feat: support for callbacks and traces in testset generation (explodi…

…nggradients#1392)

an example visualization with langsmith

![image](https://github.com/user-attachments/assets/02494276-b1fa-4706-a3cd-ce55dc111809)

docs/getstarted/rag_testset_generation.ipynb

@@ -100,7 +100,8 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "from ragas.llms.base import LangchainLLMWrapper\n",
-    "openai_model = LangchainLLMWrapper(ChatOpenAI(model_name=\"gpt-4o\"))\n"
+    "\n",
+    "openai_model = LangchainLLMWrapper(ChatOpenAI(model_name=\"gpt-4o\"))"
    ]
   },
   {

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import logging
 import math
 import random
@@ -24,6 +26,9 @@
     Themes,
 )
 
+if t.TYPE_CHECKING:
+    from langchain_core.callbacks import Callbacks
+
 logger = logging.getLogger(__name__)
 
 
@@ -41,8 +46,8 @@ def __post_init__(self):
         super().__post_init__()
         self.common_theme_prompt = CommonThemeFromSummaries()
 
-    async def generate_scenarios(
-        self, n: int, knowledge_graph: KnowledgeGraph
+    async def _generate_scenarios(
+        self, n: int, knowledge_graph: KnowledgeGraph, callbacks: Callbacks
     ) -> t.List[AbstractQuestionScenario]:
         node_clusters = knowledge_graph.find_clusters(
             relationship_condition=lambda rel: (
@@ -89,7 +94,7 @@ async def generate_scenarios(
                 summaries=summaries,
                 num_themes=num_themes,
             )
-            kw_list.append({"data": summaries, "llm": self.llm})
+            kw_list.append({"data": summaries, "llm": self.llm, "callbacks": callbacks})
 
         themes: t.List[Themes] = run_async_batch(
             desc="Generating common themes",
@@ -129,14 +134,14 @@ async def generate_scenarios(
             )
         return distributions
 
-    async def generate_sample(
-        self, scenario: AbstractQuestionScenario
+    async def _generate_sample(
+        self, scenario: AbstractQuestionScenario, callbacks: Callbacks
     ) -> SingleTurnSample:
-        user_input = await self.generate_user_input(scenario)
-        if await self.critic_question(user_input):
-            user_input = await self.modify_question(user_input, scenario)
+        user_input = await self.generate_user_input(scenario, callbacks)
+        if await self.critic_question(user_input, callbacks):
+            user_input = await self.modify_question(user_input, scenario, callbacks)
 
-        reference = await self.generate_answer(user_input, scenario)
+        reference = await self.generate_answer(user_input, scenario, callbacks)
 
         reference_contexts = []
         for node in scenario.nodes:
@@ -149,13 +154,16 @@ async def generate_sample(
             reference_contexts=reference_contexts,
         )
 
-    async def generate_user_input(self, scenario: AbstractQuestionScenario) -> str:
+    async def generate_user_input(
+        self, scenario: AbstractQuestionScenario, callbacks: Callbacks
+    ) -> str:
         question = await self.generate_user_input_prompt.generate(
             data=ThemeAndContext(
                 theme=scenario.theme,
                 context=self.make_source_text(scenario),
             ),
             llm=self.llm,
+            callbacks=callbacks,
         )
         return question.text
 
@@ -173,8 +181,8 @@ class ComparativeAbstractQuestionSimulator(QASimulator):
         default_factory=ComparativeAbstractQuestion
     )
 
-    async def generate_scenarios(
-        self, n: int, knowledge_graph: KnowledgeGraph
+    async def _generate_scenarios(
+        self, n: int, knowledge_graph: KnowledgeGraph, callbacks: Callbacks
     ) -> t.List[ComparativeAbstractQuestionScenario]:
         node_clusters = knowledge_graph.find_clusters(
             relationship_condition=lambda rel: (
@@ -220,6 +228,7 @@ async def generate_scenarios(
                         num_concepts=num_concepts,
                     ),
                     "llm": self.llm,
+                    "callbacks": callbacks,
                 }
             )
 
@@ -264,8 +273,8 @@ async def generate_scenarios(
             )
         return scenarios
 
-    async def generate_sample(
-        self, scenario: ComparativeAbstractQuestionScenario
+    async def _generate_sample(
+        self, scenario: ComparativeAbstractQuestionScenario, callbacks: Callbacks
     ) -> SingleTurnSample:
         # generate the user input
         keyphrases = []
@@ -285,15 +294,18 @@ async def generate_sample(
                 summaries=summaries,
             ),
             llm=self.llm,
+            callbacks=callbacks,
         )
         question = question.text
 
         # critic the question
-        if not await self.critic_question(question):
-            question = await self.modify_question(question, scenario)
+        if not await self.critic_question(question, callbacks):
+            question = await self.modify_question(question, scenario, callbacks)
 
         # generate the answer
-        answer = await self.generate_answer(question, scenario, "summary")
+        answer = await self.generate_answer(
+            question, scenario, callbacks, reference_property_name="summary"
+        )
 
         # make the reference contexts
         # TODO: make this more efficient. Right now we are taking only the summary

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

@@ -7,10 +7,13 @@
 
 from pydantic import BaseModel
 
+from ragas.callbacks import new_group
 from ragas.experimental.testset.graph import KnowledgeGraph, Node
 from ragas.llms import BaseRagasLLM, llm_factory
 
 if t.TYPE_CHECKING:
+    from langchain_core.callbacks import Callbacks
+
     from ragas.dataset_schema import BaseEvalSample
 
 
@@ -45,12 +48,48 @@ def __post_init__(self):
         if not self.name:
             self.name = self.__class__.__name__
 
-    @abstractmethod
     async def generate_scenarios(
-        self, n: int, knowledge_graph: KnowledgeGraph
+        self,
+        n: int,
+        knowledge_graph: KnowledgeGraph,
+        callbacks: t.Optional[Callbacks] = None,
+    ) -> t.List[Scenario]:
+        callbacks = callbacks or []
+        scenario_generation_rm, scenario_generation_group = new_group(
+            name=self.name,
+            inputs={"n": n, "knowledge_graph": str(knowledge_graph)},
+            callbacks=callbacks,
+        )
+        scenarios = await self._generate_scenarios(
+            n, knowledge_graph, scenario_generation_group
+        )
+        scenario_generation_rm.on_chain_end(outputs={"scenarios": scenarios})
+        return scenarios
+
+    @abstractmethod
+    async def _generate_scenarios(
+        self, n: int, knowledge_graph: KnowledgeGraph, callbacks: Callbacks
     ) -> t.List[Scenario]:
         pass
 
+    async def generate_sample(
+        self, scenario: Scenario, callbacks: t.Optional[Callbacks] = None
+    ) -> BaseEvalSample:
+        callbacks = callbacks or []
+
+        # new group for Sample Generation
+        sample_generation_rm, sample_generation_grp = new_group(
+            name=self.name,
+            inputs={"scenario": scenario},
+            callbacks=callbacks,
+        )
+        sample = await self._generate_sample(scenario, sample_generation_grp)
+        sample_generation_rm.on_chain_end(outputs={"sample": sample})
+
+        return sample
+
     @abstractmethod
-    async def generate_sample(self, scenario: Scenario) -> BaseEvalSample:
+    async def _generate_sample(
+        self, scenario: Scenario, callbacks: Callbacks
+    ) -> BaseEvalSample:
         pass

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

@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+import typing as t
 from dataclasses import dataclass, field
 
 from ragas.experimental.prompt import StringIO
@@ -13,6 +16,9 @@
     extend_modify_input_prompt,
 )
 
+if t.TYPE_CHECKING:
+    from langchain_core.callbacks import Callbacks
+
 
 @dataclass
 class QASimulator(BaseSimulator[Scenario]):
@@ -22,13 +28,19 @@ class QASimulator(BaseSimulator[Scenario]):
     )
     generate_reference_prompt: PydanticPrompt = field(default_factory=GenerateReference)
 
-    async def critic_question(self, question: str) -> bool:
+    async def critic_question(
+        self, question: str, callbacks: t.Optional[Callbacks] = None
+    ) -> bool:
+        callbacks = callbacks or []
         critic = await self.critic_user_input_prompt.generate(
-            data=StringIO(text=question), llm=self.llm
+            data=StringIO(text=question), llm=self.llm, callbacks=callbacks
         )
         return critic.independence > 1 and critic.clear_intent > 1
 
-    async def modify_question(self, question: str, scenario: Scenario) -> str:
+    async def modify_question(
+        self, question: str, scenario: Scenario, callbacks: t.Optional[Callbacks] = None
+    ) -> str:
+        callbacks = callbacks or []
         prompt = extend_modify_input_prompt(
             question_modification_prompt=self.user_input_modification_prompt,
             style=scenario.style,
@@ -41,21 +53,25 @@ async def modify_question(self, question: str, scenario: Scenario) -> str:
                 length=scenario.length,
             ),
             llm=self.llm,
+            callbacks=callbacks,
         )
         return modified_question.text
 
     async def generate_answer(
         self,
         question: str,
         scenario: Scenario,
+        callbacks: t.Optional[Callbacks] = None,
         reference_property_name: str = "page_content",
     ) -> str:
+        callbacks = callbacks or []
         reference = await self.generate_reference_prompt.generate(
             data=UserInputAndContext(
                 user_input=question,
                 context=self.make_source_text(scenario, reference_property_name),
             ),
             llm=self.llm,
+            callbacks=callbacks,
         )
         return reference.text