---
title: "Generative API Prototyping Stack"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Generative API Prototyping Stack

### Introduction
As the demand for generative AI applications continues to rise, having an efficient and versatile prototyping stack is crucial. Our proposed **Generative API Prototyping Stack** aims to provide developers with the essential tools to quickly build, test, and iterate on AI-driven solutions. The stack integrates popular tools like **Jupyter Notebook**, **Google Colab**, **Langchain**, **Google Gemini 1.5 Flash**, **Ollama**, **Llama 3.1 (8B)**, **ngrok**, and **pyngrok** to create a seamless environment for rapid prototyping.

### Components of the Prototyping Stack

1. **Jupyter Notebook**
   Jupyter Notebook is a widely-used tool that offers an interactive development environment. Its ability to combine code, visualizations, and markdown in a single document makes it ideal for prototyping generative AI applications. Developers can test code snippets, visualize outputs, and document their thought processes all in one place.

2. **Google Colab**
   Google Colab enhances the prototyping experience by providing a cloud-based environment with free GPU/TPU resources. This is essential for training and running large language models (LLMs) and generative AI without the need for local hardware investment. Its tight integration with Google Drive allows for easy code sharing and collaboration.

3. **Langchain**
   Langchain is a powerful framework that simplifies the development of language model-based applications by abstracting the complexity of working with APIs like OpenAI and Gemini. It enables developers to chain together prompts, models, and post-processing steps into cohesive workflows, making it easy to experiment with multiple models and different AI functionalities.

4. **Google Gemini 1.5 Flash for Generative AI API**
   Google Gemini 1.5 Flash provides high-performance generative AI capabilities, making it a critical element of our stack. This API can be integrated into workflows to perform tasks such as text generation, translation, summarization, and more. It allows developers to experiment with Google's cutting-edge AI technology, empowering them to build state-of-the-art applications. The main advantage of this model is that it is [free](https://ai.google.dev/pricing)

5. **Ollama and Llama 3.1 (8B)**
   **Ollama** offers a streamlined interface to interact with various LLMs, while **Llama 3.1 (8B)** provides a free and open-source model for generating language-based outputs. These tools are chosen for their cost-effectiveness and ability to run locally, making them accessible to developers working on a budget or without access to high-end cloud resources. Combining them with Colab, it becomes a best free stack.

6. **ngrok and pyngrok**
   **ngrok** is a tunneling tool that allows developers to expose local servers to the internet securely. **pyngrok**, its Python wrapper, enables seamless integration with Python projects, allowing developers to test and share AI prototypes easily without deploying them to a live environment. This is especially useful for quick demos or testing webhook functionality in Langchain-based applications.

### Benefits of the Stack

- **Cost-Effective**: Using free tools like Gemini 1.5 Flash, Llama 3.1 and Google Colab enables cost savings without sacrificing performance.
- **Scalable**: The stack allows for easy scaling from small local tests to cloud-based implementations using tools like Langchain and Gemini 1.5.
- **Collaborative**: Google Colab’s sharing features and Jupyter Notebook’s inline documentation support collaborative development and prototyping.
- **Flexible**: The inclusion of multiple APIs and frameworks, such as Google Gemini 1.5 and Ollama, provides flexibility for developers to experiment with different models and approaches.
- **Quick Deployment**: With ngrok and pyngrok, developers can easily expose their local environments for live testing, accelerating the development cycle.

**Evaluation Metrics**  

1. **BLEU, ROUGE, and METEOR**  
   These metrics are vital for evaluating the quality of text generated by AI models. BLEU focuses on precision, ROUGE on recall, and METEOR on alignment and stemming, ensuring a holistic evaluation of model performance across various text generation tasks.

2. **NLTK**  
   The Natural Language Toolkit (NLTK) supports a wide range of natural language processing tasks, making it an essential part of the stack for preprocessing and handling text data before and after generative tasks.

## ROUGE Calculator

As we also plan on evaluating the quality of generated text against reference texts, you would need a **ROUGE calculator**. The ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metric, especially useful for tasks like summarization and translation, measures how well the generated text matches the reference text in terms of overlap in n-grams, word sequences, and word pairs.

Tools like `ROUGECalculator` automate the process of calculating these scores, typically providing results for:
- **ROUGE-N**: Overlap of n-grams between generated and reference text.
- **ROUGE-L**: Longest common subsequence comparison between the texts.
- **ROUGE-W**: Weighted longest common subsequence.

You can either use existing ROUGE libraries such as the one available in Hugging Face’s `datasets` library, or build your own utility using Python.

Incorporating a ROUGE calculator into your stack would help you effectively measure and benchmark the performance of your generative models, making it a critical component for thorough evaluation.

### Conclusion
Our Generative API Prototyping Stack integrates powerful tools for developing, testing, and evaluating generative AI applications. With an emphasis on flexibility and scalability, this stack is designed to cater to both local and cloud environments, making it suitable for a wide range of developers. By incorporating cutting-edge models and evaluation techniques, this stack ensures that generative AI applications meet high performance and quality standards.

To enhance the **Generative API Prototyping Stack** further, a few additional tools could be valuable depending on specific needs, such as managing model performance, data visualization, or automating the development process. Here are a few suggestions:

### Additional Tools to Consider

1. **Hugging Face Transformers**
   Hugging Face provides a vast repository of pre-trained models and easy-to-use interfaces for working with them. By integrating Hugging Face Transformers into the stack, you can access a variety of models beyond Llama 3.1 (8B) and Google Gemini 1.5, including text, image, and multimodal models.

2. **Weights & Biases (W&B)**
   For tracking experiments and managing model performance, **Weights & Biases** is a great tool. It allows developers to visualize model training in real-time, track hyperparameters, and compare model runs. This is especially useful when fine-tuning models in PyTorch or TensorFlow environments.

3. **Streamlit or Gradio**
   **Streamlit** or **Gradio** provides easy-to-use frameworks for creating interactive web applications. These tools can be integrated into the stack to build front-end interfaces for generative AI models, enabling non-technical users to interact with the prototypes without needing to run notebooks or code directly.

4. **FastAPI**
   If you want to expose your AI models or workflows as REST APIs, **FastAPI** is a fast, modern, and easy-to-use web framework. Combined with **ngrok**, it allows you to quickly build and deploy API endpoints for your generative AI services.

5. **SQLModel** or **Pandas**
   If your prototypes involve handling large datasets or relational data, **SQLModel** (for databases) and **Pandas** (for in-memory data manipulation) are useful additions. They enable developers to integrate data processing and analysis pipelines seamlessly into their AI workflows.

6. **Docker**
   Using **Docker** for containerization ensures that your stack is portable and replicable across different development environments. It makes deploying the stack on other machines or cloud environments much easier. If you're using local LLMs, Docker can help ensure all dependencies are properly managed.

7. **Testcontainers**
   **Testcontainers** is useful for automating the testing of your microservices or applications. It can spin up temporary Docker containers for databases, message queues, or even model APIs, making the process of continuous integration and testing seamless.

8. **VSCode DevContainers**
   **DevContainers** allow you to define a development environment with all necessary dependencies and tooling inside a container. This ensures all developers or collaborators are working in the same environment, minimizing compatibility issues during prototyping.

9. **DVC (Data Version Control)**
   If your generative AI workflows involve handling large datasets, **DVC** helps version control them. This is useful in projects where models are trained on different data versions, allowing you to keep track of changes in both the data and model results.

### Conclusion
While the existing stack already offers a comprehensive prototyping environment, incorporating these additional tools—like Hugging Face for more model options, Streamlit or Gradio for quick UI building, FastAPI for API deployment, and Docker for containerization—could extend its capabilities, making it even more flexible, scalable, and powerful for generative AI development.


## Local Development and Cloud Deployment Stacks Needed After Prototyping

Once we have prototyped our app we will move to local developing and deployment stacks in sequence.

#### 1. **Local Development Stack**

For efficient and modular development, our local development stack will be centered around three primary tools: **VSCode**, **Docker Compose**, and **Dev Containers**. These tools provide a seamless environment for building, testing, and prototyping generative AI applications locally.

- **VSCode (Visual Studio Code)**  
  VSCode is a widely-used code editor that supports a wide range of languages, extensions, and developer tools. For our stack, it is the ideal environment for writing, debugging, and managing code, with additional support for Dev Containers and Docker integration. Key features that make VSCode essential include:
  - Integrated terminal and debugging
  - Extensions for Python, Docker, Kubernetes, and more
  - Seamless integration with Git for version control

- **Docker Compose**  
  Docker Compose simplifies the orchestration of multi-container Docker applications. In our stack, Docker Compose will allow us to define and manage services, such as databases, APIs, and microservices, all in isolated containers. This ensures that the local environment mimics the production setup, which reduces the chances of deployment issues.
  
  We can define multiple containers (e.g., AI microservices, databases) in a single `docker-compose.yml` file, making it easy to bring up the entire stack with a single command (`docker-compose up`). This also makes the local development stack highly portable and easy to replicate across different machines.

- **Dev Containers**  
  Dev Containers are a feature of VSCode that allows you to define a development environment inside a Docker container. This means that developers can work in consistent, isolated environments regardless of their local machine setup. By using Dev Containers, we ensure:
  - Developers are working in the same environment, reducing the "it works on my machine" issue.
  - Development environments are version-controlled and can be easily shared with the team via the configuration in `devcontainer.json`.
  
  Dev Containers also provide seamless integration with Docker, allowing developers to run, test, and debug code within the containerized environment, similar to how it will behave in production.

---

#### 2. **Cloud Deployment Stack**

For the cloud deployment of our applications, we will adopt a **Kubernetes-based stack** with a focus on **Azure Container Apps**, GitHub Actions for CI/CD automation, and Kubernetes for orchestration.

- **Kubernetes**  
  Kubernetes is an open-source platform designed to automate deploying, scaling, and managing containerized applications. In our cloud deployment stack, Kubernetes will serve as the core for orchestrating our services in the cloud. With Kubernetes, we will be able to:
  - Automate the deployment of our generative AI services across multiple containers.
  - Manage horizontal scaling, ensuring our applications can handle varying loads.
  - Maintain high availability by distributing workloads across nodes and managing failover.

- **Azure Container Apps**  
  Azure Container Apps is a fully managed serverless container service built on Kubernetes. It provides a simplified, developer-friendly experience for deploying containerized applications without managing the underlying Kubernetes infrastructure. Key features include:
  - **Kubernetes-Powered Serverless**: Developers benefit from the power of Kubernetes but with the ease of use of serverless technology.
  - **Auto-Scaling**: Azure Container Apps automatically scales up or down based on traffic or workload demand, reducing operational overhead.
  - **Integrated Logging and Monitoring**: Built-in observability through Azure Monitor and Log Analytics helps to monitor performance and troubleshoot issues.
  
  By leveraging Azure Container Apps, we maintain the flexibility of Kubernetes while reducing the complexity involved in managing a full Kubernetes cluster.

- **GitHub Actions for CI/CD**  
  To streamline the deployment process, we will integrate **GitHub Actions** into our cloud stack for continuous integration and continuous deployment (CI/CD). GitHub Actions allows us to automate the following tasks:
  - **Build Automation**: Automatically building Docker images from the code repository on every commit.
  - **Testing**: Running unit tests or integration tests in the CI pipeline before pushing changes to the cloud.
  - **Deployment**: Automatically deploying applications to Azure Container Apps or Kubernetes upon successful build and testing.
  
  With GitHub Actions, we ensure that our deployment process is automated, reducing manual intervention and speeding up the deployment cycle.

---

### Conclusion

The combination of a **local development stack** with VSCode, Docker Compose, and Dev Containers ensures a robust, portable, and consistent development environment. This setup mirrors the **cloud deployment stack**, which relies on **Kubernetes** and **Azure Container Apps** for seamless orchestration and scaling of containerized applications. GitHub Actions adds CI/CD automation, enabling rapid development, testing, and deployment cycles.

Together, these two stacks provide an end-to-end solution for building, testing, and deploying generative AI services in a modern, scalable, and efficient manner.
