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M2M Vector Search

M2M Vector Search is a Python vector-search engine that indexes vectors and retrieves similar items using probabilistic Gaussian-splat representations and hierarchical routing. It supports CPU, CUDA and Vulkan compute backends, and includes a native LangChain retriever integration.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

M2M Vector Search — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when an application already has query and document vectors and needs similarity-based retrieval, especially when hierarchical indexing, multiple compute backends or the documented LangChain retriever are relevant.

Who it is for

  • Python developers building semantic search or RAG retrieval components.
  • Teams seeking vector similarity search for edge-oriented or resource-constrained applications.
  • LangChain developers looking for a native vector retriever with CRUD support.

Documented uses

  • Add vectors with identifiers to an index and retrieve the closest matches for a query.
  • Use vector similarity as the retrieval layer in a LangChain pipeline.
  • Combine BM25 and vector retrieval with reciprocal-rank fusion in the described semantic-memory mode.
  • Explore feedback-driven updates and energy-based confidence outputs through the advanced API.
Before you choose

Requirements & limits

The README specifies Python 3.10+ and identifies the project as AGPL-3.0 licensed.

  • The documented quick-start expects float32 vectors and a configured latent dimension.
  • Native LangChain integration is identified, but supported versions and API details are not specified in the available documentation.
  • The README describes CPU, CUDA and Vulkan backends; the supplied material does not establish particular operating-system support.
  • The AGPL-3.0 license should be reviewed against the intended use and distribution model.
  • The repository describes edge and distributed modes, but detailed deployment requirements are not included here.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the library and run a basic vector search

Add sample vectors to a SimpleVectorDB instance and retrieve the closest matches for a query vector.

Before you start

  • Python 3.10 or later
  • The m2m-vector-search package
  • NumPy

Steps

  1. Install the package with the command below.

    Documented command
    pip install m2m-vector-search

  2. Create a SimpleVectorDB with a latent dimension matching your vectors.

  3. Add float32 vectors with IDs, then search using a query vector and a requested result count.

  4. Read the returned IDs and similarity scores; request metadata with include_metadata=True when needed.

Expected outcome

A basic in-memory vector index returns matching IDs and scores for a query.

Guide limitations

  • The example uses randomly generated vectors, so it demonstrates the API flow rather than meaningful semantic retrieval.
  • The example does not document persistence or a complete embedding-generation workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
13 October 2026
Original website title
schwabauerbriantomas-gif/m2m-vector-search · Official homepage title · retained in its source language
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