Query the user's LLM Wiki knowledge base (the LLM Wiki desktop app at 127.0.0.1:19828 — NOT Obsidian, Notion, Apple Notes, Logseq, or any other PKM tool). Trigger ONLY when the user explicitly names LLM Wiki, says 'my wiki', 'my 知识库 / 知识库 / knowledge base', or asks things like 'what does my wiki say

LLM Wiki Local API Skill Talk to the user's locally running LLM Wiki app over its built in HTTP API. This is a standard JSON API — call it directly with whatever HTTP tool is already in your environment ( curl , fetch , requests , http middleware, etc.). No client library to install, no SDK to learn. Treat the wiki as a private, structured knowledge base the user has been curating: pages live as wiki/ .md , raw documents under raw/sources/ , wikilinks form a graph. When to invoke Invoke only when the user is clearly referring to LLM Wiki specifically — by app name, by wiki framing, or by 知识库 framing. Concretely: asks a question framed as "what does my wiki / my knowledge base / 我的知识库 / LLM Wiki say about X" asks to "search my wiki / LLM Wiki project / 我的知识库 for X" references a wiki page by stem / title and wants to read or cross link asks for the wiki graph / 知识图谱 / wiki overview / wiki structure has just added or edited files under the LLM Wiki source folder and wants ingest re run / 重新索引 says "use my wiki for context" / "ground your answer in my wiki " / "check my LLM Wiki " names a wiki project (by ID, by absolute path, or by current ) Do NOT invoke when the user says: "search my notes " without further qualification — likely Obsidian / Apple Notes / Notion / Logseq / Bear / etc. "find in my notebook " — likely Jupyter / OneNote / Notability "check my Obsidian / Notion / Roam / Logseq vault " — explicitly a different tool "look up my Anki / Readwise / Pocket " — different tool "search my files / my Documents folder " — generic filesystem, not the wiki general world knowledge, current events, or anything the user clearly wants from the open web When in doubt about which knowledge tool the user means, ask: "Do you mean your LLM Wiki specifically, or another tool?" — don't silently call the LLM Wiki API on what might be an Obsidian vault. Quick start The whole API is plain HTTP + JSON. The fastest path: If you're writing TypeScript / JavaScript: Python is the same shape — urllib.request , requests , httpx , whatever you already have. Don't install anything new. Auth model The API is localhost only . The token is one of: 1. LLM WIKI API TOKEN environment variable (if set, overrides UI) 2. The user's apiConfig.token saved via Settings → API Server 3. allowUnauthenticated: true mode (no token needed; rare, user opt in only) Always check /api/v1/health first — it returns { enabled, authConfigured, allowUnauthenticated, tokenSource } . If authConfigured: false && allowUnauthenticated: false , ask the user to open Settings → API Server → Generate new token . Do not proceed without auth being set up. Three equivalent ways to send the token: Never log or echo the token. Never put it in any URL the user can see in your output (Referer / shell history / logs all leak it). Standard workflow When the user asks "look it up in my wiki": 1. Resolve project (see [Project resolution]( project resolution) below). 2. Search : POST /api/v1/projects/{id}/search with { query, topK: 5..10 } → ranked hits ( path , title , snippet , score , titleMatch , optional vectorScore , images ). Inspect response.mode to know whether hybrid retrieval kicked in. 3. Read top hits : for each promising hit, GET /api/v1/projects/{id}/files/content?path=... for the full markdown. Or pass includeContent: true to the search to avoid the round trip. 4. Cite + answer : synthesize an answer grounded in the read pages. Quote the path of each page you used so the user can verify and jump in app. Reading the score The score field's scale depends on mode : mode: "keyword" — additive keyword score. Filename exact hits are ~200; phrase in title ~50+; bag of tokens lands in single digits. Treat anything below ~5% of the top result as low confidence. mode: "hybrid" or "vector" — RRF (Reciprocal Rank Fusion) score, typically in the 0.015–0.035 range. The absolute number is small; relative ordering is what matters. Use the per result vectorScore (raw cosine 0–1) for "how strongly did the embedding match" if you need it. Don't apply a fixed score threshold across modes. Sort by score descending and rely on relative gaps. Project resolution {id} in every project scoped endpoint accepts four forms : Form When to use Example current (literal) Default for "my wiki / 我的知识库 / this project / this wiki". The user is referring to whatever is open in the desktop UI. /api/v1/projects/current/search UUID The user pasted a project ID, OR you previously resolved a name to an ID and want to re use it. /api/v1/projects/a0e90b29 fcf3 4364 9502 8bd1272de820/files Absolute filesystem path (URL encoded) The user named the path (e.g. ~/notes/research ). Useful when the user has multiple projects with similar names. /api/v1/projects/%2FUsers%2Fme%2Fwiki%2Fresearch/files Project name Not supported directly. You must GET /api/v1/projects first, find a match by name , then use that project's id . Decision tree for what the user said: Cache the resolved id for the rest of the conversation — there's no need to re GET /projects for every call. But if the user switches contexts mid conversation ("now look in my Reading project"), re resolve. When the user is silent about which project, default to current and mention it once: "Looking in your active project (Research Notes)…" . This avoids cross project surprises. For graph / cross reference questions: GET /api/v1/projects/{id}/graph?limit=200 → { nodes: [{id, label, nodeType, path, linkCount}], edges: [{source, target, weight}] } Filter via ?q=term (substring of id/label, case insensitive) and ?nodeType=entity concept ... For "I added new docs" requests: POST /api/v1/projects/{id}/sources/rescan → returns { queue: { tasks }, changedTasks: [...] } . Tell the user how many files changed. Actual ingest runs asynchronously via the desktop queue. Endpoint contract (v1) Method Path Notes GET /api/v1/health No auth. Returns { ok, status, version, enabled, authRequired, authConfigured, allowUnauthenticated, tokenSource } . GET /api/v1/projects List projects. Each: { id, name, path, current } . GET /api/v1/projects/{id}/files?root=wiki\ sources\ all&recursive=true&maxFiles=2000 Tree of { name, path, isDir, size, children } . Capped at 10000 nodes (413). GET /api/v1/projects/{id}/files/content?path=wiki/foo.md Text files only (md/mdx/txt/json/yaml/yml/csv/html/htm/xml/rtf/log). 2 MB max. 415 on binary, 413 on oversize, 403 on out of scope path. POST /api/v1/projects/{id}/search Body: { "query": "...", "topK": 10, "includeContent": false } . Hybrid (keyword + vector) when the user has embeddings configured in Settings; falls back to keyword only otherwise. Response carries mode: "keyword" \ "vector" \ "hybrid" , plus tokenHits / vectorHits and per result vectorScore . Empty query → 400. GET /api/v1/projects/{id}/graph?q=&nodeType=&limit=200 Wikilinks graph from wiki/ .md . Limit clamped to 1000. POST /api/v1/projects/{id}/sources/rescan Triggers a backend rescan using the user's Source Watch config. Returns post rescan queue + actually changed tasks. POST /api/v1/projects/{id}/chat 501 — not implemented in v1. Don't call. {id} accepts a UUID, an absolute filesystem path (URL encoded), or the literal string current . Error handling Always treat the status code as the contract: Status Meaning What to do 200 OK Use body.ok === true belt and suspenders; payload is in the same object. 400 Bad request Show body.error . Typical: empty query , invalid ?root= , oversized body. 401 Unauthorized Token missing/wrong. Tell user to set/regenerate in Settings → API Server. 403 Forbidden Path traversal or out of scope (e.g. ../app state.json ). Don't retry the same path. 404 Not found Unknown project id or unknown route. On unknown project, list projects first to recover. 405 Method not allowed Wrong HTTP verb. 413 Payload too large File 2 MB, file tree maxFiles, or request body 1 MB. Suggest narrower scope. 415 Unsupported media Binary or non UTF 8 file content. API is text only. 429 Too many requests Rate limit (120 req/sec global). Back off ≥1 second. 500 Internal error Log + report; don't loop. 501 Not implemented /chat stub. Don't retry. 503 Service unavailable Two flavors: API toggled off ( error contains "disabled"); in flight cap (64) reached ("busy"). Back off ≥2s. If the HTTP call itself fails (connection refused / ENOTFOUND): the desktop app is not running . Tell the user: "Launch LLM Wiki, then re try." Etiquette Cite paths. When you answer using wiki content, name the page: (from wiki/concepts/rope.md) . The user uses these to verify and to jump in app. Stay read only by default. Only sources/rescan mutates state; everything else is reads. Don't invent write endpoints — they don't exist in v1. Don't dump full pages unless asked. Snippet + path is usually enough. Pull full content only when reasoning genuinely needs it. Respect the project boundary. The current project is the user's active context. Do not silently switch projects. Honor the rate limit. 120 req/sec is plenty for sequential work, but parallel page reads can burst close to it. Batch where the API allows ( includeContent: true on search avoids N+1 reads). Never leak the token. Headers are safe; query params and your own output text are not. See also api reference.md — full endpoint shapes with request / response examples examples.md — common conversational patterns mapped to direct curl / fetch sequences README.md — human setup notes (token generation, port conflicts, troubleshooting)

llm-wiki