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vybe-intelligence-vault

Automated knowledge harvesting for AI engineers.
Scrapes. Scores. Commits. Every 3 hours. Zero manual effort.

Overview · How It Works · Architecture · Quick Start · Vault Stats · Contributing

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📌 Overview

Most AI knowledge bases go stale the moment you stop updating them. Vybe Intelligence Vault doesn't — it runs itself.

A GitHub Actions pipeline wakes up every 3 hours, discovers emerging AI/ML resources, evaluates them with an LLM scoring engine, and commits the ranked results back into the repo. No human in the loop. No manual curation.

The result: a self-reinforcing knowledge graph of 84,775 indexed resources spanning AI agents, RAG architectures, MCP servers, and modern web tooling — always current, always queryable by local agents via an HTTP gateway.

Built for: AI engineers who want a living knowledge base they can plug into agentic workflows, not a static awesome-list that someone forked two years ago.

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⚙️ How It Works

Every 3 hours:

  GitHub Actions Cron
       │
       ▼
  evaluate_repo.py          ← discovers candidate resources from configured topics
       │
       ▼
  LLM Scoring Engine        ← qwen2.5:14b (local) or cloud fallback
       │  scores: quality, rag_relevance, tech_stack match
       ▼
  vault-core/               ← ranked .md files committed back to repo
       │
       ├─▶ rebuild-index.yml  ← triggers on push
       │         │
       │         ▼
       │   nomic-embed-text   ← local Ollama embeddings
       │         │
       │         ▼
       │   vault-index.json   ← semantic node/edge graph (cosine sim > 0.75)
       │         │
       │         ▼
       │   React 3D Map       ← WebGL intelligence visualization (SWR polling)
       │
       └─▶ Orchestrator :3456 ← MCP gateway for agent context injection

Scoring Pipeline

Each resource is evaluated across 4 dimensions:

Signal	Method
Content quality	LLM pass via qwen2.5:14b / cloud fallback
RAG relevance	Keyword + semantic scoring
Community velocity	Stars delta, fork rate
Tech stack match	Tag overlap with config.yaml topics

Semantic Graph

Embeddings via nomic-embed-text (Ollama). Two nodes are linked if:

• Cosine similarity > 0.75 → similar_to
• Shared tech stack → depends_on
• Same category + shared tags → references

Edge weight = cosine_sim + (shared_tags × 0.08)

MCP Gateway

HTTP bridge on :3456. Send a vault file path → receive a clean, LLM-formatted context block. Agents can pull any resource into their context window without reading the filesystem directly.

# Example agent request
curl -X POST http://localhost:3456/inject \
  -d '{"path": "ai/agents/tool-use-patterns.md"}'

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🏗 Architecture

graph TD
    A[⏰ Cron / Dispatch Trigger] -->|every 3h| B(evaluate_repo.py)
    B -->|LLM score| C{Decision Engine}
    C -->|pass threshold| D[vault-core/]
    C -->|reject| X[❌ Discarded]
    D -->|git push| E(rebuild-index.yml)
    E -->|nomic-embed-text| F[vault