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title Office-assistant MVP (meeting notes + Q&A)

A minimal "office assistant" built from two manually-run workflows and a Postgres knowledge base:

  • office-assistant-ingest-meeting — paste a meeting transcript; an LLM summarizes it and extracts action items, the notes are stored in the KB, a Jira issue is opened for the action items, and the summary is posted to Teams.
  • office-assistant-ask — ask a question; the KB is full-text searched for the most relevant notes and an LLM synthesizes a grounded, cited answer.

Cross-run state lives in Postgres, so ingesting builds up a corpus that asking reads back. Both workflows are in packages/site/src/content/examples/.

Prerequisites

Create these credentials (mantle secrets create) and the KB schema:

Credential name Type Used for
openai openai ai/completion (summarize + answer)
kb-db postgres The knowledge-base database
jira basic jira/create_issue (email + API token)
teams generic teams/send_message (incoming-webhook URL)

Using Claude via Bedrock instead of OpenAI: in both ai/completion steps, add provider: bedrock and a region: to params, point credential: at an aws credential, and set model: to a Bedrock model ID. provider defaults to openai, so changing only credential/model still sends an OpenAI-format request.

Apply the KB schema (pure Postgres full-text search, no extensions) from office-assistant-kb-schema.sql:

psql "$KB_DATABASE_URL" -f packages/site/src/content/examples/office-assistant-kb-schema.sql

Then apply both workflows:

mantle apply office-assistant-ingest-meeting.yaml
mantle apply office-assistant-ask.yaml

Ingesting a meeting

Put the transcript in a values file (a YAML block scalar handles long, multi-line text cleanly — there is no input size cap on manual runs):

# meeting.values.yaml
inputs:
  title: "Q3 strategy sync"
  meeting_date: "2026-07-01"
  attendees: "Michael, CTO, Team A lead"
  transcript: |
    <paste the full transcript here — as many lines as you like>
mantle run office-assistant-ingest-meeting --values meeting.values.yaml

The run summarizes the transcript, stores the note, opens a Jira action-item issue (skipped if the LLM found none), and posts the summary to Teams.

Asking a question

mantle run office-assistant-ask \
  --input question="Who else is working with client C?" \
  --output json

The search step ranks matching notes with websearch_to_tsquery, and the answer step's output.text is the grounded answer (the CLI prints step outputs with --output json or -v). To send the answer somewhere instead of reading it from the CLI, add a final teams/send_message step.

What you own, and the caveats

This is a deliberately small v0 that fits what the engine does today. Known trade-offs:

  • Retrieval is full-text search, not semantic. The KB uses a Postgres tsvector; there are no embeddings. It's the simplest thing that works end-to-end with stock connectors. A native embeddings + vector-store retrieval layer is tracked by #153; the schema comment shows the upgrade path.
  • One Jira issue per meeting, not one per action item. The engine has no loop / for_each construct, so the workflow opens a single checklist issue rather than fanning out.
  • Manual transcription. You paste a transcript; the bot does not join meetings or transcribe audio (tracked by #154).
  • Request/response, not a live Teams chat. You ask via the CLI (or API) and optionally post the answer out; there is no conversational in-Teams bot (tracked by #155).
  • Confluence / GitHub actions aren't included here but drop in as extra http/request steps (or the native jira/linear connectors already used).

See #161 for the full MVP write-up and how these pieces map to the larger office-assistant vision.