Custom Model Client docs follow-up (#1545)

* custom model client docs followup

* fix function name in docs

* Update website/docs/Use-Cases/enhanced_inference.md

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update website/docs/Use-Cases/enhanced_inference.md

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update website/docs/Use-Cases/enhanced_inference.md

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update website/docs/Use-Cases/enhanced_inference.md

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

autogen/oai/client.py

@@ -77,6 +77,8 @@ class ModelClient(Protocol):
             class Message(Protocol):
                 content: Optional[str]
 
+            message: Message
+
         choices: List[Choice]
         model: str
 

notebook/agentchat_custom_model.ipynb

@@ -94,7 +94,9 @@
     "    class ModelClientResponseProtocol(Protocol):\n",
     "        class Choice(Protocol):\n",
     "            class Message(Protocol):\n",
-    "                content: str | None\n",
+    "                content: Optional[str]\n",
+    "\n",
+    "            message: Message\n",
     "\n",
     "        choices: List[Choice]\n",
     "        model: str\n",

website/blog/2024-01-26-Custom-Models/index.mdx

@@ -122,7 +122,9 @@ class ModelClient(Protocol):
     class ModelClientResponseProtocol(Protocol):
         class Choice(Protocol):
             class Message(Protocol):
-                content: str | None
+                content: Optional[str]
+
+            message: Message
 
         choices: List[Choice]
         model: str

website/docs/FAQ.md

@@ -89,7 +89,10 @@ In version >=1, OpenAI renamed their `api_base` parameter to `base_url`. So for
 
 ### Can I use non-OpenAI models?
 
-Yes. Autogen can work with any API endpoint which complies with OpenAI-compatible RESTful APIs - e.g. serving local LLM via FastChat or LM Studio. Please check https://microsoft.github.io/autogen/blog/2023/07/14/Local-LLMs for an example.
+Yes. You currently have two options:
+
+- Autogen can work with any API endpoint which complies with OpenAI-compatible RESTful APIs - e.g. serving local LLM via FastChat or LM Studio. Please check https://microsoft.github.io/autogen/blog/2023/07/14/Local-LLMs for an example.
+- You can supply your own custom model implementation and use it with Autogen. Please check https://microsoft.github.io/autogen/blog/2024/01/26/Custom-Models for more information.
 
 ## Handle Rate Limit Error and Timeout Error
 

website/docs/Use-Cases/enhanced_inference.md

@@ -107,9 +107,6 @@ The tuned config can be used to perform inference.
 
 ## API unification
 
-<!-- `autogen.Completion.create` is compatible with both `openai.Completion.create` and `openai.ChatCompletion.create`, and both OpenAI API and Azure OpenAI API. So models such as "text-davinci-003", "gpt-3.5-turbo" and "gpt-4" can share a common API.
-When chat models are used and `prompt` is given as the input to `autogen.Completion.create`, the prompt will be automatically converted into `messages` to fit the chat completion API requirement. One advantage is that one can experiment with both chat and non-chat models for the same prompt in a unified API. -->
-
 `autogen.OpenAIWrapper.create()` can be used to create completions for both chat and non-chat models, and both OpenAI API and Azure OpenAI API.
 
 ```python
@@ -133,7 +130,7 @@ print(client.extract_text_or_completion_object(response))
 
 For local LLMs, one can spin up an endpoint using a package like [FastChat](https://github.com/lm-sys/FastChat), and then use the same API to send a request. See [here](/blog/2023/07/14/Local-LLMs) for examples on how to make inference with local LLMs.
 
-<!-- When only working with the chat-based models, `autogen.ChatCompletion` can be used. It also does automatic conversion from prompt to messages, if prompt is provided instead of messages. -->
+For custom model clients, one can register the client with `autogen.OpenAIWrapper.register_model_client` and then use the same API to send a request. See [here](/blog/2024/01/26/Custom-Models) for examples on how to make inference with custom model clients.
 
 ## Usage Summary
 
@@ -166,6 +163,8 @@ Total cost: 0.00027
 * Model 'gpt-3.5-turbo': cost: 0.00027, prompt_tokens: 50, completion_tokens: 100, total_tokens: 150
 ```
 
+Note: if using a custom model client (see [here](/blog/2024/01/26/Custom-Models) for details) and if usage summary is not implemented, then the usage summary will not be available.
+
 ## Caching
 
 API call results are cached locally and reused when the same request is issued.
@@ -241,13 +240,6 @@ The differences between autogen's `cache_seed` and openai's `seed`:
 
 ### Runtime error
 
-<!-- It is easy to hit error when calling OpenAI APIs, due to connection, rate limit, or timeout. Some of the errors are transient. `autogen.Completion.create` deals with the transient errors and retries automatically. Request timeout, max retry period and retry wait time can be configured via `request_timeout`, `max_retry_period` and `retry_wait_time`.
-
-- `request_timeout` (int): the timeout (in seconds) sent with a single request.
-- `max_retry_period` (int): the total time (in seconds) allowed for retrying failed requests.
-- `retry_wait_time` (int): the time interval to wait (in seconds) before retrying a failed request.
-
-Moreover,  -->
 One can pass a list of configurations of different models/endpoints to mitigate the rate limits and other runtime error. For example,
 
 ```python
@@ -268,12 +260,16 @@ client = OpenAIWrapper(
         {
             "model": "llama2-chat-7B",
             "base_url": "http://127.0.0.1:8080",
+        },
+        {
+            "model": "microsoft/phi-2",
+            "model_client_cls": "CustomModelClient"
         }
     ],
 )
 ```
 
-`client.create()` will try querying Azure OpenAI gpt-4, OpenAI gpt-3.5-turbo, and a locally hosted llama2-chat-7B one by one,
+`client.create()` will try querying Azure OpenAI gpt-4, OpenAI gpt-3.5-turbo, a locally hosted llama2-chat-7B, and phi-2 using a custom model client class named `CustomModelClient`, one by one,
 until a valid result is returned. This can speed up the development process where the rate limit is a bottleneck. An error will be raised if the last choice fails. So make sure the last choice in the list has the best availability.
 
 For convenience, we provide a number of utility functions to load config lists.