KiwiFS can export your entire knowledge base (or a subset of it) as JSONL or CSV. You can export from the CLI or the REST API.Documentation Index
Fetch the complete documentation index at: https://docs.kiwifs.com/llms.txt
Use this file to discover all available pages before exploring further.
Formats
- JSONL
- CSV
Each line is a JSON object representing one file. Fields come from frontmatter.With With With
--include-content, each object includes the full markdown body.--include-links, each object includes link data.--include-embeddings, each object includes a vector embedding array. A .schema.json sidecar file is also written alongside the output file, describing the embedding model and dimensions.CLI usage
Use thekiwifs export command to export from the command line.
CLI flags
| Flag | Short | Default | Description |
|---|---|---|---|
--root | -r | ./knowledge | Knowledge root directory |
--format | jsonl | Output format: jsonl or csv | |
--output | -o | stdout | Output file path |
--path | Scope export to a subdirectory | ||
--columns | Comma-separated frontmatter fields to include | ||
--include-content | false | Include the full markdown body | |
--include-links | false | Include outgoing and incoming link arrays | |
--include-embeddings | false | Include vector embeddings | |
--limit | 0 | Max files to export (0 = unlimited) |
REST API
Export your knowledge base over HTTP using the/api/kiwi/export endpoint.
Query parameters
| Parameter | Default | Description |
|---|---|---|
format | jsonl | Output format: jsonl or csv |
path | Scope to a subdirectory | |
columns | Comma-separated frontmatter fields | |
include-content | false | Include markdown body |
include-links | false | Include link arrays |
include-embeddings | false | Include vector embeddings |
limit | 0 | Max files (0 = unlimited) |
application/x-ndjson (JSONL) or text/csv (CSV) with chunked transfer encoding. You can pipe the response directly into downstream tools.
Embeddings sidecar
When you export with--include-embeddings, KiwiFS writes a .schema.json sidecar file next to the output file. This file describes the embedding model used and the vector dimensions.
vectors.schema.json
Embeddings are only available if you have configured a vector search provider (such as pgvector with an OpenAI API key).
Common recipes
Back up metadata to git
Back up metadata to git
Export frontmatter as JSONL and commit it alongside your knowledge base for a searchable metadata snapshot.
Build a static search index
Build a static search index
Export with content and embeddings, then feed the output into your static site search pipeline.
Sync to a data warehouse
Sync to a data warehouse
Export as CSV and load into your warehouse using standard ETL tools.
Filter with jq
Filter with jq
Pipe JSONL output to
jq for ad-hoc filtering and transformation.