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The most durable design is simple: keep your notes as ordinary Markdown files in a folder you control, and treat search indexes, embeddings, and model caches as disposable data that can be rebuilt. Start with fast lexical search, measure where it misses, then add a local semantic or hybrid index only when it solves a real retrieval problem. This preserves portability while giving you meaning-based discovery.
What a local-first Markdown knowledge base is
A local-first knowledge base has a clear source of truth: readable files on your own filesystem. An editor can add backlinks, previews, and search, but the notes remain usable if that application disappears. Obsidian describes this arrangement directly: “Obsidian stores your notes as Markdown-formatted plain text files in a vault.” Its documentation also says the files can be managed with other editors and file managers. See Obsidian’s storage documentation for the supported file model.
Semantic search does not replace that source. It creates derived artifacts: a parsed representation of each note, an index, text chunks, vectors (embeddings), optional reranking data, and sometimes a model cache. If an index becomes corrupt, a folder is excluded, or you change embedding models, you should be able to delete the derived data and rebuild it from Markdown.
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- Lexical layer: Exact words, phrases, tags, properties, filenames, paths, headings, and blocks.
- Semantic layer: Embeddings and a vector index that retrieve conceptually related text even when the query uses different words.
- Control layer: Backup, exclusion, permissions, refresh, and evaluation rules.
“Local” describes the entire data path, not merely the name of a feature. Check where the notes, index, embeddings, query text, model downloads, and inference requests go before indexing private material.
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Design the file layer before adding AI
Choose one normal directory
Create a directory for Markdown notes and attachments, then open it in an editor or vault interface that understands Markdown. A vault can be located anywhere your operating system permits, according to Obsidian’s documentation. Keep the directory understandable outside the application: a new computer should be able to open the files with a text editor and copy them with ordinary file tools.
A practical starting layout is deliberately small:
Knowledge/
├── Inbox/
├── Projects/
├── Topics/
├── Meetings/
├── Indexes/
└── Attachments/Use Inbox for uncategorized capture, a few stable topic or project folders, and a small number of index notes that explain important areas. This is an implementation choice, not a universal taxonomy. Add structure when repeated retrieval problems reveal that you need it.
Avoid nested vault boundaries
If you use Obsidian, do not place one vault inside another. Its help warns that local links may not update correctly across vault boundaries. One top-level vault keeps relative links and indexing scope predictable.
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Keep links and metadata readable
Use Markdown headings for major concepts, short descriptive filenames, and consistent properties or tags for facts you routinely filter. Put enough context near a statement that a retrieved chunk remains intelligible on its own. Semantic search can find a related passage, but it cannot repair an ambiguous note.
Protect the Markdown source and treat indexes as rebuildable
Back up the files, not only the application state
Obsidian lists Obsidian Sync, Dropbox, iCloud, OneDrive, Git, and other third-party services as ways to synchronize a vault; they have different conflict, privacy, and retention trade-offs. Synchronization is not automatically an independent backup. Keep at least one copy that can be recovered after accidental deletion, corruption, or a bad sync operation.
An optional portable external SSD can hold a local copy of the vault. No particular capacity, speed, or model is required by this workflow; choose a backup method and schedule that match the amount and sensitivity of your data.
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Exclude sensitive or irrelevant paths deliberately
Decide whether private correspondence, credentials, legal records, or temporary exports belong in the retrieval corpus. Excluding a directory before indexing is safer than hoping a later prompt will prevent an unwanted result. Record the exclusion rules so a rebuild produces the same intended corpus.
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Embedding jobs and index migrations should never be your only copy of a note. Confirm that the Markdown directory is backed up, then let each search tool build its own derived database or cache. If an index cannot be recreated from the files, it is functioning as an undocumented second source of truth.
Establish the lexical baseline first
Keyword search is your control condition. It is fast, transparent, and often better than semantic retrieval for names, identifiers, exact quotations, error messages, and code symbols. Run it before installing an embedding pipeline so you can identify the cases that actually need one.
Obsidian’s Search documentation describes full-text queries, exact phrases, Boolean combinations, file and path filters, content filters, tags, properties, section matching, block matching, and sorting by filename or dates. For example:
| Need | Example query | What it narrows |
|---|---|---|
| Exact phrase | "incident response" |
The literal phrase |
| Filename filter | file:meeting |
Notes whose filenames match |
| Folder filter | path:Projects |
Notes under a path |
| Content filter | content:latency |
Notes containing the term in content |
| Boolean logic | (backup OR restore) AND encryption |
Alternative and required terms |
| Section match | section:authentication |
A matching heading section |
| Block match | block:rotation |
A matching Markdown block |
Block search parses Markdown content in every file and can take longer. That delay is useful information: a semantic index may improve discovery, but it adds its own indexing and maintenance cost.
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- Write 10–30 questions you genuinely ask, such as “What was the decision on database retention?” or “Which notes describe rotating API keys?”
- For each question, list the note paths you believe should appear.
- Run the questions with lexical search and save the top results and obvious misses.
- Mark whether the failure was vocabulary mismatch, too many generic matches, poor note structure, or an incorrect expectation.
This record gives you something concrete to beat. “The results feel smarter” is not a reliable acceptance test.
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Add semantic or hybrid retrieval for a defined gap
Semantic retrieval helps when a question and a note express the same idea with different words. Hybrid retrieval combines that signal with lexical matching, which is valuable for exact terms and rare identifiers. The following documented projects illustrate different implementation patterns; they are examples, not a product ranking.
| Implementation | Documented retrieval and storage | Requirements and limits |
|---|---|---|
| QMD Semantic Search for Obsidian | Local vector/semantic search with BM25 fallback and automatic indexing. | Separate QMD CLI, Bun, desktop filesystem access, and an internet connection for the first model download. The listed first-use model sizes are about 300 MB for embeddings, 640 MB for a reranker, and 2.2 GB for query expansion; project figures can change. |
| Obsidian Semantic Search MCP | Scans Markdown, splits on headings, embeds chunks with Ollama, and stores note data, full-text rows, and vectors in SQLite. Supports hybrid vector plus SQLite FTS5 and keyword-only modes. | Can read the filesystem without Obsidian running. The reviewed repository labels version 0.3.1 an early preview. It exposes read-only search, read, index, and status tools; configuration also permits a remote URL, so settings must be inspected rather than assumed local. |
| Vault Knowledge Base (OKB) | A separate obsidian-kb CLI combines lexical, semantic, and graph-aware related-note discovery and can expose retrieval through MCP. |
Requires Obsidian Desktop and the locally installed CLI. The community listing says it does not support Obsidian Mobile. |
Read the current project documentation before deployment: QMD Semantic Search for Obsidian, Obsidian Semantic Search MCP, and Vault Knowledge Base. Their interfaces and resource requirements are version-sensitive.
Verify that “local” is true for your whole data path
Trace one query from entry to result and answer these questions:
- Are the Markdown files read from a local directory, or synchronized through a hosted service first?
- Where are the parsed chunks, SQLite tables, and vector embeddings stored?
- Does the search request leave the machine?
- Are embedding, reranking, and query-expansion models running locally?
- Does first-run setup download models, and can later searches run without a network?
- Can an MCP or plugin write, rename, or delete notes, or is it read-only?
The QMD project explicitly requires a network connection for its initial model download, so “local model” does not necessarily mean “offline installation.” The semantic MCP project describes local Ollama embeddings and a local index but also allows a remote URL in configuration. Inspect the actual settings, logs, and network policy for the stack you select.
Build the system in a controlled sequence
1. Create and populate the vault
- Create the top-level directory and the small folder structure described earlier.
- Put a representative sample of real notes in it, including the messy notes and terminology you expect to search.
- Open the directory in your Markdown editor or vault interface and confirm that links, attachments, and edits work.
- Make a backup copy before installing an indexer.
2. Record lexical results
Run your baseline questions with exact phrases, Boolean terms, file:, path:, tags, properties, section, and block filters where appropriate. Save the query, expected paths, returned paths, and time to useful results. This is your comparison set.
3. Install one semantic layer
Choose a single documented implementation that fits your constraints. Confirm desktop or mobile support, command-line dependencies, model disk use, whether Obsidian must be open, and whether the tool can modify notes. Start with a small corpus and an explicit exclusion list.
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4. Index and inspect the derived data
Watch the first indexing run for skipped files, permission errors, unexpected paths, and network requests. Verify that a result links back to the correct Markdown file and that a deleted or edited note is not still appearing from stale data. Keep the index location documented so it can be backed up or removed independently.
5. Add hybrid search only if it wins
Compare keyword-only, semantic-only, and hybrid modes on the same questions. Hybrid search is not automatically superior: it can add latency, tuning work, and embedding cost. Keep the simplest mode that retrieves the notes you actually need.
Evaluate whether semantic search is better
Use a repeatable test rather than a universal accuracy claim. The semantic MCP repository documents an evaluation approach that reports Recall@K and mean reciprocal rank (MRR), and recommends avoiding extra ranking complexity or embedding expense unless hybrid search measurably beats the keyword baseline. Its guidance is a practical template for your vault, not an independent benchmark of every tool.
Define the metrics
- Recall@K: the proportion of expected notes found within the first K results.
- MRR: how high the first relevant result appears; a first-position hit scores better than a result near the bottom of the list.
- Time to useful answer: include query latency and the time you spend opening and checking results.
Run the same cases three ways
- Use the saved questions and expected note paths from the lexical baseline.
- Run keyword-only, semantic-only, and hybrid retrieval without changing the corpus.
- Calculate Recall@K and MRR for each mode, then inspect false positives and misses.
- Separate vocabulary failures from bad chunking, stale indexes, and unrealistic expected paths.
- Keep semantic retrieval only if its improvement matters for your recurring work.
Test exact identifiers separately from conceptual questions. Embeddings may help “notes about reducing onboarding friction,” while lexical search is usually the safer first choice for an incident number or function name.
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Refresh after file changes
Index refresh behavior differs by project. The semantic MCP documentation describes action-driven indexing before search after changes, and a full rebuild after an embedding-model or index-format change. Other tools may watch files continuously, require a manual command, or refresh only when launched. Confirm the behavior instead of assuming edits are immediately searchable.
Rebuild when the representation changes
Changing chunking rules, embedding models, excluded paths, or index formats can make old vectors incomparable with new ones. Delete or archive the derived database, run a clean rebuild from Markdown, and rerun your evaluation questions.
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Keep AI access read-only when possible
A search assistant that can search and read notes has a narrower failure mode than automation that can write, rename, or delete them. The semantic MCP project documents read-only search, read, index, and status tools. If you enable a write-capable tool, define which directories it may alter, keep backups, and require confirmation for destructive operations.
Protect the index itself
An index can contain note snippets, metadata, and vectors even when the original files are excluded from a backup. Apply suitable filesystem permissions and retention rules to the derived store. Treat model caches and exported evaluation data as potentially sensitive.
Organization helps scale, but does not guarantee better answers
Folders, headings, stable names, and overview notes reduce the amount of material a person or retrieval system must inspect. A 2026 arXiv preprint, Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability, reports that organized stores roughly halve retrieval cost when the material is large. The same study found that organization eroded for all but its strongest management agent in a growth experiment, and that measured agents did not turn organization itself into better answers. Read the study at arXiv for its benchmark context.
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Apply that result cautiously: it is not a promised speedup for an individual Markdown vault. Use organization to make notes understandable and constrain retrieval, then verify its value with your own questions and metrics.
Troubleshoot common failures
Relevant note is missing
- Run an exact keyword query to confirm the file is indexed at all.
- Check that the folder is not excluded and that file permissions allow reading.
- Inspect headings and chunk boundaries; a concept split across unrelated chunks may lose context.
- Refresh or rebuild after changing the file, embedding model, or index format.
Results are conceptually close but not useful
- Add a heading or a short context sentence to the source note.
- Try hybrid mode so rare names and exact terms retain lexical weight.
- Reduce overly broad queries and inspect the top-k cutoff.
- Measure whether reranking improves MRR before accepting its extra model download and latency.
Search is slow or disk usage is unexpectedly high
- Check whether block parsing is scanning every Markdown file.
- Review model caches: the QMD listing gives approximate first-use sizes of 300 MB for embeddings, 640 MB for reranking, and 2.2 GB for query expansion.
- Index a smaller, intentional corpus and exclude generated exports.
- Use keyword search for exact lookups and reserve semantic retrieval for conceptual questions.
“Local” behavior is unclear
- Inspect configuration for remote endpoints, especially model or embedding URLs.
- Observe first-run downloads and later network activity under your own policy.
- Check where query logs, snippets, and vector databases are written.
- Prefer a documented read-only interface when an AI agent does not need edit access.
A practical decision rule
Keep Markdown as the authoritative files, maintain a backup that is independent enough to recover them, and make lexical search your baseline. Add semantic or hybrid indexing when your recorded questions show repeated vocabulary mismatch or discovery failures. Select a documented implementation whose storage path, model runtime, refresh behavior, hardware requirements, and permissions you can verify. Then keep it only if it improves Recall@K, MRR, or time to a useful note on your own corpus without creating more operational risk than the benefit justifies.
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