# aimem-core
`aimem-core` is AiMem's Rust core library.
It provides:
- Turso-backed storage
- project mining and conversation import
- keyword and semantic retrieval
- memory layers / wake-up context generation
- knowledge graph support
- embedding profile guards
- multimodal content primitives
CLI lives in `aimem-cli`. MCP server lives in `aimem-mcp`.
## 0.3.x capabilities
Current `aimem-core` includes:
- async embedding flow
- `LocalEmbedder`
- opt-in `Gemini2Embedder`
- multimodal `ContentPart`
- embedding profile metadata:
- provider
- model
- dimension
- profile guards against mixed stores
- `Drawer` helper API
- `MemoryStack::file_text(...)`
- tighter extractor heuristics with multilingual regression tests
## Install
```toml
[dependencies]
tokio = { version = "1", features = ["full"] }
```
```bash
cargo add aimem-core
```
## Recommended API surface
Prefer either:
```rust
use aimem_core::prelude::*;
```
or explicit root imports like:
```rust
use aimem_core::{AimemDb, Drawer, LocalEmbedder, MemoryStack, Miner, Searcher};
```
## Core concepts
- **Store** — one AiMem DB
- **Wing** — project or domain
- **Room** — topic inside a wing
- **Drawer** — one stored chunk
- **ContentPart** — multimodal part inside a drawer
- **L0/L1/L2/L3** — identity, essential story, recall, deep search
## Embedding modes
### Local
- `LocalEmbedder`
- default recommendation
- embedding stays local
### Remote
- `Gemini2Embedder`
- explicit opt-in
- only explicit text / data URI / raw bytes are sent
URI-only multimedia parts are not automatically read from disk and uploaded.
## Store compatibility guard
`aimem-core` records embedding profile metadata and validates semantic writes and queries against:
- provider
- model
- dimension
This prevents silent mixing of incompatible embedding stores.
## Drawer helpers
```rust,no_run
use aimem_core::prelude::*;
let drawer = Drawer::new("id", "wing", "room", "content", "agent")
.with_source_file("README.md")
.with_chunk_index(3);
```
For multimodal drawers:
```rust,no_run
use aimem_core::prelude::*;
let drawer = Drawer::multimodal(
"id",
"wing",
"room",
"Look at this image",
vec![ContentPart::text("Look at this image")],
"agent",
);
```
## Semantic search example
```rust,no_run
use std::sync::Arc;
use aimem_core::prelude::*;
#[tokio::main]
async fn main() -> anyhow::Result<()> {
let db = AimemDb::memory().await?;
let embedder = Arc::new(LocalEmbedder::new()?);
let content = "We moved the memory backend to Turso for local vector search.";
let embedding = embedder.embed_one(content).await?;
let drawer = Drawer::new("drawer_demo_001", "demo_app", "backend", content, "example");
db.insert_drawer_with_profile(
&drawer,
Some(&embedding),
embedder.provider_name(),
embedder.model_name(),
).await?;
let searcher = Searcher::new(db, embedder);
let hits = searcher.vector_search("local vector database", Some("demo_app"), None, 5).await?;
assert!(!hits.is_empty());
Ok(())
}
```
## Filing text directly
```rust,no_run
use std::sync::Arc;
use aimem_core::prelude::*;
# #[tokio::main]
# async fn main() -> anyhow::Result<()> {
let cfg = Config::load()?;
let db = AimemDb::open(&cfg.db_path).await?;
let embedder = Arc::new(LocalEmbedder::new()?);
let stack = MemoryStack::new(db, embedder, &cfg);
let id = stack.file_text("demo_app", "notes", "remember this", "example").await?;
println!("{id}");
# Ok(())
# }
```
## Notes
- `AimemDb::open()` bootstraps schema automatically.
- `Miner::new(db, None)` and `ConvoMiner::new(db, None)` work fine for text-only filing.
- drawers without embeddings still appear in keyword search.
- `AimemDb::embedding_profile()` reports the current store profile.