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try:
from openai import OpenAI
except ImportError:
raise ImportError("If you'd like to use OpenAI models, please install the openai package by running `pip install openai`, and add 'OPENAI_API_KEY' to your environment variables.")
import os
import json
import base64
import platformdirs
from tenacity import (
retry,
stop_after_attempt,
wait_random_exponential,
)
from typing import List, Union
from .base import EngineLM, CachedEngine
import openai
from dotenv import load_dotenv
load_dotenv()
# Define global constant for structured models
# https://platform.openai.com/docs/guides/structured-outputs
# https://cookbook.openai.com/examples/structured_outputs_intro
from pydantic import BaseModel
class DefaultFormat(BaseModel):
response: str
# Define global constant for structured models
OPENAI_STRUCTURED_MODELS = ['gpt-4o', 'gpt-4o-2024-08-06','gpt-4o-mini', 'gpt-4o-mini-2024-07-18']
class ChatOpenAI(EngineLM, CachedEngine):
DEFAULT_SYSTEM_PROMPT = "You are a helpful, creative, and smart assistant."
def __init__(
self,
model_string="gpt-4o-mini-2024-07-18",
system_prompt=DEFAULT_SYSTEM_PROMPT,
is_multimodal: bool=False,
# enable_cache: bool=True,
enable_cache: bool=False, # NOTE: disable cache for now
**kwargs):
"""
:param model_string:
:param system_prompt:
:param is_multimodal:
"""
if enable_cache:
root = platformdirs.user_cache_dir("opentools")
cache_path = os.path.join(root, f"cache_openai_{model_string}.db")
# For example, cache_path = /root/.cache/opentools/cache_openai_gpt-4o-mini.db
# print(f"Cache path: {cache_path}")
self.image_cache_dir = os.path.join(root, "image_cache")
os.makedirs(self.image_cache_dir, exist_ok=True)
super().__init__(cache_path=cache_path)
self.system_prompt = system_prompt
if os.getenv("OPENAI_API_KEY") is None:
raise ValueError("Please set the OPENAI_API_KEY environment variable if you'd like to use OpenAI models.")
self.client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY"),
)
self.model_string = model_string
self.is_multimodal = is_multimodal
self.enable_cache = enable_cache
if enable_cache:
print(f"!! Cache enabled for model: {self.model_string}")
else:
print(f"!! Cache disabled for model: {self.model_string}")
@retry(wait=wait_random_exponential(min=1, max=5), stop=stop_after_attempt(5))
def generate(self, content: Union[str, List[Union[str, bytes]]], system_prompt=None, **kwargs):
try:
# Print retry attempt information
attempt_number = self.generate.retry.statistics.get('attempt_number', 0) + 1
if attempt_number > 1:
print(f"Attempt {attempt_number} of 5")
if isinstance(content, str):
return self._generate_text(content, system_prompt=system_prompt, **kwargs)
elif isinstance(content, list):
if (not self.is_multimodal):
raise NotImplementedError("Multimodal generation is only supported for GPT-4 models.")
return self._generate_multimodal(content, system_prompt=system_prompt, **kwargs)
except openai.LengthFinishReasonError as e:
print(f"Token limit exceeded: {str(e)}")
print(f"Tokens used - Completion: {e.completion.usage.completion_tokens}, Prompt: {e.completion.usage.prompt_tokens}, Total: {e.completion.usage.total_tokens}")
return {
"error": "token_limit_exceeded",
"message": str(e),
"details": {
"completion_tokens": e.completion.usage.completion_tokens,
"prompt_tokens": e.completion.usage.prompt_tokens,
"total_tokens": e.completion.usage.total_tokens
}
}
except openai.RateLimitError as e:
print(f"Rate limit error encountered: {str(e)}")
return {
"error": "rate_limit",
"message": str(e),
"details": getattr(e, 'args', None)
}
except Exception as e:
print(f"Error in generate method: {str(e)}")
print(f"Error type: {type(e).__name__}")
print(f"Error details: {e.args}")
return {
"error": type(e).__name__,
"message": str(e),
"details": getattr(e, 'args', None)
}
def _generate_text(
self, prompt, system_prompt=None, temperature=0, max_tokens=4000, top_p=0.99, response_format=None
):
sys_prompt_arg = system_prompt if system_prompt else self.system_prompt
if self.enable_cache:
cache_key = sys_prompt_arg + prompt
cache_or_none = self._check_cache(cache_key)
if cache_or_none is not None:
return cache_or_none
if self.model_string in ['o1', 'o1-mini']: # only supports base response currently
# print(f"Using structured model: {self.model_string}")
response = self.client.beta.chat.completions.parse(
model=self.model_string,
messages=[
{"role": "user", "content": prompt},
],
max_completion_tokens=max_tokens
)
if response.choices[0].finishreason == "length":
response = "Token limit exceeded"
else:
response = response.choices[0].message.parsed
elif self.model_string in OPENAI_STRUCTURED_MODELS and response_format is not None:
# print(f"Using structured model: {self.model_string}")
response = self.client.beta.chat.completions.parse(
model=self.model_string,
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": prompt},
],
frequency_penalty=0,
presence_penalty=0,
stop=None,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
response_format=response_format
)
response = response.choices[0].message.parsed
else:
# print(f"Using non-structured model: {self.model_string}")
response = self.client.chat.completions.create(
model=self.model_string,
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": prompt},
],
frequency_penalty=0,
presence_penalty=0,
stop=None,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
)
response = response.choices[0].message.content
if self.enable_cache:
self._save_cache(cache_key, response)
return response
def __call__(self, prompt, **kwargs):
return self.generate(prompt, **kwargs)
def _format_content(self, content: List[Union[str, bytes]]) -> List[dict]:
formatted_content = []
for item in content:
if isinstance(item, bytes):
base64_image = base64.b64encode(item).decode('utf-8')
formatted_content.append({
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
})
elif isinstance(item, str):
formatted_content.append({
"type": "text",
"text": item
})
else:
raise ValueError(f"Unsupported input type: {type(item)}")
return formatted_content
def _generate_multimodal(
self, content: List[Union[str, bytes]], system_prompt=None, temperature=0, max_tokens=4000, top_p=0.99, response_format=None
):
sys_prompt_arg = system_prompt if system_prompt else self.system_prompt
formatted_content = self._format_content(content)
if self.enable_cache:
cache_key = sys_prompt_arg + json.dumps(formatted_content)
cache_or_none = self._check_cache(cache_key)
if cache_or_none is not None:
# print(f"Cache hit for prompt: {cache_key[:200]}")
return cache_or_none
if self.model_string in ['o1', 'o1-mini']: # only supports base response currently
# print(f"Using structured model: {self.model_string}")
print(f'Max tokens: {max_tokens}')
response = self.client.chat.completions.create(
model=self.model_string,
messages=[
{"role": "user", "content": formatted_content},
],
max_completion_tokens=max_tokens
)
if response.choices[0].finish_reason == "length":
response_text = "Token limit exceeded"
else:
response_text = response.choices[0].message.content
elif self.model_string in OPENAI_STRUCTURED_MODELS and response_format is not None:
# print(f"Using structured model: {self.model_string}")
response = self.client.beta.chat.completions.parse(
model=self.model_string,
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": formatted_content},
],
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
response_format=response_format
)
response_text = response.choices[0].message.parsed
else:
# print(f"Using non-structured model: {self.model_string}")
response = self.client.chat.completions.create(
model=self.model_string,
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": formatted_content},
],
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
)
response_text = response.choices[0].message.content
if self.enable_cache:
self._save_cache(cache_key, response_text)
return response_text
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