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ankn a5f8fb49ab Migrate all repos into monorepo context folders

Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
      Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
      Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)

Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.

2026-06-30 20:39:52 +02:00

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Azure OpenAI setup

This project requires Azure OpenAI for embeddings only:

Embeddings (text-embedding-3-large, 3072 dims):
- role inference (against Data Lake role vocabulary)
- competence inference (against Data Lake competence vocabulary)
- similarity scoring for matching

There are no chat/LLM features anymore.

1) Create an embedding deployment in Azure

In your Azure OpenAI resource, create an embedding deployment for:

text-embedding-3-large

Notes:

The deployment name must match what you set in config.toml.
This project uses 3072 embedding dimensions.

2) Configure `config.toml`

Copy the template:

cp config.toml.example config.toml

Set the Azure section:

[azure_openai]
endpoint = "https://<your-resource>.openai.azure.com"
api_version = "2024-02-15-preview"
embedding_deployment = "text-embedding-3-large"

# Embeddings batching (number of inputs per embeddings API request)
embedding_batch_size = 128

# Optional
show_costs_in_output = false

Notes:

Embeddings requests may be batched (input=[...]) and chunked sequentially. Increase embedding_batch_size to reduce round-trips, or decrease it if you suspect request-size related failures.

3) Configure credentials via environment variables

The server reads the API key from:

AZURE_OPENAI_EMBEDDING_API_KEY

Recommended: keep secrets in a local .env file (do not commit):

AZURE_OPENAI_EMBEDDING_API_KEY="..."

The server also needs database credentials:

DATA_LAKE_USERNAME
DATA_LAKE_PASSWORD

4) Verify your setup

4.1 Quick import/run smoke check

Start the server with debug logs and confirm it loads your config:

uv run teamlandkarte-mcp --config config.toml --log-level DEBUG

4.2 Typical Azure errors

HTTP 401/403: wrong key or wrong resource
HTTP 404: deployment name mismatch
HTTP 429: throttling; warm the embedding cache and/or reduce concurrency
network/TLS: verify corporate proxy/TLS setup

See docs/troubleshooting.md for additional details.

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