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توضیحات

Tab-completion & data search server - total recall

ویژگی	مقدار
سیستم عامل	-
نام فایل	argrelay-0.0.0.dev42
نام	argrelay
نسخه کتابخانه	0.0.0.dev42
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	uvsmtid
ایمیل نویسنده	uvsmtid@gmail.com
آدرس صفحه اصلی	https://github.com/uvsmtid/argrelay
آدرس اینترنتی	https://pypi.org/project/argrelay/
مجوز	-

Project status: working prototype <a name="argrelay-secreencast"></a> [![asciicast](https://asciinema.org/a/LTHj0DHN2kfXJCHCGuJugNG4P.svg)](https://asciinema.org/a/LTHj0DHN2kfXJCHCGuJugNG4P)  <a name="argrelay-about"></a> # What's this? An integration framework to provide contextual Tab-auto-completion and search for command line interfaces (CLI) in Bash shell. <a name="argrelay-original-use-case"></a> ### Original use case Auto-complete based on arbitrary structured data sets (e.g. config or ref data) **directly from standard shell**.[^1] This requires data indexing for [responsive lookup][completion_perf_notes.md] (the client has to start and find relevant data on each Tab-request). The straightforward approach to meet performance requirements taken by `argrelay` is to run a standby data server. > For example, with several thousands of service instances, > even if someone manages to generate Bash completion config, > it takes considerable time to load it for every shell instance. Unlike static|generated|offline index, standby server also naturally supports dynamic data updates.  <a name="argrelay-name"></a> # What's in a name? Eventually, `argrelay` will "relay" command line arguments (hence, the name) with associated data to user domain-specific program. To clarify, let's compare side-by-side (independent) `argparse` _library_ and `argrelay` _framework_: ```mermaid graph RL; %% user --> library %% user --> framework subgraph `argparse` library direction LR some.py <--> argparse; end argrelay_client -. delegates = relays .-> some.py; subgraph `argrelay` framework direction TB subgraph client direction LR relay2some --> argrelay_client[argrelay client]; end subgraph server direction TB argrelay_server[argrelay server] <--> data[(data)]; end end ``` | Category | `argparse` is a library | `argrelay` is a framework | |:---------------|:-----------------------------------------------------------|:-------------------------------------------------------------------------------| | Given: | `some.py` is some script | `relay2some` is a "wrapper" command configured in Bash to call `argrelay` | | In Bash: | type `some.py` to execute it | type `relay2some` to let `argrelay` decide whether to execute `some.py` | | Execution: | `some.py` calls `argparse` library | `some.py` is called by the framework when `relay2some` is invoked | | Function: | `some.py` directly does domain-specific task | `relay2some` directly only "relays" the command line to `argrelay` | | CLI source: | `some.py` defines its CLI itself via `argparse` | CLI for `relay2some` is defined by the framework via configs/plugins/data | | CLI is: | mostly code-driven | mostly data-driven | | Modify CLI: | modify `some.py` | keep `some.py` intact, re-configure `argrelay` instead | | Prog lang: | `some.py` has to be a Python script to use `argparse` | `some.py` can be anything somehow executable by `argrelay` | | **Important:** | `some.py`/`argparse` have no domain data to query | `relay2some` may access any domain data from `argrelay` server | <a name="argrelay-includes"></a> # What's missing? `argrelay` excludes: * Any (real) domain-specific data * Any (useful) domain-specific plugins <a name="argrelay-excludes"></a> # What's in the package? `argrelay` includes: * **Client** to be invoked by Bash hook on every Tab to send command line arguments to the server. * **Server** to parse command line and propose values from pre-loaded data for the argument under the cursor. * **Plugins** to customize: * actions the client can run * objects the server can search * grammar the command line can have * **Interfaces** to bind these all together. * **Demo** example to start from. * **Testing** support and coverage. <a name="argrelay-focus"></a> # CLI-friendly completion: primary focus GUI-s are secondary for `argrelay`'s niche because GUI-s do not have the restrictions CLI-s have: * Technically, the server can handle requests from anywhere (GUI). * But primary API-s are feature-tailored to support CLI (because everyone does GUI). <details> <summary>show example</summary> For example, in GUI-s, typing a query into a search bar may easily be accompanied by (1) a separate (from the search bar) window area (2) with individually selectable (3) full-text-search results (4) populated **async-ly** with typing. In CLI-s, `grep` does (3) full-text-search, but what about the rest (1), (2), (4)? To facilitate selection of results, catalogue-like navigation with auto-completion (rather than full-text-search) seems the answer. </details> Nevertheless, GUI can also benefit from minimalist single line structured search queries. <a name="argrelay-story"></a> # Syntax: origin story When an interface is limited... You probably heard about research where apes were taught to communicate with humans in sign language (their vocal apparatus cannot reproduce speech effectively). Naturally, with limited vocabulary, they combined known words to describe unnamed things. For example, to ask for a watermelon (without knowing the exact sign), they used combination of known "drink" + "sweet". The default `argrelay` CLI-interpretation plugin (see `FuncArgsInterp`) prompts for object properties to disambiguate search results until single one is found. <details> <summary>continue story</summary> ### Narrow down options Without any context, just two words "drink" + "sweet" leave a lot of ambiguity to guess a watermelon (many drinks are sweet). A more clarified "sentence" could be: > drink striped red sweet fruit Each word narrows down matching object set to more specific candidates (including watermelon). ### Avoid strict order Notice that the word order is not important - this line provides (almost) equivalent hints for guessing: > striped sweet fruit red drink It is not valid English grammar, but it somewhat works. ### Use "enum language" Think of speaking "enum language": * Each word is an enum value of some enum type: * Color: red, green, ... * Taste: sweet, salty, ... * Temperature: hot, cold, ... * Action: drink, play, ... * Word order is irrelevant because _enum value spaces do not overlap_ (almost). * To "say" something, one keeps clarifying meaning by more enum values. Now, imagine the enum types and values are not supposed to be memorized, they are proposed to select from (based on the current context). ### Address any object Suppose enums are binary = having only two values (cardinality = 2: black/white, hot/cold, true/false, ...). For example, 5 words could slice the object space to single out (identify exactly) up to 2^5 = 32 objects. To "address" larger object spaces, larger enum cardinalities or more word places are required. * Each enum type ~ a dimension. * Each specific enum value ~ a coordinate. * Each object fills a slot in such multi-dimensional discrete space. ### Apply to CLI CLI-s are used to write commands - imperative sentences: specific actions on specific objects. The "enum language" above covers searching both an action and any object it requires. ### Suggest contextually Not every combination of enum values may point to an existing object. For data with sparse object spaces, the CLI-suggestion can be shaped by coordinates applicable to remaining (narrowed down) object sets. ### Differentiate on purpose All above may be an obvious approach to come up with, but it is not ordinary for CLI-s of most common commands (due to lack of data): | Common commands (think `ls`, `git`, `ssh`, ...): | `argrelay`-wrapped actions: | |:----------------------------------------------------------------------------|:------------------------------------------------------| | have succinct syntax and prefer single-char switches (defined by code) | prefer explicit "enum language" defined by data | | rely on humans to memorize syntax (options, ordering, etc.) | assume humans have a loose idea about the syntax | | auto-complete only for objects known to the OS (hosts, files, etc.) | auto-complete from a domain-specific data | </details> Learn more about [how search works][how_search_works.md]. <a name="argrelay-demo"></a> # Quick demo This is a non-intrusive demo (without permanent changes to user env, e.g. no `~/.bashrc` changes). Clone this repo somewhere. If `dev_shell.bash` is run for the first time, it will ask to provide `python_conf.bash` file - follow instruction on error. To start both the server and the client, two terminal windows are required. * Server: Start the first sub-shell: ```sh ./dev_shell.bash ``` In this sub-shell, start the server: ```sh # in server `dev_shell.bash`: run_argrelay_server ``` * Client: Start the second sub-shell: ```sh ./dev_shell.bash ``` While it is running (temporarily), this sub-shell is configured for Bash Tab-completion for `relay_demo` command. * Try to `Tab`-complete command `relay_demo` using [demo test data][TD_63_37_05_36.demo_services_data.md]: ```sh # in client `dev_shell.bash`: relay_demo goto host # press Tab one or multiple times ``` ```sh # in client `dev_shell.bash`: relay_demo goto host dev # press Alt+Shift+Q shortcut to describe command line args ``` * Inspect how auto-completion binds to `relay_demo` command: ```sh # in client `dev_shell.bash`: complete -p relay_demo ``` * Inspect client and server config: * server config: `~/.argrelay.conf.d/argrelay.server.yaml` * client config: `~/.argrelay.conf.d/argrelay.client.json` * To clean up, exit the sub-shells: ```sh # in client or server `dev_shell.bash`: exit ``` <a name="argrelay-backend"></a> # Data backend There are two options at the moment - both using [MongoDB][MongoDB] API: | Category | `mongomock` (default) | `PyMongo` | |:---------------|:----------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | Data set size: | practical limit ~ 10K | tested at 1M | | Pro: | nothing else to install | no practical data set size limit found (yet) for `argrelay` intended use cases | | Con: | understandably, does not meet non-functional requirements for large data sets | require some knowledge of MongoDB, additional setup, additional running processes | `PyMongo` connects to running MongoDB instance which has to be configured in `mongo_config` and `mongomock` should be disabled in `argrelay.server.yaml`: ```diff - use_mongomock_only: True + use_mongomock_only: False ``` <a name="argrelay-next-steps"></a> # What's next? * After trying non-intrusive demo, try [intrusive one][dev_env_and_target_env_diff.md] for permanent setup. * Modify [`ServiceLoader.py` plugin][link_to_load_data_envelopes] to provide data beyond [demo data set][TD_63_37_05_36.demo_services_data.md]. The data can be simply hard-coded with different `test_data` tag (other than `TD_63_37_05_36` demo) and selected in `argrelay.server.yaml`: ```diff ServiceLoader: plugin_module_name: argrelay.custom_integ.ServiceLoader plugin_class_name: ServiceLoader plugin_type: LoaderPlugin plugin_config: test_data_ids_to_load: #- TD_70_69_38_46 # no data - - TD_63_37_05_36 # demo + - TD_NN_NN_NN_NN # custom data #- TD_38_03_48_51 # large generated ``` If hard-coding is boring, soft-code to load it from external data source. * Replace [redirect to `ErrorDelegator.py` plugin][link_to_redirect_to_error] to execute something useful instead when use hits `Enter`. * ... * Many features and docs are actively taking their shape - any (minimal, unfiltered, first-thought) feedback is welcome. [Raise questions or suggestions as issues][repo_issues] to influence the dev direction. ### [footnotes] [^1]: **Brief History** Tab-completion with custom (domain-specific) arg values is constantly on a dev wish list for complex backend. * DEC 2022: Attempts to find an adequate solution for sizeable data yielded no results. * JAN 2023: The [earlier question][earlier_stack_question] received zero activity for a month (with a single silent downvote, auto-deleted by a bot). Request to restore it was 🎵 Shut Down In Flames.  * FEB 2023: The [explanation hangs on the appropriate site][later_stack_question] now - recommendations are still very welcome there. But, with some patience for integration, `argrelay` already became satisfying enough.  [completion_perf_notes.md]: docs/dev_notes/completion_perf_notes.md [MongoDB]: https://www.mongodb.com/ [dev_env_and_target_env_diff.md]: docs/dev_notes/dev_env_and_target_env_diff.md [TD_63_37_05_36.demo_services_data.md]: docs/test_data/TD_63_37_05_36.demo_services_data.md [earlier_stack_question]: https://stackoverflow.com/q/74996560/441652 [later_stack_question]: https://softwarerecs.stackexchange.com/questions/85247/ [how_search_works.md]: docs/dev_notes/how_search_works.md [link_to_redirect_to_error]: https://github.com/uvsmtid/argrelay/blob/v0.0.0.dev27/src/argrelay/custom_integ/ServiceInvocator.py#L148 [link_to_load_data_envelopes]: https://github.com/uvsmtid/argrelay/blob/v0.0.0.dev27/src/argrelay/custom_integ/ServiceLoader.py#L111 [repo_issues]: https://github.com/uvsmtid/argrelay/issues

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl argrelay-0.0.0.dev42:

pip install argrelay-0.0.0.dev42.whl

نصب پکیج tar.gz argrelay-0.0.0.dev42:

pip install argrelay-0.0.0.dev42.tar.gz