Add API Endpoints guide and update Advanced Topics index

docs/getting-started/advanced-topics/api-endpoints.md

@@ -0,0 +1,202 @@
+---
+sidebar_position: 400
+title: "🔗 API Endpoints"
+---
+
+This guide provides essential information on how to interact with the API endpoints effectively to achieve seamless integration and automation using our models. Please note that this is an experimental setup and may undergo future updates for enhancement.
+
+## Authentication
+To ensure secure access to the API, authentication is required 🛡️. You can authenticate your API requests using the Bearer Token mechanism. Obtain your API key from **Settings > Account** in the Open WebUI, or alternatively, use a JWT (JSON Web Token) for authentication.
+
+## Notable API Endpoints
+
+### 📜 Retrieve All Models
+- **Endpoint**: `GET /api/models`
+- **Description**: Fetches all models created or added via Open WebUI.
+- **Example**:
+  ```bash
+  curl -H "Authorization: Bearer YOUR_API_KEY" http://localhost:3000/api/models
+  ```
+
+### 💬 Chat Completions
+- **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**:
+  ```bash
+  curl -X POST http://localhost:3000/api/chat/completions \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+        "model": "llama3.1",
+        "messages": [
+          {
+            "role": "user",
+            "content": "Why is the sky blue?"
+          }
+        ]
+      }'
+  ```
+
+### 🧩 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.
+
+#### Uploading Files
+
+To utilize external data in RAG responses, you first need to upload the files. The content of the uploaded file is automatically extracted and stored in a vector database.
+
+- **Endpoint**: `POST /api/v1/files/`
+- **Curl Example**:
+  ```bash
+  curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Accept: application/json" \
+  -F "file=@/path/to/your/file" http://localhost:3000/api/v1/files/
+  ```
+- **Python Example**:
+  ```python
+  import requests
+  
+  def upload_file(token, file_path):
+      url = 'http://localhost:3000/api/v1/files/'
+      headers = {
+          'Authorization': f'Bearer {token}',
+          'Accept': 'application/json'
+      }
+      files = {'file': open(file_path, 'rb')}
+      response = requests.post(url, headers=headers, files=files)
+      return response.json()
+  ```
+
+#### Adding Files to Knowledge Collections
+
+After uploading, you can group files into a knowledge collection or reference them individually in chats.
+
+- **Endpoint**: `POST /api/v1/knowledge/{id}/file/add`
+- **Curl Example**:
+  ```bash
+  curl -X POST http://localhost:3000/api/v1/knowledge/{knowledge_id}/file/add \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"file_id": "your-file-id-here"}'
+  ```
+- **Python Example**:
+  ```python
+  import requests
+
+  def add_file_to_knowledge(token, knowledge_id, file_id):
+      url = f'http://localhost:3000/api/v1/knowledge/{knowledge_id}/file/add'
+      headers = {
+          'Authorization': f'Bearer {token}',
+          'Content-Type': 'application/json'
+      }
+      data = {'file_id': file_id}
+      response = requests.post(url, headers=headers, json=data)
+      return response.json()
+  ```
+
+#### Using Files and Collections in Chat Completions
+
+You can reference both individual files or entire collections in your RAG queries for enriched responses.
+
+##### Using an Individual File in Chat Completions
+
+This method is beneficial when you want to focus the chat model's response on the content of a specific file.
+
+- **Endpoint**: `POST /api/chat/completions`
+- **Curl Example**:
+  ```bash
+  curl -X POST http://localhost:3000/api/chat/completions \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+        "model": "gpt-4-turbo",
+        "messages": [
+          {"role": "user", "content": "Explain the concepts in this document."}
+        ],
+        "files": [
+          {"type": "file", "id": "your-file-id-here"}
+        ]
+      }'
+  ```
+
+- **Python Example**:
+  ```python
+  import requests
+
+  def chat_with_file(token, model, query, file_id):
+      url = 'http://localhost:3000/api/chat/completions'
+      headers = {
+          'Authorization': f'Bearer {token}',
+          'Content-Type': 'application/json'
+      }
+      payload = {
+          'model': model,
+          'messages': [{'role': 'user', 'content': query}],
+          'files': [{'type': 'file', 'id': file_id}]
+      }
+      response = requests.post(url, headers=headers, json=payload)
+      return response.json()
+  ```
+
+##### Using a Knowledge Collection in Chat Completions
+
+Leverage a knowledge collection to enhance the response when the inquiry may benefit from a broader context or multiple documents.
+
+- **Endpoint**: `POST /api/chat/completions`
+- **Curl Example**:
+  ```bash
+  curl -X POST http://localhost:3000/api/chat/completions \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+        "model": "gpt-4-turbo",
+        "messages": [
+          {"role": "user", "content": "Provide insights on the historical perspectives covered in the collection."}
+        ],
+        "files": [
+          {"type": "collection", "id": "your-collection-id-here"}
+        ]
+      }'
+  ```
+
+- **Python Example**:
+  ```python
+  import requests
+  
+  def chat_with_collection(token, model, query, collection_id):
+      url = 'http://localhost:3000/api/chat/completions'
+      headers = {
+          'Authorization': f'Bearer {token}',
+          'Content-Type': 'application/json'
+      }
+      payload = {
+          'model': model,
+          'messages': [{'role': 'user', 'content': query}],
+          'files': [{'type': 'collection', 'id': collection_id}]
+      }
+      response = requests.post(url, headers=headers, json=payload)
+      return response.json()
+  ```
+
+These methods enable effective utilization of external knowledge via uploaded files and curated knowledge collections, enhancing chat applications' capabilities using the Open WebUI API. Whether using files individually or within collections, you can customize the integration based on your specific needs.
+
+## Advantages of Using Open WebUI as a Unified LLM Provider
+Open WebUI offers a myriad of benefits, making it an essential tool for developers and businesses alike:
+- **Unified Interface**: Simplify your interactions with different LLMs through a single, integrated platform.
+- **Ease of Implementation**: Quick start integration with comprehensive documentation and community support.
+
+## Swagger Documentation Links
+Access detailed API documentation for different services provided by Open WebUI:
+
+| Application | Documentation Path      |
+|-------------|-------------------------|
+| Main        | `/docs`                 |
+| WebUI       | `/api/v1/docs`          |
+| Ollama      | `/ollama/docs`          |
+| OpenAI      | `/openai/docs`          |
+| Images      | `/images/api/v1/docs`   |
+| Audio       | `/audio/api/v1/docs`    |
+| RAG         | `/retrieval/api/v1/docs`|
+
+Each documentation portal offers interactive examples, schema descriptions, and testing capabilities to enhance your understanding and ease of use.
+
+By following these guidelines, you can swiftly integrate and begin utilizing the Open WebUI API. Should you encounter any issues or have questions, feel free to reach out through our Discord Community or consult the FAQs. Happy coding! 🌟

docs/getting-started/advanced-topics/index.mdx

@@ -11,27 +11,33 @@ Explore deeper concepts and advanced configurations of Open WebUI to enhance you
 
 ## 🔧 Environment Configuration  
 Understand how to set environment variables to customize your Open WebUI setup.  
-[Environment Configuration Guide](./EnvConfig)
+[Environment Configuration Guide](./env-configuration)
 
 ---
 
 ## 📊 Logging and Monitoring  
 Learn how to monitor, log, and troubleshoot your system effectively.  
-[Logging and Monitoring Guide](./Logging)
+[Logging and Monitoring Guide](./logging)
 
 ---
 
 ## 🛠️ Development Guide  
 Dive into the development process and learn how to contribute to Open WebUI.  
-[Development Guide](./Development)
+[Development Guide](./development)
 
 ---
 
 ## 🔒 HTTPS Encryption  
 Ensure secure communication by implementing HTTPS encryption in your deployment.  
-[HTTPS Encryption Guide](./HttpsEncryption)
+[HTTPS Encryption Guide](./https-encryption)
+
+---
+
+## 🔗 API Endpoints  
+Get essential information for API integration and automation using our models.  
+[API Endpoints Guide](./api-endpoints)
 
 ---
 
 Looking for installation instructions? Head over to our [Quick Start Guide](../quick-start).  
-Need to explore core features? Check out [Using OpenWebUI](../using-openwebui).
+Need to explore core features? Check out [Using OpenWebUI](../using-openwebui).