🎙️ Conversational Pathways API

Conversational Pathways API: This is a Django RESTful API designed to manage conversational agents and pathways, integrating seamlessly with Bland AI. This project allows you to create, update, and manage conversational agents and their pathways, providing a robust backend for conversational applications.

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📖 Table of Contents

• Features
• Getting Started
  • Prerequisites
  • Installation
  • Environment Setup
• Usage
  • Running the Server
  • API Endpoints
• Testing
  • Running Tests
• Deployment
  • Infrastructure as Code with Terraform
• Documentation
• Contributing
• License
• Contact

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✨ Features

• Agent Management: Create, update, delete, and retrieve conversational agents.
• Pathway Management: Manage conversational pathways with nodes and edges.
• Integration with Bland AI: Seamless integration with Bland AI for agent deployment.
• API Documentation: Interactive API documentation with Swagger UI and ReDoc.
• Robust Testing Suite: Unit tests, integration tests, end-to-end tests, and concurrency tests.
• Continuous Integration: Automated testing and code quality checks with GitHub Actions.
• Infrastructure as Code: Deployable infrastructure using Terraform.
• Extensible and Modular: Designed for scalability and easy integration with other services.

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🚀 Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.8+: The project is built using Python 3.8 or higher.
• PostgreSQL: A Supbase PostgreSQL database instance for development (https://supabase.com).
• Git: Version control system to clone the repository.
• Virtual Environment: Recommended to use venv or virtualenv.

Installation

1. Clone the Repository

   git clone https://github.com/Fato07/Django-REST-API-for-Conversational-Pathways.git
   cd conversational_api

2. Create a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies

   pip install --upgrade pip
   pip install -r requirements.txt

Environment Setup

Create a .env file in the root directory and add the following environment variables:

SECRET_KEY=<your-value>
BLAND_AI_API_KEY=<your-value>
BLAND_AI_BASE_URL=https://api.bland.ai/v1
DATABASE_URL=<your-value: in my case, i used supabase>

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📝 Usage

Running the Server

Apply migrations and start the development server:

python manage.py migrate
python manage.py runserver

The API will be available at http://127.0.0.1:8000/.

API Endpoints

The main endpoints are: Full documenation for the API endpoints available and can be tested with postman here:

• Agents

  • GET /api/v1/agents/ : List all agents.
  • POST /api/v1/agents/ : Create a new agent.
  • GET /api/v1/agents/{id}/ : Retrieve a specific agent.
  • PUT /api/v1/agents/{id}/ : Update an agent.
  • DELETE /api/v1/agents/{id}/ : Delete an agent.

• Conversational Pathways

  • GET /api/v1/pathways/ : List all pathways.
  • POST /api/v1/pathways/ : Create a new pathway.
  • GET /api/v1/pathways/{id}/ : Retrieve a specific pathway.
  • PUT /api/v1/pathways/{id}/ : Update a pathway.
  • DELETE /api/v1/pathways/{id}/ : Delete a pathway.

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🧪 Testing

This project includes a comprehensive test suite.

Running Tests

1. Unit and Integration Tests

   pytest

2. End-to-End Tests

   pytest agents/tests/test_end_to_end.py

3. Concurrency Tests

to DO

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☁️ Deployment

Infrastructure as Code with Terraform

To DO

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📚 Documentation

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🎉