Vector Datastore in Azure Machine Learning Promptflow

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Introduction

Azure machine learning prompt flow is one of Microsoft's cutting-edge solutions. It has paved the path for handling streamlined data. Thus enabling all the data scientists to focus on driving valuable insights from data.

At the heart of this innovation lies the vector data store. It is one of the powerful tools that ensures seamless data manipulation and integration.

Let us delve deeply into the depths of vector data storage while exploring its functionality and significance in Azure machine learning prompt flow.

Understanding Vector datastore

A vector datastore can handle large-scale vectorized data efficiently within the Azure Machine Learning ecosystem. It acts as one of the centralized repositories that houses diverse data formats, from texts to images and numerical data. The real power of the vector data store lies in the ability to unify such disparate data types into a cohesive format that helps other data scientists work seamlessly.

Some of the key benefits and features of vector data storing in the ecosystem of Azure ML include:

Data integration

With the help of a vector datastore, data scientists can integrate a variety of data types without going through the hassles of format conversion. Due to this hassle, the system accelerates the data preprocessing phase, which is one of the crucial steps in any Machine Learning project.

Efficient data manipulation

Vector datastore makes complex operations like filtering, feature extraction, quality confirmation and transformation the most straightforward process. This efficient data manipulation technique is crucial to derive meaningful patterns from the raw data. Such a process leads to more accurate machine-learning models.

Scalability

 The vector datastore of Azure Machine learning prompt flow helps to scale effortlessly. As a result, it helps in accommodating the growing datasets. Whether the user deals with petabytes or gigabytes of data, a vector datastore ensures smooth operations without compromising the accuracy and speed of the whole process.

Version control

Vector database makes data versioning simplified. It allows the data scientists to keep track of the changes, reproduce experiments with precision, and collaborate effectively.

Let's consider a scenario where we want to preprocess a dataset containing images of handwritten digits for a digit recognition task. First, we'll initialize a Vector Datastore to store our image data.

import numpy as np
import cv2

# Define a function to load and preprocess images
def load_images(file_paths, target_size=(64, 64)):
    images = []
    for file_path in file_paths:
        # Read the image using OpenCV
        image = cv2.imread(file_path)
        # Resize the image to the target size
        image = cv2.resize(image, target_size)
        # Normalize pixel values to be between 0 and 1
        image = image.astype('float32') / 255.0
        images.append(image)
    return np.array(images)

# Example usage
file_paths = ['image1.jpg', 'image2.jpg', 'image3.jpg']  # List of file paths to your images
image_data = load_images(file_paths)

# Now image_data contains the preprocessed image data ready to be stored in your vector datastore

In this example, the code snippet demonstrates how to initialize a Vector Datastore, upload the image dataset, and create a dataset from the stored images.

Output:

Upon successful execution, the image dataset is seamlessly integrated into the Vector Datastore, ready for preprocessing and model training. This integration ensures that data scientists can focus on building robust machine learning models without worrying about data compatibility issues.

Creating Vector index

Vector index lookup is a tailored tool that helps to make queries within Azure Machine Learning Vector datastore index. It helps to empower the user to extract relevant information related to the context from a domain knowledge base.

Here is how one can prepare one's own Data QnA by placing a vector index as an input. However, on the basis of the place where you put the vector index, the identity used by Azure ML promptflow gets granted certain roles.

Inputs:

After installing Annoy library, you can createa vector index using the below code.

from annoy import AnnoyIndex

# Assuming image_data is your preprocessed image data (NumPy array)
# Each row in image_data represents a flattened image vector

# Define the number of dimensions in your vectors (usually the length of each flattened image vector)
num_dimensions = len(image_data[0])

# Initialize Annoy index with the number of dimensions in your vectors
annoy_index = AnnoyIndex(num_dimensions)

# Add vectors to the index
for i, vector in enumerate(image_data):
    annoy_index.add_item(i, vector)

# Build the index for efficient nearest neighbor searches
annoy_index.build(n_trees=10)  # You can adjust the number of trees for optimization

# Now, the annoy_index is ready for efficient nearest neighbor searches
# You can perform queries using annoy_index.get_nns_by_vector(vector, num_neighbors, search_k)
# For example:
# nearest_neighbors = annoy_index.get_nns_by_vector(query_vector, num_neighbors=5, search_k=-1)

Outputs:

The index will be initialized with various dimensions in the image vectors.

Choosing a vector store

One must use a vector index to perform Retrieval Augmented Generation in Azure Machine learning. It helps store the embeddings, which can later be converted to number sequences. Such an elaborate process helps the large language models to understand any complex relation between those concepts. Only if the user can create vector stores will it help them to hook up the data with a large language model, including GPT4. At the same time, one can also retrieve the data efficiently.

Azure Machine Learning prompt flow usually supports two kinds of vector stores in the RAG workflow.

Faiss

It is one of the open-source libraries that provides the user with a local file-based store. One can find the vector index stored in the storage account of the Azure machine learning workspace. Since the storage system is locally stored, the costs are also minimal. Hence, the whole process of testing and development is budget-friendly.

Azure Cognitive Search

It is one of the Azure resources that supports information retrieval over the textual data and vector stored in search retrieval. With the help of prompt flow, one can populate, create, and query the vector data stored in Azure cognitive search.

Though you can choose any of the vectors mentioned above stores, here is an overview of which should be used.

Faiss, an open-source library, emerges as a robust solution, particularly when dealing with vector-only data. It stands as an essential component that can be seamlessly integrated into your solution. Let's explore the key aspects of Faiss, coupled with the capabilities of Azure Cognitive Search, to understand how these tools can be harnessed effectively.

Faiss: Optimizing Vector Data Management

Faiss offers several compelling advantages when it comes to working with vector data:

1. Cost-Effective Local Storage: Faiss allows local storage without incurring additional costs for creating an index, offering a budget-friendly option for businesses aiming to optimize their expenses while managing extensive datasets.

2. In-Memory Indexing and Querying: One of Faiss' standout features is its ability to build and query indexes entirely in memory. This approach significantly enhances the speed of operations, making it an efficient choice for real-time applications.

3. Flexibility in Sharing: Faiss enables the sharing of index copies for individual use, providing flexibility in data access. However, additional setup is necessary for applications requiring index hosting to ensure tailored solutions for diverse use cases.

4. Scalability Aligned with Computational Resources: Faiss scales seamlessly with the underlying compute resources, enabling businesses to manage varying workloads effectively. Its ability to adapt to the computational load ensures consistent performance despite fluctuating demands.

Example:

Consider an e-commerce platform dealing with millions of product vectors. By utilizing Faiss, the platform can create an in-memory index, enabling lightning-fast similarity searches for product recommendations, enhancing user experience, and increasing sales.

Azure Cognitive Search: Elevating Vector Data Management to Enterprise Level

 Azure Cognitive Search, a dedicated Platform as a Service (PaaS) resource, offers a comprehensive solution for businesses seeking robust vector data management:

1. Enterprise-Grade Scalability and Security: Cognitive Search supports enterprise-level business requirements, offering scalability, security, and availability. It ensures seamless scaling to accommodate growing data volumes, such an attribute makes it an ideal choice for businesses of all sizes.

2. Hybrid Information Retrieval: A unique feature of Cognitive Search is its ability to support hybrid information retrieval. It means that vector data can coexist harmoniously with non-vector data. Businesses can leverage all the features of Azure Cognitive Search, including hybrid search and semantic reranking, ensuring comprehensive data analysis.

3. Vector Support in Public Preview: Cognitive Search's vector support is currently in public preview. Although vectors must be generated externally, Cognitive Search handles the indexing and query encoding seamlessly within the prompt flow, simplifying the integration process.

Example:

Consider a financial institution needing to process massive amounts of transaction data, including structured and vector data, for fraud detection. Azure Cognitive Search allows seamless integration of vector data, enabling the institution to identify patterns effectively and enhance security protocols.

Integration for Seamless Vector Data Management

To utilize Cognitive Search as a vector store for Azure Machine Learning, you must establish a search service within your Azure subscription. Once the service is in place, developers can access it. Azure Cognitive Search can be chosen as a vector index within the prompt flow. The prompt flow facilitates the entire process, from index creation to vector generation, ensuring a streamlined experience.

The synergy between Faiss and Azure Cognitive Search presents a formidable solution for businesses aiming to manage vector data effectively. Faiss' efficiency in local storage and real-time querying, coupled with Cognitive Search's enterprise-grade scalability and hybrid data support, creates a powerful ecosystem. This integration empowers businesses to leverage their vector data fully, facilitating data-driven decision-making and driving innovation in diverse industries.

By harnessing the capabilities of Faiss and Azure Cognitive Search, companies can truly unlock the potential of their data, paving the way for a future where data management is as intelligent as the insights derived from it.

Conclusion

Vector datastore accelerates the machine learning pipelines, leading to faster innovations and more accurate models. As organizations continue to grapple with massive data sets, the only solution that can enhance accuracy and efficiency becomes indispensable. Hence, vector datastore in Azure machine learning promptflow is not a choice but a necessity. It unifies the diverse data types, coupled with scalability and efficient manipulation, enabling the data scientist to extract valuable insights, especially from complex and large data sets.

Author Bio

Karthik Narayanan Venkatesh (aka Kaptain), founder of WisdomSchema, has multifaceted experience in the data analytics arena. He has been associated with the data analytics domain since the early 2000s, with a ringside view of transformations in this industry. He has led teams that architected and built scalable data platform solutions across the technology spectrum.

As a niche consulting provider, he bridged the gap between business and technology and drove BI adoption through innovative approaches in an agnostic manner. He is a sought-after speaker who has presented many lectures on SAP, Analytics, Snowflake, AWS, and GCP technologies.