mirror of https://github.com/hwchase17/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1242 lines
47 KiB
Python
1242 lines
47 KiB
Python
from __future__ import annotations
|
|
|
|
import logging
|
|
import os
|
|
import sys
|
|
import warnings
|
|
from typing import (
|
|
AbstractSet,
|
|
Any,
|
|
AsyncIterator,
|
|
Callable,
|
|
Collection,
|
|
Dict,
|
|
Iterator,
|
|
List,
|
|
Literal,
|
|
Mapping,
|
|
Optional,
|
|
Set,
|
|
Tuple,
|
|
Union,
|
|
)
|
|
|
|
from langchain_core._api.deprecation import deprecated
|
|
from langchain_core.callbacks import (
|
|
AsyncCallbackManagerForLLMRun,
|
|
CallbackManagerForLLMRun,
|
|
)
|
|
from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator
|
|
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
|
|
from langchain_core.pydantic_v1 import Field, root_validator
|
|
from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
|
|
from langchain_core.utils.utils import build_extra_kwargs
|
|
|
|
from langchain_community.utils.openai import is_openai_v1
|
|
|
|
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),
|
|
),
|
|
)
|
|
|
|
|
|
def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
|
|
"""Update response from the stream response."""
|
|
response["choices"][0]["text"] += stream_response["choices"][0]["text"]
|
|
response["choices"][0]["finish_reason"] = stream_response["choices"][0].get(
|
|
"finish_reason", None
|
|
)
|
|
response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
|
|
|
|
|
|
def _streaming_response_template() -> Dict[str, Any]:
|
|
return {
|
|
"choices": [
|
|
{
|
|
"text": "",
|
|
"finish_reason": None,
|
|
"logprobs": None,
|
|
}
|
|
]
|
|
}
|
|
|
|
|
|
def _create_retry_decorator(
|
|
llm: Union[BaseOpenAI, OpenAIChat],
|
|
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
|
|
)
|
|
|
|
|
|
def completion_with_retry(
|
|
llm: Union[BaseOpenAI, OpenAIChat],
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Use tenacity to retry the completion call."""
|
|
if is_openai_v1():
|
|
return llm.client.create(**kwargs)
|
|
|
|
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
|
|
|
|
@retry_decorator
|
|
def _completion_with_retry(**kwargs: Any) -> Any:
|
|
return llm.client.create(**kwargs)
|
|
|
|
return _completion_with_retry(**kwargs)
|
|
|
|
|
|
async def acompletion_with_retry(
|
|
llm: Union[BaseOpenAI, OpenAIChat],
|
|
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)
|
|
|
|
|
|
class BaseOpenAI(BaseLLM):
|
|
"""Base OpenAI large language model class."""
|
|
|
|
@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", "llms", "openai"]
|
|
|
|
@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
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
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-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."""
|
|
# 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
|
|
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]] = Field(default_factory=dict)
|
|
"""Adjust the probability of specific tokens being generated."""
|
|
max_retries: int = 2
|
|
"""Maximum number of retries to make when generating."""
|
|
streaming: bool = False
|
|
"""Whether to stream the results or not."""
|
|
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
|
|
"""Set of special tokens that are allowed。"""
|
|
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
|
|
"""Set of special tokens that are not allowed。"""
|
|
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."""
|
|
|
|
def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore
|
|
"""Initialize the OpenAI object."""
|
|
model_name = data.get("model_name", "")
|
|
if (
|
|
model_name.startswith("gpt-3.5-turbo") or model_name.startswith("gpt-4")
|
|
) and "-instruct" not in model_name:
|
|
warnings.warn(
|
|
"You are trying to use a chat model. This way of initializing it is "
|
|
"no longer supported. Instead, please use: "
|
|
"`from langchain_community.chat_models import ChatOpenAI`"
|
|
)
|
|
return OpenAIChat(**data)
|
|
return super().__new__(cls)
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
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", {})
|
|
values["model_kwargs"] = build_extra_kwargs(
|
|
extra, values, all_required_field_names
|
|
)
|
|
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["streaming"] and values["n"] > 1:
|
|
raise ValueError("Cannot stream results when n > 1.")
|
|
if values["streaming"] and values["best_of"] > 1:
|
|
raise ValueError("Cannot stream results when best_of > 1.")
|
|
|
|
values["openai_api_key"] = get_from_dict_or_env(
|
|
values, "openai_api_key", "OPENAI_API_KEY"
|
|
)
|
|
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="",
|
|
)
|
|
values["openai_organization"] = (
|
|
values["openai_organization"]
|
|
or os.getenv("OPENAI_ORG_ID")
|
|
or os.getenv("OPENAI_ORGANIZATION")
|
|
)
|
|
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).completions
|
|
if not values.get("async_client"):
|
|
values["async_client"] = openai.AsyncOpenAI(**client_params).completions
|
|
elif not values.get("client"):
|
|
values["client"] = openai.Completion
|
|
else:
|
|
pass
|
|
|
|
return values
|
|
|
|
@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,
|
|
"logit_bias": self.logit_bias,
|
|
}
|
|
|
|
if self.max_tokens is not None:
|
|
normal_params["max_tokens"] = self.max_tokens
|
|
if self.request_timeout is not None and not is_openai_v1():
|
|
normal_params["request_timeout"] = self.request_timeout
|
|
|
|
# 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 completion_with_retry(
|
|
self, prompt=prompt, run_manager=run_manager, **params
|
|
):
|
|
if not isinstance(stream_resp, dict):
|
|
stream_resp = stream_resp.dict()
|
|
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 acompletion_with_retry(
|
|
self, prompt=prompt, run_manager=run_manager, **params
|
|
):
|
|
if not isinstance(stream_resp, dict):
|
|
stream_resp = stream_resp.dict()
|
|
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 = completion_with_retry(
|
|
self, prompt=_prompts, run_manager=run_manager, **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.dict()
|
|
|
|
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 acompletion_with_retry(
|
|
self, prompt=_prompts, run_manager=run_manager, **params
|
|
)
|
|
if not isinstance(response, dict):
|
|
response = response.dict()
|
|
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,
|
|
)
|
|
|
|
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:
|
|
if "stop" in params:
|
|
raise ValueError("`stop` found in both the input and default params.")
|
|
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
|
|
|
|
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."""
|
|
openai_creds: Dict[str, Any] = {}
|
|
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[assignment]
|
|
return {**openai_creds, **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"
|
|
|
|
def get_token_ids(self, text: str) -> List[int]:
|
|
"""Get the token IDs using the tiktoken package."""
|
|
# tiktoken NOT supported for Python < 3.8
|
|
if sys.version_info[1] < 8:
|
|
return super().get_num_tokens(text)
|
|
try:
|
|
import tiktoken
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import tiktoken python package. "
|
|
"This is needed in order to calculate get_num_tokens. "
|
|
"Please install it with `pip install tiktoken`."
|
|
)
|
|
|
|
model_name = self.tiktoken_model_name or self.model_name
|
|
try:
|
|
enc = tiktoken.encoding_for_model(model_name)
|
|
except KeyError:
|
|
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
|
model = "cl100k_base"
|
|
enc = tiktoken.get_encoding(model)
|
|
|
|
return enc.encode(
|
|
text,
|
|
allowed_special=self.allowed_special,
|
|
disallowed_special=self.disallowed_special,
|
|
)
|
|
|
|
@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": 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)
|
|
|
|
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
|
|
|
|
|
|
@deprecated(
|
|
since="0.0.10", removal="0.3.0", alternative_import="langchain_openai.OpenAI"
|
|
)
|
|
class OpenAI(BaseOpenAI):
|
|
"""OpenAI large language models.
|
|
|
|
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.llms import OpenAI
|
|
openai = OpenAI(model_name="gpt-3.5-turbo-instruct")
|
|
"""
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "llms", "openai"]
|
|
|
|
@property
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
return {**{"model": self.model_name}, **super()._invocation_params}
|
|
|
|
|
|
@deprecated(
|
|
since="0.0.10", removal="0.3.0", alternative_import="langchain_openai.AzureOpenAI"
|
|
)
|
|
class AzureOpenAI(BaseOpenAI):
|
|
"""Azure-specific OpenAI large language models.
|
|
|
|
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.llms import AzureOpenAI
|
|
|
|
openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
|
|
"""
|
|
|
|
azure_endpoint: Union[str, None] = None
|
|
"""Your Azure endpoint, including the resource.
|
|
|
|
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
|
|
|
|
Example: `https://example-resource.azure.openai.com/`
|
|
"""
|
|
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
|
|
"""A model deployment.
|
|
|
|
If given sets the base client URL to include `/deployments/{azure_deployment}`.
|
|
Note: this means you won't be able to use non-deployment endpoints.
|
|
"""
|
|
openai_api_version: str = Field(default="", alias="api_version")
|
|
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
|
|
openai_api_key: Union[str, None] = Field(default=None, alias="api_key")
|
|
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
|
|
azure_ad_token: Union[str, None] = None
|
|
"""Your Azure Active Directory token.
|
|
|
|
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
|
|
|
|
For more:
|
|
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
|
|
"""
|
|
azure_ad_token_provider: Union[Callable[[], str], None] = None
|
|
"""A function that returns an Azure Active Directory token.
|
|
|
|
Will be invoked on every request.
|
|
"""
|
|
openai_api_type: str = ""
|
|
"""Legacy, for openai<1.0.0 support."""
|
|
validate_base_url: bool = True
|
|
"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
|
|
infer if it is a base_url or azure_endpoint and update accordingly.
|
|
"""
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "llms", "openai"]
|
|
|
|
@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["streaming"] and values["n"] > 1:
|
|
raise ValueError("Cannot stream results when n > 1.")
|
|
if values["streaming"] and values["best_of"] > 1:
|
|
raise ValueError("Cannot stream results when best_of > 1.")
|
|
|
|
# Check OPENAI_KEY for backwards compatibility.
|
|
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
|
|
# other forms of azure credentials.
|
|
values["openai_api_key"] = (
|
|
values["openai_api_key"]
|
|
or os.getenv("AZURE_OPENAI_API_KEY")
|
|
or os.getenv("OPENAI_API_KEY")
|
|
)
|
|
|
|
values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
|
|
"AZURE_OPENAI_ENDPOINT"
|
|
)
|
|
values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
|
|
"AZURE_OPENAI_AD_TOKEN"
|
|
)
|
|
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="",
|
|
)
|
|
values["openai_organization"] = (
|
|
values["openai_organization"]
|
|
or os.getenv("OPENAI_ORG_ID")
|
|
or os.getenv("OPENAI_ORGANIZATION")
|
|
)
|
|
values["openai_api_version"] = values["openai_api_version"] or os.getenv(
|
|
"OPENAI_API_VERSION"
|
|
)
|
|
values["openai_api_type"] = get_from_dict_or_env(
|
|
values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
|
|
)
|
|
try:
|
|
import openai
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import openai python package. "
|
|
"Please install it with `pip install openai`."
|
|
)
|
|
if is_openai_v1():
|
|
# For backwards compatibility. Before openai v1, no distinction was made
|
|
# between azure_endpoint and base_url (openai_api_base).
|
|
openai_api_base = values["openai_api_base"]
|
|
if openai_api_base and values["validate_base_url"]:
|
|
if "/openai" not in openai_api_base:
|
|
values["openai_api_base"] = (
|
|
values["openai_api_base"].rstrip("/") + "/openai"
|
|
)
|
|
warnings.warn(
|
|
"As of openai>=1.0.0, Azure endpoints should be specified via "
|
|
f"the `azure_endpoint` param not `openai_api_base` "
|
|
f"(or alias `base_url`). Updating `openai_api_base` from "
|
|
f"{openai_api_base} to {values['openai_api_base']}."
|
|
)
|
|
if values["deployment_name"]:
|
|
warnings.warn(
|
|
"As of openai>=1.0.0, if `deployment_name` (or alias "
|
|
"`azure_deployment`) is specified then "
|
|
"`openai_api_base` (or alias `base_url`) should not be. "
|
|
"Instead use `deployment_name` (or alias `azure_deployment`) "
|
|
"and `azure_endpoint`."
|
|
)
|
|
if values["deployment_name"] not in values["openai_api_base"]:
|
|
warnings.warn(
|
|
"As of openai>=1.0.0, if `openai_api_base` "
|
|
"(or alias `base_url`) is specified it is expected to be "
|
|
"of the form "
|
|
"https://example-resource.azure.openai.com/openai/deployments/example-deployment. " # noqa: E501
|
|
f"Updating {openai_api_base} to "
|
|
f"{values['openai_api_base']}."
|
|
)
|
|
values["openai_api_base"] += (
|
|
"/deployments/" + values["deployment_name"]
|
|
)
|
|
values["deployment_name"] = None
|
|
client_params = {
|
|
"api_version": values["openai_api_version"],
|
|
"azure_endpoint": values["azure_endpoint"],
|
|
"azure_deployment": values["deployment_name"],
|
|
"api_key": values["openai_api_key"],
|
|
"azure_ad_token": values["azure_ad_token"],
|
|
"azure_ad_token_provider": values["azure_ad_token_provider"],
|
|
"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"],
|
|
}
|
|
values["client"] = openai.AzureOpenAI(**client_params).completions
|
|
values["async_client"] = openai.AsyncAzureOpenAI(
|
|
**client_params
|
|
).completions
|
|
|
|
else:
|
|
values["client"] = openai.Completion
|
|
|
|
return values
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
return {
|
|
**{"deployment_name": self.deployment_name},
|
|
**super()._identifying_params,
|
|
}
|
|
|
|
@property
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
if is_openai_v1():
|
|
openai_params = {"model": self.deployment_name}
|
|
else:
|
|
openai_params = {
|
|
"engine": self.deployment_name,
|
|
"api_type": self.openai_api_type,
|
|
"api_version": self.openai_api_version,
|
|
}
|
|
return {**openai_params, **super()._invocation_params}
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of llm."""
|
|
return "azure"
|
|
|
|
@property
|
|
def lc_attributes(self) -> Dict[str, Any]:
|
|
return {
|
|
"openai_api_type": self.openai_api_type,
|
|
"openai_api_version": self.openai_api_version,
|
|
}
|
|
|
|
|
|
@deprecated(
|
|
since="0.0.1",
|
|
removal="0.3.0",
|
|
alternative_import="langchain_openai.ChatOpenAI",
|
|
)
|
|
class OpenAIChat(BaseLLM):
|
|
"""OpenAI Chat large language models.
|
|
|
|
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.llms import OpenAIChat
|
|
openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
|
|
"""
|
|
|
|
client: Any = Field(default=None, exclude=True) #: :meta private:
|
|
async_client: Any = Field(default=None, exclude=True) #: :meta private:
|
|
model_name: str = "gpt-3.5-turbo"
|
|
"""Model name 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."""
|
|
# to support explicit proxy for OpenAI
|
|
openai_proxy: Optional[str] = None
|
|
max_retries: int = 6
|
|
"""Maximum number of retries to make when generating."""
|
|
prefix_messages: List = Field(default_factory=list)
|
|
"""Series of messages for Chat input."""
|
|
streaming: bool = False
|
|
"""Whether to stream the results or not."""
|
|
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
|
|
"""Set of special tokens that are allowed。"""
|
|
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
|
|
"""Set of special tokens that are not allowed。"""
|
|
|
|
@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 = {field.alias for field in cls.__fields__.values()}
|
|
|
|
extra = values.get("model_kwargs", {})
|
|
for field_name in list(values):
|
|
if field_name not in all_required_field_names:
|
|
if field_name in extra:
|
|
raise ValueError(f"Found {field_name} supplied twice.")
|
|
extra[field_name] = values.pop(field_name)
|
|
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."""
|
|
openai_api_key = get_from_dict_or_env(
|
|
values, "openai_api_key", "OPENAI_API_KEY"
|
|
)
|
|
openai_api_base = get_from_dict_or_env(
|
|
values,
|
|
"openai_api_base",
|
|
"OPENAI_API_BASE",
|
|
default="",
|
|
)
|
|
openai_proxy = get_from_dict_or_env(
|
|
values,
|
|
"openai_proxy",
|
|
"OPENAI_PROXY",
|
|
default="",
|
|
)
|
|
openai_organization = get_from_dict_or_env(
|
|
values, "openai_organization", "OPENAI_ORGANIZATION", default=""
|
|
)
|
|
try:
|
|
import openai
|
|
|
|
openai.api_key = openai_api_key
|
|
if openai_api_base:
|
|
openai.api_base = openai_api_base
|
|
if openai_organization:
|
|
openai.organization = openai_organization
|
|
if openai_proxy:
|
|
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment]
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import openai python package. "
|
|
"Please install it with `pip install openai`."
|
|
)
|
|
try:
|
|
values["client"] = openai.ChatCompletion
|
|
except AttributeError:
|
|
raise ValueError(
|
|
"`openai` has no `ChatCompletion` attribute, this is likely "
|
|
"due to an old version of the openai package. Try upgrading it "
|
|
"with `pip install --upgrade openai`."
|
|
)
|
|
warnings.warn(
|
|
"You are trying to use a chat model. This way of initializing it is "
|
|
"no longer supported. Instead, please use: "
|
|
"`from langchain_community.chat_models import ChatOpenAI`"
|
|
)
|
|
return values
|
|
|
|
@property
|
|
def _default_params(self) -> Dict[str, Any]:
|
|
"""Get the default parameters for calling OpenAI API."""
|
|
return self.model_kwargs
|
|
|
|
def _get_chat_params(
|
|
self, prompts: List[str], stop: Optional[List[str]] = None
|
|
) -> Tuple:
|
|
if len(prompts) > 1:
|
|
raise ValueError(
|
|
f"OpenAIChat currently only supports single prompt, got {prompts}"
|
|
)
|
|
messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
|
|
params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
|
|
if stop is not None:
|
|
if "stop" in params:
|
|
raise ValueError("`stop` found in both the input and default params.")
|
|
params["stop"] = stop
|
|
if params.get("max_tokens") == -1:
|
|
# for ChatGPT api, omitting max_tokens is equivalent to having no limit
|
|
del params["max_tokens"]
|
|
return messages, params
|
|
|
|
def _stream(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[GenerationChunk]:
|
|
messages, params = self._get_chat_params([prompt], stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
for stream_resp in completion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
):
|
|
if not isinstance(stream_resp, dict):
|
|
stream_resp = stream_resp.dict()
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
chunk = GenerationChunk(text=token)
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(token, chunk=chunk)
|
|
yield chunk
|
|
|
|
async def _astream(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[GenerationChunk]:
|
|
messages, params = self._get_chat_params([prompt], stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
async for stream_resp in await acompletion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
):
|
|
if not isinstance(stream_resp, dict):
|
|
stream_resp = stream_resp.dict()
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
chunk = GenerationChunk(text=token)
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(token, chunk=chunk)
|
|
yield chunk
|
|
|
|
def _generate(
|
|
self,
|
|
prompts: List[str],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
if self.streaming:
|
|
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
|
|
return LLMResult(generations=[[generation]])
|
|
|
|
messages, params = self._get_chat_params(prompts, stop)
|
|
params = {**params, **kwargs}
|
|
full_response = completion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
)
|
|
if not isinstance(full_response, dict):
|
|
full_response = full_response.dict()
|
|
llm_output = {
|
|
"token_usage": full_response["usage"],
|
|
"model_name": self.model_name,
|
|
}
|
|
return LLMResult(
|
|
generations=[
|
|
[Generation(text=full_response["choices"][0]["message"]["content"])]
|
|
],
|
|
llm_output=llm_output,
|
|
)
|
|
|
|
async def _agenerate(
|
|
self,
|
|
prompts: List[str],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
if self.streaming:
|
|
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
|
|
return LLMResult(generations=[[generation]])
|
|
|
|
messages, params = self._get_chat_params(prompts, stop)
|
|
params = {**params, **kwargs}
|
|
full_response = await acompletion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
)
|
|
if not isinstance(full_response, dict):
|
|
full_response = full_response.dict()
|
|
llm_output = {
|
|
"token_usage": full_response["usage"],
|
|
"model_name": self.model_name,
|
|
}
|
|
return LLMResult(
|
|
generations=[
|
|
[Generation(text=full_response["choices"][0]["message"]["content"])]
|
|
],
|
|
llm_output=llm_output,
|
|
)
|
|
|
|
@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-chat"
|
|
|
|
def get_token_ids(self, text: str) -> List[int]:
|
|
"""Get the token IDs using the tiktoken package."""
|
|
# tiktoken NOT supported for Python < 3.8
|
|
if sys.version_info[1] < 8:
|
|
return super().get_token_ids(text)
|
|
try:
|
|
import tiktoken
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import tiktoken python package. "
|
|
"This is needed in order to calculate get_num_tokens. "
|
|
"Please install it with `pip install tiktoken`."
|
|
)
|
|
|
|
enc = tiktoken.encoding_for_model(self.model_name)
|
|
return enc.encode(
|
|
text,
|
|
allowed_special=self.allowed_special,
|
|
disallowed_special=self.disallowed_special,
|
|
)
|