Status: True
Assertion
The square root of 16 is 5.
Results
Understanding the definition and operation of a mathematical square root is fundamental for performing calculations involving roots correctly. Knowing how to calculate or approximate square roots allows one to verify results effectively. This statement highlights the importance of understanding square roots in ensuring accurate outcomes when working with these operations.
– True: Basic arithmetic knowledge, specifically multiplication, plays an essential role when verifying the correctness of square roots because it’s involved in squaring numbers (e.g., 4 * 4 = 16). Without this foundational skill, understanding and working with square roots would be challenging. This statement emphasizes how basic arithmetic knowledge is crucial for correctly verifying square root calculations.
– True: Acceptance that the value being evaluated is correct helps identify potential errors during calculations or misunderstandings of concepts. In this example, if someone calculated a different result than 4 for the square root of 16, they could focus on where their process went wrong instead of questioning the validity of the original number (16). This statement emphasizes the importance of assessing correctness in determining errors when working with square roots.
– True: Familiarity with common numerical values and their
Assumptions
<|assistant|> – The definition and operation of a mathematical square root are understood.
– Knowledge about basic arithmetic, particularly multiplication, as it’s needed to verify square roots.
– Acceptance that the value being evaluated (16 in this case) is correct; otherwise, there might be confusion regarding the error point.
– Awareness of common numerical values and their square roots for quick reference or verification purposes.
– An understanding that mathematical statements can sometimes contain errors due to misinterpretation or calculation mistakes.
Fact Checker Output
<|assistant|> – True: Understanding the definition and operation of a mathematical square root is fundamental for performing calculations involving roots correctly. Knowing how to calculate or approximate square roots allows one to verify results effectively.
– True: Basic arithmetic knowledge, specifically multiplication, plays an essential role when verifying the correctness of square roots because it’s involved in squaring numbers (e.g., 4 * 4 = 16). Without this foundational skill, understanding and working with square roots would be challenging.
– True: Acceptance that the value being evaluated is correct helps identify potential errors during calculations or misunderstandings of concepts. In this example, if someone calculated a different result than 4 for the square root of 16, they could focus on where their process went wrong instead of questioning the validity of the original number (16).
– True: Familiarity with common numerical values and their square roots allows quick reference or verification. This knowledge can help individuals identify incorrect results more efficiently since some numbers have well-known square root values, such as 9 having a square root of 3.
– True: Recognizing that mistakes may occur due to misinterpretation or calculation
Model Used
microsoft/Phi-3-mini-4k-instruct-gguf
Script Name
fact_checker_mongodb.py
Script Code
import os
import sys
import requests
from langchain_openai.llms import OpenAI
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
from statements import get_random_statement
from mongodb_helper import insert_record # Import MongoDB helper functions
import html
# Load environment variables from .env file
load_dotenv()
# Define category IDs
CATEGORY_IDS = {
"True": 2,
"False": 3,
"Debatable": 4,
"Unknown": 6
}
def create_wordpress_post(title, content, category):
data = {
"title": title,
"content": content,
"status": "publish",
"categories": [CATEGORY_IDS[category]]
}
response = requests.post(
os.getenv("WORDPRESS_POSTS_URL"),
json=data,
auth=(os.getenv("WORDPRESS_USERNAME"), os.getenv("WORDPRESS_PASSWORD"))
)
if response.status_code == 201:
print("Blog post created successfully.")
else:
print(f"Failed to create blog post: {response.status_code} - {response.text}")
def fact_check(assertion):
llm = OpenAI(temperature=0.7, model=os.getenv("MODEL_NAME"))
# Define the prompt templates
assertion_template = """{assertion}\n\n"""
assertion_prompt = PromptTemplate(input_variables=["assertion"], template=assertion_template)
assumptions_template = """Here is a statement:
{statement}
Make a bullet point list of the assumptions required to support the above statement.\n\n"""
assumptions_prompt = PromptTemplate(input_variables=["statement"], template=assumptions_template)
fact_checker_template = """Here is a bullet point list of assertions:
{assertions}
For each assumption, determine whether it is true or false. Explain your reasoning.\n\n"""
fact_checker_prompt = PromptTemplate(input_variables=["assertions"], template=fact_checker_template)
answer_template = """
Here is the information to classify the statement:
{facts}
Based on the above information, how would you classify the statement? Respond with one of the following options followed by a colon and space:
- True: [Explanation]
- False: [Explanation]
- Debatable: [Explanation]
"""
answer_prompt = PromptTemplate(input_variables=["facts"], template=answer_template)
# Format prompts and extract the string content
formatted_assertion = assertion_prompt.format_prompt(assertion=assertion).text
assertion_output = llm.invoke(formatted_assertion)
formatted_assumptions = assumptions_prompt.format_prompt(statement=assertion_output).text
assumptions_output = llm.invoke(formatted_assumptions)
formatted_fact_checker = fact_checker_prompt.format_prompt(assertions=assumptions_output).text
fact_checker_output = llm.invoke(formatted_fact_checker)
formatted_answer = answer_prompt.format_prompt(facts=fact_checker_output).text
final_output = llm.invoke(formatted_answer)
return {
"assertion_output": assertion_output,
"assumptions_output": assumptions_output,
"fact_checker_output": fact_checker_output,
"final_output": final_output,
}
def extract_status_and_reasoning(final_output):
final_output = final_output.strip()
if "True:" in final_output:
status_start = final_output.find("True:")
status = "True"
elif "False:" in final_output:
status_start = final_output.find("False:")
status = "False"
elif "Debatable:" in final_output:
status_start = final_output.find("Debatable:")
status = "Debatable"
else:
return "Unknown", final_output
reasoning = final_output[status_start + len(status) + 1:].strip()
return status, reasoning
if __name__ == "__main__":
if len(sys.argv) > 1:
assertion = sys.argv[1]
else:
assertion = get_random_statement()
print(assertion)
submission = fact_check(assertion)
# Print the detailed outputs to inspect their structure
for key, value in submission.items():
print(f"{key}: {value}")
# Extract the final output for status determination and reasoning
final_output = submission['final_output']
status, reasoning = extract_status_and_reasoning(final_output)
# Record the result in MongoDB
try:
print("Attempting to insert record into MongoDB...")
insert_record(
script_name="fact_checker_mongodb.py",
script_code=html.escape(open(__file__).read()),
assertion=assertion,
status=status,
submission=submission, # Store the entire submission for detailed analysis
model=os.getenv("MODEL_NAME")
)
print("Record inserted into MongoDB successfully.")
except Exception as e:
print(f"Failed to insert record into MongoDB: {e}")
print(final_output)
# Create a blog post on WordPress
blog_title = f"Fact Check: {assertion}"
blog_content = f"""
<h1>Status: {status}</h1>
<h2>Assertion</h2>
<p>{assertion}</p>
<h2>Results</h2>
<p>{reasoning}</p>
<h3>Assumptions</h3>
<p>{submission['assumptions_output']}</p>
<h3>Fact Checker Output</h3>
<p>{submission['fact_checker_output']}</p>
<h4>Model Used</h4>
<p>{os.getenv("MODEL_NAME")}</p>
<h4>Script Name</h4>
<p>fact_checker_mongodb.py</p>
<h4>Script Code</h4>
<pre>{html.escape(open(__file__).read())}</pre>
"""
create_wordpress_post(blog_title, blog_content, status)
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