| Name | bkmr JSON |
| Version |
2.0.1
JSON |
| download |
| home_page | None |
| Summary | Super fast bookmark manager with semantic full text search' |
| upload_time | 2024-09-01 12:38:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | BSD-3-Clause |
| keywords |
bookmark
launcher
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# bkmr
### [Generalized Semantic Search](https://github.com/sysid/bkmr/wiki/Semantic-Search)
# Ultrafast Bookmark Manager and Launcher
> New Feature: Semantic Search (AI Embeddings)
[Elevating Bookmark Management with AI-Driven Semantic Search](https://sysid.github.io/elevating-bookmark-management-with-ai-driven-semantic-search/)
Features:
- semantic search using OpenAI embeddings (requires OpenAI API key)
- full-text search with semantic ranking (FTS5)
- fuzzy search `--fzf` (CTRL-O: copy to clipboard, CTRL-E: edit, CTRL-D: delete, Enter: open)
- tags for classification
- can handle HTTP URLs, directories, files (e.g. Office, Images, ....)
- can execute URI strings as shell commands via protocol prefix: 'shell::'
URI-Example: `shell::vim +/"## SqlAlchemy" $HOME/document.md`
- automatically enriches URLs with title and description from Web
- manages statistics about bookmark usage
**`bkmr search --fzf` is a great way to open bookmarks very fast.**
## Usage
```bash
bkmr --help
A Bookmark Manager and Launcher for the Terminal
Usage: bkmr [OPTIONS] [NAME] [COMMAND]
Commands:
search Searches Bookmarks
sem-search Semantic Search with OpenAI
open Open/launch bookmarks
add Add a bookmark
delete Delete bookmarks
update Update bookmarks
edit Edit bookmarks
show Show Bookmarks (list of ids, separated by comma, no blanks)
surprise Opens n random URLs
tags Tag for which related tags should be shown. No input: all tags are printed
create-db Initialize bookmark database
backfill Backfill embeddings for bookmarks
load-texts Load texts for semantic similarity search
help Print this message or the help of the given subcommand(s)
Arguments:
[NAME] Optional name to operate on
```
<a href="https://asciinema.org/a/ULCDIrw4pG9diaVJb17AjIAa7?autoplay=1&speed=2"><img src="https://asciinema.org/a/ULCDIrw4pG9diaVJb17AjIAa7.png" width="836"/></a>
### Examples
```bash
# FTS examples (https://www.sqlite.org/fts5.htm)
bkmr search '"https://securit" *'
bkmr search 'security NOT keycloak'
# FTS combined with tag filtering
bkmr search -t tag1,tag2 -n notag1 <searchquery>
# Search by any tag and sort by bookmark age ascending
bkmr search -T tag1,tag2 -O
# Give me the 10 oldest bookmarks
bkmr search -O --limit 10
# Adding URI to local files
bkmr add /home/user/presentation.pptx tag1,tag2 --title 'My super Presentation'
# Adding shell commands as URI
bkmr add "shell::vim +/'# SqlAlchemy' sql.md" shell,sql,doc --title 'sqlalchemy snippets'
# JSON dump of entire database
bkmr search --json
# Semantic Search based on OpenAI Embeddings
bkmr --openai sem-search "python security" # requires OPENAI_API_KEY
```
Tags must be separated by comma without blanks.
## Installation
1. `cargo install bkmr`
2. initialize the database: `bkmr create-db db_path`
3. `export "BKMR_DB_URL=db-path"`, location of created sqlite database must be known
4. add URLs
More configuration options can be found at [documentation page](https://github.com/sysid/bkmr/wiki/configuration).
### Upgrade to 1.x.x
A database migration will be performed on the first run of the new version.
This will add two columns to the bookmarks table for the OpenAI embeddings.
No destructive changes are made to the database.
## Semantic Search
`bkmr` provides now full semantic search of generalized bookmarks using OpenAI's Embeddings.
You can find more information on the [documentation page](https://github.com/sysid/bkmr/wiki/semantic-search).
## Benchmarking
- ca. 20x faster than the Python original [twbm](https://github.com/sysid/twbm) after warming up Python.
```bash
time twbm search 'zzz*' --np
0. zzzeek : Asynchronous Python and Databases [343]
https://techspot.zzzeek.org/2015/02/15/asynchronous-python-and-databases/
async, knowhow, py
Found: 1
343
real 0m0.501s
user 0m0.268s
sys 0m0.070s
time bkmr search 'zzz*' --np
1. zzzeek : Asynchronous Python and Databases [343]
https://techspot.zzzeek.org/2015/02/15/asynchronous-python-and-databases/
async knowhow py
real 0m0.027s
user 0m0.008s
sys 0m0.016s
```
[sysid blog: bkmr](https://sysid.github.io/bkmr/)
<!-- Badges -->
[pypi-image]: https://img.shields.io/pypi/v/bkmr?color=blue
[pypi-url]: https://pypi.org/project/bkmr/
[build-image]: https://github.com/sysid/bkmr/actions/workflows/build.yml/badge.svg
[build-url]: https://github.com/sysid/bkmr/actions/workflows/build.yml
[coverage-image]: https://codecov.io/gh/sysid/bkmr/branch/main/graph/badge.svg
[coverage-url]: https://codecov.io/gh/sysid/bkmr
[quality-image]: https://api.codeclimate.com/v1/badges/3130fa0ba3b7993fbf0a/maintainability
[quality-url]: https://codeclimate.com/github/nalgeon/podsearch-py
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"description": "# bkmr\n\n### [Generalized Semantic Search](https://github.com/sysid/bkmr/wiki/Semantic-Search)\n\n# Ultrafast Bookmark Manager and Launcher\n\n> New Feature: Semantic Search (AI Embeddings)\n\n[Elevating Bookmark Management with AI-Driven Semantic Search](https://sysid.github.io/elevating-bookmark-management-with-ai-driven-semantic-search/)\n\nFeatures:\n- semantic search using OpenAI embeddings (requires OpenAI API key)\n- full-text search with semantic ranking (FTS5)\n- fuzzy search `--fzf` (CTRL-O: copy to clipboard, CTRL-E: edit, CTRL-D: delete, Enter: open)\n- tags for classification\n- can handle HTTP URLs, directories, files (e.g. Office, Images, ....)\n- can execute URI strings as shell commands via protocol prefix: 'shell::'\n URI-Example: `shell::vim +/\"## SqlAlchemy\" $HOME/document.md`\n- automatically enriches URLs with title and description from Web\n- manages statistics about bookmark usage\n\n**`bkmr search --fzf` is a great way to open bookmarks very fast.**\n\n## Usage\n```bash\nbkmr --help\n\nA Bookmark Manager and Launcher for the Terminal\n\nUsage: bkmr [OPTIONS] [NAME] [COMMAND]\n\nCommands:\n search Searches Bookmarks\n sem-search Semantic Search with OpenAI\n open Open/launch bookmarks\n add Add a bookmark\n delete Delete bookmarks\n update Update bookmarks\n edit Edit bookmarks\n show Show Bookmarks (list of ids, separated by comma, no blanks)\n surprise Opens n random URLs\n tags Tag for which related tags should be shown. No input: all tags are printed\n create-db Initialize bookmark database\n backfill Backfill embeddings for bookmarks\n load-texts Load texts for semantic similarity search\n help Print this message or the help of the given subcommand(s)\n\nArguments:\n [NAME] Optional name to operate on\n```\n\n<a href=\"https://asciinema.org/a/ULCDIrw4pG9diaVJb17AjIAa7?autoplay=1&speed=2\"><img src=\"https://asciinema.org/a/ULCDIrw4pG9diaVJb17AjIAa7.png\" width=\"836\"/></a>\n\n### Examples\n```bash\n# FTS examples (https://www.sqlite.org/fts5.htm)\nbkmr search '\"https://securit\" *'\nbkmr search 'security NOT keycloak'\n\n# FTS combined with tag filtering\nbkmr search -t tag1,tag2 -n notag1 <searchquery>\n\n# Search by any tag and sort by bookmark age ascending\nbkmr search -T tag1,tag2 -O\n\n# Give me the 10 oldest bookmarks\nbkmr search -O --limit 10\n\n# Adding URI to local files\nbkmr add /home/user/presentation.pptx tag1,tag2 --title 'My super Presentation'\n\n# Adding shell commands as URI\nbkmr add \"shell::vim +/'# SqlAlchemy' sql.md\" shell,sql,doc --title 'sqlalchemy snippets'\n\n# JSON dump of entire database\nbkmr search --json\n\n# Semantic Search based on OpenAI Embeddings\nbkmr --openai sem-search \"python security\" # requires OPENAI_API_KEY\n```\nTags must be separated by comma without blanks.\n\n## Installation\n1. `cargo install bkmr`\n2. initialize the database: `bkmr create-db db_path`\n3. `export \"BKMR_DB_URL=db-path\"`, location of created sqlite database must be known\n4. add URLs\n\nMore configuration options can be found at [documentation page](https://github.com/sysid/bkmr/wiki/configuration).\n\n### Upgrade to 1.x.x\nA database migration will be performed on the first run of the new version.\nThis will add two columns to the bookmarks table for the OpenAI embeddings.\nNo destructive changes are made to the database.\n\n## Semantic Search\n`bkmr` provides now full semantic search of generalized bookmarks using OpenAI's Embeddings. \n\nYou can find more information on the [documentation page](https://github.com/sysid/bkmr/wiki/semantic-search).\n\n## Benchmarking\n- ca. 20x faster than the Python original [twbm](https://github.com/sysid/twbm) after warming up Python.\n```bash\ntime twbm search 'zzz*' --np\n0. zzzeek : Asynchronous Python and Databases [343]\n https://techspot.zzzeek.org/2015/02/15/asynchronous-python-and-databases/\n async, knowhow, py\n\n\nFound: 1\n343\n\nreal 0m0.501s\nuser 0m0.268s\nsys 0m0.070s\n\n\n\ntime bkmr search 'zzz*' --np\n1. zzzeek : Asynchronous Python and Databases [343]\n https://techspot.zzzeek.org/2015/02/15/asynchronous-python-and-databases/\n async knowhow py\n\n\nreal 0m0.027s\nuser 0m0.008s\nsys 0m0.016s\n```\n[sysid blog: bkmr](https://sysid.github.io/bkmr/)\n\n\n<!-- Badges -->\n[pypi-image]: https://img.shields.io/pypi/v/bkmr?color=blue\n[pypi-url]: https://pypi.org/project/bkmr/\n[build-image]: https://github.com/sysid/bkmr/actions/workflows/build.yml/badge.svg\n[build-url]: https://github.com/sysid/bkmr/actions/workflows/build.yml\n[coverage-image]: https://codecov.io/gh/sysid/bkmr/branch/main/graph/badge.svg\n[coverage-url]: https://codecov.io/gh/sysid/bkmr\n[quality-image]: https://api.codeclimate.com/v1/badges/3130fa0ba3b7993fbf0a/maintainability\n[quality-url]: https://codeclimate.com/github/nalgeon/podsearch-py\n\n",
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