mirror of
https://github.com/turnstonelabs/turnstone.git
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Patrick Buckley
167 lines
7.8 KiB
TOML
167 lines
7.8 KiB
TOML
# turnstone.toml — shared bootstrap configuration
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#
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# This file is read once at startup. Values here are overridden by
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# environment variables, which are in turn overridden by CLI flags.
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#
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# All sections are optional. Missing sections use binary defaults.
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# Config file location precedence:
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# 1. --config flag
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# 2. $TURNSTONE_CONFIG env var
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# 3. ~/.config/turnstone/config.toml
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# --- LLM API (turnstone, node, eval) ---
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[api]
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# base_url = "" # API endpoint; empty = binary default
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# api_key = "" # env: OPENAI_API_KEY or ANTHROPIC_API_KEY
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# --- Default Model (turnstone, node, eval) ---
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[model]
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# name = "" # Model ID; empty = provider default (gpt-5 / claude-sonnet-4)
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# temperature = 0.0 # 0 = provider default
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# reasoning_effort = "" # "none", "minimal", "low", "medium", "high", "xhigh", "max"
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# context_window = 0 # 0 = auto-detect from provider capabilities
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# max_tokens = 0 # 0 = provider default
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#
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# Sub-agent routing (task_agent tool). Falls back to agent_model when
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# unset, then to the session model.
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# agent_model = "" # legacy single-knob alias used as fallback
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# task_model = "" # task_agent override (e.g. "local" for cheap subtasks)
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# task_effort = "" # reasoning effort for task_agent (default: inherit session)
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#
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# At call time, the calling LLM may also pass `model="<alias>"` to
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# task_agent to override these per-invocation. Tool descriptions list
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# available aliases dynamically; bad aliases return an error so the
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# model retries with a valid choice.
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# --- Named Models (turnstone, node, eval) ---
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# Define model aliases with per-model overrides. Useful for local model
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# servers or mixing providers. Reference by name with --model flag.
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#
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# [models.local]
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# name = "llama-3-70b"
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# provider = "openai"
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# base_url = "http://localhost:8000/v1"
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# context_window = 8192
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# max_concurrency = 1 # Per-process generation cap for this alias; 0 = unlimited.
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# # For llama.cpp, start with its usable -np slot count.
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#
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# [models.local.capabilities]
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# supports_vision = false
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# supports_web_search = false
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#
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# [models.claude]
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# name = "claude-opus-4-8"
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# provider = "anthropic"
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# --- Database (turnstone, node, console) ---
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[database]
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# url = "" # postgres://user:pass@host/db or /path/to.db
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# env: TURNSTONE_DB_URL
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# listen_url = "" # direct-to-postgres URL for the console's
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# dedicated LISTEN connection. Set this when
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# `url` points at pgbouncer in transaction
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# pooling mode (LISTEN holds session state and
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# is incompatible with transaction pooling —
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# see docs/pgbouncer.md). Defaults to `url`
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# when unset. env: TURNSTONE_DB_LISTEN_URL
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# SSL params (passed through to SQLAlchemy connection):
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# sslmode = "prefer" # disable, allow, prefer, require, verify-ca, verify-full
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# sslrootcert = "" # path to CA cert for verify-ca/verify-full
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# sslcert = "" # path to client cert (mTLS)
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# sslkey = "" # path to client key (mTLS)
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# --- Auth (node, console) ---
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[auth]
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# Auth is always enabled. JWT secret is required.
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# jwt_secret = "" # HS256 signing secret (min 32 bytes recommended)
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# env: TURNSTONE_JWT_SECRET
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# --- Logging (turnstone, node, console) ---
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[log]
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# level = "" # "debug", "info", "warn", "error"
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# empty = binary default (warn for CLI, info for servers)
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# env: TURNSTONE_LOG_LEVEL
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# json = false # JSON output; auto-enabled when stderr is not a TTY
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# --- Session (turnstone, node) ---
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[session]
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# instructions = "" # Default system message
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# compact_max_tokens = 32768 # Max tokens for context compaction summary
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# auto_compact_pct = 0.8 # Trigger compaction at this % of context window
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# --- Tools (turnstone, node) ---
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[tools]
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# timeout = 120 # Tool execution timeout in seconds
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# skip_permissions = false # Auto-approve all tool calls
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#
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# web_search backend (local/vLLM models only — commercial providers use their
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# own native server-side search). The docker-compose stack bundles a SearxNG
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# service and points at it by default.
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# web_search_backend = "" # "" (auto), "searxng", or "mcp:server:tool"
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# searxng_url = "http://searxng:8080" # SearxNG base URL; env: TURNSTONE_SEARXNG_URL
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# (the admin Settings value, if set, wins; clear it
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# there to disable web search)
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# searxng_engines = "" # comma-separated engines (e.g. "duckduckgo,wikipedia");
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# empty = the instance's default mix
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# env: TURNSTONE_SEARXNG_ENGINES
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#
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# workspace_dir = "/workspace" # directory surfaced to the model as its workspace
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# (informational only — does not chdir or confine
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# tools; skipped if the directory doesn't exist).
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# The Docker image presets this to /workspace.
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# env: TURNSTONE_WORKSPACE
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#
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# Reranking (optional, disabled by default). Turnstone runs no reranker itself —
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# it POSTs to an external Cohere/Jina-compatible /rerank endpoint (self-hosted
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# vLLM/TEI/llama.cpp, or hosted Cohere/Jina/Voyage) to reorder results by query
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# relevance. The endpoint is a per-model definition: add a model in the admin
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# Models tab with capability {"supports_rerank": true} and base_url set to the
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# full /rerank endpoint, then pick it under Models -> Roles -> Reranker. The
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# settings below are global knobs — there is no rerank_url-style endpoint setting.
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# rerank_web_search = true # rerank web_search results (when an endpoint is set)
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# rerank_bm25 = true # rerank BM25 retrieval: tool search, skill search, memory
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# rerank_bm25_threshold = 0.0 # 0-1 relevance floor for proactive memory; 0 = off (reorder
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# only). Per-model: set via `turnstone-admin rerank-calibrate`.
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# rerank_instruction = "" # for instruction-aware rerankers (Qwen3) when the endpoint
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# does NOT apply the model's chat template, e.g. "Given a web
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# search query, retrieve relevant passages that answer the
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# query". Prefer vLLM's --chat-template; don't use both.
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# NB: serving Qwen3-Reranker via vLLM REQUIRES --chat-template
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# (the model's chat_template.jinja) or scores are near-random.
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# --- Judge (turnstone CLI; server/console use Admin -> Judge) ---
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[judge]
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# enabled = true # Enable intent validation
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# model = "" # Registered judge alias; empty = same as session
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# confidence_threshold = 0.95 # Judge verdict confidence threshold
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# timeout = 120.0 # Per-turn LLM judge timeout in seconds
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# parallel_evaluations = 1 # Concurrent LLM evaluations per tool-call batch (1-16)
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# --- Memory (turnstone, node) ---
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[memory]
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# relevance_k = 5 # Top-K memories for context injection
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# fetch_limit = 50 # Max memories to fetch for ranking
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# max_content = 32768 # Max memory content size in chars
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# nudge_cooldown = 300 # Min seconds between metacognitive nudges
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# nudges = true # Enable memory nudges
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# --- MCP (turnstone, node) ---
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[mcp]
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# config_path = "" # Path to MCP servers config file (JSON)
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# --- Server (node, console) ---
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[server]
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# max_workstreams = 50 # Maximum concurrent workstreams per node
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# env: TURNSTONE_MAX_WORKSTREAMS
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