testset generation : Improvements

requirements/test.txt

@@ -1,2 +1,3 @@
 pytest
 pytest-xdist[psutil]
+llama_index

src/ragas/testset/prompts.py

@@ -120,11 +120,10 @@
 
 FILTER_QUESTION = HumanMessagePromptTemplate.from_template(
     """\
-Determine if the given question can be clearly understood even when presented without any additional context? Reason before arriving at the answer.
+Determine if the given question can be clearly understood even when presented without any additional context. Specify reason and verdict is a valid json format.
 question: What is the keyword that best describes the paper's focus in natural language understanding tasks?
-answer: The specific paper being referred to is not mentioned in the question. Hence, No.
+{{"reason":"The specific paper being referred to is not mentioned in the question.", "verdict": "No"}}
 question:{question}
-answer:
 """  # noqa: E501
 )
 

src/ragas/testset/testset_generator.py

@@ -1,4 +1,5 @@
-import re
+from __future__ import annotations
+
 import typing as t
 import warnings
 from collections import defaultdict, namedtuple
@@ -33,6 +34,7 @@
     SCORE_CONTEXT,
     SEED_QUESTION,
 )
+from ragas.testset.utils import load_as_json, load_as_score
 
 DEFAULT_TEST_DISTRIBUTION = {
     "simple": 0.4,
@@ -138,6 +140,7 @@ def from_default(
         openai_filter_llm: str = "gpt-4",
         chat_qa: float = 0.3,
         chunk_size: int = 512,
+        testset_distribution: dict = DEFAULT_TEST_DISTRIBUTION,
     ):
         generator_llm = ChatOpenAI(model=openai_generator_llm)
         critic_llm = ChatOpenAI(model=openai_filter_llm)
@@ -148,6 +151,7 @@ def from_default(
             embeddings_model=embeddings_model,
             chat_qa=chat_qa,
             chunk_size=chunk_size,
+            testset_distribution=testset_distribution,
         )
 
     def _get_evolve_type(self) -> str:
@@ -175,12 +179,7 @@ def _filter_context(self, context: str) -> bool:
         prompt = ChatPromptTemplate.from_messages([human_prompt])
         results = generate(prompts=[prompt], llm=self.critic_llm)
         output = results.generations[0][0].text.strip()
-        pattern = r"^[\d.]+$"
-        if not re.match(pattern, output):
-            score = 0.0
-        else:
-            score = eval(output)
-
+        score = load_as_score(output)
         return score >= self.threshold
 
     def _seed_question(self, context: str) -> str:
@@ -193,7 +192,9 @@ def _filter_question(self, question: str) -> bool:
         human_prompt = FILTER_QUESTION.format(question=question)
         prompt = ChatPromptTemplate.from_messages([human_prompt])
         results = generate(prompts=[prompt], llm=self.critic_llm)
-        return bool(results.generations[0][0].text.strip().endswith("Yes."))
+        results = results.generations[0][0].text.strip()
+        json_results = load_as_json(results)
+        return json_results.get("verdict") != "No"
 
     def _reasoning_question(self, question: str, context: str) -> str:
         return self._qc_template(REASONING_QUESTION, question, context)
@@ -320,6 +321,9 @@ def generate(self, documents: t.List[Document], test_size: int) -> TestDataset:
             if not score:
                 continue
             seed_question = self._seed_question(text_chunk)
+            is_valid_question = self._filter_question(seed_question)
+            if not is_valid_question:
+                continue
 
             if evolve_type == "multi_context":
                 # Find most similar chunk in same document
@@ -361,10 +365,14 @@ def generate(self, documents: t.List[Document], test_size: int) -> TestDataset:
                 else:
                     question = self._compress_question(question=question)
 
-            context = self._generate_context(question, text_chunk)
-            answer = self._generate_answer(question, context)
-            samples.append(DataRow(question.split("\n"), context, answer, evolve_type))
-            count += 1
-            pbar.update(count)
+            is_valid_question = self._filter_question(question)
+            if is_valid_question:
+                context = self._generate_context(question, text_chunk)
+                answer = self._generate_answer(question, context)
+                samples.append(
+                    DataRow(question.split("\n"), context, answer, evolve_type)
+                )
+                count += 1
+                pbar.update(count)
 
         return TestDataset(test_data=samples)

src/ragas/testset/utils.py

@@ -0,0 +1,31 @@
+import json
+import re
+import warnings
+
+
+def load_as_json(text):
+    """
+    validate and return given text as json
+    """
+
+    try:
+        return json.loads(text)
+    except ValueError:
+        warnings.warn("Invalid json")
+
+    return {}
+
+
+def load_as_score(text):
+    """
+    validate and returns given text as score
+    """
+
+    pattern = r"^[\d.]+$"
+    if not re.match(pattern, text):
+        warnings.warn("Invalid score")
+        score = 0.0
+    else:
+        score = eval(text)
+
+    return score

tests/unit/test_simple.py

@@ -1,4 +1,6 @@
 def test_import():
     import ragas
+    from ragas.testset.testset_generator import TestsetGenerator
 
+    assert TestsetGenerator is not None
     assert ragas is not None