community[minor]: integrate with model Yuan2.0 (#15411)

1. integrate with
[`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. update `langchain.llms`
3. add a new doc for [Yuan2.0
integration](docs/docs/integrations/llms/yuan2.ipynb)

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/community/langchain_community/llms/yuan2.py

@@ -0,0 +1,192 @@
+import json
+import logging
+from typing import Any, Dict, List, Mapping, Optional, Set
+
+import requests
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.pydantic_v1 import Field
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+logger = logging.getLogger(__name__)
+
+
+class Yuan2(LLM):
+    """Yuan2.0 language models.
+
+    Example:
+        .. code-block:: python
+
+            yuan_llm = Yuan2(
+                infer_api="http://127.0.0.1:8000/yuan",
+                max_tokens=1024,
+                temp=1.0,
+                top_p=0.9,
+                top_k=40,
+            )
+            print(yuan_llm)
+            print(yuan_llm("你是谁？"))
+    """
+
+    infer_api: str = "http://127.0.0.1:8000/yuan"
+    """Yuan2.0 inference api"""
+
+    max_tokens: int = Field(1024, alias="max_token")
+    """Token context window."""
+
+    temp: Optional[float] = 0.7
+    """The temperature to use for sampling."""
+
+    top_p: Optional[float] = 0.9
+    """The top-p value to use for sampling."""
+
+    top_k: Optional[int] = 40
+    """The top-k value to use for sampling."""
+
+    do_sample: bool = False
+    """The do_sample is a Boolean value that determines whether 
+    to use the sampling method during text generation.
+    """
+
+    echo: Optional[bool] = False
+    """Whether to echo the prompt."""
+
+    stop: Optional[List[str]] = []
+    """A list of strings to stop generation when encountered."""
+
+    repeat_last_n: Optional[int] = 64
+    "Last n tokens to penalize"
+
+    repeat_penalty: Optional[float] = 1.18
+    """The penalty to apply to repeated tokens."""
+
+    streaming: bool = False
+    """Whether to stream the results or not."""
+
+    history: List[str] = []
+    """History of the conversation"""
+
+    use_history: bool = False
+    """Whether to use history or not"""
+
+    @property
+    def _llm_type(self) -> str:
+        return "Yuan2.0"
+
+    @staticmethod
+    def _model_param_names() -> Set[str]:
+        return {
+            "max_tokens",
+            "temp",
+            "top_k",
+            "top_p",
+            "do_sample",
+        }
+
+    def _default_params(self) -> Dict[str, Any]:
+        return {
+            "infer_api": self.infer_api,
+            "max_tokens": self.max_tokens,
+            "temp": self.temp,
+            "top_k": self.top_k,
+            "top_p": self.top_p,
+            "do_sample": self.do_sample,
+            "use_history": self.use_history,
+        }
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model": self._llm_type,
+            **self._default_params(),
+            **{
+                k: v for k, v in self.__dict__.items() if k in self._model_param_names()
+            },
+        }
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out to a Yuan2.0 LLM inference endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = yuan_llm("你能做什么?")
+        """
+
+        if self.use_history:
+            self.history.append(prompt)
+            input = "<n>".join(self.history)
+        else:
+            input = prompt
+
+        headers = {"Content-Type": "application/json"}
+        data = json.dumps(
+            {
+                "ques_list": [{"id": "000", "ques": input}],
+                "tokens_to_generate": self.max_tokens,
+                "temperature": self.temp,
+                "top_p": self.top_p,
+                "top_k": self.top_k,
+                "do_sample": self.do_sample,
+            }
+        )
+
+        logger.debug("Yuan2.0 prompt:", input)
+
+        # call api
+        try:
+            response = requests.put(self.infer_api, headers=headers, data=data)
+        except requests.exceptions.RequestException as e:
+            raise ValueError(f"Error raised by inference api: {e}")
+
+        logger.debug(f"Yuan2.0 response: {response}")
+
+        if response.status_code != 200:
+            raise ValueError(f"Failed with response: {response}")
+        try:
+            resp = response.json()
+
+            if resp["errCode"] != "0":
+                raise ValueError(
+                    f"Failed with error code [{resp['errCode']}], "
+                    f"error message: [{resp['errMessage']}]"
+                )
+
+            if "resData" in resp:
+                if len(resp["resData"]["output"]) >= 0:
+                    generate_text = resp["resData"]["output"][0]["ans"]
+                else:
+                    raise ValueError("No output found in response.")
+            else:
+                raise ValueError("No resData found in response.")
+
+        except requests.exceptions.JSONDecodeError as e:
+            raise ValueError(
+                f"Error raised during decoding response from inference api: {e}."
+                f"\nResponse: {response.text}"
+            )
+
+        if stop is not None:
+            generate_text = enforce_stop_tokens(generate_text, stop)
+
+        # support multi-turn chat
+        if self.use_history:
+            self.history.append(generate_text)
+
+        logger.debug(f"history: {self.history}")
+        return generate_text