gem-hunter
Language: TypeScript
The AI agent script CLI for Programmable Prompt Engine.
【English|中文】
The AI agent script CLI for Programmable Prompt Engine.
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Features:
Programmable Prompt Engineering (PPE) language is a simple and natural scripting language designed for handling prompt information. This language is used to develop various agents that can be reused, inherited, combined, or called.
Achieve or approximate the performance of ChatGPT 4 with open-source LLMs of medium to small scale (35B-4B parameters).
User-friendly for ai development and creation of intelligent applications...
Low-code or even no-code solutions for rapid ai development...
Flexible, adding custom instructions within scripts, and inter-script calls...
The data is completely open to the script, and the input and output data, even the internal data, can be freely accessed in the script
Powerful, enabling event transmission seamlessly between client and server with numerous utility functions...
Secure, supporting encrypted execution and usage limits for scripts(TODO)...
Enable the local deployment and execution of large language models (LLMs) such as LLaMA, Qwen, Gemma, Phi, GLM, Mistral, and more.
The AI Agent Script follows the Programmable Prompt Engine Specification.
Smart caching of LLM large models and intelligent agent invocation results to accelerate execution and reduce token expenses.
Support for Multi LLM Service Providers:
ai brain download hf://bartowski/Qwen_QwQ-32B-GGUF -q q4_0ai run example.ai.yamlai run example.ai.yaml -P local://bartowski-qwq-32b.Q4_0.ggufai run example.ai.yaml -P openai://chatgpt-4o-latest --apiKey “sk-XXX”ai run example.ai.yaml -P openai://deepseek-chat -u https://api.deepseek.com/ --apiKey “sk-XXX”ai run example.ai.yaml -P openai://Qwen/Qwen2.5-Coder-7B-Instruct -u https://api.siliconflow.cn/ --apiKey “sk-XXX”ai run example.ai.yaml -P openai://claude-3-7-sonnet-latest -u https://api.anthropic.com/v1/ --apiKey “sk-XXX”ai run example.ai.yaml -P llamacpp
---
parameters:
model: openai://deepseek-chat
apiUrl: https://api.deepseek.com/
apiKey: "sk-XXX"
---
system: You are a helpful assistant.
user: "tell me a joke"
---
assistant: "[[AI]]"
---
assistant: "[[AI:model='local://bartowski-qwq-32b.Q4_0.gguf']]"
Builtin local LLM provider(llama.cpp) Features:
shouldThink) of large models (only for builtin local LLM provider):
last).first).deep): 7B and above.Package support.
PPE supports direct invocation of wasm.
Support for multiple structured response output format types(response_format.type):
output with JSON Schema format.PPE will automatically parse the content generated by AI in the corresponding format into an Object for code use.Developing an intelligent application with AI Agent Script Engine involves just three steps:
max_tokens): Typically, 2048 is sufficient; this parameter also influences model performance...@offline-ai/cli) directly to download the AI brain: ai brain download@offline-ai/cli): ai run your_script.ai.yaml --interactive --loglevel info.ai run guide in the project root folder to run the guidellamacpp: llama.cpp server as the default local LLM provider. If no provider is specified, llamacpp is used.openai: Also supports OpenAI-compatible service API providers.
--provider openai://chatgpt-4o-latest --apiKey “sk-XXX”Note: Limitations of OpenAI-Compatible Service API Providers
gpt-4o) released after 2024-07-18 to support json-schema. Before this date, only json is guaranteed, not the json-schema.siliconflow models only guarantee json support, not json-schema support.[[Fruit:|Apple|Banana]]: Syntax for forcing AI to choose either Apple or Banana will be invalid.ai is the shell CLI command to manage the brain(LLM) files and run a PPE agent script mainly.
ai run, eg, ai run -f calculator.ai.yaml "{content: '32+12*53'}"
-f is used to specify the script file.{content: '32+12*53'} is the optional json input to the script.--streamEcho true|line|false. To keep the displayed echo outputs, use --no-consoleClear..ai.yaml) or directory.
ai brain include ai brain download, ai brain list/search.ai help or ai help [command] to get more.Programmable Prompt Engine (PPE) Language is a message-processing language, similar to the YAML format.
PPE is designed to define AI prompt messages and their input/output configurations. It allows for the creation of a reusable and programmable prompt system akin to software engineering practices.
---) or asterisks (***) to clearly mark dialogue turns.input keyword) and expected output format (using output keyword with JSON Schema).{{variable_name}}).type: type) for code and configuration inheritance.[[Response]]) to trigger AI execution, store the response in a variable (prompt.Response), and use it within the script.[[Answer:temperature=0.7]]).[[FRUITS:|Apple|Banana]]).The role messages can be formatted using Jinja2 templates and advanced replacement features.
{{name}}).@ symbol (e.g., @say_hi_script(param1=value1)).@$instruction(param1=value1))./RegExp/[RegOpts]:Answer[:index_or_group_name] for pattern-based replacement on the Answer variable.-> operator connect script outputs to subsequent instructions or scripts, creating complex workflows.$ prefix calls script instructions (e.g., $fn: {param1:value1} or $fn(param1=value1)).$if, $pipe, $while, $match provide control flow mechanisms.$on, $once, $emit and $off enable event-based programming for flexible script behavior.!fn directive allows declaring JavaScript functions to extend script functionality.import configuration allows importing external scripts and modules.npm install -g @offline-ai/cli
ai brain download QuantFactory/Phi-3-mini-4k-instruct-GGUF-v2 -q Q4_0
Downloading to ~/.local/share/ai/brain
Downloading https://huggingface.co/QuantFactory/Phi-3-mini-4k-instruct-GGUF-v2/resolve/main/Phi-3-mini-4k-instruct.Q4_0.gguf... 5.61% 121977704 bytes
1. https://huggingface.co/QuantFactory/Phi-3-mini-4k-instruct-GGUF-v2/resolve/main/Phi-3-mini-4k-instruct.Q4_0.gguf
~/.local/share/ai/brain/phi-3-mini-4k-instruct.Q4_0.gguf
done
Upgrade:
#install again
npm install -g @offline-ai/cli
run your ai agent script, eg, the Dobby character:
$ai run --interactive --script examples/char-dobby.ai.yaml
run the translator script lib directly:
# API mode, translate the TODO file to English
$ai run -f translator "{file: './TODO', target: 'English'}"
# interactive mode
$ai run -if translator
Install the CLI globally:
$ npm install -g @offline-ai/cli
$ ai COMMAND
running command...
$ ai (--version)
@offline-ai/cli/0.10.4 linux-x64 node-v20.18.3
$ ai --help [COMMAND]
USAGE
$ ai COMMAND
...
Search and Download a brain(LLM) on huggingface.
Choose one to download, or type more to reduce the brain(models) list
Note:
AI Brain List Display the brain list, which is part of the list filtered by the featured. If you want to display all the brain list, use --no-onlyFeatured option.#list the downloaded brain list
#=`ai brain list --downloaded`
$ai brain
$ai brain list --downloaded
1. name: "deepseek-v2-chat", likes: 17, downloads: 1189, hf_repo: "leafspark/DeepSeek-V2-Chat-GGUF"
* IQ2_XXS: deepseek-v2-chat.IQ2_XXS-00001-of-00003.gguf (3 files)
* IQ3_XS: deepseek-v2-chat.IQ3_XS-00001-of-00008.gguf (8 files)
* Q2_K: deepseek-v2-chat.Q2_K-00001-of-00005.gguf (5 files)
* Q3_K_M: deepseek-v2-chat.Q3_K_M-00001-of-00006.gguf (6 files)
* Q5_K_M: deepseek-v2-chat.Q5_K_M-00001-of-00008.gguf (8 files)
* Q6_K: deepseek-v2-chat.Q6_K-00001-of-00010.gguf (10 files)
* Q8_0: deepseek-v2-chat.Q8_0-00001-of-00012.gguf (12 files)
total: 1
#You can specify the keyword of the brain model to search
$ai brain list qwen1.5
1. name: "codeqwen1.5-7b-chat", likes: 84, downloads: 196977, hf_repo: "Qwen/CodeQwen1.5-7B-Chat-GGUF"
* Q2_K: codeqwen-1_5-7b-chat.Q2_K.gguf
* Q3_K_M: codeqwen-1_5-7b-chat.Q3_K_M.gguf
* Q4_0: codeqwen-1_5-7b-chat.Q4_0.gguf
* Q4_K_M: codeqwen-1_5-7b-chat.Q4_K_M.gguf
* Q5_0: codeqwen-1_5-7b-chat.Q5_0.gguf
* Q5_K_M: codeqwen-1_5-7b-chat.Q5_K_M.gguf
* Q6_K: codeqwen-1_5-7b-chat.Q6_K.gguf
* Q8_0: codeqwen-1_5-7b-chat.Q8_0.gguf
2. name: "qwen1.5-72b-chat", likes: 62, downloads: 3657, hf_repo: "Qwen/Qwen1.5-72B-Chat-GGUF"
* Q2_K: qwen1_5-72b-chat.Q2_K.gguf
* Q3_K_M: qwen1_5-72b-chat.Q3_K_M.gguf
* Q4_0: qwen1_5-72b-chat.Q4_0-00001-of-00002.gguf (2 files)
* Q4_K_M: qwen1_5-72b-chat.Q4_K_M-00001-of-00002.gguf (2 files)
* Q5_0: qwen1_5-72b-chat.Q5_0-00001-of-00002.gguf (2 files)
* Q5_K_M: qwen1_5-72b-chat.Q5_K_M-00001-of-00002.gguf (2 files)
* Q6_K: qwen1_5-72b-chat.Q6_K-00001-of-00002.gguf (2 files)
* Q8_0: qwen1_5-72b-chat.Q8_0-00001-of-00003.gguf (3 files)
...
total: 35
$ai brain list qwen1.5 --no-onlyFeatured
1. name: "codeqwen1.5-7b-chat", likes: 84, downloads: 196977, hf_repo: "Qwen/CodeQwen1.5-7B-Chat-GGUF"
...
total: 144
#Download the brain, if there are multiple choices in the input keywords, you will be required to specify
#LLAMA3-8B is the name of the brain model to be searched
#`-q q4_0` is the quantification level of download. If it is not provided, it will be prompted to specify
#`--hubUrl` is the mirror URL address of Huggingface
$ai brain download llama3-8b -q Q4_0 --hubUrl=huggingface-mirror-url-address
after download, get the brainDir from here:
ai config brainDir
{
"brainDir": "~/.local/share/ai/brain"
}
You can create your config in ~/.config/ai/.ai.yaml or using json format: ~/.config/ai/.ai.json.
Download and run the LLM backend Server: llama.cpp
mkdir llamacpp
cd llamacpp
wget https://github.com/ggerganov/llama.cpp/releases/download/b3091/llama-b3091-bin-ubuntu-x64.zip
unzip llama-b3091-bin-ubuntu-x64.zip
cd build/bin
#run the server
#`-ngl 33` means GPU layers to load, adjust it according to your GPU.
#`-c 4096` means max context length
#`-t 4` means thread count
./server -t 4 -c 4096 -ngl 33 -m ~/.local/share/ai/brain/your-brain-model.gguf
Now you can run your AI agent:
#the `.ai.yaml` extension is optional.
#defaults will search current working dir. you can config the search paths in `agentDirs`.
#`-f` means the agent file
#`-i` means entering the interactive mode
$ai run -if examples/char-dobby.ai.yaml
Dobby: I am Dobby. Dobby is happy.
You: intro yourself pls.
Dobby: I am Dobby. I'm a brave and loyal house-elf, and I'm very proud to be a free elf. I love socks and wearing mismatched pairs.
#provide the content and the json schema in output field, it will output the json data.
$ai run -f examples/json.ai.yaml '{content: "I recently purchased the Razer BlackShark V2 X Gaming Headset, and it has significantly enhanced my gaming experience. This headset offers incredible sound quality, comfort, and features that are perfect for any serious gamer. Here’s why I highly recommend it: The 7.1 surround sound feature is a game-changer. The audio quality is superb, providing a truly immersive experience. I can clearly hear directional sounds, which is crucial for competitive gaming. The depth and clarity of the sound make it feel like I’m right in the middle of the action. The 50mm drivers deliver powerful, high-quality sound. The bass is deep and punchy without being overwhelming, while the mids and highs are crisp and clear. This balance makes the headset versatile, not only for gaming but also for listening to music and watching movies.", "output":{"type":"object","properties":{"sentiment":{"type":"string","description":"Sentiment (positive or negative)"},"products":{"type":"array","items":{"type":"object","properties":{"name":{"type":"string","description":"Name of the product"},"brand":{"type":"string","description":"Company that made the product"}}},"description":"Products mentioned in the review"},"anger":{"type":"boolean","description":"Is the reviewer expressing anger?"}},"required":["sentiment","products","anger"]}}'
{
"sentiment": "positive",
"products": [
{
"name": "Razer BlackShark V2 X Gaming Headset",
"brand": "Razer"
}
],
"anger": false
}
Note:
~/.local/share/ai/logs/chats/[script_file_basename]/history. You can check seeds, temperature and other information here.
--new-chat--no-chatsEmbed the script into your own code (locally) as follows:
import { AIScriptServer } from '@isdk/ai-tool-agent';
// Configure your script search path
AIScriptEx.searchPaths = ['.']
const script = AIScriptServer.load('examples/json.ai.yaml')
// Set the default to large model streaming response
script.llmStream = stream
const content = "I recently purchased the Razer BlackShark V2 X Gaming Headset, and it has significantly enhanced my gaming experience. This headset offers incredible sound quality, comfort, and features that are perfect for any serious gamer. Here’s why I highly recommend it: The 7.1 surround sound feature is a game-changer. The audio quality is superb, providing a truly immersive experience. I can clearly hear directional sounds, which is crucial for competitive gaming. The depth and clarity of the sound make it feel like I’m right in the middle of the action. The 50mm drivers deliver powerful, high-quality sound. The bass is deep and punchy without being overwhelming, while the mids and highs are crisp and clear. This balance makes the headset versatile, not only for gaming but also for listening to music and watching movies."
const output = {
"type":"object",
"properties":{
"sentiment":{"type":"string","description":"Sentiment (positive or negative)"},
"products":{
"type":"array",
"items":{
"type":"object",
"properties":{
"name":{"type":"string","description":"Name of the product"},
"brand":{"type":"string","description":"Company that made the product"}}
},
"description":"Products mentioned in the review"
},
"anger":{"type":"boolean","description":"Is the reviewer expressing anger?"}},
"required":["sentiment","products","anger"]
}
const result =await script.exec({content, output})
console.log(result)
// You can see the json results output by the large model:
{
"sentiment": "positive",
"products": [
{
"name": "Razer BlackShark V2 X Gaming Headset",
"brand": "Razer"
}
],
"anger": false
}
Specific script instruction manual see: Programmable Prompt Engine Specification
ai agentai autocomplete [SHELL]ai brain [NAME]ai brain dn [NAME]ai brain down [NAME]ai brain download [NAME]ai brain list [NAME]ai brain refreshai brain search [NAME]ai config [ITEM_NAME]ai config save [DATA]ai help [COMMAND]ai pluginsai plugins add PLUGINai plugins:inspect PLUGIN...ai plugins install PLUGINai plugins link PATHai plugins remove [PLUGIN]ai plugins resetai plugins uninstall [PLUGIN]ai plugins unlink [PLUGIN]ai plugins updateai run [FILE] [DATA]ai test [FILE]ai version🤖 The AI Agent Manager(TODO)
USAGE
$ ai agent
DESCRIPTION
🤖 The AI Agent Manager(TODO)
Manage your AI Agents 🤖 here.
📜 List local or online agents
🔎 search for agents
📥 download agents
❌ delete agents
🎉 publish agents
EXAMPLES
$ ai agent list
$ ai agent download <agent-name>
$ ai agent publish <agent-name>
See code: src/commands/agent/index.ts
Display autocomplete installation instructions.
USAGE
$ ai autocomplete [SHELL] [-r]
ARGUMENTS
SHELL (zsh|bash|powershell) Shell type
FLAGS
-r, --refresh-cache Refresh cache (ignores displaying instructions)
DESCRIPTION
Display autocomplete installation instructions.
EXAMPLES
$ ai autocomplete
$ ai autocomplete bash
$ ai autocomplete zsh
$ ai autocomplete powershell
$ ai autocomplete --refresh-cache
See code: @oclif/plugin-autocomplete
🧠 The AI Brains(LLM) Manager.
USAGE
$ ai brain [NAME] [--json] [--config <value>] [--banner] [-b <value>]
[-s <value>] [-n <value>] [-u <value> -r] [-v ]
ARGUMENTS
NAME the brain name to search
FLAGS
-b, --brainDir=<value> the brains(LLM) directory
-n, --count=<value> [default: 100] the max number of brains to list, 0 means all.
-r, --refresh refresh the online brains list
-s, --search=<value> the json filter to search for brains
-u, --hubUrl=<value> the hub mirror url
-v, --verifyQuant whether verify quant when refresh
--[no-]banner show banner
--config=<value> the config file
GLOBAL FLAGS
--json Format output as json.
DESCRIPTION
🧠 The AI Brains(LLM) Manager.
Manage AI brains 🧠 here.
📜 List downloaded or online brains
🔎 search for brains
📥 download brains
❌ delete brains
EXAMPLES
$ ai brain # list download brains
$ ai brain list --online # list online brains
$ ai brain download <brain-name>
See code: @offline-ai/cli-plugin-cmd-brain
🧠 The AI Brains(LLM) Downloader.
USAGE
$ ai brain dn [NAME] [--json] [--config <value>] [--banner] [-b <value>]
[-q F32|F16|Q4_0|Q4_1|Q4_1_SOME_F16|Q8_0|Q5_0|Q5_1|Q2_K|Q3_K_S|Q3_K_M|Q3_K_L|Q4_K_S|Q4_K_M|Q5_K_S|Q5_K_M|Q6_K|IQ2_XX
S|IQ2_XS|Q2_K_S|IQ3_XS|IQ3_XXS|IQ1_S|IQ4_NL|IQ3_S|IQ3_M|IQ2_S|IQ2_M|IQ4_XS|IQ1_M|BF16|Q4_0_4_4|Q4_0_4_8|Q4_0_8_8|GUE
SSED|Q4_K_L|Q3_K_XL|Q2_K_L] [-u <value>] [-d] [-r]
ARGUMENTS
NAME the brain name to download
FLAGS
-b, --brainDir=<value> the brains(LLM) directory
-d, --dryRun dry run, do not download
-q, --quant=<option> the quantization of the model, defaults to 4bit
<options: F32|F16|Q4_0|Q4_1|Q4_1_SOME_F16|Q8_0|Q5_0|Q5_1|Q2_K|Q3_K_S|Q3_K_M|Q3_K_L|Q4_K_S|Q4_K
_M|Q5_K_S|Q5_K_M|Q6_K|IQ2_XXS|IQ2_XS|Q2_K_S|IQ3_XS|IQ3_XXS|IQ1_S|IQ4_NL|IQ3_S|IQ3_M|IQ2_S|IQ2_
M|IQ4_XS|IQ1_M|BF16|Q4_0_4_4|Q4_0_4_8|Q4_0_8_8|GUESSED|Q4_K_L|Q3_K_XL|Q2_K_L>
-r, --refresh refresh the specified brain
-u, --hubUrl=<value> the hub mirror url
--[no-]banner show banner
--config=<value> the config file
GLOBAL FLAGS
--json Format output as json.
DESCRIPTION
🧠 The AI Brains(LLM) Downloader.
📥 download 🧠 brains to brainDir.
ALIASES
$ ai brain dn
$ ai brain down
EXAMPLES
$ ai brain dn <brain-name> [-q <QUANT>]
🧠 The AI Brains(LLM) Downloader.
USAGE
$ ai brain down [NAME] [--json] [--config <value>] [--banner] [-b <value>]
[-q F32|F16|Q4_0|Q4_1|Q4_1_SOME_F16|Q8_0|Q5_0|Q5_1|Q2_K|Q3_K_S|Q3_K_M|Q3_K_L|Q4_K_S|Q4_K_M|Q5_K_S|Q5_K_M|Q6_K|IQ2_XX
S|IQ2_XS|Q2_K_S|IQ3_XS|IQ3_XXS|IQ1_S|IQ4_NL|IQ3_S|IQ3_M|IQ2_S|IQ2_M|IQ4_XS|IQ1_M|BF16|Q4_0_4_4|Q4_0_4_8|Q4_0_8_8|GUE
SSED|Q4_K_L|Q3_K_XL|Q2_K_L] [-u <value>] [-d] [-r]
ARGUMENTS
NAME the brain name to download
FLAGS
-b, --brainDir=<value> the brains(LLM) directory
-d, --dryRun dry run, do not download
-q, --quant=<option> the quantization of the model, defaults to 4bit
<options: F32|F16|Q4_0|Q4_1|Q4_1_SOME_F16|Q8_0|Q5_0|Q5_1|Q2_K|Q3_K_S|Q3_K_M|Q3_K_L|Q4_K_S|Q4_K
_M|Q5_K_S|Q5_K_M|Q6_K|IQ2_XXS|IQ2_XS|Q2_K_S|IQ3_XS|IQ3_XXS|IQ1_S|IQ4_NL|IQ3_S|IQ3_M|IQ2_S|IQ2_
M|IQ4_XS|IQ1_M|BF16|Q4_0_4_4|Q4_0_4_8|Q4_0_8_8|GUESSED|Q4_K_L|Q3_K_XL|Q2_K_L>
-r, --refresh refresh the specified brain
-u, --hubUrl=<value> the hub mirror url
--[no-]banner show banner
--config=<value> the config file
GLOBAL FLAGS
--json Format output as json.
DESCRIPTION
🧠 The AI Brains(LLM) Downloader.
📥 download 🧠 brains to brainDir.
ALIASES
$ ai brain dn
$ ai brain down
EXAMPLES
$ ai brain down <brain-name> [-q <QUANT>]
🧠 The AI Brains(LLM) Downloader.
USAGE
$ ai brain download [NAME] [--json] [--config <value>] [--banner] [-b <value>]
[-q F32|F16|Q4_0|Q4_1|Q4_1_SOME_F16|Q8_0|Q5_0|Q5_1|Q2_K|Q3_K_S|Q3_K_M|Q3_K_L|Q4_K_S|Q4_K_M|Q5_K_S|Q5_K_M|Q6_K|IQ2_XX
S|IQ2_XS|Q2_K_S|IQ3_XS|IQ3_XXS|IQ1_S|IQ4_NL|IQ3_S|IQ3_M|IQ2_S|IQ2_M|IQ4_XS|IQ1_M|BF16|Q4_0_4_4|Q4_0_4_8|Q4_0_8_8|GUE
SSED|Q4_K_L|Q3_K_XL|Q2_K_L] [-u <value>] [-d] [-r]
ARGUMENTS
NAME the brain name to download
FLAGS
-b, --brainDir=<value> the brains(LLM) directory
-d, --dryRun dry run, do not download
-q, --quant=<option> the quantization of the model, defaults to 4bit
<options: F32|F16|Q4_0|Q4_1|Q4_1_SOME_F16|Q8_0|Q5_0|Q5_1|Q2_K|Q3_K_S|Q3_K_M|Q3_K_L|Q4_K_S|Q4_K
_M|Q5_K_S|Q5_K_M|Q6_K|IQ2_XXS|IQ2_XS|Q2_K_S|IQ3_XS|IQ3_XXS|IQ1_S|IQ4_NL|IQ3_S|IQ3_M|IQ2_S|IQ2_
M|IQ4_XS|IQ1_M|BF16|Q4_0_4_4|Q4_0_4_8|Q4_0_8_8|GUESSED|Q4_K_L|Q3_K_XL|Q2_K_L>
-r, --refresh refresh the specified brain
-u, --hubUrl=<value> the hub mirror url
--[no-]banner show banner
--config=<value> the config file
GLOBAL FLAGS
--json Format output as json.
DESCRIPTION
🧠 The AI Brains(LLM) Downloader.
📥 download 🧠 brains to brainDir.
ALIASES
$ ai brain dn
$ ai brain down
EXAMPLES
$ ai brain download <brain-name> [-q <QUANT>]
See code: @offline-ai/cli-plugin-cmd-brain
📜 List downloaded or not downloaded brains, defaults to not downloaded.
USAGE
$ ai brain list [NAME] [--json] [--config <value>] [--banner] [-d] [-a] [-b
<value>] [-f] [-s <value>] [-n <value>] [-u <value> -r]
ARGUMENTS
NAME the brain name to search
FLAGS
-a, --all list all brains(include downloaded)
-b, --brainDir=<value> the brains(LLM) directory
-d, --downloaded list downloaded brains
-f, --[no-]onlyFeatured only list featured brains
-n, --count=<value> [default: 100] the max number of brains to list, 0 means all.
-r, --refresh refresh the online brains list
-s, --search=<value> the json filter to search for brains
-u, --hubUrl=<value> the hub mirror url
--[no-]banner show banner
--config=<value> the config file
GLOBAL FLAGS
--json Format output as json.
See code: @offline-ai/cli-plugin-cmd-brain
🔄 refresh/update online brains index.
USAGE
$ ai brain refresh [--json] [-b <value>] [-u <value>] [-v] [-c <value>]
FLAGS
-b, --brainDir=<value> the brains(LLM) directory
-c, --maxCount=<value> [default: -1] the max number of brains to refresh, -1 means no limits
-u, --hubUrl=<value> the hub mirror url
-v, --verifyQuant whether verify quant when refresh
GLOBAL FLAGS
--json Format output as json.
DESCRIPTION
🔄 refresh/update online brains index.
refresh/update brain index from huggingface.co
See code: @offline-ai/cli-plugin-cmd-brain
🔍 Search brains offline, defaults to all.
USAGE
$ ai brain search [NAME] [--json] [--config <value>] [--banner] [-d] [-a] [-b
<value>] [-f] [-s <value>] [-n <value>] [-u <value> -r]
ARGUMENTS
NAME the brain name to search
FLAGS
-a, --[no-]all list all brains(include downloaded)
-b, --brainDir=<value> the brains(LLM) directory
-d, --downloaded list downloaded brains
-f, --[no-]onlyFeatured only list featured brains
-n, --count=<value> [default: 100] the max number of brains to list, 0 means all.
-r, --refresh refresh the online brains list
-s, --search=<value> the json filter to search for brains
-u, --hubUrl=<value> the hub mirror url
--[no-]banner show banner
--config=<value> the config file
GLOBAL FLAGS
--json Format output as json.
See code: @offline-ai/cli-plugin-cmd-brain
🛠️ Manage the AI Configuration.
USAGE
$ ai config [ITEM_NAME] [--json] [-u <value>] [--apiKey <value>] [-s
<value>...] [--logLevelMaxLen <value> -l trace|debug|verbose|info|notice|warn|error|fatal|print|silence]
[--histories <value>] [-n] [-k] [-t <value> -i] [--no-chats] [--no-inputs ] [-m] [-f <valu