# Cerebro User Guide
Welcome to the Cerebro User Guide! This document covers how to implement the memory protocol in your application.
## 1. Initializing the Engine
Cerebro is designed to be highly modular. You can mix and match your compute backends and storage layers based on your privacy and scale needs.
```rust
use std::sync::Arc;
use cerebro::prelude::*;
use cerebro::compute::local::LocalEmbedder;
use cerebro::storage::pgvector::PgVectorStore;
#[tokio::main]
async fn main() {
// 1. Semantic Chunking (HTML aware)
let chunker = Arc::new(HtmlSemanticChunker::new(1024));
// 2. Local Privacy-First Embeddings (Runs 100% on CPU/Metal)
// Requires feature = ["local_models"]
let embedder = Arc::new(LocalEmbedder::new().await.unwrap());
// 3. Scalable Production Storage
let store = Arc::new(PgVectorStore::new("postgres://localhost/cerebro", "memories", 384).await.unwrap());
// Connect the neurons!
let engine = MemoryEngine::new(chunker, embedder, store);
println!("Brain is online.");
}
```
## 2. Ingesting Complex Data
Cerebro supports basic text, HTML, and PDF ingestion.
```rust
use cerebro::ingest::pdf::PdfIngestor;
// Ingest a PDF file directly
let pdf_ingestor = PdfIngestor::new();
let docs = pdf_ingestor.ingest("whitepaper.pdf").await.unwrap();
for doc in docs {
engine.ingest_document(doc).await.unwrap();
}
```
## 3. Hybrid Search & RRF
Cerebro automatically performs Hybrid Search (Full-Text + Vector) when using the `PgVectorStore`, merging results via **Reciprocal Rank Fusion (RRF)** for maximum accuracy.
```rust
// The query automatically triggers the hybrid merge logic
let results = engine.query("neural architecture", 5).await.unwrap();
```
## 4. Advanced Features
### Consolidation (The "Sleep Cycle")
Cerebro runs a background consolidation worker that autonomously prunes dead memory and refines the vector index during idle periods.
### Optional Feature Flags
To keep your binary lean, most integrations are opt-in:
- `local_models`: Enable `candle`-based local inference.
- `qdrant`: Enable distributed Qdrant storage.
- `pdf`: Enable PDF extraction via `pdf-extract`.
- `graph`: Enable Neo4j Knowledge Graph persistence.
- `python` / `wasm`: Enable FFI bindings.
## 5. FFI & Language Support
### Python
If compiled with the `python` feature, you can use Cerebro directly in your Python apps:
```python
import cerebro
engine = cerebro.Cerebro()
engine.ingest("Some data...")
```
### WASM
Targeting the browser or Edge workers? Compile with `--features wasm` to get native JS bindings.
```javascript
import init, { CerebroWasm } from './pkg/cerebro.js';
const engine = new CerebroWasm();
```
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