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🧠 Federation Library: Local Intelligence Initiative

Goal: Deploy a Local LLM and Vector Database stack to augment Gemini's capabilities, reduce token costs, and improve context awareness.

🏗️ Architecture

  • Host: starfleet-compute (192.168.1.35)
  • Core Engine: Ollama (Running Llama-3, Phi-3, & Nomic-Embed).
  • Memory Store: ChromaDB (Vector Database).
  • Interface: Custom Python Scripts (summarizer.py, indexer.py, query_library.py).

📋 Implementation Checklist

Phase 1: Infrastructure (The Stack)

  • [x] Configure: Created starfleet-compute/ollama/docker-compose.yml.
  • [x] Storage: Created NAS directories (app_data/ollama, app_data/chroma).
  • [x] Deploy: Launched stack via Docker Compose.
  • [x] Hydrate: Pulled models (llama3, phi3:mini, nomic-embed-text).

Phase 2: Tooling (The Scripts)

  • [x] Summarizer: Created tools/summarize_logs.py (with Streaming & Phi-3 speed).
  • [x] Indexer: Created tools/index_codebase.py (Indexes Codex docs to ChromaDB).
  • [x] Search: Created tools/query_library.py (Enables semantic retrieval).

Phase 3: Integration (The Workflow)

  • [x] Test: Verified semantic search retrieval.
  • [ ] Automate: (Optional) Set up cron jobs for nightly indexing.
  • [x] UI: Open WebUI active at http://192.168.1.35:3000.

🛡️ Status

COMPLETED. The Federation now possesses a local intelligence layer.