# gnes-ai/gnes

GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.

Repository: https://github.com/gnes-ai/gnes
Canonical: https://ross.abutalabs.com/products/gnes
Homepage: https://gnes.ai
Language: Python
License: NOASSERTION
License Family: other
Topics: gnes, cloud-native, nlp, computer-vision, deep-learning, machine-learning, docker-swarm, semantic-search, neural-network, dnn, database, tensorflow, pytorch, python, elasticsearch, search-engine, video-processing, grpc, distributed-systems, microservices
Archived: true
Last push: 2019-10-31T11:36:15+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2613, "days_push": 2498, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1263, forks 210 (observed 2026-08-28T04:04:10.486217+00:00)

## What it is
GNES (Generic Neural Elastic Search) is a cloud-native framework for building semantic search systems over text, images, and video using deep neural network encoders. It decomposes search pipelines into microservices (preprocessor, encoder, indexer, router) orchestrated by Docker Swarm or Kubernetes, configured via YAML.

## Use cases
- build a semantic search engine for text documents
- search images by image similarity using deep learning embeddings
- index and search video content at scale
- deploy a distributed neural search system on Kubernetes
- swap BERT or other encoders in a search pipeline via YAML config
- build any-to-any modality neural search

## When to choose
- you need a microservice-based, cloud-native neural search architecture
- you want to combine pluggable deep learning encoders and indexers without touching code
- you need multi-modal (text/image/video) semantic search at scale

## When to avoid
- you want an actively maintained project - GNES was superseded by Jina and its last release was in 2019
- you need a simple embedded vector database rather than a distributed microservice system
- you require a permissive, clearly defined license (the GitHub license is marked NOASSERTION)

## Facets
- artifact type: framework
- maturity: abandoned
- function: search-engine, vector-database, machine-learning, deep-learning, nlp, image-processing, video-processing, microservices, rpc
- domain: machine-learning, deep-learning, computer-vision, microservices
- platform: python, cross-platform
- tags: semantic-search, neural-search, embeddings, grpc, cloud-native, bert, tensorflow, pytorch, predecessor-of-jina, search, natural-language-processing, docker, kubernetes, linux

## Member repositories
- gnes-ai/gnes (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:10.486217+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:04:08.253414+00:00, confidence not recorded.
  - readme: https://github.com/gnes-ai/gnes (fetched 2026-08-28T04:04:10.486217+00:00, sha c70039bf86f4)
  - homepage: https://gnes.ai (fetched 2026-08-29T12:17:06.003218+00:00, sha 4655eaff9b58)
  - registry_pypi: https://pypi.org/pypi/gnes/json (fetched 2026-08-29T12:17:06.007261+00:00, sha dac854151414)
- Data as of 2026-08-30T08:39:29.467469+00:00.