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# Ref: https://docs.x.ai/docs/guides/chat
# Ref: https://docs.x.ai/docs/guides/reasoning
try:
from openai import OpenAI
except ImportError:
raise ImportError("If you'd like to use Groq models, please install the openai package by running `pip install openai`, and add 'XAI_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
from .engine_utils import get_image_type_from_bytes
from .openai import ChatOpenAI
def validate_reasoning_model(model_string: str):
# Ref: https://docs.x.ai/docs/guides/reasoning
return any(x in model_string for x in ["grok-3-mini"])
class ChatGrok(ChatOpenAI):
DEFAULT_SYSTEM_PROMPT = "You are a helpful, creative, and smart assistant."
def __init__(
self,
model_string: str="grok-3-latest",
use_cache: bool=False,
system_prompt: str=DEFAULT_SYSTEM_PROMPT,
is_multimodal: bool=False,
**kwargs):
"""
:param model_string: The Groq model to use
:param use_cache: Whether to use caching
:param system_prompt: System prompt to use
:param is_multimodal: Whether to enable multimodal capabilities
"""
self.use_cache = use_cache
self.model_string = model_string
self.system_prompt = system_prompt
self.is_multimodal = is_multimodal
self.is_reasoning_model = validate_reasoning_model(model_string)
if self.use_cache:
root = platformdirs.user_cache_dir("agentflow")
cache_path = os.path.join(root, f"cache_groq_{model_string}.db")
super().__init__(cache_path=cache_path)
if os.getenv("XAI_API_KEY") is None:
raise ValueError("Please set the XAI_API_KEY environment variable if you'd like to use Groq models.")
self.client = OpenAI(
api_key=os.getenv("XAI_API_KEY"),
base_url="https://api.x.ai/v1"
)
assert isinstance(self.system_prompt, str)
def __call__(self, prompt, **kwargs):
return self.generate(prompt, **kwargs)
@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):
if isinstance(content, str):
return self._generate_from_single_prompt(content, system_prompt=system_prompt, **kwargs)
elif isinstance(content, list):
has_multimodal_input = any(isinstance(item, bytes) for item in content)
if (has_multimodal_input) and (not self.is_multimodal):
raise NotImplementedError("Multimodal generation is not supported for Groq models.")
return self._generate_from_multiple_input(content, system_prompt=system_prompt, **kwargs)
def _generate_from_single_prompt(
self, prompt: str, system_prompt=None, temperature=0, max_tokens=2000, top_p=0.99, **kwargs
):
sys_prompt_arg = system_prompt if system_prompt else self.system_prompt
if self.use_cache:
cache_or_none = self._check_cache(sys_prompt_arg + prompt)
if cache_or_none is not None:
return cache_or_none
# Chat with reasoning model
if self.is_reasoning_model:
response = self.client.chat.completions.create(
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": prompt}
],
model=self.model_string,
reasoning_effort="medium",
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p
)
# Chat with non-reasoning model
else:
response = self.client.chat.completions.create(
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": prompt}
],
model=self.model_string,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p
)
response_text = response.choices[0].message.content
if self.use_cache:
self._save_cache(sys_prompt_arg + prompt, response_text)
return response_text
def _format_content(self, content: List[Union[str, bytes]]) -> List[dict]:
# Ref: https://docs.x.ai/docs/guides/image-understanding#image-understanding
formatted_content = []
for item in content:
if isinstance(item, bytes):
image_type = get_image_type_from_bytes(item)
base64_image = base64.b64encode(item).decode('utf-8')
formatted_content.append({
"type": "image_url",
"image_url": {
"url": f"data:image/{image_type};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_from_multiple_input(
self, content: List[Union[str, bytes]], 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
# Chat with reasoning model
if self.is_reasoning_model:
response = self.client.chat.completions.create(
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": formatted_content}
],
model=self.model_string,
reasoning_effort="medium",
temperature=temperature,
max_tokens=max_tokens
)
# Chat with non-reasoning model
else:
response = self.client.chat.completions.create(
messages=[
{"role": "system", "content": sys_prompt_arg},
{"role": "user", "content": formatted_content}
],
model=self.model_string,
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 |