Removed uneeded aio fix

scripts/generate_synthetic_testset.py

@@ -1,15 +1,24 @@
+import json
+import logging
+from argparse import ArgumentParser
+from typing import List
+
 import nest_asyncio
+from langchain.docstore.document import Document
 from langchain_community.chat_models import ChatOllama
 from langchain_community.embeddings import OllamaEmbeddings
 from ragas.run_config import RunConfig
 from ragas.testset.evolutions import multi_context, reasoning, simple
 from ragas.testset.generator import TestsetGenerator
-from langchain.docstore.document import Document
-import json
-from argparse import ArgumentParser
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(
+    level=logging.INFO,
+    handlers=[logging.StreamHandler()],
+)
 
 
-def load_metadata(metadata_file):
+def load_metadata(metadata_file: str) -> List[Document]:
     with open(metadata_file) as f:
         json_data = json.load(f)
         return [
@@ -21,16 +30,16 @@ def load_metadata(metadata_file):
         ]
 
 
-def main(metadata_file, testset_output_file, testset_size=5):
-    nest_asyncio.apply()
+def main(metadata_file: str, testset_output_file: str, testset_size: int = 5) -> None:
     docs = load_metadata(metadata_file)
+    logger.info(f"Loaded {len(docs)} documents from {metadata_file}")
     llm = ChatOllama(model="mistral-nemo", num_ctx=16384)
     embeddings = OllamaEmbeddings(model="mistral-nemo", num_ctx=16384)
     gen = TestsetGenerator.from_langchain(
-        llm, llm, embeddings, run_config=RunConfig(max_workers=1, max_retries=1)
+        llm, llm, embeddings, run_config=RunConfig(max_workers=1, max_retries=2)
     )
     dist = {simple: 0.6, multi_context: 0.2, reasoning: 0.2}
-    testset = gen.generate_with_langchain_docs(docs, testset_size, dist, is_async=False)
+    testset = gen.generate_with_langchain_docs(docs, testset_size, dist)
     df = testset.to_pandas()
     df.to_csv(testset_output_file, index=False)