Update ci

.github/workflows/docker-package.yml

@@ -0,0 +1,31 @@
+name: Python package
+
+on:
+  push:
+    branches: [ "main" ]
+
+jobs:
+
+  build_docker_image:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+    - name: Get Version Tags
+      id: versions
+      run: |
+        echo "BACKEND_VERSION=$(echo "$(<index/api/routes.py)" | grep -oP "(?<=version=\")[^\"]+")" >> "$GITHUB_OUTPUT"
+    - name: Docker Login
+      uses: docker/login-action@v1
+      with:
+        registry: ghcr.io
+        username: ${{ github.actor }}
+        password: ${{ secrets.GITHUB_TOKEN }}
+    - name: Build & push backend
+      uses: docker/build-push-action@v2
+      with:
+        file: Dockerfile
+        push: true
+        tags: |
+          ghcr.io/scai-bio/backend:latest
+          ghcr.io/scai-bio/backend:${{ steps.versions.outputs.BACKEND_VERSION }}

.github/workflows/python-package.yml

@@ -35,27 +35,3 @@ jobs:
     - name: Test with pytest
       run: |
         pytest
-
-  build_docker_image:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout
-      uses: actions/checkout@v2
-    - name: Get Version Tags
-      id: versions
-      run: |
-        echo "BACKEND_VERSION=$(echo "$(<index/api/routes.py)" | grep -oP "(?<=version=\")[^\"]+")" >> "$GITHUB_OUTPUT"
-    - name: Docker Login
-      uses: docker/login-action@v1
-      with:
-        registry: ghcr.io
-        username: ${{ github.actor }}
-        password: ${{ secrets.GITHUB_TOKEN }}
-    - name: Build & push backend
-      uses: docker/build-push-action@v2
-      with:
-        file: Dockerfile
-        push: true
-        tags: |
-          ghcr.io/scai-bio/backend:latest
-          ghcr.io/scai-bio/backend:${{ steps.versions.outputs.BACKEND_VERSION }}

README.md

@@ -1,2 +1,58 @@
 # INDEX – the Intelligent Data Steward Toolbox
-Intelligent data steward toolbox using Large Language Model embeddings for automated Data-Harmonization
+
+INDEX is an intelligent data steward toolbox that leverages Large Language Model embeddings for automated Data-Harmonization. 
+
+## Table of Contents
+- [Introduction](##ntroduction)
+- [Installation & Usage](#installation)
+- [Configuration](#configuration)
+
+## Introduction
+
+INDEX relies on vector embeddings calculated based on variable descriptions to generate mapping suggestions for any 
+dataset, enabling efficient and accurate data indexing and retrieval. Confirmed mappings are stored alongside their 
+vectorized representations in a knowledge base, facilitating rapid search and retrieval operations, ultimately enhancing 
+data management and analysis capabilities. New mappings may be added to the knowledge base in an iterative procedure,
+allowing for improved mapping suggestions in subsequent harmonization tasks.
+
+## Installation & Usage
+
+Clone the repository:
+
+```bash
+git clone https://github.com/SCAI-BIO/index
+cd index
+```
+
+### Starting the Backend locally
+
+Install python requirements:
+
+```bash
+pip install -r requirements.txt
+```
+
+
+Run the Backend API on port 5000:
+
+```bash
+uvicorn main:app --reload --port 5000
+```
+
+### Run the Backend via Docker
+
+Download the latest docker build:
+
+```bash
+docker pull ghcr.io/scai-bio/backend:latest
+```
+
+## Configuration
+
+### Description Embeddings
+
+You can configure INDEX to use either a local language model or call OPenAPIs embedding API. While using the OpenAI API
+is significantly faster, you will need to provide an API key that is linked to your OpenAI account. 
+
+Currently, the following local models are implemented:
+* [MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)