Extractors

Extractors

Extractors are the second stage in the SmartMotion pipeline. They receive data yielded from a collector and generate targets — positional objects describing where actions can apply.

Tip

If collectors define where to look, extractors decide what to act on.

---

✅ What an Extractor Does

Extractors operate on each yielded item from the collector (often a line object) and generate one or more SmartMotionTarget entries. These targets include position info, visual text, and metadata that can be used for labeling, filtering, and execution.

Extractors are also written as coroutines, allowing for:

• Early-exit target detection (e.g. flow state or quick jump)
• Scalable, memory-efficient target generation

Important

Extractors should not apply logic for direction, cursor filtering, or context sensitivity. That logic belongs to filters. Extractors should only describe all possible targets.

---

🧠 Coroutine Design

Like collectors, extractors are implemented as coroutines.

• The collector is resumed to yield an item
• The extractor processes the item and can coroutine.yield() one or more targets
• If the pipeline needs only one match, it stops early

This system minimizes overhead and makes SmartMotion fast, even on large files.

Important

One coroutine loop pulls from the collector, and one loop extracts all targets — and that’s it. No extra passes are needed.

---

📦 Built-in Extractors

Name	Description
lines	Extracts whole-line targets
words	Extracts words using regex-based word boundaries
text_search	Extracts fixed-length search targets (1-2 chars)

Note

All built-in extractors are registered using register_many() in the extractors registry. You can override or extend them.

---

🧱 Example Usage

Used inside a pipeline:

pipeline = {
  collector = "lines",
  extractor = "words",
  visualizer = "hint_start",
}

This example extracts visible words from all lines in the buffer.

---

✨ Example: Custom Word Extractor

Here’s a simplified version of a custom words extractor that finds word matches using regex and yields them as targets. This version omits cursor and direction logic entirely — such filtering should be handled in the filters module.

---@type SmartMotionExtractorModuleEntry
local M = {}

function M.run(collector, opts)
  return coroutine.create(function(ctx, cfg, motion_state)
    while true do
      local ok, data_or_error = coroutine.resume(collector, ctx, cfg, motion_state)

      if not ok then
        log.error("Collector Coroutine Error: " .. tostring(data_or_error))
        break
      end

      if data_or_error == nil then
        break
      end

      local row = line_data.line_number
      local line = line_data.text

      for match_text, start_pos in line:gmatch("()%w+") do
        local end_pos = start_pos + #match_text - 1

        coroutine.yield({
          row = row,
          col = start_pos,
          text = match_text,
          start_pos = { row = row, col = start_pos },
          end_pos = { row = row, col = end_pos },
          type = "words",
        })
      end
    end
  end)
end

return M

---

🛠 Registering Your Extractor

local extractors = require("smart-motion.core.registries"):get().extractors
extractors.register("my_extractor", MyModule)

You can also provide metadata and keys to link extractors to motion keys if using infer = true.

---

📚 Advanced Example: Word Extractor

The built-in words extractor:

• Uses vim.fn.matchstrpos to find regex-based words
• Applies cursor logic and direction-specific filtering
• Reorders results for BEFORE_CURSOR

Note

This behavior will be moved into filters in the future to better separate concerns.

---

🔗 Next Steps

Now that you're generating targets, it's time to:

• Visualize them with visualizers.md
• Define what happens when selected using actions.md