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Lars ea0fd951f2 Enhance LLM validation zone extraction in graph_derive_edges.py 
Implement support for H2 headers in LLM validation zone detection, allowing for improved flexibility in header recognition. Update the extraction logic to track zones during callout processing, ensuring accurate differentiation between LLM validation and standard zones. This enhancement improves the handling of callouts and their associated metadata, contributing to more precise edge construction.
2026-01-11 20:58:33 +01:00
.gitea/workflows .gitea/workflows/deploy.yml aktualisiert 2025-11-07 09:37:02 +01:00
.vscode Neue Dokumentationsdateien 2025-12-07 15:49:44 +01:00
app Enhance LLM validation zone extraction in graph_derive_edges.py 2026-01-11 20:58:33 +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
docker docker/embeddings.Dockerfile aktualisiert 2025-09-04 08:00:52 +02:00
docs Update graph_derive_edges.py to version 4.2.1: Implement Clean-Context enhancements, including consolidated callout extraction and smart scope prioritization. Refactor callout handling to avoid duplicates and improve processing efficiency. Update documentation to reflect changes in edge extraction logic and prioritization strategy. 2026-01-10 22:17:03 +01:00
scripts Enhance logging capabilities across multiple modules for version 4.5.0: Introduce detailed debug statements in decision_engine.py, retriever_scoring.py, retriever.py, and logging_setup.py to improve traceability during retrieval processes. Implement dynamic log level configuration based on environment variables, allowing for more flexible debugging and monitoring of application behavior. 2026-01-11 17:30:34 +01:00
tests Update graph_db_adapter.py, graph_derive_edges.py, graph_subgraph.py, graph_utils.py, ingestion_processor.py, and retriever.py to version 4.1.0: Introduce Scope-Awareness and Section-Filtering features, enhancing edge retrieval and processing. Implement Note-Scope Zones extraction from Markdown, improve edge ID generation with target_section, and prioritize Note-Scope Links during de-duplication. Update documentation for clarity and consistency across modules. 2026-01-10 19:55:51 +01:00
vault vault/leitbild/templates/obsidian_review_daily.md hinzugefügt 2025-11-01 15:08:47 +01:00
vault_master Pfad auflösen zum dictionary 2025-12-18 18:37:23 +01:00
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