python-lingualens 1.0.2

A flexible evaluation framework for content using LLMs

LinguaLens (LSADA)

LinguaLens (formerly LSADA - Language Services and Data Analysis) is a flexible Python framework for evaluating content quality using Large Language Models (LLMs).

It provides a structured way to:

• Define evaluation tasks with specific metrics and weighting.
• Use different LLM providers (currently OpenAI, Cloudverse supported).
• Generate prompts tailored to your evaluation criteria.
• Parse LLM responses to extract scores and justifications.
• Aggregate results from multiple evaluations for robustness.

Installation

You can install LinguaLens directly from PyPI:

pip install python-lingualens

Quick Start Example

1. Set your LLM API Key: Make sure you have your API key (e.g., for OpenAI) set as an environment variable:

   export OPENAI_API_KEY="your_api_key_here"
   # On Windows (Command Prompt)
   # set OPENAI_API_KEY=your_api_key_here
   # On Windows (PowerShell)
   # $env:OPENAI_API_KEY="your_api_key_here"
   

2. Run the basic usage script: Navigate to the examples directory and run the script:

   cd examples
   python 1_basic_usage.py
   

   This script demonstrates:

   • Initializing an OpenAI client.
   • Initializing the Evaluator for a specific task (conversation_evaluation).
   • Evaluating sample content.
   • Printing the detailed results, including the overall score, individual metric scores, and justifications.

   # examples/1_basic_usage.py (Simplified Snippet)
   import os
   import logging
   from lingualens import Evaluator, LLMManager
   
   logging.basicConfig(level=logging.INFO)
   
   api_key = os.getenv("OPENAI_API_KEY")
   task_type = "conversation_evaluation"
   content_to_evaluate = "... (your content here) ..."
   
   if not api_key:
       logging.error("OPENAI_API_KEY not set.")
   else:
       try:
           llm_client = LLMManager.initialize_client(vendor="openai", api_key=api_key)
           evaluator = Evaluator(task_type=task_type, include_justification=True)
           result = evaluator.evaluate(content=content_to_evaluate, llm_client=llm_client)
   
           print("\n----- Evaluation Results -----")
           print(f"Task Type: {result.get('metadata', {}).get('task_type')}")
           print(f"Total Weighted Score: {result.get('total_weighted_score')}")
           # ... (print detailed scores and justifications) ...
   
       except Exception as e:
           logging.error(f"An error occurred: {e}", exc_info=True)
   

Core Components

• Evaluator: The main class to orchestrate the evaluation.
• LLMManager: Manages and initializes clients for different LLM vendors.
• TaskManager: Handles task definitions, metrics, and can auto-identify tasks.
• PromptGenerator: Creates detailed prompts for the LLM based on configuration.
• LLMResponseParser: Extracts structured data from LLM responses.
• MetricsCalculator: Aggregates scores and performs calculations.
• ConfigManager: Loads configurations from pool