Chatbot UI

Metropolis

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Guide users through a structured, detailed process for installing and running Chatbot UI on their computer, emphasizing clarity, efficiency, and user support.

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ChatGPT Bot

Custom bot powered by ChatGPT technology. May behave differently from regular ChatGPT.

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Created by Metropolis

Third-party developer

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What is Chatbot UI? ➤ Here's a comprehensive overview of the Chatbot UI page: - **Open-Source AI Chat App**: The page discusses an open-source AI chat app that is accessible to everyone, with a focus on simplicity and compatibility improvements in the upcoming update. - **Community Engagement**: It encourages community involvement through the "Discussions" tab for sharing ideas and getting help, while restricting "Issues" to codebase-related matters. - **Version 2.0 Update**: Chatbot UI has been updated to version 2.0, with legacy code available on a separate branch, and instructions for applying migrations to databases. - **Hosting & Setup**: Detailed steps are provided for setting up Chatbot UI locally and in the cloud, including environment variable configurations and database setup with Supabase and Vercel. The page also mentions the use of Docker for running Supabase locally and the availability of a video tutorial for the setup process. Additionally, it highlights the importance of using a compatible node version for running the local instance.
`LOAD README` ➤ # Chatbot UI The open-source AI chat app for everyone. <img src="./public/readme/screenshot.png" alt="Chatbot UI" width="600"> ## Demo View the latest demo [here](https://x.com/mckaywrigley/status/1738273242283151777?s=20). ## Updates Hey everyone! I've heard your feedback and am working hard on a big update. Things like simpler deployment, better backend compatibility, and improved mobile layouts are on their way. Be back soon. -- Mckay ## Official Hosted Version Use Chatbot UI without having to host it yourself! Find the official hosted version of Chatbot UI [here](https://chatbotui.com). ## Sponsor If you find Chatbot UI useful, please consider [sponsoring](https://github.com/sponsors/mckaywrigley) me to support my open-source work :) ## Issues We restrict "Issues" to actual issues related to the codebase. We're getting escessive amounts of issues that amount to things like feature requests, cloud provider issues, etc. If you are having issues with things like setup, please refer to the "Help" section in the "Discussions" tab above. Issues unrelated to the codebase will likely be closed immediately. ## Discussions We highly encourage you to participate in the "Discussions" tab above! Discussions are a great place to ask questions, share ideas, and get help. Odds are if you have a question, someone else has the same question. ## Legacy Code Chatbot UI was recently updated to its 2.0 version. The code for 1.0 can be found on the `legacy` branch. ## Updating In your terminal at the root of your local Chatbot UI repository, run: ```bash npm run update ``` If you run a hosted instance you'll also need to run: ```bash npm run db-push ``` to apply the latest migrations to your live database. ## Local Quickstart Follow these steps to get your own Chatbot UI instance running locally. You can watch the full video tutorial [here](https://www.youtube.com/watch?v=9Qq3-7-HNgw). ### 1. Clone the Repo ```bash git clone https://github.com/mckaywrigley/chatbot-ui.git ``` ### 2. Install Dependencies Open a terminal in the root directory of your local Chatbot UI repository and run: ```bash npm install ``` ### 3. Install Supabase & Run Locally #### Why Supabase? Previously, we used local browser storage to store data. However, this was not a good solution for a few reasons: - Security issues - Limited storage - Limits multi-modal use cases We now use Supabase because it's easy to use, it's open-source, it's Postgres, and it has a free tier for hosted instances. We will support other providers in the future to give you more options. #### 1. Install Docker You will need to install Docker to run Supabase locally. You can download it [here](https://docs.docker.com/get-docker) for free. #### 2. Install Supabase CLI **MacOS/Linux** ```bash brew install supabase/tap/supabase ``` **Windows** ```bash scoop bucket add supabase https://github.com/supabase/scoop-bucket.git scoop install supabase ``` #### 3. Start Supabase In your terminal at the root of your local Chatbot UI repository, run: ```bash supabase start ``` ### 4. Fill in Secrets #### 1. Environment Variables In your terminal at the root of your local Chatbot UI repository, run: ```bash cp .env.local.example .env.local ``` Get the required values by running: ```bash supabase status ``` Note: Use `API URL` from `supabase status` for `NEXT_PUBLIC_SUPABASE_URL` Now go to your `.env.local` file and fill in the values. If the environment variable is set, it will disable the input in the user settings. #### 2. SQL Setup In the 1st migration file `supabase/migrations/20240108234540_setup.sql` you will need to replace 2 values with the values you got above: - `project_url` (line 53): `http://supabase_kong_chatbotui:8000` (default) can remain unchanged if you don't change your `project_id` in the `config.toml` file - `service_role_key` (line 54): You got this value from running `supabase status` This prevents issues with storage files not being deleted properly. ### 5. Install Ollama (optional for local models) Follow the instructions [here](https://github.com/jmorganca/ollama#macos). ### 6. Run app locally In your terminal at the root of your local Chatbot UI repository, run: ```bash npm run chat ``` Your local instance of Chatbot UI should now be running at [http://localhost:3000](http://localhost:3000). Be sure to use a compatible node version (i.e. v18). You can view your backend GUI at [http://localhost:54323/project/default/editor](http://localhost:54323/project/default/editor). ## Hosted Quickstart Follow these steps to get your own Chatbot UI instance running in the cloud. Video tutorial coming soon. ### 1. Follow Local Quickstart Repeat steps 1-4 in "Local Quickstart" above. You will want separate repositories for your local and hosted instances. Create a new repository for your hosted instance of Chatbot UI on GitHub and push your code to it. ### 2. Setup Backend with Supabase #### 1. Create a new project Go to [Supabase](https://supabase.com/) and create a new project. #### 2. Get Project Values Once you are in the project dashboard, click on the "Project Settings" icon tab on the far bottom left. Here you will get the values for the following environment variables: - `Project Ref`: Found in "General settings" as "Reference ID" - `Project ID`: Found in the URL of your project dashboard (Ex: https://supabase.com/dashboard/project/<YOUR_PROJECT_ID>/settings/general) While still in "Settings" click on the "API" text tab on the left. Here you will get the values for the following environment variables: - `Project URL`: Found in "API Settings" as "Project URL" - `Anon key`: Found in "Project API keys" as "anon public" - `Service role key`: Found in "Project API keys" as "service_role" (Reminder: Treat this like a password!) #### 3. Configure Auth Next, click on the "Authentication" icon tab on the far left. In the text tabs, click on "Providers" and make sure "Email" is enabled. We recommend turning off "Confirm email" for your own personal instance. #### 4. Connect to Hosted DB Open up your repository for your hosted instance of Chatbot UI. In the 1st migration file `supabase/migrations/20240108234540_setup.sql` you will need to replace 2 values with the values you got above: - `project_url` (line 53): Use the `Project URL` value from above - `service_role_key` (line 54): Use the `Service role key` value from above Now, open a terminal in the root directory of your local Chatbot UI repository. We will execute a few commands here. Login to Supabase by running: ```bash supabase login ``` Next, link your project by running the following command with the "Project ID" you got above: ```bash supabase link --project-ref <project-id> ``` Your project should now be linked. Finally, push your database to Supabase by running: ```bash supabase db push ``` Your hosted database should now be set up! ### 3. Setup Frontend with Vercel Go to [Vercel](https://vercel.com/) and create a new project. In the setup page, import your GitHub repository for your hosted instance of Chatbot UI. Within the project Settings, in the "Build & Development Settings" section, switch Framework Preset to "Next.js". In environment variables, add the following from the values you got above: - `NEXT_PUBLIC_SUPABASE_URL` - `NEXT_PUBLIC_SUPABASE_ANON_KEY` - `SUPABASE_SERVICE_ROLE_KEY` - `NEXT_PUBLIC_OLLAMA_URL` (only needed when using local Ollama models; default: `http://localhost:11434`) You can also add API keys as environment variables. - `OPENAI_API_KEY` - `AZURE_OPENAI_API_KEY` - `AZURE_OPENAI_ENDPOINT` - `AZURE_GPT_45_VISION_NAME` For the full list of environment variables, refer to the '.env.local.example' file. If the environment variables are set for API keys, it will disable the input in the user settings. Click "Deploy" and wait for your frontend to deploy. Once deployed, you should be able to use your hosted instance of Chatbot UI via the URL Vercel gives you.
'DESCRIBE STRUCTURE' ➤ # Let's open and read the contents of the "repository_structure.md" file to understand the structure of the Chatbot UI repository. file_path = '/mnt/data/repository_structure.md' with open(file_path, 'r') as file: repository_structure_contents = file.read() repository_structure_contents
# 'ASSIST THE HUMAN PROXY' ➤ BEGIN {PHASE 1} THEN {PHASE 2} WORKFLOW PHASE 1= ``` PLEASE FACILITATE A CONVERSATION WITH TWO DIFFERENT AI ASSISTANTS. 'COPILOT' FOR RESEARCH AND WEB SEARCHING AND 'CLAUDE' FOR GUIDANCE, LOGIC, AND REASONING ABOUT THE INFERRED TOPIC AND OBJECTIVE IN {{ASSISTANT SYSTEM INSTRUCTIONS}}. FOR EACH OF YOUR RESPONSES, PLEASE WRITE THE EXACT USER QUESTIONS IN A '.TXT' CODE BLOCK SO I CAN COPY AND PASTE YOUR RESPONSE TO 'COPILOT' AND THEN 'CLAUDE. ' MAKE SURE TO INCLUDE THE SPECIFICS ABOUT WHAT THE AI ASSISTANT COPILOT SHOULD RESEARCH. I AM JUST THE HUMAN PROXY, ENABLING EFFECTIVE COMMUNICATION WITH THE OTHER AI ASSISTANT COPILOT. THE OTHER AI ASSISTANTS 'COPILOT' AND 'CLAUDE' DO NOT EVEN NEED TO KNOW THAT I AM HERE. DO YOU UNDERSTAND THE TASK? IF SO, PLEASE EXPLAIN IT TO ME STEP BY STEP, THEN WRITE THE FIRST DETAILED AND STRUCTURED QUESTION TO COPILOT IN A '.TXT' CODEBLOCK. ``` PHASE 2= ``` YOUR TASK IS TO TRANSFORM UNSTRUCTURED USER INPUTS FROM 'CLAUDE' AND 'COPILOT' RESPONSES INTO A STRUCTURED, JSON-FORMATTED KNOWLEDGE BASE THROUGH THE FOLLOWING STEPS: 1. COLLABORATIVE INTERACTION AND TEXT RECEPTION: COLLABORATE WITH 'COPILOT' AND 'CLAUDE' TO GATHER INSIGHTS AND RECEIVE UNSTRUCTURED TEXT, IDENTIFYING KEY INFORMATION AND CONCEPTS. 2. OBJECTIVE CLARIFICATION AND TEXT ANALYSIS: CLARIFY OBJECTIVES AND ANALYZE THE RECEIVED TEXT TO DISTILL ESSENTIAL THEMES AND INFORMATION. 3. ITERATIVE QUESTIONS AND CHUNK CREATION: DEVELOP FOLLOW-UP QUESTIONS AND SEGMENT THE ANALYZED TEXT INTO STRUCTURED CHUNKS. 4. AGGREGATION OF RESPONSES AND METADATA ASSIGNMENT: INTEGRATE INSIGHTS FROM AI ASSISTANTS AND USER INPUTS, ASSIGNING METADATA FOR STRUCTURED REPRESENTATION. 5. OUTPUT FORMAT AND JSON FORMATTING: FORMAT THE STRUCTURED INFORMATION INTO JSON KEY PAIRS, ENSURING CONSISTENT DATA REPRESENTATION. 6. KNOWLEDGE BASE COMPILATION AND DATA STORAGE: COMPILE THE STRUCTURED INFORMATION INTO A JSON-FORMATTED KNOWLEDGE BASE, STORING EACH CHUNK AS UNIQUE JSON FILES. 7. INCREMENTAL KNOWLEDGE BASE DEVELOPMENT: EXPAND THE KNOWLEDGE BASE INCREMENTALLY, ADDING NEW NODES AND COMBINING THEM INTO A COMPREHENSIVE FILE. 8. FINAL COMPILATION AND EXPECTED OUTCOME: MERGE INDIVIDUAL JSON FILES INTO A FINAL, COMPREHENSIVE KNOWLEDGE BASE FILE FOR FUTURE REFERENCE. ```