Merge pull request #406 from jjasghar/patch-1

Added python chat completion example

docs/getting-started/api-endpoints.md

@@ -25,7 +25,8 @@ To ensure secure access to the API, authentication is required 🛡️. You can
 
 - **Endpoint**: `POST /api/chat/completions`
 - **Description**: Serves as an OpenAI API compatible chat completion endpoint for models on Open WebUI including Ollama models, OpenAI models, and Open WebUI Function models.
-- **Example**:
+
+- **Curl Example**:
 
   ```bash
   curl -X POST http://localhost:3000/api/chat/completions \
@@ -42,6 +43,29 @@ To ensure secure access to the API, authentication is required 🛡️. You can
       }'
   ```
   
+- **Python Example**:
+  ```python
+  import requests
+  
+  def chat_with_model(token):
+      url = 'http://localhost:3000/api/chat/completions'
+      headers = {
+          'Authorization': f'Bearer {token}',
+          'Content-Type': 'application/json'
+      }
+      data = {
+        "model": "granite3.1-dense:8b",
+        "messages": [
+          {
+            "role": "user",
+            "content": "Why is the sky blue?"
+          }
+        ]
+      }
+      response = requests.post(url, headers=headers, data=data)
+      return response.json()
+  ```
+
 ### 🧩 Retrieval Augmented Generation (RAG)
 
 The Retrieval Augmented Generation (RAG) feature allows you to enhance responses by incorporating data from external sources. Below, you will find the methods for managing files and knowledge collections via the API, and how to use them in chat completions effectively.