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# Reference: https://github.com/zou-group/textgrad/tree/main/textgrad/engine
try:
from together import Together
except ImportError:
raise ImportError("If you'd like to use Together models, please install the together package by running `pip install together`, and add 'TOGETHER_API_KEY' to your environment variables.")
import os
import platformdirs
from tenacity import (
retry,
stop_after_attempt,
wait_random_exponential,
)
from typing import List, Union
import json
import base64
from .base import EngineLM, CachedEngine
class ChatTogether(EngineLM, CachedEngine):
DEFAULT_SYSTEM_PROMPT = "You are a helpful, creative, and smart assistant."
def __init__(
self,
model_string="meta-llama/Llama-3-70b-chat-hf",
use_cache: bool=False,
system_prompt=DEFAULT_SYSTEM_PROMPT,
is_multimodal: bool=False):
"""
:param model_string:
:param system_prompt:
:param is_multimodal:
"""
self.use_cache = use_cache
self.system_prompt = system_prompt
self.model_string = model_string
# Check if model supports multimodal inputs
self.is_multimodal = is_multimodal or any(x in model_string.lower() for x in [
"llama-4",
"qwen2-vl",
"qwen-vl",
"vl",
"visual"
])
if self.use_cache:
root = platformdirs.user_cache_dir("agentflow")
cache_path = os.path.join(root, f"cache_together_{model_string}.db")
super().__init__(cache_path=cache_path)
if os.getenv("TOGETHER_API_KEY") is None:
raise ValueError("Please set the TOGETHER_API_KEY environment variable if you'd like to use OpenAI models.")
self.client = Together(
api_key=os.getenv("TOGETHER_API_KEY"),
)
def _format_content(self, content):
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
@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, max_tokens=4000, top_p=0.99, **kwargs):
try:
if isinstance(content, str):
return self._generate_text(content, system_prompt=system_prompt, max_tokens=max_tokens, top_p=top_p, **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, max_tokens=max_tokens, top_p=top_p, **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, temperature=temperature, max_tokens=max_tokens, top_p=top_p, **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, temperature=0.7, max_tokens=4000, top_p=0.99, **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
# # Adjust max_tokens to ensure total tokens don't exceed Together's limit
# MAX_TOTAL_TOKENS = 8000
# max_tokens = min(max_tokens, MAX_TOTAL_TOKENS - 1000)
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.use_cache:
self._save_cache(cache_key, response)
return response
def _generate_multimodal(
self, content, system_prompt=None, temperature=0, max_tokens=4000, top_p=0.99, **kwargs
):
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
# # Adjust max_tokens to ensure total tokens don't exceed Together's limit
# MAX_TOTAL_TOKENS = 8000
# max_tokens = min(max_tokens, MAX_TOTAL_TOKENS - 1000)
response = self.client.chat.completions.create(
model=self.model_string,
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": formatted_content},
],
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.use_cache:
self._save_cache(cache_key, response)
return response
@retry(wait=wait_random_exponential(min=1, max=5), stop=stop_after_attempt(5))
def __call__(self, prompt, **kwargs):
return self.generate(prompt, **kwargs) |