Welcome to our custom RAG (Retrieval Augmented Generation) project! We've crafted this using a micro-services architecture to bring you a powerful, yet flexible, solution.

Our project is composed of three key services 🛠️

1. Embedding API This service turns your text into numerical vectors, crucial for our machine learning magic! You have the flexibility to choose between:

   • Sentence-transformers embedding models from Hugging Face.
   • OpenAI Embeddings for advanced performance.

2. Knowledge Retriever Think of this as the brain of our project. It can:

   • Extracts text from various documents (PDF, Word, PPTX) using the versatile unstructured Python library (also capable of OCR).
   • Manages a 'vectorstore' for efficient similarity searches, powered by ChromaDB.
   • Connects to Azure OpenAI GPT and Vertex AI Palm Text Generation Models, bringing cutting-edge AI into play.

3. Front App A user-friendly chat interface built with Gradio, making it easy for you to interact with our service.

Here's how to get everything up and running 🚀

1. Installation Process Simply build and run the images using our Docker compose file. Execute this command from the project's root directory: docker compose up --build

2. Software Dependencies Our Embedding & Knowledge Retriever services use a PyTorch image from DockerHub. For local testing, ensure that the torch library is installed in your Python environment.

3. API References To extend and enrich our services, we're leveraging: - GCP Vertex AI services - Azure OpenAI Services

The application is Kubernetes-ready! 🌟

1. Kubernetes Deployment You'll find everything you need in the Kubernetes Folder within the project. This includes all the necessary manifests to smoothly deploy the application on a Kubernetes cluster.

2. Continuous Delivery Made Easy

In our Pipelines Folder, we have set up a streamlined continuous delivery process using Azure DevOps Pipelines. There's a dedicated pipeline for each of our three services, and each pipeline includes two key stages:

- Build and Push: This stage takes care of building each service's image and pushing it to a Container Registry. It ensures that your latest code is always ready to be deployed.

- Update and Sync: In this stage, the Kubernetes manifests are updated with the commitID. This is crucial for ArgoCD, as it allows for synchronization with the latest image pushed to the container registry.