Advanced Features for Rust-based LLM CLI Tool

Here are some advanced and innovative features to consider adding to the Rust-based LLM CLI tool to enhance its utility and functionality:

Enable the tool to execute code, scripts, or functions based on the model's response. This allows the tool to interpret code snippets, run calculations, or trigger system operations based on user input.

• Code Execution Mode: After generating a response, the tool can interpret and run code snippets in languages like Python, Rust, or Bash:

  lm -exec "Write a Python function to calculate the Fibonacci sequence."

  The tool generates the code and executes it:

  def fibonacci(n):
      a, b = 0, 1
      for i in range(n):
          yield a
          a, b = b, a + b
  
  for num in fibonacci(10):
      print(num)

  Execution result:

  0
  1
  1
  2
  3
  5
  8
  13
  21
  34
  

• Function Invocation from Response: The tool could automatically detect and execute functions or scripts from the output:

  lm -exec "Write a shell script to back up my Documents folder."

  And then execute it directly:

  lm -run "back_up_script.sh"

To use these plugins or actions, you need to define them in a TOML configuration file. Below is an example of how to create a config.toml file that defines custom actions and plugins:

default_model = "gemini-1.5-flash-002"
default_system_prompt = "You are a helpful assistant response in the shortest possible text."
project_id = "google_project_idea"

[plugins]
weather = "Fetch real-time weather data from an API."

[hooks]
write_report_summary = "Send the generated summary via email."

Place this config.toml file in the appropriate directory so the CLI tool can reference it when executing commands.

Allow users to define custom actions (e.g., shell commands, API requests, local file operations) that can be triggered by the LLM's response or specific user commands.

• Custom Action Hooks: Define actions (via a config file or command) that get executed after the response:

  lm -hook "Write a report summary" -action email

  The tool generates a summary and sends it via email using a predefined mail client or API.

• Predefined or Customizable Plugins: A plugin system that lets users extend functionality. Plugins can trigger web searches, interact with APIs, or perform system tasks:

  lm -plugin weather "What's the weather in London?"

  The "weather" plugin fetches real-time weather data by calling a weather API.

Allow integration with external APIs and webhooks to make LLM-based workflows more powerful.

• API Calls from Prompts: Enable the LLM to interface with APIs directly:

  lm -api "fetch_stock_price AAPL"

  This command queries a stock price API and returns the current price.

• Webhook Triggering: Set up webhooks that are triggered based on specific output:

  lm -webhook "generate_invoice" -url "https://example.com/webhook"

Integrate real-time data feeds directly into the prompt or output.

• Real-Time Data Enrichment: Inject real-time data into responses by linking live data streams:

  lm -inject "stock_price" "Write a report on AAPL stock performance over the last week."

Allow users to chain multiple tasks or create complex workflows using LLMs.

• Task Automation: Automate a series of steps:

  lm -workflow "Create a GitHub repo, clone it locally, and push the initial commit."

• Multi-Step Workflow Chaining: Chain multiple related tasks into a single command:

  lm -chain "Fetch data from API" "Clean data" "Generate report" "Send email"

Enable users to schedule LLM-based tasks or actions at specific times.

• Task Scheduling: Set a task to run at a later time, integrated with cron jobs:

  lm -schedule "daily_summary" "Generate a daily business summary" --time "09:00"

• Recurring Tasks: Repeat actions at specified intervals:

  lm -cron "Update my blog every Monday at 10am."

Enable the tool to remember previous conversations or commands across sessions.

• Persistent Context: Store conversations across sessions to maintain state:

  lm -context "Enable persistent memory for this project."

• Session Recall: Recall a specific session or task-related conversation:

  lm -recall "project_alpha"

Allow users to manage files, directories, and databases using natural language.

• File Manipulation: Create, modify, or search files:

  lm "Create a markdown file called 'meeting_notes.md' and list all points discussed today."

• Database Queries: Query a database directly via natural language:

  lm -db-query "Get the top 10 highest-paying customers from our database."

Facilitate real-time collaboration or sharing of prompts and responses between multiple users.

• Shared Sessions: Allow multiple users to join the same LLM session:

  lm -collab "Invite teammates to this session."

• Collaboration over Network: Share session output or conversation with another terminal user:

  lm -share "Allow a remote team member to collaborate on this prompt."

Enable conditional logic between prompts.

• Conditional Prompt Execution: Run different prompts based on the result of a previous one:

  lm -if "price > 1000" "Write a report on high-value sales."

• Dynamic Prompt Adaptation: Adjust prompts based on current data or system conditions:

  lm "If I receive an email from X, draft a response."

Offer built-in error handling for both generated code and user actions.

• Error Detection and Explanation: If errors are detected in generated code, provide debugging help:

  lm -debug "Generate a Python script to scrape a webpage."

  If an error occurs:

  lm "Explain why this script isn't working."

Support multiple users with different roles or permissions.

• User Roles: Assign roles to limit or expand capabilities:

  lm -user create --role editor

• User-Specific Prompts: Allow personalized environments:

  lm -n "user123_system" "What's the project status?"