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- Incremented version numbers to reflect updates: authoring guidelines to 1.4.0, knowledge design manual to 4.6.0, and Obsidian integration guide to 1.1.0.
- Introduced detailed documentation on Section Types and Intra-Note-Edges, including syntax, rules, and examples for implementation.
- Enhanced context in the knowledge design manual to include new features related to section types and intra-note edges.
- Updated scopes in all relevant documents to reflect the addition of section types and intra-note edges, ensuring comprehensive coverage of the new functionalities.
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mindnet API (bundle)

This bundle provides a minimal FastAPI app for embeddings and Qdrant upserts/queries plus a Markdown importer.

Quick start

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Environment (adjust as needed)
export QDRANT_URL=http://127.0.0.1:6333
export MINDNET_PREFIX=mindnet
export MINDNET_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Run API
uvicorn app.main:app --host 0.0.0.0 --port 8001 --workers 1

# (optional) Ensure collections exist (or use setup_mindnet_collections.py you already have)
# python3 scripts/setup_mindnet_collections.py --qdrant-url $QDRANT_URL --prefix $MINDNET_PREFIX --dim 384 --distance Cosine

# Import some notes
python3 scripts/import_markdown.py --vault /path/to/Obsidian

Endpoints

  • POST /embed{ "texts": [...] } → 384-d vectors
  • POST /qdrant/upsert_note
  • POST /qdrant/upsert_chunk
  • POST /qdrant/upsert_edge
  • POST /qdrant/query → semantic search over chunks with optional filters

See scripts/quick_test.sh for a runnable example.

Anmerkung: Diese Datei ist veraltet und muss auf Stand 2.6.0 gebracht werden