d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
144 lines
3.6 KiB
Markdown
144 lines
3.6 KiB
Markdown
# AgenticDB Quick Start Guide
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Get started with Ruvector's AgenticDB API in 5 minutes.
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## Installation
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```bash
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# Add to Cargo.toml
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[dependencies]
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ruvector-core = "0.1"
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```
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## Basic Usage
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```rust
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use ruvector_core::{AgenticDB, DbOptions, Result};
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use std::collections::HashMap;
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fn main() -> Result<()> {
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// 1. Initialize database
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let db = AgenticDB::with_dimensions(128)?;
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// 2. Store a learning episode
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let episode_id = db.store_episode(
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"Learn to optimize code".to_string(),
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vec!["analyzed bottleneck".to_string(), "applied optimization".to_string()],
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vec!["code 2x faster".to_string()],
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"Profiling first helps identify real bottlenecks".to_string(),
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)?;
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println!("Stored episode: {}", episode_id);
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// 3. Retrieve similar past experiences
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let similar = db.retrieve_similar_episodes("code optimization", 5)?;
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println!("Found {} similar experiences", similar.len());
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// 4. Create a reusable skill
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let skill_id = db.create_skill(
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"Code Profiler".to_string(),
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"Profile code to find performance bottlenecks".to_string(),
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HashMap::new(),
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vec!["run profiler".to_string(), "analyze hotspots".to_string()],
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)?;
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println!("Created skill: {}", skill_id);
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// 5. Add causal knowledge
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db.add_causal_edge(
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vec!["inefficient loop".to_string()],
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vec!["slow performance".to_string()],
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0.9,
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"Performance analysis".to_string(),
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)?;
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// 6. Start RL training
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let session = db.start_session("Q-Learning".to_string(), 4, 2)?;
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db.add_experience(&session, vec![1.0; 4], vec![1.0; 2], 1.0, vec![0.0; 4], false)?;
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// 7. Get predictions
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let prediction = db.predict_with_confidence(&session, vec![1.0; 4])?;
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println!("Predicted action: {:?}", prediction.action);
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Ok(())
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}
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```
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## Five Core APIs
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### 1. Reflexion Memory
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Learn from past mistakes:
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```rust
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// Store mistake
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db.store_episode(task, actions, observations, critique)?;
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// Learn from history
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let similar = db.retrieve_similar_episodes("similar situation", 5)?;
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```
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### 2. Skill Library
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Build reusable patterns:
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```rust
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// Create skill
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db.create_skill(name, description, params, examples)?;
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// Find relevant skills
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let skills = db.search_skills("what I need to do", 5)?;
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```
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### 3. Causal Memory
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Understand cause and effect:
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```rust
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// Add relationship (supports multiple causes → multiple effects)
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db.add_causal_edge(
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vec!["cause1", "cause2"],
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vec!["effect1", "effect2"],
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confidence,
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context,
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)?;
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// Query with utility function
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let results = db.query_with_utility(query, k, 0.7, 0.2, 0.1)?;
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```
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### 4. Learning Sessions
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Train RL models:
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```rust
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// Start training
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let session = db.start_session("DQN", state_dim, action_dim)?;
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// Add experience
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db.add_experience(&session, state, action, reward, next_state, done)?;
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// Make predictions
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let pred = db.predict_with_confidence(&session, current_state)?;
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```
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### 5. Vector Search
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Fast similarity search:
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```rust
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// All text is automatically embedded and indexed
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// Just use the high-level APIs above!
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```
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## Complete Example
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See `examples/agenticdb_demo.rs` for a full demonstration.
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## Documentation
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- Full API reference: `docs/AGENTICDB_API.md`
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- Implementation details: `docs/PHASE3_SUMMARY.md`
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## Performance
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- 10-100x faster than original agenticDB
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- O(log n) search with HNSW index
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- SIMD-optimized distance calculations
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- Concurrent access with lock-free reads
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## Next Steps
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1. Try the example: `cargo run --example agenticdb_demo`
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2. Read the API docs: `docs/AGENTICDB_API.md`
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3. Run tests: `cargo test -p ruvector-core agenticdb`
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4. Build your agentic AI system!
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