PromptLab

by irahulpandey

6 stars

Not rated

GitHub

About

Transforms basic user queries into optimized prompts for AI systems by automatically detecting content type and applying tailored templates from a YAML-based system.

Details

Author: irahulpandey
Repository: iRahulPandey/PromptLab
GitHub stars: 6
License: Apache License 2.0
Categories: Design, AI, Infrastructure, Developer Tools

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name PromptLab

Command (node, npx, python, etc.) npx
Arguments
- Argument 1 -y
- Argument 2 @highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

From the repository

pip install -r requirements.txt

export OPENAI_API_KEY="your-openai-api-key"

```bash

`register_prompt`

`update_prompt`

Update an existing prompt, archiving the previous production version.

`list_prompts`

List all registered prompts in MLflow.

`register_from_file`

`register_sample_prompts`

Initialize the system with standard sample prompts.

`load_all_prompts`

Load all available prompts from MLflow at server startup.

`match_prompt`

Match user queries to appropriate prompt templates.

`enhance_query`

Apply the selected prompt template to the user's query.

`validate_query`

Validate the enhanced queries to ensure quality.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "promptlab": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Linux

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Macos

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Windows

{
    "env": [],
    "args": [
        "/c",
        "npx",
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "cmd"
}

PromptLab: AI Query Enhancement with MLflow Integration

PromptLab is an intelligent system that transforms basic user queries into optimized prompts for AI systems using MLflow Prompt Registry. It dynamically matches user requests to the most appropriate prompt template and applies it with extracted parameters.

🔍 Overview

PromptLab combines MLflow Prompt Registry with dynamic prompt matching to create a powerful, flexible system for prompt engineering:

- Centralized Prompt Management: Store, version, and manage prompts in MLflow
- Dynamic Matching: Intelligently match user queries to the best prompt template
- Version Control: Track prompt history with production and archive aliases
- Extensible: Easily add new prompt types without code changes

🏗️ Architecture

The system consists of three main components:

1. Prompt Registry (register_prompts.py) - Tool for registering and managing prompts in MLflow
2. Server (promptlab_server.py) - Server with dynamic prompt matching and LangGraph workflow
3. Client (promptlab_client.py) - Lightweight client for processing user queries

Workflow Process

PromptLab Workflow

1. Prompt Registration: Register prompt templates in MLflow with versioning and aliasing
2. Prompt Loading: Server loads all available prompts from MLflow at startup
3. Query Submission: User submits a natural language query via the client
4. Intelligent Matching: LLM analyzes the query and selects the most appropriate prompt template
5. Parameter Extraction: System extracts required parameters from the query
6. Template Application: Selected template is applied with extracted parameters
7. Validation & Adjustment: Enhanced prompt is validated and adjusted if needed
8. Response Generation: Optimized prompt produces a high-quality response

📂 Code Structure

promptlab/
├── promptlab_server.py            # Main server with LangGraph workflow
├── promptlab_client.py            # Client for processing queries
├── register_prompts.py            # MLflow prompt management tool
├── requirements.txt               # Project dependencies
├── advanced_prompts.json          # Additional prompt templates
└── README.md                      # Project documentation

Core Components:

`register_prompts.py`

- Purpose: Manages prompts in MLflow Registry - Key Functions: - register_prompt(): Register a new prompt or version - update_prompt(): Update an existing prompt (archives previous production) - list_prompts(): List all registered prompts - register_from_file(): Register multiple prompts from JSON - register_sample_prompts(): Initialize with standard prompts

`promptlab_server.py`

- Purpose: Processes queries using LangGraph workflow - Key Components: - load_all_prompts(): Loads prompts from MLflow - match_prompt(): Matches queries to appropriate templates - enhance_query(): Applies selected template - validate_query(): Validates enhanced queries - LangGraph workflow: Orchestrates the query enhancement process

`promptlab_client.py`

- Purpose: Provides user interface to the service - Key Features: - Process queries with enhanced prompts - List available prompts - Display detailed prompt matching information

🚀 Getting Started

Prerequisites

- Python 3.12
- Dependencies in requirements.txt
- OpenAI API key for LLM capabilities

Installation

```bash

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About

Details

Jump to

Setting up with Highlight

From the repository

register_prompt

update_prompt

list_prompts

register_from_file

register_sample_prompts

load_all_prompts

match_prompt

enhance_query

validate_query

Claude Desktop / Cursor

Linux

Macos

Windows

PromptLab: AI Query Enhancement with MLflow Integration

🔍 Overview

🏗️ Architecture

Workflow Process

📂 Code Structure

Core Components:

register_prompts.py

promptlab_server.py

promptlab_client.py

🚀 Getting Started

Prerequisites

Installation

Install

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`register_prompt`

`update_prompt`

`list_prompts`

`register_from_file`

`register_sample_prompts`

`load_all_prompts`

`match_prompt`

`enhance_query`

`validate_query`

`register_prompts.py`

`promptlab_server.py`

`promptlab_client.py`