Building an Agentic AI Solutions Using Open-Source in Azure Cloud

In the rapidly evolving world of artificial intelligence, businesses and developers are increasingly looking for cost-effective, flexible, and scalable solutions. One powerful approach to achieving this is through the use of an Open Source AI Stack. This allows organizations to tap into a robust set of free, community-driven tools and technologies that offer the flexibility to create and deploy AI models, integrate them into systems, and scale them to meet business needs.

For Agentic AI solutions—those that aim to deliver intelligent, autonomous agents capable of interacting with the world and solving complex tasks—an open-source AI stack can provide the ideal foundation. In this article, we'll explore how an open-source AI stack can be utilized to build an Agentic AI solution using several cutting-edge frameworks and tools.

The Components of an Open Source AI Stack for Agentic AI

An open-source AI stack typically comprises several key layers or components that work together to deliver a complete AI-powered solution. Let's break down the essential layers and discuss how they play a role in the architecture of an Agentic AI solution.

1. Frontend Layer (User Interface)

Technologies:

• NextJS: To build the frontend UI, enabling fast server-side rendering and efficient client-side interactions.
• Streamlit: To build interactive data-driven AI applications, especially for data visualization or quick prototype AI models.
• Vercel: For deployment of static sites and serverless functions.

Deployment on Azure:

• Azure Static Web Apps: Vercel’s equivalent on Azure would be Azure Static Web Apps, which integrates well with GitHub actions for CI/CD. It will host the frontend built with Next.js and Streamlit.
• Azure App Service or Azure Functions: For backend services that complement the frontend when needed, such as for API calls.

Scalability Considerations:

• Azure Static Web Apps can automatically scale based on user traffic and has built-in integration for CI/CD with GitHub repositories.
• Azure Functions can scale according to demand, allowing for quick response times and low cost when there’s no traffic.

2. Embedding and RAG Libraries Layer

Technologies:

• Nomic, JinaAI, Cognito, LLMAware: These frameworks are used for embedding models and retrieval-augmented generation (RAG) libraries. These tools enable the building of intelligent search features for querying large datasets effectively.

Deployment on Azure:

• Azure Kubernetes Service (AKS): Host these RAG libraries and embedding models as containerized microservices. This provides flexibility and scalability as you can dynamically adjust the number of pods based on the load.
• Azure Container Registry: For storing and managing container images for these services.

Scalability Considerations:

• AKS provides automatic scaling with Horizontal Pod Autoscaler (HPA) based on CPU or memory usage, which ensures that resources are allocated dynamically based on traffic demands.

3. Backend and Model Access Layer

Technologies:

• FastAPI: A modern, fast (high-performance) web framework for building APIs with Python, ideal for building the backend.
• Langchain: For integrating language models into workflows, such as chaining LLM calls and managing context.
• Netflix Metaflow: For managing machine learning workflows, data pipelines, and model training in a simplified, user-friendly manner.
• Ollama, Huggingface: For model access, especially for open-source large language models (LLMs).

Deployment on Azure:

• Azure App Service or Azure Kubernetes Service (AKS): Host the FastAPI backend services. This will handle API calls and LLM model integrations.
• Azure Machine Learning (AML): Azure Machine Learning can be leveraged for training, fine-tuning, and managing ML models in the backend.
• Azure Cognitive Services: If any additional language models or NLP services are required, Azure Cognitive Services can complement Huggingface or Ollama.

Scalability Considerations:

• Azure App Service provides auto-scaling based on demand, ensuring the backend is always available and responsive.
• Azure Machine Learning can scale model training pipelines as needed using Azure Compute instances.

4. Data Storage and Retrieval Layer

Technologies:

• Postgres, Milvus, Weaviate, PGVector, FAISS: These tools are used for managing and querying vector embeddings and structured data.

Deployment on Azure:

• Azure Database for PostgreSQL: A fully managed database service that can handle Postgres deployments.
• Azure Cognitive Search: A managed search service that supports vector search and can work with Milvus or Weaviate for large-scale data indexing and retrieval.
• Azure Blob Storage: For unstructured data, large datasets, or model files.

Scalability Considerations:

• Azure Database for PostgreSQL and Cognitive Search scale horizontally and automatically depending on the query load. Indexing in Cognitive Search is optimized for large datasets.
• Azure Blob Storage can grow indefinitely and is designed for high availability and low-latency access to large datasets.

5. Large Language Models (LLMs) Layer

Technologies:

• Llama, Mistral, Qwen, Phi, Gemma: Open-source LLMs that can be fine-tuned and deployed for specific tasks.

Deployment on Azure:

• Azure Machine Learning (AML): This service will be used to deploy, manage, and scale LLMs for inference. It integrates well with the open-source libraries like Huggingface and Ollama, enabling the use of these LLMs in production.
• Azure Kubernetes Service (AKS): For containerizing and deploying LLMs at scale, ensuring that workloads can be distributed efficiently.

Scalability Considerations:

• Azure Machine Learning can scale compute clusters as needed, especially for inference-heavy workloads like LLMs.
• AKS can scale automatically depending on resource usage, ensuring that multiple instances of LLMs can run in parallel, reducing response times during high demand.

Key Benefits of Using an Open-Source AI Stack for Agentic AI

1. Cost Efficiency

Using open-source tools and frameworks significantly reduces the cost of development and deployment. There are no licensing fees for most of the open-source components, allowing organizations to allocate resources to other areas of development or infrastructure.

2. Flexibility and Customization

Open-source tools offer the ability to customize and adapt components to meet the specific needs of an organization or project. You can swap models, experiment with new algorithms, and integrate additional libraries without being locked into a proprietary ecosystem.

3. Scalability

The stack's components are designed for scalability. Azure services like Azure Kubernetes Service (AKS) and Azure Machine Learning enable the dynamic scaling of AI workloads, while tools like Milvus, Weaviate, and FAISS are optimized for high-performance search at scale.

4. Community Support

Open-source tools come with the added benefit of a vibrant and active community of developers. This means access to a wealth of knowledge, tutorials, and resources, as well as the opportunity to contribute back to the community.

Complete Architecture Flow

1. Frontend (NextJS / Streamlit): The user interacts with the UI hosted on Azure Static Web Apps, which makes API calls to the FastAPI backend or directly interacts with data retrieval services.
2. Backend (FastAPI / Langchain): API calls made from the frontend interact with FastAPI endpoints. FastAPI leverages Langchain for chaining model calls and managing context.
3. Embedding and RAG (JinaAI, Nomic): The backend queries the embedding models and RAG libraries to enhance the data search and model responses.
4. Model Access (Ollama / Huggingface): LLMs accessed via Ollama or Huggingface APIs perform NLP tasks such as language generation or understanding.
5. Data Retrieval (Postgres / Milvus / FAISS): The backend interacts with Azure Cognitive Search or Postgres for managing structured and unstructured data, ensuring fast retrieval.
6. LLMs (Llama / Mistral / Qwen): Large Language Models are deployed in Azure Machine Learning, either fine-tuned or used as-is to generate responses to user queries.

The deployment solution for Agentic AI will leverage Azure's flexible cloud services to host, scale, and manage the entire AI stack, from frontend interfaces to LLMs and data retrieval. The architecture will emphasize ease of use, flexibility, and scalability while integrating open-source technologies.

The open-source AI stack provides a powerful, flexible, and cost-effective solution for building an Agentic AI system. By leveraging modern frameworks like NextJS, FastAPI, Langchain, Milvus, Huggingface, and Azure services, businesses can create intelligent, scalable, and high-performance AI applications that meet the needs of modern enterprises.

The combination of these open-source tools and services allows for rapid development, seamless scaling, and adaptability, ensuring that the Agentic AI solution is ready to tackle complex challenges while staying on the cutting edge of AI technology.