text': query,
        "limit": 4
    }

    # Set the request headers with the API key
    headers = {
        "Authorization": f"Bearer {ALGOBOOST_API_KEY}"
    }

    try:
        # Make a POST request to the API with form data and files
        response = requests.post(
            f"https://app.algoboost.ai/api/model/similarity/{model}/{endpoint}",
            headers=headers,
            data=form_data,
        )

        # Check the HTTP status code
        if response.status_code == 200:
            # Parse the JSON response
            results = response.json()
            return results
        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

Output return json of top results and respective vector ids which you can use to fetch the original text

{
    "results": {
        "distance": [
            0.9669929146766663,
            0.9375953078269958,
            0.9375621676445007
        ],
        "ids": [
            450061302305389208,
            450061302305389264,
            450061302305389182
        ]
    }
}

Now we will fetch the original text which we will use to generate a response


def retrieve_content_by_id(vector_ids):
    json_data = {
        'vectors': vector_ids,
    }

    # Set the request headers with the API key
    headers = {
        "Authorization": f"Bearer {ALGOBOOST_API_KEY}",
    }

    try:
        response = requests.post(
            "https://app.algoboost.ai/api/model/retrieve_content_by_id",
            headers=headers,
            json=json_data,
        )

        # Check the HTTP status code
        if response.status_code == 200:
            # Parse the JSON response
            results = response.json()
            return results
        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


def content(content_urls):
    headers = {
        "Authorization": f"Bearer {ALGOBOOST_API_KEY}",
    }
    content = []

    for url in content_urls:
        try:
            response = requests.post(
                url["content_url"],
                headers=headers,
            )

            # Check the HTTP status code
            if response.status_code == 200:
                # Parse the JSON response
                results = response.json()
                content.append(results)
            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
    return content

Output

['Machine learning algorithms are used to analyze large datasets efficiently.',
'Artificial intelligence can detect patterns in data that humans might miss.',
'Artificial intelligence is transforming the way we interact with technology.',
'Artificial intelligence assists in automating repetitive tasks.']

Generate Responses

Use a generative model to produce a response based on the retrieved documents.

import requests
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Initialize the model and tokenizer
model_name = 'gpt2'  # Or any other generation model you prefer
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
generator_model = GPT2LMHeadModel.from_pretrained(model_name)

def generate_response(retrieved_documents):
    """
    Generate a response using a generation model and retrieved documents.

    Args:
        query (str): The input query.
        retrieved_documents (list): List of retrieved documents.

    Returns:
        str: Generated response.
    """
    context = " ".join(retrieved_documents)
    input_text = f"Context: {context} Query: {query}"

    inputs = tokenizer.encode(input_text, return_tensors='pt')
    outputs = generator_model.generate(inputs, max_length=300, num_return_sequences=1)

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

Output

Context: Machine learning algorithms are used to analyze large datasets efficiently. Artificial intelligence can detect patterns in data that humans might miss. Artificial intelligence is transforming the way we interact with technology. Artificial intelligence assists in automating repetitive tasks. Query: What are the benefits of machine learning in data analysis?

Machine learning is a new field of research that has been gaining momentum in recent years. It is a new way to analyze data and to understand the underlying mechanisms that drive it.

Conclusion

In the dynamic realm of AI and machine learning, having the right tools can make all the difference. Algoboost offers a cutting-edge solution for embedding model inference and vector storage, enabling you to drive innovation and achieve remarkable results.

Ready to unlock the full potential of your AI projects? Sign up for Algoboost today and experience the difference firsthand."

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A Quick Guide to RAG Using Algoboost for Embedding Vector Inference

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Understanding Retrieval Augmented Generation

Implementing RAG with Algoboost: A Step-by-Step Guide

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