first deploy

.github/workflows/build_deploy.yaml

@@ -0,0 +1,71 @@
+name: Build and Deploy to GKE
+
+on:
+  push:
+    branches:
+      - main
+
+env:
+  PROJECT_ID: ${{ secrets.GKE_PROJECT }} 
+  GKE_CLUSTER: llama-gke-cluster      # Cluster Name
+  GKE_ZONE: europe-west6-a                # Cluster zone 
+  DEPLOYMENT_NAME: llama-gke-deploy   # Deployment name
+  IMAGE: llama-app-gke-image              # Image Name
+
+jobs:
+  setup-build-publish-deploy:
+    name: Setup, Build, Publish, and Deploy
+    runs-on: ubuntu-latest
+    environment: production
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v4
+
+    # Setup gcloud CLI
+    - id: 'auth'
+      uses: 'google-github-actions/auth@v2'
+      with:
+        credentials_json: '${{ secrets.GKE_SA_KEY }}'
+
+    # Configure Docker to use the gcloud command-line tool as a credential
+    # helper for authentication
+    - run: |-
+        gcloud --quiet auth configure-docker
+
+    # Get the GKE credentials so we can deploy to the cluster
+    - uses: google-github-actions/get-gke-credentials@db150f2cc60d1716e61922b832eae71d2a45938f
+      with:
+        cluster_name: ${{ env.GKE_CLUSTER }}
+        location: ${{ env.GKE_ZONE }}
+        credentials: ${{ secrets.GKE_SA_KEY }}
+
+    # Build the Docker image
+    - name: Build
+      run: |-
+        docker build --no-cache \
+          --tag "gcr.io/$PROJECT_ID/$IMAGE:$GITHUB_SHA" \
+          --build-arg GITHUB_SHA="$GITHUB_SHA" \
+          --build-arg GITHUB_REF="$GITHUB_REF" \
+          .
+
+    # Push the Docker image to Google Container Registry
+    - name: Publish
+      run: |-
+        docker push "gcr.io/$PROJECT_ID/$IMAGE:$GITHUB_SHA"
+
+    # Set up kustomize
+    - name: Set up Kustomize
+      run: |-
+        curl -sfLo kustomize https://github.com/kubernetes-sigs/kustomize/releases/download/v3.1.0/kustomize_3.1.0_linux_amd64
+        chmod u+x ./kustomize 
+        kubectl create secret generic openai-secret --from-literal=OPENAI_API_KEY=${{secrets.OPENAI_API_KEY}} || true 
+        kubectl create secret generic qdrant-secret --from-literal=QDRANT_API_KEY=${{secrets.QDRANT_API_KEY}} --from-literal=QDRANT_URL=${{secrets.QDRANT_URL}} --from-literal=COLLECTION_NAME=${{secrets.COLLECTION_NAME}} || true 
+
+    # Deploy the Docker image to the GKE cluster
+    - name: Deploy
+      run: |-
+        ./kustomize edit set image gcr.io/PROJECT_ID/IMAGE:TAG=gcr.io/$PROJECT_ID/$IMAGE:$GITHUB_SHA
+        ./kustomize build . | kubectl apply -f -
+        kubectl rollout status deployment/$DEPLOYMENT_NAME
+        kubectl get services -o wide

.github/workflows/ci.yaml

@@ -0,0 +1,36 @@
+name: Continuous Integration
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  lint-and-test:
+    runs-on: ubuntu-latest
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
+      QDRANT_URL: ${{ secrets.QDRANT_URL }}
+      COLLECTION_NAME: ${{ secrets.COLLECTION_NAME }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
+      - name: Run linting
+        run: |
+          make lint
+
+      - name: Run tests
+        run: |
+          make test

Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.10
+
+WORKDIR /app
+
+# Copy application code
+COPY . .
+
+# Clear pip cache
+RUN pip cache purge
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose port
+EXPOSE 8000
+
+# Command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
+

Makefile

@@ -0,0 +1,21 @@
+# Makefile
+
+.PHONY: req lint clean
+
+# Variables
+PIP := pip
+RUFF := ruff
+
+all: req lint test clean ## Run all tasks
+
+req: ## Install the requirements
+	$(PIP) install -r requirements.txt
+
+lint: ## Run linter and code formatter (ruff)
+	$(RUFF) check . --fix  
+
+test: ## Run tests using pytest
+	pytest tests/
+
+clean: ## Clean up generated files
+	rm -rf __pycache__

README.md

@@ -1 +1,191 @@
-# scale-gke-qdrant-llama
+# Q&A Pipeline Deployment on GKE for Scalability with LlamaIndex and Qdrant". 🚀
+
+<p align="center">
+  <img width="976" alt="aws" src="https://github.com/benitomartin/mlops-aws-insurance/assets/116911431/4bfeb7ce-b151-4042-8cf6-c83299a2765a">
+</p>
+
+This repository contains a full Q&A pipeline using the LlamaIndex framework, Qdrant as a vector database, and deployment on Google Kubernetes Engine (GKE) using a FastAPI app and Dockerfile. Python files from my repositories are loaded into the vector database, and the FastAPI app processes requests. The main goal is to provide fast access to your own code, enabling reuse of functions.
+
+For detailed project descriptions, refer to this [Medium article](XXX).
+
+Main Steps
+
+- **Data Ingestion**: Load data from GitHub repositories.
+- **Indexing**: Use SentenceSplitter for indexing in nodes.
+- **Embedding**: Implement FastEmbedEmbedding.
+- **Vector Store**: Use Qdrant for inserting metadata.
+- **Query Retrieval**: Implement RetrieverQueryEngine.
+- **FastAPI and GKE**: Handle requests via the FastAPI app deployed on GKE.
+- **Streamlit**: UI component.
+
+Feel free to ⭐ and clone this repo 😉
+
+## Tech Stack
+
+![Visual Studio Code](https://img.shields.io/badge/Visual%20Studio%20Code-0078d7.svg?style=for-the-badge&logo=visual-studio-code&logoColor=white)
+![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)
+![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)
+![OpenAI](https://img.shields.io/badge/OpenAI-74aa9c?style=for-the-badge&logo=openai&logoColor=white)
+![Anaconda](https://img.shields.io/badge/Anaconda-%2344A833.svg?style=for-the-badge&logo=anaconda&logoColor=white)
+![Linux](https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=white)
+![Ubuntu](https://img.shields.io/badge/Ubuntu-E95420?style=for-the-badge&logo=ubuntu&logoColor=white)
+![Google Cloud](https://img.shields.io/badge/GoogleCloud-%234285F4.svg?style=for-the-badge&logo=google-cloud&logoColor=white)
+![Kubernetes](https://img.shields.io/badge/kubernetes-%23326ce5.svg?style=for-the-badge&logo=kubernetes&logoColor=white)
+![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi)
+![Git](https://img.shields.io/badge/git-%23F05033.svg?style=for-the-badge&logo=git&logoColor=white)
+![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white)
+![GitHub Actions](https://img.shields.io/badge/github%20actions-%232671E5.svg?style=for-the-badge&logo=githubactions&logoColor=white)
+![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge&logo=Streamlit&logoColor=white)
+
+
+## Project Structure
+
+The project has been structured with the following files:
+
+- `.github/workflows:` CI/CD pipelines
+- `tests`: unittest
+- `Dockerfile:`Dockerfile
+- `Makefile`: install requirements, formating, linting, testing and clean up
+- `app.py:` FastAPI
+- `pyproject.toml`: linting and formatting using ruff
+- `create_qdrant_collection.py:` script to create the collection in Qdrant
+- `deploy-gke.yaml:` deployment function
+- `kustomization.yaml:` kustomize deployment function
+- `requirements.txt:` project requirements
+
+
+## Project Set Up
+
+The Python version used for this project is Python 3.10. You can follow along the medium article.
+
+1. Clone the repo (or download it as a zip file):
+
+   ```bash
+   git clone https://github.com/benitomartin/rag-aws-qdrant.git
+   ```
+
+2. Create the virtual environment named `main-env` using Conda with Python version 3.10:
+
+   ```bash
+   conda create -n main-env python=3.10
+   conda activate main-env
+   ```
+
+3. Execute the `Makefile` script and install the project dependencies included in the requirements.txt:
+
+    ```bash
+    pip install -r requirements.txt
+
+    or
+
+    make install
+    ```
+
+4. You can test the app locally running:
+
+   ```bash
+   uvicorn app:app --host 0.0.0.0 --port 8000
+   ```
+
+   then go to one of these addresses
+
+   http://localhost:8000/docs
+   http://127.0.0.1:8000/docs
+
+5. Create **GCP Account**, project, service account key, and activate GKE API
+
+6. Make sure the `.env` file is complete:
+
+   ```bash
+   OPENAI_API_KEY=
+   QDRANT_API_KEY=
+   QDRANT_URL=
+   COLLECTION_NAME=
+   ACCESS_TOKEN=
+   GITHUB_USERNAME=
+   ```
+
+7. Add the following secrets into github:
+   ```bash
+   OPENAI_API_KEY
+   QDRANT_API_KEY
+   QDRANT_URL
+    COLLECTION_NAME
+   GKE_SA_KEY
+   GKE_PROJECT # PROJECT_ID
+   ```
+
+8. Be sure to authenticate in GCP:
+    ```bash
+    gcloud auth login
+    ```
+
+    ```bash
+    gcloud config set project PROJECT_ID
+    ```
+
+9. Create Kubernetes Cluster
+
+    ```bash
+    gcloud container clusters create llama-gke-cluster \
+        --zone=europe-west6-a \
+        --num-nodes=5 \
+        --enable-autoscaling \
+        --min-nodes=1 \
+        --max-nodes=10 \
+        --machine-type=n1-standard-4 \
+        --enable-vertical-pod-autoscaling
+    ```
+
+    after creation check the nodes
+
+    ```bash
+    kubectl get nodes
+    ```
+
+10. Push the GitHub Actions workflows to start the deployment
+
+11. Verify Kubernetes is running after deployment
+
+    ```bash
+    kubectl get po 
+    kubectl get svc 
+    ```
+
+    <p align="center">
+    <img width="940" alt="Pods Running" src="https://github.com/benitomartin/mlops-car-prices/assets/116911431/d4dee27d-383f-4375-9a21-29996a5b5089">
+    </p>
+
+    under svc the external ip is the endpoint (34.65.3.225), that can be added in the streamlit app
+
+    <p align="center">
+    <img width="767" alt="lambda-gke" src="https://github.com/benitomartin/mlops-car-prices/assets/116911431/b4a7e10c-52f9-4ca2-ade3-f2136ff6bbdf">
+    </p>
+
+    http://34.65.191.211:8000
+
+12. Check some pods and logs
+
+    ```bash
+    kubectl logs llama-app-gke-deploy-79bf48d7d8-4b77z
+    kubectl describe pod llama-app-gke-deploy-79bf48d7d8-4b77z
+    ```
+
+13. Clean up to avoid costs deleting the cluster and the docker image
+
+    ```bash
+    gcloud container clusters delete app-llama-gke-cluster --zone=europe-west6-a
+    kubectl delete deployment llama-app-gke-deploy
+    ```
+
+## Streamlit UI
+
+Run the streamlit app adding the endpoint url that you get after deployment:
+
+   ```bash
+   streamlit run streamlit_app.py
+   ```
+
+    <p align="center">
+    <img width="767" alt="lambda-gke" src="https://github.com/benitomartin/mlops-car-prices/assets/116911431/b4a7e10c-52f9-4ca2-ade3-f2136ff6bbdf">
+    </p>