ankn
a5f8fb49ab
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.
83 lines
2.0 KiB
Markdown
83 lines
2.0 KiB
Markdown
# Azure OpenAI setup
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This project requires Azure OpenAI for **embeddings only**:
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- **Embeddings** (`text-embedding-3-large`, **3072 dims**):
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- role inference (against Data Lake role vocabulary)
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- competence inference (against Data Lake competence vocabulary)
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- similarity scoring for matching
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There are **no chat/LLM features** anymore.
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## 1) Create an embedding deployment in Azure
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In your Azure OpenAI resource, create an embedding deployment for:
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- `text-embedding-3-large`
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Notes:
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- The deployment **name** must match what you set in `config.toml`.
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- This project uses 3072 embedding dimensions.
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## 2) Configure `config.toml`
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Copy the template:
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- `cp config.toml.example config.toml`
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Set the Azure section:
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```toml
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[azure_openai]
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endpoint = "https://<your-resource>.openai.azure.com"
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api_version = "2024-02-15-preview"
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embedding_deployment = "text-embedding-3-large"
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# Embeddings batching (number of inputs per embeddings API request)
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embedding_batch_size = 128
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# Optional
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show_costs_in_output = false
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```
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Notes:
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- Embeddings requests may be batched (`input=[...]`) and chunked sequentially.
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Increase `embedding_batch_size` to reduce round-trips, or decrease it if you
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suspect request-size related failures.
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## 3) Configure credentials via environment variables
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The server reads the API key from:
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- `AZURE_OPENAI_EMBEDDING_API_KEY`
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Recommended: keep secrets in a local `.env` file (do not commit):
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```bash
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AZURE_OPENAI_EMBEDDING_API_KEY="..."
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```
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The server also needs database credentials:
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- `DATA_LAKE_USERNAME`
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- `DATA_LAKE_PASSWORD`
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## 4) Verify your setup
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### 4.1 Quick import/run smoke check
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Start the server with debug logs and confirm it loads your config:
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- `uv run teamlandkarte-mcp --config config.toml --log-level DEBUG`
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### 4.2 Typical Azure errors
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- HTTP 401/403: wrong key or wrong resource
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- HTTP 404: deployment name mismatch
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- HTTP 429: throttling; warm the embedding cache and/or reduce concurrency
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- network/TLS: verify corporate proxy/TLS setup
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See `docs/troubleshooting.md` for additional details.
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