aethershell 0.3.1

The world's first multi-agent shell with typed functional pipelines and multi-modal AI
Documentation 
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# Provider System Example
# Demonstrates AetherShell's universal LLM provider interface

# List all supported providers
let providers = [
  "openai:gpt-4o",
  "anthropic:claude-3-5-sonnet-20241022", 
  "google:gemini-1.5-pro",
  "ollama:llama3",
  "groq:mixtral-8x7b-32768",
  "together:meta-llama/Llama-3-70b-chat-hf",
  "deepseek:deepseek-chat",
  "xai:grok-beta",
  "openrouter:anthropic/claude-3"
]

print "=== AetherShell Universal Provider System ==="
print ""

# Simple chat with any provider (using environment's default)
let response = ai "Hello, what is 2+2?" 
print response

# Specify a specific model via URI
let response2 = ai "Explain quantum computing in one sentence" --model "openai:gpt-4o"
print response2

# Multi-turn conversation
let conversation = [
  { role: "system", content: "You are a helpful shell assistant." },
  { role: "user", content: "How do I list files in Linux?" },
]
let response3 = ai_chat conversation --model "anthropic:claude-3-5-sonnet-20241022"
print response3

# Using tools with the provider system
let tools = [
  {
    name: "read_file",
    description: "Read contents of a file",
    parameters: {
      type: "object",
      properties: {
        path: { type: "string", description: "File path to read" }
      },
      required: ["path"]
    }
  },
  {
    name: "list_directory",
    description: "List files in a directory", 
    parameters: {
      type: "object",
      properties: {
        path: { type: "string", description: "Directory path" }
      },
      required: ["path"]
    }
  }
]

# AI can request tool calls
let agent_response = ai "What files are in the current directory?" --tools tools --model "openai:gpt-4o"
print agent_response

# Pipeline integration - AI processes structured data
let files = ls "."
let summary = files | ai "Summarize these files by type"
print summary

# Embeddings for semantic search
let texts = ["shell scripting", "file management", "process control"]
let embeddings = embed texts --model "openai:text-embedding-3-small"
print "Generated embeddings for semantic search"

# Provider-specific features
# Streaming (when supported)
ai_stream "Tell me a short story" --model "openai:gpt-4o" | fn(chunk) => print chunk

# Local models via Ollama
ai "What is the meaning of life?" --model "ollama:llama3"

print ""
print "=== Provider System Features ==="
print "• Universal model URI scheme (provider:model)"
print "• 19 providers supported"
print "• Automatic API key detection from environment"
print "• Tool/function calling normalization"
print "• Multi-modal support (images, audio)"
print "• Streaming support"
print "• Structured data integration with pipelines"