from __future__ import annotations
import logging
import sys
from typing import (
AbstractSet,
Any,
AsyncIterator,
Collection,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,
Union,
)
import openai
import tiktoken
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.utils import get_pydantic_field_names
from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
from pydantic import ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
logger = logging.getLogger(__name__)
def _update_token_usage(
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
) -> None:
"""Update token usage."""
_keys_to_use = keys.intersection(response["usage"])
for _key in _keys_to_use:
if _key not in token_usage:
token_usage[_key] = response["usage"][_key]
else:
token_usage[_key] += response["usage"][_key]
def _stream_response_to_generation_chunk(
stream_response: Dict[str, Any],
) -> GenerationChunk:
"""Convert a stream response to a generation chunk."""
if not stream_response["choices"]:
return GenerationChunk(text="")
return GenerationChunk(
text=stream_response["choices"][0]["text"],
generation_info=dict(
finish_reason=stream_response["choices"][0].get("finish_reason", None),
logprobs=stream_response["choices"][0].get("logprobs", None),
),
)
[docs]
class BaseOpenAI(BaseLLM):
"""Base OpenAI large language model class."""
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-instruct", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
max_tokens: int = 256
"""The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size."""
top_p: float = 1
"""Total probability mass of tokens to consider at each step."""
frequency_penalty: float = 0
"""Penalizes repeated tokens according to frequency."""
presence_penalty: float = 0
"""Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[SecretStr] = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
openai_api_base: Optional[str] = Field(
alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
)
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
openai_organization: Optional[str] = Field(
alias="organization",
default_factory=from_env(
["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None
),
)
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
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."""
logit_bias: Optional[Dict[str, float]] = None
"""Adjust the probability of specific tokens being generated."""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
seed: Optional[int] = None
"""Seed for generation"""
logprobs: Optional[int] = None
"""Include the log probabilities on the logprobs most likely output tokens,
as well the chosen tokens."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowedใ"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowedใ"""
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. Only used for sync invocations. Must specify
http_async_client as well if you'd like a custom client for async invocations.
"""
http_async_client: Union[Any, None] = None
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you'd like a custom client for sync invocations."""
extra_body: Optional[Mapping[str, Any]] = None
"""Optional additional JSON properties to include in the request parameters when
making requests to OpenAI compatible APIs, such as vLLM."""
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)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
if self.streaming and self.n > 1:
raise ValueError("Cannot stream results when n > 1.")
if self.streaming and self.best_of > 1:
raise ValueError("Cannot stream results when best_of > 1.")
client_params: dict = {
"api_key": (
self.openai_api_key.get_secret_value() if self.openai_api_key else None
),
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if not self.client:
sync_specific = {"http_client": self.http_client}
self.client = openai.OpenAI(**client_params, **sync_specific).completions # type: ignore[arg-type]
if not self.async_client:
async_specific = {"http_client": self.http_async_client}
self.async_client = openai.AsyncOpenAI(
**client_params,
**async_specific, # type: ignore[arg-type]
).completions
return self
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
normal_params: Dict[str, Any] = {
"temperature": self.temperature,
"top_p": self.top_p,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"n": self.n,
"seed": self.seed,
"logprobs": self.logprobs,
}
if self.logit_bias is not None:
normal_params["logit_bias"] = self.logit_bias
if self.max_tokens is not None:
normal_params["max_tokens"] = self.max_tokens
if self.extra_body is not None:
normal_params["extra_body"] = self.extra_body
# Azure gpt-35-turbo doesn't support best_of
# don't specify best_of if it is 1
if self.best_of > 1:
normal_params["best_of"] = self.best_of
return {**normal_params, **self.model_kwargs}
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = {**self._invocation_params, **kwargs, "stream": True}
self.get_sub_prompts(params, [prompt], stop) # this mutates params
for stream_resp in self.client.create(prompt=prompt, **params):
if not isinstance(stream_resp, dict):
stream_resp = stream_resp.model_dump()
chunk = _stream_response_to_generation_chunk(stream_resp)
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=self.verbose,
logprobs=(
chunk.generation_info["logprobs"]
if chunk.generation_info
else None
),
)
yield chunk
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
params = {**self._invocation_params, **kwargs, "stream": True}
self.get_sub_prompts(params, [prompt], stop) # this mutates params
async for stream_resp in await self.async_client.create(
prompt=prompt, **params
):
if not isinstance(stream_resp, dict):
stream_resp = stream_resp.model_dump()
chunk = _stream_response_to_generation_chunk(stream_resp)
if run_manager:
await run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=self.verbose,
logprobs=(
chunk.generation_info["logprobs"]
if chunk.generation_info
else None
),
)
yield chunk
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint with k unique prompts.
Args:
prompts: The prompts to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The full LLM output.
Example:
.. code-block:: python
response = openai.generate(["Tell me a joke."])
"""
# TODO: write a unit test for this
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = None
for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
choices.append(
{
"text": generation.text,
"finish_reason": (
generation.generation_info.get("finish_reason")
if generation.generation_info
else None
),
"logprobs": (
generation.generation_info.get("logprobs")
if generation.generation_info
else None
),
}
)
else:
response = self.client.create(prompt=_prompts, **params)
if not isinstance(response, dict):
# V1 client returns the response in an PyDantic object instead of
# dict. For the transition period, we deep convert it to dict.
response = response.model_dump()
# Sometimes the AI Model calling will get error, we should raise it.
# Otherwise, the next code 'choices.extend(response["choices"])'
# will throw a "TypeError: 'NoneType' object is not iterable" error
# to mask the true error. Because 'response["choices"]' is None.
if response.get("error"):
raise ValueError(response.get("error"))
choices.extend(response["choices"])
_update_token_usage(_keys, response, token_usage)
if not system_fingerprint:
system_fingerprint = response.get("system_fingerprint")
return self.create_llm_result(
choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint async with k unique prompts."""
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = None
async for chunk in self._astream(
_prompts[0], stop, run_manager, **kwargs
):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
choices.append(
{
"text": generation.text,
"finish_reason": (
generation.generation_info.get("finish_reason")
if generation.generation_info
else None
),
"logprobs": (
generation.generation_info.get("logprobs")
if generation.generation_info
else None
),
}
)
else:
response = await self.async_client.create(prompt=_prompts, **params)
if not isinstance(response, dict):
response = response.model_dump()
choices.extend(response["choices"])
_update_token_usage(_keys, response, token_usage)
return self.create_llm_result(
choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
)
[docs]
def get_sub_prompts(
self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
params["stop"] = stop
if params["max_tokens"] == -1:
if len(prompts) != 1:
raise ValueError(
"max_tokens set to -1 not supported for multiple inputs."
)
params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
sub_prompts = [
prompts[i : i + self.batch_size]
for i in range(0, len(prompts), self.batch_size)
]
return sub_prompts
[docs]
def create_llm_result(
self,
choices: Any,
prompts: List[str],
params: Dict[str, Any],
token_usage: Dict[str, int],
*,
system_fingerprint: Optional[str] = None,
) -> LLMResult:
"""Create the LLMResult from the choices and prompts."""
generations = []
n = params.get("n", self.n)
for i, _ in enumerate(prompts):
sub_choices = choices[i * n : (i + 1) * n]
generations.append(
[
Generation(
text=choice["text"],
generation_info=dict(
finish_reason=choice.get("finish_reason"),
logprobs=choice.get("logprobs"),
),
)
for choice in sub_choices
]
)
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
if system_fingerprint:
llm_output["system_fingerprint"] = system_fingerprint
return LLMResult(generations=generations, llm_output=llm_output)
@property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
return self._default_params
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "openai"
[docs]
def get_token_ids(self, text: str) -> List[int]:
"""Get the token IDs using the tiktoken package."""
if self.custom_get_token_ids is not None:
return self.custom_get_token_ids(text)
# tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
model_name = self.tiktoken_model_name or self.model_name
try:
enc = tiktoken.encoding_for_model(model_name)
except KeyError:
enc = tiktoken.get_encoding("cl100k_base")
return enc.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
[docs]
@staticmethod
def modelname_to_contextsize(modelname: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a model.
Args:
modelname: The modelname we want to know the context size for.
Returns:
The maximum context size
Example:
.. code-block:: python
max_tokens = openai.modelname_to_contextsize("gpt-3.5-turbo-instruct")
"""
model_token_mapping = {
"gpt-4o-mini": 128_000,
"gpt-4o": 128_000,
"gpt-4o-2024-05-13": 128_000,
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-0613": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-4-32k-0613": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"gpt-3.5-turbo-0613": 4096,
"gpt-3.5-turbo-16k": 16385,
"gpt-3.5-turbo-16k-0613": 16385,
"gpt-3.5-turbo-instruct": 4096,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 2040,
"babbage": 2049,
"text-curie-001": 2049,
"curie": 2049,
"davinci": 2049,
"text-davinci-003": 4097,
"text-davinci-002": 4097,
"code-davinci-002": 8001,
"code-davinci-001": 8001,
"code-cushman-002": 2048,
"code-cushman-001": 2048,
}
# handling finetuned models
if "ft-" in modelname:
modelname = modelname.split(":")[0]
context_size = model_token_mapping.get(modelname, None)
if context_size is None:
raise ValueError(
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
"Known models are: " + ", ".join(model_token_mapping.keys())
)
return context_size
@property
def max_context_size(self) -> int:
"""Get max context size for this model."""
return self.modelname_to_contextsize(self.model_name)
[docs]
def max_tokens_for_prompt(self, prompt: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a prompt.
Args:
prompt: The prompt to pass into the model.
Returns:
The maximum number of tokens to generate for a prompt.
Example:
.. code-block:: python
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
"""
num_tokens = self.get_num_tokens(prompt)
return self.max_context_size - num_tokens
[docs]
class OpenAI(BaseOpenAI):
"""OpenAI completion model integration.
Setup:
Install ``langchain-openai`` and set environment variable ``OPENAI_API_KEY``.
.. code-block:: bash
pip install -U langchain-openai
export OPENAI_API_KEY="your-api-key"
Key init args โ completion params:
model: str
Name of OpenAI model to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
logprobs: Optional[bool]
Whether to return logprobs.
stream_options: Dict
Configure streaming outputs, like whether to return token usage when
streaming (``{"include_usage": True}``).
Key init args โ client params:
timeout: Union[float, Tuple[float, float], Any, None]
Timeout for requests.
max_retries: int
Max number of retries.
api_key: Optional[str]
OpenAI API key. If not passed in will be read from env var OPENAI_API_KEY.
base_url: Optional[str]
Base URL for API requests. Only specify if using a proxy or service
emulator.
organization: Optional[str]
OpenAI organization ID. If not passed in will be read from env
var OPENAI_ORG_ID.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_openai import OpenAI
llm = OpenAI(
model="gpt-3.5-turbo-instruct",
temperature=0,
max_retries=2,
# api_key="...",
# base_url="...",
# organization="...",
# other params...
)
Invoke:
.. code-block:: python
input_text = "The meaning of life is "
llm.invoke(input_text)
.. code-block:: none
"a philosophical question that has been debated by thinkers and scholars for centuries."
Stream:
.. code-block:: python
for chunk in llm.stream(input_text):
print(chunk, end="|")
.. code-block:: none
a| philosophical| question| that| has| been| debated| by| thinkers| and| scholars| for| centuries|.
.. code-block:: python
"".join(llm.stream(input_text))
.. code-block:: none
"a philosophical question that has been debated by thinkers and scholars for centuries."
Async:
.. code-block:: python
await llm.ainvoke(input_text)
# stream:
# async for chunk in (await llm.astream(input_text)):
# print(chunk)
# batch:
# await llm.abatch([input_text])
.. code-block:: none
"a philosophical question that has been debated by thinkers and scholars for centuries."
""" # noqa: E501
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "llms", "openai"]
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params}
@property
def lc_secrets(self) -> Dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@property
def lc_attributes(self) -> Dict[str, Any]:
attributes: Dict[str, Any] = {}
if self.openai_api_base:
attributes["openai_api_base"] = self.openai_api_base
if self.openai_organization:
attributes["openai_organization"] = self.openai_organization
if self.openai_proxy:
attributes["openai_proxy"] = self.openai_proxy
return attributes
