Implement path normalization to ensure consistent hash checks by converting file paths to absolute paths. Update change detection logic to handle hash comparisons more robustly, treating missing hashes as content changes for safety. This prevents redundant processing and improves efficiency in the ingestion workflow. |
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|---|---|---|
| .gitea/workflows | ||
| .vscode | ||
| app | ||
| config | ||
| docker | ||
| docs | ||
| scripts | ||
| tests | ||
| vault | ||
| vault_master | ||
| ANALYSE_TYPES_YAML_ZUGRIFFE.md | ||
| 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