GlyphDeck

The glyphdeck library is a comprehensive toolkit designed to streamline & simplify various aspects of asynchronous LLM data processing workflows over high-volume, dense semantic data - like customer feedback, reviews and comments.

Common aspects of the LLM data workflow are handled end-to-end including async llm calls, data validation, sanitisation, transformation, caching and step chaining, facilitating the fast development of robust, error free LLM data workflows.

Its also equiped with caching and a configurable logging facility that makes complex asyncronous LLM workflows much easier to configure, understand and debug.

Features

• Asynchronous: Rip through tabular data with asynchronous llm usage
• Auto-scaling: Don't worry about api errors - the LLMHandler automatically scales to your api limits
• Rapid config: Simple syntax in a highly abstracted interface makes set up a breeze
• Data chaining: Automatically pass data between steps with the Cascade class
• Self validating: The Cascade class automatically maintains dataset integrity
• Pydantic models: Use built-in or custom Pydantic data models for structured llm output
• Type enforcement: Get your structured data in the correct format from the llm every time
• Private data: Sanitiser class strips out common private data patterns before making api calls
• Automatic caching: Save on API costs and re-use results
• Logging: Built-in logging facility helps to debug your set-ups & peer inside the event loop

Set-up

🔧 Install

pip install glyphdeck

🔑 Set API Key

Set your OPENAI_API_KEY with a command, or add it to your environment variables.

setx OPENAI_API_KEY "your_api_key_here"

📥 Import

import glyphdeck as gd

Usage

🌊 Cascades

The Cascade class is the primary interface for the glyphdeck library. It handles and processes data in a record-like structure, providing easy to use syntax for LLM data handling workflows.

It validates and enforces all data movements against a common id, ensuring that each record has a unique, immutable identifier that remains consistent, regardless of other changes.

This all combines to let you spin up LLM workflows in record time.

🏄 Example

import glyphdeck as gd

# Provide a dataframe or a path to a file (csv or xlsx)
data_source = r"tests\testdata.pizzashopreviews.xlsx"

# Intialise cascade instance and identify the unique id (required) and target data
cascade = gd.Cascade(data_source, "Review", "Review Text")

# Optionally remove private information
cascade.sanitiser.run()

# Prepare the llm
cascade.set_llm_handler(
    provider="OpenAI",
    model="gpt-4o-mini",
    system_message=(
        "You are an expert pizza shop customer feedback analyst system."
        "Analyse the feedback and return results in the correct format."
    ),
    validation_model=gd.validators.SubCatsSentiment,
    cache_identifier="pizzshop_sentiment",
)

# Run the llm_handler
cascade.llm_handler.run("llm_category_sentiment")

✨ Output

🚀 That's just the start!

Glyphdeck is highly customisable with useful optional commands in every function, class & method. Use the Cascade and other tools to mix and match, create feedback loops and more - all with blazing asynchronous speed through the OpenAI API.

See the full capabilities in the extensive documentation.