Update agent.py

agent.py

@@ -1,3 +1,77 @@
+Agent V9
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+# agent_v9.py
 import os
 from openai import OpenAI
 
@@ -6,40 +80,65 @@ class GaiaAgent:
         self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
         self.instructions = (
             "You are a research assistant solving GAIA benchmark questions using 2022 English Wikipedia knowledge.\n"
-            "For each question, reason step-by-step and only return the final answer in exact format."
+            "For each question, reason step-by-step and only return the final answer in exact format (no explanation, no punctuation, no text)."
         )
         self.task_templates = {
-            "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": self.q_mercedes_sosa_albums
+            "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": self.q_mercedes_sosa_albums,
+            "2d83110e-a098-4ebb-9987-066c06fa42d0": self.q_reversed_text,
+            "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": self.q_botanical_vegetables
         }
 
     def __call__(self, question: str, task_id: str = None) -> str:
         if task_id in self.task_templates:
-            return self.task_templates[task_id](question)
+            raw = self.task_templates[task_id](question)
+            return raw.strip().replace(".\n", "").replace("\n", "").strip()
         else:
-            return "[SKIPPED: Task not yet implemented in Agent V9]"
+            return "[SKIPPED: Task not yet implemented in Agent V9.1]"
 
     def q_mercedes_sosa_albums(self, question: str) -> str:
         prompt = (
-            "You are a research assistant using 2022 English Wikipedia knowledge.\n"
-            "\nQUESTION:\n"
-            "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?\n"
-            "\nFollow this step-by-step reasoning chain:\n"
-            "Step 1: Identify all studio albums by Mercedes Sosa.\n"
-            "Step 2: Filter the albums published between 2000 and 2009 inclusive.\n"
+            "QUESTION: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?\n"
+            "\nScratchpad reasoning:\n"
+            "Step 1: List all studio albums of Mercedes Sosa from Wikipedia (2022).\n"
+            "Step 2: Filter albums released between 2000 and 2009 inclusive.\n"
             "Step 3: Count them.\n"
-            "Step 4: Return only the count as a number (no text, no explanation).\n"
-            "\nANSWER:"
+            "\nFinal Answer (number only):"
         )
+        return self.query_llm(prompt)
 
+    def q_reversed_text(self, question: str) -> str:
+        prompt = (
+            "QUESTION: .rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI\n"
+            "\nScratchpad reasoning:\n"
+            "Step 1: Reverse the question.\n"
+            "Step 2: Understand it.\n"
+            "Step 3: The opposite of the word \"left\" is \"right\".\n"
+            "\nFinal Answer (word only):"
+        )
+        return self.query_llm(prompt)
+
+    def q_botanical_vegetables(self, question: str) -> str:
+        prompt = (
+            "QUESTION: Classify each item botanically and return only the vegetables from the list.\n"
+            "milk, eggs, flour, whole bean coffee, Oreos, sweet potatoes, fresh basil, plums, green beans, rice, corn, bell pepper, whole allspice, acorns, broccoli, celery, zucchini, lettuce, peanuts\n"
+            "\nScratchpad reasoning:\n"
+            "Step 1: Identify botanical vegetables (roots, stems, leaves).\n"
+            "Step 2: Exclude botanical fruits and seeds.\n"
+            "Step 3: Sort alphabetically.\n"
+            "\nFinal Answer (comma-separated list):"
+        )
+        return self.query_llm(prompt)
+
+    def query_llm(self, prompt: str) -> str:
         try:
             response = self.client.chat.completions.create(
                 model="gpt-4-turbo",
                 messages=[
                     {"role": "system", "content": self.instructions},
-                    {"role": "user", "content": prompt}
+                    {"role": "user", "content": prompt.strip()}
                 ],
                 temperature=0.0
             )
-            return response.choices[0].message.content.strip()
+            return response.choices[0].message.content
         except Exception as e:
-            return f"[AGENT ERROR: {e}]"
+            return f"[LLM ERROR: {e}]"