Function Calling Evaluation

This repository is designed for evaluating pythonic function calling abilities. Designed by Dria.

def extract_text_from_pdf_document(file_path: str) -> str:
    """Extracts text from a PDF document at the specified file path.
        Args:
            file_path (str): The path to the PDF document

        Returns:
            str: The extracted text from the PDF document
    """
                            
        pass

def extract_text_from_pdf_document(file_path: str) -> str:
    mock_outputs = {
        'empty.pdf': '',
        'large.pdf': 'This is a large document with lots of text...',
        'error.pdf': 'Error: Unable to extract text.',
    }
    return mock_outputs.get(file_path, 'Default mock text from the PDF document.')

JSON Mode Eval

Proposed format for json-mode eval data:

Prompt	Completion	Schema	SchemaJSON

Prompt

[
  {
     "content":"You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{'title': 'PortfolioPerformance', 'type': 'object', 'properties': {'capitalGains': {'title': 'Capital Gains', 'type': 'number', 'format': 'currency'}, 'interestIncome': {'title': 'Interest Income', 'type': 'number', 'format': 'currency'}, 'totalReturn': {'title': 'Total Return', 'type': 'number', 'format': 'percentage'}}, 'required': ['capitalGains', 'interestIncome', 'totalReturn']}\n</schema>\n",
     "role":"system"
  },
  {
     "content":"I am analyzing the performance of our mortgage REIT's investment portfolio and need to track key financial metrics. Specifically, I require a JSON object that captures the following data points: capital gains realized this quarter, which are $1,250,000.00, interest income generated from mortgage-backed securities, which is $375,000.00, and the total return percentage over the past year, which is 8.5%. This information will be used to update our investors and for internal performance reviews. Please provide a JSON object that includes these metrics with appropriate formatting for currency and percentages.",
     "role":"user"
  }]

Completion

{"capitalGains": 1250000.0, "interestIncome": 375000.0, "totalReturn": 8.5} 

Schema

from pydantic import BaseModel, Field

class PortfolioPerformance(BaseModel):
    capitalGains: float = Field(..., title="Capital Gains", description="Capital Gains", example=0.0)
    interestIncome: float = Field(..., title="Interest Income", description="Interest Income", example=0.0)
    totalReturn: float = Field(..., title="Total Return", description="Total Return as a percentage", example=0.0)
    
    class Config:
        schema_extra = {
            "example": {
                "capitalGains": 1000.50,
                "interestIncome": 500.25,
                "totalReturn": 0.12,  # Represented as a percentage
            }
        }

SchemaJSON

{"title": "PortfolioPerformance", "type": "object", "properties": {"capitalGains": {"title": "Capital Gains", "type": "number", "format": "currency"}, "interestIncome": {"title": "Interest Income", "type": "number", "format": "currency"}, "totalReturn": {"title": "Total Return", "type": "number", "format": "percentage"}}, "required": ["capitalGains", "interestIncome", "totalReturn"]}

Evaluation Method

Evaluate by deserializing completion into given python class Schema.

Pythonic Function Calling Eval

Proposed format for pythonic function calling eval data:

Field	Description	Example
needs_judge	Whether human evaluation is needed	true/false
function_schema_json	JSON representation of available functions	{"name": "get_tweets", "parameters": {...}}
function_schema_python	Python function definitions with types	def get_tweets(hashtag: str) -> list[str]
expected_output	Expected output from function calls	["tweet1", "tweet2"]
mock_function	Mock implementation returning expected output	def mock_get_tweets(): return ["tweet1"]
completion	The assistant's response/code	result = get_tweets("#AI")
user_query	The user's original request	"Get latest AI tweets"
function_results	Results from executing the functions	<function_results>tweets = ["tweet1", "tweet2"]</function_results>

System Prompt Template

You are an expert AI assistant that specializes in providing Python code to solve the task/problem at hand provided by the user.

You can use Python code freely, including the following available functions:

<|functions_schema|> {{functions_schema}} <|end_functions_schema|>

Think step by step and provide your reasoning, outside of the function calls. You can write Python code and use the available functions. The multi-turn conversation between you and the user starts now. The user will provide you with the results of the code execution, in between <|function_results|> and <|end_function_results|> tags and you will answer as if you were directly answering the user. In this second response, be concise and to the point. Provide all your python code in a SINGLE markdown code block like the following:

result = example_function(arg1, "string")
result2 = example_function2(result, arg2)

DO NOT use print() statements AT ALL. Avoid mutating variables whenever possible.

functions_schema: functions_schema is just function definitions in Python, with typed arguments and return type(s), the docstring and a mock implementation.

Example Row

Needs Judge:

True

Function Schema JSON:

{
  "functions": [
    {
      "name": "get_tweets",
      "parameters": {
        "hashtag": {"type": "string"},
        "num_tweets": {"type": "integer"}
      }
    },
    {
      "name": "get_fighter_stats", 
      "parameters": {
        "fighter": {"type": "string"}
      }
    },
    {
      "name": "get_sentiment",
      "parameters": {
        "text": {"type": "string"}
      }
    },
    {
      "name": "send_email",
      "parameters": {
        "to": {"type": "string"},
        "subject": {"type": "string"},
        "body": {"type": "string"}
      }
    }
  ]
}

Functions Schema:

def get_tweets(hashtag: str, num_tweets: int) -> list[str]:
    """Get the latest tweets with a given hashtag."""
    pass

def get_fighter_stats(fighter: str) -> dict:
    """Get the stats for a given fighter."""
    pass

def get_sentiment(text: str) -> float:
    """Get the sentiment of a given text."""
    pass

def send_email(to: str, subject: str, body: str) -> str:
    """Send an email to a given recipient."""
    pass

Mock Function:

def get_tweets(hashtag: str, num_tweets: int) -> list[str]:
    """Get the latest tweets with a given hashtag."""
    return ["good tweet"] * num_tweets

def get_fighter_stats(fighter: str) -> dict:
    """Get the stats for a given fighter."""
    return {"name": fighter, "wins": 20, "losses": 5, "draws": 1}

def get_sentiment(text: str) -> float:
    """Get the sentiment of a given text."""
    return 0.8

def send_email(to: str, subject: str, body: str) -> str:
    """Send an email to a given recipient."""
    return f"Email with title {subject} sent to {to} with body {body}."

Expected Output:

tweets = get_tweets("#MMA", 10)
sentiment = sum([get_sentiment(tweet) for tweet in tweets]) / len(tweets)
fighter_stats = get_fighter_stats("Sean Strickland")
email_body = f"The sentiment about MMA is {str(sentiment)} and the stats for Sean Strickland are {fighter_stats}."
send_email("[email protected]", "MMA Sentiment and Stats for Sean Strickland", email_body)

Completion:

tweets = get_tweets("#MMA", 10)
sentiment_total = sum([get_sentiment(tweet) for tweet in tweets])
sentiment = sentiment_total / len(tweets)
fighter_stats = get_fighter_stats("Sean Strickland")
email_body = f"The sentiment about MMA is {str(sentiment)} and the stats for Sean Strickland are {fighter_stats}."
send_email("[email protected]", "MMA Sentiment and Stats for Sean Strickland", email_body)

User Query:

Can you send my friend ([email protected]) an email about the current sentiment about MMA in twitter and the stats for Sean Strickland please?

Function Results: