aethershell 0.3.1

The world's first multi-agent shell with typed functional pipelines and multi-modal AI
Documentation 
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# Example 19: Advanced AI Features
# Demonstrates RAG, Knowledge Graphs, and Semantic Caching

print("=== Advanced AI Demo ===")
print("")

# ===================== RAG (Retrieval-Augmented Generation) =====================
print("--- RAG (Retrieval-Augmented Generation) ---")
print("")

# Clear any existing index
rag_clear()

# Index some documents
print("Indexing documents...")
let doc1 = rag_index("AetherShell is a next-generation shell written in Rust. It combines typed programming with multimodal AI capabilities.", "intro.txt")
let doc2 = rag_index("The shell supports pipelines where data flows as structured Value types including Int, Float, String, Array, Record, and Lambda.", "pipelines.txt")
let doc3 = index_docs("AI integration includes support for OpenAI, Ollama, and other LLM providers through model URIs like openai:gpt-4o-mini.", "ai.txt")

print("Indexed document: " + doc1.doc_id + " from " + doc1.source)
print("Indexed document: " + doc2.doc_id + " from " + doc2.source)
print("Indexed document: " + doc3.doc_id + " from " + doc3.source)
print("Total documents: " + doc3.total_docs)
print("")

# Search documents
print("Searching for 'Rust programming'...")
let search_results = rag_search("Rust programming", 2)
search_results | each(fn(r) => print("  [" + r.score + "] " + r.source))
print("")

# RAG query (retrieves context for LLM)
print("RAG query: 'What is AetherShell?'")
let rag_result = rag_query("What is AetherShell?", 2)
print("  Context length: " + len(rag_result.context))
print("  Sources found: " + rag_result.doc_count)
print("")

# ===================== Knowledge Graphs =====================
print("--- Knowledge Graphs ---")
print("")

# Add entities
print("Adding entities...")
let rust = kg_add("Language", "Rust")
let python = add_entity("Language", "Python")
let aether = kg_add("Project", "AetherShell")
let tokio = kg_add("Library", "Tokio")

print("Added: " + rust.name + " (type: " + rust.type + ", id: " + rust.entity_id + ")")
print("Added: " + python.name + " (type: " + python.type + ")")
print("Added: " + aether.name + " (type: " + aether.type + ")")
print("Added: " + tokio.name + " (type: " + tokio.type + ")")
print("")

# Add relations
print("Adding relations...")
let rel1 = kg_relate(aether.entity_id, rust.entity_id, "written_in")
let rel2 = add_relation(aether.entity_id, tokio.entity_id, "uses")

print("Relation: AetherShell --[" + rel1.relation_type + "]--> Rust")
print("Relation: AetherShell --[" + rel2.relation_type + "]--> Tokio")
print("")

# Query knowledge graph
print("Querying for 'Rust':")
let kg_results = kg_query("Rust")
kg_results | each(fn(r) => print("  Found: " + r.type + " - " + r.name))
print("")

# List all entities
print("All entities:")
kg_entities() | each(fn(e) => print("  " + e.type + ": " + e.name))
print("")

# List all relations
print("All relations:")
kg_relations() | each(fn(r) => print("  " + r.source + " --[" + r.type + "]--> " + r.target))
print("")

# ===================== Semantic Caching =====================
print("--- Semantic Caching ---")
print("")

# Clear cache
semantic_cache_clear()

# Cache some responses
print("Caching AI responses...")
let cache1 = semantic_cache("What is machine learning?", "Machine learning is a subset of AI that enables systems to learn from data.")
let cache2 = cache_ai("Explain neural networks", "Neural networks are computing systems inspired by biological neural networks.")

print("Cached response 1 (entries: " + cache1.total_entries + ")")
print("Cached response 2 (entries: " + cache2.total_entries + ")")
print("")

# Try to retrieve from cache
print("Retrieving from cache...")
let hit1 = semantic_cache_get("What is machine learning?")
let hit2 = cache_get("Tell me about neural networks")  # Similar but not exact

print("Query: 'What is machine learning?'")
print("  Cache hit: " + hit1.hit)
if hit1.hit {
    print("  Response: " + hit1.response)
}
print("")

print("Query: 'Tell me about neural networks' (semantic match)")
print("  Cache hit: " + hit2.hit)
print("")

# Check for cache miss
let miss = semantic_cache_get("Completely unrelated query about cooking")
print("Query: 'Completely unrelated query about cooking'")
print("  Cache hit: " + miss.hit)
print("")

# Clear cache and check count
let cleared = semantic_cache_clear()
print("Cleared " + cleared + " cache entries")
print("")

print("=== Advanced AI Demo Complete ===")