diff --git a/core/quivr_core/rag/entities/config.py b/core/quivr_core/rag/entities/config.py
index cb455ab42..1e848bdcd 100644
--- a/core/quivr_core/rag/entities/config.py
+++ b/core/quivr_core/rag/entities/config.py
@@ -230,6 +230,7 @@ class LLMEndpointConfig(QuivrBaseConfig):
 
         if not self.llm_api_key:
             logger.warning(f"The API key for supplier '{self.supplier}' is not set. ")
+            logger.warning(f"Please set the environment variable: '{self.env_variable_name}'. ")
 
     def set_llm_model_config(self):
         # Automatically set context_length and tokenizer_hub based on the supplier and model
diff --git a/examples/chatbot/README.md b/examples/chatbot/README.md
index 3cfa916ae..2602d54bb 100644
--- a/examples/chatbot/README.md
+++ b/examples/chatbot/README.md
@@ -8,23 +8,32 @@ This example demonstrates how to create a simple chatbot using Quivr and Chainli
 
 ## Installation
 
-1. Clone the repository or navigate to the `core/examples/chatbot` directory.
+1. Clone the repository and navigate to the `examples/chatbot` directory.
 
-2. Install the required dependencies:
+2. Make sure you have [rye](https://rye.astral.sh/) installed.
 
+3. Install the requirements using `rye`:
+
+   ```sh
+   rye sync
    ```
-   pip install -r requirements.lock
+4. Activate the venv
+
+   ```sh
+   source ./venv/bin/activate
    ```
 
 ## Running the Chatbot
 
-1. Start the Chainlit server:
+1. Define your API key as environment variable. e.g. `export OPENAI_API_KEY=your-key-here`
+
+2. Start the Chainlit server:
 
    ```
    chainlit run main.py
    ```
 
-2. Open your web browser and go to the URL displayed in the terminal (usually `http://localhost:8000`).
+3. Open your web browser and go to the URL displayed in the terminal (usually `http://localhost:8000`).
 
 ## Using the Chatbot