base on AI on the command line # Mods <p> <img src="https://github.com/charmbracelet/mods/assets/25087/5442bf46-b908-47af-bf4e-60f7c38951c4" width="630" alt="Mods product art and type treatment"/> <br> <a href="https://github.com/charmbracelet/mods/releases"><img src="https://img.shields.io/github/release/charmbracelet/mods.svg" alt="Latest Release"></a> <a href="https://github.com/charmbracelet/mods/actions"><img src="https://github.com/charmbracelet/mods/workflows/build/badge.svg" alt="Build Status"></a> </p> AI for the command line, built for pipelines. <p><img src="https://vhs.charm.sh/vhs-5Uyj0U6Hlqi1LVIIRyYKM5.gif" width="900" alt="a GIF of mods running"></p> Large Language Models (LLM) based AI is useful to ingest command output and format results in Markdown, JSON, and other text based formats. Mods is a tool to add a sprinkle of AI in your command line and make your pipelines artificially intelligent. It works great with LLMs running locally through [LocalAI]. You can also use [OpenAI], [Cohere], [Groq], or [Azure OpenAI]. [LocalAI]: https://github.com/go-skynet/LocalAI [OpenAI]: https://platform.openai.com/account/api-keys [Cohere]: https://dashboard.cohere.com/api-keys [Groq]: https://console.groq.com/keys [Azure OpenAI]: https://azure.microsoft.com/en-us/products/cognitive-services/openai-service ### Installation Use a package manager: ```bash # macOS or Linux brew install charmbracelet/tap/mods # Windows (with Winget) winget install charmbracelet.mods # Arch Linux (btw) yay -S mods # Nix nix-shell -p mods ``` <details> <summary>Debian/Ubuntu</summary> ```bash sudo mkdir -p /etc/apt/keyrings curl -fsSL https://repo.charm.sh/apt/gpg.key | sudo gpg --dearmor -o /etc/apt/keyrings/charm.gpg echo "deb [signed-by=/etc/apt/keyrings/charm.gpg] https://repo.charm.sh/apt/ * *" | sudo tee /etc/apt/sources.list.d/charm.list sudo apt update && sudo apt install mods ``` </details> <details> <summary>Fedora/RHEL</summary> ```bash echo '[charm] name=Charm baseurl=https://repo.charm.sh/yum/ enabled=1 gpgcheck=1 gpgkey=https://repo.charm.sh/yum/gpg.key' | sudo tee /etc/yum.repos.d/charm.repo sudo yum install mods ``` </details> Or, download it: - [Packages][releases] are available in Debian and RPM formats - [Binaries][releases] are available for Linux, macOS, and Windows [releases]: https://github.com/charmbracelet/mods/releases Or, just install it with `go`: ```sh go install github.com/charmbracelet/mods@latest ``` <details> <summary>Shell Completions</summary> All the packages and archives come with pre-generated completion files for Bash, ZSH, Fish, and PowerShell. If you built it from source, you can generate them with: ```bash mods completion bash -h mods completion zsh -h mods completion fish -h mods completion powershell -h ``` If you use a package (like Homebrew, Debs, etc), the completions should be set up automatically, given your shell is configured properly. </details> ## What Can It Do? Mods works by reading standard in and prefacing it with a prompt supplied in the `mods` arguments. It sends the input text to an LLM and prints out the result, optionally asking the LLM to format the response as Markdown. This gives you a way to "question" the output of a command. Mods will also work on standard in or an argument supplied prompt individually. Be sure to check out the [examples](examples.md) and a list of all the [features](features.md). Mods works with OpenAI compatible endpoints. By default, Mods is configured to support OpenAI's official API and a LocalAI installation running on port 8080. You can configure additional endpoints in your settings file by running `mods --settings`. ## Saved Conversations Conversations are saved locally by default. Each conversation has a SHA-1 identifier and a title (like `git`!). <p> <img src="https://vhs.charm.sh/vhs-6MMscpZwgzohYYMfTrHErF.gif" width="900" alt="a GIF listing and showing saved conversations."> </p> Check the [`./features.md`](./features.md) for more details. ## Usage - `-m`, `--model`: Specify Large Language Model to use. - `-f`, `--format`: Ask the LLM to format the response in a given format. - `--format-as`: Specify the format for the output (used with `--format`). - `-P`, `--prompt`: Prompt should include stdin and args. - `-p`, `--prompt-args`: Prompt should only include args. - `-q`, `--quiet`: Only output errors to standard err. - `-r`, `--raw`: Print raw response without syntax highlighting. - `--settings`: Open settings. - `-x`, `--http-proxy`: Use HTTP proxy to connect to the API endpoints. - `--max-retries`: Maximum number of retries. - `--max-tokens`: Specify maximum tokens with which to respond. - `--no-limit`: Do not limit the response tokens. - `--role`: Specify the role to use (See [custom roles](#custom-roles)). - `--word-wrap`: Wrap output at width (defaults to 80) - `--reset-settings`: Restore settings to default. #### Conversations - `-t`, `--title`: Set the title for the conversation. - `-l`, `--list`: List saved conversations. - `-c`, `--continue`: Continue from last response or specific title or SHA-1. - `-C`, `--continue-last`: Continue the last conversation. - `-s`, `--show`: Show saved conversation for the given title or SHA-1. - `-S`, `--show-last`: Show previous conversation. - `--delete-older-than=<duration>`: Deletes conversations older than given duration (`10d`, `1mo`). - `--delete`: Deletes the saved conversation for the given title or SHA-1. - `--no-cache`: Do not save conversations. #### Advanced - `--fanciness`: Level of fanciness. - `--temp`: Sampling temperature. - `--topp`: Top P value. - `--topk`: Top K value. ## Custom Roles Roles allow you to set system prompts. Here is an example of a `shell` role: ```yaml roles: shell: - you are a shell expert - you do not explain anything - you simply output one liners to solve the problems you're asked - you do not provide any explanation whatsoever, ONLY the command ``` Then, use the custom role in `mods`: ```sh mods --role shell list files in the current directory ``` ## Setup ### Open AI Mods uses GPT-4 by default. It will fall back to GPT-3.5 Turbo. Set the `OPENAI_API_KEY` environment variable. If you don't have one yet, you can grab it the [OpenAI website](https://platform.openai.com/account/api-keys). Alternatively, set the [`AZURE_OPENAI_KEY`] environment variable to use Azure OpenAI. Grab a key from [Azure](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service). ### Cohere Cohere provides enterprise optimized models. Set the `COHERE_API_KEY` environment variable. If you don't have one yet, you can get it from the [Cohere dashboard](https://dashboard.cohere.com/api-keys). ### Local AI Local AI allows you to run models locally. Mods works with the GPT4ALL-J model as setup in [this tutorial](https://github.com/go-skynet/LocalAI#example-use-gpt4all-j-model). ### Groq Groq provides models powered by their LPU inference engine. Set the `GROQ_API_KEY` environment variable. If you don't have one yet, you can get it from the [Groq console](https://console.groq.com/keys). ## Whatcha Think? We’d love to hear your thoughts on this project. Feel free to drop us a note. - [Twitter](https://twitter.com/charmcli) - [The Fediverse](https://mastodon.social/@charmcli) - [Discord](https://charm.sh/chat) ## License [MIT](https://github.com/charmbracelet/mods/raw/main/LICENSE) --- Part of [Charm](https://charm.sh). <a href="https://charm.sh/"><img alt="The Charm logo" width="400" src="https://stuff.charm.sh/charm-badge.jpg" /></a> <!--prettier-ignore--> Charm热爱开源 • Charm loves open source ", Assign "at most 3 tags" to the expected json: {"id":"2317","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"