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CoACT initialize (#292)

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Linxin Song
2025-07-30 19:35:20 -07:00
committed by GitHub
parent 862d704b8c
commit b968155757
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mm_agents/coact/autogen/token_count_utils.py Normal file
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# Copyright (c) 2023 - 2025, AG2ai, Inc., AG2ai open-source projects maintainers and core contributors
#
# SPDX-License-Identifier: Apache-2.0
#
# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
# SPDX-License-Identifier: MIT
import json
import logging
import re
from typing import Any, Union
import tiktoken
from .agentchat.contrib.img_utils import num_tokens_from_gpt_image
from .import_utils import optional_import_block
# if PIL is not imported, we will redefine num_tokens_from_gpt_image to return 0 tokens for images
# Otherwise, it would raise an ImportError
with optional_import_block() as result:
import PIL # noqa: F401
pil_imported = result.is_successful
if not pil_imported:
def num_tokens_from_gpt_image(*args, **kwargs):
return 0
logger = logging.getLogger(__name__)
logger.img_dependency_warned = False # member variable to track if the warning has been logged
def get_max_token_limit(model: str = "gpt-3.5-turbo-0613") -> int:
# Handle common azure model names/aliases
model = re.sub(r"^gpt\-?35", "gpt-3.5", model)
model = re.sub(r"^gpt4", "gpt-4", model)
max_token_limit = {
"gpt-3.5-turbo": 16385,
"gpt-3.5-turbo-0125": 16385,
"gpt-3.5-turbo-0301": 4096,
"gpt-3.5-turbo-0613": 4096,
"gpt-3.5-turbo-instruct": 4096,
"gpt-3.5-turbo-16k": 16385,
"gpt-3.5-turbo-16k-0613": 16385,
"gpt-3.5-turbo-1106": 16385,
"gpt-4": 8192,
"gpt-4-turbo": 128000,
"gpt-4-turbo-2024-04-09": 128000,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768, # deprecate in Sep
"gpt-4-0314": 8192, # deprecate in Sep
"gpt-4-0613": 8192,
"gpt-4-32k-0613": 32768,
"gpt-4-1106-preview": 128000,
"gpt-4-0125-preview": 128000,
"gpt-4-turbo-preview": 128000,
"gpt-4-vision-preview": 128000,
"gpt-4o": 128000,
"gpt-4o-2024-05-13": 128000,
"gpt-4o-2024-08-06": 128000,
"gpt-4o-2024-11-20": 128000,
"gpt-4o-mini": 128000,
"gpt-4o-mini-2024-07-18": 128000,
}
return max_token_limit[model]
def percentile_used(input, model="gpt-3.5-turbo-0613"):
return count_token(input) / get_max_token_limit(model)
def token_left(input: Union[str, list[str], dict[str, Any]], model="gpt-3.5-turbo-0613") -> int:
"""Count number of tokens left for an OpenAI model.
Args:
input: (str, list, dict): Input to the model.
model: (str): Model name.
Returns:
int: Number of tokens left that the model can use for completion.
"""
return get_max_token_limit(model) - count_token(input, model=model)
def count_token(input: Union[str, list[str], dict[str, Any]], model: str = "gpt-3.5-turbo-0613") -> int:
"""Count number of tokens used by an OpenAI model.
Args:
input: (str, list, dict): Input to the model.
model: (str): Model name.
Returns:
int: Number of tokens from the input.
"""
if isinstance(input, str):
return _num_token_from_text(input, model=model)
elif isinstance(input, (list, dict)):
return _num_token_from_messages(input, model=model)
else:
raise ValueError(f"input must be str, list or dict, but we got {type(input)}")
def _num_token_from_text(text: str, model: str = "gpt-3.5-turbo-0613"):
"""Return the number of tokens used by a string."""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning(f"Model {model} not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
return len(encoding.encode(text))
def _num_token_from_messages(messages: Union[list[str], dict[str, Any]], model="gpt-3.5-turbo-0613"):
"""Return the number of tokens used by a list of messages.
retrieved from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb/
"""
if isinstance(messages, dict):
messages = [messages]
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning(f"Model {model} not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model in {
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k-0613",
"gpt-4-0314",
"gpt-4-32k-0314",
"gpt-4-0613",
"gpt-4-32k-0613",
"gpt-4-turbo-preview",
"gpt-4-vision-preview",
"gpt-4o",
"gpt-4o-2024-05-13",
"gpt-4o-2024-08-06",
"gpt-4o-2024-11-20",
"gpt-4o-mini",
"gpt-4o-mini-2024-07-18",
}:
tokens_per_message = 3
tokens_per_name = 1
elif model == "gpt-3.5-turbo-0301":
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
elif "gpt-3.5-turbo" in model:
logger.info("gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
return _num_token_from_messages(messages, model="gpt-3.5-turbo-0613")
elif "gpt-4" in model:
logger.info("gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
return _num_token_from_messages(messages, model="gpt-4-0613")
elif "gemini" in model:
logger.info("Gemini is not supported in tiktoken. Returning num tokens assuming gpt-4-0613.")
return _num_token_from_messages(messages, model="gpt-4-0613")
elif "claude" in model:
logger.info("Claude is not supported in tiktoken. Returning num tokens assuming gpt-4-0613.")
return _num_token_from_messages(messages, model="gpt-4-0613")
elif "mistral-" in model or "mixtral-" in model:
logger.info("Mistral.AI models are not supported in tiktoken. Returning num tokens assuming gpt-4-0613.")
return _num_token_from_messages(messages, model="gpt-4-0613")
elif "deepseek" in model:
logger.info("Deepseek models are not supported in tiktoken. Returning num tokens assuming gpt-4-0613.")
return _num_token_from_messages(messages, model="gpt-4-0613")
else:
raise NotImplementedError(
f"""_num_token_from_messages() is not implemented 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
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
if value is None:
continue
# handle content if images are in GPT-4-vision
if key == "content" and isinstance(value, list):
for part in value:
if not isinstance(part, dict) or "type" not in part:
continue
if part["type"] == "text":
num_tokens += len(encoding.encode(part["text"]))
if "image_url" in part:
if not pil_imported and not logger.img_dependency_warned:
logger.warning(
"img_utils or PIL not imported. Skipping image token count."
"Please install autogen with [lmm] option.",
)
logger.img_dependency_warned = True
try:
num_tokens += num_tokens_from_gpt_image(
image_data=part["image_url"]["url"], model=model
)
except ValueError as e:
logger.warning(f"Error in num_tokens_from_gpt_image: {e}")
continue
# function calls
if not isinstance(value, str):
try:
value = json.dumps(value)
except TypeError:
logger.warning(
f"Value {value} is not a string and cannot be converted to json. It is a type: {type(value)} Skipping."
)
continue
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, model="gpt-3.5-turbo-0613") -> int:
"""Return the number of tokens used by a list of functions.
Args:
functions: (list): List of function descriptions that will be passed in model.
model: (str): Model name.
Returns:
int: Number of tokens from the function descriptions.
"""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning(f"Model {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"]))
function_tokens -= 2
if "parameters" in function:
parameters = function["parameters"]
if "properties" in parameters:
for properties_key in parameters["properties"]:
function_tokens += len(encoding.encode(properties_key))
v = parameters["properties"][properties_key]
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 == "enum":
function_tokens -= 3
for o in v["enum"]:
function_tokens += 3
function_tokens += len(encoding.encode(o))
else:
logger.warning(f"Not supported field {field}")
function_tokens += 11
if len(parameters["properties"]) == 0:
function_tokens -= 2
num_tokens += function_tokens
num_tokens += 12
return num_tokens