RAG hub
A practical hub for embedding models, retrieval systems, vector search, reranking, document agents, and grounded answer evaluation.
Built for: Knowledge product teams, search teams, support automation teams
5
Ranked entries
2
Verified repos
6
Decision pages
92
Top score
Does retrieval find the right passage before the answer model writes?
Can the system preserve permissions, freshness, and citation quality?
How expensive is reindexing if the embedding model changes later?
Ranking
These entries come from the current SkillRank dataset. Scores help discovery; final decisions should use your own workflow tests.
Embedding / RAG
OpenAI
92
Score
OpenAI’s latest large embedding model tuned for retrieval, deduping, and RAG backends.
Embedding / RAG
90
Score
Shipping Gemini-class embedding endpoint for Vertex AI and Gemini API retrieval stacks.
Embedding / RAG
Cohere
88
Score
Latest multilingual Cohere embed family for retrieval, rerank, and search stacks.
Embedding / RAG
BAAI
84
Score
Flagship multilingual BGE checkpoints for dense, sparse, and hybrid retrieval setups.
Embedding / RAG
Jina AI
82
Score
Late-interaction friendly embeddings and small specialized models.
Daily signals
Signals are generated from recorded snapshots and verified source metadata. They keep the hub connected to the daily crawler.
text-embedding-4-large remained stable across recorded snapshots this week.
Gemini embedding 004 remained stable across recorded snapshots this week.
Cohere Embed v4 remained stable across recorded snapshots this week.
BGE M3 remained stable across recorded snapshots this week.
Jina Embeddings remained stable across recorded snapshots this week.
Shubhamsaboo/awesome-llm-apps is verified on GitHub with 129,267 stars, 19,060 forks, and last push date 2026-07-31.
Verified repositories
GitHub metadata is useful for discovery, but production fit still depends on license, docs, security posture, and local maintainability.
Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
Graphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.
Guides
A practical framework for choosing chat, reasoning, multimodal, coding, and retrieval models without relying on launch hype.
A grounded checklist for choosing embedding models, retrieval pipelines, rerankers, and document-agent workflows.
A practical guide to routing prompts across fast, cheap, specialist, and frontier models without creating brittle AI infrastructure.
How to protect retrieval systems from malicious documents, stale knowledge, permission leaks, and unsupported answers.
Compare
A practical comparison of embedding model choices for RAG systems, semantic search, hybrid retrieval, and enterprise knowledge bases.
Compare n8n, AutoGen, CrewAI, Flowise, LlamaIndex, and other open-source agent frameworks by workflow fit and operational risk.
Source boundaries
