| .gitea/workflows | ||
| .vscode | ||
| app | ||
| config | ||
| docker | ||
| docs | ||
| scripts | ||
| tests | ||
| vault | ||
| vault_master/leitbild2025 | ||
| README.md | ||
| requirements.txt | ||
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 vectorsPOST /qdrant/upsert_notePOST /qdrant/upsert_chunkPOST /qdrant/upsert_edgePOST /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