"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import os
import sys
import warnings
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.runnables import Runnable
from langchain_core.tools import BaseTool
from langchain_core.utils import (
get_from_dict_or_env,
get_pydantic_field_names,
pre_init,
)
from pydantic import BaseModel, ConfigDict, Field, model_validator
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
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, # type: ignore[attr-defined]
openai.error.APIError, # type: ignore[attr-defined]
openai.error.APIConnectionError, # type: ignore[attr-defined]
openai.error.RateLimitError, # type: ignore[attr-defined]
openai.error.ServiceUnavailableError, # type: ignore[attr-defined]
]
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)
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]
def _update_token_usage(
overall_token_usage: Union[int, dict], new_usage: Union[int, dict]
) -> Union[int, dict]:
# Token usage is either ints or dictionaries
# `reasoning_tokens` is nested inside `completion_tokens_details`
if isinstance(new_usage, int):
if not isinstance(overall_token_usage, int):
raise ValueError(
f"Got different types for token usage: "
f"{type(new_usage)} and {type(overall_token_usage)}"
)
return new_usage + overall_token_usage
elif isinstance(new_usage, dict):
if not isinstance(overall_token_usage, dict):
raise ValueError(
f"Got different types for token usage: "
f"{type(new_usage)} and {type(overall_token_usage)}"
)
return {
k: _update_token_usage(overall_token_usage.get(k, 0), v)
for k, v in new_usage.items()
}
else:
warnings.warn(f"Unexpected type for token usage: {type(new_usage)}")
return new_usage
[docs]
@deprecated(
since="0.0.10", removal="1.0", 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"}
@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
@classmethod
def is_lc_serializable(cls) -> bool:
"""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)
"""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."""
model_config = ConfigDict(
populate_by_name=True,
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> 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
[docs]
@pre_init
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.
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 # type: ignore[attr-defined]
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] = _update_token_usage(
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]
if choice["delta"] is None:
continue
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],
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"])
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]
if choice["delta"] is None:
continue
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,
}
if not is_openai_v1():
openai_creds.update(
{
"api_key": self.openai_api_key,
"api_base": self.openai_api_base,
"organization": self.openai_organization,
}
)
if self.openai_proxy:
import openai
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[attr-defined]
return {**self._default_params, **openai_creds}
def _get_invocation_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
return {
"model": self.model_name,
**super()._get_invocation_params(stop=stop),
**self._default_params,
**kwargs,
}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "openai-chat"
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
tiktoken_ = _import_tiktoken()
if self.tiktoken_model_name is not None:
model = self.tiktoken_model_name
else:
model = self.model_name
if model == "gpt-3.5-turbo":
# gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
# gpt-4 may change over time.
# Returning num tokens assuming gpt-4-0314.
model = "gpt-4-0314"
# Returns the number of tokens used by a list of messages.
try:
encoding = tiktoken_.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken_.get_encoding(model)
return model, encoding
[docs]
def get_token_ids(self, text: str) -> List[int]:
"""Get the tokens present in the text with tiktoken package."""
# tiktoken NOT supported for Python 3.7 or below
if sys.version_info[1] <= 7:
return super().get_token_ids(text)
_, encoding_model = self._get_encoding_model()
return encoding_model.encode(text)
[docs]
def get_num_tokens_from_messages(
self,
messages: List[BaseMessage],
tools: Optional[
Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
] = None,
) -> int:
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
if tools is not None:
warnings.warn(
"Counting tokens in tool schemas is not yet supported. Ignoring tools."
)
if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model.startswith("gpt-3.5-turbo-0301"):
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if there's a name, the role is omitted
tokens_per_name = -1
elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"):
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"get_num_tokens_from_messages() is not presently implemented "
f"for model {model}."
"See https://github.com/openai/openai-python/blob/main/chatml.md for "
"information on how messages are converted to tokens."
)
num_tokens = 0
messages_dict = [convert_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
# Cast str(value) in case the message value is not a string
# This occurs with function messages
num_tokens += len(encoding.encode(str(value)))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
return num_tokens
[docs]
def bind_functions(
self,
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
function_call: Optional[str] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind functions (and other objects) to this chat model.
Args:
functions: A list of function definitions to bind to this chat model.
Can be a dictionary, pydantic model, or callable. Pydantic
models and callables will be automatically converted to
their schema dictionary representation.
function_call: Which function to require the model to call.
Must be the name of the single provided function or
"auto" to automatically determine which function to call
(if any).
kwargs: Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
"""
from langchain.chains.openai_functions.base import convert_to_openai_function
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
if function_call is not None:
if len(formatted_functions) != 1:
raise ValueError(
"When specifying `function_call`, you must provide exactly one "
"function."
)
if formatted_functions[0]["name"] != function_call:
raise ValueError(
f"Function call {function_call} was specified, but the only "
f"provided function was {formatted_functions[0]['name']}."
)
function_call_ = {"name": function_call}
kwargs = {**kwargs, "function_call": function_call_}
return super().bind(
functions=formatted_functions,
**kwargs,
)
