pop-python 1.0.0

Prompt Oriented Programming (POP): reusable, composable prompt functions for LLMs.

Prompt Oriented Programming (POP)

from POP import PromptFunction

pf = PromptFunction(
    prompt="Draw a simple ASCII art of <<<object>>>.",
    client = "openai"
)

print(pf.execute(object="a cat"))
print(pf.execute(object="a rocket"))

 /\_/\  
( o.o )
 > ^ <  

   /\
  /  \
 /    \
 |    |
 |    |

---

Reusable, composable prompt functions for LLM workflows.

This release cleans the architecture, moves all LLM client logic to a separate LLMClient module, and extends multi-LLM backend support.

PyPI: https://pypi.org/project/pop-python/

GitHub: https://github.com/sgt1796/POP

---

Table of Contents

1. Overview

2. Major Updates

3. Features

4. Installation

5. Setup

6. PromptFunction

   • Placeholders
   • Reserved Keywords
   • Executing prompts
   • Improving prompts

7. Function Schema Generation

8. Embeddings

9. Web Snapshot Utility

10. Examples

11. Contributing

---

1. Overview

Prompt Oriented Programming (POP) is a lightweight framework for building reusable, parameterized prompt functions. Instead of scattering prompt strings across your codebase, POP lets you:

• encapsulate prompts as objects
• pass parameters cleanly via placeholders
• select a backend LLM client dynamically
• improve prompts using meta-prompting
• generate OpenAI-compatible function schemas
• use unified embedding tools
• work with multiple LLM providers through LLMClient subclasses

POP is designed to be simple, extensible, and production-friendly.

---

2. Major Updates

This version introduces structural and functional improvements:

2.1. LLMClient moved into its own module

LLMClient.py now holds all LLM backends:

• OpenAI
• Gemini
• Deepseek
• Doubao
• Local PyTorch stub
• Extensible architecture for adding new backends

2.2. Expanded multi-LLM support

Each backend now has consistent interface behavior and multimodal (text + image) support where applicable.

---

3. Features

• Reusable Prompt Functions Use <<<placeholder>>> syntax to inject dynamic content.

• Multi-LLM Backend Choose between OpenAI, Gemini, Deepseek, Doubao, or local models.

• Prompt Improvement Improve or rewrite prompts using Fabric-style metaprompts.

• Function Schema Generation Convert natural language descriptions into OpenAI-function schemas.

• Unified Embedding Interface Supports OpenAI, Jina AI embeddings, and local HuggingFace models.

• Webpage Snapshot Utility Convert any URL into structured text using r.jina.ai with optional image captioning.

---

4. Installation

Install from PyPI:

pip install pop-python

Or install in development mode from GitHub:

git clone https://github.com/sgt1796/POP.git
cd POP
pip install -e .

---

5. Setup

Create a .env file in your project root:

OPENAI_API_KEY=your_openai_key
GEMINI_API_KEY=your_gcp_gemini_key
DEEPSEEK_API_KEY=your_deepseek_key
DOUBAO_API_KEY=your_volcengine_key
JINAAI_API_KEY=your_jina_key

All clients automatically read keys from environment variables.

---

6. PromptFunction

The core abstraction of POP is the PromptFunction class.

from POP import PromptFunction

pf = PromptFunction(
    sys_prompt="You are a helpful AI.",
    prompt="Give me a summary about <<<topic>>>."
)

print(pf.execute(topic="quantum biology"))

---

6.1. Placeholder Syntax

Use angle-triple-brackets inside your prompt:

<<<placeholder>>>

These are replaced at execution time.

Example:

prompt = "Translate <<<sentence>>> to French."

---

6.2. Reserved Keywords

Within .execute(), the following keyword arguments are reserved and should not be used as placeholder names:

• model
• sys
• fmt
• tools
• temp
• images
• ADD_BEFORE
• ADD_AFTER

Most keywords are used for parameters. ADD_BEFORE and ADD_AFTER will attach input string to head/tail of the prompt.

---

6.3. Executing prompts

result = pf.execute(
    topic="photosynthesis",
    model="gpt-4o-mini",
    temp=0.3
)

---

6.4. Improving Prompts

You can ask POP to rewrite or enhance your system prompt:

better = pf._improve_prompt()
print(better)

This uses a Fabric-inspired meta-prompt bundled in the prompts/ directory.

---

7. Function Schema Generation

POP supports generating OpenAI function-calling schemas from natural language descriptions.

schema = pf.generate_schema(
    description="Return the square and cube of a given integer."
)

print(schema)

What this does:

• Applies a standard meta-prompt
• Uses the selected LLM backend
• Produces a valid JSON Schema for OpenAI function calling
• Optionally saves it under functions/

---

8. Embeddings

POP includes a unified embedding interface:

from POP.Embedder import Embedder

embedder = Embedder(use_api="openai")
vecs = embedder.get_embedding(["hello world"])

Supported modes:

• OpenAI embeddings
• JinaAI embeddings
• Local HuggingFace model embeddings (cpu/gpu)

Large inputs are chunked automatically when needed.

---

9. Web Snapshot Utility

from POP import get_text_snapshot

text = get_text_snapshot("https://example.com", image_caption=True)
print(text[:500])

Supports:

• optional image removal
• optional image captioning
• DOM selector filtering
• returning JSON or plain text

---

10. Examples

from POP import PromptFunction

pf = PromptFunction(prompt="Give me 3 creative names for a <<<thing>>>.")

print(pf.execute(thing="robot"))
print(pf.execute(thing="new language"))

---

11. Contributing

Steps:

1. Fork the GitHub repo
2. Create a feature branch
3. Add tests or examples
4. Submit a PR with a clear explanation