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# Wiki: local web interface and agent API, as a modular .NET solution
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## Goal
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A web interface for the Notes wiki that runs entirely in the cluster, and an
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HTTP API through which agents ask the wiki typed questions and deposit facts.
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Built as one C# solution with a MudBlazor front end, and built so that every
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external dependency is replaceable: where the notes live, which model embeds
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them, what judges a typed question, what runs an agent, how the vector index
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is stored. The first adapters are what the homelab uses today. The point of
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the design is that the next one is a project and a config line, not a
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rewrite.
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## Where we are
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- `wiki-index` (Python, ns `ai`) syncs `wiki/` from `k.renner/notes` by blob
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SHA, embeds through `wiki-embed` (bge-m3 on llama-server), answers
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`POST /rank`. It keeps slug, title and vector, not the text. LAN-only behind
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Traefik BasicAuth; the Gitea webhook hits the ClusterIP.
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- `~/Notes/bin/wiki_rank.py` and `wiki_ask.py` rank through the index, build a
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document context from a local vault checkout, send it with typed questions
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to TypeSafe, gate on `covered`. The wiki-synth agent composes answers from
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those judgements.
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- New knowledge enters as a file in `raw/` (facts with provenance, never
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hypotheses), distilled nightly in CI.
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- The house C# project, shelly-manager, sets the conventions: net10.0, an
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`.slnx` with `src/` and `test/`, xUnit, MudBlazor 9, Blazor Web App with
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interactive server rendering, minimal APIs, an Authentik proxy-header
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authentication handler for humans and an API-key handler for machines, a
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multi-stage Dockerfile, `dotnet test` on every push and a kaniko release
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pipeline to the Gitea registry. Human-facing apps sit behind an Authentik
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outpost (`<app>-gate`) with a CNP that admits only that outpost.
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## Architecture: ports in the core, adapters around it
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```
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┌──────────────────────────────────────────┐
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│ Wiki.Host (ASP.NET Core + Blazor/Mud) │ composition root,
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│ /api/v1, /rank, /webhook, UI pages │ config, auth
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└───────────────┬──────────────────────────┘
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│ uses
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┌───────────────▼──────────────────────────┐
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│ Wiki.Application (use cases) │ Sync, Rank, Ask,
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│ │ DepositFact, Queue-
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└───────────────┬──────────────────────────┘ Question, Evaluate
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│ depends only on
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┌───────────────▼──────────────────────────┐
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│ Wiki.Core (domain + ports) │ Note, RawSource,
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│ no package references │ Report, LinkGraph,
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└──────────────────────────────────────────┘ the interfaces
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implemented by ▲ ▲ ▲ ▲ ▲
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Adapters.Gitea Adapters.LlamaCpp Adapters.TypeSafe Adapters.FileIndex Adapters.Anthropic
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(later: Database, Ollama/Onnx, local Jev, pgvector/Qdrant, OpenAI-compatible answer models)
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```
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Rules that keep it modular, enforced by project references and a test:
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- `Wiki.Core` references nothing. It holds the domain types and the port
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interfaces. Adapters reference Core only; they never reference each other
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or the host. `Wiki.Host` is the only project that knows which adapters
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exist.
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- Every port has a **contract test suite** in `test/Wiki.Contracts` that
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runs against every adapter of that port. A new adapter is done when the
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suite is green. That is what makes "swap the storage" a safe operation.
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- Adapters are selected by configuration and registered by name (keyed
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services), so several can be active at once. The UI's lab page and the
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evaluator use that to compare, the API uses the one marked active.
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- Nothing in Core or Application knows about HTTP, Gitea, TypeSafe, files or
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Kubernetes.
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### Ports
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| port | what it abstracts | first adapter | examples of the next ones |
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|---|---|---|---|
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| `INoteStore` | read notes, raw sources and reports; enumerate by kind; observe changes (content hash per document) | Gitea repository API (`k.renner/notes`, blob SHA) | git checkout on a volume; a database (CNPG Postgres) with the vault imported; an Obsidian sync folder |
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| `IFactSink` | append a new raw source with provenance | Gitea contents API, new file on `main` | database insert; git commit on a checkout |
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| `IEmbeddingModel` | text → vector, with a model id and dimension | llama-server `/v1/embeddings` (bge-m3 via wiki-embed, the model that won the German-document comparison on accuracy per cost) | the qwen3 embedding variants from that comparison; ONNX in-process; any OpenAI-compatible endpoint |
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| `IVectorIndex` | store vectors per (model id, document), query top-k | in-memory cosine with a file snapshot per model, as today | pgvector in the database; Qdrant |
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| `IChunker` | split a document into addressable units for typed questions | paragraph splitter (headings, fences), as in `wiki_ask.py` | sentence windows; semantic chunks |
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| `IJudge` | typed questions over a state: Choice, Noul, Score, with probabilities | TypeSafe System One (`jev-latest`, cloud; the wiki records no locally hosted Jev today) | a locally hosted Jev with the same request shape, should TypeSafe offer one; a constrained-output local LLM adapter, flagged as uncalibrated |
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| `IAnswerModel` | prose from a question plus evidence: the selected paragraphs with their notes and the judgements; must cite by slug; streaming | Anthropic Messages API (Claude, model from config, Sonnet by default) | any OpenAI-compatible endpoint, including the local llama-server; a different Claude model per question |
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| `IEvaluator` | measure a retrieval or judge configuration against the labelled set (questions and cited sources in `output/`) | recall@k, hit rate, MRR, as `wiki_rank_eval.py` | judge accuracy against hand-labelled paragraphs |
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| `IIdentity` | who is calling, for attribution of facts and saved answers | Authentik proxy headers (humans), API key (machines), as in shelly-manager | OIDC bearer tokens |
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Two ports deserve a sentence of scope each. The judge returns judgements
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with probabilities, never prose, and the UI never presents its output as an
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answer, only as ranked, quoted evidence. The answer model turns that
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evidence into words and is allowed nothing else: it sees the selected
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paragraphs and the judgements, not the whole context, and its output is
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shown next to the evidence it was given. Both are single model calls inside
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the request. The service runs no agent: no tool loop, no vault edits. The
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wiki-synth agent in a Claude session remains the path for artifacts and for
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multi-question synthesis, and it can call `/questions` itself.
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### Solution layout
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```
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Wiki.slnx
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src/
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Wiki.Core/ domain types, ports, frontmatter and link parsing
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Wiki.Application/ use cases; depends on Core only
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Wiki.Adapters.Gitea/ INoteStore, IFactSink
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Wiki.Adapters.LlamaCpp/ IEmbeddingModel (OpenAI-compatible embeddings)
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Wiki.Adapters.TypeSafe/ IJudge (System One client)
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Wiki.Adapters.FileIndex/ IVectorIndex (memory + snapshot per model)
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Wiki.Adapters.Anthropic/ IAnswerModel (Messages API, streaming)
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Wiki.Host/ ASP.NET Core: API, webhook, MudBlazor UI, auth,
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composition root, appsettings
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test/
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Wiki.Core.Tests/ parsing, link graph, chunker
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Wiki.Contracts/ one suite per port, parameterised over adapters
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Wiki.Application.Tests/ use cases with fake ports
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Wiki.Host.Tests/ API surface, auth handlers
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```
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Own repository `k.renner/wiki`, same shape as shelly-manager: `Dockerfile`,
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`.woodpecker/test.yml` on pull requests, `.woodpecker/release.yml` building
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the image with kaniko into the Gitea registry on push to `main`, tagged
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`build.<unix-ts>` plus the commit SHA so Flux image automation can follow
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it. The homelab repo holds only the manifests. `claude-bot` needs write
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access to the new repository.
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Two things the shelly-manager history settles in advance: the TypeSafe
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client is written from the documented field contract (endpoint, bearer key,
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`jev-latest`, Noul/Choice/Score, 255-option cap, 32k-token state budget),
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there is no prior C# client to copy; and the UI needs no login page, because
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identity comes from the outpost's headers, which avoids the Blazor-circuit
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sign-in constraint shelly-manager ran into.
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### Configuration
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Adapters are declared in `appsettings` and selected by name; secrets come
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through environment variables, filled by ExternalSecret, never from the file.
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```json
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"Wiki": {
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"NoteStore": { "Active": "gitea", "gitea": { "Repository": "k.renner/notes", "Branch": "main" } },
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"Embeddings": {
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"Active": "bge-m3",
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"Models": {
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"bge-m3": { "Adapter": "LlamaCpp", "Url": "http://wiki-embed.ai.svc:8080", "Model": "bge-m3" },
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"e5-large": { "Adapter": "Ollama", "Url": "http://ollama.ai.svc:11434", "Model": "multilingual-e5-large" }
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}
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},
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"Judges": {
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"Active": "typesafe",
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"Models": {
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"typesafe": { "Adapter": "TypeSafe", "Url": "https://api.typesafe.ai/v1/systemone", "Model": "jev-latest" },
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"jev-local": { "Adapter": "TypeSafe", "Url": "http://jev.ai.svc:8080/v1/systemone", "Model": "jev" } // illustration: same adapter, other endpoint
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}
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},
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"Index": { "Active": "file", "file": { "Path": "/data/index" } },
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"AnswerModels": {
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"Active": "claude-sonnet",
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"Models": {
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"claude-sonnet": { "Adapter": "Anthropic", "Model": "claude-sonnet-5" },
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"qwen-local": { "Adapter": "OpenAI", "Url": "http://llama-server.ai.svc:8080/v1", "Model": "qwen3.6-35b-a3b" }
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}
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}
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}
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```
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Every embedding model gets its own index namespace. Switching the active
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model is a config change after the new index has been built in the
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background; until then both exist and can be compared. Nothing is
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re-embedded in place.
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## The web interface (MudBlazor)
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Blazor Web App, interactive server rendering, hosted in `Wiki.Host`. Static
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assets vendored into the image, no CDN. Pages:
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1. **Search**: a query box, results ranked by the active index with score and
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tags; for a phrased question, optionally the paragraph-level hits from the
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judge with their probabilities, shown as quoted evidence.
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2. **Note**: frontmatter, rendered body with `[[wikilinks]]` resolved,
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backlinks, sources linking to their raw file.
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3. **Browse**: by tag, by `updated`, recently changed, orphans.
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4. **Answers**: a question box. The answer streams in within seconds: rank,
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judge, compose, all in the request. Below it the evidence: the paragraphs
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the judge selected with their notes and probabilities, and the `covered`
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gate; a low gate is shown as "the wiki does not cover this" instead of an
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answer. A save button writes the answer to `output/` with the wiki-synth
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frontmatter (`question`, `sources`, `confidence`), so it joins the
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labelled set. Underneath, the saved answers, newest first, including the
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ones the wiki-synth agent wrote from a session.
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5. **Maintenance**: the raw queue with ages, the last consolidation and audit
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reports, index and sync status.
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6. **Lab**: pick any registered embedding model and judge, run the same query
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through them side by side, and run the evaluator on the labelled set to
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get recall@k per configuration. This is the page for "play around with
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different models".
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7. **Settings**: read-only view of the active adapters and their health.
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No editing of notes in the browser. The vault stays a git repository edited
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by the consolidate agent and by hand.
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## API v1
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Reads are authenticated by the hostname's gate (humans) or API key
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(machines); writes always need an API key so a fact carries its author.
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| method and path | purpose |
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|---|---|
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| `GET /api/v1/status` | index commit and age, counts, active adapters |
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| `GET /api/v1/notes?q=&tag=&limit=` | list; `q` ranks through the active index |
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| `GET /api/v1/notes/{slug}` | frontmatter, body, links, backlinks, sources |
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| `GET /api/v1/tags` | tags with counts |
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| `POST /api/v1/rank` | `{question, k, model?}` → ranked slugs; also served at `/rank` for the existing scripts |
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| `POST /api/v1/ask` | `{queries, questions, k, judge?, model?}` → context, typed judgements with the answering paragraphs verbatim, `covered`. For agents and code. |
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| `POST /api/v1/questions` | `{question, k?, judge?, model?, answerModel?, save?}` → the answer text with `[[slug]]` citations, the evidence (paragraphs, probabilities), `covered`, model and token usage; streamed as server-sent events when requested; with `save`, the `output/` path and commit |
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| `POST /api/v1/facts` | deposit a raw source: title, source, `kind: fact`, observed_at, content, tags → path and commit |
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| `GET /api/v1/raw` | the distill queue with ages |
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| `GET /api/v1/reports` | `output/` documents by type, newest first |
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| `POST /api/v1/lab/eval` | run the evaluator for a (model, judge) pair |
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| `POST /webhook` | Gitea push, HMAC as today |
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`POST /api/v1/facts` is rejected without `source`, with a `kind` other than
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`fact`, or with content under a minimum length. The raw rule is enforced by
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shape: there is no field for a hypothesis. The file gets the vault's
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provenance frontmatter with `captured_by` set from the API key's identity.
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`POST /api/v1/ask` is `wiki_ask.py` moved server-side, so the scripts become
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thin clients and agents no longer need a vault checkout. Its result is
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judgements and quotations, never prose.
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`POST /api/v1/questions` is `/ask` plus one answer-model call, in process:
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rank the question, build the context, run the default typed questions
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(answered, where, covered), hand the selected paragraphs and judgements to
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the answer model, stream the prose back. Below the `covered` threshold it
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returns the gap instead of an answer. Time is the sum of the parts, about
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0.2 s rank, 1 to 4 s judge, a few seconds of generation. Cost is one model
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call over a few thousand tokens, a few cents on Sonnet, against a quarter
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dollar for a full agent run.
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## Auth and network
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- Humans: `wiki.renner.uno`, LAN-only (no `dns.renner.uno/public` label),
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Ingress backend `ak-outpost-wiki-gate:9000`, a `wiki-gate-egress` CNP in
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`authentik`, the proxy-header handler trusts `X-Authentik-*` only from that
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outpost.
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- Machines: `wiki-index.renner.uno` with the API-key handler; the workstation
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key stays where it is. CI reaches the ClusterIP.
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- Secrets (Gitea token, webhook secret, API keys, TypeSafe key, Anthropic
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key) reach the
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pod as environment variables from Kubernetes Secrets filled by
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ExternalSecret from the `openbao` ClusterSecretStore. The pod itself has
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no OpenBao access. The Python service fetched its secrets from OpenBao
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directly through its own Kubernetes-auth role; that role, its policy and
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the `ai` → `openbao` CNP entry are retired with it.
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- Egress from the pod: Gitea, wiki-embed, `api.typesafe.ai:443`,
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`api.anthropic.com:443`. Every
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further adapter that talks to a new endpoint gets its own CNP line, which
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is the one place adding an adapter touches the cluster.
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## Health endpoints
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Three endpoints with three different meanings, wired to the .NET health
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checks with tags so that the probes and the humans see the right thing:
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| endpoint | meaning | probe |
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|---|---|---|
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| `GET /health` | the process serves HTTP. Always 200 once Kestrel listens; no dependency checks. | liveness |
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| `GET /ready` | the active note store has synced at least once and the active index is loaded; 503 while the first sync or index build runs. A failed later sync keeps the last good state and stays ready. | readiness, plus a startup probe with enough budget for the first sync (the Python service was killed mid-build when its `/health` returned 503 during the initial sync) |
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| `GET /api/v1/status` | the full picture for humans and the Settings page: index commit and age, counts, queue lengths, and one entry per registered adapter with its last check result and latency. | none |
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Adapter checks (Gitea reachable, embeddings endpoint answering, judge
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endpoint answering, answer model answering) are reported in `/status` and
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on the Settings page and are never part of `/ready`: a judge outage must not
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take the reader down. Their failures show up as red entries, not as
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restarts.
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## Phases
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One PR each, verified before the next. The Python `wiki-index` keeps running
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until phase 3 proves parity.
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0. **Skeleton.** Repository, solution, `Wiki.Core` with the ports and domain
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types, `Wiki.Contracts` with the suites, `Wiki.Host` serving `/health`,
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`/ready` and `/api/v1/status`. CI: test and release pipelines. Verify:
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image in the registry, pipeline green, the Deployment's three probes
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wired and the pod Ready.
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1. **Parity with the Python service.** Gitea, LlamaCpp and FileIndex
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adapters, the sync use case, `/rank` and `/webhook`. Verify: the 40-query
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recall eval gives the same numbers as the Python service on the same
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commit; `wiki_rank.py --probe-index` against the new service passes.
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2. **Read API and UI.** Notes, tags, raw, reports; Search, Note, Browse,
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Maintenance pages; `wiki-gate`. Verify: a note renders with resolved
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links; an unauthenticated request is redirected to Authentik.
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3. **Ask.** Chunker, TypeSafe adapter, `/api/v1/ask`, scripts as clients.
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Verify: `test_wiki_ask.py` passes against the API; a wiki-synth run with
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the definition unchanged. Then the Python service is retired and the
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`wiki-index` hostname points at the new one.
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4. **Facts.** API keys as an ExternalSecret, `IFactSink`, `/api/v1/facts`. Verify:
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a fact posted from the workstation shows as a `claude-bot` commit and in
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the next distill.
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5. **Questions.** Anthropic adapter, `/api/v1/questions` with streaming,
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the Answers page with save. Verify: a question answered in under ten
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seconds with citations that resolve to the shown evidence; a question the
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wiki does not cover returns the gap; a saved answer appears in `output/`
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with the wiki-synth frontmatter and is picked up by the evaluator.
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6. **Lab.** Evaluator, a second embedding model and a second answer model
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registered, the Lab page. Verify: two models compared on the labelled set
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from the UI.
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## Not in scope
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- Editing notes in the browser.
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- Replacing Obsidian; the vault stays a git repository and `notes-push.path`
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keeps pushing.
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- Public exposure; LAN and VPN only.
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- Running or triggering agents. The service makes single model calls inside
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a request; it never runs a tool loop or edits the vault. Distill,
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consolidate and multi-question synthesis stay Claude processes in CI or in
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a session; the service shows their inputs and outputs and serves their API
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calls.
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claude-botandClaude Fable 5.1
security-bot