William Valentin
28c78d469d
- README.md: Update audio config format to match schema (enabled + provider.* fields instead of old transcription_endpoint fields), add whisper.cpp server Docker example - CHANGELOG.md: Add '### Fixed' section with voice message failure handling details - config/default.yaml: Update audio section with new schema format and Docker setup example
141 lines
5.0 KiB
YAML
141 lines
5.0 KiB
YAML
# Flynn Configuration
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# Copy to ~/.config/flynn/config.yaml and customize
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# Log verbosity: debug | info | warn | error | silent (default: info)
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# Set to 'debug' to see model fallback details.
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# log_level: info
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telegram:
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bot_token: ${FLYNN_TELEGRAM_TOKEN}
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allowed_chat_ids: [] # Add your Telegram chat ID
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server:
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tailscale_only: true
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localhost: true
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port: 18800
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models:
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# ── Model tiers ────────────────────────────────────────────────────
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# Each tier (default, fast, complex, local) defines a primary model.
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# When an Anthropic tier fails, Flynn automatically tries the same
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# model via GitHub Models before falling through to the global chain.
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#
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# You can override auto-fallback with an inline `fallback:` block:
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#
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# default:
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# provider: anthropic
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# model: claude-sonnet-4-20250514
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# fallback: # ← inline per-tier fallback
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# provider: openai
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# model: gpt-4o
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#
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# ── Fallback order ─────────────────────────────────────────────────
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# 1. Primary client for the tier
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# 2. Auto same-model fallback via GitHub Models (Anthropic tiers only,
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# skipped when an inline `fallback:` block is present)
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# 3. Inline `fallback:` client (if configured)
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# 4. Global fallback_chain (tried in order)
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#
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default:
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provider: anthropic
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model: claude-sonnet-4-20250514
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# supports_audio: false # Override native audio detection per tier
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local:
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provider: ollama
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model: glm-4.7-flash
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# ── Global fallback chain ──────────────────────────────────────────
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# Entries can be tier names (default, fast, complex, local) or keys
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# from local_providers below. Tried in order after per-tier fallbacks
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# are exhausted.
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fallback_chain: [local]
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# ── Named providers (optional) ─────────────────────────────────────
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# Define additional providers that can be referenced by name in
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# fallback_chain. Useful for secondary API accounts or self-hosted
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# endpoints that aren't tied to a specific tier.
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#
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# local_providers:
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# openrouter-backup:
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# provider: openrouter
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# model: anthropic/claude-sonnet-4
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# ollama-big:
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# provider: ollama
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# model: llama3.1:70b
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# endpoint: http://gpu-server:11434
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#
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# Then reference them in fallback_chain:
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# fallback_chain: [openrouter-backup, ollama-big, local]
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hooks:
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confirm:
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- shell.*
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- file.write
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- file.patch
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log:
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- web.*
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- file.read
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silent:
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- notify
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# ── Automation ──────────────────────────────────────────────────────
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# Uncomment and configure any automation sources you need.
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# automation:
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# cron:
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# - name: daily-summary
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# schedule: "0 9 * * *"
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# message: "Give me a summary of today's tasks"
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# output:
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# channel: telegram
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# peer: "123456789"
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#
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# webhooks:
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# - name: github-push
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# secret: "whsec_..."
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# message: "GitHub push to {{json.repository.full_name}}: {{json.head_commit.message}}"
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# output:
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# channel: telegram
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# peer: "123456789"
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#
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# gmail:
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# enabled: false
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# credentials_file: ~/.config/flynn/gmail-credentials.json
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# token_file: ~/.config/flynn/gmail-token.json
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# watch_labels: [INBOX]
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# poll_interval: "60s"
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# message: "New email from {{from}}: {{subject}}\n\n{{snippet}}"
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# output:
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# channel: telegram
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# peer: "123456789"
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#
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# heartbeat:
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# enabled: false
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# interval: "5m"
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# checks: [gateway, model, channels, memory, disk]
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# notify:
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# channel: telegram
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# peer: "123456789"
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# failure_threshold: 2
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# disk_threshold_mb: 100
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# ── Audio ────────────────────────────────────────────────────────────
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# Configure a Whisper-compatible endpoint for audio transcription.
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# Models that support native audio input (Gemini, OpenAI, GitHub) will
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# receive raw audio directly; others fall back to this endpoint.
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#
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# For local transcription with whisper.cpp:
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# docker run -d --name whisper-server -p 18801:8080 \
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# ghcr.io/ggml-org/whisper.cpp:main \
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# --model /app/models/ggml-base.en.bin \
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# --host 0.0.0.0 --port 8080 --convert --language en \
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# --inference-path /v1/audio/transcriptions
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#
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# audio:
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# enabled: true
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# provider:
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# type: custom # openai, groq, ollama, llamacpp, custom
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# endpoint: "http://localhost:18801/v1/audio/transcriptions"
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# api_key: "${WHISPER_API_KEY}" # Optional Bearer token
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# model: "whisper-1" # Model name (default: whisper-1)
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