---
paths:
  - backend/matrix_intake/**
---

# Matrix intake bot

Read this before editing `backend/matrix_intake/`. The bot turns a typed message in a
dedicated Matrix room into a proposed fundraising-grid add/edit, gated on **in-thread human
approval** before any write. Phase status: **M1 + M2 deployed & live** (text intake + approval + write; bot on the Spark,
CRM endpoints on the box at **v0.1.0:86**; live-smoked 2026-06-17). **M3 (business-card photo)
deferred** — Spark Control has no vision model yet.

**Post-deploy UX pass — DEPLOYED & LIVE 2026-06-17:** fuzzy investor matching (server-side,
**v0.1.0:86**, installed to the box — `candidates` endpoint verified live) + in-thread
disambiguation and conversational natural-language edits (bot-side, pulled + restarted on the
Spark). See *Fuzzy matching* below. Tests green (27/27 backend + the offline bot suite); the
**Matrix live-smoke** of the disambiguation grammar and the Qwen `revise` leg is still pending.

## What it is (and isn't)

- A **separate process**, not part of the CRM. Its only third-party dep, `matrix-nio`, lives
  in `backend/matrix_intake/requirements.txt` and **must never** be added to the stdlib CRM
  (`backend/server.py`). Runs on the Spark (placement per `standards/guides/placement.md`).
- It **drafts; a human approves.** Nothing is written autonomously — every CRM write follows a
  `yes` reply in the proposal thread. This is exempt from "agents draft, humans send" the same
  way the digest is: it's internal data entry to our own CRM, not outward LP contact.
- It is **not** a parallel write path. It reuses the CRM's own canonical endpoint
  `POST /api/fundraising/log-communication` (create-if-missing + contact upsert + note +
  relational sync + audit) for both new-investor and existing-note cases. Don't reimplement
  grid mutation in the bot.

## Flow

1. Top-level message in the intake room → `parse.parse_message` → local **Qwen via Spark
   Control** (`spark.py` reuses `backend/ingest/llm.py`; temp 0, JSON only) extracts
   `{intent, investor_name, contact_name, contact_email, contact_title, note}`. The original
   message text is stashed on the proposal as `_source_text` (needed later for `revise`'s
   email-integrity check).
2. `crm_client.match` (`GET /api/intake/match`) resolves new-vs-existing. It returns **both** an
   exact `match` (returns the **grid row id** so an approved note lands on exactly that investor,
   no duplicate) **and**, when there's no exact match, a ranked list of fuzzy `candidates` (see
   *Fuzzy matching* below).
3. Three outcomes drive what gets posted, all **in a thread** rooted at the user's message, plus a
   brief **main-timeline nudge** (a plain reply — `matrix_io.make_reply`) so it isn't missed:
   - **Exact match** → auto-attach: proposal flips to `meeting_note` with `_match_id` set, rendered
     as the normal approval card.
   - **Fuzzy candidates, no exact** → a **disambiguation** card (`proposals.render_disambiguation`):
     the proposal is held at `_stage="disambiguate"` with `_candidates`, and the human must pick a
     **number** / `new` / `no` before it becomes an approval-stage proposal.
   - **Neither** → the new-investor approval card.
   The nudge is a **pointer only, not a reply target** — you need the thread to act. The pending
   proposal is held in memory keyed by the thread root (`proposals.ProposalStore`).
4. User replies **in the thread**. `handle_reply` branches on `_stage`:
   - **disambiguate** (`handle_disambiguation`): a number attaches to that candidate (→ `meeting_note`
     + `_match_id`, re-rendered for approval); `new` proceeds as a new investor; `no` discards.
   - **approval**: `yes` commits; `no` discards; `edit field=value` is the deterministic fast-path
     edit; **anything else is treated as a natural-language revision** — `parse.revise` sends
     `{current proposal + instruction}` back through local Qwen and re-renders the revised card (a
     no-op revision is detected via `proposals.same_fields` and re-prompts instead of saying
     "Updated"). On `yes`, `crm_client.commit` POSTs to `log-communication` tagged
     `source="matrix_intake"` (provenance in the audit log).
   A bare `yes`/`no` typed **top-level** (not in the thread) while a proposal is pending gets a
   "reply in the thread" redirect (`store.any_pending()` guard in `handle_intake`), not a
   misparsed new intake.

## Fuzzy matching (server-side, ships in the s9pk)

`GET /api/intake/match` returns `{match, candidates}`. `find_intake_match` is unchanged —
**exact-after-normalization**, and an exact match still auto-attaches without disambiguation.
`find_intake_candidates` (new) is the fuzzy layer, **deterministic, no LLM**: it scans the same
canonical grid blob and scores each row by `max(`name similarity`, `email near-match`)`, keeping
rows ≥ `min_score` (0.62), ranked, capped at 5:
- **Name** (`_name_similarity`): max of stdlib `difflib` sequence ratio (near-spellings —
  "Charlie"/"Charles") and token-set Jaccard (word-order). **Legal-entity suffixes**
  (LLC/LP/Inc/… via `_strip_legal_suffix`) are stripped first, so "Acme Capital" ~ "Acme Capital
  LLC" scores 1.0 (a near-certain duplicate `find_intake_match` misses because it compares the
  full string) — and is surfaced as a candidate, **never auto-attached** (the human still confirms).
- **Email** (`_email_edit_distance`): Levenshtein ≤ 2 against each contact email (dist 1→0.9,
  2→0.8). Distance 0 is an exact email — that's `find_intake_match`'s job, skipped here.
- **Recall-favoring by design:** a shared common name-word ("… Capital") can lift an unrelated firm
  into the 0.6–0.8 band. Acceptable — it's a *ranked, human-confirmed* shortlist, and the cost of an
  occasional stray suggestion is far lower than missing a real near-duplicate. **Semantic pruning of
  the shortlist (the "Charlie really is Charles" judgment) is a deferred LLM-judge re-rank** — fed
  only the shortlist, never the whole LP list — intentionally NOT built in this pass, because the
  deterministic filter already surfaces every duplicate the human then resolves.

## Rules / gotchas

- **Module-name collision:** the intake config module is `settings.py`, **not** `config.py`,
  because `backend/ingest/config.py` is imported (as bare `config`) through `spark → llm`. A
  second `config` module would shadow it in `sys.modules` and break `llm` (`CHAT_MODEL`).
  Keep intake module names from colliding with ingest's (`config`, `http_util`, `llm`).
- **Email integrity:** `parse.normalize` only keeps an address that literally appears in the
  source message — the model must never mint one (a wrong email is worse than none). It takes
  the **first** address in the text, so a two-person message ("Alice a@x.com and Bob b@y.com")
  could attach the wrong one; the human sees it in the proposal and can `edit email=…` before
  approving. Cross-referencing multiple addresses to the named contact is a deliberate non-goal
  for v1.
- **Conversational revise keeps the email rule:** `parse.revise` re-runs a free-form correction
  through Qwen but **never trusts the model's email field**. A changed address is accepted only if
  it literally appears in the *instruction text* (searched first), else the existing
  integrity-checked address is kept (`_apply_revision`). The model can edit name/contact/title/note
  freely but cannot mint an email. A revision that nulls both investor and contact is rejected (the
  proposal can't be emptied to something unactionable). Revise edits fields on the current proposal;
  it does **not** re-run the matcher if you rename the firm mid-thread (a known v1 limit — the human
  still approves).
- **Deploy is split across two surfaces** (mind which one carries a change): the fuzzy
  **`candidates`** come from `server.py` → ship in the **s9pk** (build + install, version-bumped).
  The bot's **disambiguation flow + `revise`** live in `backend/matrix_intake/` → ship on the
  **Spark** via `git pull` + restart. A bot restart alone won't deliver `candidates` (the box would
  return an empty list and the bot just proposes new — safe, but no fuzzy surfacing until the s9pk
  is installed). Same lesson as the v83→v84 `/api/intake/match` 404.
- **Double-approve guard:** `handle_reply` pops the pending proposal from the store *before*
  awaiting the commit, so a second `yes` arriving mid-write is a no-op (asyncio is cooperative;
  the pop is atomic w.r.t. other events). On commit failure the proposal is restored for retry.
  *Known minor:* in the **disambiguate** stage the pick re-stores an approval-stage proposal
  before its `await say`, so a rapidly-repeated `1` can have the second one fall through to the
  NL-revise path (a wasted Spark round-trip that re-prompts) — harmless, nothing commits, not
  guarded (low likelihood on a ~5-person team).
- **Local-only parse:** intake text is real LP substance but goes ONLY to local Qwen via Spark
  Control, never Claude — so no scrub boundary applies (same basis as the digest). Never call a
  Spark directly; always go through `SPARK_CONTROL_URL`.
- **Auth:** the CRM has no service-key path; the bot logs in as a dedicated CRM user
  (`CRM_BOT_USERNAME`/`CRM_BOT_PASSWORD`) → Bearer JWT, re-login once on 401.
- **Tests** are offline: `test_parse.py` / `test_proposals.py` / `test_crm_client.py` stub the
  network; `backend/test_intake_endpoints.py` boots the real server against a temp DB and
  covers `/api/intake/match` + the create→match (no-duplicate) contract + provenance. A **live
  Matrix smoke** needs creds + `matrix-nio` installed on the Spark — it can't run in CI.
- **Grid note line:** the bot sends a **blank `subject`** when there's a note so the CRM's
  one-line note summary shows the note text (the CRM renders subject-or-body); a provenance
  label is sent only when there's no note. v0.1.0:85 also dropped the redundant `[note]` type
  tag from that server-side line (informative types like `[call]` keep theirs).

## Deployment & ops

- **Runs on the Spark** (SSH alias `modelo32`, host `spark-32d0`): repo at
  `/home/modelo/ten31-database`, deps in a venv (`.venv`; only `matrix-nio`). Launched detached:
  `nohup ./.venv/bin/python backend/matrix_intake/bot.py >/tmp/intake-bot.log 2>&1 &`, pid in
  `/tmp/intake-bot.pid`; startup logs `listening as … in room …`.
- **Restart after a `git pull` of bot code:** `kill $(cat /tmp/intake-bot.pid)`, relaunch as
  above, re-write the pid. A restart **drops in-memory pending proposals** (re-send to recover).
- **NOT a managed service yet** — won't survive a Spark reboot; restart-on-boot (systemd) is an
  open TODO.
- **Server-side endpoints ship in the s9pk, not the bot.** `GET /api/intake/match` and the
  `source` provenance on `log-communication` live in `backend/server.py`, so they reach the box
  only via an **s9pk build + install** — a bot restart won't deliver them. (Missed in v83: the
  box 404'd `/api/intake/match` until **v0.1.0:84**.)
- **`CRM_API_BASE` is the box over the LAN, not localhost** (bot on the Spark, CRM on the box).
  `https://immense-voyage.local` (443) is the **StartOS dashboard**, not the CRM — the CRM has
  its own interface address (the URL you open in a browser); container port 8080 isn't
  LAN-reachable.

## Config

All in `.env` (names in `.env.example`): `MATRIX_HOMESERVER`, `MATRIX_USER`,
`MATRIX_ACCESS_TOKEN`, `MATRIX_DEVICE_ID`, `MATRIX_INTAKE_ROOM`; `CRM_API_BASE`,
`CRM_BOT_USERNAME`, `CRM_BOT_PASSWORD`, `CRM_API_VERIFY_TLS`. Spark settings are inherited from
the ingest client (`SPARK_CONTROL_URL`, `CRM_CHAT_MODEL`).
