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token_utils.py
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token_utils.py
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import tiktoken
from logging_util import get_logger
logger = get_logger()
def num_tokens_from_messages(messages):
"""Return the number of tokens used by a list of messages."""
try:
# we only support a few models for now, and they all use the same encoding
model = "gpt-3.5-turbo-0613"
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
tokens_per_name = -1 # if there's a name, the role is omitted
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
def num_tokens_from_functions(functions):
"""Return the number of tokens used by a list of functions."""
try:
# we only support a few models for now, and they all use the same encoding
model = "gpt-3.5-turbo-0613"
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
num_tokens = 0
for function in functions:
function_tokens = len(encoding.encode(function['name']))
function_tokens += len(encoding.encode(function['description']))
if 'parameters' in function:
parameters = function['parameters']
if 'properties' in parameters:
for propertiesKey in parameters['properties']:
function_tokens += len(encoding.encode(propertiesKey))
v = parameters['properties'][propertiesKey]
for field in v:
if field == 'type':
function_tokens += 2
function_tokens += len(encoding.encode(v['type']))
elif field == 'description':
function_tokens += 2
function_tokens += len(encoding.encode(v['description']))
elif field == 'items':
function_tokens -= 3
for _, o in v['items'].items():
function_tokens += 2
if isinstance(o, str):
function_tokens += len(encoding.encode(o))
elif isinstance(o, dict):
for _, oo in o.items():
function_tokens += 2
function_tokens += len(encoding.encode(oo))
else:
logger.warning(f"Warning: not supported field {field}")
function_tokens += 11
num_tokens += function_tokens
num_tokens += 12
return num_tokens
def num_tokens_from_completions(completion_text):
"""Return the number of tokens used by a list of functions."""
try:
# we only support a few models for now, and they all use the same encoding
model = "gpt-3.5-turbo-0613"
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if not completion_text:
return 0
num_tokens = len(encoding.encode(completion_text))
return num_tokens