feat: universal LLM gateway with embedded multilingual admin console
- OpenAI-compatible gateway with intelligent routing via cortiq-router (allaigate) - Embedded build-free admin SPA at /admin: 7 languages (en/ru/de/fr/es/zh/tr), light/dark themes, hot config reload (no restart) - Admin API, per-request statistics + Prometheus /metrics, secret store, playground - English documentation (primary) with Russian translations (*.ru.md) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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infosave2007
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# ============================================================================
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# Cortiq Gateway — example configuration
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# Copy to gateway.toml and edit for your own model pool.
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# Do NOT store secrets here — provide them via *_env (environment variables)
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# or set them from the admin console (stored in config/secrets.toml).
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# ============================================================================
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listen = "0.0.0.0:9000"
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# --- connection to cortiq-router (decides task type / complexity) -----------
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# Hosted allaigate router: url = "https://138.226.222.209", verify_tls = false
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# (cert is bound to an IP), taxonomy_id = "data-assistant", a cortiq_… key in
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# CORTIQ_ROUTER_KEY.
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# NOTE: on hard prompts the router escalates to an oracle LLM (~10 s) — for the
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# hosted router set timeout_ms = 12000+, otherwise the gateway degrades to default.
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[router]
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url = "http://localhost:8080"
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api_key_env = "CORTIQ_ROUTER_KEY" # name of the env var holding the router key
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timeout_ms = 800 # timeout for the /v1/route call (12000+ for oracle)
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taxonomy_id = "general.task-type" # for allaigate: "data-assistant"
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verify_tls = true # false if the router uses an IP / self-signed cert
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# --- how to pick the text to classify from the conversation -----------------
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[route]
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text_strategy = "last_user" # last_user | last_user_plus_system | concat_all
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max_chars = 4000
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cache_ttl = "60s"
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profile = "balanced" # cost-saver | balanced | quality-first
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# ============================================================================
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# Model pool: mix cheap local models and expensive cloud ones.
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# provider = "openai" covers OpenAI, OpenRouter, Together, Groq, and also
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# LOCAL servers vLLM / llama.cpp / LM Studio / Ollama (/v1).
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# ============================================================================
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[[models]]
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id = "local-qwen"
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provider = "openai"
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base_url = "http://localhost:8000/v1"
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model = "qwen2.5-7b-instruct"
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cost_tier = "cheap"
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price_in = 0.0
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price_out = 0.0
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caps = ["tools"]
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[[models]]
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id = "gpt-4o-mini"
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provider = "openai"
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base_url = "https://api.openai.com/v1"
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model = "gpt-4o-mini"
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api_key_env = "OPENAI_API_KEY"
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cost_tier = "mid"
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price_in = 0.15
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price_out = 0.60
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caps = ["tools", "vision"]
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[[models]]
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id = "claude-opus"
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provider = "anthropic"
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base_url = "https://api.anthropic.com"
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model = "claude-opus-4"
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api_key_env = "ANTHROPIC_API_KEY"
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cost_tier = "expensive"
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price_in = 15.0
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price_out = 75.0
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caps = ["tools", "vision"]
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# --- embedding model (for /v1/embeddings in provider mode) ------------------
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[[models]]
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id = "local-embed"
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provider = "openai"
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base_url = "http://localhost:8000/v1"
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model = "nomic-embed-text"
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cost_tier = "cheap"
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kind = "embedding"
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# ============================================================================
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# Routing table: complexity tier → ordered pool (priority = fallback chain)
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# ============================================================================
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[routing]
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low = ["local-qwen"]
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medium = ["gpt-4o-mini", "local-qwen"]
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high = ["claude-opus", "gpt-4o-mini"]
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default = "gpt-4o-mini" # used when the router is unreachable
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[routing.policy]
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mode = "fixed_table" # fixed_table | cost_aware
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# for cost_aware:
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# min_class = { low = "cheap", medium = "mid", high = "expensive" }
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# max_cost_usd_per_request = 0.50
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# --- resilience -------------------------------------------------------------
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[breaker]
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threshold = 5
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cooldown = "30s"
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# ============================================================================
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# Inbound protocols — enable only what you need (toggles apply live in the UI)
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# ============================================================================
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[protocols]
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openai_chat = true
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openai_completions = true
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openai_embeddings = true
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openai_models = true
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anthropic_messages = false
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mcp = false
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native_passthrough = true
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# --- response routing metadata ----------------------------------------------
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[cortiq]
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echo = true # add a "cortiq" field to the response body (X-Cortiq-* headers are always sent)
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# ============================================================================
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# Authentication: virtual gateway keys (agents get these, not provider keys).
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# If this section is empty, the gateway runs in open mode (local development).
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# ============================================================================
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[[api_keys]]
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key = "sk-gw-team-a"
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account = "team-a"
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rate_per_min = 600
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# allow_models = ["cortiq-auto", "local-qwen"] # restrict available models (optional)
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# --- idempotency and logging ------------------------------------------------
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[idempotency]
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ttl = "10m"
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[log]
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bodies = false # do NOT log message bodies (privacy) — default
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level = "info"
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[telemetry]
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metrics = true # GET /metrics (Prometheus)
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otlp_endpoint_env = "OTEL_EXPORTER_OTLP_ENDPOINT" # optional
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# ============================================================================
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# Admin web console — embedded in the binary, served at /admin
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# ============================================================================
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[admin]
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enabled = true
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# Name of the env var holding the admin token. If unset (or empty), a token is
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# generated at startup and printed to the log (see also the --admin-token flag).
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token_env = "CORTIQ_ADMIN_TOKEN"
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# listen = "127.0.0.1:9001" # optional: separate bind for admin (default: main listen)
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# ============================================================================
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# Request statistics (dashboard + /metrics). Kept in memory + appended to JSONL.
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# ============================================================================
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[stats]
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enabled = true
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file = "config/stats.jsonl" # empty = in-memory only
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ring_size = 500 # how many recent requests to keep in the feed
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retention = "7d" # aggregate/series depth (replayed on startup)
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