leo-prompt-optimizer 0.1.5

A Python library to optimize prompt drafts using LLMs

🧠 leo-prompt-optimizer

leo-prompt-optimizer is a Python library that helps developers optimize raw LLM prompts into structured, high-performance instructions using real LLM intelligence.

It leverages open-source models via Groq API (like Mixtral or LLaMA 3), and also supports OpenAI, making it fast, flexible, and production-ready.

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🚀 Features

• 🛠️ Refines vague, messy, or unstructured prompts
• 🧠 Follows a 9-step prompt engineering framework
• 🧩 Supports contextual optimization (with user input & LLM output)
• 🔁 Works with both Groq and OpenAI
• ⚡ Blazing-fast open models via Groq
• 🔐 Secure API key management with .env or helper function
• 🎛️ Let users choose model (gpt-3.5-turbo, mixtral-8x7b, llama3, etc.)

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📦 Installation

pip install leo-prompt-optimizer

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🔧 Setup: API Keys

You can provide your API key in two ways:

✅ Option A: .env file (recommended)

At the root of your project, create a .env file:

GROQ_API_KEY=sk-your-groq-key
or
OPENAI_API_KEY=sk-your-openai-key

Then, in your Python script:

from dotenv import load_dotenv
load_dotenv()  # 👈 Required to load the API keys from .env

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✅ Option B: Set programmatically

from leo_prompt_optimizer import set_groq_api_key, set_openai_api_key

set_groq_api_key("sk-your-groq-key")
set_openai_api_key("sk-your-openai-key")

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✍️ Usage Example

from dotenv import load_dotenv
load_dotenv()  # Only needed if using .env for API keys

from leo_prompt_optimizer import optimize_prompt, set_groq_api_key, set_openai_api_key

# Optional: Set API key manually (Groq or OpenAI)
# set_openai_api_key("sk-...")
# set_groq_api_key("sk-...")

optimized = optimize_prompt(
    prompt_draft="[YOUR PROMPT]",
    user_input="[POTENTIAL INPUT EXAMPLE]", # Optional
    llm_output="[POTENTIAL OUTPUT EXAMPLE]", # Optional
    provider="[YOUR PROVIDER]",               # "groq" (default) or "openai"
    model="[YOUR MODEL]"            # Optional: model choie based on your provider(e.g. "gpt-4", "llama3-70b", etc.)
)

print(optimized)

🧠 user_input and llm_output are optional but helpful when refining an existing prompt flow. 🎛️ You can also specify the provider (groq or openai) and the exact model you want.

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📘 Output Format

The returned optimized prompt follows a structured format:

Role:
[Define the LLM's persona]

Task:
[Clearly state the specific objective]

Instructions:
* Step-by-step subtasks

Context:
[Any relevant background, constraints, domain]

Output Format:
[e.g., bullet list, JSON, summary]

User Input:
[Original user input or example]

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🧪 Quick Test (Optional)

python3 test_import.py

This will check:

• ✅ Import works
• ✅ API keys are detected
• ✅ LLM returns optimized result

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🧯 Common Errors & Fixes

Error	Solution
Missing GROQ_API_KEY	Ensure it's in .env and loaded with load_dotenv(), or passed via set_groq_api_key()
Missing OPENAI_API_KEY	Same as above, but with set_openai_api_key()
Invalid model or 403	The model may be deprecated or restricted. Try another model or check Groq Models
ModuleNotFoundError	Ensure leo-prompt-optimizer is installed in the right Python environment

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💡 Why Use It?

Prompt quality is critical when building with LLMs.

leo-prompt-optimizer helps you:

✅ Make prompts explicit and structured 🚫 Reduce hallucinations 🔁 Increase consistency and reuse 🧱 Standardize prompt formats across your stack

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