anda_db

anda_db is the core crate of Anda DB: an embedded Rust database for AI agents that stores documents, builds multiple index types, and runs structured queries and hybrid retrieval.

It is designed to be linked into your application (not a hosted database service) and persists data via the object_store ecosystem.

Highlights

• Embedded & async: Integrate as a Rust library; built around tokio.
• Object-store persistence: Use local filesystem, S3-compatible storage, in-memory, etc.
• Document schema: Define typed documents via AndaDBSchema derive.
• Indexes:
  • B-Tree for exact match and range filters (powered by anda_db_btree).
  • BM25 for full-text retrieval (powered by anda_db_tfs, requires feature full).
  • HNSW for ANN vector search (powered by anda_db_hnsw).
• Hybrid search: Combine BM25 + vector search with RRF.
• Encryption (optional): Wrap storage with anda_object_store for metadata + AES-256-GCM at rest.

Installation

Add dependencies (pick the object_store backend you need):

[dependencies]
anda_db = "0.7"
tokio = { version = "1", features = ["full"] }
serde = { version = "1", features = ["derive"] }

# Required to provide an ObjectStore implementation:
object_store = { version = "0.13", features = ["fs"] }

# Optional (recommended for local filesystem): adds metadata + conditional put support
anda_object_store = "0.3"

Feature flags

• default: no extra features
• full: enables full-text search dependencies (BM25 via Tantivy + jieba integration)

If you want BM25 / hybrid search, enable:

anda_db = { version = "0.7", features = ["full"] }

Quickstart (minimal, in-memory)

This example creates a collection with an HNSW vector index and performs a vector search.

use anda_db::{
    collection::CollectionConfig,
    database::{AndaDB, DBConfig},
    error::DBError,
    index::HnswConfig,
    query::{Query, Search},
    schema::{AndaDBSchema, Vector, vector_from_f32},
};
use object_store::memory::InMemory;
use serde::{Deserialize, Serialize};
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize, AndaDBSchema)]
pub struct Doc {
    pub _id: u64,
    pub text: String,
    pub embedding: Vector,
}

#[tokio::main]
async fn main() -> Result<(), DBError> {
    let db = AndaDB::connect(Arc::new(InMemory::new()), DBConfig::default()).await?;

    let collection = db
        .open_or_create_collection(
            Doc::schema()?,
            CollectionConfig {
                name: "docs".to_string(),
                description: "Demo documents".to_string(),
            },
            async |c| {
                c.create_hnsw_index_nx(
                    "embedding",
                    HnswConfig {
                        dimension: 4,
                        ..Default::default()
                    },
                )
                .await?;
                Ok(())
            },
        )
        .await?;

    collection
        .add_from(&Doc {
            _id: 0,
            text: "Rust is focused on safety.".to_string(),
            embedding: vector_from_f32(vec![0.1, 0.2, 0.3, 0.4]),
        })
        .await?;
    collection.flush(anda_db::unix_ms()).await?;

    let hits: Vec<Doc> = collection
        .search_as(Query {
            search: Some(Search {
                vector: Some(vec![0.1, 0.2, 0.3, 0.4]),
                ..Default::default()
            }),
            ..Default::default()
        })
        .await?;

    println!("hits={}", hits.len());
    db.close().await?;
    Ok(())
}

Full demo (persistent, BM25 + HNSW + hybrid)

The repo includes a complete runnable example:

• Source: examples/db_demo.rs
• Run: cargo run -p anda_db --example db_demo --features full

Notes:

• BM25 / hybrid search requires feature full.
• For better tokenization (especially CJK), set a tokenizer (the demo uses anda_db_tfs::jieba_tokenizer).

Storage backends

AndaDB::connect accepts any Arc<dyn object_store::ObjectStore>.

Local filesystem (recommended)

For local filesystem, wrapping with anda_object_store::MetaStoreBuilder improves correctness/performance by providing metadata and conditional put support:

use anda_object_store::MetaStoreBuilder;
use object_store::local::LocalFileSystem;

let store = MetaStoreBuilder::new(LocalFileSystem::new_with_prefix("./db")?, 10000).build();

Encryption at rest (optional)

See the EncryptedStoreBuilder examples in ../anda_object_store/README.md.

License

Copyright © 2026 LDC Labs.

ldclabs/anda-db is licensed under the MIT License. See LICENSE for details.