Metadata-Version: 2.4
Name: textprompts
Version: 1.6.0
Summary: Minimal text-based prompt-loader with TOML/YAML front-matter
Keywords: prompts,toml,yaml,frontmatter,template
Author: Jan Siml
Author-email: Jan Siml <49557684+svilupp@users.noreply.github.com>
License-Expression: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: pydantic~=2.7
Requires-Dist: pyyaml>=6.0
Requires-Dist: tomli>=1.0.0 ; python_full_version < '3.11'
Requires-Python: >=3.11
Project-URL: Homepage, https://github.com/svilupp/textprompts
Project-URL: Bug Tracker, https://github.com/svilupp/textprompts/issues
Project-URL: Documentation, https://github.com/svilupp/textprompts#readme
Description-Content-Type: text/markdown

# textprompts

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> **So simple, it's not even worth vibe coding yet it just makes so much sense.**

Are you tired of vendors trying to sell you fancy UIs for prompt management that just make your system more confusing and harder to debug? Isn't it nice to just have your prompts **next to your code**?

But then you worry: *Did my formatter change my prompt? Are those spaces at the beginning actually part of the prompt or just indentation?*

**textprompts** solves this elegantly: treat your prompts as **text files** and keep your linters and formatters away from them.

## Why textprompts?

- ✅ **Prompts live next to your code** - no external systems to manage
- ✅ **Git is your version control** - diff, branch, and experiment with ease
- ✅ **No formatter headaches** - your prompts stay exactly as you wrote them
- ✅ **Minimal markup** - just TOML or YAML front-matter when you need metadata (or no metadata if you prefer!)
- ✅ **Zero dependencies** - well, almost (just Pydantic)
- ✅ **Safe formatting** - catch missing variables before they cause problems
- ✅ **Works with everything** - OpenAI, Anthropic, local models, function calls

## Cross-Language Support

**textprompts** uses a cross-language compatible prompt template format:

- **Python** (this package): Available on PyPI as `textprompts`
- **Node/TypeScript**: Available in `packages/textprompts-ts` folder
- **Julia**: Available in `packages/TextPrompts.jl` folder
- **Go** (alpha): Available in `packages/textprompts-go` folder ([docs](https://pkg.go.dev/github.com/svilupp/textprompts/packages/textprompts-go))

This means you can share prompt files across different parts of your stack without any conversion or compatibility issues.

## Installation

```bash
uv add textprompts # or pip install textprompts
```

## Quick Start

**Flexible by default** - TextPrompts loads metadata when present and still works fine without it:

1. **Create a prompt file** (`greeting.txt`):
```
---
title = "Customer Greeting"
version = "1.0.0"
description = "Friendly greeting for customer support"
---
Hello {customer_name}!

Welcome to {company_name}. We're here to help you with {issue_type}.

Best regards,
{agent_name}
```

2. **Load and use it** (no configuration needed):
```python
import textprompts

# Just load it - works with or without metadata
prompt = textprompts.load_prompt("greeting.txt")
# Or simply
alt = textprompts.Prompt.from_path("greeting.txt")

# Use it safely - all placeholders must be provided
message = prompt.prompt.format(
    customer_name="Alice",
    company_name="ACME Corp",
    issue_type="billing question",
    agent_name="Sarah"
)

print(message)

# Or use partial formatting when needed
partial = prompt.prompt.format(
    customer_name="Alice",
    company_name="ACME Corp",
    skip_validation=True
)
# Result: "Hello Alice!\n\nWelcome to ACME Corp. We're here to help you with {issue_type}.\n\nBest regards,\n{agent_name}"

# Prompt objects expose `.meta` and `.prompt`.
# Use `prompt.prompt.format()` for safe formatting or `str(prompt)` for raw text.
```

**Even simpler** - no metadata required:
```python
# simple_prompt.txt contains just: "Analyze this data: {data}"
prompt = textprompts.load_prompt("simple_prompt.txt")  # Just works!
result = prompt.prompt.format(data="sales figures")
```

## Core Features

### Safe String Formatting

Never ship a prompt with missing variables again:

```python
from textprompts import PromptString

template = PromptString("Hello {name}, your order {order_id} is {status}")

# ✅ Strict formatting - all placeholders must be provided
result = template.format(name="Alice", order_id="12345", status="shipped")

# ❌ This catches the error by default
try:
    result = template.format(name="Alice")  # Missing order_id and status
except ValueError as e:
    print(f"Error: {e}")  # Missing format variables: ['order_id', 'status']

# ✅ Partial formatting - replace only what you have
partial = template.format(name="Alice", skip_validation=True)
print(partial)  # "Hello Alice, your order {order_id} is {status}"
```

### Simple & Flexible Metadata Handling

TextPrompts is designed to be **flexible** by default - load metadata when it's present, and fall back gracefully when it isn't. No configuration needed!

```python
import textprompts

# Default behavior: load metadata if available, otherwise just use the file content
prompt = textprompts.load_prompt("my_prompt.txt")  # Just works!

# Three modes available for different use cases:
# 1. ALLOW (default): Load metadata if present, don't worry if it's incomplete
textprompts.set_metadata("allow")  # Flexible metadata loading (default)
prompt = textprompts.load_prompt("prompt.txt")  # Loads any metadata found

# 2. IGNORE: Treat as simple text file, use filename as title
textprompts.set_metadata("ignore")  # Super simple file loading
prompt = textprompts.load_prompt("prompt.txt")  # No metadata parsing
print(prompt.meta.title)  # "prompt" (from filename)

# 3. STRICT: Require complete metadata for production use
textprompts.set_metadata("strict")  # Prevent errors in production
prompt = textprompts.load_prompt("prompt.txt")  # Must have title, description, version

# Override per prompt when needed
prompt = textprompts.load_prompt("prompt.txt", meta="strict")
```

**Why this design?**
- **Default = Flexible**: Parse metadata when it's available, no friction when it's not
- **No configuration needed**: Just load files and it works
- **Production-Safe**: Use strict mode to catch missing metadata before deployment

## Real-World Examples

### OpenAI Integration

```python
import openai
from textprompts import load_prompt

system_prompt = load_prompt("prompts/customer_support_system.txt")
user_prompt = load_prompt("prompts/user_query_template.txt")

response = openai.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[
        {
            "role": "system",
            "content": system_prompt.prompt.format(
                company_name="ACME Corp",
                support_level="premium"
            )
        },
        {
            "role": "user",
            "content": user_prompt.prompt.format(
                query="How do I return an item?",
                customer_tier="premium"
            )
        }
    ]
)
```

### Function Calling (Tool Definitions)

Yes, you can version control your whole tool schemas too:

```python
# tools/search_products.txt
---
title = "Product Search Tool"
version = "2.1.0"
description = "Search our product catalog"
---
{
    "type": "function",
    "function": {
        "name": "search_products",
        "description": "Search for products in our catalog",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Search query for products"
                },
                "category": {
                    "type": "string",
                    "enum": ["electronics", "clothing", "books"],
                    "description": "Product category to search within"
                },
                "max_results": {
                    "type": "integer",
                    "default": 10,
                    "description": "Maximum number of results to return"
                }
            },
            "required": ["query"]
        }
    }
}
```

```python
import json
from textprompts import load_prompt

# Load and parse the tool definition
tool_prompt = load_prompt("tools/search_products.txt")
tool_schema = json.loads(tool_prompt.prompt)

# Use with OpenAI
response = openai.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "Find me some electronics"}],
    tools=[tool_schema]
)
```

### Environment-Specific Prompts

```python
import os
from textprompts import load_prompt

env = os.getenv("ENVIRONMENT", "development")
system_prompt = load_prompt(f"prompts/{env}/system.txt")

# prompts/development/system.txt - verbose logging
# prompts/production/system.txt - concise responses
```

### Prompt Versioning & Experimentation

```python
from textprompts import load_prompt

# Easy A/B testing
prompt_version = "v2"  # or "v1", "experimental", etc.
prompt = load_prompt(f"prompts/{prompt_version}/system.txt")

# Git handles the rest:
# git checkout experiment-branch
# git diff main -- prompts/
```

## File Format

TextPrompts uses TOML front-matter (optional) followed by your prompt content.
YAML front-matter is also supported as an alternative.

**TOML (default):**
```
---
title = "My Prompt"
version = "1.0.0"
author = "Your Name"
description = "What this prompt does"
created = "2024-01-15"
tags = ["customer-support", "greeting"]
---
Your prompt content goes here.

Use {variables} for templating.
```

**YAML alternative:**
```
---
title: "My Prompt"
version: "1.0.0"
author: "Your Name"
description: "What this prompt does"
created: "2024-01-15"
tags:
  - customer-support
  - greeting
---
Your prompt content goes here.

Use {variables} for templating.
```

Both formats are auto-detected based on the front-matter content.

### Metadata Modes

Choose the right level of strictness for your use case:

1. **ALLOW** (default) - Load metadata if present, don't worry about completeness
2. **IGNORE** - Simple text file loading, filename becomes title
3. **STRICT** - Require complete metadata (title, description, version) for production safety

You can also set the environment variable `TEXTPROMPTS_METADATA_MODE` to one of
`strict`, `allow`, or `ignore` before importing the library to configure the
default mode.

```python
# Set globally
textprompts.set_metadata("allow")    # Default: load metadata when available
textprompts.set_metadata("ignore")   # Simple: no metadata parsing
textprompts.set_metadata("strict")   # Production: require complete metadata

# Or override per prompt
prompt = textprompts.load_prompt("file.txt", meta="strict")
```

## API Reference

### `load_prompt(path, *, meta=None)`

Load a single prompt file.

- `path`: Path to the prompt file
- `meta`: Metadata handling mode - `MetadataMode.STRICT`, `MetadataMode.ALLOW`, `MetadataMode.IGNORE`, or string equivalents. None uses global config.

Returns a `Prompt` object with:
- `prompt.meta`: Metadata from TOML/YAML front-matter (always present)
- `prompt.prompt`: The prompt content as a `PromptString`
- `prompt.path`: Path to the original file

### `set_metadata(mode)` / `get_metadata()`

Set or get the global metadata handling mode.

- `mode`: `MetadataMode.STRICT`, `MetadataMode.ALLOW`, `MetadataMode.IGNORE`, or string equivalents

```python
import textprompts

# Set global mode
textprompts.set_metadata(textprompts.MetadataMode.STRICT)
textprompts.set_metadata("allow")  # String also works

# Get current mode
current_mode = textprompts.get_metadata()
```

### `save_prompt(path, content, *, format="toml")`

Save a prompt to a file.

- `path`: Path to save the prompt file
- `content`: Either a string (creates template with required fields) or a `Prompt` object
- `format`: Front-matter format to use - `"toml"` (default) or `"yaml"`

```python
from textprompts import save_prompt

# Save a simple prompt with metadata template
save_prompt("my_prompt.txt", "You are a helpful assistant.")

# Save with YAML front-matter
save_prompt("my_prompt.txt", "You are a helpful assistant.", format="yaml")

# Save a Prompt object with full metadata
save_prompt("my_prompt.txt", prompt_object)
```

### `parse_sections(text)` and section utilities

Parse mixed Markdown/XML prompt structure without going through the file loader.

- `parse_sections(text)`: Returns a `ParseResult` with `sections`, `anchors`, `duplicate_anchors`, `frontmatter`, and `total_chars`
- `generate_slug(heading)`: Creates the same auto-anchor slug used by the parser (lowercase, non-alphanumeric runs -> `_`)
- `inject_anchors(text)`: Inserts missing `<a id="..."></a>` lines before Markdown headings and returns `(text, result)`
- `render_toc(result, path)`: Renders a human-readable table of contents

```python
from textprompts import inject_anchors, parse_sections, render_toc

result = parse_sections("## Intro\n\nBody.")
print(result.sections[0].anchor_id)  # "intro"

anchored_text, anchored = inject_anchors("## Intro\n\nBody.")
print(anchored_text)  # <a id="intro"></a>\n## Intro...

print(render_toc(anchored, "prompt.txt"))
```

Anchor IDs use a canonical underscore form. For example, `generate_slug("My Section")` returns `my_section`, `id="my-section"` normalizes to `my_section`, and generic XML sections default to the normalized tag name when no explicit `id` is present.

### `PromptString`

A string subclass that validates `format()` calls:

```python
from textprompts import PromptString

template = PromptString("Hello {name}, you are {role}")

# Strict formatting (default) - all placeholders required
result = template.format(name="Alice", role="admin")  # ✅ Works
result = template.format(name="Alice")  # ❌ Raises ValueError

# Partial formatting - replace only available placeholders
partial = template.format(name="Alice", skip_validation=True)  # ✅ "Hello Alice, you are {role}"

# Access placeholder information
print(template.placeholders)  # {'name', 'role'}
```

## Error Handling

TextPrompts provides specific exception types:

```python
from textprompts import (
    TextPromptsError,       # Base exception
    FileMissingError,       # File not found
    MissingMetadataError,   # No TOML front-matter when required
    InvalidMetadataError,   # Invalid TOML syntax
    MalformedHeaderError,   # Malformed front-matter structure
    MetadataMode,           # Metadata handling mode enum
    set_metadata,           # Set global metadata mode
    get_metadata            # Get global metadata mode
)
```

## CLI Tool

TextPrompts includes a CLI for quick prompt inspection:

```bash
# View a single prompt
textprompts show greeting.txt

# List all prompts in a directory
textprompts list prompts/ --recursive

# Validate prompts
textprompts validate prompts/
```

## Best Practices

1. **Organize by purpose**: Group related prompts in folders
   ```
   prompts/
   ├── customer-support/
   ├── content-generation/
   └── code-review/
   ```

2. **Use semantic versioning**: Version your prompts like code
   ```
   version = "1.2.0"  # major.minor.patch
   ```

3. **Document your variables**: List expected variables in descriptions
   ```
   description = "Requires: customer_name, issue_type, agent_name"
   ```

4. **Test your prompts**: Write unit tests for critical prompts
   ```python
   def test_greeting_prompt():
    prompt = load_prompt("greeting.txt")
    result = prompt.prompt.format(customer_name="Test")
       assert "Test" in result
   ```

5. **Use environment-specific prompts**: Different prompts for dev/prod
   ```python
   env = os.getenv("ENV", "development")
   prompt = load_prompt(f"prompts/{env}/system.txt")
   ```

## Why Not Just Use String Templates?

You could, but then you lose:
- **Metadata tracking** (versions, authors, descriptions)
- **Safe formatting** (catch missing variables)
- **Organized storage** (searchable, documentable)
- **Version control benefits** (proper diffs, blame, history)
- **Tooling support** (CLI, validation, testing)

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

MIT License - see [LICENSE](LICENSE) for details.

---

**textprompts** - Because your prompts deserve better than being buried in code strings. 🚀
