Autorag Setup

AutoRAG setup

Use this skill when AutoRAG is unconfigured, the autorag CLI is missing, model resolution fails, indexes are missing or stale, or the user wants to change the document collection or datasources.

Safety

• Inspect only non-secret provider/model metadata and credential availability.
• Never print, copy, migrate, compare, or persist credential values. Store only environment-variable names such as apiKeyEnv.
• Do not scan the whole filesystem or home directory without explicit approval.
• Never move, rename, edit, or delete source documents.
• Do not index system trees, app bundles, caches, credential stores, node_modules, .git, dist, build, target, .cache, .autorag, or .jikji.

Install the CLI if needed

The CLI is @autorag/librarian (autorag). Runtime is Node.js ≥ 24 or Bun.

command -v autorag >/dev/null || bun install -g @autorag/librarian
autorag --help

If Bun is unavailable, npm install -g @autorag/librarian is acceptable.

Inspect existing configuration

Check --config, AUTORAG_CONFIG, $AUTORAG_HOME/config.json, or ~/.autorag/config.json. Relevant fields are:

• searchPaths, workspacePath, and memoryPath
• model.provider, model.id, model.api, model.baseUrl, model.apiKeyEnv
• bm25, minSync, and jikji
• datasources, datasourceAccess, and ui

Preserve explicit user choices and a working config unless the user asks to replace them or health checks fail.

Configure one search model

AutoRAG is the specialized librarian agent. One configured model plans the search, calls retrieval and filesystem tools, reads sources, judges evidence, and curates the answer in one loop. There are no orchestrator/explorer roles.

Prefer a model with reliable tool calling and structured output, enough context for source excerpts, high output TPS, and low first-token latency. Use a larger reasoning model only when difficult synthesis or domain judgment matters more than latency.

Start from a provider/model the current runtime can actually call. A Pi-usable ChatGPT, Claude, Gemini, or other authenticated subscription is valid; an installed CLI or subscription that Pi cannot invoke is not. For custom OpenAI-compatible endpoints, record the real wire API, base URL, and credential environment-variable name.

Allowed api values:

• openai-completions
• openai-responses
• anthropic-messages
• openai-codex-responses
• azure-openai-responses

If no callable setup can be established, ask the user for the provider, model id, API protocol, base URL when custom, and credential environment-variable name. Do not invent provider identities or model ids.

Propose and approve document roots

Explicit user paths win. Otherwise inspect only these likely document-dense candidates for existence and approximate supported-file counts, then ask for approval before indexing:

Supported parsed formats are md, markdown, txt, text, pdf, docx, pptx, xlsx, xls, hwp, hwpx, and eml. OCR for jpg, jpeg, png, bmp, and tiff is optional (parserOptions.ocr.enabled). Do not present legacy .doc as a supported parsed format.

Keep the first-run set small, usually one to three roots. Present a concrete proposal and require yes, a narrowed keep-list, a custom list, or skip before running refresh.

Initialize

autorag init \
  --search-paths "/path/to/documents,/path/to/notes" \
  --workspace "/path/to/workspace" \
  --model-provider PROVIDER \
  --model-id MODEL

If the authenticated local runtime already supplies the intended model, model flags may be omitted. For a custom endpoint, add api, baseUrl, and apiKeyEnv to the single model object in the trusted config:

{
  "searchPaths": ["/path/to/documents"],
  "model": {
    "provider": "openrouter",
    "id": "anthropic/claude-sonnet-4",
    "api": "openai-completions",
    "baseUrl": "https://openrouter.ai/api/v1",
    "apiKeyEnv": "OPENROUTER_API_KEY"
  }
}

Use --force only when intentionally replacing an existing config. Legacy cwd autorag.config.json is a migration source only and is never deleted by init.

Retrieval defaults

MinSync and Jikji are enabled by default. Leave them enabled unless the user explicitly asks otherwise. MinSync auto-installs a verified GitHub release into <workspace>/.autorag/bin on first use (minSync.autoInstall defaults to true). Set "autoInstall": false only when managing the binary yourself. Jikji auto-installs jikji-cli through cargo when enabled (jikji.autoInstall defaults to true; requires the Rust toolchain).

Exact duplicate exclusion is enabled by default. AutoRAG invokes the external dupey CLI before parsed-mirror indexing, keeps the newest filesystem copy for each exact canonical-text hash, and excludes older copies from the mirror. Install dupey during setup when it is missing (command -v dupey || cargo install dupey) and tell the user the feature is available; when installation is impossible, refresh continues without this optimization and the user is told duplicate exclusion is off. Set "excludeExactDuplicates": false to index every copy.

MinSync's default embedder is local EmbeddingGemma (768 dimensions, served locally via Ollama). The default needs no embedder flags and no API key, and no corpus text is sent to a remote embedding service. During setup, verify the local model is available (ollama pull embeddinggemma with ollama serve running) instead of silently falling back.

Override the embedder only when intentionally using a different, for example remote, provider:

autorag init \
  --embedder-id "text-embedding-3-small" \
  --embedder-base-url "https://api.openai.com/v1" \
  --embedder-api-key-env "OPENAI_API_KEY" \
  --embedder-dimension 1536 \
  --embedder-batch-size 64

Only store the environment-variable name, never its value. Dimension and batch size must be positive integers, and the dimension must match the embedder (EmbeddingGemma is 768; text-embedding-3-small is 1536).

Probe and configure datasource skills (setup wizard)

autorag ui is still in development — do not recommend it for datasource setup. Configure datasources directly in trusted config, wizard-style:

1. Probe every datasource for setup feasibility before asking the user anything: the backing CLI exists (katok, discrawl, slacrawl, wacrawl, telecrawl, notcrawl, qmd, mailcrawl, rclone), its local store or archive is present, and any credentials it needs are available as environment variables or in the tool's own external configuration.
2. Auto-configure every datasource that probes feasible — write its trusted datasources / datasourceAccess entries without asking. For example, when Slack (slacrawl) and Discord (discrawl) are installed, set both up automatically.
3. Skip every datasource that probes infeasible (for example Notion or Telegram when their CLIs are not installed) and always report the skipped list to the user, with what is missing for each.
4. Set up a skipped datasource only when the user explicitly asks for it: install or authenticate the backing CLI first, then configure it.
5. E-mail datasources (gmail, mail-export, mailcrawl) matter to most users — always probe them and report their status, even when they end up skipped.

Datasource skills belong in trusted config and remain default-deny. Builtin template names are kakao, whatsapp, telegram, slack, discord, clawgallery, notion, github, cloud-drive, gmail, mail-export, mailcrawl, obsidian, rss, and spotlight. Config keys may be connection aliases with "type": "<template>". Unknown names are skipped with an unknown-datasource-skill warning; they do not fail config resolution. scope and tags can narrow trusted access but cannot grant it.

{
  "datasources": {
    "github": { "connector": { "repos": ["owner/repo"], "tokenEnv": "GITHUB_TOKEN" } },
    "google-drive": { "type": "cloud-drive", "connector": { "provider": "google-drive", "remote": "gdrive:" } },
    "archive-drive": { "type": "cloud-drive", "connector": { "remote": "archive:" } },
    "gmail": { "connector": { "tokenEnv": "GMAIL_ACCESS_TOKEN", "labelIds": ["INBOX"] } },
    "mailcrawl": { "instanceId": "personal", "connector": { "account": "personal", "mailbox": "INBOX", "binaryPath": "mailcrawl" } },
    "obsidian": { "connector": { "vaultPath": "/path/to/vault" } },
    "rss": { "connector": { "feeds": [{ "url": "https://example.com/feed.xml" }] } }
  },
  "datasourceAccess": {
    "allowedTags": ["github", "cloud-drive", "gmail", "mailcrawl", "obsidian", "rss"],
    "allowedScopes": ["/github/**", "/google-drive/**", "/archive-drive/**", "/gmail/**", "/mailcrawl/**", "/obsidian/**", "/rss/**"]
  }
}

Tokens are environment-variable names, not raw secrets. CLI-backed connectors keep authentication in their external tool configuration.

Mailcrawl must be installed separately (@nomadamas/mailcrawl@0.1.4 or newer) and configured through its own Himalaya account. AutoRAG runs its local sync and index lifecycle, then uses the mailcrawl CLI for BM25, semantic, or hybrid search. Do not use 0.1.3 or earlier: a no-op sync followed by index fails with text array must be non-empty. Use mailcrawl for Himalaya-backed IMAP/Maildir retrieval. The legacy gmail connector option backend: "himalaya" is no longer registered; migrate that configuration to an explicit mailcrawl datasource.

Verify and build indexes

Configuration alone is not a successful setup:

autorag status --json
autorag health --json
autorag refresh --json
autorag search "summarize the collection" --top-k 3 --json --debug

• status is model-free and path-opaque.
• health resolves the single model, checks credential presence, and normally performs one live completion probe.
• health --skip-probes is only for intentionally offline validation and does not prove live provider access.
• refresh syncs parsed mirrors, MinSync, Jikji, and authorized datasources. --method <csv> may deliberately narrow it (parsed,minsync,datasources,jikji,all).
• Use refresh --force for a full resync only when incremental refresh is not enough. Keep destructive reset/rebuild operations scoped to workspace .autorag indexes, never source documents.
• After a search, use autorag evidence <sessionId> --json to inspect the exact source chunks behind numbered results, including source, method, stable evidence ID, excerpt/content, chunk index, and line number.

Keep indexes fresh

For continuous freshness, create or verify an OS-appropriate scheduled autorag watch --once job, hourly by default (every 1 hour; shorten only when the user asks for fresher indexes). Prefer cron or launchd on macOS, cron or a user systemd timer on Linux, and Task Scheduler on Windows. Use the same config as search, avoid overlapping runs, and keep logs outside source trees. Once the schedule is installed, tell the user right away that hourly freshness is set up.

Environment overrides

• AUTORAG_HOME
• AUTORAG_CONFIG
• AUTORAG_SEARCH_PATHS
• AUTORAG_WORKSPACE
• AUTORAG_MEMORY_PATH
• AUTORAG_MODEL_PROVIDER
• AUTORAG_MODEL_ID

Completion condition

Setup is complete only when the CLI is installed, roots are approved, a non-secret single-model config is written, every datasource has been probed and the auto-configured and skipped lists reported to the user, dupey is installed or its absence reported, status is acceptable, live health passes, refresh builds the requested indexes, one real structured search succeeds, and any requested ongoing schedule is installed or verified with the user told it is active.