prompttools-core-ai 1.0.0

Shared foundation library for the prompttools suite

prompttools-core

Shared foundation library for the prompttools suite.

What It Provides

prompttools-core is the shared library used by all tools in the prompttools suite (promptfmt, promptcost, prompttest, promptlint). It provides:

• Multi-format prompt parsing -- YAML, JSON, Markdown, and plain text
• Tokenization engine -- tiktoken-based token counting with model-aware encoding selection
• Model profiles -- Built-in profiles for OpenAI, Anthropic, and Google models with pricing and context window data
• Configuration system -- Hierarchical config file discovery and merging
• Content caching -- SHA256-based file-system cache to avoid redundant computation
• Plugin system -- Discover and load custom plugin classes from directories
• Template variable extraction -- Detects {{var}}, {var}, and <var> syntax across all formats

Installation

pip install prompttools-core-ai

Dependencies: pydantic >= 2.0, pyyaml >= 6.0, tiktoken >= 0.7

Supported Formats

Extension	Format	Description
.yaml, .yml	YAML	Structured messages with front-matter metadata
.json	JSON	Structured messages with metadata
.md	Markdown	Heading-delimited message sections
.txt	Text	Plain text, single or --- delimited messages

API Reference

Parsing

from prompttools_core import parse_file, parse_directory, parse_stdin, parse_pipeline

# Parse a single prompt file (auto-detects format from extension)
prompt = parse_file("prompts/greeting.yaml")

# Parse from stdin
prompt = parse_stdin(content, format="yaml")  # format: text, md, yaml, json

# Parse all prompt files in a directory
prompts = parse_directory("prompts/")

# Parse a pipeline manifest
pipeline = parse_pipeline("pipeline.yaml")

parse_file(path, config=None) -> PromptFile Parse a prompt file, auto-detecting format by extension. Raises ParseError for unsupported formats and FileNotFoundError if the file does not exist.

parse_directory(path, config=None, patterns=None) -> list[PromptFile] Recursively parse all prompt files in a directory. Skips files that fail to parse. Respects config.exclude patterns.

parse_stdin(content, format) -> PromptFile Parse prompt content from stdin. The format parameter must be one of: text, md, yaml, json.

parse_pipeline(manifest_path, config=None) -> PromptPipeline Parse a pipeline manifest YAML and all referenced prompt files. The manifest defines stages with name, file, depends_on, expected_output_tokens, and persona fields.

Tokenization

from prompttools_core import Tokenizer, count_tokens, get_encoding

# Quick token count
tokens = count_tokens("Hello, world!", encoding="cl100k_base")

# Model-aware tokenizer
tokenizer = Tokenizer.for_model("gpt-4o")
tokens = tokenizer.count("Some text")

# Count tokens for a full prompt file (populates token_count on each message)
total = tokenizer.count_file(prompt)

# Count tokens across messages including role overhead
total = tokenizer.count_messages(prompt.messages)

Tokenizer(encoding="cl100k_base", provider="default") Create a tokenizer with a specific encoding. The provider parameter controls per-message role overhead (OpenAI: 4 tokens, Anthropic: 3, Google: 3).

Tokenizer.for_model(model_name) -> Tokenizer Factory method that looks up the model profile and creates a tokenizer with the correct encoding and provider.

count_tokens(text, encoding="cl100k_base") -> int Standalone convenience function to count tokens in a string.

Model Profiles

from prompttools_core import get_profile, list_profiles, register_profile, ModelProfile

# Look up a built-in profile
profile = get_profile("gpt-4o")
print(f"Context: {profile.context_window}, Encoding: {profile.encoding}")
print(f"Input: ${profile.input_price_per_mtok}/Mtok")

# List all profiles
for name, p in list_profiles().items():
    print(f"{name}: {p.provider}, {p.context_window} tokens")

# Register a custom profile
register_profile(ModelProfile(
    name="my-model",
    context_window=32000,
    encoding="cl100k_base",
    provider="custom",
    input_price_per_mtok=1.0,
    output_price_per_mtok=3.0,
))

Built-in Profiles

Model	Provider	Context	Encoding	Input $/Mtok	Output $/Mtok
gpt-4	openai	8,192	cl100k_base	$30.00	$60.00
gpt-4-turbo	openai	128,000	cl100k_base	$10.00	$30.00
gpt-4o	openai	128,000	o200k_base	$2.50	$10.00
gpt-4o-mini	openai	128,000	o200k_base	$0.15	$0.60
claude-3-haiku	anthropic	200,000	cl100k_base	$0.25	$1.25
claude-3-sonnet	anthropic	200,000	cl100k_base	$3.00	$15.00
claude-3-opus	anthropic	200,000	cl100k_base	$15.00	$75.00
claude-4-sonnet	anthropic	200,000	cl100k_base	$3.00	$15.00
gemini-1.5-pro	google	1,000,000	cl100k_base	$1.25	$5.00
gemini-2.0-flash	google	1,048,576	cl100k_base	$0.10	$0.40

Non-OpenAI models use cl100k_base as an approximation (indicated by approximate_tokenizer=True on the profile).

Configuration

from prompttools_core import load_config, find_config_file

# Auto-discover and load config
config = load_config(tool_name="fmt", start_dir=Path("."))

# Load with explicit path and CLI overrides
config = load_config(
    tool_name="cost",
    config_path=Path(".prompttools.yaml"),
    cli_overrides={"model": "gpt-4o"},
)

Config file discovery order (at each directory level, walking upward):

1. .prompt{tool_name}.yaml (e.g., .promptfmt.yaml)
2. .prompttools.yaml
3. .promptlint.yaml (backward compatibility)

Merge priority (highest to lowest):

1. CLI overrides
2. Config file
3. Model profile defaults
4. Built-in defaults

Configuration File Format

# .prompttools.yaml
model: gpt-4o
exclude:
  - "vendor/**"
  - "*.generated.*"
plugins:
  - "./my-plugins"
cache:
  enabled: true
  dir: .prompttools-cache
tokenizer:
  encoding: cl100k_base

# Tool-specific sections
fmt:
  delimiter_style: "###"
  variable_style: double_brace
cost:
  default_output_tokens: 1000

Caching

from prompttools_core import PromptCache

cache = PromptCache(cache_dir=Path(".prompttools-cache"))

# Generate a content-based key
key = PromptCache.content_key(content="Hello", encoding="cl100k_base")

# Store and retrieve values (with optional TTL in seconds)
cache.set(key, 42, ttl=3600)
value = cache.get(key)  # Returns None if expired

# Invalidate a single key or clear all
cache.invalidate(key)
cache.clear()

Plugin System

from prompttools_core import discover_plugins, load_plugin

# Load plugin classes from a single file
classes = load_plugin(Path("my_plugin.py"), base_class=MyBaseClass)

# Discover all plugins in directories
classes = discover_plugins(
    plugin_dirs=[Path("./plugins"), Path("./custom-rules")],
    base_class=MyBaseClass,
)

Plugins are Python files containing classes that subclass a given base class. The plugin system is used by promptlint for custom lint rules and by promptfmt for custom formatters.

Data Models

PromptFile -- Represents a parsed prompt file.

• path: Path -- Source file path
• format: PromptFormat -- Detected format (TEXT, MARKDOWN, YAML, JSON)
• raw_content: str -- Original file content
• messages: list[Message] -- Parsed messages
• variables: dict[str, str] -- Template variables mapped to syntax style
• variable_defaults: dict[str, str] -- Default values from metadata
• metadata: dict[str, Any] -- Front-matter or top-level metadata
• total_tokens: Optional[int] -- Populated by tokenizer
• content_hash: str -- SHA256 hash of raw_content

Message -- A single turn in a prompt conversation.

• role: Literal["system", "user", "assistant", "tool"]
• content: str
• line_start: Optional[int], line_end: Optional[int]
• token_count: Optional[int]
• metadata: dict[str, Any]

ModelProfile -- Configuration for a specific LLM model.

• name, context_window, encoding, provider
• input_price_per_mtok, output_price_per_mtok
• max_output_tokens, supports_system_message, supports_tools
• approximate_tokenizer: bool

ToolConfig -- Base configuration shared by all tools.

• model, tokenizer_encoding, exclude, plugins
• cache_enabled, cache_dir, extra

PromptPipeline / PipelineStage -- Multi-stage pipeline support.

Error Hierarchy

All exceptions inherit from PromptToolsError:

• ParseError -- File cannot be parsed (also a ValueError)
• ConfigError -- Configuration is invalid
• TokenizerError -- Tokenization fails
• ProfileNotFoundError -- Unknown model profile
• PluginError -- Plugin loading or execution fails
• CacheError -- Cache read/write fails

Template Variable Extraction

from prompttools_core.parser import extract_variables

vars = extract_variables("Hello {{name}}, your order {id} is in <status>")
# {'name': 'jinja', 'id': 'fstring', 'status': 'xml'}

Common HTML tags (div, span, p, etc.) are automatically excluded from XML-style variable detection.

License

MIT License. Author: Scott Converse.