rename stuff

README.md

@@ -1,4 +1,4 @@
-# Gemini DeepSearch LangGraph Quickstart
+# Gemini Fullstack LangGraph Quickstart
 
 This project demonstrates a fullstack application using a React frontend and a LangGraph-powered backend agent. The agent is designed to perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search, reflecting on the results to identify knowledge gaps, and iteratively refining its search until it can provide a well-supported answer with citations. This application serves as an example of building research-augmented conversational AI using LangGraph and Google's Gemini models.
 
@@ -63,7 +63,7 @@ _Alternatively, you can run the backend and frontend development servers separat
 
 The core of the backend is a LangGraph agent defined in `backend/src/agent/graph.py`. It follows these steps:
 
-![Agent Flow](./deepsearch.png)
+![Agent Flow](./agent.png)
 
 1.  **Generate Initial Queries:** Based on your input, it generates a set of initial search queries using a Gemini model.
 2.  **Web Research:** For each query, it uses the Gemini model with the Google Search API to find relevant web pages.
@@ -83,7 +83,7 @@ _Note: If you are not running the docker-compose.yml example or exposing the bac
 
    Run the following command from the **project root directory**:
    ```bash
-   docker build -t deepsearch -f Dockerfile .
+   docker build -t gemini-fullstack-langgraph -f Dockerfile .
    ```
 **2. Run the Production Server:**