docs: Add ChatSparkLLM docstrings (langchain-ai#24449)

- **Description:** 
  - Add `ChatSparkLLM` docstrings, the issue langchain-ai#22296 
  - To support `stream` method

libs/community/langchain_community/chat_models/sparkllm.py

@@ -92,34 +92,116 @@ def _convert_delta_to_message_chunk(
 
 
 class ChatSparkLLM(BaseChatModel):
-    """iFlyTek Spark large language model.
+    """IFlyTek Spark chat model integration.
+
+    Setup:
+        To use, you should have the environment variable``IFLYTEK_SPARK_API_KEY``,
+        ``IFLYTEK_SPARK_API_SECRET`` and ``IFLYTEK_SPARK_APP_ID``.
+
+    Key init args — completion params:
+        model: Optional[str]
+            Name of IFLYTEK SPARK model to use.
+        temperature: Optional[float]
+            Sampling temperature.
+        top_k: Optional[float]
+            What search sampling control to use.
+        streaming: Optional[bool]
+             Whether to stream the results or not.
+
+    Key init args — client params:
+        api_key: Optional[str]
+            IFLYTEK SPARK API KEY. If not passed in will be read from env var IFLYTEK_SPARK_API_KEY.
+        api_secret: Optional[str]
+            IFLYTEK SPARK API SECRET. If not passed in will be read from env var IFLYTEK_SPARK_API_SECRET.
+        api_url: Optional[str]
+            Base URL for API requests.
+        timeout: Optional[int]
+            Timeout for requests.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.chat_models import ChatSparkLLM
 
-    To use, you should pass `app_id`, `api_key`, `api_secret`
-    as a named parameter to the constructor OR set environment
-    variables ``IFLYTEK_SPARK_APP_ID``, ``IFLYTEK_SPARK_API_KEY`` and
-    ``IFLYTEK_SPARK_API_SECRET``
+            chat = MiniMaxChat(
+                api_key=api_key,
+                api_secret=ak,
+                model='Spark4.0 Ultra',
+                # temperature=...,
+                # other params...
+            )
 
-    Example:
+    Invoke:
         .. code-block:: python
 
-        client = ChatSparkLLM(
-            spark_app_id="<app_id>",
-            spark_api_key="<api_key>",
-            spark_api_secret="<api_secret>"
-        )
+            messages = [
+                ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
+                ("human", "我喜欢编程。"),
+            ]
+            chat.invoke(messages)
 
-    Extra infos:
-        1. Get app_id, api_key, api_secret from the iFlyTek Open Platform Console:
-            https://console.xfyun.cn/services/bm35
-        2. By default, iFlyTek Spark LLM V3.5 is invoked.
-            If you need to invoke other versions, please configure the corresponding
-            parameters(spark_api_url and spark_llm_domain) according to the document:
-            https://www.xfyun.cn/doc/spark/Web.html
-        3. It is necessary to ensure that the app_id used has a license for
-            the corresponding model version.
-        4. If you encounter problems during use, try getting help at:
-            https://console.xfyun.cn/workorder/commit
-    """
+        .. code-block:: python
+
+            AIMessage(
+                content='I like programming.',
+                response_metadata={
+                    'token_usage': {
+                        'question_tokens': 3,
+                        'prompt_tokens': 16,
+                        'completion_tokens': 4,
+                        'total_tokens': 20
+                    }
+                },
+                id='run-af8b3531-7bf7-47f0-bfe8-9262cb2a9d47-0'
+            )
+
+    Stream:
+        .. code-block:: python
+
+            for chunk in chat.stream(messages):
+                print(chunk)
+
+        .. code-block:: python
+
+            content='I' id='run-fdbb57c2-2d32-4516-b894-6c5a67605d83'
+            content=' like programming' id='run-fdbb57c2-2d32-4516-b894-6c5a67605d83'
+            content='.' id='run-fdbb57c2-2d32-4516-b894-6c5a67605d83'
+
+        .. code-block:: python
+
+            stream = chat.stream(messages)
+            full = next(stream)
+            for chunk in stream:
+                full += chunk
+            full
+
+        .. code-block:: python
+
+            AIMessageChunk(
+                content='I like programming.',
+                id='run-aca2fa82-c2e4-4835-b7e2-865ddd3c46cb'
+            )
+
+    Response metadata
+        .. code-block:: python
+
+            ai_msg = chat.invoke(messages)
+            ai_msg.response_metadata
+
+        .. code-block:: python
+
+            {
+                'token_usage': {
+                    'question_tokens': 3,
+                    'prompt_tokens': 16,
+                    'completion_tokens': 4,
+                    'total_tokens': 20
+                }
+            }
+
+    """  # noqa: E501
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
@@ -257,7 +339,7 @@ def _stream(
             [_convert_message_to_dict(m) for m in messages],
             self.spark_user_id,
             self.model_kwargs,
-            self.streaming,
+            streaming=True,
         )
         for content in self.client.subscribe(timeout=self.request_timeout):
             if "data" not in content:
@@ -274,9 +356,10 @@ def _generate(
         messages: List[BaseMessage],
         stop: Optional[List[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
+        stream: Optional[bool] = None,
         **kwargs: Any,
     ) -> ChatResult:
-        if self.streaming:
+        if stream or self.streaming:
             stream_iter = self._stream(
                 messages=messages, stop=stop, run_manager=run_manager, **kwargs
             )

libs/community/tests/integration_tests/chat_models/test_sparkllm.py

@@ -53,3 +53,10 @@ def test_chat_spark_llm_with_temperature() -> None:
     print(response)  # noqa: T201
     assert isinstance(response, AIMessage)
     assert isinstance(response.content, str)
+
+
+def test_chat_spark_llm_streaming_with_stream_method() -> None:
+    chat = ChatSparkLLM()  # type: ignore[call-arg]
+    for chunk in chat.stream("Hello!"):
+        assert isinstance(chunk, AIMessageChunk)
+        assert isinstance(chunk.content, str)