@ -27,14 +27,10 @@ from langchain.llms.base import BaseLLM, create_base_retry_decorator
from langchain . pydantic_v1 import Field , root_validator
from langchain . schema import Generation , LLMResult
from langchain . schema . output import GenerationChunk
<< << << < HEAD
from langchain . utils import get_from_dict_or_env , get_pydantic_field_names
== == == =
from langchain . utils import (
get_from_dict_or_env ,
get_pydantic_field_names ,
)
>> >> >> > master
from langchain . utils . utils import build_extra_kwargs
@ -51,13 +47,9 @@ def is_openai_v1() -> bool:
def update_token_usage (
<< << << < HEAD
keys : Set [ str ] , response : Dict [ str , Any ] , token_usage : Dict [ str , Any ]
== == == =
keys : Set [ str ] ,
response : Dict [ str , Any ] ,
token_usage : Dict [ str , Any ] ,
>> >> >> > master
) - > None :
""" Update token usage. """
_keys_to_use = keys . intersection ( response [ " usage " ] )
@ -75,30 +67,16 @@ def _stream_response_to_generation_chunk(
return GenerationChunk (
text = stream_response [ " choices " ] [ 0 ] [ " text " ] ,
generation_info = dict (
<< << << < HEAD
finish_reason = stream_response [ " choices " ] [ 0 ] . get ( " finish_reason " , None ) ,
logprobs = stream_response [ " choices " ] [ 0 ] . get ( " logprobs " , None ) ,
== == == =
finish_reason = stream_response [ " choices " ] [ 0 ] . get (
" finish_reason " , None
) ,
logprobs = stream_response [ " choices " ] [ 0 ] . get (
" logprobs " , None
) ,
>> >> >> > master
) ,
)
<< << << < HEAD
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 _update_response (
response : Dict [ str , Any ] , stream_response : Dict [ str , Any ]
) - > None :
@ -112,7 +90,6 @@ def _update_response(
response [ " choices " ] [ 0 ] [ " logprobs " ] = stream_response [ " choices " ] [
0
] [ " logprobs " ]
>> >> >> > master
def _streaming_response_template ( ) - > Dict [ str , Any ] :
@ -127,26 +104,6 @@ def _streaming_response_template() -> Dict[str, Any]:
}
<< << << < HEAD
# def _create_retry_decorator(
# llm: Union[BaseOpenAI, OpenAIChat],
# run_manager: Optional[
# Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
# ] = None,
# ) -> Callable[[Any], Any]:
# import openai
# errors = [
# openai.Timeout,
# openai.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 _create_retry_decorator (
llm : Union [ BaseOpenAI , OpenAIChat ] ,
run_manager : Optional [
@ -167,7 +124,6 @@ def _create_retry_decorator(
max_retries = llm . max_retries ,
run_manager = run_manager ,
)
>> >> >> > master
def completion_with_retry (
@ -176,17 +132,11 @@ def completion_with_retry(
* * kwargs : Any ,
) - > Any :
""" Use tenacity to retry the completion call. """
<< << << < HEAD
# retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
# @retry_decorator
== == == =
retry_decorator = _create_retry_decorator (
llm , run_manager = run_manager
)
@retry_decorator
>> >> >> > master
def _completion_with_retry ( * * kwargs : Any ) - > Any :
return llm . client . create ( * * kwargs )
@ -199,17 +149,11 @@ async def acompletion_with_retry(
* * kwargs : Any ,
) - > Any :
""" Use tenacity to retry the async completion call. """
<< << << < HEAD
# retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
# @retry_decorator
== == == =
retry_decorator = _create_retry_decorator (
llm , run_manager = run_manager
)
@retry_decorator
>> >> >> > master
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 )
@ -231,13 +175,9 @@ class BaseOpenAI(BaseLLM):
attributes [ " openai_api_base " ] = self . openai_api_base
if self . openai_organization != " " :
<< << << < HEAD
attributes [ " openai_organization " ] = self . openai_organization
== == == =
attributes [ " openai_organization " ] = (
self . openai_organization
)
>> >> >> > master
if self . openai_proxy != " " :
attributes [ " openai_proxy " ] = self . openai_proxy
@ -276,11 +216,6 @@ class BaseOpenAI(BaseLLM):
openai_proxy : Optional [ str ] = None
batch_size : int = 20
""" Batch size to use when passing multiple documents to generate. """
<< << << < HEAD
request_timeout : Optional [ Union [ float , Tuple [ float , float ] ] ] = None
""" Timeout for requests to OpenAI completion API. Default is 600 seconds. """
logit_bias : Optional [ Dict [ str , float ] ] = Field ( default_factory = dict )
== == == =
request_timeout : Optional [ Union [ float , Tuple [ float , float ] ] ] = (
None
)
@ -288,7 +223,6 @@ class BaseOpenAI(BaseLLM):
logit_bias : Optional [ Dict [ str , float ] ] = Field (
default_factory = dict
)
>> >> >> > master
""" Adjust the probability of specific tokens being generated. """
max_retries : int = 6
""" Maximum number of retries to make when generating. """
@ -365,13 +299,9 @@ class BaseOpenAI(BaseLLM):
if values [ " streaming " ] and values [ " n " ] > 1 :
raise ValueError ( " Cannot stream results when n > 1. " )
if values [ " streaming " ] and values [ " best_of " ] > 1 :
<< << << < HEAD
raise ValueError ( " Cannot stream results when best_of > 1. " )
== == == =
raise ValueError (
" Cannot stream results when best_of > 1. "
)
>> >> >> > master
return values
@property
@ -403,13 +333,9 @@ class BaseOpenAI(BaseLLM):
* * kwargs : Any ,
) - > Iterator [ GenerationChunk ] :
params = { * * self . _invocation_params , * * kwargs , " stream " : True }
<< << << < HEAD
self . get_sub_prompts ( params , [ prompt ] , stop ) # this mutates params
== == == =
self . get_sub_prompts (
params , [ prompt ] , stop
) # this mutates params
>> >> >> > master
for stream_resp in completion_with_retry (
self , prompt = prompt , run_manager = run_manager , * * params
) :
@ -420,17 +346,11 @@ class BaseOpenAI(BaseLLM):
chunk . text ,
chunk = chunk ,
verbose = self . verbose ,
<< << << < HEAD
logprobs = chunk . generation_info [ " logprobs " ]
if chunk . generation_info
else None ,
== == == =
logprobs = (
chunk . generation_info [ " logprobs " ]
if chunk . generation_info
else None
) ,
>> >> >> > master
)
async def _astream (
@ -441,13 +361,9 @@ class BaseOpenAI(BaseLLM):
* * kwargs : Any ,
) - > AsyncIterator [ GenerationChunk ] :
params = { * * self . _invocation_params , * * kwargs , " stream " : True }
<< << << < HEAD
self . get_sub_prompts ( params , [ prompt ] , stop ) # this mutate params
== == == =
self . get_sub_prompts (
params , [ prompt ] , stop
) # this mutate params
>> >> >> > master
async for stream_resp in await acompletion_with_retry (
self , prompt = prompt , run_manager = run_manager , * * params
) :
@ -458,17 +374,11 @@ class BaseOpenAI(BaseLLM):
chunk . text ,
chunk = chunk ,
verbose = self . verbose ,
<< << << < HEAD
logprobs = chunk . generation_info [ " logprobs " ]
if chunk . generation_info
else None ,
== == == =
logprobs = (
chunk . generation_info [ " logprobs " ]
if chunk . generation_info
else None
) ,
>> >> >> > master
)
def _generate (
@ -504,12 +414,6 @@ class BaseOpenAI(BaseLLM):
for _prompts in sub_prompts :
if self . streaming :
if len ( _prompts ) > 1 :
<< << << < HEAD
raise ValueError ( " Cannot stream results with multiple prompts. " )
generation : Optional [ GenerationChunk ] = None
for chunk in self . _stream ( _prompts [ 0 ] , stop , run_manager , * * kwargs ) :
== == == =
raise ValueError (
" Cannot stream results with multiple prompts. "
)
@ -518,7 +422,6 @@ class BaseOpenAI(BaseLLM):
for chunk in self . _stream (
_prompts [ 0 ] , stop , run_manager , * * kwargs
) :
>> >> >> > master
if generation is None :
generation = chunk
else :
@ -527,14 +430,6 @@ class BaseOpenAI(BaseLLM):
choices . append (
{
" text " : generation . text ,
<< << << < HEAD
" 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 ,
== == == =
" finish_reason " : (
generation . generation_info . get (
" finish_reason "
@ -547,19 +442,14 @@ class BaseOpenAI(BaseLLM):
if generation . generation_info
else None
) ,
>> >> >> > master
}
)
else :
response = completion_with_retry (
<< << << < HEAD
self , prompt = _prompts , run_manager = run_manager , * * params
== == == =
self ,
prompt = _prompts ,
run_manager = run_manager ,
* * params ,
>> >> >> > master
)
choices . extend ( response [ " choices " ] )
update_token_usage ( _keys , response , token_usage )
@ -584,13 +474,9 @@ class BaseOpenAI(BaseLLM):
for _prompts in sub_prompts :
if self . streaming :
if len ( _prompts ) > 1 :
<< << << < HEAD
raise ValueError ( " Cannot stream results with multiple prompts. " )
== == == =
raise ValueError (
" Cannot stream results with multiple prompts. "
)
>> >> >> > master
generation : Optional [ GenerationChunk ] = None
async for chunk in self . _astream (
@ -604,14 +490,6 @@ class BaseOpenAI(BaseLLM):
choices . append (
{
" text " : generation . text ,
<< << << < HEAD
" 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 ,
== == == =
" finish_reason " : (
generation . generation_info . get (
" finish_reason "
@ -624,19 +502,14 @@ class BaseOpenAI(BaseLLM):
if generation . generation_info
else None
) ,
>> >> >> > master
}
)
else :
response = await acompletion_with_retry (
<< << << < HEAD
self , prompt = _prompts , run_manager = run_manager , * * params
== == == =
self ,
prompt = _prompts ,
run_manager = run_manager ,
* * params ,
>> >> >> > master
)
choices . extend ( response [ " choices " ] )
update_token_usage ( _keys , response , token_usage )
@ -651,30 +524,20 @@ class BaseOpenAI(BaseLLM):
""" Get the sub prompts for llm call. """
if stop is not None :
if " stop " in params :
<< << << < HEAD
raise ValueError ( " `stop` found in both the input and default params. " )
== == == =
raise ValueError (
" `stop` found in both the input and default "
" params. "
)
>> >> >> > master
params [ " stop " ] = stop
if params [ " max_tokens " ] == - 1 :
if len ( prompts ) != 1 :
raise ValueError (
<< << << < HEAD
" max_tokens set to -1 not supported for multiple inputs. "
)
params [ " max_tokens " ] = self . max_tokens_for_prompt ( prompts [ 0 ] )
== == == =
" max_tokens set to -1 not supported for multiple "
" inputs. "
)
params [ " max_tokens " ] = self . max_tokens_for_prompt (
prompts [ 0 ]
)
>> >> >> > master
sub_prompts = [
prompts [ i : i + self . batch_size ]
for i in range ( 0 , len ( prompts ) , self . batch_size )
@ -682,14 +545,10 @@ class BaseOpenAI(BaseLLM):
return sub_prompts
def create_llm_result (
<< << << < HEAD
self , choices : Any , prompts : List [ str ] , token_usage : Dict [ str , int ]
== == == =
self ,
choices : Any ,
prompts : List [ str ] ,
token_usage : Dict [ str , int ] ,
>> >> >> > master
) - > LLMResult :
""" Create the LLMResult from the choices and prompts. """
generations = [ ]
@ -707,10 +566,6 @@ class BaseOpenAI(BaseLLM):
for choice in sub_choices
]
)
<< << << < HEAD
llm_output = { " token_usage " : token_usage , " model_name " : self . model_name }
return LLMResult ( generations = generations , llm_output = llm_output )
== == == =
llm_output = {
" token_usage " : token_usage ,
" model_name " : self . model_name ,
@ -718,7 +573,6 @@ class BaseOpenAI(BaseLLM):
return LLMResult (
generations = generations , llm_output = llm_output
)
>> >> >> > master
@property
def _invocation_params ( self ) - > Dict [ str , Any ] :
@ -726,37 +580,21 @@ class BaseOpenAI(BaseLLM):
openai_creds : Dict [ str , Any ] = {
" api_key " : self . openai_api_key ,
" api_base " : self . openai_api_base ,
<< << << < HEAD
" organization " : self . openai_organization ,
== == == =
" organization " : self . openai_organization ,
>> >> >> > master
}
if self . openai_proxy :
import openai
<< << << < HEAD
# raise Exception("The 'openai.proxy' option isn't read in the client API. You will need to pass it when you instantiate the client, e.g.",
# 'OpenAI(proxy={
# "http": self.openai_proxy,
# "https": self.openai_proxy,
# })'") # type: ignore[assignment] # noqa: E501
== == == =
openai . proxy = { " http " : self . openai_proxy , " https " : self . openai_proxy } # type: ignore[assignment] # noqa: E501
>> >> >> > master
return { * * openai_creds , * * self . _default_params }
@property
def _identifying_params ( self ) - > Mapping [ str , Any ] :
""" Get the identifying parameters. """
<< << << < HEAD
return { * * { " model_name " : self . model_name } , * * self . _default_params }
== == == =
return {
* * { " model_name " : self . model_name } ,
* * self . _default_params ,
}
>> >> >> > master
@property
def _llm_type ( self ) - > str :
@ -772,29 +610,19 @@ class BaseOpenAI(BaseLLM):
import tiktoken
except ImportError :
raise ImportError (
<< << << < HEAD
" Could not import tiktoken python package. "
" This is needed in order to calculate get_num_tokens. "
" Please install it with `pip install tiktoken`. "
== == == =
" Could not import tiktoken python package. This is "
" needed in order to calculate get_num_tokens. Please "
" install it with `pip install tiktoken`. "
>> >> >> > master
)
model_name = self . tiktoken_model_name or self . model_name
try :
enc = tiktoken . encoding_for_model ( model_name )
except KeyError :
<< << << < HEAD
logger . warning ( " Warning: model not found. Using cl100k_base encoding. " )
== == == =
logger . warning (
" Warning: model not found. Using cl100k_base "
" encoding. "
)
>> >> >> > master
model = " cl100k_base "
enc = tiktoken . get_encoding ( model )
@ -855,14 +683,9 @@ class BaseOpenAI(BaseLLM):
if context_size is None :
raise ValueError (
<< << << < HEAD
f " Unknown model: { modelname } . Please provide a valid OpenAI model name. "
" Known models are: " + " , " . join ( model_token_mapping . keys ( ) )
== == == =
f " Unknown model: { modelname } . Please provide a valid "
" OpenAI model name.Known models are: "
+ " , " . join ( model_token_mapping . keys ( ) )
>> >> >> > master
)
return context_size
@ -908,14 +731,10 @@ class OpenAI(BaseOpenAI):
@property
def _invocation_params ( self ) - > Dict [ str , Any ] :
<< << << < HEAD
return { * * { " model " : self . model_name } , * * super ( ) . _invocation_params }
== == == =
return {
* * { " model " : self . model_name } ,
* * super ( ) . _invocation_params ,
}
>> >> >> > master
class AzureOpenAI ( BaseOpenAI ) :
@ -989,8 +808,6 @@ class OpenAIChat(BaseLLM):
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 .
<< << << < HEAD
== == == =
Args :
model_name : The model name to use .
@ -1006,7 +823,6 @@ class OpenAIChat(BaseLLM):
>> >> >> > master
Example :
. . code - block : : python
@ -1015,20 +831,10 @@ class OpenAIChat(BaseLLM):
"""
client : Any #: :meta private:
<< << << < HEAD
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. """
openai_api_key : Optional [ str ] = None
openai_api_base : Optional [ str ] = None
# to support explicit proxy for OpenAI
== == == =
model_name : str = " gpt-3.5-turbo-1106 "
model_kwargs : Dict [ str , Any ] = Field ( default_factory = dict )
openai_api_key : Optional [ str ] = None
openai_api_base : Optional [ str ] = None
>> >> >> > master
openai_proxy : Optional [ str ] = None
max_retries : int = 6
""" Maximum number of retries to make when generating. """
@ -1044,25 +850,17 @@ class OpenAIChat(BaseLLM):
@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. """
<< << << < HEAD
all_required_field_names = { field . alias for field in cls . __fields__ . values ( ) }
== == == =
all_required_field_names = {
field . alias for field in cls . __fields__ . values ( )
}
>> >> >> > master
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 :
<< << << < HEAD
raise ValueError ( f " Found { field_name } supplied twice. " )
== == == =
raise ValueError (
f " Found { field_name } supplied twice. "
)
>> >> >> > master
extra [ field_name ] = values . pop ( field_name )
values [ " model_kwargs " ] = extra
return values
@ -1086,25 +884,14 @@ class OpenAIChat(BaseLLM):
default = " " ,
)
openai_organization = get_from_dict_or_env (
<< << << < HEAD
values , " openai_organization " , " OPENAI_ORGANIZATION " , default = " "
== == == =
values ,
" openai_organization " ,
" OPENAI_ORGANIZATION " ,
default = " " ,
>> >> >> > master
)
try :
import openai
<< << << < HEAD
if openai_api_base :
raise Exception ( " The ' openai.api_base ' option isn ' t read in the client API. You will need to pass it when you instantiate the client, e.g. ' OpenAI(api_base=openai_api_base) ' " )
if openai_organization :
raise Exception ( " The ' openai.organization ' option isn ' t read in the client API. You will need to pass it when you instantiate the client, e.g. ' OpenAI(organization=openai_organization) ' " )
== == == =
openai . api_key = openai_api_key
if openai_api_base :
openai . api_base = openai_api_base
@ -1112,7 +899,6 @@ class OpenAIChat(BaseLLM):
openai . organization = openai_organization
if openai_proxy :
openai . proxy = { " http " : openai_proxy , " https " : openai_proxy } # type: ignore[assignment] # noqa: E501
>> >> >> > master
except ImportError :
raise ImportError (
" Could not import openai python package. "
@ -1122,16 +908,10 @@ class OpenAIChat(BaseLLM):
values [ " client " ] = openai . ChatCompletion
except AttributeError :
raise ValueError (
<< << << < HEAD
" `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`. "
== == == =
" `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`. "
>> >> >> > master
)
return values
@ -1145,15 +925,6 @@ class OpenAIChat(BaseLLM):
) - > Tuple :
if len ( prompts ) > 1 :
raise ValueError (
<< << << < HEAD
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. " )
== == == =
" OpenAIChat currently only supports single prompt, "
f " got { prompts } "
)
@ -1170,7 +941,6 @@ class OpenAIChat(BaseLLM):
" `stop` found in both the input and default "
" params. "
)
>> >> >> > master
params [ " stop " ] = stop
if params . get ( " max_tokens " ) == - 1 :
# for ChatGPT api, omitting max_tokens is equivalent to having no limit
@ -1189,13 +959,9 @@ class OpenAIChat(BaseLLM):
for stream_resp in completion_with_retry (
self , messages = messages , run_manager = run_manager , * * params
) :
<< << << < HEAD
token = stream_resp [ " choices " ] [ 0 ] [ " delta " ] . get ( " content " , " " )
== == == =
token = stream_resp [ " choices " ] [ 0 ] [ " delta " ] . get (
" content " , " "
)
>> >> >> > master
chunk = GenerationChunk ( text = token )
yield chunk
if run_manager :
@ -1213,13 +979,9 @@ class OpenAIChat(BaseLLM):
async for stream_resp in await acompletion_with_retry (
self , messages = messages , run_manager = run_manager , * * params
) :
<< << << < HEAD
token = stream_resp [ " choices " ] [ 0 ] [ " delta " ] . get ( " content " , " " )
== == == =
token = stream_resp [ " choices " ] [ 0 ] [ " delta " ] . get (
" content " , " "
)
>> >> >> > master
chunk = GenerationChunk ( text = token )
yield chunk
if run_manager :
@ -1234,13 +996,9 @@ class OpenAIChat(BaseLLM):
) - > LLMResult :
if self . streaming :
generation : Optional [ GenerationChunk ] = None
<< << << < HEAD
for chunk in self . _stream ( prompts [ 0 ] , stop , run_manager , * * kwargs ) :
== == == =
for chunk in self . _stream (
prompts [ 0 ] , stop , run_manager , * * kwargs
) :
>> >> >> > master
if generation is None :
generation = chunk
else :
@ -1259,9 +1017,6 @@ class OpenAIChat(BaseLLM):
}
return LLMResult (
generations = [
<< << << < HEAD
[ Generation ( text = full_response [ " choices " ] [ 0 ] [ " message " ] [ " content " ] ) ]
== == == =
[
Generation (
text = full_response [ " choices " ] [ 0 ] [ " message " ] [
@ -1269,7 +1024,6 @@ class OpenAIChat(BaseLLM):
]
)
]
>> >> >> > master
] ,
llm_output = llm_output ,
)
@ -1283,13 +1037,9 @@ class OpenAIChat(BaseLLM):
) - > LLMResult :
if self . streaming :
generation : Optional [ GenerationChunk ] = None
<< << << < HEAD
async for chunk in self . _astream ( prompts [ 0 ] , stop , run_manager , * * kwargs ) :
== == == =
async for chunk in self . _astream (
prompts [ 0 ] , stop , run_manager , * * kwargs
) :
>> >> >> > master
if generation is None :
generation = chunk
else :
@ -1308,9 +1058,6 @@ class OpenAIChat(BaseLLM):
}
return LLMResult (
generations = [
<< << << < HEAD
[ Generation ( text = full_response [ " choices " ] [ 0 ] [ " message " ] [ " content " ] ) ]
== == == =
[
Generation (
text = full_response [ " choices " ] [ 0 ] [ " message " ] [
@ -1318,7 +1065,6 @@ class OpenAIChat(BaseLLM):
]
)
]
>> >> >> > master
] ,
llm_output = llm_output ,
)
@ -1326,14 +1072,10 @@ class OpenAIChat(BaseLLM):
@property
def _identifying_params ( self ) - > Mapping [ str , Any ] :
""" Get the identifying parameters. """
<< << << < HEAD
return { * * { " model_name " : self . model_name } , * * self . _default_params }
== == == =
return {
* * { " model_name " : self . model_name } ,
* * self . _default_params ,
}
>> >> >> > master
@property
def _llm_type ( self ) - > str :
@ -1349,15 +1091,9 @@ class OpenAIChat(BaseLLM):
import tiktoken
except ImportError :
raise ImportError (
<< << << < HEAD
" Could not import tiktoken python package. "
" This is needed in order to calculate get_num_tokens. "
" Please install it with `pip install tiktoken`. "
== == == =
" Could not import tiktoken python package. This is "
" needed in order to calculate get_num_tokens. Please "
" install it with `pip install tiktoken`. "
>> >> >> > master
)
enc = tiktoken . encoding_for_model ( self . model_name )
Zack
1 year ago
