feat: running basic tests

llama-index-packs/llama-index-packs-ersatz-o1/examples/simple_planing.py

@@ -8,19 +8,17 @@
 llm = OpenAI(model="gpt-4-turbo")
 
 task_context = """
-Every day, Wendi feeds each of her chickens three cups of mixed chicken feed, containing seeds, mealworms and vegetables
-to help keep them healthy. She gives the chickens their feed in three separate meals. In the morning, she gives her flock of
-chickens 15 cups of feed. In the afternoon, she gives her chickens another 25 cups of feed.  
+Tim gets a promotion that offers him a 5% raise on his $20000 a month salary. It also gives him a bonus worth half a month’s salary. 
 """
 
 query_engine = ErsatzO1QueryEngine(
     context=task_context,
     llm=llm,
-    reasoning_paths=5,
+    reasoning_paths=20,
     verbose=True,
 )
 
 if __name__ == "__main__":
-    res = query_engine.query("How many cups of feed does she need to give her chickens in the final meal of the day if the size of Wendi’s flock is 20 chickens?")
+    res = query_engine.query("How much money will he make in a year?")
     print(res)
 

llama-index-packs/llama-index-packs-ersatz-o1/llama_index/packs/ersatz_o1/base.py

@@ -79,7 +79,7 @@ async def async_textual_reasoning(
         llm: LLM,
         num_paths: int = 5,
         verbose: bool = False,
-        temperature: float = 0.01,
+        temperature: float = 1.99,
 ) -> List[Tuple[str, int]]:
     """
     Perform asynchronous textual reasoning using the ErsatzO1 approach.
@@ -96,8 +96,11 @@ async def async_textual_reasoning(
             question=query_str,
             context=context,
         )
-        answer, confidence = parse_response(str(response))
-        results.append((answer, confidence))
+        try:
+            answer, confidence = parse_response(str(response))
+            results.append((answer, confidence))
+        except:
+            print(f"Error parsing response: {response}")
 
     return results