meeting-lab/PROJECT_KNOWLEDGE.md

Project Knowledge

This is a compact operational summary of the current Meeting Lab state.

Objective

Meeting Lab develops and evaluates local methods for extracting structured organizational knowledge from real meeting recordings and transcripts. The project is a research and validation environment for a future Meeting Assistant, not a finished product.

Implemented Pipeline Stages

Implemented:

• Whisper JSON cleanup via scripts/clean_whisper_json.py.
• Transcript normalization in src/meeting_lab/normalization/.
• Technical chunking in src/meeting_lab/chunking/.
• Local per-chunk extraction in src/meeting_lab/extraction/extract_chunks.py.
• Prompt loading from src/meeting_lab/llm/prompts.py.
• Interim Markdown protocol generation in src/meeting_lab/protocol/.
• Non-LLM unit tests for chunking, extraction helpers, protocol rendering and gold-test runner validation.

Experimental/prototype:

• Topic segmentation in src/meeting_lab/segmentation/.
• Windowed segmentation and review output in samples/chunks/.
• Gold Standard extraction corpus under tests/gold/.

Planned:

• Consolidation of extraction results.
• Canonical Meeting Knowledge implementation as the semantic source of truth.
• Final Working Protocol / Arbeitsprotokoll, Distribution Protocol / Verteilerprotokoll and Knowledge Objects / Wissensdatenbankeintrag renderers.

Source Tree

src/meeting_lab/
  chunking/        technical transcript chunking
  consolidation/   planned merge/consolidation area
  extraction/      current local LLM extraction flow
  io/              lightweight file and JSON helpers
  llm/             Ollama and prompt support
  models/          current lightweight model definitions
  normalization/   deterministic transcript cleanup
  protocol/        interim Markdown protocol builder
  segmentation/    experimental topic segmentation tooling

Supporting areas:

• docs/: architecture, pipeline, data models and output-view concepts.
• prompts/: active prompt files. Only common.md and decisions.md contain substantive extraction prompt text in the current tree.
• tests/gold/: semantic gold tests and prompt-engineering methodology.
• samples/: sample inputs and generated or experimental artifacts.
• scripts/: operational scripts for cleanup and gold-test execution.

Current Model Strategy

The current extraction strategy is one normalized chunk per LLM call. This is preferred over expanding context windows or asking one model call to analyze a full meeting.

Known working models from current project notes and experiment practice:

• qwen3:1.7b: useful for smoke tests.
• qwen3.5:9b: useful for meaningful extraction and segmentation work.

LLM calls use Ollama locally. The current extractor defaults to qwen3:8b, but validated work may specify another model explicitly.

Important Findings

• Whisper JSON chunking must use segments[*].text, not only the top-level text field.
• Independent chunk extraction is currently preferred.
• Larger context windows can change classification behavior and increase instability.
• Extraction and consolidation are separate problems.
• Generation limits can truncate JSON.
• Qwen thinking may be returned separately by the Ollama API.
• Gold Standard tests are also a formal specification of meeting semantics.
• Raw model responses should be preserved when diagnosing parser or truncation failures.

Decision Taxonomy

Accepted decision semantics:

• A decision is an explicit agreement that creates a binding change in action, process, responsibility, approval status, timing or next step.
• Included: substantive decisions, organizational decisions, process decisions, approvals, rejections, deferrals, explicit agreement not to decide yet, and explicit agreement to gather more information before deciding.
• Excluded: opinions, preferences, proposals without agreement, open questions, current-state descriptions and explanations without commitment.
• A process decision to defer a substantive decision is still a decision.
• "No decision was reached" is different from "the group decided to defer the decision."

Current Prompt Version 2 decision baseline:

• decision_simple: passing.
• decision_deferred: passing.
• decision_none: passing.
• Prompt Version 2 explicitly supports process decisions where the group agrees to defer a substantive decision until more information is available.

Canonical Knowledge Architecture

Canonical Meeting Knowledge is the planned semantic intermediate model and future single source of truth. It should preserve topics, facts, decisions, action items, open questions, positions, technical details, rationale, uncertainty, contradictions and source evidence.

Output views are planned as independent renderings from that canonical model:

• Working Protocol / Arbeitsprotokoll: relatively complete, optimized for recall and traceability.
• Distribution Protocol / Verteilerprotokoll: concise and outcome-oriented, optimized for circulation.
• Knowledge Objects / Wissensdatenbankeintrag: durable organizational knowledge optimized for reuse.

The current meeting_protocol.md builder is an interim technical validation tool, not the final output-view architecture.

Current Limitations

• Discussion Blocks are documented as a stable semantic unit but are not yet a separate implemented pipeline artifact.
• Topic segmentation exists as prototype tooling, not a stable pipeline stage.
• Extraction is still a combined current flow, even though separate extractors are the intended architecture.
• Consolidation is not implemented.
• Canonical Meeting Knowledge is documented but not implemented.
• Final output views are documented but not implemented.
• Most prompt files are placeholders except the common and decision prompts.
• Gold tests currently emphasize extraction semantics, especially decisions.

Next Recommended Engineering Step

Stabilize repeatable local extraction evaluation before broadening the pipeline: expand Gold Standard coverage by category, keep one-chunk extraction as the baseline, and use small prompt experiments with immediate non-regression checks. After extraction behavior is stable enough, implement consolidation with evidence retention as the next major pipeline stage.