From 76b5fb4b09ac75afbbe0977e210532c6b6aebe4d Mon Sep 17 00:00:00 2001
From: Benito Martin <116911431+benitomartin@users.noreply.github.com>
Date: Mon, 1 Jul 2024 12:41:28 +0200
Subject: [PATCH] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index fcd3608..b081374 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # Q&A Pipeline Deployment on GKE for Scalability with LlamaIndex and Qdrant 🚀
 
 <p align="center">
-<img width="767" alt="lambda-gke" src="https://github.com/benitomartin/mlops-car-prices/assets/116911431/b4a7e10c-52f9-4ca2-ade3-f2136ff6bbdf">
+<img width="737" alt="cover_gke_medium" src="https://github.com/benitomartin/rag-gke-llama-qdrant/assets/116911431/1fea3c27-66b8-4751-b5e2-c045ac4d1b93">
 </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.
@@ -202,5 +202,5 @@ Run the streamlit app adding the endpoint url that you get after deployment:
    ```
 
 <p align="center">
-<img width="793" alt="qdrant-gke" src="https://github.com/benitomartin/rag-aws-qdrant/assets/116911431/74faa65a-f662-40c3-adf6-9dc18a08f712">
+<img width="767" alt="lambda-gke" src="https://github.com/benitomartin/mlops-car-prices/assets/116911431/b4a7e10c-52f9-4ca2-ade3-f2136ff6bbdf">
 </p>