Introduction

This is the Sunholo Python project, a comprehensive toolkit for working with language models and vector stores on Google Cloud Platform. It provides a wide range of functionalities and utilities to facilitate the development and deployment of language model applications.

This is the sunholo-py project. It provides a collection of useful tools and functionalities for working with language models, databases, and various bots.

sunholo-py

(draft release https://pypi.org/project/sunholo/ )

Table of Contents

• Agents
• Archive
• Bots
• Chunker
• Components
• Database
• Embedder
• PubSub
• QnA
• Streaming
• Summarise
• Utils

pip install sunholo

A python library to enable LLMOps within cloud environments

sunholo provides utilities to help manage LLM operations on Google Cloud Platform at first, but it is hoped that making it open source will help it support other clouds in the future. A lot of the functionality is not Google Cloud Platform specific, so still may be helpful.

It is derived from the Edmonbrain project, the original blog post you can read here: https://code.markedmondson.me/running-llms-on-gcp/ and owes a lot to Langchain ( https://github.com/langchain-ai/langchain )

The package includes:

• agents/ - functions for working with agents, including easy flask apps, parsing chat history and dispatching requests to different agent endpoints
• archive/ - functions to record all Q&A activity to BigQuery via PubSub
• bots/ - functions for special cases regarding frontend bots such as GChat, Web Apps, Discord and Slack
• chunker/ - functions to slice up documents for sending into vector stores
• components/ - functions to help configure which LLM, prompt, vectorstore or document retriever you will use based on a yaml config file
• database/ - database setup functions and SQL to run on those sources such as Supabase
• embedder/ - functions to send chunks into embedding vector stores
• pubsub/ - use of PubSub for a message queue between components
• qna/ - utilities for running agents such as retry strats and parsing of output/input
• streaming/ - creation of streaming responses from LLM bots
• summarise/ - creation of summaries of large documents
• utils/ - reading configuration files, Google Cloud Platform metadata

Configuration

The library uses the config specifications that some examples are given in the config/ folder.

When using the functions, make sure to have the config/ folder in the root of where your application is running (usually $HOME/config)

   Copyright [2024] [Holosun ApS]

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