config.example.yaml

# Primkit configuration file.
# All four primitives (taskprim, stateprim, knowledgeprim, queueprim) read this format.
# Copy to config.yaml and edit. Each primitive uses its own config file.
# Environment variables override any value here (see docs for prefix rules).
# Use ${ENV_VAR} syntax for secrets — they're interpolated at startup.

storage:
  # Path to the SQLite database file. Created automatically if it doesn't exist.
  # Override: TASKPRIM_DB, STATEPRIM_DB, KNOWLEDGEPRIM_DB, or QUEUEPRIM_DB
  db: ~/.taskprim/default.db

  # Litestream replication (optional). Embeds Litestream as a Go library —
  # no sidecar process needed. Continuously streams WAL frames to object
  # storage for durability. Runs automatically in serve and MCP modes.
  # Restore with: taskprim restore --config config.yaml
  replicate:
    enabled: false
    # provider: r2          # r2 | s3 | b2 | gcs
    # bucket: ${R2_BUCKET}
    # path: taskprim.db
    # endpoint: ${R2_ENDPOINT}              # required for R2/B2: https://<account>.r2.cloudflarestorage.com
    # access_key_id: ${R2_ACCESS_KEY_ID}
    # secret_access_key: ${R2_SECRET_ACCESS_KEY}

# Authentication for serve and MCP SSE modes.
# CLI mode uses filesystem permissions — no auth needed.
auth:
  keys: []
  # - key: "tp_sk_your_api_key_here"
  #   name: "johanna"         # identifies task source in API mode
  # - key: "sp_sk_your_api_key_here"
  #   name: "johanna"
  # - key: "kp_sk_your_api_key_here"
  #   name: "thomas"
  # - key: "qp_sk_your_api_key_here"
  #   name: "worker-01"

# HTTP server settings (serve mode and MCP SSE transport).
server:
  port: 8090                  # taskprim: 8090, stateprim: 8091, knowledgeprim: 8092, queueprim: 8093

# taskprim-specific settings.
taskprim:
  # Default list for new tasks when --list is omitted.
  # Override: TASKPRIM_LIST env var.
  default_list: ""

# knowledgeprim-specific settings (ignored by taskprim and stateprim).

# Vector embedding (optional). When configured, knowledgeprim generates embeddings
# on capture and enables vector and hybrid search modes.
# Without embedding, FTS5 search, manual edges, and discovery still work.
embedding:
  provider: ""                # gemini | openai | custom (empty = disabled)
  # model: text-embedding-004            # Gemini (768d default; supports Matryoshka dimensions)
  # model: text-embedding-3-small       # OpenAI
  # dimensions: 768                     # must match model output
  # api_key: ${EMBEDDING_API_KEY}
  # endpoint: ""                        # custom endpoint for local models

# Auto-connect (optional). When embedding is enabled, new entities are
# automatically linked to semantically similar existing entities on capture.
auto_connect:
  enabled: true
  threshold: 0.35             # cosine distance (lower = more similar)
  max_connections: 10         # max auto-connections per capture