[langchain_community.llms.xinference]: Rewrite _stream() method and support stream() method in xinference.py (#29259)

- [ ] **PR title**:[langchain_community.llms.xinference]: Rewrite
_stream() method and support stream() method in xinference.py

- [ ] **PR message**: Rewrite the _stream method so that the
chain.stream() can be used to return data streams.

       chain = prompt | llm
       chain.stream(input=user_input)


- [ ] **tests**: 
      from langchain_community.llms import Xinference
      from langchain.prompts import PromptTemplate

      llm = Xinference(
server_url="http://0.0.0.0:9997", # replace your xinference server url
model_uid={model_uid} # replace model_uid with the model UID return from
launching the model
          stream = True
       )
prompt = PromptTemplate(input=['country'], template="Q: where can we
visit in the capital of {country}? A:")
      chain = prompt | llm
      chain.stream(input={'country': 'France'})

libs/community/langchain_community/llms/xinference.py

@@ -1,7 +1,20 @@
-from typing import TYPE_CHECKING, Any, Dict, Generator, List, Mapping, Optional, Union
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Generator,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Union,
+)
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
+from langchain_core.outputs import GenerationChunk
 
 if TYPE_CHECKING:
     from xinference.client import RESTfulChatModelHandle, RESTfulGenerateModelHandle
@@ -73,6 +86,26 @@ class Xinference(LLM):
             generate_config={"max_tokens": 1024, "stream": True},
         )
 
+    Example:
+
+    .. code-block:: python
+
+        from langchain_community.llms import Xinference
+        from langchain.prompts import PromptTemplate
+
+        llm = Xinference(
+            server_url="http://0.0.0.0:9997",
+            model_uid={model_uid}, # replace model_uid with the model UID return from launching the model
+            stream=True
+        )
+        prompt = PromptTemplate(
+            input=['country'],
+            template="Q: where can we visit in the capital of {country}? A:"
+        )
+        chain = prompt | llm
+        chain.stream(input={'country': 'France'})
+
+
     To view all the supported builtin models, run:
 
     .. code-block:: bash
@@ -216,3 +249,59 @@ class Xinference(LLM):
                                 token=token, verbose=self.verbose, log_probs=log_probs
                             )
                         yield token
+
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        generate_config = kwargs.get("generate_config", {})
+        generate_config = {**self.model_kwargs, **generate_config}
+        if stop:
+            generate_config["stop"] = stop
+        for stream_resp in self._create_generate_stream(prompt, generate_config):
+            if stream_resp:
+                chunk = self._stream_response_to_generation_chunk(stream_resp)
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
+                yield chunk
+
+    def _create_generate_stream(
+        self, prompt: str, generate_config: Optional[Dict[str, List[str]]] = None
+    ) -> Iterator[str]:
+        if self.client is None:
+            raise ValueError("Client is not initialized!")
+        model = self.client.get_model(self.model_uid)
+        yield from model.generate(prompt=prompt, generate_config=generate_config)
+
+    @staticmethod
+    def _stream_response_to_generation_chunk(
+        stream_response: str,
+    ) -> GenerationChunk:
+        """Convert a stream response to a generation chunk."""
+        token = ""
+        if isinstance(stream_response, dict):
+            choices = stream_response.get("choices", [])
+            if choices:
+                choice = choices[0]
+                if isinstance(choice, dict):
+                    token = choice.get("text", "")
+
+                    return GenerationChunk(
+                        text=token,
+                        generation_info=dict(
+                            finish_reason=choice.get("finish_reason", None),
+                            logprobs=choice.get("logprobs", None),
+                        ),
+                    )
+                else:
+                    raise TypeError("choice type error!")
+            else:
+                return GenerationChunk(text=token)
+        else:
+            raise TypeError("stream_response type error!")