Update documentation and remove deprecated files

README.md

@@ -1,6 +1,6 @@
-# PromptLab: LLM Prompt Management System
+# LLMPromptKit: LLM Prompt Management System
 
-PromptLab is a comprehensive library for managing, versioning, testing, and evaluating prompts for Large Language Models (LLMs). It provides a structured framework to help data scientists and developers create, optimize, and maintain high-quality prompts.
+LLMPromptKit is a comprehensive library for managing, versioning, testing, and evaluating prompts for Large Language Models (LLMs). It provides a structured framework to help data scientists and developers create, optimize, and maintain high-quality prompts.
 
 ## Features
 
@@ -24,11 +24,11 @@ For detailed documentation, see the [docs](./docs) directory:
 ## Installation
 
 ```bash
-pip install promptlab
+pip install llmpromptkit
 
 Quick Start
 
-from promptlab import PromptManager, VersionControl, PromptTesting, Evaluator
+from llmpromptkit import PromptManager, VersionControl, PromptTesting, Evaluator
 
 # Initialize components
 prompt_manager = PromptManager()
@@ -91,25 +91,25 @@ evaluation_result = asyncio.run(evaluator.evaluate_prompt(
 print(f"Evaluation metrics: {evaluation_result['aggregated_metrics']}")
 
 Command-line Interface
-PromptLab comes with a powerful CLI for managing prompts:
+LLMPromptKit comes with a powerful CLI for managing prompts:
 
 # Create a prompt
-promptlab prompt create "Summarization" --content "Summarize: {text}" --tags "summarization,basic"
+llmpromptkit prompt create "Summarization" --content "Summarize: {text}" --tags "summarization,basic"
 
 # List all prompts
-promptlab prompt list
+llmpromptkit prompt list
 
 # Create a new version
-promptlab version commit <prompt_id> --message "Updated prompt"
+llmpromptkit version commit <prompt_id> --message "Updated prompt"
 
 # Run tests
-promptlab test run-all <prompt_id> --llm openai
+llmpromptkit test run-all <prompt_id> --llm openai
 
 Advanced Usage
 Advanced Templating
-PromptLab supports advanced templating with conditionals and loops:
+LLMPromptKit supports advanced templating with conditionals and loops:
 
-from promptlab import PromptTemplate
+from llmpromptkit import PromptTemplate
 
 template = PromptTemplate("""
 {system_message}
@@ -134,7 +134,7 @@ rendered = template.render(
 
 Custom Evaluation Metrics
 Create custom metrics to evaluate prompt performance:
-from promptlab import EvaluationMetric, Evaluator
+from llmpromptkit import EvaluationMetric, Evaluator
 
 class CustomMetric(EvaluationMetric):
     def __init__(self):

docs/advanced_features.md

@@ -1,15 +1,15 @@
 # Advanced Features
 
-PromptLab provides several advanced features for sophisticated prompt engineering.
+LLMPromptKit provides several advanced features for sophisticated prompt engineering.
 
 ## Advanced Templating
 
-PromptLab's templating system goes beyond simple variable substitution, offering conditionals and loops.
+LLMPromptKit's templating system goes beyond simple variable substitution, offering conditionals and loops.
 
 ### Basic Variable Substitution
 
 ```python
-from promptlab import PromptTemplate
+from llmpromptkit import PromptTemplate
 
 # Simple variable substitution
 template = PromptTemplate("Hello, {name}!")
@@ -101,7 +101,7 @@ You can create custom metrics to evaluate prompt outputs based on your specific
 ### Creating a Custom Metric
 
 ```python
-from promptlab import EvaluationMetric
+from llmpromptkit import EvaluationMetric
 
 class RelevanceMetric(EvaluationMetric):
     """Evaluates relevance of output to a given topic."""
@@ -129,7 +129,7 @@ class RelevanceMetric(EvaluationMetric):
 ### Using Custom Metrics
 
 ```python
-from promptlab import Evaluator, PromptManager
+from llmpromptkit import Evaluator, PromptManager
 
 # Initialize components
 prompt_manager = PromptManager()
@@ -156,7 +156,7 @@ print(f"Relevance score: {results['aggregated_metrics']['relevance']}")
 
 ## Customizing Storage
 
-PromptLab allows you to customize where and how prompts and related data are stored.
+LLMPromptKit allows you to customize where and how prompts and related data are stored.
 
 ### Custom Storage Locations
 
@@ -180,13 +180,13 @@ with open("exported_prompt.json", "r") as f:
 
 ## LLM Integration
 
-PromptLab is designed to work with any LLM through callback functions. Here are examples of integrating with popular LLM APIs.
+LLMPromptKit is designed to work with any LLM through callback functions. Here are examples of integrating with popular LLM APIs.
 
 ### OpenAI Integration
 
 ```python
 import openai
-from promptlab import PromptManager, PromptTesting
+from llmpromptkit import PromptManager, PromptTesting
 
 prompt_manager = PromptManager()
 testing = PromptTesting(prompt_manager)
@@ -212,7 +212,7 @@ test_results = await testing.run_all_tests("abc123", openai_callback)
 
 ```python
 import anthropic
-from promptlab import PromptManager, Evaluator
+from llmpromptkit import PromptManager, Evaluator
 
 prompt_manager = PromptManager()
 evaluator = Evaluator(prompt_manager)
@@ -242,7 +242,7 @@ eval_results = await evaluator.evaluate_prompt(
 ```python
 from transformers import pipeline
 import asyncio
-from promptlab import PromptManager, VersionControl
+from llmpromptkit import PromptManager, VersionControl
 
 prompt_manager = PromptManager()
 version_control = VersionControl(prompt_manager)

docs/api_reference.md

@@ -1,13 +1,13 @@
 # API Reference
 
-This document provides detailed API documentation for the main components of PromptLab.
+This document provides detailed API documentation for the main components of LLMPromptKit.
 
 ## PromptManager
 
 The `PromptManager` class is the core component for managing prompts.
 
 ```python
-from promptlab import PromptManager
+from llmpromptkit import PromptManager
 ```
 
 ### Methods
@@ -15,7 +15,7 @@ from promptlab import PromptManager
 #### `__init__(storage_path=None)`
 - **Description**: Initialize a new PromptManager.
 - **Parameters**:
-  - `storage_path` (str, optional): Path to store prompts. Defaults to "~/promptlab_storage".
+  - `storage_path` (str, optional): Path to store prompts. Defaults to "~/llmpromptkit_storage".
 
 #### `create(content, name, description='', tags=None, metadata=None)`
 - **Description**: Create a new prompt.
@@ -66,7 +66,7 @@ from promptlab import PromptManager
 The `VersionControl` class manages prompt versions.
 
 ```python
-from promptlab import VersionControl
+from llmpromptkit import VersionControl
 ```
 
 ### Methods
@@ -117,7 +117,7 @@ from promptlab import VersionControl
 The `PromptTesting` class provides testing capabilities.
 
 ```python
-from promptlab import PromptTesting
+from llmpromptkit import PromptTesting
 ```
 
 ### Methods
@@ -166,7 +166,7 @@ from promptlab import PromptTesting
 The `Evaluator` class handles prompt evaluation.
 
 ```python
-from promptlab import Evaluator
+from llmpromptkit import Evaluator
 ```
 
 ### Methods
@@ -196,7 +196,7 @@ from promptlab import Evaluator
 The `PromptTemplate` class provides advanced templating.
 
 ```python
-from promptlab import PromptTemplate
+from llmpromptkit import PromptTemplate
 ```
 
 ### Methods
@@ -217,7 +217,7 @@ from promptlab import PromptTemplate
 The `EvaluationMetric` is the base class for evaluation metrics.
 
 ```python
-from promptlab import EvaluationMetric
+from llmpromptkit import EvaluationMetric
 ```
 
 ### Methods
@@ -243,5 +243,5 @@ from promptlab import EvaluationMetric
 - `LengthMetric`: Scores based on output length.
 
 ```python
-from promptlab import ExactMatchMetric, ContainsKeywordsMetric, LengthMetric
+from llmpromptkit import ExactMatchMetric, ContainsKeywordsMetric, LengthMetric
 ```

docs/cli_usage.md

@@ -1,6 +1,6 @@
 # CLI Usage
 
-PromptLab provides a command-line interface (CLI) for managing prompts, versions, tests, and evaluations.
+LLMPromptKit provides a command-line interface (CLI) for managing prompts, versions, tests, and evaluations.
 
 ## Basic Commands
 
@@ -8,78 +8,78 @@ PromptLab provides a command-line interface (CLI) for managing prompts, versions
 
 ```bash
 # Create a prompt
-promptlab prompt create "Weather Forecast" --content "Provide a weather forecast for {location} on {date}" --tags "weather,forecast"
+llmpromptkit prompt create "Weather Forecast" --content "Provide a weather forecast for {location} on {date}" --tags "weather,forecast"
 
 # List all prompts
-promptlab prompt list
+llmpromptkit prompt list
 
 # Get prompt details
-promptlab prompt get <prompt_id>
+llmpromptkit prompt get <prompt_id>
 
 # Update a prompt
-promptlab prompt update <prompt_id> --content "New content" --tags "new,tags"
+llmpromptkit prompt update <prompt_id> --content "New content" --tags "new,tags"
 
 # Delete a prompt
-promptlab prompt delete <prompt_id>
+llmpromptkit prompt delete <prompt_id>
 ```
 
 ### Version Control
 
 ```bash
 # Commit a version
-promptlab version commit <prompt_id> --message "Version description"
+llmpromptkit version commit <prompt_id> --message "Version description"
 
 # List versions
-promptlab version list <prompt_id>
+llmpromptkit version list <prompt_id>
 
 # Check out (revert to) a specific version
-promptlab version checkout <prompt_id> <version_number>
+llmpromptkit version checkout <prompt_id> <version_number>
 
 # Compare versions
-promptlab version diff <prompt_id> <version1> <version2>
+llmpromptkit version diff <prompt_id> <version1> <version2>
 ```
 
 ### Testing
 
 ```bash
 # Create a test case
-promptlab test create <prompt_id> --input '{"location": "New York", "date": "tomorrow"}' --expected "Expected output"
+llmpromptkit test create <prompt_id> --input '{"location": "New York", "date": "tomorrow"}' --expected "Expected output"
 
 # List test cases
-promptlab test list <prompt_id>
+llmpromptkit test list <prompt_id>
 
 # Run a specific test case
-promptlab test run <test_case_id> --llm openai
+llmpromptkit test run <test_case_id> --llm openai
 
 # Run all test cases for a prompt
-promptlab test run-all <prompt_id> --llm openai
+llmpromptkit test run-all <prompt_id> --llm openai
 
 # Run an A/B test between two prompts
-promptlab test ab <prompt_id_a> <prompt_id_b> --inputs '[{"var": "value1"}, {"var": "value2"}]' --llm openai
+llmpromptkit test ab <prompt_id_a> <prompt_id_b> --inputs '[{"var": "value1"}, {"var": "value2"}]' --llm openai
 ```
 
 ### Evaluation
 
 ```bash
 # Evaluate a prompt
-promptlab eval run <prompt_id> --inputs '[{"var": "value1"}, {"var": "value2"}]' --llm openai
+llmpromptkit eval run <prompt_id> --inputs '[{"var": "value1"}, {"var": "value2"}]' --llm openai
 
 # List available metrics
-promptlab eval metrics
+llmpromptkit eval metrics
 
 # Register a custom metric
-promptlab eval register-metric <metric_file.py>
+llmpromptkit eval register-metric <metric_file.py>
 ```
 
 ## Environment Configuration
 
 The CLI supports environment variables for configuration:
 
-- `PROMPTLAB_STORAGE`: Path to store prompts and related data
-- `PROMPTLAB_OPENAI_API_KEY`: OpenAI API key for built-in LLM support
-- `PROMPTLAB_DEFAULT_LLM`: Default LLM to use for testing and evaluation
+- `LLMPROMPTKIT_STORAGE`: Path to store prompts and related data
+- `LLMPROMPTKIT_OPENAI_API_KEY`: OpenAI API key for built-in LLM support
+- `LLMPROMPTKIT_DEFAULT_LLM`: Default LLM to use for testing and evaluation
 
-You can also create a config file at `~/.promptlab/config.json`:
+You can also create a config file at `~/.llmpromptkit/config.json`:
 
 ```json
 {
@@ -97,22 +97,22 @@ You can also create a config file at `~/.promptlab/config.json`:
 
 ```bash
 # Specify a storage location for a command
-promptlab --storage /path/to/storage prompt list
+llmpromptkit --storage /path/to/storage prompt list
 
 # Export a prompt to another storage
-promptlab prompt export <prompt_id> --output /path/to/output.json
+llmpromptkit prompt export <prompt_id> --output /path/to/output.json
 
 # Import a prompt from a file
-promptlab prompt import /path/to/prompt.json
+llmpromptkit prompt import /path/to/prompt.json
 ```
 
 ### Automation and Scripting
 
 ```bash
 # Get output in JSON format
-promptlab --json prompt list
+llmpromptkit --json prompt list
 
 # Use in shell scripts
-PROMPT_ID=$(promptlab --json prompt create "Script Prompt" --content "Content" | jq -r '.id')
+PROMPT_ID=$(llmpromptkit --json prompt create "Script Prompt" --content "Content" | jq -r '.id')
 echo "Created prompt with ID: $PROMPT_ID"
 ```

docs/getting_started.md

@@ -1,11 +1,11 @@
-# Getting Started with PromptLab
+# Getting Started with LLMPromptKit
 
-This guide will help you get started with PromptLab, a comprehensive library for managing LLM prompts.
+This guide will help you get started with LLMPromptKit, a comprehensive library for managing LLM prompts.
 
 ## Installation
 
 ```bash
-pip install promptlab
+pip install llmpromptkit
 ```
 
 ## Basic Usage
@@ -13,7 +13,7 @@ pip install promptlab
 ### Initialize Components
 
 ```python
-from promptlab import PromptManager, VersionControl, PromptTesting, Evaluator
+from llmpromptkit import PromptManager, VersionControl, PromptTesting, Evaluator
 
 # Initialize with default storage location
 prompt_manager = PromptManager()

docs/integration_examples.md

@@ -1,13 +1,13 @@
 # Integration Examples
 
-This document provides concrete examples of integrating PromptLab into various applications and workflows.
+This document provides concrete examples of integrating LLMPromptKit into various applications and workflows.
 
 ## Customer Support Chatbot
 
 ### Setup
 
 ```python
-from promptlab import PromptManager, VersionControl
+from llmpromptkit import PromptManager, VersionControl
 import openai
 
 # Initialize components
@@ -71,7 +71,7 @@ def handle_customer_message(customer_name, message, is_new_conversation):
 ### Setup
 
 ```python
-from promptlab import PromptManager, PromptTesting, Evaluator
+from llmpromptkit import PromptManager, PromptTesting, Evaluator
 import asyncio
 
 # Initialize components
@@ -155,7 +155,7 @@ async def generate_content(content_type, parameters):
 ### Setup
 
 ```python
-from promptlab import PromptManager, VersionControl
+from llmpromptkit import PromptManager, VersionControl
 import json
 import openai
 
@@ -261,7 +261,7 @@ def save_research_data(research_project, data_type, content):
 ### Setup
 
 ```python
-from promptlab import PromptManager, PromptTemplate
+from llmpromptkit import PromptManager, PromptTemplate
 import asyncio
 import aiohttp
 
@@ -409,7 +409,7 @@ async def generate_quiz(topic, difficulty, num_questions, grade_level, include_e
 ### Setup
 
 ```python
-from promptlab import PromptManager, PromptTesting
+from llmpromptkit import PromptManager, PromptTesting
 import asyncio
 import subprocess
 import tempfile

hello.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python3
+print("Hello World test!")

{promptlab → llmpromptkit}/__init__.py

@@ -1,7 +1,7 @@
 """
-PromptLab - A comprehensive LLM Prompt Management System
+LLMPromptKit - A comprehensive LLM Prompt Management System
 
-PromptLab is a Python library that provides tools for managing, versioning,
+LLMPromptKit is a Python library that provides tools for managing, versioning,
 testing, and evaluating prompts for Large Language Models.
 
 Features:

{promptlab → llmpromptkit}/cli/commands.py

@@ -12,14 +12,14 @@ from ..core.evaluation import Evaluator, ContainsKeywordsMetric, LengthMetric
 
 
 class CLI:
-    """Command-line interface for PromptLab."""
+    """Command-line interface for LLMPromptKit."""
     def __init__(self):
         self.prompt_manager = PromptManager()
         self.version_control = VersionControl(self.prompt_manager)
         self.testing = PromptTesting(self.prompt_manager)
         self.evaluator = Evaluator(self.prompt_manager)
         
-        self.parser = argparse.ArgumentParser(description="PromptLab - LLM Prompt Management System")
+        self.parser = argparse.ArgumentParser(description="LLMPromptKit - LLM Prompt Management System")
         self._setup_commands()
     
     def _setup_commands(self) -> None:

{promptlab → llmpromptkit}/core/prompt_manager.py

@@ -80,7 +80,7 @@ class Prompt:
 
 class PromptManager:
     def __init__(self, storage_path: Optional[str] = None):
-        self.storage_path = storage_path or os.path.join(os.getcwd(), "promptlab_storage")
+        self.storage_path = storage_path or os.path.join(os.getcwd(), "llmpromptkit_storage")
         self.prompts: Dict[str, Prompt] = {}
         self._ensure_storage_dir()
         self._load_prompts()

{promptlab → llmpromptkit}/examples/ab_testing.py

@@ -1,13 +1,13 @@
 """
-A/B testing example for PromptLab.
+A/B testing example for LLMPromptKit.
 
-This example demonstrates how to use PromptLab to perform A/B testing
+This example demonstrates how to use LLMPromptKit to perform A/B testing
 on different prompt variations to find the most effective one.
 """
 
 import asyncio
 import os
-from promptlab import PromptManager, PromptTesting
+from llmpromptkit import PromptManager, PromptTesting
 
 async def llm_callback(prompt, vars):
     """
@@ -25,7 +25,7 @@ async def llm_callback(prompt, vars):
 
 async def main():
     # Initialize the prompt manager with a custom storage path
-    storage_path = os.path.join(os.getcwd(), "promptlab_storage")
+    storage_path = os.path.join(os.getcwd(), "llmpromptkit_storage")
     prompt_manager = PromptManager(storage_path)
     
     # Initialize testing

{promptlab → llmpromptkit}/examples/basic_usage.py

@@ -1,18 +1,18 @@
 
 """
-Basic usage example for PromptLab.
+Basic usage example for LLMPromptKit.
 
-This example demonstrates the fundamental features of PromptLab
+This example demonstrates the fundamental features of LLMPromptKit
 including creating prompts, versioning, and rendering.
 """
 
 import asyncio
 import os
-from promptlab import PromptManager, VersionControl
+from llmpromptkit import PromptManager, VersionControl
 
 async def main():
     # Initialize the prompt manager with a custom storage path
-    storage_path = os.path.join(os.getcwd(), "promptlab_storage")
+    storage_path = os.path.join(os.getcwd(), "llmpromptkit_storage")
     prompt_manager = PromptManager(storage_path)
     
     # Initialize version control

{promptlab → llmpromptkit}/examples/evaluation_example.py

@@ -1,13 +1,13 @@
 """
-Evaluation example for PromptLab.
+Evaluation example for LLMPromptKit.
 
-This example demonstrates how to use PromptLab's evaluation framework
+This example demonstrates how to use LLMPromptKit's evaluation framework
 to measure the quality of prompts using various metrics.
 """
 
 import asyncio
 import os
-from promptlab import PromptManager, Evaluator, ContainsKeywordsMetric, LengthMetric
+from llmpromptkit import PromptManager, Evaluator, ContainsKeywordsMetric, LengthMetric
 
 async def llm_callback(prompt, vars):
     """
@@ -29,7 +29,7 @@ async def llm_callback(prompt, vars):
 
 async def main():
     # Initialize the prompt manager with a custom storage path
-    storage_path = os.path.join(os.getcwd(), "promptlab_storage")
+    storage_path = os.path.join(os.getcwd(), "llmpromptkit_storage")
     prompt_manager = PromptManager(storage_path)
     
     # Initialize evaluator

{promptlab → llmpromptkit}/tests/test_prompt_manager.py

@@ -2,7 +2,7 @@ import unittest
 import os
 import shutil
 import tempfile
-from promptlab.core.prompt_manager import PromptManager, Prompt
+from llmpromptkit.core.prompt_manager import PromptManager, Prompt
 
 class TestPromptManager(unittest.TestCase):
     def setUp(self):

{promptlab → llmpromptkit}/utils/storage.py

@@ -4,7 +4,7 @@ import shutil
 from typing import Dict, Any, Optional, List
 
 class Storage:
-    """Handles persistent storage for PromptLab."""
+    """Handles persistent storage for LLMPromptKit."""
     def __init__(self, base_path: str):
         self.base_path = base_path
         os.makedirs(base_path, exist_ok=True)

llmpromptkit_storage/prompts/0a6614b65f.json

@@ -0,0 +1,15 @@
+{
+  "id": "0a6614b65f",
+  "name": "Weather Forecast",
+  "content": "Provide a detailed weather forecast for {location} on {date}. Include temperature, precipitation chances, and wind information.",
+  "description": "A comprehensive weather forecast prompt",
+  "tags": [
+    "weather",
+    "forecast",
+    "testing"
+  ],
+  "metadata": {},
+  "created_at": "2025-05-21T10:55:45.716023",
+  "updated_at": "2025-05-21T11:06:17.983763",
+  "version": 1
+}

llmpromptkit_storage/prompts/4e9d34fb56.json

@@ -0,0 +1,15 @@
+{
+  "id": "4e9d34fb56",
+  "name": "Test Prompt",
+  "content": "This is a test prompt for {parameter}",
+  "description": "",
+  "tags": [
+    "test",
+    "cli",
+    "llmpromptkit"
+  ],
+  "metadata": {},
+  "created_at": "2025-05-21T10:19:04.907396",
+  "updated_at": "2025-05-21T10:19:04.907396",
+  "version": 1
+}

llmpromptkit_storage/tests/test_cases/6c30b375-7.json

@@ -0,0 +1,12 @@
+{
+  "id": "6c30b375-7",
+  "prompt_id": "0a6614b65f",
+  "input_vars": {
+    "location": "New York",
+    "date": "tomorrow"
+  },
+  "expected_output": "Weather forecast for New York tomorrow should include temperature, precipitation, wind, UV index, air quality, and alerts.",
+  "name": "Test case 6c30b375-7",
+  "description": "",
+  "created_at": "2025-05-21T11:01:26.603328"
+}

llmpromptkit_storage/versions/0a6614b65f/v1.json

@@ -0,0 +1,8 @@
+{
+  "prompt_id": "0a6614b65f",
+  "version": 1,
+  "content": "Provide a detailed weather forecast for {location} on {date}. Include temperature, precipitation chances, and wind information.",
+  "metadata": {},
+  "commit_message": "Initial detailed weather forecast version",
+  "created_at": "2025-05-21T10:56:43.548028"
+}

llmpromptkit_storage/versions/0a6614b65f/v2.json

@@ -0,0 +1,8 @@
+{
+  "prompt_id": "0a6614b65f",
+  "version": 2,
+  "content": "Provide a comprehensive weather forecast for {location} on {date}. Please include the following information:\\n- High and low temperatures (in both Celsius and Fahrenheit)\\n- Precipitation chances and amount\\n- Wind speed and direction\\n- UV index\\n- Air quality\\n- Any weather warnings or alerts",
+  "metadata": {},
+  "commit_message": "Enhanced with more detailed weather parameters",
+  "created_at": "2025-05-21T11:00:51.632374"
+}

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools>=42", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "promptlab"
+name = "llmpromptkit"
 version = "0.1.0"
 description = "A comprehensive LLM Prompt Management System"
 readme = "README.md"
@@ -33,13 +33,13 @@ dependencies = [
 ]
 
 [project.urls]
-"Homepage" = "https://github.com/biswanathroul/promptlab"
-"Bug Tracker" = "https://github.com/biswanathroul/promptlab/issues"
-"Documentation" = "https://github.com/biswanathroul/promptlab/wiki"
-"Source Code" = "https://github.com/biswanathroul/promptlab"
+"Homepage" = "https://github.com/biswanathroul/llmpromptkit"
+"Bug Tracker" = "https://github.com/biswanathroul/llmpromptkit/issues"
+"Documentation" = "https://github.com/biswanathroul/llmpromptkit/wiki"
+"Source Code" = "https://github.com/biswanathroul/llmpromptkit"
 
 [project.scripts]
-promptlab = "promptlab.cli.commands:main"
+llmpromptkit = "llmpromptkit.cli.commands:main"
 
 [tool.setuptools]
-packages = ["promptlab", "promptlab.core", "promptlab.cli", "promptlab.utils"]
+packages = ["llmpromptkit", "llmpromptkit.core", "llmpromptkit.cli", "llmpromptkit.utils"]

simple_test.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+"""
+A simple test script for the llmpromptkit library.
+"""
+
+from llmpromptkit import PromptManager
+
+# Initialize the prompt manager
+prompt_manager = PromptManager()
+
+# List all prompts
+prompts = prompt_manager.list()
+print(f"Found {len(prompts)} prompts:")
+for prompt in prompts:
+    print(f"ID: {prompt.id} | Name: {prompt.name} | Tags: {', '.join(prompt.tags)}")

test_script.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+"""
+A test script to demonstrate using the llmpromptkit library programmatically.
+"""
+
+import os
+import asyncio
+from llmpromptkit import PromptManager, VersionControl, PromptTesting, Evaluator
+
+async def main():
+    # Initialize components with a custom storage path
+    storage_path = os.path.join(os.getcwd(), "llmpromptkit_storage")
+    prompt_manager = PromptManager(storage_path)
+    version_control = VersionControl(prompt_manager)
+    testing = PromptTesting(prompt_manager)
+    evaluator = Evaluator(prompt_manager)
+    
+    # Print all existing prompts
+    print("===== Existing Prompts =====")
+    prompts = prompt_manager.list()
+    for prompt in prompts:
+        print(f"ID: {prompt.id} | Name: {prompt.name} | Tags: {', '.join(prompt.tags)}")
+    
+    # Create a new prompt
+    print("\n===== Creating a New Prompt =====")
+    translation_prompt = prompt_manager.create(
+        content="Translate the following text from {source_language} to {target_language}: {text}",
+        name="Translation Prompt",
+        description="A prompt for translating text between languages",
+        tags=["translation", "multilingual", "test"]
+    )
+    print(f"Created prompt with ID: {translation_prompt.id}")
+    
+    # Render the prompt with variables
+    print("\n===== Rendering the Prompt =====")
+    rendered = translation_prompt.render(
+        source_language="English",
+        target_language="French",
+        text="Hello, how are you today?"
+    )
+    print(f"Rendered prompt: {rendered}")
+    
+    # Create a version
+    print("\n===== Creating a Version =====")
+    version = version_control.commit(
+        prompt_id=translation_prompt.id,
+        commit_message="Initial translation prompt"
+    )
+    print(f"Created version {version.version} for prompt {translation_prompt.id}")
+    
+    # List all versions for this prompt
+    print("\n===== Listing Versions =====")
+    versions = version_control.list_versions(translation_prompt.id)
+    print(f"Found {len(versions)} versions for prompt {translation_prompt.id}:")
+    for v in versions:
+        print(f"Version: {v.version} | Created: {v.created_at} | Message: {v.commit_message}")
+    
+    # Create a test case
+    print("\n===== Creating a Test Case =====")
+    test_case = testing.create_test_case(
+        prompt_id=translation_prompt.id,
+        input_vars={
+            "source_language": "English", 
+            "target_language": "French", 
+            "text": "Hello, world!"
+        },
+        expected_output="Bonjour, monde!"
+    )
+    print(f"Created test case with ID: {test_case.id}")
+    
+    # List all test cases
+    print("\n===== Listing Test Cases =====")
+    test_cases = testing.list_test_cases()
+    print(f"Found {len(test_cases)} test cases:")
+    for tc in test_cases:
+        print(f"ID: {tc.id} | Prompt ID: {tc.prompt_id}")
+    
+    print("\n===== Test Complete =====")
+
+if __name__ == "__main__":
+    asyncio.run(main())