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Lars 43641441ef Refactor hash input and body/frontmatter handling in ingestion_processor.py for improved accuracy Update the ingestion process to utilize the parsed object instead of note_pl for hash input, body, and frontmatter extraction. This change ensures that the correct content is used for comparisons, enhancing the reliability of change detection diagnostics and improving overall ingestion accuracy.		2026-01-12 08:19:43 +01:00
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app	Refactor hash input and body/frontmatter handling in ingestion_processor.py for improved accuracy	2026-01-12 08:19:43 +01:00
config	Implement WP-24c v4.2.0: Introduce configurable header names and levels for LLM validation and Note-Scope zones in the chunking system. Update chunking models, parser, and processor to support exclusion of edge zones during chunking. Enhance documentation and configuration files to reflect new environment variables for improved flexibility in Markdown processing.	2026-01-10 21:46:51 +01:00
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ANALYSE_TYPES_YAML_ZUGRIFFE.md	Update type_registry, graph_utils, ingestion_note_payload, and discovery services for dynamic edge handling: Integrate EdgeRegistry for improved edge defaults and topology management (WP-24c). Enhance type loading and edge resolution logic to ensure backward compatibility while transitioning to a more robust architecture. Version bumps to 1.1.0 for type_registry, 1.1.0 for graph_utils, 2.5.0 for ingestion_note_payload, and 1.1.0 for discovery service.	2026-01-09 15:20:12 +01:00
README.md	README.md aktualisiert	2025-12-13 06:48:13 +01:00
requirements.txt	Anpassung WP20	2025-12-23 15:57:50 +01:00

README.md

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