Tony20100/langchain_full huggingface dataset:
m.get("delta"), default_chunk_class
                )
                default_chunk_class = chunk.__class__
                cg_chunk = ChatGenerationChunk(message=chunk)
                if run_manager:
                    run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk)
                yield cg_chunk
    def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
        parameters = {**self._default_params, **kwargs}
        model = parameters.pop("model")
        headers = parameters.pop("headers", {})
        temperature = parameters.pop("temperature", 0.3)
        top_k = parameters.pop("top_k", 5)
        top_p = parameters.pop("top_p", 0.85)
        with_search_enhance = parameters.pop("with_search_enhance", False)
        stream = parameters.pop("stream", False)
        payload = {
            "model": model,
            "messages": [_convert_message_to_dict(m) for m in messages],
            "top_k": top_k,
            "top_p": top_p,
            "temperature": temperature,
            "with_search_enhance": with_search_enhance,
            "stream": stream,
        }
        url = self.baichuan_api_base
        api_key = ""
        if self.baichuan_api_key:
            api_key = self.baichuan_api_key.get_secret_value()
        res = requests.post(
            url=url,
            timeout=self.request_timeout,
            headers={
                "Content-Type": "application/json",
                "Authorization": f"Bearer {api_key}",
                **headers,
            },
            json=payload,
            stream=self.streaming,
        )
        return res
json=payload,
            stream=self.streaming,
        )
        return res
    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
        generations = []
        for c in response["choices"]:
            message = _convert_dict_to_message(c["message"])
            gen = ChatGeneration(message=message)
            generations.append(gen)
        token_usage = response["usage"]
        llm_output = {"token_usage": token_usage, "model": self.model}
        return ChatResult(generations=generations, llm_output=llm_output)
    @property
    def _llm_type(self) -> str:
        return "baichuan-chat"
Source code for langchain_community.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import os
import sys
from typing import (
    TYPE_CHECKING,
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
)
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
    BaseChatModel,
    agenerate_from_stream,
    generate_from_stream,
)
from langchain_core.language_models.llms import create_base_retry_decorator
from langchain_core.messages import (
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    ChatMessageChunk,
    FunctionMessageChunk,
    HumanMessageChunk,
    SystemMessageChunk,
    ToolMessageChunk,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_core.runnables import Runnable
from langchain_core.utils import (
    get_from_dict_or_env,
    get_pydantic_field_names,
)
from langchain_community.adapters.openai import (
    convert_dict_to_message,
    convert_message_to_dict,
)
from langchain_community.utils.openai import is_openai_v1
if TYPE_CHECKING:
    import tiktoken
logger = logging.getLogger(__name__)
def _import_tiktoken() -> Any:
    try:
        import tiktoken
def _import_tiktoken() -> Any:
    try:
        import tiktoken
    except ImportError:
        raise ImportError(
            "Could not import tiktoken python package. "
            "This is needed in order to calculate get_token_ids. "
            "Please install it with `pip install tiktoken`."
        )
    return tiktoken
def _create_retry_decorator(
    llm: ChatOpenAI,
    run_manager: Optional[
        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
    ] = None,
) -> Callable[[Any], Any]:
    import openai
    errors = [
        openai.error.Timeout,
        openai.error.APIError,
        openai.error.APIConnectionError,
        openai.error.RateLimitError,
        openai.error.ServiceUnavailableError,
    ]
    return create_base_retry_decorator(
        error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
    )
[docs]async def acompletion_with_retry(
    llm: ChatOpenAI,
    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
    **kwargs: Any,
) -> Any:
    """Use tenacity to retry the async completion call."""
    if is_openai_v1():
        return await llm.async_client.create(**kwargs)
    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
    @retry_decorator
    async def _completion_with_retry(**kwargs: Any) -> Any:
        # Use OpenAI's async api https://github.com/openai/openai-python#async-api
        return await llm.client.acreate(**kwargs)
    return await _completion_with_retry(**kwargs)
return await _completion_with_retry(**kwargs)
def _convert_delta_to_message_chunk(
    _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
    role = _dict.get("role")
    content = _dict.get("content") or ""
    additional_kwargs: Dict = {}
    if _dict.get("function_call"):
        function_call = dict(_dict["function_call"])
        if "name" in function_call and function_call["name"] is None:
            function_call["name"] = ""
        additional_kwargs["function_call"] = function_call
    if _dict.get("tool_calls"):
        additional_kwargs["tool_calls"] = _dict["tool_calls"]
    if role == "user" or default_class == HumanMessageChunk:
        return HumanMessageChunk(content=content)
    elif role == "assistant" or default_class == AIMessageChunk:
        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
    elif role == "system" or default_class == SystemMessageChunk:
        return SystemMessageChunk(content=content)
    elif role == "function" or default_class == FunctionMessageChunk:
        return FunctionMessageChunk(content=content, name=_dict["name"])
    elif role == "tool" or default_class == ToolMessageChunk:
        return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
    elif role or default_class == ChatMessageChunk:
        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
    else:
        return default_class(content=content)  # type: ignore[call-arg]
[docs]@deprecated(
[docs]@deprecated(
    since="0.0.10", removal="0.3.0", alternative_import="langchain_openai.ChatOpenAI"
)
class ChatOpenAI(BaseChatModel):
    """`OpenAI` Chat large language models API.
    To use, you should have the ``openai`` python package installed, and the
    environment variable ``OPENAI_API_KEY`` set with your API key.
    Any parameters that are valid to be passed to the openai.create call can be passed
    in, even if not explicitly saved on this class.
    Example:
        .. code-block:: python
            from langchain_community.chat_models import ChatOpenAI
            openai = ChatOpenAI(model="gpt-3.5-turbo")
    """
    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {"openai_api_key": "OPENAI_API_KEY"}
[docs]    @classmethod
    def get_lc_namespace(cls) -> List[str]:
        """Get the namespace of the langchain object."""
        return ["langchain", "chat_models", "openai"]
    @property
    def lc_attributes(self) -> Dict[str, Any]:
        attributes: Dict[str, Any] = {}
        if self.openai_organization:
            attributes["openai_organization"] = self.openai_organization
        if self.openai_api_base:
            attributes["openai_api_base"] = self.openai_api_base
        if self.openai_proxy:
            attributes["openai_proxy"] = self.openai_proxy
        return attributes
[docs]    @classmethod
    def is_lc_serializable(cls) -> bool:
        """Return whether this model can be serialized by Langchain."""
        return True
"""Return whether this model can be serialized by Langchain."""
        return True
    client: Any = Field(default=None, exclude=True)  #: :meta private:
    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
    model_name: str = Field(default="gpt-3.5-turbo", alias="model")
    """Model name to use."""
    temperature: float = 0.7
    """What sampling temperature to use."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Holds any model parameters valid for `create` call not explicitly specified."""
    # When updating this to use a SecretStr
    # Check for classes that derive from this class (as some of them
    # may assume openai_api_key is a str)
    openai_api_key: Optional[str] = Field(default=None, alias="api_key")
    """Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
    openai_api_base: Optional[str] = Field(default=None, alias="base_url")
    """Base URL path for API requests, leave blank if not using a proxy or service 
        emulator."""
    openai_organization: Optional[str] = Field(default=None, alias="organization")
    """Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
    # to support explicit proxy for OpenAI
    openai_proxy: Optional[str] = None
    request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
        default=None, alias="timeout"
    )
    """Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or 
        None."""
    max_retries: int = Field(default=2)
None."""
    max_retries: int = Field(default=2)
    """Maximum number of retries to make when generating."""
    streaming: bool = False
    """Whether to stream the results or not."""
    n: int = 1
    """Number of chat completions to generate for each prompt."""
    max_tokens: Optional[int] = None
    """Maximum number of tokens to generate."""
    tiktoken_model_name: Optional[str] = None
    """The model name to pass to tiktoken when using this class. 
    Tiktoken is used to count the number of tokens in documents to constrain 
    them to be under a certain limit. By default, when set to None, this will 
    be the same as the embedding model name. However, there are some cases 
    where you may want to use this Embedding class with a model name not 
    supported by tiktoken. This can include when using Azure embeddings or 
    when using one of the many model providers that expose an OpenAI-like 
    API but with different models. In those cases, in order to avoid erroring 
    when tiktoken is called, you can specify a model name to use here."""
    default_headers: Union[Mapping[str, str], None] = None
    default_query: Union[Mapping[str, object], None] = None
    # Configure a custom httpx client. See the
    # [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
    http_client: Union[Any, None] = None
    """Optional httpx.Client."""
    class Config:
        """Configuration for this pydantic object."""
        allow_population_by_field_name = True
    @root_validator(pre=True)
allow_population_by_field_name = True
    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Build extra kwargs from additional params that were passed in."""
        all_required_field_names = get_pydantic_field_names(cls)
        extra = values.get("model_kwargs", {})
        for field_name in list(values):
            if field_name in extra:
                raise ValueError(f"Found {field_name} supplied twice.")
            if field_name not in all_required_field_names:
                logger.warning(
                    f"""WARNING! {field_name} is not default parameter.
                    {field_name} was transferred to model_kwargs.
                    Please confirm that {field_name} is what you intended."""
                )
                extra[field_name] = values.pop(field_name)
        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
        if invalid_model_kwargs:
            raise ValueError(
                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
                f"Instead they were passed in as part of `model_kwargs` parameter."
            )
        values["model_kwargs"] = extra
        return values
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        if values["n"] < 1:
            raise ValueError("n must be at least 1.")
        if values["n"] > 1 and values["streaming"]:
            raise ValueError("n must be 1 when streaming.")
        values["openai_api_key"] = get_from_dict_or_env(
            values, "openai_api_key", "OPENAI_API_KEY"
        )
        # Check OPENAI_ORGANIZATION for backwards compatibility.
)
        # Check OPENAI_ORGANIZATION for backwards compatibility.
        values["openai_organization"] = (
            values["openai_organization"]
            or os.getenv("OPENAI_ORG_ID")
            or os.getenv("OPENAI_ORGANIZATION")
        )
        values["openai_api_base"] = values["openai_api_base"] or os.getenv(
            "OPENAI_API_BASE"
        )
        values["openai_proxy"] = get_from_dict_or_env(
            values,
            "openai_proxy",
            "OPENAI_PROXY",
            default="",
        )
        try:
            import openai
        except ImportError:
            raise ImportError(
                "Could not import openai python package. "
                "Please install it with `pip install openai`."
            )
        if is_openai_v1():
            client_params = {
                "api_key": values["openai_api_key"],
                "organization": values["openai_organization"],
                "base_url": values["openai_api_base"],
                "timeout": values["request_timeout"],
                "max_retries": values["max_retries"],
                "default_headers": values["default_headers"],
                "default_query": values["default_query"],
                "http_client": values["http_client"],
            }
            if not values.get("client"):
                values["client"] = openai.OpenAI(**client_params).chat.completions
            if not values.get("async_client"):
                values["async_client"] = openai.AsyncOpenAI(
                    **client_params
                ).chat.completions
        elif not values.get("client"):
            values["client"] = openai.ChatCompletion
        else:
            pass
        return values
    @property
else:
            pass
        return values
    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling OpenAI API."""
        params = {
            "model": self.model_name,
            "stream": self.streaming,
            "n": self.n,
            "temperature": self.temperature,
            **self.model_kwargs,
        }
        if self.max_tokens is not None:
            params["max_tokens"] = self.max_tokens
        if self.request_timeout is not None and not is_openai_v1():
            params["request_timeout"] = self.request_timeout
        return params
[docs]    def completion_with_retry(
        self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
    ) -> Any:
        """Use tenacity to retry the completion call."""
        if is_openai_v1():
            return self.client.create(**kwargs)
        retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
        @retry_decorator
        def _completion_with_retry(**kwargs: Any) -> Any:
            return self.client.create(**kwargs)
        return _completion_with_retry(**kwargs)
    def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
        overall_token_usage: dict = {}
        system_fingerprint = None
        for output in llm_outputs:
            if output is None:
                # Happens in streaming
                continue
            token_usage = output["token_usage"]
            if token_usage is not None:
                for k, v in token_usage.items():
                    if k in overall_token_usage:
                        overall_token_usage[k] += v
                    else:
                        overall_token_usage[k] = v
else:
                        overall_token_usage[k] = v
            if system_fingerprint is None:
                system_fingerprint = output.get("system_fingerprint")
        combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
        if system_fingerprint:
            combined["system_fingerprint"] = system_fingerprint
        return combined
    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {**params, **kwargs, "stream": True}
        default_chunk_class = AIMessageChunk
        for chunk in self.completion_with_retry(
            messages=message_dicts, run_manager=run_manager, **params
        ):
            if not isinstance(chunk, dict):
                chunk = chunk.dict()
            if len(chunk["choices"]) == 0:
                continue
            choice = chunk["choices"][0]
            chunk = _convert_delta_to_message_chunk(
                choice["delta"], default_chunk_class
            )
            finish_reason = choice.get("finish_reason")
            generation_info = (
                dict(finish_reason=finish_reason) if finish_reason is not None else None
            )
            default_chunk_class = chunk.__class__
            cg_chunk = ChatGenerationChunk(
                message=chunk, generation_info=generation_info
            )
            if run_manager:
                run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk)
            yield cg_chunk
    def _generate(
        self,
        messages: List[BaseMessage],
def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        stream: Optional[bool] = None,
        **kwargs: Any,
    ) -> ChatResult:
        should_stream = stream if stream is not None else self.streaming
        if should_stream:
            stream_iter = self._stream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
            return generate_from_stream(stream_iter)
        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {
            **params,
            **({"stream": stream} if stream is not None else {}),
            **kwargs,
        }
        response = self.completion_with_retry(
            messages=message_dicts, run_manager=run_manager, **params
        )
        return self._create_chat_result(response)
    def _create_message_dicts(
        self, messages: List[BaseMessage], stop: Optional[List[str]]
    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
        params = self._client_params
        if stop is not None:
            if "stop" in params:
                raise ValueError("`stop` found in both the input and default params.")
            params["stop"] = stop
        message_dicts = [convert_message_to_dict(m) for m in messages]
        return message_dicts, params
    def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
        generations = []
        if not isinstance(response, dict):
            response = response.dict()
        for res in response["choices"]:
            message = convert_dict_to_message(res["message"])
message = convert_dict_to_message(res["message"])
            generation_info = dict(finish_reason=res.get("finish_reason"))
            if "logprobs" in res:
                generation_info["logprobs"] = res["logprobs"]
            gen = ChatGeneration(
                message=message,
                generation_info=generation_info,
            )
            generations.append(gen)
        token_usage = response.get("usage", {})
        llm_output = {
            "token_usage": token_usage,
            "model_name": self.model_name,
            "system_fingerprint": response.get("system_fingerprint", ""),
        }
        return ChatResult(generations=generations, llm_output=llm_output)
    async def _astream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ChatGenerationChunk]:
        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {**params, **kwargs, "stream": True}
        default_chunk_class = AIMessageChunk
        async for chunk in await acompletion_with_retry(
            self, messages=message_dicts, run_manager=run_manager, **params
        ):
            if not isinstance(chunk, dict):
                chunk = chunk.dict()
            if len(chunk["choices"]) == 0:
                continue
            choice = chunk["choices"][0]
            chunk = _convert_delta_to_message_chunk(
                choice["delta"], default_chunk_class
            )
            finish_reason = choice.get("finish_reason")
            generation_info = (
                dict(finish_reason=finish_reason) if finish_reason is not None else None
            )
)
            default_chunk_class = chunk.__class__
            cg_chunk = ChatGenerationChunk(
                message=chunk, generation_info=generation_info
            )
            if run_manager:
                await run_manager.on_llm_new_token(token=cg_chunk.text, chunk=cg_chunk)
            yield cg_chunk
    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        stream: Optional[bool] = None,
        **kwargs: Any,
    ) -> ChatResult:
        should_stream = stream if stream is not None else self.streaming
        if should_stream:
            stream_iter = self._astream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
            return await agenerate_from_stream(stream_iter)
        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {
            **params,
            **({"stream": stream} if stream is not None else {}),
            **kwargs,
        }
        response = await acompletion_with_retry(
            self, messages=message_dicts, run_manager=run_manager, **params
        )
        return self._create_chat_result(response)
    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {**{"model_name": self.model_name}, **self._default_params}
    @property
    def _client_params(self) -> Dict[str, Any]:
        """Get the parameters used for the openai client."""
        openai_creds: Dict[str, Any] = {
            "model": self.model_name,
        }