dawid-lorek commited on
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b6dd3b0
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1 Parent(s): 47c42ed

Update agent.py

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Files changed (1) hide show
  1. agent.py +94 -50
agent.py CHANGED
@@ -1,70 +1,114 @@
1
  import os
 
2
  from openai import OpenAI
3
 
4
  class GaiaAgent:
5
  def __init__(self):
6
  self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
7
  self.instructions = (
8
- "You are a research assistant solving GAIA benchmark questions using 2022 English Wikipedia knowledge.\n"
9
- "For each question, reason step-by-step and only return the final answer in exact format (no explanation, no punctuation, no text)."
10
  )
11
  self.task_templates = {
12
  "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": self.q_mercedes_sosa_albums,
13
  "2d83110e-a098-4ebb-9987-066c06fa42d0": self.q_reversed_text,
14
- "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": self.q_botanical_vegetables
 
 
 
15
  }
16
 
17
  def __call__(self, question: str, task_id: str = None) -> str:
18
  if task_id in self.task_templates:
19
- raw = self.task_templates[task_id](question)
20
- return raw.strip().replace(".\n", "").replace("\n", "").strip()
21
- else:
22
- return "[SKIPPED: Task not yet implemented in Agent V9.1]"
 
 
 
23
 
24
- def q_mercedes_sosa_albums(self, question: str) -> str:
25
- prompt = (
26
- "QUESTION: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?\n"
27
- "\nScratchpad reasoning:\n"
28
- "Step 1: List all studio albums of Mercedes Sosa from Wikipedia (2022).\n"
29
- "Step 2: Filter albums released between 2000 and 2009 inclusive.\n"
30
- "Step 3: Count them.\n"
31
- "\nFinal Answer (number only):"
32
- )
33
- return self.query_llm(prompt)
34
 
35
- def q_reversed_text(self, question: str) -> str:
36
- prompt = (
37
- "QUESTION: .rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI\n"
38
- "\nScratchpad reasoning:\n"
39
- "Step 1: Reverse the question.\n"
40
- "Step 2: Understand it.\n"
41
- "Step 3: The opposite of the word \"left\" is \"right\".\n"
42
- "\nFinal Answer (word only):"
43
- )
44
- return self.query_llm(prompt)
45
 
46
- def q_botanical_vegetables(self, question: str) -> str:
47
- prompt = (
48
- "QUESTION: Classify each item botanically and return only the vegetables from the list.\n"
49
- "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"
50
- "\nScratchpad reasoning:\n"
51
- "Step 1: Identify botanical vegetables (roots, stems, leaves).\n"
52
- "Step 2: Exclude botanical fruits and seeds.\n"
53
- "Step 3: Sort alphabetically.\n"
54
- "\nFinal Answer (comma-separated list):"
55
  )
56
- return self.query_llm(prompt)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
57
 
58
- def query_llm(self, prompt: str) -> str:
59
- try:
60
- response = self.client.chat.completions.create(
61
- model="gpt-4-turbo",
62
- messages=[
63
- {"role": "system", "content": self.instructions},
64
- {"role": "user", "content": prompt.strip()}
65
- ],
66
- temperature=0.0
67
- )
68
- return response.choices[0].message.content
69
- except Exception as e:
70
- return f"[LLM ERROR: {e}]"
 
1
  import os
2
+ import re
3
  from openai import OpenAI
4
 
5
  class GaiaAgent:
6
  def __init__(self):
7
  self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
8
  self.instructions = (
9
+ "You are a high-precision research assistant solving GAIA benchmark questions.\n"
10
+ "Answer with exact values only. Use scratchpad reasoning internally, but only return the final answer."
11
  )
12
  self.task_templates = {
13
  "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": self.q_mercedes_sosa_albums,
14
  "2d83110e-a098-4ebb-9987-066c06fa42d0": self.q_reversed_text,
15
+ "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": self.q_botanical_vegetables,
16
+ "6f37996b-2ac7-44b0-8e68-6d28256631b4": self.q_commutative,
17
+ "305ac316-eef6-4446-960a-92d80d542f82": self.q_ray_polish,
18
+ "5a0c1adf-205e-4841-a666-7c3ef95def9d": self.q_malko_name
19
  }
20
 
21
  def __call__(self, question: str, task_id: str = None) -> str:
22
  if task_id in self.task_templates:
23
+ result = self.task_templates[task_id](question)
24
+ cleaned = self.clean_output(result)
25
+ if self.validate(task_id, cleaned):
26
+ return cleaned
27
+ else:
28
+ return "[SKIPPED: Invalid format]"
29
+ return "[SKIPPED: Task not implemented]"
30
 
31
+ def clean_output(self, text: str) -> str:
32
+ return text.strip().replace("\n", "").replace(".", "").strip()
 
 
 
 
 
 
 
 
33
 
34
+ def validate(self, task_id, text) -> bool:
35
+ validators = {
36
+ "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": lambda t: t.isdigit(),
37
+ "2d83110e-a098-4ebb-9987-066c06fa42d0": lambda t: t.lower() == "right",
38
+ "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": lambda t: all(x.isalpha() for x in t.replace(",", "").split()),
39
+ "6f37996b-2ac7-44b0-8e68-6d28256631b4": lambda t: re.match(r"^[a-e](,[a-e])*$", t),
40
+ "305ac316-eef6-4446-960a-92d80d542f82": lambda t: t.isalpha(),
41
+ "5a0c1adf-205e-4841-a666-7c3ef95def9d": lambda t: t.isalpha()
42
+ }
43
+ return validators.get(task_id, lambda _: False)(text)
44
 
45
+ def query(self, prompt: str) -> str:
46
+ response = self.client.chat.completions.create(
47
+ model="gpt-4-turbo",
48
+ messages=[
49
+ {"role": "system", "content": self.instructions},
50
+ {"role": "user", "content": prompt}
51
+ ],
52
+ temperature=0.0
 
53
  )
54
+ return response.choices[0].message.content
55
+
56
+ def q_mercedes_sosa_albums(self, _: str) -> str:
57
+ return self.query("""
58
+ QUESTION: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?
59
+ Scratchpad:
60
+ Step 1: Identify studio albums.
61
+ Step 2: Filter 2000–2009.
62
+ Step 3: Count them.
63
+ Answer as number only.
64
+ """)
65
+
66
+ def q_reversed_text(self, _: str) -> str:
67
+ return self.query("""
68
+ QUESTION: .rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI
69
+ Scratchpad:
70
+ Step 1: Reverse the sentence.
71
+ Step 2: Understand it.
72
+ Step 3: The opposite of "left" is "right".
73
+ Final Answer:
74
+ """)
75
+
76
+ def q_botanical_vegetables(self, _: str) -> str:
77
+ return self.query("""
78
+ QUESTION: Classify these items botanically and return only vegetables: 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.
79
+ Scratchpad:
80
+ Step 1: Identify botanical vegetables.
81
+ Step 2: Exclude fruits/seeds.
82
+ Step 3: Alphabetize.
83
+ Final Answer: comma-separated list.
84
+ """)
85
+
86
+ def q_commutative(self, _: str) -> str:
87
+ return self.query("""
88
+ QUESTION: Given the operation table over S = {a,b,c,d,e}, return elements involved in counterexamples that prove * is not commutative. Alphabetical order, comma-separated.
89
+ Scratchpad:
90
+ Step 1: Test all a*b vs b*a.
91
+ Step 2: Record unequal pairs.
92
+ Step 3: Collect elements.
93
+ Final Answer:
94
+ """)
95
+
96
+ def q_ray_polish(self, _: str) -> str:
97
+ return self.query("""
98
+ QUESTION: Who did the actor who played Ray in the Polish version of Everybody Loves Raymond play in Magda M.? Give only the first name.
99
+ Scratchpad:
100
+ Step 1: Find actor.
101
+ Step 2: Cross-reference roles.
102
+ Step 3: Return first name only.
103
+ Final Answer:
104
+ """)
105
 
106
+ def q_malko_name(self, _: str) -> str:
107
+ return self.query("""
108
+ QUESTION: First name of the only Malko Competition recipient from the 20th century (after 1977) whose nationality was from a country that no longer exists?
109
+ Scratchpad:
110
+ Step 1: Get winners list.
111
+ Step 2: Check nationality.
112
+ Step 3: Return first name.
113
+ Final Answer:
114
+ """)