add Groq Model LLM for faster llm inference

src/beyondllm/llms/__init__.py

@@ -5,4 +5,5 @@
 from .azurechat import AzureOpenAIModel
 from .ollama import OllamaModel
 from .multimodal import GeminiMultiModal
-from .gpt4o import GPT4oOpenAIModel
+from .gpt4o import GPT4oOpenAIModel
+from .chatgroq import GroqModel

src/beyondllm/llms/chatgroq.py

@@ -0,0 +1,59 @@
+from beyondllm.llms.base import BaseLLMModel, ModelConfig
+from typing import Any, Dict, List, Optional
+import os
+from dataclasses import dataclass,field
+
+@dataclass
+class GroqModel:
+    """
+    Class representing a Chat Language Model (LLM) model using OpenAI
+    Example:
+    from beyondllm.llms import GroqModel
+    llm = ChatOpenAIModel(model="gpt-3.5-turbo",api_key = "",model_kwargs = {"max_tokens":512,"temperature":0.1})
+    """
+    groq_api_key: str = ""
+    model: str = field(default="mixtral-8x7b-32768")
+    system_prompt: str = field(default="You are an AI assistant")
+
+    def __post_init__(self):
+        if not self.groq_api_key:  
+            self.groq_api_key = os.getenv('GROQ_API_KEY') 
+            if not self.groq_api_key: 
+                raise ValueError("GROQ_API_KEY is not provided and not found in environment variables.")
+        self.load_llm()
+
+    def load_llm(self):
+        try:
+            from groq import Groq
+        except ImportError:
+            raise ImportError("Groq library is not installed. Please install it with ``pip install groq``.")
+
+        try:
+            self.client = Groq(api_key=self.groq_api_key)
+
+        except Exception as e:
+            raise Exception("Failed to load the model from Groq:", str(e))
+
+        return self.client
+
+    def predict(self,prompt:Any):
+        response = self.client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": f"{self.system_prompt}",
+                },
+                {
+                    "role": "user",
+                    "content": f"{prompt}",
+                },
+            ],
+            model=self.model,
+        )
+        return response.choices[0].message.content
+
+    @staticmethod
+    def load_from_kwargs(self,kwargs): 
+        model_config = ModelConfig(**kwargs)
+        self.config = model_config
+        self.load_llm()