Practical overview
When it fits
Use Mememo when an application needs approximate-nearest-neighbor retrieval over supplied vectors in a browser or Node.js project, particularly as the retrieval component of a RAG workflow.
Who it is for
- Developers adding vector retrieval to browser-based RAG applications.
- Teams building interactive document or embedding-search interfaces.
- Researchers prototyping on-device retrieval and personalized content workflows.
Documented uses
- Create an HNSW vector index and insert records in batches.
- Query an index for nearest neighbors and receive their keys and distances.
- Add a vector-retrieval component to a RAG or interactive search application.
- Explore a local RAG Playground example with browser-based explainers.
Requirements & limits
The repository identifies the software as available under the MIT License and documents installation through npm.
- The documented interface centers on HNSW indexing, batch insertion and nearest-neighbor queries.
- The browser implementation uses technologies including IndexedDB and Web Workers; Node.js is also documented.
- The library performs retrieval over supplied vectors; the README's example does not document embedding generation.
- Image-vector search is a plausible adaptation, but the repository material provided does not show a specific image-search implementation.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Mememo and query a vector index
Set up the package and follow the documented browser vector-index workflow through a nearest-neighbor query.
Before you start
- A JavaScript or TypeScript project using npm
- Vector keys and corresponding numeric vector values
- A numeric query vector and the number of neighbors to request
Steps
Install the package with the command below.
npm install mememoImport
HNSWfrommememoand create an index with a distance function such ascosine.Pass keys and vector values to
index.bulkInsert(keys, values).Call
index.query(query, k)to retrieve keys and distances for the nearest neighbors.
Expected outcome
An HNSW vector index populated with supplied vectors and a query result containing neighbor keys and distances.
Guide limitations
- The guide covers indexing and retrieval, not embedding generation or the complete RAG application.
- The README gives example variables rather than a dataset or production configuration.
Start the RAG Playground development example
Install and launch the repository’s documented RAG Playground example locally.
Before you start
- A local clone or download of the Mememo repository
- npm
Steps
Clone or download the repository.
Change into
./examples/rag-playground.Run the command below in that directory.
npm installRun the command below and open
localhost:3000.npm run dev
Expected outcome
The documented local development page, described by the README as showing three explainers in the browser.
Guide limitations
- This is a development setup for the example application, not instructions for deploying it.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- poloclub/mememo · Official homepage title · retained in its source language