Crate anda_db 

Core embedded database primitives for building durable AI memory systems.

anda_db combines three retrieval modes behind a single collection model:

• B-Tree indexes for exact match and range filters
• BM25 indexes for full-text retrieval
• HNSW indexes for vector similarity search

The crate is designed for agent memory workloads where data must be:

• schema-validated on write
• incrementally searchable through multiple index types
• persisted to a generic object_store backend
• recoverable after partial flushes or process restarts

Typical usage:

1. Create or connect to an database::AndaDB
2. Create or open a collection::Collection
3. Define a schema via schema or AndaDBSchema
4. Add B-Tree, BM25, and/or HNSW indexes
5. Insert documents and query them through query

Feature flags:

• full: enables full-text search integrations exposed by the workspace setup
• tantivy: enables the Tantivy-backed text search dependency
• tantivy-jieba: enables Tantivy plus Jieba tokenization support

See also the technical guide in docs/anda_db.md for architecture, lifecycle, indexing, and operational guidance.

Modules

collection

Collection-level document storage, indexing, and query execution.

database

Database-level lifecycle and collection management.

error

Error types returned by the core library. Error types for schema module

index

Index abstractions and concrete B-Tree, BM25, and HNSW integrations.

query

Query structures for hybrid search, filters, and reranking.

schema

Schema types re-exported from anda_db_schema.

storage

Object-store-backed persistence, compression, and cached I/O.

Functions

unix_ms

Returns the current Unix timestamp in milliseconds.