feat: get and set prompts mixin

src/ragas/experimental/testset/simulators/base.py

@@ -1,12 +1,14 @@
 from __future__ import annotations
 
+import inspect
 import typing as t
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from enum import Enum
 
 from pydantic import BaseModel
 
+from ragas.experimental.prompt import PydanticPrompt as Prompt
 from ragas.experimental.testset.graph import KnowledgeGraph, Node
 from ragas.llms import BaseRagasLLM, llm_factory
 
@@ -54,3 +56,23 @@ async def generate_scenarios(
     @abstractmethod
     async def generate_sample(self, scenario: Scenario) -> BaseEvalSample:
         pass
+
+    def get_prompts(self) -> t.Dict[str, Prompt]:
+        prompts = {}
+        for name, value in inspect.getmembers(self):
+            if isinstance(value, Prompt):
+                prompts.update({name: value})
+        return prompts
+
+    def set_prompts(self, **prompts):
+        available_prompts = self.get_prompts()
+        for key, value in prompts.items():
+            if key not in available_prompts:
+                raise ValueError(
+                    f"Prompt with name '{key}' does not exist in the simulator {self.name}. Use get_prompts() to see available prompts."
+                )
+            if not isinstance(value, Prompt):
+                raise ValueError(
+                    f"Prompt with name '{key}' must be an instance of 'Prompt'"
+                )
+            setattr(self, key, value)

src/ragas/experimental/testset/transforms/base.py

@@ -1,8 +1,10 @@
+import inspect
 import logging
 import typing as t
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 
+from ragas.experimental.prompt import PydanticPrompt as Prompt
 from ragas.experimental.testset.graph import KnowledgeGraph, Node, Relationship
 from ragas.llms import BaseRagasLLM, llm_factory
 
@@ -90,7 +92,11 @@ class Extractor(BaseGraphTransformation):
         Abstract method to extract a specific property from a node.
     """
 
-    filter_nodes: t.Callable[[Node], bool] = lambda _: True
+    @staticmethod
+    def default_filter(node: Node) -> bool:
+        return True
+
+    filter_nodes: t.Callable[[Node], bool] = default_filter
 
     async def transform(
         self, kg: KnowledgeGraph
@@ -184,6 +190,26 @@ class LLMBasedExtractor(Extractor):
     llm: BaseRagasLLM = field(default_factory=llm_factory)
     merge_if_possible: bool = True
 
+    def get_prompts(self) -> t.Dict[str, Prompt]:
+        prompts = {}
+        for name, value in inspect.getmembers(self):
+            if isinstance(value, Prompt):
+                prompts.update({name: value})
+        return prompts
+
+    def set_prompts(self, **prompts):
+        available_prompts = self.get_prompts()
+        for key, value in prompts.items():
+            if key not in available_prompts:
+                raise ValueError(
+                    f"Prompt with name '{key}' does not exist in the extractor. Use get_prompts() to see available prompts."
+                )
+            if not isinstance(value, Prompt):
+                raise ValueError(
+                    f"Prompt with name '{key}' must be an instance of 'Prompt'"
+                )
+            setattr(self, key, value)
+
 
 class Splitter(BaseGraphTransformation):
     """