# Ghaymah Docs RAG API

This project implements a Retrieval-Augmented Generation (RAG) API using FastAPI to answer questions about Ghaymah Cloud documentation. It features a sophisticated two-stage retrieval process involving an initial vector search followed by a more precise re-ranking step to ensure high-quality answers.

## Key Features

- **FastAPI Backend:** A robust and fast API for serving the RAG pipeline.
- **Two-Stage Retrieval:**
  1.  **Initial Search:** Uses `sentence-transformers` to perform a broad vector search and retrieve an initial set of candidate documents.
  2.  **Re-ranking:** Employs a `CrossEncoder` model to re-rank the initial candidates for greater relevance and precision.
- **Dockerized:** Comes with a `Dockerfile` for easy, repeatable deployment on any platform that supports containers.
- **Visualization:** Includes a `rerank_test.html` page to visually compare the results before and after the re-ranking step.

## Getting Started

### Prerequisites

- Docker
- A Git client

### Deployment

This application is designed to be deployed as a Docker container. It can be deployed via a Git-based workflow on a platform like Ghaymah Cloud.

1.  **Push to Git:** Push the code to a GitHub or GitLab repository.
2.  **Connect Platform:** Connect your cloud platform to the Git repository.
3.  **Build and Deploy:** The platform will use the included `Dockerfile` to automatically build and deploy the application.

### Configuration

The application requires the following environment variables to be set in the deployment environment:

- `GITPASHA_HOST`: The URL for the remote vector store (GitPasha).
- `OPENAI_API_KEY`: Your API key for the LLM provider (e.g., OpenAI).