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Iamsdt/function_calling.py

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TOON Format Benchmark: Efficient Structured Output with Gemini
Raw
function_calling.py
from google.genai import types
from google.genai import Client
from dotenv import load_dotenv
from toon_format import encode, decode
load_dotenv()
client = Client()
def get_current_weather(location: str) -> str:
"""Returns the current weather and 15-day forecast.
Args:
location: The city and state, e.g. San Francisco, CA
"""
forecast = []
for i in range(16): # 0 for today, 1-15 for next 15 days
day_data = {
"date": f"2025-12-{21 + i:02d}", # Starting from Dec 21, 2025
"temperature": f"{70 + (i % 10)} F", # Varying temperatures
"condition": ["sunny", "cloudy", "rainy", "partly cloudy"][i % 4],
}
forecast.append(day_data)
data = {
"location": location,
"current": {
"temperature": "72 F",
"condition": "sunny",
},
"forecast": forecast,
}
return encode(data)
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="What is the weather like in New York? Share Next 15 days forecast as well.",
config=types.GenerateContentConfig(
tools=[get_current_weather],
thinking_config=types.ThinkingConfig(
include_thoughts=False,
thinking_budget=0,
),
),
)
print(response.text)
print(response.usage_metadata)
Raw
jd_extractions-pydantic-flash-2.5.json
[
{
"JD Index": 0,
"Tokens Used": 1008,
"Prompt Toknen": 639,
"Completion Tokens": 369,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 1,
"Tokens Used": 1191,
"Prompt Toknen": 716,
"Completion Tokens": 475,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 2,
"Tokens Used": 1088,
"Prompt Toknen": 726,
"Completion Tokens": 362,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 3,
"Tokens Used": 1002,
"Prompt Toknen": 646,
"Completion Tokens": 356,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 4,
"Tokens Used": 1065,
"Prompt Toknen": 648,
"Completion Tokens": 417,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 5,
"Tokens Used": 1034,
"Prompt Toknen": 635,
"Completion Tokens": 399,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 6,
"Tokens Used": 1137,
"Prompt Toknen": 688,
"Completion Tokens": 449,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 7,
"Tokens Used": 964,
"Prompt Toknen": 593,
"Completion Tokens": 371,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 8,
"Tokens Used": 956,
"Prompt Toknen": 594,
"Completion Tokens": 362,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 9,
"Tokens Used": 872,
"Prompt Toknen": 584,
"Completion Tokens": 288,
"Parsing Status": "Success",
"method": "json"
}
]
Raw
jd_extractions-toon.json
[
{
"JD Index": 0,
"Tokens Used": 1200,
"Prompt Toknen": 794,
"Completion Tokens": 406,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 1,
"Tokens Used": 1353,
"Prompt Toknen": 871,
"Completion Tokens": 482,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 2,
"Tokens Used": 1291,
"Prompt Toknen": 881,
"Completion Tokens": 410,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 3,
"Tokens Used": 1243,
"Prompt Toknen": 801,
"Completion Tokens": 442,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 4,
"Tokens Used": 1188,
"Prompt Toknen": 803,
"Completion Tokens": 385,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 5,
"Tokens Used": 1364,
"Prompt Toknen": 790,
"Completion Tokens": 574,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 6,
"Tokens Used": 1276,
"Prompt Toknen": 843,
"Completion Tokens": 433,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 7,
"Tokens Used": 1282,
"Prompt Toknen": 748,
"Completion Tokens": 534,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 8,
"Tokens Used": 1182,
"Prompt Toknen": 749,
"Completion Tokens": 433,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 9,
"Tokens Used": 1194,
"Prompt Toknen": 739,
"Completion Tokens": 455,
"Parsing Status": "Success",
"method": "toon"
}
]
Raw
jd_extractions.json
[
{
"JD Index": 0,
"Tokens Used": 1136,
"Prompt Toknen": 715,
"Completion Tokens": 421,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 1,
"Tokens Used": 1278,
"Prompt Toknen": 792,
"Completion Tokens": 486,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 2,
"Tokens Used": 1277,
"Prompt Toknen": 802,
"Completion Tokens": 475,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 3,
"Tokens Used": 1284,
"Prompt Toknen": 722,
"Completion Tokens": 562,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 4,
"Tokens Used": 1159,
"Prompt Toknen": 724,
"Completion Tokens": 435,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 5,
"Tokens Used": 1236,
"Prompt Toknen": 711,
"Completion Tokens": 525,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 6,
"Tokens Used": 1199,
"Prompt Toknen": 764,
"Completion Tokens": 435,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 7,
"Tokens Used": 1113,
"Prompt Toknen": 669,
"Completion Tokens": 444,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 8,
"Tokens Used": 1151,
"Prompt Toknen": 670,
"Completion Tokens": 481,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 9,
"Tokens Used": 1101,
"Prompt Toknen": 660,
"Completion Tokens": 441,
"Parsing Status": "Success",
"method": "json"
},
{
"JD Index": 0,
"Tokens Used": 907,
"Prompt Toknen": 721,
"Completion Tokens": 186,
"Parsing Status": "Failed",
"method": "toon"
},
{
"JD Index": 1,
"Tokens Used": 938,
"Prompt Toknen": 798,
"Completion Tokens": 140,
"Parsing Status": "Failed",
"method": "toon"
},
{
"JD Index": 2,
"Tokens Used": 1056,
"Prompt Toknen": 808,
"Completion Tokens": 248,
"Parsing Status": "Failed",
"method": "toon"
},
{
"JD Index": 3,
"Tokens Used": 1084,
"Prompt Toknen": 728,
"Completion Tokens": 356,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 4,
"Tokens Used": 881,
"Prompt Toknen": 730,
"Completion Tokens": 151,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 5,
"Tokens Used": 994,
"Prompt Toknen": 717,
"Completion Tokens": 277,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 6,
"Tokens Used": 1083,
"Prompt Toknen": 770,
"Completion Tokens": 313,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 7,
"Tokens Used": 914,
"Prompt Toknen": 675,
"Completion Tokens": 239,
"Parsing Status": "Failed",
"method": "toon"
},
{
"JD Index": 8,
"Tokens Used": 887,
"Prompt Toknen": 676,
"Completion Tokens": 211,
"Parsing Status": "Success",
"method": "toon"
},
{
"JD Index": 9,
"Tokens Used": 815,
"Prompt Toknen": 666,
"Completion Tokens": 149,
"Parsing Status": "Success",
"method": "toon"
}
]
Raw
pydantic_code.py
import json
from google.genai import Client
from dotenv import load_dotenv
from pydantic import BaseModel
from toon_format import encode, decode
load_dotenv()
class JobModel(BaseModel):
Role: str
Primary_Skills: list[str]
Secondary_Skills: list[str]
Minimum_Experience: str
Maximum_Experience: str
Location: str
Employment_Type: str
Summary: str
Responsibilities: list[str]
client = Client()
with open("jds.json") as f:
jds = json.load(f)
stats = []
def check_json(jds):
for i, v in enumerate(jds):
prompt = f"""Extract Role, Primary Skills, Secondary Skills,
Minimum Experience, Maximum Experience,
Location, Employment Type, Summary, and Responsibilities
from the following job description in JSON format.
Job Description:
```
{v}
```
"""
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config={
"thinking_config": {
"include_thoughts": False,
"thinking_budget": 0,
},
"response_mime_type": "application/json",
"response_schema": JobModel,
},
)
print(f"JD#{i}", "Tokens used:", response.usage_metadata.total_token_count)
text = response.text or ""
try:
if not response.parsed:
raise json.JSONDecodeError("No parsed data", text, 0)
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Success",
"method": "json",
}
)
except json.JSONDecodeError:
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Failed",
"method": "json",
}
)
check_json(jds)
print("done")
with open("jd_extractions-pydantic-flash-2.5.json", "w") as f:
json.dump(stats, f, indent=4)
Raw
test.py
import json
from google.genai import Client
from dotenv import load_dotenv
from toon_format import encode, decode
load_dotenv()
client = Client()
with open("jds.json") as f:
jds = json.load(f)
stats = []
def check_json(jds):
for i, v in enumerate(jds):
prompt = f"""Extract Role, Primary Skills, Secondary Skills,
Minimum Experience, Maximum Experience,
Location, Employment Type, Summary, and Responsibilities
from the following job description in JSON format.
Job Description:
```
{v}
```
JSON Output:
```
{{
"Role": "",
"Primary Skills": [],
"Secondary Skills": [],
"Minimum Experience": "",
"Maximum Experience": "",
"Location": "",
"Employment Type": "",
"Summary": "",
"Responsibilities": []
}}
```
"""
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config={
"thinking_config": {
"include_thoughts": False,
"thinking_budget": 0,
}
},
)
print(f"JD#{i}", "Tokens used:", response.usage_metadata.total_token_count)
text = response.text or ""
try:
text = text.replace("```", "").strip()
text = text.replace("json", "").strip()
data = json.loads(text)
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Success",
"method": "json",
}
)
except json.JSONDecodeError:
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Failed",
"method": "json",
}
)
def check_toon(jds):
for i, v in enumerate(jds):
prompt = f"""Extract Role, Primary Skills, Secondary Skills,
Minimum Experience, Maximum Experience,
Location, Employment Type, Summary, and Responsibilities
from the following job description in TOON format.
Job Description:
```
{v}
```
TOON Output:
```
Role: ""
"Primary Skills"[2]: Python,JavaScript
"Secondary Skills"[2]: Responsibility,Communication
"Minimum Experience": ""
"Maximum Experience": ""
Location: ""
"Employment Type": ""
Summary: ""
Responsibilities[2]: Task A,Task B
```
"""
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config={
"thinking_config": {
"include_thoughts": False,
"thinking_budget": 0,
}
},
)
print(f"JD#{i}", "Tokens used:", response.usage_metadata.total_token_count)
text = response.text or ""
try:
text = text.replace("```", "").strip()
data = decode(text)
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Success",
"method": "toon",
}
)
except Exception as e:
print(f"Error parsing TOON format for JD#{i}: {e}")
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Failed",
"method": "toon",
}
)
check_json(jds)
check_toon(jds)
print("done")
with open("jd_extractions.json", "w") as f:
json.dump(stats, f, indent=4)
Raw
toon_improved.py
import json
from google.genai import Client
from dotenv import load_dotenv
from toon_format import encode, decode
load_dotenv()
client = Client()
with open("jds.json") as f:
jds = json.load(f)
stats = []
def check_toon(jds):
for i, v in enumerate(jds):
prompt = f"""
Extract Role, Primary_Skills, Secondary_Skills,
Minimum_Experience, Maximum_Experience,
Location, Employment_Type, Summary, and Responsibilities
Job Description:
```
{v}
```
Output in TOON format. Example structure:
```
Role: "Senior Data Scientist"
Primary_Skills:
[0]: "Python"
[1]: "Machine Learning"
[2]: "Statistical Analysis"
Secondary_Skills:
[0]: "Big Data"
[1]: "Cloud Platforms"
Minimum_Experience: "5 years"
Maximum_Experience: "10 years"
Location: "New York, NY or Remote"
Employment_Type: "Full-time"
Summary: "Lead data science initiatives"
Responsibilities:
[0]: "Design ML models"
[1]: "Analyze datasets"
```
Now provide the extraction in TOON format. Keep the format exactly as shown above.
"""
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config={
"thinking_config": {
"include_thoughts": False,
"thinking_budget": 0,
}
},
)
print(f"JD#{i}", "Tokens used:", response.usage_metadata.total_token_count)
text = response.text or ""
try:
text = text.replace("```", "").strip()
print("TOON Response:")
data = decode(text)
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Success",
"method": "toon",
}
)
except Exception as e:
print(f"Error parsing TOON format for JD#{i}: {e}")
stats.append(
{
"JD Index": i,
"Tokens Used": response.usage_metadata.total_token_count,
"Prompt Toknen": response.usage_metadata.prompt_token_count,
"Completion Tokens": response.usage_metadata.candidates_token_count,
"Parsing Status": "Failed",
"method": "toon",
}
)
check_toon(jds)
print("done")
with open("jd_extractions-toon.json", "w") as f:
json.dump(stats, f, indent=4)
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