Spaces:
Sleeping
Sleeping
File size: 6,848 Bytes
d12a6df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
# Reference: https://github.com/zou-group/textgrad/blob/main/textgrad/engine/openai.py
try:
import vllm
except ImportError:
raise ImportError("If you'd like to use VLLM models, please install the vllm package by running `pip install vllm`.")
try:
from openai import OpenAI
except ImportError:
raise ImportError("If you'd like to use VLLM models, please install the openai package by running `pip install openai`.")
import os
import json
import base64
import platformdirs
from typing import List, Union
from tenacity import (
retry,
stop_after_attempt,
wait_random_exponential,
)
from .base import EngineLM, CachedEngine
class ChatVLLM(EngineLM, CachedEngine):
DEFAULT_SYSTEM_PROMPT = "You are a helpful, creative, and smart assistant."
def __init__(
self,
model_string="Qwen/Qwen2.5-VL-3B-Instruct",
system_prompt=DEFAULT_SYSTEM_PROMPT,
is_multimodal: bool=False,
use_cache: bool=True,
base_url=None,
api_key=None,
check_model: bool=True,
**kwargs):
"""
:param model_string:
:param system_prompt:
:param is_multimodal:
"""
self.model_string = model_string
self.use_cache = use_cache
self.system_prompt = system_prompt
self.is_multimodal = is_multimodal
if self.use_cache:
root = platformdirs.user_cache_dir("agentflow")
cache_path = os.path.join(root, f"cache_vllm_{self.model_string}.db")
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.base_url = base_url or os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
self.api_key = api_key or os.environ.get("VLLM_API_KEY", "dummy-token")
try:
self.client = OpenAI(
base_url=self.base_url,
api_key=self.api_key,
)
except Exception as e:
raise ValueError(f"Failed to connect to VLLM server at {self.base_url}. Please ensure the server is running and try again.")
@retry(wait=wait_random_exponential(min=1, max=3), stop=stop_after_attempt(3))
def generate(self, content: Union[str, List[Union[str, bytes]]], system_prompt=None, **kwargs):
try:
if isinstance(content, str):
return self._generate_text(content, system_prompt=system_prompt, **kwargs)
elif isinstance(content, list):
if all(isinstance(item, str) for item in content):
full_text = "\n".join(content)
return self._generate_text(full_text, system_prompt=system_prompt, **kwargs)
elif any(isinstance(item, bytes) for item in content):
if not self.is_multimodal:
raise NotImplementedError(
f"Multimodal generation is only supported for {self.model_string}. "
"Consider using a multimodal model like 'gpt-4o'."
)
return self._generate_multimodal(content, system_prompt=system_prompt, **kwargs)
else:
raise ValueError("Unsupported content in list: only str or bytes are allowed.")
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, max_tokens=2048, top_p=0.99, response_format=None, **kwargs
):
sys_prompt_arg = system_prompt if system_prompt else self.system_prompt
if self.use_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
# Chat models without structured outputs
response = self.client.chat.completions.create(
model=self.model_string,
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": prompt},
],
frequency_penalty=kwargs.get("frequency_penalty", 1.2),
presence_penalty=0,
stop=None,
temperature=kwargs.get("temperature", 0.7),
max_tokens=max_tokens,
top_p=top_p,
)
response = response.choices[0].message.content
if self.use_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=2048, 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.use_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:
return cache_or_none
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.use_cache:
self._save_cache(cache_key, response_text)
return response_text
|