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YantrikDB

Quick Start

Installation

Section titled “Installation”

Python

Section titled “Python”
Terminal window
pip install yantrikdb

Rust

Section titled “Rust”
Terminal window
cargo add yantrikdb

Python Quick Start

Section titled “Python Quick Start”
from yantrikdb import YantrikDB


# Open or create a database (single file, like SQLite)
db = YantrikDB("memory.db", embedding_dim=384)


# Set up an embedder (sentence-transformers)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
db.set_embedder(lambda text: model.encode(text).tolist())


# Store memories with importance and emotional valence
db.record("User's name is Pranab", importance=1.0)
db.record("User works at Walmart as a senior developer", importance=0.9)
db.record("User is excited about their AI project", importance=0.8, valence=0.7)
db.record("User prefers Rust over Python for systems work", importance=0.6)


# Recall with relevance-conditioned scoring
results = db.recall("What does the user do for work?", top_k=3)
for r in results:
    print(f"[{r['score']:.3f}] {r['text']}")


# Build the cognitive graph
db.relate("Pranab", "Walmart", "works_at", weight=0.9)
db.relate("Pranab", "Rust", "prefers", weight=0.8)
db.relate("Pranab", "AI project", "excited_about", weight=0.7)


# Run autonomous cognition
result = db.think()
print(f"Triggers: {result['triggers']}")
print(f"Consolidations: {result['consolidation_count']}")
print(f"Conflicts found: {result['conflicts_found']}")
print(f"New patterns: {result['patterns_new']}")

Rust Quick Start

Section titled “Rust Quick Start”
use yantrikdb::{YantrikDB, Embedder};


// Implement the Embedder trait with your preferred model
struct MyEmbedder;


impl Embedder for MyEmbedder {
    fn embed(&self, text: &str) -> Result<Vec<f32>, Box<dyn std::error::Error + Send + Sync>> {
        // Your embedding logic here
        Ok(vec![0.0; 384])
    }
    fn dim(&self) -> usize { 384 }
}


fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut db = YantrikDB::open("memory.db", 384)?;
    db.set_embedder(Box::new(MyEmbedder));


    // Record a memory
    let embedding = db.embed("User likes Rust")?;
    db.record_with_embedding("User likes Rust", &embedding, "semantic", 0.8, 0.5)?;


    // Recall
    let query_emb = db.embed("What programming languages?")?;
    let results = db.recall(&query_emb, 5)?;


    for r in &results.results {
        println!("[{:.3}] {}", r.score, r.text);
    }


    Ok(())
}

What’s Next?

Section titled “What’s Next?”