gnes-ai/gnes

Health v2 · maintenance only

Flags: no_releases archived no_license

Adoption not part of the score

1263 stars · 210 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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• you want an actively maintained project - GNES was superseded by Jina and its last release was in 2019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

framework · maturity abandoned

search-engine vector-database machine-learning deep-learning nlp image-processing video-processing microservices rpc machine-learning deep-learning computer-vision microservices python cross-platform semantic-search neural-search embeddings grpc cloud-native bert tensorflow pytorch predecessor-of-jina search natural-language-processing docker kubernetes linux

Member repositories

For agents

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