Status: True
Assertion
A duck’s quack doesn’t echo.
Results
A duck’s quack typically does not produce an audible echo due to several factors. First, the environment where a duck is usually located lacks large hard surfaces necessary for sound reflection that can create noticeable echoes. Secondly, although ducks do make short vocalizations, they are not commonly associated with producing sounds strong enough or of sufficient duration to generate an audible echo as humans typically perceive them. Lastly, human hearing sensitivity may not be adequate to detect an echo from a duck’s quack even if one were present due to the lower frequency and limited propagation distance of such sounds in everyday environments.
Assumptions
Assumptions supporting the statement “A duck’s quack doesn’t echo”:
– The environment where the duck is located does not have large, hard surfaces at sufficient distances that could reflect sound waves and produce an audible echo.
– The duration of a typical duck’s quack is relatively short compared to other sounds known to create echoes (e.g., clapping hands).
– Human hearing sensitivity for detecting echoes from such sources as a duck’s quack may not be sufficient, even if an echo were present.
– The frequency of the sound produced by a duck’s quack is typically lower and doesn’t travel far enough to reach reflective surfaces at distances where the time delay would meet the threshold for human perception of distinct echoes.
– Other ambient noises in the environment might mask any potential echo, making it even less noticeable or distinguishable from background sounds.
– The acoustic properties of the environment (e.g., temperature, humidity) and their effect on sound propagation do not significantly favor the creation of audible echoes for a duck’s quack.
– The size and density of objects in
Fact Checker Output
<|assistant|> – True: The environment where the duck is located does not have large, hard surfaces at sufficient distances that could reflect sound waves and produce an audible echo. This is generally true since natural environments are usually filled with vegetation, soft soil, water bodies, etc., which absorb or diffuse sound rather than reflecting it to create echoes.
– True: The duration of a typical duck’s quack is relatively short compared to other sounds known to create echoes (e.g., clapping hands). Ducks produce a series of short, abrupt vocalizations for communication and signaling rather than long continuous sounds that are commonly associated with producing audible echoes.
– True: Human hearing sensitivity for detecting echoes from such sources as a duck’s quack may not be sufficient, even if an echo were present. The human ear is more attuned to pick up on longer durations and higher frequency sounds that are more commonly associated with producing audible echoes in everyday environments.
– True: The frequency of the sound produced by a duck’s quack is typically lower and doesn’t travel far enough to reach reflective surfaces at distances where the time
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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