feat: 1. Add system parameters, 2. Align with the QianfanChatEndpoint…

… for function calling (#14275)

- **Description:** 
1. Add system parameters to the ERNIE LLM API to set the role of the
LLM.
2. Add support for the ERNIE-Bot-turbo-AI model according from the
document https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Alp0kdm0n.
3. For the function call of ErnieBotChat, align with the
QianfanChatEndpoint.

With this PR, the `QianfanChatEndpoint()` can use the `function calling`
ability with `create_ernie_fn_chain()`. The example is as the following:

```
from langchain.prompts import ChatPromptTemplate
import json
from langchain.prompts.chat import (
    ChatPromptTemplate,
)

from langchain.chat_models import QianfanChatEndpoint
from langchain.chains.ernie_functions import (
    create_ernie_fn_chain,
)

def get_current_news(location: str) -> str:
    """Get the current news based on the location.'

    Args:
        location (str): The location to query.
    
    Returs:
        str: Current news based on the location.
    """

    news_info = {
        "location": location,
        "news": [
            "I have a Book.",
            "It's a nice day, today."
        ]
    }

    return json.dumps(news_info)

def get_current_weather(location: str, unit: str="celsius") -> str:
    """Get the current weather in a given location

    Args:
        location (str): location of the weather.
        unit (str): unit of the tempuature.
    
    Returns:
        str: weather in the given location.
    """

    weather_info = {
        "location": location,
        "temperature": "27",
        "unit": unit,
        "forecast": ["sunny", "windy"],
    }
    return json.dumps(weather_info)

template = ChatPromptTemplate.from_messages([
    ("user", "{user_input}"),
])

chat = QianfanChatEndpoint(model="ERNIE-Bot-4")
chain = create_ernie_fn_chain([get_current_weather, get_current_news], chat, template, verbose=True)
res = chain.run("北京今天的新闻是什么？")
print(res)
```

The result of the above code:
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 北京今天的新闻是什么？
> Finished chain.
{'name': 'get_current_news', 'arguments': {'location': '北京'}}
```

For the `ErnieBotChat`, now can use the `system` parameter to set the
role of the LLM.

```
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from langchain.chat_models import ErnieBotChat

llm = ErnieBotChat(model_name="ERNIE-Bot-turbo-AI", system="你是一个能力很强的机器人，你的名字叫 小叮当。无论问你什么问题，你都可以给出答案。")
prompt = ChatPromptTemplate.from_messages(
    [
        ("human", "{query}"),
    ]
)
chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
res = chain.run(query="你是谁？")
print(res)
```

The result of the above code:

```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 你是谁？
> Finished chain.
我是小叮当，一个智能机器人。我可以为你提供各种服务，包括回答问题、提供信息、进行计算等。如果你需要任何帮助，请随时告诉我，我会尽力为你提供最好的服务。
```

libs/langchain/langchain/chat_models/ernie.py

@@ -1,4 +1,3 @@
-import json
 import logging
 import threading
 from typing import Any, Dict, List, Mapping, Optional
@@ -46,7 +45,8 @@ class ErnieBotChat(BaseChatModel):
     and will be regenerated after expiration (30 days).
 
     Default model is `ERNIE-Bot-turbo`,
-    currently supported models are `ERNIE-Bot-turbo`, `ERNIE-Bot`
+    currently supported models are `ERNIE-Bot-turbo`, `ERNIE-Bot`, `ERNIE-Bot-8K`,
+    `ERNIE-Bot-4`, `ERNIE-Bot-turbo-AI`.
 
     Example:
         .. code-block:: python
@@ -87,6 +87,11 @@ class ErnieBotChat(BaseChatModel):
     """model name of ernie, default is `ERNIE-Bot-turbo`.
       Currently supported `ERNIE-Bot-turbo`, `ERNIE-Bot`"""
 
+    system: Optional[str] = None
+    """system is mainly used for model character design, 
+    for example, you are an AI assistant produced by xxx company.
+    The length of the system is limiting of 1024 characters."""
+
     request_timeout: Optional[int] = 60
     """request timeout for chat http requests"""
 
@@ -123,6 +128,7 @@ def _chat(self, payload: object) -> dict:
             "ERNIE-Bot": "completions",
             "ERNIE-Bot-8K": "ernie_bot_8k",
             "ERNIE-Bot-4": "completions_pro",
+            "ERNIE-Bot-turbo-AI": "ai_apaas",
             "BLOOMZ-7B": "bloomz_7b1",
             "Llama-2-7b-chat": "llama_2_7b",
             "Llama-2-13b-chat": "llama_2_13b",
@@ -180,6 +186,7 @@ def _generate(
             "top_p": self.top_p,
             "temperature": self.temperature,
             "penalty_score": self.penalty_score,
+            "system": self.system,
             **kwargs,
         }
         logger.debug(f"Payload for ernie api is {payload}")
@@ -195,14 +202,19 @@ def _generate(
 
     def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
         if "function_call" in response:
-            fc_str = '{{"function_call": {}}}'.format(
-                json.dumps(response.get("function_call"))
-            )
-            generations = [ChatGeneration(message=AIMessage(content=fc_str))]
+            additional_kwargs = {
+                "function_call": dict(response.get("function_call", {}))
+            }
         else:
-            generations = [
-                ChatGeneration(message=AIMessage(content=response.get("result")))
-            ]
+            additional_kwargs = {}
+        generations = [
+            ChatGeneration(
+                message=AIMessage(
+                    content=response.get("result"),
+                    additional_kwargs={**additional_kwargs},
+                )
+            )
+        ]
         token_usage = response.get("usage", {})
         llm_output = {"token_usage": token_usage, "model_name": self.model_name}
         return ChatResult(generations=generations, llm_output=llm_output)

libs/langchain/langchain/output_parsers/ernie_functions.py

@@ -72,12 +72,8 @@ def parse_result(self, result: List[Generation], *, partial: bool = False) -> An
                 "This output parser can only be used with a chat generation."
             )
         message = generation.message
-        message.additional_kwargs["function_call"] = {}
-        if "function_call" in message.content:
-            function_call = json.loads(str(message.content))
-            if "function_call" in function_call:
-                fc = function_call["function_call"]
-                message.additional_kwargs["function_call"] = fc
+        if "function_call" not in message.additional_kwargs:
+            return None
         try:
             function_call = message.additional_kwargs["function_call"]
         except KeyError as exc: