docs: fix incorrect reference to LLM in NonLLMContextPrecisionWithRef…

…erence metric (#1863)

Updated the documentation for NonLLMContextPrecisionWithReference to
clarify that it uses non-LLM-based similarity measures, correcting the
misleading reference to LLMs.

fixes #1861

docs/concepts/metrics/available_metrics/context_precision.md

@@ -68,11 +68,11 @@ Output
 
 ## Non LLM Based Context Precision
 
-The following metrics uses traditional methods to identify if a retrieved context is relevant or not. You can use any non LLM based metrics as distance measure to identify if a retrieved context is relevant or not.
+This metric uses traditional methods to determine whether a retrieved context is relevant. It relies on non-LLM-based metrics as a distance measure to evaluate the relevance of retrieved contexts.
 
 ### Context Precision with reference contexts
 
-`NonLLMContextPrecisionWithReference` metric is can be used when you have both retrieved contexts and also reference contexts associated with a `user_input`. To estimate if a retrieved contexts is relevant or not this method uses the LLM to compare each of the retrieved context or chunk present in `retrieved_contexts` with each ones present in `reference_contexts`. 
+The `NonLLMContextPrecisionWithReference` metric is designed for scenarios where both retrieved contexts and reference contexts are available for a `user_input`. To determine if a retrieved context is relevant, this method compares each retrieved context or chunk in `retrieved_context`s with every context in `reference_contexts` using a non-LLM-based similarity measure.
 
 #### Example
 

src/ragas/testset/synthesizers/multi_hop/base.py

@@ -161,7 +161,7 @@ async def _generate_sample(
         self, scenario: Scenario, callbacks: Callbacks
     ) -> SingleTurnSample:
         if not isinstance(scenario, MultiHopScenario):
-            raise TypeError('scenario type should be MultiHopScenario')
+            raise TypeError("scenario type should be MultiHopScenario")
         reference_context = self.make_contexts(scenario)
         prompt_input = QueryConditions(
             persona=scenario.persona,

src/ragas/testset/synthesizers/single_hop/base.py

@@ -122,7 +122,7 @@ async def _generate_sample(
         self, scenario: Scenario, callbacks: Callbacks
     ) -> SingleTurnSample:
         if not isinstance(scenario, SingleHopScenario):
-            raise TypeError('scenario type should be SingleHopScenario')
+            raise TypeError("scenario type should be SingleHopScenario")
         reference_context = scenario.nodes[0].properties.get("page_content", "")
         prompt_input = QueryCondition(
             persona=scenario.persona,