Q&A Pipeline Deployment on GKE for Scalability with LlamaIndex and Qdrant 🚀

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.

This article was featured in the GKE Newsletter (This week in GKE, ISSUE#19, 12 July 2024)

Main Steps

Data Ingestion: Load data from GitHub repositories
Indexing: Use SentenceSplitter for indexing in nodes
Embedding and Model: OpenAI
Vector Store: Use Qdrant for inserting metadata
Query Retrieval: Implement RetrieverQueryEngine with SentenceTransformerRerank
FastAPI and GKE: Handle requests via the FastAPI app deployed on GKE
Streamlit: UI

Feel free to ⭐ and clone this repo 😉

Tech Stack

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
streamlit_app.py: streamlit app

Project Set Up

The Python version used for this project is Python 3.10. You can follow along the medium article.

Clone the repo (or download it as a zip file):

git clone https://github.com/benitomartin/scale-gke-qdrant-llama.git

Create the virtual environment named main-env using Conda with Python version 3.10:
```
conda create -n main-env python=3.10
conda activate main-env
```
Execute the Makefile script and install the project dependencies included in the requirements.txt:
```
pip install -r requirements.txt

or

make install
```

Make sure the .env file is complete:

OPENAI_API_KEY=
QDRANT_API_KEY=
QDRANT_URL=
COLLECTION_NAME=
ACCESS_TOKEN=
GITHUB_USERNAME=

You can test the app locally running:
```
uvicorn app:app --host 0.0.0.0 --port 8000
```
then go to one of these addresses

http://localhost:8000/docs or http://127.0.0.1:8000/docs
Create GCP Account, project, service account key, and activate GKE API

Add the following secrets into github:

OPENAI_API_KEY
QDRANT_API_KEY
QDRANT_URL
COLLECTION_NAME
GKE_SA_KEY
GKE_PROJECT # PROJECT_ID

Be sure to authenticate in GCP:

gcloud auth login

gcloud config set project PROJECT_ID

Create Kubernetes Cluster

gcloud container clusters create llama-gke-cluster \
    --zone=europe-west6-a \
    --num-nodes=5 \
    --enable-autoscaling \
    --min-nodes=2 \
    --max-nodes=10 \
    --machine-type=n1-standard-4 \
    --enable-vertical-pod-autoscaling

after creation check the nodes

kubectl get nodes

Push the GitHub Actions workflows to start the deployment

Verify Kubernetes is running after deployment

# Get the Pods
kubectl get po

# Get the Nodes
kubectl get nodes

# Get the Services
kubectl get svc 

# Get the logs of a pod
kubectl logs llama-gke-deploy-668b58b455-fjwvq 

# Describe a pod
kubectl describe pod llama-gke-deploy-668b58b455-fjwvq

# Check CPU usage
kubectl top pod llama-gke-deploy-668b58b455-fjwvq

Under svc the external ip is the endpoint (34.65.157.134), that can be added in the streamlit app
```
# Set the FastAPI endpoint
FASTAPI_ENDPOINT = "http://34.65.157.134:8000/query/"
```

Check some pods and logs

kubectl logs llama-gke-deploy-668b58b455-fjwvq 
kubectl describe pod llama-gke-deploy-668b58b455-fjwvq
kubectl top pod llama-gke-deploy-668b58b455-8xfhf

Clean up to avoid costs deleting the cluster and the docker image

gcloud container clusters delete llama-gke-cluster --zone=europe-west6-a

Streamlit UI

Run the streamlit app adding the endpoint url that you get after deployment:

streamlit run streamlit_app.py