Added New LLM Cohere

src/beyondllm/llms/__init__.py

@@ -8,4 +8,6 @@
 from .gpt4o import GPT4oOpenAIModel
 from .chatgroq import GroqModel
 from .claude import ClaudeModel
-from .mistral import MistralModel
+from .mistral import MistralModel
+from .cohere import CohereModel  
+from .together import TogetherModel 

src/beyondllm/llms/cohere.py

@@ -0,0 +1,63 @@
+from beyondllm.llms.base import BaseLLMModel, ModelConfig
+from typing import Any, Dict
+from dataclasses import dataclass, field
+import os
+
+@dataclass
+class CohereModel:
+    """
+    Class representing a Language Model (LLM) model using Cohere.
+
+    Example:
+    ```
+    >>> llm = CohereModel(api_key="<your_api_key>", model_kwargs={"temperature": 0.5})
+    ```
+    or
+    ```
+    >>> import os
+    >>> os.environ['COHERE_API_KEY'] = "***********" #replace with your key
+    >>> llm = CohereModel()
+    ```
+    """
+    api_key: str =" "
+    model_name: str = "command-r-plus-08-2024"
+    model_kwargs: dict = field(default_factory=lambda: {
+        "temperature": 0.5,
+        "top_p": 1,
+        "max_tokens": 2048,
+    })
+
+    def __post_init__(self):
+        if not self.api_key:
+            self.api_key = os.getenv('COHERE_API_KEY')
+            if not self.api_key:
+                raise ValueError("COHERE_API_KEY is not provided and not found in environment variables.")
+        self.load_llm()
+
+    def load_llm(self):
+        try:
+            import cohere
+        except ImportError:
+            print("The cohere module is not installed. Please install it with 'pip install cohere'.")
+
+        try:
+            self.client = cohere.ClientV2(api_key=self.api_key)
+        except Exception as e:
+            raise Exception(f"Failed to initialize Cohere client: {str(e)}")
+
+    def predict(self, prompt: Any) -> str:
+        try:
+            response = self.client.chat(
+                model=self.model_name, 
+                messages=[{"role": "user", "content": prompt}]
+            )
+            return response.message.content[0].text
+        except Exception as e:
+            raise Exception(f"Failed to generate prediction: {str(e)}")
+
+    @staticmethod
+    def load_from_kwargs(self, kwargs: Dict):
+        model_config = ModelConfig(**kwargs)
+        self.config = model_config
+        self.load_llm()
+

src/beyondllm/llms/together.py

@@ -0,0 +1,68 @@
+from beyondllm.llms.base import BaseLLMModel, ModelConfig
+from typing import Any, Dict
+from dataclasses import dataclass, field
+import os
+
+@dataclass
+class TogetherModel:
+    """
+    Class representing a Language Model (LLM) using Together AI.
+
+    Example:
+    ```
+    >>> llm = TogetherModel(api_key="<your_api_key>", model_kwargs={"temperature": 0.7})
+    ```
+    or
+    ```
+    >>> import os
+    >>> os.environ['TOGETHER_API_KEY'] = "***********" #replace with your key
+    >>> llm = TogetherModel()
+    ```
+    """
+    api_key: str = " "
+    model_name: str = "meta-llama/Llama-3-8b-chat-hf"
+    model_kwargs: dict = field(default_factory=lambda: {
+        "temperature": 0.7,
+        "top_p": 0.9,
+        "max_tokens": 1024,
+    })
+
+    def __post_init__(self):
+        if not self.api_key:
+            self.api_key = os.getenv('TOGETHER_API_KEY')
+            if not self.api_key:
+                raise ValueError("TOGETHER_API_KEY is not provided and not found in environment variables.")
+        self.load_llm()
+
+    def load_llm(self):
+        try:
+            from together import Together
+        except ImportError:
+            print("The together module is not installed. Please install it with 'pip install together'.")
+
+        try:
+            self.client = Together(api_key=self.api_key)
+        except Exception as e:
+            raise Exception(f"Failed to initialize Together client: {str(e)}")
+
+    def predict(self, prompt: Any) -> str:
+        """Generate a response from the model based on the provided prompt."""
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=[{"role": "system", "content": "You are a highly skilled software engineer. Provide detailed explanations and code examples when relevant."},
+                          {"role": "user", "content": prompt}]
+
+            )
+            response_text = response.choices[0].message.content
+            return response_text
+
+        except Exception as e:
+            raise Exception(f"Failed to generate prediction: {str(e)}")
+
+    @staticmethod
+    def load_from_kwargs(self, kwargs: Dict):
+        model_config = ModelConfig(**kwargs)
+        self.config = model_config
+        self.load_llm()
+