About wiki-retrieve
A local retrieval skill for finding relevant passages in Obsidian vault wiki pages. It combines contextual prefixes, BM25 search, and optional reranking while leaving canonical notes unchanged.
Key features
- 01Split pages and add contextual prefixes
- 02Build a local BM25 index
- 03Retrieve snippets and deduplicate by page
- 04Optionally rerank with Ollama
- 05Validate paths, hashes, and index integrity
Use cases
- 01Find relevant passages across wiki pages
- 02Run lexical retrieval without calling a model
- 03Rerank BM25 candidate results
- 04Check index status and evaluate retrieval
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ agricidaniel/ claude-obsidian --skill wiki-retrieveAfter installing, type/wiki-retrievein Claude Code to confirm it loaded.
Details
From the project READMERetrieve relevant passages
This extension derives search data from wiki/ into .vault-meta/. It never
changes canonical notes. Always pass the selected vault explicitly.
Resolve the installed product root from this skill's own location, not from the vault or current working directory:
PRODUCT_ROOT=/absolute/path/to/installed/claude-obsidian
PREFIX="$PRODUCT_ROOT/scripts/contextual-prefix.py"
BM25="$PRODUCT_ROOT/scripts/bm25-index.py"
RETRIEVE="$PRODUCT_ROOT/scripts/retrieve.py"
RERANK="$PRODUCT_ROOT/scripts/rerank.py"
test -f "$PREFIX" && test -f "$BM25" && test -f "$RETRIEVE" && test -f "$RERANK"
Pipeline
contextual-prefix.pysplits pages on paragraph boundaries and stores the raw chunk plus a short page-level prefix.bm25-index.pybuilds a local, standard-library BM25 index over the contextualized text.retrieve.pyselects BM25 candidates, optionally reranks them, rejects invalid records, deduplicates by page, and returns paths and snippets.- The caller reads the returned pages and performs synthesis; retrieval output is not itself evidence.
Provision locally
Preview first, then build synthetic prefixes without network egress:
python3 "$PREFIX" --vault "$VAULT" --all --no-llm --peek
python3 "$PREFIX" --vault "$VAULT" --all --no-llm
python3 "$BM25" --vault "$VAULT" build
python3 "$RETRIEVE" --vault "$VAULT" "wiki" --top 1 --no-rerank --explain
Chunk and index files are disposable runtime state. Incremental prefixing skips records whose chunk hashes, page hashes and provider/model profile still match. Legacy records without that profile regenerate on an explicit build. A complete scan removes surplus records for deleted pages, and the prefixer invalidates the BM25 index before changing its chunk set so a mixed stale index is not served. Prefix and BM25 build operations share the vault-wide mutation lock with every other writer; a busy vault fails closed instead of publishing a partial index.
Contextual-prefix privacy
Synthetic prefixes use only local frontmatter and page text. The explicit
Anthropic API profile can send page bodies off-machine and requires the user's
consent plus --allow-egress and a configured API key. There is no Claude CLI
generation tier or ambient subprocess fallback. Never infer consent from an
API key or installed binary. Preview the scope first and state which provider
will receive what data.
Remote Ollama endpoints also require explicit approval and
--allow-remote-ollama; the default reranker accepts localhost only.
Local model prefixes are a separate explicit option. Use an already installed
exact name:tag; no server is started and no model is installed or pulled:
python3 "$PREFIX" --vault "$VAULT" --all --local-ollama --ollama-model installed:tag --peek
python3 "$PREFIX" --vault "$VAULT" --all --local-ollama --ollama-model installed:tag
The prefix adapter verifies cloud-disabled server status and local model
identity before sending page text. Unsupported or unavailable preflight falls
back to synthetic prefixes for new or changed chunks only; prefixes from the
same endpoint and model stay in place, so a transient outage leaves an
unchanged vault's chunks and BM25 index untouched. This requirement can exclude
older Ollama servers.
Reranking and tiling use the same local-only proof before query, page, chunk,
or embedding cache work. --allow-remote-ollama approves the endpoint, not
cloud delegation by the server.
See prefix providers for the exact policy,
cloud model selection and verification limits. The reranker has its own model
and endpoint policy; selecting local prefixes does not enable reranking.
Query
For a strictly read-only lookup, use the prebuilt BM25 index:
python3 "$RETRIEVE" --vault "$VAULT" "$QUERY" --top 5 --no-rerank --explain
For an explicitly requested rerank, omit --no-rerank. The default is Ollama's
multilingual nomic-embed-text-v2-moe model (approximately 958 MB); the product
never pulls it automatically. To use an already-installed, smaller,
English-oriented v1.5 model, pass --model nomic-embed-text explicitly.
Nomic models use search_query: for the query and search_document: for
candidate text. Nomic v2 has a 512-token input context and Ollama truncates
longer embedding inputs by default; BM25 still scores the complete chunk.
Embeddings are cached by exact model, verified digest, input scheme, and hash
of the exact prefixed input. A missing local Ollama service, malformed HTTP
reply, missing selected model, unusable vector, or any candidate embedding
failure falls back for the complete result set to the original BM25 order; it
never mixes cosine and BM25 score scales.
Query input is bounded at 8,000 normalized characters and result counts must be
between 1 and 1,000. Oversized queries and invalid limits fail with an
actionable usage error instead of looking like an empty successful search.
An untagged model request matches only the installed untagged name or its
:latest alias; select any other tag explicitly.
Use direct diagnostics when needed:
python3 "$BM25" --vault "$VAULT" stats
python3 "$BM25" --vault "$VAULT" query "$QUERY" --top 10
python3 "$RERANK" --vault "$VAULT" "$QUERY" --peek
python3 "$RERANK" --vault "$VAULT" "$QUERY" --model nomic-embed-text --peek
Integrity rules
- Accept only relative chunk and page paths whose resolved targets remain under
$VAULT/.vault-meta/chunks/and$VAULT/wiki/respectively. - Reject hashless legacy chunk records and require chunk-body, page, and index hashes to match before a cached record can be built or served.
- Reject absolute paths, symlink escapes, missing pages, mismatched chunk IDs, changed page hashes, and stale index/chunk hash pairs.
- Rerank the full candidate set, then deduplicate by page, then apply
--top. - An empty index is an honest no-result state. A missing or corrupt index makes
retrieve.pyexit 10 with a stable rebuild command; callers fall back to the standard vault query/text-search path and do not fabricate matches. - Do not cite benchmark percentages unless a reproducible vault-specific benchmark produced them.
Measure a prebuilt index against explicit labels with bench --queries FILE --vault VAULT. See offline retrieval evaluation
for recall, MRR, missing labels and negative cases. This is a read-only
measurement and never silently rebuilds an index or calls a model.
Checkpoint
Observe cache readiness and privacy boundaries, think about whether lexical or semantic ranking is needed, verify returned paths and source freshness, and grow by measuring retrieval misses against a maintained local query set.