SaltAI_Documentation_Tools

Welcome to the SaltAI Documentation Tools Pack, a toolkit designed to automate the generation of documentation for ComfyUI components.

Leveraging the power of Large Language Models (LLM) and Retrieval-Augmented Generation (RAG), this pack streamlines the process, making it effortless to create, update, and maintain comprehensive documentation for any ComfyUI node or node pack.

The documentation for our platform Salt was generated with only the use of the proposed pipeline, please visit our site to check it out.

Getting Started

Prerequisites

This project requires the following dependencies:

• ComfyUI: Ensure you have ComfyUI installed. The pack has been validated with specific revisions of ComfyUI, which can be found on GitHub. The validated revisions are:

  • 36f7face37320ec3c6ac85ec3c625738241deaa9
  • 327ca1313d756c4b443790a53ab0afa1945d3f3e

• ComfyUI-Custom-Scripts: This is required to run an example workflow. You can find it on GitHub.

Installing

This project has been tested with Python 3.11.0.

Environmental Variables

Set the following environmental variables before running the project:

• OPENAI_KEY: Your personal OpenAI API key, required for sending requests to the OpenAI API. The price will depend on the model you use.
• ENABLE_PHOENIX_LOGGING: Set this to true to enable tracing callbacks with Phoenix (optional).

Installation Steps

To install the necessary dependencies, run the following command:

pip install -r requirements.txt

If you encounter the error AttributeError: type object 'SpanAttributes' has no attribute 'HTTP_REQUEST_METHOD' while executing the pack, try installing opentelemetry-semantic-conventions=0.44.b0.

Usage

Nodes

LoadOpenAIModel

Load OpenAI language model and embedding model based on provided names, return dictionary with models objects.

DocumentPack and DocumentNode

Locate all nodes loaded in the system (including ComfyUI default nodes and custom node packs) and generate documentation for the selected pack (node) in json format.

Generation process includes:

• Embed pack code and store it as a vector store index (will be cached in cache/indices/<parameters_signature>/<pack_name> directory).
• For each node, retrieve relevant context from the index and pass it to LLM guided by a set of prompts and forced to return structured json output.
• Evaluate the generated documentation against a set of questions. If any problems detected, refine query with the evaluation feedback and repeat the request.
• The resulting documentation will be stored in cache/documented_nodes/<parameters_signature>/json directory, split by packs, one json file per node.
• Optionally, you convert json files to markdown format and save those to cache/documented_nodes/<parameters_signature>/md directory.
• Node output is a string containing debug information - statistics on errors and mismatches in documentation. Analyse it and regenerate if required.

Example outputs are provided in the folder examples/json and examples/md.

Additionally, you can setup CI/CD to deploy markdown files to your repo github pages.

LogAllNodesToTable

Creates csv table containing information about all the nodes available in the system.

Columns include : Node name, Node pack name, Github link, Commit hash.

Example workflow

Please try this simple example workflow

License

This project is licensed under the MIT License - see the LICENSE.txt file for details