fix issue 1857

ragas/src/ragas/llms/__init__.py

@@ -5,11 +5,13 @@
     llm_factory,
 )
 from ragas.llms.haystack_wrapper import HaystackLLMWrapper
+from ragas.llms.ollama_wrapper import OllamaLLMWrapper
 
 __all__ = [
     "BaseRagasLLM",
     "HaystackLLMWrapper",
     "LangchainLLMWrapper",
     "LlamaIndexLLMWrapper",
+    "OllamaLLMWrapper",
     "llm_factory",
 ]

ragas/src/ragas/llms/ollama_wrapper.py

@@ -0,0 +1,102 @@
+import typing as t
+from langchain_core.callbacks import Callbacks
+from langchain_core.outputs import Generation, LLMResult
+from langchain_core.prompt_values import PromptValue
+from langchain_ollama import ChatOllama
+from langchain_openai import ChatOpenAI
+from ragas.cache import CacheInterface
+from ragas.llms import BaseRagasLLM
+from ragas.run_config import RunConfig
+
+
+class OllamaLLMWrapper(BaseRagasLLM):
+    """
+    A wrapper class for using Ollama LLM within the Ragas framework.
+
+    This class integrates Ollama's LLM into Ragas, enabling both synchronous and
+    asynchronous text generation.
+
+    Parameters
+    ----------
+    ollama_llm : ChatOllama
+        An instance of Ollama chat model.
+    run_config : RunConfig, optional
+        Configuration object to manage LLM execution settings, by default None.
+    cache : CacheInterface, optional
+        A cache instance for storing results, by default None.
+    """
+
+    def __init__(
+        self,
+        ollama_llm: ChatOllama,
+        run_config: t.Optional[RunConfig] = None,
+        cache: t.Optional[CacheInterface] = None,
+    ):
+        super().__init__(cache=cache)
+
+        self.llm = ChatOpenAI(
+            api_key="ollama",
+             model=ollama_llm.model,
+             base_url=f"{ollama_llm.base_url}/v1"
+            )
+
+        if run_config is None:
+            run_config = RunConfig()
+        self.set_run_config(run_config)
+
+    def is_finished(self, response: LLMResult) -> bool:
+        """Check if the generation is finished."""
+        return True
+
+    def generate_text(
+        self,
+        prompt: str,
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> str:
+        """Generate text from the model."""
+        response = self.llm.invoke(prompt)
+        return response.content
+
+    async def agenerate_text(
+        self,
+        prompt: str,
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> str:
+        """Generate text from the model asynchronously."""
+        response = await self.llm.ainvoke(prompt)
+        return response.content
+
+    def _generate(
+        self,
+        prompts: t.List[PromptValue],
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> LLMResult:
+        """Generate text from the model."""
+        generations = []
+        for prompt in prompts:
+            response = self.llm.invoke(prompt.to_messages())
+            generations.append([Generation(text=response.content)])
+        return LLMResult(generations=generations)
+
+    async def _agenerate(
+        self,
+        prompts: t.List[PromptValue],
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> LLMResult:
+        """Generate text from the model asynchronously."""
+        generations = []
+        for prompt in prompts:
+            response = await self.llm.ainvoke(prompt.to_messages())
+            generations.append([Generation(text=response.content)])
+        return LLMResult(generations=generations)
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(llm={self.llm.model}(...))"