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Latest Updates

1. A new workflow intermediary has been added, which allows your workflow to call other workflows!
2. Added a omnipotent interpreter node that allows the large model to execute any task. The large model operates within a virtual environment, downloading necessary third-party libraries and executing generated code. Please use this tool with caution, as the large model gains the ability to control your computer for any task!
3. Introducing a cool "Matryoshka" feature: Disable the main_brain attribute of an LLM node to use it as a tool. Link this node's tool to another regular LLM node, and you'll find that the second LLM can call it like a tool!
4. New start_workflow and end_workflow nodes allow you to define the starting and ending points of a workflow. Place your workflow in the workflow subfolder of this project, then run setup_streamlit_app.bat in the project folder. In the Streamlit interface, click "Settings" and replace it with your workflow.

COMFYUI LLM PARTY—A Node Library for LLM Workflow Development in ComfyUI

Introduction

comfyui is an extremely minimalist UI interface, primarily used for AI drawing and other workflows based on the SD model. This project aims to develop a complete set of nodes for LLM workflow construction based on comfyui. It allows users to quickly and conveniently build their own LLM workflows and easily integrate them into their existing SD workflows.The image is a workflow of an intelligent customer service, for more workflows please refer to the workflow folder.

User Guide

Building a Modular AI with ComfyUI×LLM: A Step-by-Step Tutorial (Super Easy!)

Features

1. You can right-click in the comfyui interface, select llm from the context menu, and you will find the nodes for this project. how to use nodes
2. Supports API integration or local large model integration. Modular implementation for tool invocation.When entering the base_url, please use a URL that ends with /v1/.You can use ollama to manage your model. Then, enter http://localhost:11434/v1/ for the base_url, ollama for the api_key, and your model name for the model_name, such as: llama3. If the call fails with a 503 error, you can try turning off the proxy server.
3. Local knowledge base integration with RAG support.
4. Ability to invoke code interpreters.
5. Enables online queries, including Google search support.
6. Implement conditional statements within ComfyUI to categorize user queries and provide targeted responses.
7. Supports looping links for large models, allowing two large models to engage in debates.
8. Attach any persona mask, customize prompt templates.
9. Supports various tool invocations, including weather lookup, time lookup, knowledge base, code execution, web search, and single-page search.
10. Use LLM as a tool node.
11. Rapidly develop your own web applications using API + Streamlit.The picture below is an example of a drawing application.
12. Added a dangerous omnipotent interpreter node that allows the large model to perform any task.
13. It is recommended to use the show_text node under the function submenu of the right-click menu as the display output for the LLM node.

Download

Baidu Cloud Download (Recommended! Includes a compressed package of comfyui with the environment setup completed, and a folder for this project. After downloading the former, there’s no need for further environment configuration!)

Or install using one of the following methods:

Method 1:

1. Search for comfyui_LLM_party in the comfyui manager and install it with one click.
2. Restart comfyui. During the first restart, it will take some time to download the embedding model used for RAG.

Method 2:

1. Navigate to the custom_nodes subfolder under the ComfyUI root folder.
2. Clone this repository with git clone https://github.com/heshengtao/comfyui_LLM_party.git.
3. Copy the word embedding model to the model folder, click the link to download the model.

Method 3:

1. Click CODE in the upper right corner.
2. Click download zip.
3. Unzip the downloaded package into the custom_nodes subfolder under the ComfyUI root folder.
4. Copy the word embedding model to the model folder, click the link to download the model.

Environment Deployment

1. Navigate to the comfyui_LLM_party project folder.
2. Enter pip install -r requirements.txt in the terminal to deploy the third-party libraries required by the project into the comfyui environment. Please ensure you are installing within the comfyui environment and pay attention to any pip errors in the terminal.
3. If you are using the comfyui launcher, you need to enter path_in_launcher_configuration\python_embeded\python.exe path_in_launcher_configuration\python_embeded\Scripts\pip.exe install -r requirements.txt in the terminal to install. The python_embeded folder is usually at the same level as your ComfyUI folder.

Configuration

Configure the APIKEY using one of the following methods:

Method One:

1. Open the config.ini file in the comfyui_LLM_party project folder.
2. Enter your openai_api_key and base_url in config.ini.
3. If you want to use the Google search tool, enter your google_api_key and cse_id in config.ini.

Method Two:

1. Open the comfyui interface.
2. Create a new Large Language Model (LLM) node and directly enter your openai_api_key and base_url in the node.
3. Create a new Google Search Tool (google_tool) node and directly enter your google_api_key and cse_id in the node.

Next Steps Plan:

1. More model adaptations, at least covering the API interfaces of mainstream large models and local calls of mainstream open-source models, as well as more LVM model adaptations. Currently, I have only adapted the visual function calls of GPT-4;
2. More ways to build agents. The work I have completed in this area includes importing an LLM as a tool to another LLM, achieving radial construction of LLM workflows, and importing one workflow as a node into another workflow. I might develop some cooler functions in this area in the future.
3. More automation features. In the future, I will introduce more nodes that automatically push images, text, videos, and audio to other applications, as well as listening nodes that implement automatic replies to mainstream social software and forums.
4. More knowledge base management functions. The project already supports local file search and web search. In the future, I will introduce knowledge graph search and long-term memory search. This will allow agents to think logically about professional knowledge and always remember certain key information when conversing with users.
5. More tools, more persona. This part is the easiest to do but also requires the most accumulation. I hope that in the future, this project can have as many custom nodes as comfyui, with a multitude of tools and persona.

Disclaimer:

This open-source project and its contents (hereinafter referred to as "Project") are provided for reference purposes only and do not imply any form of warranty, either expressed or implied. The contributors of the Project shall not be held responsible for the completeness, accuracy, reliability, or suitability of the Project. Any reliance you place on the Project is strictly at your own risk. In no event shall the contributors of the Project be liable for any indirect, special, or consequential damages or any damages whatsoever resulting from the use of the Project.

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