README formatting and CI fixes

.github/workflows/pr_qc.yml

@@ -9,11 +9,16 @@ jobs:
     steps:
       - name: Check out repository code
         uses: actions/checkout@v4
-      - name: Set up Python
-        uses: actions/setup-python@v5
+      - name: Install the latest version of uv
+        uses: astral-sh/setup-uv@v7
         with:
+          enable-cache: false
           python-version: 3.11
       - name: Run qa
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: |
-          pip install ".[dev]"
+          python -m venv ./.venv
+          source ./.venv/bin/activate
+          make dev
           make qa

README.md

@@ -153,16 +153,18 @@ Note:
 
 **Parameters**
 
-- **`value`** _(Any)_: The input value to validate.
-- **`metadata`** _(dict)_: A dictionary containing metadata required for validation. Keys and values must match the expectations of this validator.
+- **`value`** *(Any)*: The input value to validate.
+- **`metadata`** *(dict)*: A dictionary containing metadata required for validation. Keys and values must match the expectations of this validator.
+
+
   | Key | Type | Description | Default |
   | --- | --- | --- | --- |
   | `user_input` | String | The original user query passed into the RAG system. | N/A (Required) |
   | `retrieved_context` | String | The context retrieved and used by the RAG system. | N/A (Required) |
   | `min_range_value` | String | The minimum value for the rating range used by the LLM judge. | 0 (the default of the validator class) |
   | `max_range_value` | String | The maximum value for the rating range used by the LLM judge. | 1 (the default of the validator class) |
-  </ul>
+</ul>
 
 **Returns**
 
-**`ValidationResult`**: Returns a `PassResult` if the LLM judge's rating meets or exceeds the pass threshold, or a `FailResult` with detailed explanation if the rating is below the threshold.
+**`ValidationResult`**: Returns a `PassResult` if the LLM judge's rating meets or exceeds the pass threshold, or a `FailResult` with detailed explanation if the rating is below the threshold.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "rag-context-evaluator"
-version = "0.0.1"
+version = "0.0.2"
 description = "ML3 RAG Context Evaluator"
 authors = [
     {name = "MLcube", email = "info@mlcube.com"}

validator/main.py

@@ -1,6 +1,4 @@
 from typing import Any, Callable, Dict, Optional
-from guardrails import Guard
-import openai
 import logging
 from guardrails.validator_base import (
     FailResult,