anda-db

name

anda-db

description

Use this skill whenever you need to help users work with AndaDB in Rust programs or update AndaDB repository documentation. This includes creating databases and collections, deriving schemas, CRUD operations, B-Tree filters, BM25 full-text search, HNSW vector search, hybrid retrieval, object-store persistence, encrypted storage, KIP/Cognitive Nexus integration, and avoiding outdated ciborium-era examples. Invoke it when users mention AndaDB, AI agent memory, embedding storage, hybrid search, KIP, or Cognitive Nexus in this repository.

AndaDB Skill

This skill provides source-aligned guidance for working with the current Anda DB workspace. Prefer the patterns below over older examples that used direct AndaDB::create(store, ...), unwrapped LocalFileSystem, Vec<bf16> literals, or ciborium.

Quick Start Template

use anda_db::{
    collection::CollectionConfig,
    database::{AndaDB, DBConfig},
    index::HnswConfig,
    query::{Query, Search},
    schema::{AndaDBSchema, Vector, vector_from_f32},
    storage::StorageConfig,
};
use object_store::local::LocalFileSystem;
use serde::{Deserialize, Serialize};
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize, AndaDBSchema)]
struct Memory {
    _id: u64,
    title: String,
    body: String,
    embedding: Vector,
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let store = Arc::new(LocalFileSystem::new_with_prefix("./db")?);

    let db = AndaDB::connect(
        store,
        DBConfig {
            name: "agent_memory".into(),
            description: "Embedded AI memory".into(),
            storage: StorageConfig::default(),
            lock: None,
        },
    )
    .await?;

    let memories = db
        .open_or_create_collection(
            Memory::schema()?,
            CollectionConfig {
                name: "memories".into(),
                description: "Long-term memory collection".into(),
            },
            async |c| {
                c.create_bm25_index_nx(&["title", "body"]).await?;
                c.create_hnsw_index_nx(
                    "embedding",
                    HnswConfig {
                        dimension: 4,
                        ..Default::default()
                    },
                )
                .await?;
                Ok(())
            },
        )
        .await?;

    let id = memories
        .add_from(&Memory {
            _id: 0,
            title: "Rust".into(),
            body: "Rust is well suited to embedded AI memory services.".into(),
            embedding: vector_from_f32(vec![0.1, 0.2, 0.3, 0.4]),
        })
        .await?;

    let results: Vec<Memory> = memories
        .search_as(Query {
            search: Some(Search {
                text: Some("embedded AI memory".into()),
                vector: Some(vec![0.1, 0.2, 0.3, 0.4]),
                ..Default::default()
            }),
            limit: Some(10),
            ..Default::default()
        })
        .await?;

    let loaded: Memory = memories.get_as(id).await?;
    println!("Loaded {}, found {}", loaded.title, results.len());

    db.close().await?;
    Ok(())
}

Core Dependencies

[dependencies]
anda_db = { version = "0.9", features = ["full"] }
object_store = { version = "0.14", features = ["fs"] }
tokio = { version = "1", features = ["full"] }
serde = { version = "1", features = ["derive"] }

Add direct low-level crates only when using their public APIs directly:

anda_db_hnsw = "0.9"       # e.g. DistanceMetric
anda_object_store = "0.9" # MetaStoreBuilder / EncryptedStoreBuilder
cbor2 = "1"               # direct CBOR values, readers, writers, size

Current API Rules

• Use Arc<dyn object_store::ObjectStore> when opening a database.
• Prefer AndaDB::connect for application startup; use create or open only when the failure mode matters.
• Prefer open_or_create_collection plus _nx index creation methods for idempotent startup.
• Use add_from(&typed_value), get_as::<T>(id), and search_as::<T>(query) for typed application structs.
• search_as, search, and search_ids take Query by value, not &Query.
• Stored vector fields use anda_db::schema::Vector; query vectors use Vec<f32>. Convert stored vectors with vector_from_f32.
• For filters, wrap values in Fv/FieldValue, for example RangeQuery::Eq(Fv::Text("active".into())).

Type Mapping (Rust -> AndaDB)

Rust Type	AndaDB FieldType
bool	Bool
i8 through i64, isize	I64
u8 through u64, usize	U64
f32	F32
f64	F64
String, &str	Text
Vec<u8>, [u8; N]	Bytes
Vector / Vec<bf16>	Vector
Vec<T>	Array(T)
BTreeMap<String, V> / HashMap<String, V>	Map(String, V)
Option<T>	Option(T)
serde_json::Value	Json

For arbitrary binary payloads serialized through Serde, use explicit bytes types such as FieldValue::Bytes or Serde byte helpers where appropriate. Do not assume every Vec<u8> inside a nested arbitrary type will be treated as a CBOR byte string.

Derive Macro Attributes

use anda_db::schema::{AndaDBSchema, Vector};
use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Serialize, Deserialize, AndaDBSchema)]
struct Doc {
    _id: u64,                 // reserved document id field
    #[serde(rename = "name")] // schema field name follows serde rename
    title: String,
    #[field_type = "Bytes"]   // override inferred field type
    #[unique]                 // enforce uniqueness when indexed
    checksum: [u8; 32],
    /// Used by LLM context and schema documentation.
    embedding: Vector,
}

Index Types

Create indexes during open_or_create_collection startup. Prefer _nx methods when startup can run repeatedly.

HNSW (vector search)

c.create_hnsw_index_nx(
    "embedding",
    HnswConfig {
        dimension: 384,
        ..Default::default()
    },
)
.await?;

If you need a non-default distance metric, add anda_db_hnsw as a direct dependency and set distance_metric: anda_db_hnsw::DistanceMetric::Cosine.

B-Tree (exact match / range filters)

c.create_btree_index_nx(&["category"]).await?;
c.create_btree_index_nx(&["tenant", "created_at"]).await?;

BM25 (full-text search)

c.create_bm25_index_nx(&["title", "body"]).await?;

For Chinese tokenization with the full feature, see rs/anda_db/examples/db_demo.rs for jieba_tokenizer().

Document CRUD

use anda_db::schema::Fv;
use std::collections::BTreeMap;

let id = collection.add_from(&doc).await?;
let loaded: MyDoc = collection.get_as(id).await?;

let mut fields = BTreeMap::new();
fields.insert("status".to_string(), Fv::Text("archived".into()));
let updated = collection.update(id, fields).await?;

let removed = collection.remove(id).await?;

Use collection.add(document) only when you are already working with an anda_db::schema::Document.

Search with Filters

use anda_db::query::{Filter, Query, RRFReranker, RangeQuery, Search};
use anda_db::schema::Fv;

let results: Vec<MyDoc> = collection
    .search_as(Query {
        search: Some(Search {
            text: Some("query text".into()),
            vector: Some(vec![0.1_f32; 384]),
            reranker: Some(RRFReranker::default()),
            ..Default::default()
        }),
        filter: Some(Filter::Field((
            "category".into(),
            RangeQuery::Eq(Fv::Text("news".into())),
        ))),
        limit: Some(20),
    })
    .await?;

CBOR Rules

Use cbor2 directly:

use cbor2::{from_reader, serialized_size, to_writer};

let mut buf = Vec::new();
to_writer(&value, &mut buf)?;
let size = serialized_size(&value)?;
let decoded = from_reader(buf.as_slice())?;

Do not introduce new direct ciborium usage. The old cbor_size helper module has been replaced by cbor2::serialized_size.

Workspace Crates

Crate	Purpose
anda_db	Core embedded database
anda_db_schema	Schema, field values, documents
anda_db_derive	Derive macros
anda_db_btree	Exact-match and range index
anda_db_tfs	BM25 full-text search
anda_db_hnsw	HNSW vector index
anda_db_utils	Shared utilities
anda_object_store	Metadata and encrypted object-store wrappers
anda_kip	Knowledge Interaction Protocol
anda_cognitive_nexus	Reference AI memory graph runtime
anda_db_server	HTTP server for core database APIs
anda_cognitive_nexus_server	HTTP/JSON-RPC server for Cognitive Nexus
anda_db_shard_proxy	Shard proxy for multi-tenant deployments

Reference Documentation

Read these before making broad API or documentation changes:

• references/anda_db_quick_ref.md - compact API reference for agents
• README.md - workspace overview and current quick start
• docs/anda_db.md - core database design and usage
• docs/anda_db_schema.md - field types, values, schemas, documents
• docs/anda_db_derive.md - derive macro behavior
• docs/anda_db_btree.md - B-Tree index behavior
• docs/anda_db_tfs.md - BM25 full-text engine
• docs/anda_db_hnsw.md - HNSW vector search
• docs/anda_object_store.md - metadata and encryption wrappers
• docs/anda_kip.md - KIP protocol
• docs/anda_cognitive_nexus.md - Cognitive Nexus runtime