vbi-evaluate-blogs 0.1.6

A package for evaluating PDFs with text, image, and fact-checking modules.

Evaluate Blogs

Evaluate Blogs is a Python package designed to evaluate the content of PDF documents comprehensively. It combines advanced text analysis, image evaluation, and fact-checking capabilities to ensure the quality, relevance, and credibility of the document. The package leverages Azure OpenAI and other state-of-the-art tools to provide accurate and insightful evaluations.

Features

1. Text Content Evaluation

• Analyzes the grammar, structure, and coherence of the text.
• Provides feedback on the readability and relevance of the content.
• Detects potential issues such as redundancy or lack of clarity.

2. Image Relevance Analysis

• Evaluates the quality and relevance of images in the document.
• Ensures that images align with the context and purpose of the document.
• Detects low-quality or irrelevant images.

3. Fact-Checking

• Verifies the factual accuracy of the content using external sources.
• Highlights potential inaccuracies or unsupported claims.
• Ensures the credibility of the information presented.

4. Modular Design

• Each feature is implemented as a separate module, allowing for flexible usage.
• Users can choose to run specific evaluations or combine them as needed.

Install

pip install vbi-evaluate-blogs
playwright install

Usage

Basic Example

Here is an example of how to use the vbi_evaluate_blogs package to analyze a PDF:

from evaluate_module import evaluate
from langchain_openai import AzureChatOpenAI

# Initialize the Azure OpenAI model
model = AzureChatOpenAI(api_key="your_api_key")

# Path to the PDF file
pdf_path = "path/to/your/pdf_file.pdf"

# Evaluate the PDF
result = evaluate(pdf_path, model=model)

# Print the evaluation result
print(result)

Detailed Usage

1. Text Evaluation

The text evaluation module analyzes the PDF's text content for grammar, structure, and relevance. It provides insights into the quality of the written content.

from evaluate_module import evaluate_text

# Path to the PDF file
pdf_path = "path/to/your/pdf_file.pdf"

# Analyze text content
text_result = evaluate_text(pdf_path, model=model)
print("Text Evaluation Result:", text_result)

2. Image Analysis

The image analysis module checks the relevance and quality of images in the PDF. It ensures that images align with the document's context.

from evaluate_module import evaluate_images

# Path to the PDF file
pdf_path = "path/to/your/pdf_file.pdf"

# Analyze images in the PDF
image_result = evaluate_images(pdf_path)
print("Image Analysis Result:", image_result)

3. Fact-Checking

The fact-checking module verifies the factual accuracy of the content using external sources. This ensures the credibility of the information presented.

from evaluate_module import evaluate_facts

# Path to the PDF file
pdf_path = "path/to/your/pdf_file.pdf"

# Perform fact-checking
fact_result = evaluate_facts(pdf_path, model=model)
print("Fact-Checking Result:", fact_result)

Command-Line Usage

You can also use the package via the command line for quick evaluations:

python -m evaluate_module --file path/to/your/pdf_file.pdf

Additional Options

• --text: Perform only text evaluation.
• --images: Perform only image analysis.
• --facts: Perform only fact-checking.

Example:

python -m evaluate_module --file path/to/your/pdf_file.pdf --text --images

Advanced Usage

Customizing the Model

You can customize the Azure OpenAI model by providing additional parameters during initialization:

model = AzureChatOpenAI(api_key="your_api_key", temperature=0.7, max_tokens=1000)

Combining Modules

You can combine multiple modules to perform a comprehensive evaluation:

from evaluate_module import evaluate_text, evaluate_images, evaluate_facts

# Path to the PDF file
pdf_path = "path/to/your/pdf_file.pdf"

# Perform evaluations
text_result = evaluate_text(pdf_path, model=model)
image_result = evaluate_images(pdf_path)
fact_result = evaluate_facts(pdf_path, model=model)

# Combine results
combined_result = {
    "text": text_result,
    "images": image_result,
    "facts": fact_result
}

print("Combined Evaluation Result:", combined_result)

License

This project is licensed under the MIT License. See the LICENSE file for details.