feat: add ERNIE-Bot-4 Function Calling (#13320)

- **Description:** ERNIE-Bot-Chat-4 Large Language Model adds the
ability of `Function Calling` by passing parameters through the
`functions` parameter in the request. To simplify function calling for
ERNIE-Bot-Chat-4, the `create_ernie_fn_chain()` function has been added.
The definition and usage of the `create_ernie_fn_chain()` function is
similar to that of the `create_openai_fn_chain()` function.

Examples as the follows:

```
import json

from langchain.chains.ernie_functions import (
    create_ernie_fn_chain,
)
from langchain.chat_models import ErnieBotChat
from langchain.prompts import ChatPromptTemplate

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)

llm = ErnieBotChat(model_name="ERNIE-Bot-4")
prompt = ChatPromptTemplate.from_messages(
    [
        ("human", "{query}"),
    ]
)

chain = create_ernie_fn_chain([get_current_weather, get_current_news], llm, prompt, verbose=True)
res = chain.run("北京今天的新闻是什么？")
print(res)
```

The running results of the above program are shown below：
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 北京今天的新闻是什么？



> Finished chain.
{'name': 'get_current_news', 'thoughts': '用户想要知道北京今天的新闻。我可以使用get_current_news工具来获取这些信息。', 'arguments': {'location': '北京'}}
```

libs/langchain/langchain/chains/ernie_functions/__init__.py

@@ -0,0 +1,17 @@
+from langchain.chains.ernie_functions.base import (
+    convert_to_ernie_function,
+    create_ernie_fn_chain,
+    create_ernie_fn_runnable,
+    create_structured_output_chain,
+    create_structured_output_runnable,
+    get_ernie_output_parser,
+)
+
+__all__ = [
+    "convert_to_ernie_function",
+    "create_structured_output_chain",
+    "create_ernie_fn_chain",
+    "create_structured_output_runnable",
+    "create_ernie_fn_runnable",
+    "get_ernie_output_parser",
+]

libs/langchain/langchain/chains/ernie_functions/base.py

@@ -0,0 +1,547 @@
+"""Methods for creating chains that use Ernie function-calling APIs."""
+import inspect
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+    cast,
+)
+
+from langchain.base_language import BaseLanguageModel
+from langchain.chains import LLMChain
+from langchain.output_parsers.ernie_functions import (
+    JsonOutputFunctionsParser,
+    PydanticAttrOutputFunctionsParser,
+    PydanticOutputFunctionsParser,
+)
+from langchain.prompts import BasePromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema import BaseLLMOutputParser
+from langchain.schema.output_parser import BaseGenerationOutputParser, BaseOutputParser
+from langchain.schema.runnable import Runnable
+from langchain.utils.ernie_functions import convert_pydantic_to_ernie_function
+
+PYTHON_TO_JSON_TYPES = {
+    "str": "string",
+    "int": "number",
+    "float": "number",
+    "bool": "boolean",
+}
+
+
+def _get_python_function_name(function: Callable) -> str:
+    """Get the name of a Python function."""
+    return function.__name__
+
+
+def _parse_python_function_docstring(function: Callable) -> Tuple[str, dict]:
+    """Parse the function and argument descriptions from the docstring of a function.
+
+    Assumes the function docstring follows Google Python style guide.
+    """
+    docstring = inspect.getdoc(function)
+    if docstring:
+        docstring_blocks = docstring.split("\n\n")
+        descriptors = []
+        args_block = None
+        past_descriptors = False
+        for block in docstring_blocks:
+            if block.startswith("Args:"):
+                args_block = block
+                break
+            elif block.startswith("Returns:") or block.startswith("Example:"):
+                # Don't break in case Args come after
+                past_descriptors = True
+            elif not past_descriptors:
+                descriptors.append(block)
+            else:
+                continue
+        description = " ".join(descriptors)
+    else:
+        description = ""
+        args_block = None
+    arg_descriptions = {}
+    if args_block:
+        arg = None
+        for line in args_block.split("\n")[1:]:
+            if ":" in line:
+                arg, desc = line.split(":")
+                arg_descriptions[arg.strip()] = desc.strip()
+            elif arg:
+                arg_descriptions[arg.strip()] += " " + line.strip()
+    return description, arg_descriptions
+
+
+def _get_python_function_arguments(function: Callable, arg_descriptions: dict) -> dict:
+    """Get JsonSchema describing a Python functions arguments.
+
+    Assumes all function arguments are of primitive types (int, float, str, bool) or
+    are subclasses of pydantic.BaseModel.
+    """
+    properties = {}
+    annotations = inspect.getfullargspec(function).annotations
+    for arg, arg_type in annotations.items():
+        if arg == "return":
+            continue
+        if isinstance(arg_type, type) and issubclass(arg_type, BaseModel):
+            # Mypy error:
+            # "type" has no attribute "schema"
+            properties[arg] = arg_type.schema()  # type: ignore[attr-defined]
+        elif arg_type.__name__ in PYTHON_TO_JSON_TYPES:
+            properties[arg] = {"type": PYTHON_TO_JSON_TYPES[arg_type.__name__]}
+        if arg in arg_descriptions:
+            if arg not in properties:
+                properties[arg] = {}
+            properties[arg]["description"] = arg_descriptions[arg]
+    return properties
+
+
+def _get_python_function_required_args(function: Callable) -> List[str]:
+    """Get the required arguments for a Python function."""
+    spec = inspect.getfullargspec(function)
+    required = spec.args[: -len(spec.defaults)] if spec.defaults else spec.args
+    required += [k for k in spec.kwonlyargs if k not in (spec.kwonlydefaults or {})]
+
+    is_class = type(function) is type
+    if is_class and required[0] == "self":
+        required = required[1:]
+    return required
+
+
+def convert_python_function_to_ernie_function(
+    function: Callable,
+) -> Dict[str, Any]:
+    """Convert a Python function to an Ernie function-calling API compatible dict.
+
+    Assumes the Python function has type hints and a docstring with a description. If
+        the docstring has Google Python style argument descriptions, these will be
+        included as well.
+    """
+    description, arg_descriptions = _parse_python_function_docstring(function)
+    return {
+        "name": _get_python_function_name(function),
+        "description": description,
+        "parameters": {
+            "type": "object",
+            "properties": _get_python_function_arguments(function, arg_descriptions),
+            "required": _get_python_function_required_args(function),
+        },
+    }
+
+
+def convert_to_ernie_function(
+    function: Union[Dict[str, Any], Type[BaseModel], Callable]
+) -> Dict[str, Any]:
+    """Convert a raw function/class to an Ernie function.
+
+    Args:
+        function: Either a dictionary, a pydantic.BaseModel class, or a Python function.
+            If a dictionary is passed in, it is assumed to already be a valid Ernie
+            function.
+
+    Returns:
+        A dict version of the passed in function which is compatible with the
+            Ernie function-calling API.
+    """
+    if isinstance(function, dict):
+        return function
+    elif isinstance(function, type) and issubclass(function, BaseModel):
+        return cast(Dict, convert_pydantic_to_ernie_function(function))
+    elif callable(function):
+        return convert_python_function_to_ernie_function(function)
+
+    else:
+        raise ValueError(
+            f"Unsupported function type {type(function)}. Functions must be passed in"
+            f" as Dict, pydantic.BaseModel, or Callable."
+        )
+
+
+def get_ernie_output_parser(
+    functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
+) -> Union[BaseOutputParser, BaseGenerationOutputParser]:
+    """Get the appropriate function output parser given the user functions.
+
+    Args:
+        functions: Sequence where element is a dictionary, a pydantic.BaseModel class,
+            or a Python function. If a dictionary is passed in, it is assumed to
+            already be a valid Ernie function.
+
+    Returns:
+        A PydanticOutputFunctionsParser if functions are Pydantic classes, otherwise
+            a JsonOutputFunctionsParser. If there's only one function and it is
+            not a Pydantic class, then the output parser will automatically extract
+            only the function arguments and not the function name.
+    """
+    function_names = [convert_to_ernie_function(f)["name"] for f in functions]
+    if isinstance(functions[0], type) and issubclass(functions[0], BaseModel):
+        if len(functions) > 1:
+            pydantic_schema: Union[Dict, Type[BaseModel]] = {
+                name: fn for name, fn in zip(function_names, functions)
+            }
+        else:
+            pydantic_schema = functions[0]
+        output_parser: Union[
+            BaseOutputParser, BaseGenerationOutputParser
+        ] = PydanticOutputFunctionsParser(pydantic_schema=pydantic_schema)
+    else:
+        output_parser = JsonOutputFunctionsParser(args_only=len(functions) <= 1)
+    return output_parser
+
+
+def create_ernie_fn_runnable(
+    functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
+    llm: Runnable,
+    prompt: BasePromptTemplate,
+    *,
+    output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
+    **kwargs: Any,
+) -> Runnable:
+    """Create a runnable sequence that uses Ernie functions.
+
+    Args:
+        functions: A sequence of either dictionaries, pydantic.BaseModels classes, or
+            Python functions. If dictionaries are passed in, they are assumed to
+            already be a valid Ernie functions. If only a single
+            function is passed in, then it will be enforced that the model use that
+            function. pydantic.BaseModels and Python functions should have docstrings
+            describing what the function does. For best results, pydantic.BaseModels
+            should have descriptions of the parameters and Python functions should have
+            Google Python style args descriptions in the docstring. Additionally,
+            Python functions should only use primitive types (str, int, float, bool) or
+            pydantic.BaseModels for arguments.
+        llm: Language model to use, assumed to support the Ernie function-calling API.
+        prompt: BasePromptTemplate to pass to the model.
+        output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
+            will be inferred from the function types. If pydantic.BaseModels are passed
+            in, then the OutputParser will try to parse outputs using those. Otherwise
+            model outputs will simply be parsed as JSON. If multiple functions are
+            passed in and they are not pydantic.BaseModels, the chain output will
+            include both the name of the function that was returned and the arguments
+            to pass to the function.
+
+    Returns:
+        A runnable sequence that will pass in the given functions to the model when run.
+
+    Example:
+        .. code-block:: python
+
+                from typing import Optional
+
+                from langchain.chains.ernie_functions import create_ernie_fn_chain
+                from langchain.chat_models import ErnieBotChat
+                from langchain.prompts import ChatPromptTemplate
+                from langchain.pydantic_v1 import BaseModel, Field
+
+
+                class RecordPerson(BaseModel):
+                    \"\"\"Record some identifying information about a person.\"\"\"
+
+                    name: str = Field(..., description="The person's name")
+                    age: int = Field(..., description="The person's age")
+                    fav_food: Optional[str] = Field(None, description="The person's favorite food")
+
+
+                class RecordDog(BaseModel):
+                    \"\"\"Record some identifying information about a dog.\"\"\"
+
+                    name: str = Field(..., description="The dog's name")
+                    color: str = Field(..., description="The dog's color")
+                    fav_food: Optional[str] = Field(None, description="The dog's favorite food")
+
+
+                llm = ErnieBotChat(model_name="ERNIE-Bot-4")
+                prompt = ChatPromptTemplate.from_messages(
+                    [
+                        ("user", "Make calls to the relevant function to record the entities in the following input: {input}"),
+                        ("assistant", "OK!"),
+                        ("user", "Tip: Make sure to answer in the correct format"),
+                    ]
+                )
+                chain = create_ernie_fn_runnable([RecordPerson, RecordDog], llm, prompt)
+                chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
+                # -> RecordDog(name="Harry", color="brown", fav_food="chicken")
+    """  # noqa: E501
+    if not functions:
+        raise ValueError("Need to pass in at least one function. Received zero.")
+    ernie_functions = [convert_to_ernie_function(f) for f in functions]
+    llm_kwargs: Dict[str, Any] = {"functions": ernie_functions, **kwargs}
+    if len(ernie_functions) == 1:
+        llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]}
+    output_parser = output_parser or get_ernie_output_parser(functions)
+    return prompt | llm.bind(**llm_kwargs) | output_parser
+
+
+def create_structured_output_runnable(
+    output_schema: Union[Dict[str, Any], Type[BaseModel]],
+    llm: Runnable,
+    prompt: BasePromptTemplate,
+    *,
+    output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
+    **kwargs: Any,
+) -> Runnable:
+    """Create a runnable that uses an Ernie function to get a structured output.
+
+    Args:
+        output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary
+            is passed in, it's assumed to already be a valid JsonSchema.
+            For best results, pydantic.BaseModels should have docstrings describing what
+            the schema represents and descriptions for the parameters.
+        llm: Language model to use, assumed to support the Ernie function-calling API.
+        prompt: BasePromptTemplate to pass to the model.
+        output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
+            will be inferred from the function types. If pydantic.BaseModels are passed
+            in, then the OutputParser will try to parse outputs using those. Otherwise
+            model outputs will simply be parsed as JSON.
+
+    Returns:
+        A runnable sequence that will pass the given function to the model when run.
+
+    Example:
+        .. code-block:: python
+
+                from typing import Optional
+
+                from langchain.chains.ernie_functions import create_structured_output_chain
+                from langchain.chat_models import ErnieBotChat
+                from langchain.prompts import ChatPromptTemplate
+                from langchain.pydantic_v1 import BaseModel, Field
+
+                class Dog(BaseModel):
+                    \"\"\"Identifying information about a dog.\"\"\"
+
+                    name: str = Field(..., description="The dog's name")
+                    color: str = Field(..., description="The dog's color")
+                    fav_food: Optional[str] = Field(None, description="The dog's favorite food")
+
+                llm = ErnieBotChat(model_name="ERNIE-Bot-4")
+                prompt = ChatPromptTemplate.from_messages(
+                    [
+                        ("user", "Use the given format to extract information from the following input: {input}"),
+                        ("assistant", "OK!"),
+                        ("user", "Tip: Make sure to answer in the correct format"),
+                    ]
+                )
+                chain = create_structured_output_chain(Dog, llm, prompt)
+                chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
+                # -> Dog(name="Harry", color="brown", fav_food="chicken")
+    """  # noqa: E501
+    if isinstance(output_schema, dict):
+        function: Any = {
+            "name": "output_formatter",
+            "description": (
+                "Output formatter. Should always be used to format your response to the"
+                " user."
+            ),
+            "parameters": output_schema,
+        }
+    else:
+
+        class _OutputFormatter(BaseModel):
+            """Output formatter. Should always be used to format your response to the user."""  # noqa: E501
+
+            output: output_schema  # type: ignore
+
+        function = _OutputFormatter
+        output_parser = output_parser or PydanticAttrOutputFunctionsParser(
+            pydantic_schema=_OutputFormatter, attr_name="output"
+        )
+    return create_ernie_fn_runnable(
+        [function],
+        llm,
+        prompt,
+        output_parser=output_parser,
+        **kwargs,
+    )
+
+
+""" --- Legacy --- """
+
+
+def create_ernie_fn_chain(
+    functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
+    llm: BaseLanguageModel,
+    prompt: BasePromptTemplate,
+    *,
+    output_key: str = "function",
+    output_parser: Optional[BaseLLMOutputParser] = None,
+    **kwargs: Any,
+) -> LLMChain:
+    """[Legacy] Create an LLM chain that uses Ernie functions.
+
+    Args:
+        functions: A sequence of either dictionaries, pydantic.BaseModels classes, or
+            Python functions. If dictionaries are passed in, they are assumed to
+            already be a valid Ernie functions. If only a single
+            function is passed in, then it will be enforced that the model use that
+            function. pydantic.BaseModels and Python functions should have docstrings
+            describing what the function does. For best results, pydantic.BaseModels
+            should have descriptions of the parameters and Python functions should have
+            Google Python style args descriptions in the docstring. Additionally,
+            Python functions should only use primitive types (str, int, float, bool) or
+            pydantic.BaseModels for arguments.
+        llm: Language model to use, assumed to support the Ernie function-calling API.
+        prompt: BasePromptTemplate to pass to the model.
+        output_key: The key to use when returning the output in LLMChain.__call__.
+        output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
+            will be inferred from the function types. If pydantic.BaseModels are passed
+            in, then the OutputParser will try to parse outputs using those. Otherwise
+            model outputs will simply be parsed as JSON. If multiple functions are
+            passed in and they are not pydantic.BaseModels, the chain output will
+            include both the name of the function that was returned and the arguments
+            to pass to the function.
+
+    Returns:
+        An LLMChain that will pass in the given functions to the model when run.
+
+    Example:
+        .. code-block:: python
+
+                from typing import Optional
+
+                from langchain.chains.ernie_functions import create_ernie_fn_chain
+                from langchain.chat_models import ErnieBotChat
+                from langchain.prompts import ChatPromptTemplate
+
+                from langchain.pydantic_v1 import BaseModel, Field
+
+
+                class RecordPerson(BaseModel):
+                    \"\"\"Record some identifying information about a person.\"\"\"
+
+                    name: str = Field(..., description="The person's name")
+                    age: int = Field(..., description="The person's age")
+                    fav_food: Optional[str] = Field(None, description="The person's favorite food")
+
+
+                class RecordDog(BaseModel):
+                    \"\"\"Record some identifying information about a dog.\"\"\"
+
+                    name: str = Field(..., description="The dog's name")
+                    color: str = Field(..., description="The dog's color")
+                    fav_food: Optional[str] = Field(None, description="The dog's favorite food")
+
+
+                llm = ErnieBotChat(model_name="ERNIE-Bot-4")
+                prompt = ChatPromptTemplate.from_messages(
+                    [
+                        ("user", "Make calls to the relevant function to record the entities in the following input: {input}"),
+                        ("assistant", "OK!"),
+                        ("user", "Tip: Make sure to answer in the correct format"),
+                    ]
+                )
+                chain = create_ernie_fn_chain([RecordPerson, RecordDog], llm, prompt)
+                chain.run("Harry was a chubby brown beagle who loved chicken")
+                # -> RecordDog(name="Harry", color="brown", fav_food="chicken")
+    """  # noqa: E501
+    if not functions:
+        raise ValueError("Need to pass in at least one function. Received zero.")
+    ernie_functions = [convert_to_ernie_function(f) for f in functions]
+    output_parser = output_parser or get_ernie_output_parser(functions)
+    llm_kwargs: Dict[str, Any] = {
+        "functions": ernie_functions,
+    }
+    if len(ernie_functions) == 1:
+        llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]}
+    llm_chain = LLMChain(
+        llm=llm,
+        prompt=prompt,
+        output_parser=output_parser,
+        llm_kwargs=llm_kwargs,
+        output_key=output_key,
+        **kwargs,
+    )
+    return llm_chain
+
+
+def create_structured_output_chain(
+    output_schema: Union[Dict[str, Any], Type[BaseModel]],
+    llm: BaseLanguageModel,
+    prompt: BasePromptTemplate,
+    *,
+    output_key: str = "function",
+    output_parser: Optional[BaseLLMOutputParser] = None,
+    **kwargs: Any,
+) -> LLMChain:
+    """[Legacy] Create an LLMChain that uses an Ernie function to get a structured output.
+
+    Args:
+        output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary
+            is passed in, it's assumed to already be a valid JsonSchema.
+            For best results, pydantic.BaseModels should have docstrings describing what
+            the schema represents and descriptions for the parameters.
+        llm: Language model to use, assumed to support the Ernie function-calling API.
+        prompt: BasePromptTemplate to pass to the model.
+        output_key: The key to use when returning the output in LLMChain.__call__.
+        output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
+            will be inferred from the function types. If pydantic.BaseModels are passed
+            in, then the OutputParser will try to parse outputs using those. Otherwise
+            model outputs will simply be parsed as JSON.
+
+    Returns:
+        An LLMChain that will pass the given function to the model.
+
+    Example:
+        .. code-block:: python
+
+                from typing import Optional
+
+                from langchain.chains.ernie_functions import create_structured_output_chain
+                from langchain.chat_models import ErnieBotChat
+                from langchain.prompts import ChatPromptTemplate
+
+                from langchain.pydantic_v1 import BaseModel, Field
+
+                class Dog(BaseModel):
+                    \"\"\"Identifying information about a dog.\"\"\"
+
+                    name: str = Field(..., description="The dog's name")
+                    color: str = Field(..., description="The dog's color")
+                    fav_food: Optional[str] = Field(None, description="The dog's favorite food")
+
+                llm = ErnieBotChat(model_name="ERNIE-Bot-4")
+                prompt = ChatPromptTemplate.from_messages(
+                    [
+                        ("user", "Use the given format to extract information from the following input: {input}"),
+                        ("assistant", "OK!"),
+                        ("user", "Tip: Make sure to answer in the correct format"),
+                    ]
+                )
+                chain = create_structured_output_chain(Dog, llm, prompt)
+                chain.run("Harry was a chubby brown beagle who loved chicken")
+                # -> Dog(name="Harry", color="brown", fav_food="chicken")
+    """  # noqa: E501
+    if isinstance(output_schema, dict):
+        function: Any = {
+            "name": "output_formatter",
+            "description": (
+                "Output formatter. Should always be used to format your response to the"
+                " user."
+            ),
+            "parameters": output_schema,
+        }
+    else:
+
+        class _OutputFormatter(BaseModel):
+            """Output formatter. Should always be used to format your response to the user."""  # noqa: E501
+
+            output: output_schema  # type: ignore
+
+        function = _OutputFormatter
+        output_parser = output_parser or PydanticAttrOutputFunctionsParser(
+            pydantic_schema=_OutputFormatter, attr_name="output"
+        )
+    return create_ernie_fn_chain(
+        [function],
+        llm,
+        prompt,
+        output_key=output_key,
+        output_parser=output_parser,
+        **kwargs,
+    )

libs/langchain/langchain/chat_models/ernie.py

@@ -1,3 +1,4 @@
+import json
 import logging
 import threading
 from typing import Any, Dict, List, Mapping, Optional
@@ -179,9 +180,15 @@ class ErnieBotChat(BaseChatModel):
         return self._create_chat_result(resp)
 
     def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
-        generations = [
-            ChatGeneration(message=AIMessage(content=response.get("result")))
-        ]
+        if "function_call" in response:
+            fc_str = '{{"function_call": {}}}'.format(
+                json.dumps(response.get("function_call"))
+            )
+            generations = [ChatGeneration(message=AIMessage(content=fc_str))]
+        else:
+            generations = [
+                ChatGeneration(message=AIMessage(content=response.get("result")))
+            ]
         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

@@ -0,0 +1,184 @@
+import copy
+import json
+from typing import Any, Dict, List, Optional, Type, Union
+
+import jsonpatch
+
+from langchain.output_parsers.json import parse_partial_json
+from langchain.pydantic_v1 import BaseModel, root_validator
+from langchain.schema import (
+    ChatGeneration,
+    Generation,
+    OutputParserException,
+)
+from langchain.schema.output_parser import (
+    BaseCumulativeTransformOutputParser,
+    BaseGenerationOutputParser,
+)
+
+
+class OutputFunctionsParser(BaseGenerationOutputParser[Any]):
+    """Parse an output that is one of sets of values."""
+
+    args_only: bool = True
+    """Whether to only return the arguments to the function call."""
+
+    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
+        generation = result[0]
+        if not isinstance(generation, ChatGeneration):
+            raise OutputParserException(
+                "This output parser can only be used with a chat generation."
+            )
+        message = generation.message
+        try:
+            func_call = copy.deepcopy(message.additional_kwargs["function_call"])
+        except KeyError as exc:
+            raise OutputParserException(f"Could not parse function call: {exc}")
+
+        if self.args_only:
+            return func_call["arguments"]
+        return func_call
+
+
+class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
+    """Parse an output as the Json object."""
+
+    strict: bool = False
+    """Whether to allow non-JSON-compliant strings.
+    
+    See: https://docs.python.org/3/library/json.html#encoders-and-decoders
+    
+    Useful when the parsed output may include unicode characters or new lines.
+    """
+
+    args_only: bool = True
+    """Whether to only return the arguments to the function call."""
+
+    @property
+    def _type(self) -> str:
+        return "json_functions"
+
+    def _diff(self, prev: Optional[Any], next: Any) -> Any:
+        return jsonpatch.make_patch(prev, next).patch
+
+    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
+        if len(result) != 1:
+            raise OutputParserException(
+                f"Expected exactly one result, but got {len(result)}"
+            )
+        generation = result[0]
+        if not isinstance(generation, ChatGeneration):
+            raise OutputParserException(
+                "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
+        try:
+            function_call = message.additional_kwargs["function_call"]
+        except KeyError as exc:
+            if partial:
+                return None
+            else:
+                raise OutputParserException(f"Could not parse function call: {exc}")
+        try:
+            if partial:
+                if self.args_only:
+                    return parse_partial_json(
+                        function_call["arguments"], strict=self.strict
+                    )
+                else:
+                    return {
+                        **function_call,
+                        "arguments": parse_partial_json(
+                            function_call["arguments"], strict=self.strict
+                        ),
+                    }
+            else:
+                if self.args_only:
+                    try:
+                        return json.loads(
+                            function_call["arguments"], strict=self.strict
+                        )
+                    except (json.JSONDecodeError, TypeError) as exc:
+                        raise OutputParserException(
+                            f"Could not parse function call data: {exc}"
+                        )
+                else:
+                    try:
+                        return {
+                            **function_call,
+                            "arguments": json.loads(
+                                function_call["arguments"], strict=self.strict
+                            ),
+                        }
+                    except (json.JSONDecodeError, TypeError) as exc:
+                        raise OutputParserException(
+                            f"Could not parse function call data: {exc}"
+                        )
+        except KeyError:
+            return None
+
+    # This method would be called by the default implementation of `parse_result`
+    # but we're overriding that method so it's not needed.
+    def parse(self, text: str) -> Any:
+        raise NotImplementedError()
+
+
+class JsonKeyOutputFunctionsParser(JsonOutputFunctionsParser):
+    """Parse an output as the element of the Json object."""
+
+    key_name: str
+    """The name of the key to return."""
+
+    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
+        res = super().parse_result(result, partial=partial)
+        if partial and res is None:
+            return None
+        return res.get(self.key_name) if partial else res[self.key_name]
+
+
+class PydanticOutputFunctionsParser(OutputFunctionsParser):
+    """Parse an output as a pydantic object."""
+
+    pydantic_schema: Union[Type[BaseModel], Dict[str, Type[BaseModel]]]
+    """The pydantic schema to parse the output with."""
+
+    @root_validator(pre=True)
+    def validate_schema(cls, values: Dict) -> Dict:
+        schema = values["pydantic_schema"]
+        if "args_only" not in values:
+            values["args_only"] = isinstance(schema, type) and issubclass(
+                schema, BaseModel
+            )
+        elif values["args_only"] and isinstance(schema, Dict):
+            raise ValueError(
+                "If multiple pydantic schemas are provided then args_only should be"
+                " False."
+            )
+        return values
+
+    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
+        _result = super().parse_result(result)
+        if self.args_only:
+            pydantic_args = self.pydantic_schema.parse_raw(_result)  # type: ignore
+        else:
+            fn_name = _result["name"]
+            _args = _result["arguments"]
+            pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args)  # type: ignore  # noqa: E501
+        return pydantic_args
+
+
+class PydanticAttrOutputFunctionsParser(PydanticOutputFunctionsParser):
+    """Parse an output as an attribute of a pydantic object."""
+
+    attr_name: str
+    """The name of the attribute to return."""
+
+    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
+        result = super().parse_result(result)
+        return getattr(result, self.attr_name)

libs/langchain/langchain/utils/ernie_functions.py

@@ -0,0 +1,51 @@
+from typing import Literal, Optional, Type, TypedDict
+
+from langchain.pydantic_v1 import BaseModel
+from langchain.utils.json_schema import dereference_refs
+
+
+class FunctionDescription(TypedDict):
+    """Representation of a callable function to the Ernie API."""
+
+    name: str
+    """The name of the function."""
+    description: str
+    """A description of the function."""
+    parameters: dict
+    """The parameters of the function."""
+
+
+class ToolDescription(TypedDict):
+    """Representation of a callable function to the Ernie API."""
+
+    type: Literal["function"]
+    function: FunctionDescription
+
+
+def convert_pydantic_to_ernie_function(
+    model: Type[BaseModel],
+    *,
+    name: Optional[str] = None,
+    description: Optional[str] = None,
+) -> FunctionDescription:
+    """Converts a Pydantic model to a function description for the Ernie API."""
+    schema = dereference_refs(model.schema())
+    schema.pop("definitions", None)
+    return {
+        "name": name or schema["title"],
+        "description": description or schema["description"],
+        "parameters": schema,
+    }
+
+
+def convert_pydantic_to_ernie_tool(
+    model: Type[BaseModel],
+    *,
+    name: Optional[str] = None,
+    description: Optional[str] = None,
+) -> ToolDescription:
+    """Converts a Pydantic model to a function description for the Ernie API."""
+    function = convert_pydantic_to_ernie_function(
+        model, name=name, description=description
+    )
+    return {"type": "function", "function": function}