Context and container diagrams, sequence diagrams for the core summarize-new-video use case and the local-first/BYO AI routing fallback, and the Clean Architecture layering. Ports & adapters keep the engine provider- and sink-agnostic, making standalone-vs-homelab a wiring choice (ADR-003), not two codebases.
6.5 KiB
Tapir — Architecture (C4 + sequences)
Diagrams describe current assumptions, not a frozen design. They are diffable Mermaid
so they live in version control and render in Gitea. When a decision here changes, record it
in DECISIONS.md and update the relevant diagram in the same commit.
Altitude: C4 Level 1 (Context) and Level 2 (Container), plus sequence diagrams for the two behaviours that carry the most design risk. Clean Architecture layering is described after the diagrams.
C4 L1 — System Context
Who and what Tapir talks to. (Brain and external AI are dashed — both optional.)
graph TB
user["User<br/>(maintainer; later, trusted users)"]
tapir["Tapir<br/>watches subscriptions,<br/>summarizes new videos"]
yt["YouTube<br/>(Data API + captions)"]
vimeo["Vimeo<br/>(API + text tracks)"]
local["Local AI stack<br/>(LiteLLM / piguard alias)"]
byo["User's BYO AI<br/>(Claude / OpenAI / Gemini)"]
brain["brain<br/>(brain-mcp)"]
user -->|connects accounts,<br/>reads summaries| tapir
tapir -->|subscriptions,<br/>new-video events,<br/>captions| yt
tapir -->|subscriptions,<br/>text tracks| vimeo
tapir -->|summarize<br/>PRIMARY| local
tapir -.->|summarize<br/>FALLBACK, opt-in| byo
tapir -.->|optional sink:<br/>brain_ingest| brain
classDef opt stroke-dasharray: 5 5;
class byo,brain opt;
C4 L2 — Containers
Inside Tapir. The engine is provider- and sink-agnostic; everything external is an adapter behind an interface (Clean Architecture ports & adapters).
graph TB
subgraph tapir["Tapir (Go)"]
http["HTTP server<br/>OAuth callbacks +<br/>user-facing API"]
watcher["Watcher<br/>detects new videos<br/>(WebSub + poll)"]
engine["Summarization engine<br/>(use-case core)"]
resolver["Transcript resolver<br/>(captions-first)"]
router["AI router<br/>(llm.Router:<br/>Primary -> Fallback)"]
store[("User store<br/>(Postgres,<br/>per-user isolated)")]
subgraph sinks["Sink adapters (Sink interface)"]
sink_store["Store sink<br/>(primary)"]
sink_brain["Brain sink<br/>(HTTP brain-mcp)"]
end
subgraph providers["Provider adapters (VideoSource interface)"]
p_yt["YouTube adapter"]
p_vimeo["Vimeo adapter"]
end
end
http --> store
watcher --> p_yt
watcher --> p_vimeo
watcher -->|NewVideo event| engine
engine --> resolver
resolver --> p_yt
resolver --> p_vimeo
engine --> router
engine --> sink_store
engine -.-> sink_brain
sink_store --> store
classDef opt stroke-dasharray: 5 5;
class sink_brain opt;
Interfaces that keep the engine pure (the ports):
VideoSource— list subscriptions, detect new videos, fetch transcript. Implemented by YouTube and Vimeo adapters.Summarizer— turn a transcript + context into highlights/takeaways. Backed by thellm.Router(Primary local, Fallback BYO).Sink— deliver a summary. Implemented by the store sink (primary) and brain sink (optional). New sinks add an implementation, nothing else.SecretStore— fetch/store per-user OAuth tokens and BYO keys. Backed by ESO/1Password.
The engine depends only on these interfaces, never on YouTube, brain, or a concrete model. This is what makes "standalone vs homelab" a configuration of which adapters are wired, not two codebases (ADR-003).
Sequence — core use case: new video summarized
sequenceDiagram
participant Src as VideoSource<br/>(YouTube/Vimeo)
participant W as Watcher
participant E as Engine
participant R as Transcript resolver
participant AI as AI router
participant S as Sink(s)
Src->>W: new upload (WebSub push / poll)
W->>E: NewVideo{user, channel, videoID}
E->>R: resolve transcript(videoID)
R->>Src: fetch captions
alt captions available
Src-->>R: captions text
R-->>E: Transcript{source: captions}
E->>AI: summarize(transcript, userContext)
AI-->>E: Summary{highlights, takeaways}
E->>S: deliver(summary)
S-->>E: ok
else no transcript
Src-->>R: none
R-->>E: NoTranscript
E->>S: deliver(skipped: no transcript)
end
Sequence — AI routing (local-first, BYO fallback)
sequenceDiagram
participant E as Engine
participant R as llm.Router
participant P as Primary<br/>(local: LiteLLM/piguard)
participant F as Fallback<br/>(user BYO key)
E->>R: summarize(transcript)
R->>P: complete(prompt)
alt local succeeds
P-->>R: summary
R-->>E: summary (fallback_used = false)
else local fails (error/timeout/unavailable)
P-->>R: error
alt user has BYO configured
R->>F: complete(prompt)
F-->>R: summary
R-->>E: summary (fallback_used = true)
else no BYO
R-->>E: error (queued for retry)
end
end
"Reliably" (VISION / the BYO trigger) is operationalized as: Primary returned without error within timeout. Richer quality scoring can layer on later without changing the interface.
fallback_usedis recorded per summary so "is the local stack good enough?" becomes a query, not a guess.
Clean Architecture layering
graph LR
subgraph domain["Domain (entities)"]
d["User, Subscription, Video,<br/>Transcript, Summary"]
end
subgraph usecase["Use cases (engine)"]
u["SummarizeNewVideo,<br/>ConnectAccount,<br/>RouteAI"]
end
subgraph ports["Ports (interfaces)"]
po["VideoSource, Summarizer,<br/>Sink, SecretStore"]
end
subgraph adapters["Adapters (infra)"]
a["YouTube, Vimeo, llm.Router,<br/>Store sink, Brain sink,<br/>ESO secret store, HTTP, Postgres"]
end
a --> po
po --> u
u --> d
Dependencies point inward only. Domain knows nothing of YouTube, brain, Postgres, or any
model. Adapters are swappable; tests target the use-case core through fake adapters (see the
Gherkin features in docs/use-cases/).
Out of scope in these diagrams (deferred per ADRs)
- Audio-download + speech-to-text resolver (ADR-007) — would be an additional
VideoSourcefallback path, drawn when built. - Multi-tenant isolation primitives (per-tenant Postgres role, NetworkPolicy, tenant label) — activate at Stage 1 (ADR-002); single-user Stage 0 doesn't exercise them.
- Public SaaS surface (sign-up, billing) — Future C, not built (ADR-008).