---
description: Help configure the Teamlandkarte matching system — weights, thresholds, similarity strategy, caching, and Azure OpenAI settings.
---

$ARGUMENTS

## Configuration Guide

All runtime configuration lives in `config.toml`. See `config.toml.example` for the full schema.

### Key Sections

**`[database]`** — Trino connection
- `host`, `port`, `catalog`, `schema`
- Credentials are in `.env` (DATA_LAKE_USERNAME, DATA_LAKE_PASSWORD)

**`[matching]`** — Scoring and behavior
- `role_weight` / `competence_weight` — how much each contributes to overall score
- `require_confirmation` — whether the confirmation gate is enforced (default: true)
- `category_thresholds` — score boundaries for Top/Good/Partial/Low/Irrelevant

**`[matching.similarity]`** — Similarity strategy
- `use_bm25_search` — false = embeddings (default), true = BM25+RRF
- `use_auto_tagging` — LLM-based synonym expansion for BM25 mode (requires `use_bm25_search = true`)

**`[cache]`** — TTL settings
- `db_cache_ttl` — Trino query cache (default: 12h)
- `search_cache_ttl` — Search session cache (default: 60min)

**`[embedding_cache]`** — Persistent SQLite cache
- `db_path` — SQLite file path
- `ttl_days` — How long embeddings are cached (default: 30)

**`[azure_openai]`** — Azure OpenAI embeddings
- `endpoint`, `deployment`, `api_version`
- `embedding_batch_size` — Batch size for embedding requests (default: 128)
- API key in `.env` (AZURE_OPENAI_EMBEDDING_API_KEY)

### Common Tuning Scenarios

**"Results are too broad"** → Increase category thresholds or add more specific competences
**"Missing valid matches"** → Lower thresholds, or switch from BM25 to embeddings for semantic matching
**"Slow responses"** → Check cache TTLs, increase embedding batch size, verify network to Azure
**"BM25 misses synonyms"** → Enable `use_auto_tagging = true` (requires LLM API key)

### Security Reminder
- Never put credentials in `config.toml`
- All secrets go in `.env` only
- `config.toml` is not in version control (see `.gitignore`)