fix: lint and format issues

Author: anistark

pyproject.toml

@@ -117,7 +117,7 @@ preview = false
 
 [tool.pyright]
 include = ["src/ragas"]
-excludeTypeshedPaths = ["@types/*"]
+exclude = ["@types/*"]
 pythonVersion = "3.9"
 pythonPlatform = "All"
 typeCheckingMode = "basic"

src/ragas/config.py

@@ -34,6 +34,3 @@ class InstructionConfig(BaseModel):
     optimizer_config: t.Dict[str, t.Any] = Field(
         default_factory=lambda: DEFAULT_OPTIMIZER_CONFIG
     )
-
-
-InstructionConfig.model_rebuild()

src/ragas/evaluation.py

@@ -169,7 +169,7 @@ async def main():
 
                 client = OpenAI()
                 llm = llm_factory("gpt-4o-mini", client=client)
-            metric.llm = llm
+            metric.llm = t.cast(t.Optional[BaseRagasLLM], llm)
             llm_changed.append(i)
         if isinstance(metric, MetricWithEmbeddings) and metric.embeddings is None:
             if embeddings is None:

src/ragas/metrics/_nv_metrics.py

@@ -9,6 +9,7 @@
 from langchain_core.prompt_values import StringPromptValue
 
 from ragas.dataset_schema import SingleTurnSample
+from ragas.llms.base import BaseRagasLLM
 from ragas.metrics.base import MetricType, MetricWithLLM, SingleTurnMetric
 
 logger = logging.getLogger(__name__)
@@ -115,7 +116,7 @@ async def _single_turn_ascore(
                         sentence_true=sample.reference,
                     )
                 )
-                req0 = self.llm.agenerate_text(
+                req0 = t.cast(BaseRagasLLM, self.llm).agenerate_text(
                     formatted_prompt,
                     n=1,
                     temperature=0.10,
@@ -138,7 +139,7 @@ async def _single_turn_ascore(
                         sentence_true=sample.response,
                     )
                 )
-                req1 = self.llm.agenerate_text(
+                req1 = t.cast(BaseRagasLLM, self.llm).agenerate_text(
                     formatted_prompt,
                     n=1,
                     temperature=0.10,
@@ -255,7 +256,7 @@ async def _single_turn_ascore(
                         context="\n".join(sample.retrieved_contexts),
                     )
                 )
-                req = self.llm.agenerate_text(
+                req = t.cast(BaseRagasLLM, self.llm).agenerate_text(
                     formatted_prompt,
                     n=1,
                     temperature=0.1,
@@ -274,7 +275,7 @@ async def _single_turn_ascore(
                         context="\n".join(sample.retrieved_contexts),
                     )
                 )
-                req = self.llm.agenerate_text(
+                req = t.cast(BaseRagasLLM, self.llm).agenerate_text(
                     formatted_prompt,
                     n=1,
                     temperature=0.1,
@@ -389,7 +390,7 @@ async def _single_turn_ascore(
                         response=sample.response,
                     )
                 )
-                req = self.llm.agenerate_text(
+                req = t.cast(BaseRagasLLM, self.llm).agenerate_text(
                     formatted_prompt,
                     n=1,
                     temperature=0.1,
@@ -408,7 +409,7 @@ async def _single_turn_ascore(
                         response=sample.response,
                     )
                 )
-                req = self.llm.agenerate_text(
+                req = t.cast(BaseRagasLLM, self.llm).agenerate_text(
                     formatted_prompt,
                     n=1,
                     temperature=0.1,

src/ragas/metrics/base.py

@@ -15,6 +15,7 @@
 from ragas.async_utils import apply_nest_asyncio, run
 from ragas.callbacks import ChainType, new_group
 from ragas.dataset_schema import MetricAnnotation, MultiTurnSample, SingleTurnSample
+from ragas.llms import BaseRagasLLM
 from ragas.losses import BinaryMetricLoss, MSELoss
 from ragas.metrics.validators import AllowedValuesType
 from ragas.prompt import FewShotPydanticPrompt, PromptMixin
@@ -28,8 +29,6 @@
     from ragas.config import DemonstrationConfig, InstructionConfig
     from ragas.dataset import Dataset
     from ragas.embeddings import BaseRagasEmbedding, BaseRagasEmbeddings
-    from ragas.llms import BaseRagasLLM
-    from ragas.llms.base import InstructorBaseRagasLLM
     from ragas.metrics.result import MetricResult
     from ragas.prompt.simple_prompt import Prompt
 
@@ -232,11 +231,12 @@ class MetricWithLLM(Metric, PromptMixin):
 
     Attributes
     ----------
-    llm : Optional[BaseRagasLLM | InstructorBaseRagasLLM]
-        The language model used for the metric.
+    llm : Optional[BaseRagasLLM]
+        The language model used for the metric. Both BaseRagasLLM and InstructorBaseRagasLLM
+        are accepted at runtime via duck typing (both have compatible methods).
     """
 
-    llm: t.Optional[t.Union[BaseRagasLLM, "InstructorBaseRagasLLM"]] = None
+    llm: t.Optional[BaseRagasLLM] = None
     output_type: t.Optional[MetricOutputType] = None
 
     def init(self, run_config: RunConfig) -> None:
@@ -257,7 +257,9 @@ def init(self, run_config: RunConfig) -> None:
             raise ValueError(
                 f"Metric '{self.name}' has no valid LLM provided (self.llm is None). Please instantiate the metric with an LLM to run."
             )
-        self.llm.set_run_config(run_config)
+        # Only BaseRagasLLM has set_run_config method, not InstructorBaseRagasLLM
+        if isinstance(self.llm, BaseRagasLLM):
+            self.llm.set_run_config(run_config)
 
     def _optimize_instruction(
         self,