do-memory-core 0.1.29

Core episodic learning system for AI agents with pattern extraction, reward scoring, and dual storage backend
Documentation 
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[package]
name = "do-memory-core"
version.workspace = true
edition.workspace = true
authors.workspace = true
license.workspace = true
repository.workspace = true
homepage = "https://github.com/d-o-hub/rust-self-learning-memory"
readme = "README.md"
description = "Core episodic learning system for AI agents with pattern extraction, reward scoring, and dual storage backend"
keywords = ["ai", "memory", "learning", "episodic", "patterns"]
categories = ["database", "development-tools", "science"]
documentation = "https://docs.rs/do-memory-core"

[features]
default = []
# Enable OpenAI embedding provider
openai = ["reqwest"]
# Enable Mistral AI embedding provider
mistral = ["reqwest"]
# Enable all embedding providers
embeddings-full = ["openai", "mistral"]
# Enable local embedding models (requires model files)
local-embeddings = ["ort", "tokenizers", "ndarray", "reqwest/stream"]
# Enable hybrid search with FTS5
hybrid_search = []
# Enable proptest Arbitrary implementations for property testing
proptest-arbitrary = ["proptest"]
# Enable AgentFS SDK integration for external signals
agentfs = []

[dependencies]
# Async runtime
tokio = { workspace = true }
async-trait = { workspace = true }

# Error handling
anyhow = { workspace = true }
thiserror = { workspace = true }

# Serialization
serde = { workspace = true }
serde_json = { workspace = true }

# UUID and time
uuid = { workspace = true }
chrono = { workspace = true }

# Logging
tracing = { workspace = true }
tracing-subscriber = { version = "0.3.23", features = ["env-filter", "json"] }

# Futures utilities
futures = { workspace = true }

# LRU cache for query caching (using workspace version for consistency)
lru = { workspace = true }

# Synchronization
parking_lot = "0.12.5"

# HTTP client for OpenAI API (optional)
reqwest = { version = "0.13", features = ["json"], optional = true }

# ONNX Runtime for local embeddings (optional)
ort = { version = "2.0.0-rc.12", optional = true }

# Tokenizers for local embeddings (optional)
tokenizers = { version = "0.22.2", optional = true }

# ndarray for tensor operations (required by ort)
# Using 0.17.x to match ort 2.0.0-rc.11's ndarray dependency
ndarray = { version = "0.17", optional = true }

# Changepoint detection using PELT algorithm
  # augurs-changepoint wraps changepoint crate for time series analysis
  augurs-changepoint = "0.10.2"
  # Direct dependency to force changepoint 0.15.0 which uses fixed rv
  # This fixes RUSTSEC-2026-0002 (rv unsound IterMut implementation)
  changepoint = "0.15.0"

# Random number generation for tests
rand = "0.10"

# Fuzzy string matching for search
strsim = "0.11.1"

# Regular expressions for pattern matching
regex = "1.12"

# Property testing (optional, enabled via feature flag)
proptest = { version = "1.11", optional = true }

[dev-dependencies]
tokio = { workspace = true, features = ["test-util"] }
tempfile = { workspace = true }
do-memory-storage-turso = { path = "../memory-storage-turso", features = ["keepalive-pool"] }
do-memory-storage-redb = { path = "../memory-storage-redb" }
libsql = { workspace = true }
serial_test = "3.4"
criterion = "0.8"
proptest = "1.11"
insta = "1.47"
postcard = { workspace = true }
# Enable proptest-arbitrary feature for tests
do-memory-core = { path = ".", features = ["proptest-arbitrary"] }

[lints.clippy]
# Core library gets pedantic linting for best practices
pedantic = { level = "warn" }
missing_docs_in_private_items = { level = "allow" }
missing_errors_doc = { level = "warn" }
missing_safety_doc = { level = "warn" }
doc_markdown = { level = "warn" }