Lumotia/docs/architecture-map/04-llm-formatting-mcp/llm-extract-tasks.md

name, type, slice, last_verified

name	type	slice	last_verified
LLM extract_tasks surface	architecture-map-page	04-llm-formatting-mcp	2026/05/09

LLM extract_tasks surface

Where you are: Architecture map → LLM, Formatting, MCP → extract_tasks

Plain English summary. extract_tasks reads a transcript and pulls out the action items the speaker actually committed to. Output is a JSON array of imperative sentences, possibly empty. The GBNF accepts an empty array, the prompt asks the model to return one when nothing was committed. A feedback-conditioned variant supports HITL few-shot examples.

At a glance

• Crate: lumotia-llm
• Path: crates/llm/src/lib.rs:294 (extract_tasks) and :358 (extract_tasks_with_feedback)
• LOC: ~50 across both methods
• Public surface:
  • pub fn extract_tasks(&self, transcript: &str) -> Result<Vec<String>, EngineError>
  • pub fn extract_tasks_with_feedback(&self, transcript: &str, examples: &[prompts::FeedbackExample]) -> Result<Vec<String>, EngineError>
• External deps that matter: GBNF sampler from llama-cpp-2; serde_json for the array parse.
• Tauri command that calls this (slice 2, best guess): commands::tasks::extract_tasks_from_transcript_cmd (src-tauri/src/commands/tasks.rs:346), wired via src-tauri/src/lib.rs:366. The actual call is at tasks.rs:364 — tokio::task::spawn_blocking(move || engine.extract_tasks_with_feedback(&transcript, &examples)).

What's in here

extract_tasks (crates/llm/src/lib.rs:294)

Convenience wrapper that calls extract_tasks_with_feedback(transcript, &[]).

extract_tasks_with_feedback (crates/llm/src/lib.rs:358)

Steps:

1. If transcript.trim().is_empty(), return Ok(Vec::new()) immediately. No model touch — distinct from cleanup_text which returns an empty string. The empty-vec convention matches what callers expect from "the speaker said nothing actionable".
2. Borrow the loaded model via loaded_model_arc(). EngineError::NotLoaded if no model is loaded.
3. Build the system prompt: prompts::build_conditioned_system_prompt(prompts::EXTRACT_TASKS_SYSTEM, examples). Same conditioning logic as decompose_task_with_feedback.
4. Render a chat prompt with two messages — ("system", system) and ("user", &format!("Transcript:\n{transcript}")).
5. Call generate with:
   • max_tokens: 768
   • temperature: 0.0
   • stop_sequences: ["<|im_end|>", "<|im_end_of_text|>"]
   • grammar: Some(grammars::OPTIONAL_TASK_ARRAY_GRAMMAR.to_string())
6. Parse the raw output via parse_string_array.

The empty-array case is the difference from decompose_task: OPTIONAL_TASK_ARRAY_GRAMMAR permits [] as a valid root expansion; TASK_ARRAY_GRAMMAR does not.

Data flow

(transcript: &str, examples: &[FeedbackExample])
  → empty-transcript short-circuit (returns Vec::new())
  → loaded_model_arc()
  → build_conditioned_system_prompt(EXTRACT_TASKS_SYSTEM, examples)
  → render_chat_prompt([(system, system), (user, "Transcript:\n{transcript}")])
  → generate(prompt, GenerationConfig {
       max_tokens: 768, temp: 0.0,
       stops: [<|im_end|>, <|im_end_of_text|>],
       grammar: Some(OPTIONAL_TASK_ARRAY_GRAMMAR),
     })
  → parse_string_array(raw)
  → Vec<String>

Prompts and grammars

• System prompt: prompts::EXTRACT_TASKS_SYSTEM at crates/llm/src/prompts.rs:40. Crucial line: "Output an empty array if there are no action items."
• GBNF: grammars::OPTIONAL_TASK_ARRAY_GRAMMAR at crates/llm/src/grammars.rs:30. Two root alternatives: "[" ws "]" for the empty case, or "[" ws string tail ws "]" with a recursive tail for one-or-more strings.

See llm-prompts-and-grammars.md for full text.

Watch-outs

• Empty array is a valid, expected output. UI and downstream code must treat Ok(Vec::new()) as the "no tasks" success path, not as an error. extract_corrections-style learning loops should skip empty results entirely.
• max_tokens: 768 is larger than decompose_task's 512. A long meeting transcript can produce many actions; the looser cap reflects that. It does not bound the count of items, only the total tokens of the JSON literal.
• The system prompt is the only thing telling the model "explicit commitments only". A model that ignores instruction would happily list observations and wishes; the GBNF is silent on content. If the cleanup_text-style "translator not editor" framing is ever copied here, that is a deliberate prompt-engineering decision, not a refactor.
• Feedback conditioning weight. Recent-first ordering in examples is preserved; the prompt builder appends them as a - Input: ... \n Good output: ... bullet list. Long lists dilute the weighting; the Tauri command at src-tauri/src/commands/tasks.rs decides how many examples to pass.
• Same GBNF parse-vs-serde concerns as decompose_task. The grammar guarantees a valid JSON array shape; serde_json::from_str can still fail on an unfortunate truncation when output approaches max_tokens.

See also

• LLM engine
• LLM decompose_task (sibling array surface, stricter GBNF)
• Prompts and grammars catalogue
• Slice README