add common litellm call to remove duplicate code

Author: asamal4

src/lightspeed_evaluation/core/llm/__init__.py

@@ -1,5 +1,6 @@
 """LLM management for Evaluation Framework."""
 
+from lightspeed_evaluation.core.llm.custom import BaseCustomLLM
 from lightspeed_evaluation.core.llm.deepeval import DeepEvalLLMManager
 from lightspeed_evaluation.core.llm.manager import LLMManager
 from lightspeed_evaluation.core.llm.ragas import RagasLLMManager
@@ -11,6 +12,7 @@
     "LLMConfig",
     "LLMError",
     "LLMManager",
+    "BaseCustomLLM",
     "DeepEvalLLMManager",
     "RagasLLMManager",
     "validate_provider_env",

src/lightspeed_evaluation/core/llm/custom.py

@@ -0,0 +1,75 @@
+"""Base Custom LLM class for evaluation framework."""
+
+from typing import Any, Optional, Union
+
+import litellm
+
+from lightspeed_evaluation.core.system.exceptions import LLMError
+
+
+class BaseCustomLLM:  # pylint: disable=too-few-public-methods
+    """Base LLM class with core calling functionality."""
+
+    def __init__(self, model_name: str, llm_params: dict[str, Any]):
+        """Initialize with model configuration."""
+        self.model_name = model_name
+        self.llm_params = llm_params
+
+    def call(
+        self,
+        prompt: str,
+        n: int = 1,
+        temperature: Optional[float] = None,
+        return_single: bool = True,
+        **kwargs: Any,
+    ) -> Union[str, list[str]]:
+        """Make LLM call and return response(s).
+
+        Args:
+            prompt: Text prompt to send
+            n: Number of responses to generate (default 1)
+            temperature: Override temperature (uses config default if None)
+            return_single: If True and n=1, return single string. If False, always return list.
+            **kwargs: Additional LLM parameters
+
+        Returns:
+            Single string if return_single=True and n=1, otherwise list of strings
+        """
+        temp = (
+            temperature
+            if temperature is not None
+            else self.llm_params.get("temperature", 0.0)
+        )
+
+        call_params = {
+            "model": self.model_name,
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": temp,
+            "n": n,
+            "max_tokens": self.llm_params.get("max_tokens"),
+            "timeout": self.llm_params.get("timeout"),
+            "num_retries": self.llm_params.get("num_retries", 3),
+            **kwargs,
+        }
+
+        try:
+            response = litellm.completion(**call_params)
+
+            # Extract content from all choices
+            results = []
+            for choice in response.choices:  # type: ignore
+                content = choice.message.content  # type: ignore
+                if content is None:
+                    content = ""
+                results.append(content.strip())
+
+            # Return format based on parameters
+            if return_single and n == 1:
+                if not results:
+                    raise LLMError("LLM returned empty response")
+                return results[0]
+
+            return results
+
+        except Exception as e:
+            raise LLMError(f"LLM call failed: {str(e)}") from e