reasonkit-mem 0.1.7

High-performance vector database & RAG memory layer - hybrid search, embeddings, RAPTOR trees, BM25 fusion, and semantic retrieval for AI systems

reasonkit-mem

There is very little structured metadata to build this page from currently. You should check the main library docs, readme, or Cargo.toml in case the author documented the features in them.

This version has 49 feature flags, 0 of them enabled by default.

default

This feature flag does not enable additional features.

axum

blake3

bm25

cacache

caching

columnar

compression

content-addressed

data-integrity

docset

document-extraction

fast-embed

fast-hash

fastembed

full

graph

graphalgs

http-server

lancedb

local-embeddings

lopdf

lz4_flex

moka

ndarray

ort

pdf-extract

pdf-parsing

persistent-db

petgraph

pyo3

python

quick_cache

rag

rag-toolchain

readability

redb

research-full

research-phase2

research-phase3

research-phase4

rkyv

rs_merkle

serde_yaml

tokenizers

tower-http

tracing-subscriber

vdreamteam

zero-copy