Back to directory

LodeDB

An embedded, multimodal vector database built for local retrieval-augmented generation, positioned as a drop-in, GPU-accelerated backend for frameworks such as LangChain, LlamaIndex, and mem0.

LodeDB

LodeDB is an embedded vector database that runs in-process and stores its index on disk, meant to replace the default in-memory or lightweight stores that LangChain, LlamaIndex, and mem0 use out of the box. It reports a smaller on-disk footprint and lower query latency than those default stores and other embedded options such as sqlite-vec, qdrant, pgvector, lancedb, and chroma across published benchmark comparisons.

It runs the same on-disk index on a GPU when one is available for higher-throughput batched search, in addition to CPU-only operation, and is exact by default rather than relying only on approximate search. A migration path is documented for pointing an existing project's coding assistant at moving its current vector store onto LodeDB.

It is written in Python and released under the Apache-2.0 license.

More in RAG

See all 540
  • ai·rete·rag

    ai-rete-rag.com

    A platform that pairs a Rete rule engine with retrieval-augmented generation, so deterministic rules decide an outcome and grounded retrieval explains the decision in plain language.

  • Xtriever

    github.com

    A hybrid retrieval engine for retrieval-augmented generation, written in Rust to run on-device on a phone or a laptop, combining lexical BM25 search, dense embedding search, reciprocal-rank fusion and cross-encoder re-ranking with no server.

  • FileFlux

    github.com

    A .NET library that turns PDF, DOCX, HWP and other documents into optimized chunks for RAG systems, through a five-stage pipeline built on high-performance Rust FFI readers.