Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
184
crates/ruvector-core/examples/embeddings_example.rs
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184
crates/ruvector-core/examples/embeddings_example.rs
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//! Example of using different embedding providers with AgenticDB
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//!
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//! Run with:
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//! ```bash
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//! # Default hash-based (testing only)
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//! cargo run --example embeddings_example
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//!
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//! # With OpenAI API (requires OPENAI_API_KEY env var)
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//! OPENAI_API_KEY=sk-... cargo run --example embeddings_example --features real-embeddings
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//! ```
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use ruvector_core::types::DbOptions;
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use ruvector_core::{AgenticDB, ApiEmbedding, HashEmbedding};
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use std::sync::Arc;
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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println!("=== AgenticDB Embeddings Example ===\n");
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// Determine which provider to use
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let use_api = std::env::var("OPENAI_API_KEY").is_ok();
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let (db, provider_name) = if use_api {
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println!("Using OpenAI API embeddings (real semantic search)");
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let api_key = std::env::var("OPENAI_API_KEY")?;
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let provider = Arc::new(ApiEmbedding::openai(&api_key, "text-embedding-3-small"));
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let mut options = DbOptions::default();
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options.dimensions = 1536; // OpenAI text-embedding-3-small
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options.storage_path = "/tmp/agenticdb_api.db".to_string();
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let db = AgenticDB::with_embedding_provider(options, provider)?;
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(db, "OpenAI API")
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} else {
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println!("Using hash-based embeddings (testing only - not semantic)");
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println!("Set OPENAI_API_KEY to use real embeddings\n");
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let mut options = DbOptions::default();
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options.dimensions = 128;
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options.storage_path = "/tmp/agenticdb_hash.db".to_string();
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let db = AgenticDB::new(options)?;
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(db, "Hash-based")
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};
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println!("Provider: {}\n", db.embedding_provider_name());
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// Store some reflexion episodes
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println!("--- Storing Reflexion Episodes ---");
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let ep1 = db.store_episode(
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"Fix Rust borrow checker error".to_string(),
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vec![
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"Identified lifetime issue".to_string(),
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"Added explicit lifetime annotations".to_string(),
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"Refactored to use references".to_string(),
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],
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vec!["Code compiles now".to_string()],
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"Should explain borrow checker rules better".to_string(),
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)?;
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println!(
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"✓ Stored episode: Fix Rust borrow checker error (ID: {})",
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ep1
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);
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let ep2 = db.store_episode(
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"Optimize Python data processing".to_string(),
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vec![
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"Profiled with cProfile".to_string(),
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"Vectorized with NumPy".to_string(),
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"Parallelized with multiprocessing".to_string(),
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],
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vec!["10x performance improvement".to_string()],
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"Could have used Pandas for better readability".to_string(),
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)?;
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println!(
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"✓ Stored episode: Optimize Python data processing (ID: {})",
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ep2
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);
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let ep3 = db.store_episode(
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"Debug JavaScript async issue".to_string(),
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vec![
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"Added console.log statements".to_string(),
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"Used Chrome DevTools debugger".to_string(),
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"Fixed Promise chain".to_string(),
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],
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vec!["Race condition resolved".to_string()],
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"Should use async/await instead of callbacks".to_string(),
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)?;
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println!(
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"✓ Stored episode: Debug JavaScript async issue (ID: {})\n",
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ep3
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);
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// Create some skills
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println!("--- Creating Skills ---");
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let skill1 = db.create_skill(
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"Memory Profiling".to_string(),
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"Profile application memory usage to detect leaks and optimize allocation".to_string(),
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Default::default(),
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vec![
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"valgrind".to_string(),
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"massif".to_string(),
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"heaptrack".to_string(),
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],
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)?;
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println!("✓ Created skill: Memory Profiling (ID: {})", skill1);
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let skill2 = db.create_skill(
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"Async Programming".to_string(),
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"Write asynchronous code using promises, async/await, or futures".to_string(),
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Default::default(),
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vec![
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"Promise.all()".to_string(),
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"async/await".to_string(),
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"tokio".to_string(),
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],
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)?;
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println!("✓ Created skill: Async Programming (ID: {})", skill2);
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let skill3 = db.create_skill(
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"Performance Optimization".to_string(),
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"Profile and optimize code performance using profilers and benchmarks".to_string(),
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Default::default(),
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vec![
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"perf".to_string(),
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"criterion".to_string(),
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"flamegraph".to_string(),
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],
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)?;
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println!(
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"✓ Created skill: Performance Optimization (ID: {})\n",
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skill3
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);
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// Search episodes
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println!("--- Searching Episodes ---");
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let query = "memory problems in programming";
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println!("Query: \"{}\"", query);
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let episodes = db.retrieve_similar_episodes(query, 3)?;
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println!("Found {} similar episodes:\n", episodes.len());
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for (i, episode) in episodes.iter().enumerate() {
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println!("{}. Task: {}", i + 1, episode.task);
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println!(" Critique: {}", episode.critique);
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println!(" Actions: {}", episode.actions.join(" → "));
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println!();
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}
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if use_api {
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println!("ℹ️ With OpenAI embeddings, results are semantically similar!");
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println!(" 'memory problems' should match 'Rust borrow checker' and 'memory profiling'");
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} else {
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println!("⚠️ Hash-based embeddings are NOT semantic!");
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println!(" Results are based on character overlap, not meaning.");
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println!(" Set OPENAI_API_KEY to see real semantic search.");
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}
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// Search skills
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println!("\n--- Searching Skills ---");
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let query = "handling asynchronous operations";
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println!("Query: \"{}\"", query);
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let skills = db.search_skills(query, 3)?;
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println!("Found {} similar skills:\n", skills.len());
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for (i, skill) in skills.iter().enumerate() {
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println!("{}. {}", i + 1, skill.name);
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println!(" Description: {}", skill.description);
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println!(" Examples: {}", skill.examples.join(", "));
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println!();
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}
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println!("=== Example Complete ===");
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println!("\nTips:");
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println!("- Use hash-based embeddings for testing/development");
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println!("- Use API embeddings (OpenAI, Cohere, Voyage) for production");
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println!("- Implement ONNX provider for offline/edge deployment");
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println!("- See docs/EMBEDDINGS.md for full guide");
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Ok(())
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}
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