Release 2026.09.30.1: backfill embeddings, flatten list and dict text

backfill_embeddings reads stored call errors (including rows from before the
outcome column) and events, rebuilds the records and sends them through the
live sink's extraction, redaction and truncation, so later records dedupe
against the backfilled rows. backfill_feedback does the same for feedback
kept in a host's own tables. A CLI wraps it:
python -m fastmcp_feedback.instrumentation.backfill.

List and tuple attrs are embedded one item per line instead of as a Python
repr; dicts as key: value lines sorted by key, since JSONB drops key order.

.env.example

@@ -0,0 +1,13 @@
+# Copy to .env (make does this for you on first run) and adjust as needed.
+COMPOSE_PROJECT_NAME=fastmcp-feedback-pkg
+
+FFB_PG_IMAGE=pgvector/pgvector:pg16
+FFB_PG_USER=ffb
+FFB_PG_PASSWORD=change-me
+FFB_PG_DB=ffb_test
+# Published on 127.0.0.1 only. 55432 is commonly taken by other local stacks.
+FFB_PG_PORT=55433
+
+# Read by the test suite. Unset, the Postgres test cases skip.
+# Each test gets its own database created on this server and dropped after.
+FFB_TEST_DATABASE_URL=postgresql+asyncpg://ffb:change-me@127.0.0.1:55433/ffb_test

.gitea/workflows/test.yml

@@ -36,11 +36,62 @@ jobs:
           uv pip show fastmcp | grep -i '^version'
 
       - name: Lint
-        run: uv run --no-sync ruff check src/ examples/ tests/_compat.py tests/unit/test_instrumentation.py
+        run: uv run --no-sync ruff check src/ examples/ tests/_compat.py tests/_databases.py tests/unit/test_test_databases.py tests/unit/test_instrumentation.py
 
       - name: Test
         run: uv run --no-sync pytest -q --no-cov
 
+  # The DatabaseSink tests again, on Postgres + pgvector instead of SQLite.
+  # Oldest and newest supported FastMCP; the SQLite legs above cover the rest.
+  # act_runner runs the job in a container, so the service is reached by name.
+  test-postgres:
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    strategy:
+      fail-fast: false
+      matrix:
+        fastmcp: ['==2.12.2', '>=4,<5']
+
+    services:
+      ffb-postgres:
+        image: pgvector/pgvector:pg16
+        env:
+          POSTGRES_USER: ffb
+          POSTGRES_PASSWORD: ffb-ci
+          POSTGRES_DB: ffb_test
+        options: >-
+          --health-cmd "pg_isready -U ffb -d ffb_test"
+          --health-interval 2s
+          --health-timeout 3s
+          --health-retries 30
+
+    env:
+      FFB_TEST_DATABASE_URL: postgresql+asyncpg://ffb:ffb-ci@ffb-postgres:5432/ffb_test
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: astral-sh/setup-uv@v6
+        with:
+          python-version: '3.13'
+
+      - name: Install against fastmcp${{ matrix.fastmcp }}
+        run: |
+          uv venv
+          uv pip install -e ".[dev]" "fastmcp${{ matrix.fastmcp }}"
+          uv pip show fastmcp | grep -i '^version'
+
+      - name: Test with the Postgres cases enabled
+        run: make test-pg
+
+      - name: Postgres cases ran rather than skipped
+        run: |
+          out=$(uv run --no-sync pytest -q --no-cov -m postgres -rs)
+          echo "$out" | tail -3
+          if echo "$out" | grep -q 'SKIPPED'; then
+            echo "Postgres cases skipped; is the service reachable?"; exit 1
+          fi
+
   # Newest FastMCP with no ceiling. Allowed to fail: it exists to say
   # "a new major version is out and here is what breaks" before users do.
   canary:

CHANGELOG.md

@@ -5,6 +5,228 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Calendar Versioning](https://calver.org/) (YYYY.MM.DD).
 
+## [2026.09.30.1] - 2026-09-30
+
+Embed what was stored before embedding was switched on, and embed list and
+dict attrs as text rather than as Python reprs.
+
+### Added
+- `await backfill_embeddings(embedding_sink, *, sources=None, since=None,
+  until=None, limit=None, batch_size=500, dry_run=False)`. Reads the
+  `DatabaseSink`'s tables for calls with outcome `error` or `soft_error`
+  (and, for rows from before the `outcome` column, outcome NULL with an
+  `error_message`), `feedback.submitted` events, `llm.call` events with
+  prompt or completion, and other events with any of the sink's
+  `event_text_keys`. Each record goes through the live sink's extraction,
+  redactor and `max_chars`, so text, `text_hash` and `source_id` match and
+  later records dedupe against backfilled rows. Idempotent: a second run
+  embeds nothing. Keyset-paged by timestamp and id, `batch_size` rows per
+  query; embeds in chunks of the sink's `batch_size`. `limit` embeds at most
+  that many texts, oldest first. Returns counts per source type plus
+  `skipped` and `errors`; embed failures are counted, never raised. Raises
+  only for bad arguments or a missing table. `sources` defaults to the
+  sink's.
+- `await backfill_feedback(embedding_sink, items, *, batch_size=500,
+  dry_run=False)` for feedback in the host's own tables, as
+  `(ref, title, description)` tuples; the same text a `feedback.submitted`
+  event with that ref gives.
+- `python -m fastmcp_feedback.instrumentation.backfill --database-url ...
+  --embed-base-url ... [--model --dim --prefix --sources --since --until
+  --limit --batch-size --max-chars --event-text-keys --create-tables
+  --dry-run]`. The API key comes from `FFB_EMBED_API_KEY` or the variable
+  named by `--api-key-env`. Prints counts as JSON; exits 1 when any text
+  failed, 2 on a setup error.
+
+### Changed
+- **Embedded text of list, tuple and dict values.** Lists and tuples are
+  embedded one cleaned, non-empty item per line instead of as their repr
+  (`"['a', 'b']"`); dicts as `key: value` lines, sorted by key because
+  PostgreSQL JSONB does not keep key order and a backfill must rebuild the
+  same text. Nested values are flattened the same way, to a depth of 32 and
+  about 64k characters before `max_chars` applies. This applies to event
+  text keys, feedback title and description, and LLM prompt and completion.
+  Existing rows embedded from such values keep their old text; a backfill
+  run replaces them, since their text changed.
+
+## [2026.09.30] - 2026-09-30
+
+Embed feedback, tool errors, event text and (opt-in) LLM text, and search
+them by meaning: "has anyone reported this before?"
+
+### Added
+- `EmbeddingSink(embedder, database_sink, *, sources=("feedback",
+  "call_error", "event", "llm"), event_text_keys=("error_excerpt", "error",
+  "message", "reason"), max_chars=4000, max_queue=1000, batch_size=32,
+  timeout=30.0, scan_limit=5000)`. `write` extracts text and puts it on the
+  sink's own bounded queue, returning at once, so a slow embedding backend
+  never reaches the dispatcher's `sink_timeout` or a tool call. A background
+  task embeds in batches and upserts through the `DatabaseSink`'s engine. A
+  full queue drops and counts (`dropped`); embedder and database failures are
+  logged and counted (`errors`), never raised. An unchanged text (same
+  `text_hash`) is not embedded again; a changed one replaces its row. Text is
+  redacted with the middleware's `Redactor` (adopted when the sink is passed
+  to `instrument`) and truncated before it leaves the process. `aclose()`
+  drains for up to `timeout`, then drops and counts the rest.
+- `Embedder` protocol (`model`, `dim`, `async embed(texts)`) and
+  `OpenAIEmbedder(base_url, api_key=None, model="mxbai-embed-large", dim=1024,
+  batch_size=32, timeout=30.0)` for any OpenAI-compatible `/v1/embeddings`
+  endpoint. It uses `httpx`, or `httpx2` under FastMCP 4; no new dependency.
+  A vector of the wrong length raises `ValueError`.
+- `await mw.similar(text, *, source_types=None, k=10, min_score=None)` and
+  `await mw.similar_feedback(feedback_ref, *, k=10, source_types=None,
+  min_score=None)`. Results carry `source_type`, `source_id`, `text`, `score`
+  (1 - cosine distance, clamped to [0, 1]) and `created_at`. PostgreSQL ranks
+  with `embedding <=> :q` on an HNSW index; other databases compare in Python
+  against the newest `scan_limit` rows. `similar_feedback` reuses the stored
+  vector and leaves the item itself out. Neither raises; failures log and
+  return [].
+- **Feedback flows as an event.** With `instrumentation=`, `submit_feedback`
+  records `feedback.submitted` keyed by the feedback id, with `type`, `title`,
+  `description` and `submitter`. Hosts with their own feedback tools record
+  the same event with their own ref (`bug-7Q2X`).
+- `InstrumentationMiddleware(capture_llm_text=False, max_llm_text_chars=None)`
+  and `record_llm_call(..., prompt=None, completion=None)`. Prompt and
+  completion are stored (redacted, cut to `8 * max_error_chars`, 4000 by
+  default) only when `capture_llm_text=True`.
+- `embeddings` extra: `pgvector>=0.5.0`. Postgres hosts also need `asyncpg`.
+- `build_metadata(prefix, metadata=None, embedding_dim=1024)` and
+  `DatabaseSink(embedding_dim=1024)`. `embedding_dim=None` in `build_metadata`
+  leaves the embeddings table out.
+
+### Changed
+- **New table `{prefix}embeddings`** (`id`, `source_type`, `source_id`,
+  `model`, `dim`, `text_hash`, `text`, `created_at`, `embedding`), unique on
+  (`source_type`, `source_id`, `model`). `embedding` is pgvector `vector(dim)`
+  with an HNSW `vector_cosine_ops` index on PostgreSQL, a JSON float list
+  elsewhere. `build_metadata()` now includes it, so Alembic hosts need a
+  migration; the README has the SQL, starting with
+  `CREATE EXTENSION IF NOT EXISTS vector`. With `create_tables=True` the table
+  is created when an `EmbeddingSink` first writes, not at startup, so hosts
+  that do not embed need neither pgvector nor the extension. On PostgreSQL
+  that step first runs `CREATE EXTENSION IF NOT EXISTS vector`; without the
+  privilege it logs a warning, keeps the sink running, and retries at most
+  once a minute.
+- `DatabaseSink.prune()` also deletes embeddings older than the cutoff,
+  except those of feedback, and returns the total from all three tables.
+- `record_llm_call` now drops `prompt` and `completion` keys passed in
+  `attrs` unless `capture_llm_text=True`. Earlier versions stored them.
+
+### Documentation
+- README: `DatabaseSink` needs the `instrumentation` extra even with
+  SQLAlchemy installed, since SQLAlchemy 2.1 no longer installs `greenlet`.
+
+## [2026.09.29.1] - 2026-09-29
+
+### Fixed
+- `DatabaseSink(create_tables=True)`: two processes starting at the same
+  moment against a database without the tables both ran `create_all`, and one
+  failed (SQLite: `table ... already exists`; Postgres: a unique violation on
+  `pg_type_typname_nsp_index`). That process dropped its first batch and
+  counted a sink error. `ensure_tables()` now waits 50 to 250 ms after a
+  database error and runs `create_all` once more, which skips what the other
+  process created; the recovery is logged at INFO. A second failure is raised
+  as before. Measured on two-writer cold starts: SQLite 9 of 10 runs lost a
+  batch before, 0 of 10 after; Postgres 16 12 of 12 before, 0 of 12 after.
+
+## [2026.09.29] - 2026-09-29
+
+Record things that are not tool calls: background job transitions, LLM
+requests a tool makes, anything with a dotted name.
+
+### Added
+- `mw.record_event(kind, *, key=None, attrs=None, user_sub=None,
+  call_id=None) -> bool`. Synchronous; never raises, never blocks. `attrs` is
+  redacted like call arguments and made JSON-safe (`NaN`/infinities become
+  strings, since Postgres JSONB rejects them). Returns False when the event
+  was not recorded: mode `off`, an empty or over-128-character `kind`, a `key`
+  over 255 characters, non-mapping `attrs`, a full queue, or no event loop.
+  Events share the call records' queue and drop-and-count policy.
+- **Linking to the running call.** The middleware now picks a call's record id
+  before the tool runs and exposes it through a ContextVar, so an event
+  recorded inside a tool gets that call's id as `call_id` (and its session),
+  matching the stored `ffb_tool_calls` row. Outside a call, `call_id` is None;
+  a task a tool started that records after the tool returned counts as
+  outside.
+- **Thread safety.** `record_event` works from any thread: off the server's
+  loop, the event is handed over with `call_soon_threadsafe`. The loop is
+  known from the first tool call (the dispatcher now binds to it as each call
+  starts), so sync tools running in FastMCP's worker threads link too. Before
+  any call or record, a thread with no loop of its own gets False and a
+  counted drop.
+- `mw.record_llm_call(*, model, provider=None, duration_ms=None,
+  input_tokens=None, output_tokens=None, ok=None, error=None, key=None,
+  attrs=None) -> bool` records kind `llm.call` with plain attribute names:
+  `model`, `provider`, `duration_ms`, `input_tokens`, `output_tokens`, `ok`
+  (defaults to `error is None`), `error` (redacted, truncated to
+  `max_error_chars`) and `error_type` for exceptions. The README maps them to
+  OTel GenAI names.
+- `await mw.events_for(key, *, kinds=None, limit=1000)`: a key's events, oldest
+  first (the newest `limit`), from the `DatabaseSink` plus events still in the
+  queue, deduplicated by id, `occurred_at` in UTC. Without a `DatabaseSink` it
+  answers from memory (`recent_events=2000` on the middleware).
+- `EventRecord`, exported from `fastmcp_feedback.instrumentation`.
+  `MemorySink` has `.calls` and `.events`.
+- Exclusion and sampling still apply to tool calls only. A sampled tool's call
+  that recorded an event is always kept (no `sample_rate`), so the event's
+  `call_id` has a row.
+
+### Changed
+- Sinks receive mixed batches of `ToolCallRecord` and `EventRecord` once
+  events are recorded. `record.record_type` is `tool_call` or `event`. A
+  `CallbackSink` callback that assumes every record is a call needs a check.
+- `JsonLinesSink` lines carry `"record": "tool_call"` or `"record": "event"`
+  (a new field; existing fields unchanged).
+- `DatabaseSink.write` stores calls and events in separate transactions,
+  calls first, so a database without the events table still stores calls.
+- `DatabaseSink.prune()` also deletes events whose `occurred_at` is before
+  the cutoff, in `prune_batch` batches, and **returns the total rows deleted
+  from both tables**; each table's count is logged. It skips events on a
+  database that has no events table.
+
+### Migration
+- New table `{prefix}events` (`ffb_events` by default): `id`, `occurred_at`,
+  `kind`, `key`, `call_id`, `session_id`, `user_sub`, `caller_kind`,
+  `client_id`, `server_version`, `attrs` (JSON/JSONB), indexed on
+  `occurred_at`, `kind`, `key`, `call_id` and `user_sub`. Hosts that manage the
+  schema with Alembic need a migration creating it (`build_metadata()`
+  includes it). `create_tables=True` creates it on first use. Until it exists,
+  event writes fail and are dropped (logged); calls are unaffected. No change
+  to existing tables.
+
+## [2026.09.28] - 2026-09-28
+
+Keep the tool-call table small: leave out or sample busy tools, and prune old
+rows.
+
+### Added
+- `InstrumentationMiddleware(..., exclude_tools=...)`: tools that are never
+  recorded and never kept for feedback links. The calls themselves are
+  untouched.
+- `InstrumentationMiddleware(..., sample_rates={"tool": 0.01})`: record that
+  fraction of a tool's `ok` calls. **Failures are always recorded**: calls with
+  outcome `error` or `soft_error` are kept whatever the rate. Rates outside
+  (0, 1] raise `ValueError`. Calls sampled out skip the hooks and redaction and
+  are not kept for feedback links. `random=` takes a `() -> float` for tests.
+- Records have a `sample_rate`: the configured rate on sampled-in `ok` rows,
+  None on everything recorded unconditionally (including failures of sampled
+  tools). `SUM(1.0 / COALESCE(sample_rate, 1))` estimates the true call count.
+- `DatabaseSink(..., retention=timedelta(days=30), prune_interval=...,
+  prune_batch=5000)`. With `retention` set, the background writer prunes after
+  an insert at most once per `prune_interval` (one hour by default). A failing
+  prune is logged and never stops the insert.
+- `DatabaseSink.prune(older_than=None) -> int` deletes older rows in batches
+  of `prune_batch`, one transaction each, on SQLite and Postgres. **Calls
+  linked to feedback are never pruned**, and links are never deleted.
+
+### Migration
+- New nullable `sample_rate` column (`Float`) on `{prefix}tool_calls`. Hosts
+  that manage the schema with Alembic need a migration adding it.
+  `create_tables=True` does not alter a table that already exists, so add it by
+  hand there (`ALTER TABLE ffb_tool_calls ADD COLUMN sample_rate FLOAT`). Until
+  the column exists, `DatabaseSink` writes fail and those records are dropped
+  (logged). Hosts still on 2026.09.27.3 or earlier also need the `outcome`
+  column from 2026.09.27.4.
+
 ## [2026.09.27.4] - 2026-09-27
 
 Count errors that tools return as data instead of raising.

CONTRIBUTING.md

@@ -97,6 +97,50 @@ uv run pytest -m unit
 uv run pytest -m integration
 ```
 
+### Running the tests against Postgres
+
+The `DatabaseSink` tests run on SQLite by default. Each one also has a
+`[postgres]` variant that runs against a real Postgres 16 with pgvector
+when `FFB_TEST_DATABASE_URL` is set, and skips otherwise. The repo ships a
+compose stack for it (Docker required):
+
+```bash
+make pg-up      # start ffb-postgres and wait until healthy (creates .env on first run)
+make test-pg    # whole suite, Postgres cases enabled
+make test       # whole suite, SQLite only
+make test-all   # both
+make pg-down    # stop it; the data volume is kept (pg-reset deletes it)
+```
+
+The server listens on `127.0.0.1` only, on `FFB_PG_PORT` from `.env`
+(55433 by default; change it if that port is taken). `make pg-psql` opens a
+shell on it. Every Postgres test gets its own freshly created database with
+the `vector` extension, dropped when the test ends, so tests never see each
+other's rows. To write a test that runs on both backends, take the `db_url`
+fixture from `tests/conftest.py`: `db_url("name")` returns an async URL for
+an empty database, the same one for the same name within a test.
+
+To point the suite at another server, set the variable yourself; the user
+needs permission to create and drop databases:
+
+```bash
+FFB_TEST_DATABASE_URL=postgresql+asyncpg://user:pass@host:5432/postgres uv run pytest -m postgres
+```
+
+### Embedding tests against a real endpoint
+
+`tests/unit/test_embeddings.py` uses a deterministic fake embedder. One test
+embeds through a real OpenAI-compatible endpoint when you point it at one, and
+skips otherwise:
+
+```bash
+FFB_TEST_EMBED_URL=https://api.example.com/v1 FFB_TEST_EMBED_KEY=... \
+  uv run pytest tests/unit/test_embeddings.py -k live
+```
+
+`FFB_TEST_EMBED_MODEL` (default `mxbai-embed-large`) and `FFB_TEST_EMBED_DIM`
+(default 1024) pick the model.
+
 ### Writing Tests
 
 - Place unit tests in `tests/unit/`

Makefile

@@ -0,0 +1,63 @@
+# Dev/test helpers. The Postgres stack exists only so the test suite can run
+# against a real server; see CONTRIBUTING.md.
+
+COMPOSE ?= docker compose
+PYTEST ?= uv run --no-sync pytest
+PYTEST_ARGS ?= -q --no-cov
+
+# FFB_TEST_DATABASE_URL and friends come from .env when it exists. A value
+# already in the environment (CI points it at a service container) wins.
+ENV_TEST_DATABASE_URL := $(FFB_TEST_DATABASE_URL)
+-include .env
+ifneq ($(ENV_TEST_DATABASE_URL),)
+FFB_TEST_DATABASE_URL := $(ENV_TEST_DATABASE_URL)
+endif
+
+.PHONY: help pg-up pg-down pg-logs pg-psql pg-reset test test-pg test-all lint
+
+help:
+	@echo "pg-up     start Postgres + pgvector and wait until healthy"
+	@echo "pg-down   stop it (the data volume is kept)"
+	@echo "pg-reset  stop it and delete the data volume"
+	@echo "pg-logs   follow its logs"
+	@echo "pg-psql   psql into the test database"
+	@echo "test      test suite on SQLite only (Postgres cases skip)"
+	@echo "test-pg   test suite with the Postgres cases enabled (needs pg-up)"
+	@echo "test-all  both"
+	@echo "lint      ruff"
+
+# First run: copy the example and give it a random password.
+.env:
+	@pw=$$(openssl rand -hex 16); sed "s/change-me/$$pw/g" .env.example > .env
+	@echo "wrote .env from .env.example"
+
+pg-up: .env
+	$(COMPOSE) up -d --wait ffb-postgres
+	$(COMPOSE) logs --tail=20 ffb-postgres
+
+pg-down:
+	$(COMPOSE) down
+
+pg-reset:
+	$(COMPOSE) down -v
+
+pg-logs:
+	$(COMPOSE) logs -f ffb-postgres
+
+pg-psql:
+	$(COMPOSE) exec ffb-postgres psql -U "$(FFB_PG_USER)" -d "$(FFB_PG_DB)"
+
+test:
+	env -u FFB_TEST_DATABASE_URL $(PYTEST) $(PYTEST_ARGS)
+
+test-pg: .env
+	@test -n "$(FFB_TEST_DATABASE_URL)" || { echo "FFB_TEST_DATABASE_URL is not set (environment or .env)"; exit 1; }
+	@echo "$(PYTEST) $(PYTEST_ARGS)  (Postgres cases enabled)"
+	@FFB_TEST_DATABASE_URL="$(FFB_TEST_DATABASE_URL)" $(PYTEST) $(PYTEST_ARGS)
+
+test-all: test test-pg
+
+lint:
+	uv run --no-sync ruff check src/ examples/ tests/_compat.py tests/_databases.py \
+		tests/unit/test_test_databases.py tests/unit/test_instrumentation.py \
+		tests/unit/test_embeddings.py tests/unit/test_backfill.py

README.md

@@ -181,12 +181,15 @@ instrument(app, [
 
 `DatabaseSink` needs the `instrumentation` extra
 (`uv add "fastmcp-feedback[instrumentation]"`), plus `asyncpg` for Postgres.
+Install the extra even if you already have SQLAlchemy: SQLAlchemy 2.1 no longer
+pulls in `greenlet` by itself, and its async engine cannot run without it.
 
 ### Using your own database and migrations
 
 Pass the `AsyncEngine` you already have. Records go to a `ffb_tool_calls`
-table (the prefix is configurable), and nothing is created at startup unless you
-ask. To manage the table with Alembic, add it to your `env.py`:
+table (events to `ffb_events`; the prefix is configurable), and nothing is
+created at startup unless you ask. To manage the tables with Alembic, add them
+to your `env.py`:
 
 ```python
 from fastmcp_feedback.instrumentation import build_metadata
@@ -336,6 +339,441 @@ Code and FastMCP 4's own `Client`, do not open sessions over HTTP. Their calls
 link by user only, so configure an `identity_resolver` for HTTP servers. Stdio
 servers link by session with every client.
 
+### Busy tools: exclusion and sampling
+
+A polling tool can make up most of the rows. Leave it out entirely, or keep a
+fraction of its successful calls:
+
+```python
+from fastmcp_feedback.instrumentation import instrument
+
+instrument(
+    app,
+    exclude_tools={"heartbeat"},                     # never recorded
+    sample_rates={"pending_dispatches": 0.01},       # 1% of ok calls
+)
+```
+
+Excluded tools are not recorded at all and are left out of feedback links.
+Sampled tools keep a random fraction of their `ok` calls, but **failures are
+always recorded**: a call whose outcome is `error` or `soft_error` is kept
+whatever the rate. Calls sampled out cost no hook or redaction work and are
+left out of feedback links too. Rates must be in (0, 1]; anything else raises
+`ValueError`. Neither setting changes what the client receives.
+
+Sampled-in `ok` rows carry the rate in a `sample_rate` column; everything
+recorded unconditionally, including failures of sampled tools, has NULL there.
+So each row stands for `1 / coalesce(sample_rate, 1)` calls, and this estimates
+the true number of calls per tool:
+
+```sql
+SELECT tool, SUM(1.0 / COALESCE(sample_rate, 1)) AS est_calls
+FROM ffb_tool_calls GROUP BY tool;
+```
+
+### Retention
+
+`ffb_tool_calls` grows with every call. Give the sink a retention period and it
+deletes older rows as it goes:
+
+```python
+from datetime import timedelta
+from fastmcp_feedback.instrumentation import DatabaseSink, instrument
+
+sink = DatabaseSink(engine, prefix="ffb_", retention=timedelta(days=30))
+mw = instrument(app, [sink])
+```
+
+Pruning runs in the background writer after an insert, at most once per
+`prune_interval` (an hour by default; the first write after startup prunes).
+It deletes `prune_batch` rows (5000) per transaction, so it never holds a long
+lock, and a failing prune is logged without affecting the insert. **Calls linked
+to feedback are never pruned**, and neither are the links, so a report keeps its
+evidence. Without `retention`, nothing is deleted.
+
+To prune on your own schedule, call it directly; it returns the rows deleted:
+
+```python
+from datetime import UTC, datetime, timedelta
+
+deleted = await sink.prune()                                          # now - retention
+deleted = await sink.prune(older_than=datetime.now(UTC) - timedelta(days=7))
+```
+
+Automatic pruning runs inside a sink write, so it is bounded by the
+dispatcher's `sink_timeout` (10 s). Batches finished before a timeout stay
+deleted and the rest goes on the next run, but for a large backlog it is
+quicker to run `await sink.prune()` once yourself.
+
+**Upgrading:** `sample_rate` is a new nullable `FLOAT` column in 2026.09.28.
+Hosts that manage the schema with Alembic need a migration adding it, and a
+table made earlier by `create_tables=True` needs
+`ALTER TABLE ffb_tool_calls ADD COLUMN sample_rate FLOAT` (`DOUBLE PRECISION`
+on Postgres also works), since existing tables are not altered. Until the
+column exists, database writes fail and those records are dropped (logged).
+Retention needs no schema change.
+
+### Events: things that aren't tool calls
+
+Some things worth recording happen outside a tool call, or inside one without
+being one: a background job changing state minutes after the tool that
+started it returned, or an LLM request a tool makes on the caller's behalf.
+Record them as events:
+
+```python
+from fastmcp_feedback.instrumentation import DatabaseSink, instrument
+
+mw = instrument(app, [DatabaseSink("sqlite+aiosqlite:///calls.db", create_tables=True)])
+
+@app.tool
+async def submit(case: str) -> dict:
+    job_id = f"job-{case}"
+    mw.record_event("job.submitted", key=job_id, attrs={"case": case, "nproc": 8})
+    return {"job_id": job_id}
+
+# Later, from a watcher task or thread, with no tool call running:
+mw.record_event("job.finished", key="job-wing", attrs={"duration_s": 3512, "converged": True})
+```
+
+`record_event(kind, *, key=None, attrs=None, user_sub=None, call_id=None)`
+returns True when the event was queued and False when it was not. It never
+raises and never blocks, and it takes the same path as call records: the same
+background queue, the same drop-and-count policy when the queue is full.
+
+- `kind` is a dotted name (`job.submitted`, `llm.call`), a non-empty string of
+  at most 128 characters.
+- `key` is what you join on, such as a job id. It is stored as a string, at
+  most 255 characters.
+- `attrs` is any JSON-like dict. It is redacted like call arguments, then made
+  JSON-safe: `NaN` and infinities become the strings `"nan"`/`"inf"` (Postgres
+  JSONB rejects them), datetimes become ISO strings, anything else unknown
+  becomes `str(value)`.
+- **Inside a tool call**, the event gets that call's id as `call_id` and its
+  session, so it joins to the call's row in `ffb_tool_calls`. Outside a call,
+  `call_id` is None. A task started by a tool that records after the tool has
+  returned counts as outside. Pass `call_id=` to set it yourself.
+- **From any thread.** Called off the server's event loop (a watcher thread, a
+  sync tool running in a worker thread), the event is handed to the loop with
+  `call_soon_threadsafe`. The loop is known once the server has handled a tool
+  call or recorded something on it; before that, a call from a thread with no
+  event loop of its own returns False and counts a drop.
+- In mode `off` it does nothing and returns False.
+
+Read a key's events back, oldest first, with `occurred_at` as UTC:
+
+```python
+for ev in await mw.events_for("job-wing"):
+    print(ev["occurred_at"], ev["kind"], ev["call_id"], ev["attrs"])
+
+steps = await mw.events_for("job-wing", kinds=["step.started", "step.finished"], limit=100)
+```
+
+`events_for` reads the `DatabaseSink` and adds events still waiting in the
+queue, so an event is visible as soon as `record_event` returns. With more
+than `limit` (1000) matches it returns the newest `limit`. Without a
+`DatabaseSink` it answers from the last `recent_events` (2000) events in
+memory; a failing database read is logged and memory is used.
+
+#### LLM calls
+
+`record_llm_call` records an event of kind `llm.call`. Call it from inside the
+tool that made the request and it links to that call:
+
+```python
+mw.record_llm_call(
+    model="claude-opus-4",
+    provider="anthropic",
+    duration_ms=812.5,
+    input_tokens=1200,
+    output_tokens=90,
+    attrs={"cache_read_input_tokens": 1000},
+)
+```
+
+Its attributes use plain names, which are easier to query from SQL than
+dotted ones (`attrs->>'model'` on Postgres, `json_extract(attrs, '$.model')`
+on SQLite):
+
+| Attribute | OTel GenAI equivalent | Notes |
+|-----------|-----------------------|-------|
+| `model` | `gen_ai.request.model` | Always set |
+| `provider` | `gen_ai.provider.name` | When given |
+| `duration_ms` | `gen_ai.client.operation.duration` (seconds) | When given |
+| `input_tokens` | `gen_ai.usage.input_tokens` | When given |
+| `output_tokens` | `gen_ai.usage.output_tokens` | When given |
+| `ok` | | Defaults to `error is None` |
+| `error` | | Redacted, truncated to `max_error_chars` |
+| `error_type` | `error.type` | When `error` is an exception |
+
+Anything in `attrs` is kept alongside, but these names win. `key=` works as
+for `record_event`, for example a chat turn id.
+
+The prompt and the answer are not stored unless you opt in, since chat text
+can hold anything a user typed. With `capture_llm_text=True` on the
+middleware, `record_llm_call(..., prompt=..., completion=...)` stores them as
+`prompt` and `completion` attributes, redacted and cut to `max_llm_text_chars`
+(by default `8 * max_error_chars`, so 4000 characters):
+
+```python
+from fastmcp_feedback.instrumentation import instrument
+
+mw = instrument(app, capture_llm_text=True)
+mw.record_llm_call(
+    model="claude-opus-4",
+    prompt="Why does the render time out?",
+    completion="The scene has 40 million polygons.",
+)
+```
+
+With the default `capture_llm_text=False` both are discarded, and so are
+`prompt` and `completion` keys passed in `attrs`.
+
+#### Storage and sinks
+
+Events go to a `ffb_events` table: `id`, `occurred_at`, `kind`, `key`,
+`call_id`, `session_id`, `user_sub`, `caller_kind`, `client_id`,
+`server_version`, `attrs` (JSON; JSONB on Postgres), indexed on
+`occurred_at`, `kind`, `key`, `call_id` and `user_sub`. It has no foreign key
+to `ffb_tool_calls`, since calls are pruned and may be written later.
+
+Sinks get calls and events in the same batches. `DatabaseSink` writes each to
+its table, calls first in their own transaction, so a database without
+`ffb_events` still stores calls. `JsonLinesSink` adds `"record": "tool_call"`
+or `"record": "event"` to every line. A `CallbackSink` callback receives both
+kinds (`record.record_type` tells them apart), and `MemorySink` has `.calls`
+and `.events` next to `.records`.
+
+Retention covers events too: `prune()` deletes events whose `occurred_at` is
+before the cutoff, in the same batches, and returns the rows deleted from both
+tables (each table's count is logged). Exclusion and sampling apply to tool
+calls only; events are always recorded. A call of a sampled tool that recorded
+an event is always kept, with no `sample_rate`, so the event's `call_id` has a
+row to join to. Events recorded inside an excluded tool have no `call_id`.
+
+**Upgrading:** `ffb_events` is a new table in this release. Hosts that manage
+the schema with Alembic need a migration creating it (`build_metadata()`
+includes it). `create_tables=True` creates it on first use, since it creates
+missing tables. Until the table exists, event writes fail and those events
+are dropped (logged); tool calls and pruning are unaffected.
+
+### Embeddings: finding similar reports and errors
+
+"Has anyone reported this before?" is hard to answer with `LIKE`. An
+`EmbeddingSink` turns the text in the records into vectors and stores them, so
+you can search by meaning. It embeds four kinds of text:
+
+| `source_type` | Text | `source_id` |
+|---------------|------|-------------|
+| `feedback` | title and description of a `feedback.submitted` event | the feedback ref |
+| `call_error` | `"{tool}: {error_type}: {error_message}"` of calls with outcome `error` or `soft_error` | the call id |
+| `event` | the values of `error_excerpt`, `error`, `message`, `reason` in an event's attrs (`event_text_keys=`) | the event id |
+| `llm` | `"prompt: ...\ncompletion: ..."` of an `llm.call` event, when `capture_llm_text=True` | the event id |
+
+An `llm.call` without prompt or completion counts as a plain event, so a
+failed LLM call's `error` is still embedded. A list or tuple value is
+embedded one item per line, a dict as `key: value` lines sorted by key (at
+any depth), rather than as its Python repr.
+
+It needs a `DatabaseSink` to store into and an embedder. `OpenAIEmbedder`
+talks to any OpenAI-compatible `/v1/embeddings` endpoint (OpenAI, Ollama,
+LiteLLM, vLLM):
+
+```python
+import os
+
+from fastmcp_feedback import add_feedback_tools
+from fastmcp_feedback.instrumentation import (
+    DatabaseSink,
+    EmbeddingSink,
+    OpenAIEmbedder,
+    instrument,
+)
+
+embedder = OpenAIEmbedder(
+    os.environ["EMBED_URL"],               # e.g. https://api.example.com/v1
+    api_key=os.environ["EMBED_API_KEY"],
+    model="mxbai-embed-large",
+    dim=1024,
+)
+db = DatabaseSink("sqlite+aiosqlite:///calls.db", create_tables=True, embedding_dim=1024)
+mw = instrument(app, [db, EmbeddingSink(embedder, db)])
+add_feedback_tools(app, database_url="sqlite:///feedback.db", instrumentation=mw)
+```
+
+Then search:
+
+```python
+for hit in await mw.similar("render timed out", k=5):
+    print(f"{hit['score']:.2f}", hit["source_type"], hit["source_id"], hit["text"][:60])
+
+# Reports and errors like feedback 42, leaving 42 itself out.
+related = await mw.similar_feedback("42", source_types=["feedback", "call_error"])
+```
+
+Each hit has `source_type`, `source_id`, `text` (what was embedded),
+`score` and `created_at`. `score` is 1 minus the cosine distance, clamped to
+[0, 1]: 1 is the same direction, 0 unrelated or opposite. `min_score=` drops
+weaker hits, and `source_types=` limits the search. Only vectors from the
+embedder's `model` are compared. `similar_feedback` uses the stored vector of
+that feedback, so it embeds nothing, and returns [] until the feedback has
+been embedded. Neither ever raises; on any failure they log and return [].
+
+On PostgreSQL the search runs in the database (`ORDER BY embedding <=> :q`)
+on an HNSW index. pgvector applies `source_types` and the model filter after
+the index scan, so a narrow filter over a large table can return fewer than
+`k` hits; `SET hnsw.iterative_scan = relaxed_order` (pgvector 0.8) fixes that.
+Elsewhere it compares in Python against the newest `scan_limit` (5000) rows,
+and logs once when there are more.
+
+**It stays off the call path.** `write` only picks out the text and puts it on
+the sink's own bounded queue (`max_queue`, 1000), so a slow embedding backend
+never holds up the dispatcher or a tool call. A background task embeds up to
+`batch_size` (32) texts per request, each request limited to `timeout` (30 s),
+and upserts the rows. A full queue drops texts and counts them in
+`sink.dropped`; failures of the embedder or the database are logged and
+counted in `sink.errors`, never raised. A text whose row already holds the
+same text (by `text_hash`) is not embedded again; a changed text replaces its
+row. `await sink.flush()` waits for the queue, and `mw.aclose()` lets it drain
+for up to `timeout` before stopping.
+
+**Nothing new leaves the process unredacted.** Text is redacted with the
+middleware's `Redactor` (the sink adopts it when passed to `instrument`) and
+cut to `max_chars` (4000) before it is sent to the embedder or stored, on top
+of the redaction every record already had.
+
+**Feedback flows as an event.** With `instrumentation=mw`, `submit_feedback`
+records a `feedback.submitted` event keyed by the feedback id, with `type`,
+`title`, `description` and `submitter` attributes. If you have your own
+feedback tools, record the same event with your own ref and it is embedded
+the same way:
+
+```python
+mw.record_event(
+    "feedback.submitted",
+    key="bug-7Q2X",
+    attrs={"type": "bug", "title": "Render hangs", "description": "Stuck at 99%"},
+)
+```
+
+**Storage.** Vectors go to `ffb_embeddings`: `id`, `source_type`,
+`source_id`, `model`, `dim`, `text_hash` (sha256 of `text`), `text`,
+`created_at` and `embedding`, unique on (`source_type`, `source_id`,
+`model`). `embedding` is a pgvector `vector(dim)` on PostgreSQL and a JSON
+list of floats elsewhere. `DatabaseSink(embedding_dim=...)` sets `dim` and must
+equal the embedder's `dim`, or `EmbeddingSink` raises `ValueError`. Retention
+covers it: `prune()` deletes embeddings older than the cutoff, **except those
+of feedback**, which are kept like the calls linked to feedback.
+
+On PostgreSQL this needs the `embeddings` extra (`pgvector`) and `asyncpg`:
+
+```bash
+uv add "fastmcp-feedback[instrumentation,embeddings]" asyncpg
+```
+
+With `create_tables=True`, the table is created when an `EmbeddingSink` first
+writes, not before, so hosts that do not embed never need pgvector. On
+PostgreSQL it first runs `CREATE EXTENSION IF NOT EXISTS vector`. Creating an
+extension needs a superuser or the database owner on most setups; without
+that it logs a warning, and so does the table creation if the extension is
+still missing. The sink keeps working and counts the failed writes, and
+retries the creation at most once a minute.
+
+**Upgrading:** `ffb_embeddings` is new in 2026.09.30. `build_metadata()`
+includes it (pass `embedding_dim=None` to leave it out). Hosts on Alembic need
+a migration; for PostgreSQL, with the default prefix and 1024 dimensions:
+
+```sql
+CREATE EXTENSION IF NOT EXISTS vector;
+
+CREATE TABLE ffb_embeddings (
+    id VARCHAR(36) NOT NULL,
+    source_type VARCHAR(16) NOT NULL,
+    source_id VARCHAR(255) NOT NULL,
+    model VARCHAR(128) NOT NULL,
+    dim INTEGER NOT NULL,
+    text_hash VARCHAR(64) NOT NULL,
+    text TEXT NOT NULL,
+    created_at TIMESTAMP WITH TIME ZONE NOT NULL,
+    embedding VECTOR(1024) NOT NULL,
+    PRIMARY KEY (id),
+    CONSTRAINT ffb_embeddings_source_uq UNIQUE (source_type, source_id, model)
+);
+CREATE INDEX ffb_embeddings_hnsw ON ffb_embeddings USING hnsw (embedding vector_cosine_ops);
+CREATE INDEX ix_ffb_embeddings_created_at ON ffb_embeddings (created_at);
+CREATE INDEX ix_ffb_embeddings_source_id ON ffb_embeddings (source_id);
+CREATE INDEX ix_ffb_embeddings_source_type ON ffb_embeddings (source_type);
+```
+
+Wrap it in `op.execute(...)` in the migration's `upgrade()`, and
+`DROP TABLE ffb_embeddings` in `downgrade()`. Autogenerate also works but
+renders the column as `fastmcp_feedback.instrumentation.storage.EmbeddingVector(dim=1024)`,
+which ties the migration to this package.
+
+#### Backfilling existing data
+
+The sink embeds records as they arrive. Calls, events and feedback stored
+before it was running can be embedded afterwards with `backfill_embeddings`,
+which reads them back from the `DatabaseSink`'s tables:
+
+```python
+from fastmcp_feedback.instrumentation import backfill_embeddings
+
+counts = await backfill_embeddings(mw.embedding_sink)
+# {"feedback": 4, "call_error": 90, "event": 12, "llm": 0, "skipped": 0, "errors": 0}
+```
+
+It picks up the same four kinds of text as the live sink: calls with outcome
+`error` or `soft_error`, `feedback.submitted` events, `llm.call` events with
+a prompt or completion, and other events with any of the sink's
+`event_text_keys`. Calls recorded before the `outcome` column existed
+(2026.09.27.4) have it NULL; those with an `error_message` count as errors.
+
+Each text goes through the sink's own extraction, redactor and `max_chars`,
+so it comes out with the same text, `text_hash` and `source_id` the live sink
+would give that record. That makes it idempotent: a second run embeds nothing
+and reports the rows as `skipped`, and records arriving later dedupe against
+what was backfilled. When the middleware has a custom `Redactor`, build the
+sink with it (`EmbeddingSink(..., redactor=mw.redactor)`), or use
+`mw.embedding_sink`, which already has it.
+
+`sources=` narrows it (default: the sink's sources), `since=` and `until=`
+select rows by timestamp (naive means UTC), and `limit=` embeds at most that
+many texts, oldest first; running it again carries on where it stopped. Rows
+are read `batch_size` (500) at a time and embedded in chunks of the sink's
+`batch_size`, so a large table never sits in memory. A failed chunk is
+logged and counted in `errors`, never raised; only a missing table or bad
+arguments raise. `dry_run=True` counts what would be embedded without calling
+the embedder.
+
+Feedback kept in your own tables rather than as events goes through
+`backfill_feedback`, which makes the same text a `feedback.submitted` event
+with that ref would:
+
+```python
+from fastmcp_feedback.instrumentation import backfill_feedback
+
+rows = [("bug-7Q2X", "Render hangs", "Stuck at 99%"), ("bug-8K1D", "Crash", None)]
+await backfill_feedback(mw.embedding_sink, rows)  # (ref, title, description)
+```
+
+From a shell, the same over a database URL. The API key comes from
+`FFB_EMBED_API_KEY` (or the variable `--api-key-env` names), never a flag:
+
+```bash
+export FFB_EMBED_API_KEY=...
+python -m fastmcp_feedback.instrumentation.backfill \
+    --database-url postgresql+asyncpg://user:pass@db/app \
+    --embed-base-url https://api.example.com/v1 \
+    --model mxbai-embed-large --dim 1024 --prefix ffb_ \
+    --since 2026-09-01 --sources call_error,feedback --dry-run
+```
+
+It prints the counts as JSON and exits 1 if any text failed to embed, 2 on
+a setup error. `--create-tables` creates `ffb_embeddings` (and on PostgreSQL
+the vector extension) when it is missing; without it a missing table is an
+error. `--help` lists the rest (`--until`, `--limit`, `--batch-size`,
+`--max-chars`, `--event-text-keys`).
+
 ### Middleware order and shutdown
 
 FastMCP runs the first middleware added as the outermost. Call `instrument()`

docker-compose.yml

@@ -0,0 +1,31 @@
+# Postgres + pgvector for running the test suite against a real server.
+# Test-only: nothing here serves HTTP, so there is no caddy network.
+# The port is published on 127.0.0.1 only, for pytest running on the host.
+
+services:
+  ffb-postgres:
+    image: ${FFB_PG_IMAGE:-pgvector/pgvector:pg16}
+    restart: unless-stopped
+    environment:
+      POSTGRES_USER: ${FFB_PG_USER}
+      POSTGRES_PASSWORD: ${FFB_PG_PASSWORD}
+      POSTGRES_DB: ${FFB_PG_DB}
+    ports:
+      - "127.0.0.1:${FFB_PG_PORT:-55433}:5432"
+    volumes:
+      - ffb-postgres-data:/var/lib/postgresql/data
+      - ./docker/postgres-init:/docker-entrypoint-initdb.d:ro
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U \"$${POSTGRES_USER}\" -d \"$${POSTGRES_DB}\""]
+      interval: 2s
+      timeout: 3s
+      retries: 30
+      start_period: 5s
+    networks:
+      - internal
+
+volumes:
+  ffb-postgres-data:
+
+networks:
+  internal:

docker/pgvector-alpine/Dockerfile

@@ -0,0 +1,13 @@
+# Postgres on Alpine with pgvector built in. Keeps the musl C library, so an
+# existing data directory's collation and text indexes are unaffected.
+# with_llvm=no: the base image's Postgres was built with a specific clang for
+# JIT bitcode that Alpine may no longer ship; pgvector itself builds with gcc.
+ARG PG_MAJOR=16
+FROM postgres:${PG_MAJOR}-alpine
+ARG PGVECTOR_VERSION=v0.8.6
+RUN apk add --no-cache --virtual .build-deps git build-base \
+ && git clone --depth 1 --branch ${PGVECTOR_VERSION} https://github.com/pgvector/pgvector.git /tmp/pgvector \
+ && make -C /tmp/pgvector OPTFLAGS="" with_llvm=no \
+ && make -C /tmp/pgvector install with_llvm=no \
+ && rm -rf /tmp/pgvector \
+ && apk del .build-deps

docker/pgvector-alpine/README.md

@@ -0,0 +1,28 @@
+# Postgres on Alpine with pgvector
+
+For stacks that already run `postgres:NN-alpine` and want the `vector`
+extension for fastmcp-feedback embeddings.
+
+Why not switch to the `pgvector/pgvector` image: it's Debian (glibc), and the
+Alpine images use musl. The two C libraries sort text differently under
+locales such as `en_US.utf8`, so reusing an Alpine data directory under a
+Debian image changes the order of every text index. The indexes need a full
+`REINDEX` or they silently return wrong results. Building pgvector into the
+same Alpine image keeps the data directory's collation unchanged.
+
+```bash
+docker build --build-arg PG_MAJOR=16 -t postgres-pgvector:16-alpine .
+docker build --build-arg PG_MAJOR=17 -t postgres-pgvector:17-alpine .
+```
+
+Then point the stack's Postgres service at the new image (same major version
+as the existing data directory) and run `CREATE EXTENSION IF NOT EXISTS
+vector;` once, or let a migration do it.
+
+Tested 2026-09-30 with pgvector v0.8.6 on postgres:16-alpine and
+postgres:17-alpine: the extension loads, and an HNSW `vector_cosine_ops`
+index is used for `ORDER BY embedding <=> ...` queries.
+
+`with_llvm=no` skips the LLVM bitcode used for JIT inlining. The base images'
+Postgres was built with a clang version Alpine no longer ships, so the bitcode
+step fails; pgvector itself still builds with gcc and works normally.

docker/postgres-init/01-vector.sql

@@ -0,0 +1,6 @@
+-- Runs once, on first start with an empty volume.
+-- The test fixtures create a fresh database per test from template1, so the
+-- extension goes there too; every database created afterwards inherits it.
+CREATE EXTENSION IF NOT EXISTS vector;
+\connect template1
+CREATE EXTENSION IF NOT EXISTS vector;

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "fastmcp-feedback"
-version = "2026.09.27.4"  # YYYYMMDD.patch versioning - update for each release
+version = "2026.09.30.1"  # YYYYMMDD.patch versioning - update for each release
 description = "Production-ready feedback collection system for FastMCP servers"
 readme = "README.md"
 license = "MIT"
@@ -52,10 +52,14 @@ dependencies = [
 postgresql = ["psycopg2-binary>=2.9.0"]
 # Async database sink for per-call instrumentation. Postgres hosts add asyncpg.
 instrumentation = ["sqlalchemy[asyncio]>=2.0.43", "aiosqlite>=0.22.1"]
+# Vector column for EmbeddingSink on Postgres. Postgres hosts add asyncpg.
+embeddings = ["pgvector>=0.5.0"]
 mysql = ["pymysql>=1.1.0"]
 dev = [
     "sqlalchemy[asyncio]>=2.0.43",
     "aiosqlite>=0.22.1",
+    "asyncpg>=0.31.0",
+    "pgvector>=0.5.0",
     "pytest>=8.2.0",
     "pytest-asyncio>=0.23.0",
     "pytest-cov>=5.0.0",
@@ -67,6 +71,8 @@ dev = [
 test = [
     "sqlalchemy[asyncio]>=2.0.43",
     "aiosqlite>=0.22.1",
+    "asyncpg>=0.31.0",
+    "pgvector>=0.5.0",
     "pytest>=8.2.0",
     "pytest-asyncio>=0.23.0",
     "pytest-cov>=5.0.0",
@@ -119,6 +125,7 @@ markers = [
     "integration: Integration tests for component interaction",
     "slow: Slow running tests",
     "database: Tests requiring database setup",
+    "postgres: Runs against the Postgres named by FFB_TEST_DATABASE_URL; skipped when unset",
 ]
 filterwarnings = [
     "ignore::DeprecationWarning",

src/fastmcp_feedback/__init__.py

@@ -44,7 +44,7 @@ Quick Start:
 import logging
 
 # Version information - CalVer YYYY.MM.DD
-__version__ = "2026.09.27.4"
+__version__ = "2026.09.30.1"
 __author__ = "Ryan Malloy"
 __email__ = "ryan@supported.systems"
 

src/fastmcp_feedback/instrumentation/__init__.py

@@ -5,14 +5,30 @@ from fastmcp_feedback.instrumentation import instrument, DatabaseSink
 mw = instrument(app)  # every tool call as JSON lines on stderr
 mw = instrument(app, [DatabaseSink(engine)], identity_resolver=who)
 mw = instrument(app, result_classifier=soft_error_classifier(extra_statuses=("unknown_target",)))
+mw.record_event("job.finished", key=job_id, attrs={"duration_s": 3600})
+
+db = DatabaseSink(engine)
+mw = instrument(app, [db, EmbeddingSink(OpenAIEmbedder(url, key), db)])
+await mw.similar("render timed out")
+await backfill_embeddings(mw.embedding_sink)  # rows stored before embedding
 """
 
+from .backfill import backfill_embeddings, backfill_feedback
 from .classify import (
     DEFAULT_RESULT_CLASSIFIER,
     ResultClassifier,
     soft_error_classifier,
 )
 from .dispatch import RecordDispatcher
+from .embeddings import (
+    DEFAULT_EVENT_TEXT_KEYS,
+    Embedder,
+    EmbeddingSink,
+    EmbeddingText,
+    OpenAIEmbedder,
+    extract_texts,
+)
+from .events import EventRecord
 from .middleware import MODE_ENV_VAR, MODES, InstrumentationMiddleware, instrument
 from .record import ToolCallRecord
 from .redaction import (
@@ -22,27 +38,46 @@ from .redaction import (
     Redactor,
 )
 from .sinks import CallbackSink, JsonLinesSink, MemorySink, Sink
-from .storage import DEFAULT_TABLE_PREFIX, DatabaseSink, build_metadata
+from .storage import (
+    DEFAULT_EMBEDDING_DIM,
+    DEFAULT_TABLE_PREFIX,
+    EMBEDDING_SOURCES,
+    DatabaseSink,
+    EmbeddingVector,
+    build_metadata,
+)
 
 __all__ = [
     "CallbackSink",
     "DatabaseSink",
+    "DEFAULT_EMBEDDING_DIM",
+    "DEFAULT_EVENT_TEXT_KEYS",
     "DEFAULT_RESULT_CLASSIFIER",
     "DEFAULT_SECRET_KEYS",
     "DEFAULT_SECRET_PATTERNS",
     "DEFAULT_TABLE_PREFIX",
+    "EMBEDDING_SOURCES",
+    "Embedder",
+    "EmbeddingSink",
+    "EmbeddingText",
+    "EmbeddingVector",
+    "EventRecord",
     "InstrumentationMiddleware",
     "JsonLinesSink",
     "MemorySink",
     "MODE_ENV_VAR",
     "MODES",
+    "OpenAIEmbedder",
     "RecordDispatcher",
     "REDACTED",
     "Redactor",
     "ResultClassifier",
     "Sink",
     "ToolCallRecord",
+    "backfill_embeddings",
+    "backfill_feedback",
     "build_metadata",
+    "extract_texts",
     "instrument",
     "soft_error_classifier",
 ]

src/fastmcp_feedback/instrumentation/backfill/__init__.py

@@ -0,0 +1,338 @@
+"""Embed records that were stored before an EmbeddingSink was running.
+
+``backfill_embeddings`` reads tool-call errors and events back out of the
+DatabaseSink's tables, rebuilds the records, and sends them through the same
+extraction, redaction and truncation as ``EmbeddingSink.write``. The texts,
+hashes and source ids therefore match what the live sink would produce, so
+records arriving later dedupe against backfilled rows, and a second run
+embeds nothing new.
+
+Command line: ``python -m fastmcp_feedback.instrumentation.backfill --help``.
+"""
+
+import asyncio
+import logging
+from collections.abc import AsyncIterator, Iterable
+from datetime import UTC, datetime
+from typing import Any
+
+from sqlalchemy import and_, inspect, or_, select, tuple_
+
+from ..embeddings import EmbeddingSink, extract_texts
+from ..events import EventRecord
+from ..record import ToolCallRecord
+from ..sinks import Record
+from ..storage import EMBEDDING_SOURCES
+
+logger = logging.getLogger(__name__)
+
+FEEDBACK_KIND = "feedback.submitted"
+LLM_KIND = "llm.call"
+
+_CALL_COLUMNS = (
+    "id",
+    "tool",
+    "started_at",
+    "duration_ms",
+    "mode",
+    "ok",
+    "error_type",
+    "error_message",
+)
+
+
+def _utc(value: datetime | None) -> datetime | None:
+    if value is None or value.tzinfo is None:
+        return value if value is None else value.replace(tzinfo=UTC)
+    return value.astimezone(UTC)
+
+
+class _Run:
+    """Counts and limits for one backfill call."""
+
+    def __init__(
+        self,
+        sink: EmbeddingSink,
+        sources: Iterable[str],
+        limit: int | None,
+        dry_run: bool,
+    ):
+        self.sink = sink
+        self.sources = tuple(sources)
+        self.limit = limit
+        self.dry_run = dry_run
+        self.counts = dict.fromkeys(self.sources, 0)
+        self.skipped = 0
+        self.errors = 0
+        self.sent = 0
+        self.have_table = True
+
+    @property
+    def done(self) -> bool:
+        return self.limit is not None and self.sent >= self.limit
+
+    def result(self) -> dict[str, int]:
+        return {**self.counts, "skipped": self.skipped, "errors": self.errors}
+
+    async def feed(self, records: list[Record]) -> None:
+        sink = self.sink
+        items = [
+            sink._prepare(i)
+            for i in extract_texts(
+                records, sources=self.sources, event_text_keys=sink.event_text_keys
+            )
+        ]
+        # The last text for a source wins, as in the live sink.
+        latest = list({(i.source_type, i.source_id): i for i in items}.values())
+        if self.have_table:
+            todo, stored = await sink._unembedded(latest)
+        else:
+            todo, stored = latest, set()
+        self.skipped += len(latest) - len(todo)
+        if self.limit is not None:
+            todo = todo[: max(0, self.limit - self.sent)]
+        self.sent += len(todo)
+        if self.dry_run:
+            for item in todo:
+                self.counts[item.source_type] += 1
+            return
+        for start in range(0, len(todo), sink.batch_size):
+            chunk = todo[start : start + sink.batch_size]
+            try:
+                vectors = await asyncio.wait_for(
+                    sink.embedder.embed([i.text for i in chunk]), sink.timeout
+                )
+                await sink._store(chunk, vectors, stored)
+            except Exception:
+                self.errors += len(chunk)
+                logger.warning(
+                    "Backfill failed to embed %d texts with %s",
+                    len(chunk),
+                    sink.embedder.model,
+                    exc_info=True,
+                )
+                continue
+            for item in chunk:
+                self.counts[item.source_type] += 1
+
+
+async def _has_table(engine: Any, name: str) -> bool:
+    async with engine.connect() as conn:
+        return await conn.run_sync(lambda sync: inspect(sync).has_table(name))
+
+
+async def _columns(engine: Any, name: str) -> set[str]:
+    async with engine.connect() as conn:
+        return await conn.run_sync(
+            lambda sync: {c["name"] for c in inspect(sync).get_columns(name)}
+        )
+
+
+async def _pages(
+    engine: Any,
+    table: Any,
+    columns: list[Any],
+    ts: Any,
+    where: Any,
+    since: datetime | None,
+    until: datetime | None,
+    page_size: int,
+) -> AsyncIterator[list[dict[str, Any]]]:
+    """Rows matching ``where``, oldest first, ``page_size`` at a time.
+
+    Keyset paging on (timestamp, id), so rows written during the run neither
+    shift the pages nor get read twice.
+    """
+    conditions = [where]
+    if since is not None:
+        conditions.append(ts >= since)
+    if until is not None:
+        conditions.append(ts < until)
+    last: tuple[Any, Any] | None = None
+    while True:
+        query = select(*columns).where(*conditions)
+        if last is not None:
+            query = query.where(tuple_(ts, table.c.id) > tuple_(*last))
+        query = query.order_by(ts, table.c.id).limit(page_size)
+        async with engine.connect() as conn:
+            rows = [dict(r) for r in (await conn.execute(query)).mappings()]
+        if not rows:
+            return
+        yield rows
+        if len(rows) < page_size:
+            return
+        last = (rows[-1][ts.name], rows[-1]["id"])
+
+
+async def _backfill_calls(run: _Run, since, until, page_size) -> None:
+    db = run.sink.database_sink
+    t = db.table
+    have = await _columns(db.engine, t.name)
+    if "outcome" in have:
+        # Rows from before the outcome column was added have it NULL and
+        # were errors exactly when they carry an error message.
+        where = or_(
+            t.c.outcome.in_(("error", "soft_error")),
+            and_(t.c.outcome.is_(None), t.c.error_message.is_not(None)),
+        )
+        columns = [t.c[c] for c in (*_CALL_COLUMNS, "outcome")]
+    else:
+        where = t.c.error_message.is_not(None)
+        columns = [t.c[c] for c in _CALL_COLUMNS]
+    async for rows in _pages(
+        db.engine, t, columns, t.c.started_at, where, since, until, page_size
+    ):
+        records: list[Record] = []
+        for row in rows:
+            row["outcome"] = row.get("outcome") or "error"
+            records.append(ToolCallRecord(**row))
+        await run.feed(records)
+        if run.done:
+            return
+
+
+async def _backfill_events(run: _Run, since, until, page_size) -> None:
+    db = run.sink.database_sink
+    e = db.events
+    kinds = []
+    if "feedback" in run.sources:
+        kinds.append(e.c.kind == FEEDBACK_KIND)
+    if "llm" in run.sources:
+        kinds.append(e.c.kind == LLM_KIND)
+    if "event" in run.sources:
+        # Includes llm.call: one without prompt or completion is a plain event.
+        kinds.append(e.c.kind != FEEDBACK_KIND)
+    columns = [e.c.id, e.c.kind, e.c.key, e.c.occurred_at, e.c.attrs]
+    async for rows in _pages(
+        db.engine, e, columns, e.c.occurred_at, or_(*kinds), since, until, page_size
+    ):
+        records: list[Record] = [
+            EventRecord(**{**row, "attrs": row["attrs"] or {}}) for row in rows
+        ]
+        await run.feed(records)
+        if run.done:
+            return
+
+
+async def _start(
+    sink: EmbeddingSink,
+    sources: Iterable[str] | None,
+    limit: int | None,
+    batch_size: int,
+    dry_run: bool,
+) -> _Run:
+    chosen = tuple(sink.sources if sources is None else sources)
+    unknown = set(chosen) - set(EMBEDDING_SOURCES)
+    if unknown:
+        raise ValueError(
+            f"unknown sources {sorted(unknown)}; choose from {EMBEDDING_SOURCES}"
+        )
+    if batch_size < 1:
+        raise ValueError(f"batch_size must be at least 1, got {batch_size!r}")
+    if limit is not None and limit < 0:
+        raise ValueError(f"limit must not be negative, got {limit!r}")
+    run = _Run(sink, sorted(chosen, key=EMBEDDING_SOURCES.index), limit, dry_run)
+    db = sink.database_sink
+    if not dry_run:
+        await db.ensure_embeddings()
+    run.have_table = await _has_table(db.engine, db.embeddings.name)
+    if not run.have_table and not dry_run:
+        raise RuntimeError(
+            f"{db.embeddings.name} does not exist; create it with a migration "
+            "or pass create_tables=True to the DatabaseSink"
+        )
+    return run
+
+
+async def backfill_embeddings(
+    embedding_sink: EmbeddingSink,
+    *,
+    sources: Iterable[str] | None = None,
+    since: datetime | None = None,
+    until: datetime | None = None,
+    limit: int | None = None,
+    batch_size: int = 500,
+    dry_run: bool = False,
+) -> dict[str, int]:
+    """Embed stored tool-call errors and events that have no embedding yet.
+
+    Reads the tables of ``embedding_sink.database_sink``:
+
+    - ``call_error``: calls with outcome ``error`` or ``soft_error``, and
+      calls with outcome NULL and an error message (rows written before the
+      outcome column existed).
+    - ``feedback``: ``feedback.submitted`` events.
+    - ``llm``: ``llm.call`` events with a prompt or completion.
+    - ``event``: other events with any of the sink's ``event_text_keys``.
+
+    Each text goes through the sink's extraction, redactor and ``max_chars``,
+    so it matches what the live sink makes of the same record. Texts already
+    stored with the same hash are skipped; a changed text replaces its row.
+
+    Args:
+        sources: which of the above; defaults to the sink's ``sources``.
+        since, until: only rows timestamped in [since, until). Naive
+            datetimes are taken as UTC.
+        limit: embed at most this many texts, oldest first. A later run
+            carries on, since what was embedded is then skipped.
+        batch_size: rows read per query. Texts are embedded in chunks of
+            the sink's ``batch_size``, each limited to its ``timeout``.
+        dry_run: count what would be embedded without calling the embedder
+            or writing anything.
+
+    Returns counts of texts embedded per source type, plus ``skipped``
+    (already embedded) and ``errors`` (texts whose embed or store failed;
+    logged, never raised). Raises ValueError for bad arguments and
+    RuntimeError when a table it needs does not exist.
+    """
+    run = await _start(embedding_sink, sources, limit, batch_size, dry_run)
+    db = embedding_sink.database_sink
+    since, until = _utc(since), _utc(until)
+    if "call_error" in run.sources:
+        if not await _has_table(db.engine, db.table.name):
+            raise RuntimeError(f"{db.table.name} does not exist")
+        if not run.done:
+            await _backfill_calls(run, since, until, batch_size)
+    if set(run.sources) & {"feedback", "event", "llm"}:
+        if not await _has_table(db.engine, db.events.name):
+            raise RuntimeError(f"{db.events.name} does not exist")
+        if not run.done:
+            await _backfill_events(run, since, until, batch_size)
+    return run.result()
+
+
+async def backfill_feedback(
+    embedding_sink: EmbeddingSink,
+    items: Iterable[tuple[str, str, str | None]],
+    *,
+    batch_size: int = 500,
+    dry_run: bool = False,
+) -> dict[str, int]:
+    """Embed feedback kept in the host's own tables, as (ref, title, description).
+
+    The text and source id are those of a ``feedback.submitted`` event with
+    key ``ref`` and those attrs, so feedback recorded later as an event
+    dedupes against these rows. Returns ``{"feedback", "skipped", "errors"}``
+    like ``backfill_embeddings``.
+    """
+    run = await _start(embedding_sink, ("feedback",), None, batch_size, dry_run)
+    now = datetime.now(UTC)
+    page: list[Record] = []
+    for ref, title, description in items:
+        page.append(
+            EventRecord(
+                kind=FEEDBACK_KIND,
+                occurred_at=now,
+                key=str(ref),
+                attrs={"title": title, "description": description},
+            )
+        )
+        if len(page) >= batch_size:
+            await run.feed(page)
+            page = []
+    if page:
+        await run.feed(page)
+    return run.result()
+
+
+__all__ = ["backfill_embeddings", "backfill_feedback"]

src/fastmcp_feedback/instrumentation/backfill/__main__.py

@@ -0,0 +1,5 @@
+import sys
+
+from .cli import main
+
+sys.exit(main())

src/fastmcp_feedback/instrumentation/backfill/cli.py

@@ -0,0 +1,143 @@
+"""python -m fastmcp_feedback.instrumentation.backfill
+
+Prints the counts as JSON. Exits 1 when any text failed to embed, 2 on a
+setup error (bad arguments, missing table, unreachable database).
+"""
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+import sys
+from datetime import datetime
+
+from ..embeddings import DEFAULT_EVENT_TEXT_KEYS, EmbeddingSink, OpenAIEmbedder
+from ..storage import DEFAULT_TABLE_PREFIX, EMBEDDING_SOURCES, DatabaseSink
+from . import backfill_embeddings
+
+API_KEY_ENV = "FFB_EMBED_API_KEY"
+
+
+def _sources(value: str) -> tuple[str, ...]:
+    chosen = tuple(s.strip() for s in value.split(",") if s.strip())
+    unknown = set(chosen) - set(EMBEDDING_SOURCES)
+    if not chosen or unknown:
+        raise argparse.ArgumentTypeError(
+            f"choose from {','.join(EMBEDDING_SOURCES)}, comma separated"
+        )
+    return chosen
+
+
+def _when(value: str) -> datetime:
+    try:
+        return datetime.fromisoformat(value)
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError(f"not an ISO 8601 time: {value!r}") from exc
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        prog="python -m fastmcp_feedback.instrumentation.backfill",
+        description=(
+            "Embed stored tool-call errors and events that have no embedding "
+            f"yet. The embedder's API key is read from ${API_KEY_ENV} (or the "
+            "variable named by --api-key-env)."
+        ),
+    )
+    parser.add_argument(
+        "--database-url",
+        required=True,
+        help="async SQLAlchemy URL, e.g. postgresql+asyncpg://... or sqlite+aiosqlite:///calls.db",
+    )
+    parser.add_argument(
+        "--embed-base-url",
+        help="OpenAI-compatible base URL up to the version, e.g. https://api.example.com/v1 "
+        "(required unless --dry-run)",
+    )
+    parser.add_argument("--api-key-env", default=API_KEY_ENV, metavar="NAME")
+    parser.add_argument("--model", default="mxbai-embed-large")
+    parser.add_argument("--dim", type=int, default=1024)
+    parser.add_argument("--prefix", default=DEFAULT_TABLE_PREFIX)
+    parser.add_argument(
+        "--sources",
+        type=_sources,
+        default=EMBEDDING_SOURCES,
+        help=f"comma separated, default {','.join(EMBEDDING_SOURCES)}",
+    )
+    parser.add_argument(
+        "--event-text-keys",
+        default=",".join(DEFAULT_EVENT_TEXT_KEYS),
+        help="attrs making up an event text, comma separated",
+    )
+    parser.add_argument("--since", type=_when, help="ISO 8601; naive means UTC")
+    parser.add_argument("--until", type=_when, help="ISO 8601, exclusive")
+    parser.add_argument("--limit", type=int, help="embed at most this many texts")
+    parser.add_argument("--batch-size", type=int, default=500, help="rows per query")
+    parser.add_argument("--max-chars", type=int, default=4000)
+    parser.add_argument(
+        "--create-tables",
+        action="store_true",
+        help="create the embeddings table (and vector extension) if missing",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="count what would be embedded; call no embedder, write nothing",
+    )
+    parser.add_argument("-v", "--verbose", action="store_true")
+    return parser
+
+
+async def _run(args: argparse.Namespace) -> dict[str, int]:
+    embedder = OpenAIEmbedder(
+        args.embed_base_url or "http://embedder.invalid/v1",
+        api_key=os.environ.get(args.api_key_env) or None,
+        model=args.model,
+        dim=args.dim,
+    )
+    db = DatabaseSink(
+        args.database_url,
+        prefix=args.prefix,
+        create_tables=args.create_tables and not args.dry_run,
+        embedding_dim=args.dim,
+    )
+    try:
+        sink = EmbeddingSink(
+            embedder,
+            db,
+            sources=args.sources,
+            event_text_keys=[k for k in args.event_text_keys.split(",") if k],
+            max_chars=args.max_chars,
+        )
+        return await backfill_embeddings(
+            sink,
+            since=args.since,
+            until=args.until,
+            limit=args.limit,
+            batch_size=args.batch_size,
+            dry_run=args.dry_run,
+        )
+    finally:
+        await db.aclose()
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = build_parser()
+    args = parser.parse_args(argv)
+    if not args.dry_run and not args.embed_base_url:
+        parser.error("--embed-base-url is required unless --dry-run")
+    # force: importing fastmcp_feedback already configured the root logger
+    # at INFO, which would print every embedding request.
+    logging.basicConfig(
+        level=logging.INFO if args.verbose else logging.WARNING,
+        format="%(levelname)s %(name)s: %(message)s",
+        force=True,
+    )
+    try:
+        counts = asyncio.run(_run(args))
+    except Exception as exc:
+        print(f"backfill failed: {type(exc).__name__}: {exc}", file=sys.stderr)
+        return 2
+    print(json.dumps({"dry_run": args.dry_run, **counts}))
+    return 1 if counts["errors"] else 0

src/fastmcp_feedback/instrumentation/dispatch.py

@@ -3,10 +3,10 @@
 import asyncio
 import contextlib
 import logging
-from collections.abc import Sequence
+import threading
+from collections.abc import Callable, Sequence
 
-from .record import ToolCallRecord
-from .sinks import Sink
+from .sinks import Record, Sink
 
 logger = logging.getLogger(__name__)
 
@@ -33,11 +33,13 @@ class RecordDispatcher:
         self.sink_timeout = sink_timeout
         self.dropped = 0
         self.sink_errors = 0
-        self._queue: asyncio.Queue[ToolCallRecord] | None = None
+        self._queue: asyncio.Queue[Record] | None = None
         self._task: asyncio.Task | None = None
         self._loop: asyncio.AbstractEventLoop | None = None
+        # submit_threadsafe can drop from any thread.
+        self._drop_lock = threading.Lock()
 
-    def submit(self, record: ToolCallRecord) -> bool:
+    def submit(self, record: Record) -> bool:
         """Queue a record for delivery. Returns False if it was dropped."""
         try:
             loop = asyncio.get_running_loop()
@@ -45,12 +47,7 @@ class RecordDispatcher:
             self._drop("no running event loop")
             return False
 
-        # A queue belongs to one loop; tests and some hosts run several.
-        if self._queue is None or self._loop is not loop:
-            self._loop = loop
-            self._queue = asyncio.Queue(maxsize=self.max_queue)
-            self._task = loop.create_task(self._run(self._queue))
-
+        self._bind(loop)
         try:
             self._queue.put_nowait(record)
         except asyncio.QueueFull:
@@ -58,11 +55,79 @@ class RecordDispatcher:
             return False
         return True
 
+    def bind_loop(self) -> None:
+        """Adopt the running loop, so other threads can submit before any record.
+
+        Called by the middleware as each tool call starts. Never raises.
+        """
+        with contextlib.suppress(Exception):
+            self._bind(asyncio.get_running_loop())
+
+    def _bind(self, loop: asyncio.AbstractEventLoop) -> None:
+        # A queue belongs to one loop; tests and some hosts run several.
+        if self._queue is None or self._loop is not loop:
+            self._loop = loop
+            self._queue = asyncio.Queue(maxsize=self.max_queue)
+            self._task = loop.create_task(self._run(self._queue))
+
+    def submit_threadsafe(
+        self,
+        record: Record,
+        on_queued: Callable[[Record], None] | None = None,
+    ) -> bool:
+        """``submit`` that may be called from any thread. Never blocks or raises.
+
+        On the loop the dispatcher runs on, or on any loop when it has none
+        running, this is ``submit``. From another thread the record is handed
+        to the dispatcher's loop with ``call_soon_threadsafe``; True then means
+        it was handed over, and a queue that fills before it lands still drops
+        and counts it. With no running loop anywhere the record is dropped.
+        ``on_queued(record)`` runs on the loop thread once the record is queued.
+        """
+        try:
+            try:
+                running = asyncio.get_running_loop()
+            except RuntimeError:
+                running = None
+            home = self._loop
+            home_alive = home is not None and home.is_running()
+            if running is not None and (not home_alive or home is running):
+                return self._submit_and_notify(record, on_queued)
+            if not home_alive:
+                self._drop("no running event loop")
+                return False
+            queue = self._queue
+            if queue is not None and queue.full():
+                self._drop("queue full")
+                return False
+            home.call_soon_threadsafe(self._submit_and_notify, record, on_queued)
+            return True
+        except Exception:
+            # call_soon_threadsafe raises if the loop closed after the check.
+            self._drop("event loop unavailable")
+            return False
+
+    def _submit_and_notify(
+        self, record: Record, on_queued: Callable[[Record], None] | None
+    ) -> bool:
+        if not self.submit(record):
+            return False
+        if on_queued is not None:
+            try:
+                on_queued(record)
+            except Exception:
+                logger.warning("on_queued callback failed", exc_info=True)
+        return True
+
     def _drop(self, reason: str) -> None:
-        self.dropped += 1
-        if self.dropped == 1 or self.dropped % 1000 == 0:
+        with self._drop_lock:
+            self.dropped += 1
+            dropped = self.dropped
+        if dropped == 1 or dropped % 1000 == 0:
             logger.warning(
-                "Dropped tool-call record (%s); %d dropped so far", reason, self.dropped
+                "Dropped instrumentation record (%s); %d dropped so far",
+                reason,
+                dropped,
             )
 
     async def _run(self, queue: asyncio.Queue) -> None:
@@ -79,7 +144,7 @@ class RecordDispatcher:
                 for _ in batch:
                     queue.task_done()
 
-    async def _deliver(self, batch: list[ToolCallRecord]) -> None:
+    async def _deliver(self, batch: list[Record]) -> None:
         for sink in self.sinks:
             try:
                 await asyncio.wait_for(sink.write(batch), self.sink_timeout)

src/fastmcp_feedback/instrumentation/embeddings.py

@@ -0,0 +1,670 @@
+"""Embed feedback, tool errors, event text and LLM text for similarity search.
+
+EmbeddingSink sits next to a DatabaseSink. It picks text out of each batch,
+queues it on its own bounded queue and returns at once, so a slow embedding
+backend never holds up the dispatcher. A background task embeds and stores
+the vectors in ``{prefix}embeddings``.
+"""
+
+import asyncio
+import contextlib
+import hashlib
+import logging
+import math
+import operator
+import uuid
+from collections.abc import Iterable, Sequence
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from typing import Any, Protocol, runtime_checkable
+
+from sqlalchemy import Float, and_, literal, not_, select, tuple_
+
+from .events import EventRecord
+from .record import ToolCallRecord
+from .redaction import Redactor
+from .sinks import Record
+from .storage import EMBEDDING_SOURCES, EmbeddingVector
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_EVENT_TEXT_KEYS = ("error_excerpt", "error", "message", "reason")
+
+
+@runtime_checkable
+class Embedder(Protocol):
+    """Turns texts into vectors of length ``dim``, one per text, in order."""
+
+    model: str
+    dim: int
+
+    async def embed(self, texts: list[str]) -> list[list[float]]: ...
+
+
+def _http_module() -> Any:
+    # FastMCP 2.x and 3.x depend on httpx, FastMCP 4 on its successor httpx2.
+    try:
+        import httpx
+
+        return httpx
+    except ImportError:
+        pass
+    try:
+        import httpx2
+
+        return httpx2
+    except ImportError as exc:
+        raise ImportError("OpenAIEmbedder needs httpx or httpx2") from exc
+
+
+class OpenAIEmbedder:
+    """An OpenAI-compatible ``POST {base_url}/embeddings`` endpoint.
+
+    Args:
+        base_url: up to and including the version, e.g.
+            ``https://api.example.com/v1``.
+        api_key: sent as ``Authorization: Bearer ...`` when given.
+        model: the ``model`` field of each request.
+        dim: expected vector length. A response with any other length raises
+            ValueError, since it cannot be stored or compared.
+        batch_size: texts per request.
+        timeout: seconds per request.
+        transport: passed to the HTTP client. For tests.
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        api_key: str | None = None,
+        model: str = "mxbai-embed-large",
+        dim: int = 1024,
+        batch_size: int = 32,
+        timeout: float = 30.0,
+        transport: Any = None,
+    ):
+        if batch_size < 1:
+            raise ValueError(f"batch_size must be at least 1, got {batch_size!r}")
+        self.base_url = base_url.rstrip("/")
+        self.api_key = api_key
+        self.model = model
+        self.dim = dim
+        self.batch_size = batch_size
+        self.timeout = timeout
+        self.transport = transport
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        if not texts:
+            return []
+        http = _http_module()
+        headers = {"Content-Type": "application/json"}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+        vectors: list[list[float]] = []
+        kwargs: dict[str, Any] = {"timeout": self.timeout}
+        if self.transport is not None:
+            kwargs["transport"] = self.transport
+        async with http.AsyncClient(**kwargs) as client:
+            for start in range(0, len(texts), self.batch_size):
+                chunk = texts[start : start + self.batch_size]
+                response = await client.post(
+                    f"{self.base_url}/embeddings",
+                    json={"model": self.model, "input": chunk},
+                    headers=headers,
+                )
+                response.raise_for_status()
+                data = sorted(
+                    response.json()["data"], key=lambda item: item.get("index", 0)
+                )
+                if len(data) != len(chunk):
+                    raise ValueError(
+                        f"asked for {len(chunk)} embeddings, got {len(data)}"
+                    )
+                for item in data:
+                    vector = [float(v) for v in item["embedding"]]
+                    if len(vector) != self.dim:
+                        raise ValueError(
+                            f"{self.model} returned a {len(vector)}-dim vector, "
+                            f"expected {self.dim}"
+                        )
+                    vectors.append(vector)
+        return vectors
+
+
+@dataclass
+class EmbeddingText:
+    """One text waiting to be embedded, and where it came from."""
+
+    source_type: str
+    source_id: str
+    text: str
+
+    @property
+    def text_hash(self) -> str:
+        return hashlib.sha256(self.text.encode()).hexdigest()
+
+
+# Flattening stops adding items past this many characters; max_chars cuts
+# the result again later. Keeps a huge list from being joined in full.
+_FLAT_CHARS = 65536
+_FLAT_DEPTH = 32
+
+
+def _clean(value: Any, depth: int = 0) -> str | None:
+    """``value`` as stripped text, or None when there is none.
+
+    Lists and tuples become their items' texts, one per line. Dicts become
+    ``key: value`` lines sorted by key: PostgreSQL JSONB does not keep key
+    order, and the text has to come out the same when a backfill rebuilds it
+    from a stored row. Other values go through ``str()``.
+    """
+    if value is None:
+        return None
+    if isinstance(value, str):
+        text = value
+    elif isinstance(value, list | tuple | dict):
+        if depth >= _FLAT_DEPTH:
+            return None
+        if isinstance(value, dict):
+            pairs = sorted(value.items(), key=lambda kv: str(kv[0]))
+            parts = ((f"{k}: ", _clean(v, depth + 1)) for k, v in pairs)
+        else:
+            parts = (("", _clean(v, depth + 1)) for v in value)
+        lines: list[str] = []
+        size = 0
+        for label, part in parts:
+            if not part:
+                continue
+            lines.append(label + part)
+            size += len(lines[-1]) + 1
+            if size > _FLAT_CHARS:
+                break
+        text = "\n".join(lines)
+    else:
+        text = str(value)
+    text = text.strip()
+    return text or None
+
+
+def extract_texts(
+    records: Iterable[Record],
+    *,
+    sources: Iterable[str] = EMBEDDING_SOURCES,
+    event_text_keys: Sequence[str] = DEFAULT_EVENT_TEXT_KEYS,
+) -> list[EmbeddingText]:
+    """The texts worth embedding in a batch of records, unredacted and untruncated.
+
+    ``call_error``: calls with outcome ``error`` or ``soft_error``, as
+    ``"{tool}: {error_type}: {error_message}"``, keyed by the call id.
+    ``feedback``: ``feedback.submitted`` events, title and description,
+    keyed by the event's ``key`` (the feedback ref).
+    ``llm``: ``llm.call`` events with ``prompt`` or ``completion`` attrs.
+    ``event``: any other event with one of ``event_text_keys`` in its attrs,
+    those values joined by newlines, keyed by the event id. An ``llm.call``
+    without prompt or completion counts as a plain event, so a failed LLM
+    call's ``error`` is still embedded.
+
+    List and tuple values become one line per item, dicts ``key: value``
+    lines sorted by key, at any depth.
+    """
+    wanted = frozenset(sources)
+    out: list[EmbeddingText] = []
+    for record in records:
+        if isinstance(record, ToolCallRecord):
+            if "call_error" in wanted and record.outcome in ("error", "soft_error"):
+                parts = [record.tool, record.error_type, _clean(record.error_message)]
+                out.append(
+                    EmbeddingText(
+                        "call_error", record.id, ": ".join(p for p in parts if p)
+                    )
+                )
+            continue
+        if not isinstance(record, EventRecord):
+            continue
+        attrs = record.attrs or {}
+        if record.kind == "feedback.submitted":
+            if "feedback" in wanted:
+                body = "\n".join(
+                    t
+                    for t in (
+                        _clean(attrs.get("title")),
+                        _clean(attrs.get("description")),
+                    )
+                    if t
+                )
+                if body:
+                    out.append(EmbeddingText("feedback", record.key or record.id, body))
+            continue
+        if record.kind == "llm.call":
+            prompt, completion = (
+                _clean(attrs.get("prompt")),
+                _clean(attrs.get("completion")),
+            )
+            if prompt or completion:
+                if "llm" in wanted:
+                    lines = []
+                    if prompt:
+                        lines.append(f"prompt: {prompt}")
+                    if completion:
+                        lines.append(f"completion: {completion}")
+                    out.append(EmbeddingText("llm", record.id, "\n".join(lines)))
+                continue
+        if "event" in wanted:
+            values = [_clean(attrs.get(k)) for k in event_text_keys]
+            body = "\n".join(v for v in values if v)
+            if body:
+                out.append(EmbeddingText("event", record.id, body))
+    return out
+
+
+def _dot(a: Sequence[float], b: Sequence[float]) -> float:
+    sumprod = getattr(math, "sumprod", None)  # 3.12+
+    if sumprod is not None:
+        return sumprod(a, b)
+    return sum(map(operator.mul, a, b))
+
+
+def cosine_score(a: Sequence[float], b: Sequence[float]) -> float | None:
+    """1 - cosine distance, clamped to [0, 1]. None for a zero vector."""
+    na, nb = math.sqrt(_dot(a, a)), math.sqrt(_dot(b, b))
+    if not na or not nb:
+        return None
+    return _clamp(_dot(a, b) / (na * nb))
+
+
+def _clamp(score: float) -> float:
+    return min(1.0, max(0.0, score))
+
+
+class EmbeddingSink:
+    """Embed text from records and store the vectors through a DatabaseSink.
+
+    ``write`` only queues, so it returns at once whatever the embedder is
+    doing. A background task embeds up to ``batch_size`` texts at a time and
+    upserts them into ``{prefix}embeddings`` on ``database_sink.engine``. A
+    full queue drops the text and counts it in ``dropped``; embedder and
+    database failures are logged and counted in ``errors``, never raised.
+    A text whose (source_type, source_id, model) row already holds the same
+    text is not embedded again; a changed text replaces the row.
+
+    Text is redacted with ``redactor`` (the middleware's, when this sink is
+    passed to ``instrument``) and cut to ``max_chars`` before it is embedded
+    or stored.
+
+    Args:
+        embedder: anything with ``model``, ``dim`` and ``async embed(texts)``.
+        database_sink: the DatabaseSink whose engine and tables to use. Its
+            ``embedding_dim`` must equal ``embedder.dim``.
+        sources: which of ``feedback``, ``call_error``, ``event`` and ``llm``
+            to embed.
+        event_text_keys: attrs whose values make up an ``event`` text.
+        max_chars: longest text embedded.
+        max_queue: texts waiting to be embedded before new ones are dropped.
+        batch_size: texts per embed call.
+        timeout: seconds one embed call may take, and how long ``aclose``
+            waits for the queue to drain.
+        scan_limit: without pgvector, ``similar`` compares against the newest
+            this many rows.
+    """
+
+    def __init__(
+        self,
+        embedder: Embedder,
+        database_sink: Any,
+        *,
+        sources: Iterable[str] = EMBEDDING_SOURCES,
+        event_text_keys: Sequence[str] = DEFAULT_EVENT_TEXT_KEYS,
+        max_chars: int = 4000,
+        max_queue: int = 1000,
+        batch_size: int = 32,
+        timeout: float = 30.0,
+        scan_limit: int = 5000,
+        redactor: Redactor | None = None,
+    ):
+        self.sources = frozenset(sources)
+        unknown = self.sources - set(EMBEDDING_SOURCES)
+        if unknown:
+            raise ValueError(
+                f"unknown sources {sorted(unknown)}; choose from {EMBEDDING_SOURCES}"
+            )
+        if embedder.dim != database_sink.embedding_dim:
+            raise ValueError(
+                f"embedder {embedder.model!r} makes {embedder.dim}-dim vectors but "
+                f"the DatabaseSink's embeddings table holds "
+                f"{database_sink.embedding_dim}; pass embedding_dim={embedder.dim} "
+                "to DatabaseSink"
+            )
+        if max_chars < 1 or max_queue < 1 or batch_size < 1 or scan_limit < 1:
+            raise ValueError(
+                "max_chars, max_queue, batch_size and scan_limit must be positive"
+            )
+        self.embedder = embedder
+        self.database_sink = database_sink
+        self.table = database_sink.embeddings
+        self.event_text_keys = tuple(event_text_keys)
+        self.max_chars = max_chars
+        self.max_queue = max_queue
+        self.batch_size = batch_size
+        self.timeout = timeout
+        self.scan_limit = scan_limit
+        self.redactor = redactor
+        self._default_redactor = Redactor()
+        self.dropped = 0
+        self.errors = 0
+        self.embedded = 0
+        self.skipped = 0
+        self._queue: asyncio.Queue[EmbeddingText] | None = None
+        self._task: asyncio.Task | None = None
+        self._loop: asyncio.AbstractEventLoop | None = None
+        self._warned_scan = False
+
+    # Writing
+
+    async def write(self, records: Sequence[Record]) -> None:
+        try:
+            items = extract_texts(
+                records, sources=self.sources, event_text_keys=self.event_text_keys
+            )
+        except Exception:
+            self.errors += 1
+            logger.warning("Failed to extract texts to embed", exc_info=True)
+            return
+        for item in items:
+            self._enqueue(self._prepare(item))
+
+    def _prepare(self, item: EmbeddingText) -> EmbeddingText:
+        redactor = self.redactor or self._default_redactor
+        item.text = redactor.redact_text(item.text)[: self.max_chars]
+        return item
+
+    def _enqueue(self, item: EmbeddingText) -> None:
+        loop = asyncio.get_running_loop()
+        if self._queue is None or self._loop is not loop:
+            self._loop = loop
+            self._queue = asyncio.Queue(maxsize=self.max_queue)
+            self._task = loop.create_task(self._run(self._queue))
+        try:
+            self._queue.put_nowait(item)
+        except asyncio.QueueFull:
+            self.dropped += 1
+            if self.dropped == 1 or self.dropped % 1000 == 0:
+                logger.warning(
+                    "Embedding queue full; %d texts dropped so far", self.dropped
+                )
+
+    async def _run(self, queue: asyncio.Queue) -> None:
+        while True:
+            batch = [await queue.get()]
+            while len(batch) < self.batch_size:
+                try:
+                    batch.append(queue.get_nowait())
+                except asyncio.QueueEmpty:
+                    break
+            try:
+                await self._process(batch)
+            except Exception:
+                self.errors += 1
+                logger.warning(
+                    "Failed to embed %d texts with %s",
+                    len(batch),
+                    self.embedder.model,
+                    exc_info=True,
+                )
+            finally:
+                for _ in batch:
+                    queue.task_done()
+
+    async def _process(self, batch: list[EmbeddingText]) -> None:
+        # The last text for a source wins within a batch.
+        latest = {(i.source_type, i.source_id): i for i in batch}
+        db = self.database_sink
+        await db._ready()
+        await db.ensure_embeddings()
+        todo, stored = await self._unembedded(list(latest.values()))
+        self.skipped += len(batch) - len(todo)
+        if not todo:
+            return
+        vectors = await asyncio.wait_for(
+            self.embedder.embed([i.text for i in todo]), self.timeout
+        )
+        await self._store(todo, vectors, stored)
+        self.embedded += len(todo)
+
+    async def _unembedded(
+        self, items: list[EmbeddingText]
+    ) -> tuple[list[EmbeddingText], set[tuple[str, str]]]:
+        """The items whose stored row is missing or holds another text, and
+        the keys that have a row (to be replaced). Items must be unique by
+        (source_type, source_id)."""
+        if not items:
+            return [], set()
+        t = self.table
+        keys = [(i.source_type, i.source_id) for i in items]
+        async with self.database_sink.engine.connect() as conn:
+            rows = (
+                await conn.execute(
+                    select(t.c.source_type, t.c.source_id, t.c.text_hash).where(
+                        t.c.model == self.embedder.model,
+                        tuple_(t.c.source_type, t.c.source_id).in_(keys),
+                    )
+                )
+            ).all()
+        stored = {(r.source_type, r.source_id): r.text_hash for r in rows}
+        todo = [
+            i for i in items if stored.get((i.source_type, i.source_id)) != i.text_hash
+        ]
+        return todo, set(stored)
+
+    async def _store(
+        self,
+        todo: list[EmbeddingText],
+        vectors: list[list[float]],
+        replace: set[tuple[str, str]],
+    ) -> None:
+        db = self.database_sink
+        if len(vectors) != len(todo):
+            raise ValueError(
+                f"embedder returned {len(vectors)} vectors for {len(todo)}"
+            )
+        for vector in vectors:
+            if len(vector) != db.embedding_dim:
+                raise ValueError(
+                    f"embedder returned a {len(vector)}-dim vector, "
+                    f"expected {db.embedding_dim}"
+                )
+        now = datetime.now(UTC)
+        values = [
+            {
+                "id": str(uuid.uuid4()),
+                "source_type": i.source_type,
+                "source_id": i.source_id,
+                "model": self.embedder.model,
+                "dim": len(v),
+                "text_hash": i.text_hash,
+                "text": i.text,
+                "created_at": now,
+                "embedding": [float(x) for x in v],
+            }
+            for i, v in zip(todo, vectors, strict=True)
+        ]
+        await self._upsert(values, replace=replace)
+
+    async def _upsert(self, values: list[dict[str, Any]], replace: set) -> None:
+        t = self.table
+        engine = self.database_sink.engine
+        dialect = engine.dialect.name
+        changing = ("text_hash", "text", "created_at", "embedding", "dim")
+        if dialect in ("postgresql", "sqlite"):
+            if dialect == "postgresql":
+                from sqlalchemy.dialects.postgresql import insert
+            else:
+                from sqlalchemy.dialects.sqlite import insert
+            stmt = insert(t)
+            stmt = stmt.on_conflict_do_update(
+                index_elements=[t.c.source_type, t.c.source_id, t.c.model],
+                set_={c: stmt.excluded[c] for c in changing},
+            )
+            async with engine.begin() as conn:
+                await conn.execute(stmt, values)
+            return
+        async with engine.begin() as conn:
+            for row in values:
+                key = (row["source_type"], row["source_id"])
+                if key in replace:
+                    await conn.execute(
+                        t.update()
+                        .where(
+                            t.c.source_type == row["source_type"],
+                            t.c.source_id == row["source_id"],
+                            t.c.model == row["model"],
+                        )
+                        .values({c: row[c] for c in changing})
+                    )
+                else:
+                    await conn.execute(t.insert(), [row])
+
+    async def flush(self) -> None:
+        """Wait until every queued text has been embedded (or failed)."""
+        if self._queue is not None and self._loop is asyncio.get_running_loop():
+            await self._queue.join()
+
+    async def aclose(self) -> None:
+        """Drain the queue for up to ``timeout`` seconds, then stop.
+
+        Texts still queued after that are dropped and counted. Disposes the
+        DatabaseSink's engine again if it owns it, since the dispatcher may
+        have closed that sink first.
+        """
+        if self._queue is not None and self._loop is asyncio.get_running_loop():
+            try:
+                await asyncio.wait_for(self._queue.join(), self.timeout)
+            except TimeoutError:
+                left = self._queue.qsize()
+                self.dropped += left
+                logger.warning(
+                    "Embedding queue did not drain in %ss; %d texts dropped",
+                    self.timeout,
+                    left,
+                )
+        if self._task is not None:
+            self._task.cancel()
+            with contextlib.suppress(asyncio.CancelledError, RuntimeError):
+                await self._task
+            self._task = None
+            self._queue = None
+        if getattr(self.database_sink, "_owns_engine", False):
+            await self.database_sink.engine.dispose()
+
+    # Searching
+
+    async def similar(
+        self,
+        text: str,
+        *,
+        source_types: Iterable[str] | None = None,
+        k: int = 10,
+        min_score: float | None = None,
+    ) -> list[dict[str, Any]]:
+        """Stored texts closest to ``text``. Raises on failure; see the middleware."""
+        text = (self.redactor or self._default_redactor).redact_text(text)[
+            : self.max_chars
+        ]
+        [vector] = await asyncio.wait_for(self.embedder.embed([text]), self.timeout)
+        return await self.nearest(
+            vector, source_types=source_types, k=k, min_score=min_score
+        )
+
+    async def vector_for(self, source_type: str, source_id: str) -> list[float] | None:
+        """The stored vector for a source under this embedder's model, if any."""
+        t = self.table
+        async with self.database_sink.engine.connect() as conn:
+            row = (
+                await conn.execute(
+                    select(t.c.embedding).where(
+                        t.c.source_type == source_type,
+                        t.c.source_id == str(source_id),
+                        t.c.model == self.embedder.model,
+                    )
+                )
+            ).first()
+        return None if row is None else list(row.embedding)
+
+    async def nearest(
+        self,
+        vector: Sequence[float],
+        *,
+        source_types: Iterable[str] | None = None,
+        k: int = 10,
+        min_score: float | None = None,
+        exclude: tuple[str, str] | None = None,
+    ) -> list[dict[str, Any]]:
+        """Rows closest to ``vector`` by cosine, best first, as dicts with
+        ``source_type``, ``source_id``, ``text``, ``score`` and ``created_at``.
+
+        ``score`` is 1 - cosine distance, clamped to [0, 1]. Only rows made by
+        this embedder's model are compared. ``exclude`` is a
+        (source_type, source_id) left out of the results.
+        """
+        if k < 1:
+            return []
+        t = self.table
+        conditions = [t.c.model == self.embedder.model]
+        if source_types is not None:
+            conditions.append(t.c.source_type.in_(list(source_types)))
+        if exclude is not None:
+            conditions.append(
+                not_(and_(t.c.source_type == exclude[0], t.c.source_id == exclude[1]))
+            )
+        engine = self.database_sink.engine
+        cols = (t.c.source_type, t.c.source_id, t.c.text, t.c.created_at)
+        results: list[dict[str, Any]] = []
+        if engine.dialect.name == "postgresql":
+            distance = t.c.embedding.op("<=>", return_type=Float)(
+                literal(list(vector), type_=EmbeddingVector(len(vector)))
+            )
+            query = (
+                select(*cols, distance.label("distance"))
+                .where(*conditions)
+                .order_by(distance)
+                .limit(k)
+            )
+            async with engine.connect() as conn:
+                for row in (await conn.execute(query)).mappings():
+                    row = dict(row)
+                    distance_value = row.pop("distance")
+                    if distance_value is None or math.isnan(distance_value):
+                        continue
+                    row["score"] = _clamp(1.0 - distance_value)
+                    results.append(row)
+        else:
+            query = (
+                select(*cols, t.c.embedding)
+                .where(*conditions)
+                .order_by(t.c.created_at.desc())
+                .limit(self.scan_limit + 1)
+            )
+            async with engine.connect() as conn:
+                rows = [dict(r) for r in (await conn.execute(query)).mappings()]
+            if len(rows) > self.scan_limit:
+                rows = rows[: self.scan_limit]
+                if not self._warned_scan:
+                    self._warned_scan = True
+                    logger.info(
+                        "Similarity search without pgvector compares only the "
+                        "newest %d embeddings (scan_limit)",
+                        self.scan_limit,
+                    )
+            for row in rows:
+                score = cosine_score(vector, row.pop("embedding"))
+                if score is None:
+                    continue
+                row["score"] = score
+                results.append(row)
+            results.sort(key=lambda r: r["score"], reverse=True)
+            results = results[:k]
+        if min_score is not None:
+            results = [r for r in results if r["score"] >= min_score]
+        for row in results:
+            created = row["created_at"]
+            if created is not None and created.tzinfo is None:
+                row["created_at"] = created.replace(tzinfo=UTC)
+        return results

src/fastmcp_feedback/instrumentation/events.py

@@ -0,0 +1,89 @@
+"""Events: things worth recording that are not tool calls.
+
+A background job changing state, an LLM request made on a tool's behalf. Each
+event has a dotted ``kind``, an optional ``key`` to join on (a job id), and,
+when recorded while a tool call is running, that call's id.
+"""
+
+import math
+import uuid
+from dataclasses import asdict, dataclass, field
+from datetime import datetime
+from typing import Any, ClassVar
+
+# Column widths in {prefix}events. Longer values are refused rather than
+# truncated, since a cut key would silently stop joining.
+MAX_KIND_CHARS = 128
+MAX_KEY_CHARS = 255
+
+
+@dataclass
+class EventRecord:
+    """One event, as passed to sinks next to ``ToolCallRecord``.
+
+    ``call_id`` is the id of the tool call that was running when the event was
+    recorded, None outside a call. ``attrs`` is already redacted.
+    """
+
+    record_type: ClassVar[str] = "event"
+
+    kind: str
+    occurred_at: datetime
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    key: str | None = None
+    call_id: str | None = None
+    session_id: str | None = None
+    user_sub: str | None = None
+    caller_kind: str | None = None
+    client_id: str | None = None
+    server_version: str | None = None
+    attrs: dict[str, Any] = field(default_factory=dict)
+
+    def to_row(self) -> dict[str, Any]:
+        """Column values for the events table, with ``attrs`` made JSON-safe."""
+        data = asdict(self)
+        data["attrs"] = jsonable(data["attrs"])
+        return data
+
+    def to_dict(self) -> dict[str, Any]:
+        """JSON-friendly form, with the timestamp as ISO 8601."""
+        data = self.to_row()
+        data["occurred_at"] = self.occurred_at.isoformat()
+        return data
+
+
+@dataclass
+class CallScope:
+    """The tool call in progress, visible to ``record_event`` through a ContextVar.
+
+    Tasks started inside a tool inherit a copy of the context and outlive the
+    call, so the scope is switched off when the call ends instead of relying
+    on the ContextVar reset alone.
+    """
+
+    id: str
+    session_id: str | None
+    active: bool = True
+    events: int = 0
+
+
+def jsonable(value: Any, depth: int = 0) -> Any:
+    """A copy of ``value`` that any JSON column accepts.
+
+    Non-finite floats (a diverged residual) become strings, since Postgres
+    JSONB rejects NaN and one bad row would fail the whole batch. Other
+    unknown types become ``str(value)``.
+    """
+    if depth > 32:
+        return "[TRUNCATED]"
+    if value is None or isinstance(value, bool | int | str):
+        return value
+    if isinstance(value, float):
+        return value if math.isfinite(value) else str(value)
+    if isinstance(value, dict):
+        return {str(k): jsonable(v, depth + 1) for k, v in value.items()}
+    if isinstance(value, list | tuple | set | frozenset):
+        return [jsonable(v, depth + 1) for v in value]
+    if isinstance(value, datetime):
+        return value.isoformat()
+    return str(value)

src/fastmcp_feedback/instrumentation/middleware.py

@@ -1,14 +1,17 @@
-"""FastMCP middleware that records every tool call."""
+"""FastMCP middleware that records every tool call, and events you report."""
 
 import asyncio
+import contextlib
+import contextvars
 import inspect
 import json
 import logging
 import os
+import random as _random
 import time
 import uuid
 from collections import deque
-from collections.abc import Awaitable, Callable, Iterable, Sequence
+from collections.abc import Awaitable, Callable, Iterable, Mapping, Sequence
 from datetime import UTC, datetime, timedelta
 from typing import Any
 
@@ -24,6 +27,14 @@ from .correlation import (
     select_calls,
 )
 from .dispatch import RecordDispatcher
+from .embeddings import EmbeddingSink
+from .events import (
+    MAX_KEY_CHARS,
+    MAX_KIND_CHARS,
+    CallScope,
+    EventRecord,
+    jsonable,
+)
 from .record import ToolCallRecord
 from .redaction import Redactor
 from .sinks import JsonLinesSink, Sink
@@ -83,6 +94,10 @@ def session_key(ctx: Any) -> str | None:
     return _attr(ctx, "session_id")
 
 
+def _tool_name(context: MiddlewareContext) -> str:
+    return getattr(context.message, "name", None) or "unknown"
+
+
 def _result_payload(result: Any) -> Any:
     structured = getattr(result, "structured_content", None)
     if structured is not None:
@@ -137,6 +152,26 @@ class InstrumentationMiddleware(Middleware):
             error (``outcome="soft_error"``, ``ok=False``) and becomes the
             error message. Defaults to ``DEFAULT_RESULT_CLASSIFIER``; None
             turns it off. If it raises, the call is recorded as ok.
+        exclude_tools: tools that are never recorded, nor kept for
+            correlation. The calls themselves are untouched.
+        sample_rates: tool name to the fraction of its ok calls recorded, in
+            (0, 1]. Failures (``error`` and ``soft_error``) are always
+            recorded. Sampled-in ok rows carry ``sample_rate``; calls
+            sampled out are not kept for correlation either.
+        random: ``() -> float`` in [0, 1) deciding sampling; a call is kept
+            when it returns less than the tool's rate. For tests.
+        recent_events: how many recent events to keep in memory for
+            ``events_for``, including ones still waiting to reach the sinks.
+        capture_llm_text: store ``prompt`` and ``completion`` given to
+            ``record_llm_call``. Off by default, since chat text can hold
+            anything a user typed; when off they are discarded, including
+            ``prompt``/``completion`` keys passed in its ``attrs``.
+        max_llm_text_chars: longest prompt or completion kept. Defaults to
+            ``8 * max_error_chars`` (4000).
+
+    Exclusion and sampling apply to tool calls only; events are always
+    recorded. A sampled tool's call is kept whenever an event was recorded
+    during it, so the event's ``call_id`` always has a row.
     """
 
     def __init__(
@@ -157,6 +192,12 @@ class InstrumentationMiddleware(Middleware):
         recent_calls: int = 2000,
         correlation_timeout: float = 1.0,
         result_classifier: ResultClassifier | None = DEFAULT_RESULT_CLASSIFIER,
+        exclude_tools: Iterable[str] = (),
+        sample_rates: Mapping[str, float] | None = None,
+        random: Callable[[], float] = _random.random,
+        recent_events: int = 2000,
+        capture_llm_text: bool = False,
+        max_llm_text_chars: int | None = None,
     ):
         mode = (mode or os.getenv(MODE_ENV_VAR) or "meta").lower()
         if mode not in MODES:
@@ -171,9 +212,35 @@ class InstrumentationMiddleware(Middleware):
         self.max_error_chars = max_error_chars
         self.correlation_timeout = correlation_timeout
         self.result_classifier = result_classifier
+        self.exclude_tools = frozenset(exclude_tools)
+        self.sample_rates: dict[str, float] = {}
+        for tool, rate in (sample_rates or {}).items():
+            rate = float(rate)
+            if not 0 < rate <= 1:  # also rejects NaN
+                raise ValueError(
+                    f"sample rate for {tool!r} must be in (0, 1], got {rate!r}"
+                )
+            self.sample_rates[tool] = rate
+        self.random = random
         self.recent: deque[ToolCallRecord] = deque(maxlen=recent_calls)
+        self.recent_events: deque[EventRecord] = deque(maxlen=recent_events)
+        # One per middleware, so events only link to calls this one records.
+        self._call_scope: contextvars.ContextVar[CallScope | None] = (
+            contextvars.ContextVar(f"ffb_call_scope_{id(self):x}", default=None)
+        )
+        self.capture_llm_text = capture_llm_text
+        self.max_llm_text_chars = (
+            max_llm_text_chars
+            if max_llm_text_chars is not None
+            else 8 * max_error_chars
+        )
+        sinks = list(sinks) if sinks is not None else [JsonLinesSink()]
+        for sink in sinks:
+            # Embedded text gets this middleware's redaction rules too.
+            if isinstance(sink, EmbeddingSink) and sink.redactor is None:
+                sink.redactor = self.redactor
         self.dispatcher = RecordDispatcher(
-            sinks if sinks is not None else [JsonLinesSink()],
+            sinks,
             max_queue=max_queue,
             batch_size=batch_size,
             sink_timeout=sink_timeout,
@@ -182,11 +249,25 @@ class InstrumentationMiddleware(Middleware):
     async def on_call_tool(self, context: MiddlewareContext, call_next):
         if self.mode == "off":
             return await call_next(context)
+        # Sync tools run in worker threads; their events need the loop known.
+        self.dispatcher.bind_loop()
+        if _tool_name(context) in self.exclude_tools:
+            return await call_next(context)
 
         started_at = datetime.now(UTC)
         start = time.perf_counter()
         result = None
         error: BaseException | None = None
+        scope = token = None
+        try:
+            # The record id exists before the tool runs, so events recorded
+            # inside it carry the id the stored call row will have.
+            scope = CallScope(
+                id=str(uuid.uuid4()), session_id=session_key(context.fastmcp_context)
+            )
+            token = self._call_scope.set(scope)
+        except Exception:
+            logger.warning("Failed to open call scope", exc_info=True)
         try:
             result = await call_next(context)
             return result
@@ -195,15 +276,50 @@ class InstrumentationMiddleware(Middleware):
             raise
         finally:
             duration_ms = (time.perf_counter() - start) * 1000
+            if scope is not None:
+                scope.active = False
+            if token is not None:
+                # ValueError when finishing in another context (a task group).
+                with contextlib.suppress(ValueError):
+                    self._call_scope.reset(token)
             try:
-                record = await self._build_record(
-                    context, started_at, duration_ms, result, error
+                await self._record(
+                    context, started_at, duration_ms, result, error, scope
                 )
-                self.recent.append(record)
-                self.dispatcher.submit(record)
             except Exception:
                 logger.warning("Failed to record tool call", exc_info=True)
 
+    async def _record(
+        self,
+        context: MiddlewareContext,
+        started_at: datetime,
+        duration_ms: float,
+        result: Any,
+        error: BaseException | None,
+        scope: CallScope | None = None,
+    ) -> None:
+        tool = _tool_name(context)
+        payload = _result_payload(result) if result is not None else None
+        soft = None
+        if error is None and self.result_classifier is not None and payload is not None:
+            soft = self._classify(tool, result, payload)
+        rate = self.sample_rates.get(tool)
+        # Failures are always kept, and their rows carry no sample_rate. So are
+        # calls that recorded events, which would otherwise point at nothing.
+        if rate is not None and (
+            error is not None or soft is not None or (scope and scope.events)
+        ):
+            rate = None
+        elif rate is not None and not self.random() < rate:
+            return
+        record = await self._build_record(
+            context, started_at, duration_ms, payload, error, soft, rate
+        )
+        if scope is not None:
+            record.id = scope.id
+        self.recent.append(record)
+        self.dispatcher.submit(record)
+
     async def _call_hook(self, hook: Callable | None, *args: Any) -> dict[str, Any]:
         if hook is None:
             return {}
@@ -237,14 +353,14 @@ class InstrumentationMiddleware(Middleware):
         context: MiddlewareContext,
         started_at: datetime,
         duration_ms: float,
-        result: Any,
+        payload: Any,
         error: BaseException | None,
+        soft: str | None,
+        sample_rate: float | None,
     ) -> ToolCallRecord:
-        message = context.message
-        tool = getattr(message, "name", None) or "unknown"
-        args = getattr(message, "arguments", None) or {}
+        tool = _tool_name(context)
+        args = getattr(context.message, "arguments", None) or {}
         ctx = context.fastmcp_context
-        payload = _result_payload(result) if result is not None else None
 
         identity = await self._call_hook(self.identity_resolver, context)
         extra = await self._call_hook(self.enricher, tool, args, payload, context)
@@ -266,14 +382,12 @@ class InstrumentationMiddleware(Middleware):
             error_message = self.redactor.redact_text(str(cause))[
                 : self.max_error_chars
             ]
-        elif self.result_classifier is not None and payload is not None:
-            soft = self._classify(tool, result, payload)
-            if soft is not None:
-                outcome = "soft_error"
-                error_type = "SoftError"
-                error_message = (
-                    self.redactor.redact_text(soft)[: self.max_error_chars] or None
-                )
+        elif soft is not None:
+            outcome = "soft_error"
+            error_type = "SoftError"
+            error_message = (
+                self.redactor.redact_text(soft)[: self.max_error_chars] or None
+            )
 
         return ToolCallRecord(
             tool=tool,
@@ -298,8 +412,233 @@ class InstrumentationMiddleware(Middleware):
             if full and payload is not None
             else None,
             extra=extra,
+            sample_rate=sample_rate,
         )
 
+    def record_event(
+        self,
+        kind: str,
+        *,
+        key: Any = None,
+        attrs: Mapping[str, Any] | None = None,
+        user_sub: str | None = None,
+        call_id: str | None = None,
+    ) -> bool:
+        """Record something that is not a tool call. Never raises or blocks.
+
+        Safe from any thread. Inside a tool call the event gets that call's id
+        and session; ``call_id`` given here wins. ``attrs`` is redacted like
+        call arguments and made JSON-safe. ``kind`` must be a non-empty string
+        of at most 128 characters and ``key`` at most 255 once made a string.
+
+        Returns False when the event was not recorded: mode ``off``, an invalid
+        ``kind``, ``key`` or ``attrs``, a full queue, or no event loop to hand
+        it to (called from a thread before anything was recorded on the
+        server's loop). From another thread, True means handed to the loop.
+        """
+        try:
+            if self.mode == "off":
+                return False
+            if not isinstance(kind, str) or not kind.strip():
+                logger.warning("record_event needs a non-empty str kind, got %r", kind)
+                return False
+            if len(kind) > MAX_KIND_CHARS:
+                logger.warning(
+                    "Event kind longer than %d chars: %r", MAX_KIND_CHARS, kind
+                )
+                return False
+            if key is not None:
+                key = str(key)
+                if len(key) > MAX_KEY_CHARS:
+                    logger.warning("Event key longer than %d chars", MAX_KEY_CHARS)
+                    return False
+            if attrs is not None and not isinstance(attrs, Mapping):
+                logger.warning("Event attrs must be a mapping, got %s", type(attrs))
+                return False
+            session_id = None
+            scope = self._call_scope.get()
+            if scope is not None and scope.active:
+                scope.events += 1
+                session_id = scope.session_id
+                if call_id is None:
+                    call_id = scope.id
+            record = EventRecord(
+                kind=kind,
+                occurred_at=datetime.now(UTC),
+                key=key,
+                call_id=None if call_id is None else str(call_id),
+                session_id=session_id,
+                user_sub=None if user_sub is None else str(user_sub),
+                server_version=self.server_version,
+                attrs=jsonable(self.redactor.redact(dict(attrs or {}))),
+            )
+            return self.dispatcher.submit_threadsafe(record, self.recent_events.append)
+        except Exception:
+            logger.warning("Failed to record event %r", kind, exc_info=True)
+            return False
+
+    def record_llm_call(
+        self,
+        *,
+        model: str,
+        provider: str | None = None,
+        duration_ms: float | None = None,
+        input_tokens: int | None = None,
+        output_tokens: int | None = None,
+        ok: bool | None = None,
+        error: str | BaseException | None = None,
+        key: Any = None,
+        attrs: Mapping[str, Any] | None = None,
+        prompt: str | None = None,
+        completion: str | None = None,
+    ) -> bool:
+        """Record one LLM request as an ``llm.call`` event. Never raises.
+
+        Attributes: ``model``, ``provider``, ``duration_ms``, ``input_tokens``,
+        ``output_tokens`` (each only when given), ``ok``, and on failure
+        ``error`` (redacted, truncated to ``max_error_chars``) plus
+        ``error_type`` when ``error`` is an exception. ``ok`` defaults to
+        ``error is None``. Extra ``attrs`` are kept, but these names win.
+
+        ``prompt`` and ``completion`` are stored, redacted and truncated to
+        ``max_llm_text_chars``, only with ``capture_llm_text=True``;
+        otherwise they are discarded.
+        """
+        try:
+            fields: dict[str, Any] = dict(attrs or {})
+            fields.pop("prompt", None)
+            fields.pop("completion", None)
+            if self.capture_llm_text:
+                for name, value in (("prompt", prompt), ("completion", completion)):
+                    if value is None and attrs is not None:
+                        value = attrs.get(name)
+                    if value is not None:
+                        text = self.redactor.redact_text(str(value))
+                        fields[name] = text[: self.max_llm_text_chars]
+            fields["model"] = None if model is None else str(model)
+            for name, value in (
+                ("provider", provider),
+                ("duration_ms", duration_ms),
+                ("input_tokens", input_tokens),
+                ("output_tokens", output_tokens),
+            ):
+                if value is not None:
+                    fields[name] = value
+            fields["ok"] = (error is None) if ok is None else bool(ok)
+            if error is not None:
+                if isinstance(error, BaseException):
+                    fields["error_type"] = type(error).__name__
+                text = self.redactor.redact_text(str(error))
+                fields["error"] = text[: self.max_error_chars]
+            return self.record_event("llm.call", key=key, attrs=fields)
+        except Exception:
+            logger.warning("Failed to record LLM call", exc_info=True)
+            return False
+
+    async def events_for(
+        self,
+        key: Any,
+        *,
+        kinds: Iterable[str] | None = None,
+        limit: int = 1000,
+    ) -> list[dict[str, Any]]:
+        """Events for ``key``, oldest first: the newest ``limit`` of them.
+
+        Reads the DatabaseSink when there is one and adds events still only in
+        memory, so an event is visible as soon as ``record_event`` returns (on
+        the loop thread). Without a DatabaseSink, memory only. ``occurred_at``
+        is a UTC datetime. A database failure is logged and memory is used.
+        """
+        key = str(key)
+        kinds = None if kinds is None else frozenset(kinds)
+        found: dict[str, dict[str, Any]] = {}
+        sink = self.database_sink
+        if sink is not None:
+            try:
+                for row in await sink.events_for(key, kinds=kinds, limit=limit):
+                    found[row["id"]] = row
+            except Exception:
+                logger.warning("Failed to read events for %r", key, exc_info=True)
+        for record in list(self.recent_events):
+            if record.key == key and (kinds is None or record.kind in kinds):
+                found.setdefault(record.id, record.to_row())
+        rows = list(found.values())
+        for row in rows:
+            row["occurred_at"] = as_utc(row["occurred_at"])
+        rows.sort(key=lambda r: r["occurred_at"])
+        return rows[-limit:] if limit > 0 else []
+
+    @property
+    def embedding_sink(self) -> EmbeddingSink | None:
+        """The first EmbeddingSink among the sinks, if any."""
+        return next(
+            (s for s in self.dispatcher.sinks if isinstance(s, EmbeddingSink)), None
+        )
+
+    async def similar(
+        self,
+        text: str,
+        *,
+        source_types: Iterable[str] | None = None,
+        k: int = 10,
+        min_score: float | None = None,
+    ) -> list[dict[str, Any]]:
+        """Embedded texts most like ``text``, best first. Never raises.
+
+        Needs an EmbeddingSink. Each result has ``source_type``,
+        ``source_id``, ``text``, ``score`` (1 - cosine distance, in [0, 1])
+        and ``created_at``. ``source_types`` limits the search to some of
+        ``feedback``, ``call_error``, ``event`` and ``llm``; ``min_score``
+        drops weaker matches. Returns [] on any failure, logged.
+        """
+        sink = self.embedding_sink
+        if sink is None:
+            logger.warning("similar() needs an EmbeddingSink")
+            return []
+        try:
+            return await sink.similar(
+                str(text),
+                source_types=None if source_types is None else list(source_types),
+                k=k,
+                min_score=min_score,
+            )
+        except Exception:
+            logger.warning("Similarity search failed", exc_info=True)
+            return []
+
+    async def similar_feedback(
+        self,
+        feedback_ref: Any,
+        *,
+        k: int = 10,
+        source_types: Iterable[str] | None = None,
+        min_score: float | None = None,
+    ) -> list[dict[str, Any]]:
+        """Texts most like an embedded feedback item, excluding the item itself.
+
+        Uses the feedback's stored vector, so nothing is embedded. Returns []
+        when the feedback is not embedded yet, and on any failure. Never raises.
+        """
+        sink = self.embedding_sink
+        if sink is None:
+            logger.warning("similar_feedback() needs an EmbeddingSink")
+            return []
+        ref = str(feedback_ref)
+        try:
+            vector = await sink.vector_for("feedback", ref)
+            if vector is None:
+                return []
+            return await sink.nearest(
+                vector,
+                source_types=None if source_types is None else list(source_types),
+                k=k,
+                min_score=min_score,
+                exclude=("feedback", ref),
+            )
+        except Exception:
+            logger.warning("similar_feedback(%r) failed", ref, exc_info=True)
+            return []
+
     @property
     def database_sink(self) -> DatabaseSink | None:
         """The first DatabaseSink among the sinks, if any."""

src/fastmcp_feedback/instrumentation/record.py

@@ -3,7 +3,7 @@
 import uuid
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
-from typing import Any
+from typing import Any, ClassVar
 
 
 @dataclass
@@ -16,8 +16,14 @@ class ToolCallRecord:
     ``outcome`` is ``ok``, ``error`` (the tool raised) or ``soft_error`` (it
     returned a result the classifier flagged). ``ok`` is ``outcome == "ok"``;
     when ``outcome`` is not given it is derived from ``ok``.
+
+    ``sample_rate`` is set on ok calls of a sampled tool: each such row stands
+    for ``1 / sample_rate`` calls. It is None for everything recorded
+    unconditionally, including failures of sampled tools.
     """
 
+    record_type: ClassVar[str] = "tool_call"
+
     tool: str
     started_at: datetime
     duration_ms: float
@@ -39,6 +45,7 @@ class ToolCallRecord:
     args: Any = None
     result: Any = None
     extra: dict[str, Any] = field(default_factory=dict)
+    sample_rate: float | None = None
 
     def __post_init__(self) -> None:
         if self.outcome is None:

src/fastmcp_feedback/instrumentation/sinks.py

@@ -1,4 +1,4 @@
-"""Destinations for tool-call records."""
+"""Destinations for tool-call and event records."""
 
 import inspect
 import json
@@ -6,27 +6,38 @@ import sys
 from collections.abc import Callable, Sequence
 from typing import IO, Any, Protocol, runtime_checkable
 
+from .events import EventRecord
 from .record import ToolCallRecord
 
+Record = ToolCallRecord | EventRecord
+
 
 @runtime_checkable
 class Sink(Protocol):
-    """Anything with ``async write(records)``. ``async aclose()`` is optional."""
+    """Anything with ``async write(records)``. ``async aclose()`` is optional.
 
-    async def write(self, records: Sequence[ToolCallRecord]) -> None: ...
+    A batch can mix ``ToolCallRecord`` and ``EventRecord``; tell them apart
+    with ``isinstance`` or ``record.record_type`` (``tool_call``, ``event``).
+    """
+
+    async def write(self, records: Sequence[Record]) -> None: ...
 
 
 class JsonLinesSink:
-    """One JSON object per record, to stderr by default."""
+    """One JSON object per record, to stderr by default.
+
+    Each line has ``"record": "tool_call"`` or ``"record": "event"``.
+    """
 
     def __init__(self, stream: IO[str] | None = None):
         self._stream = stream
 
-    async def write(self, records: Sequence[ToolCallRecord]) -> None:
+    async def write(self, records: Sequence[Record]) -> None:
         # Resolved per write so a stderr swapped by the host or a test is honored.
         stream = self._stream or sys.stderr
         for record in records:
-            stream.write(json.dumps(record.to_dict(), default=str) + "\n")
+            line = {"record": record.record_type, **record.to_dict()}
+            stream.write(json.dumps(line, default=str) + "\n")
         stream.flush()
 
 
@@ -36,20 +47,32 @@ class CallbackSink:
     A sync callback runs on the event loop, so keep it quick.
     """
 
-    def __init__(self, callback: Callable[[Sequence[ToolCallRecord]], Any]):
+    def __init__(self, callback: Callable[[Sequence[Record]], Any]):
         self.callback = callback
 
-    async def write(self, records: Sequence[ToolCallRecord]) -> None:
+    async def write(self, records: Sequence[Record]) -> None:
         result = self.callback(records)
         if inspect.isawaitable(result):
             await result
 
 
 class MemorySink:
-    """Keep records in a list. Meant for tests."""
+    """Keep records in a list. Meant for tests.
+
+    ``records`` holds everything in arrival order; ``calls`` and ``events``
+    are the two kinds on their own.
+    """
 
     def __init__(self):
-        self.records: list[ToolCallRecord] = []
+        self.records: list[Record] = []
 
-    async def write(self, records: Sequence[ToolCallRecord]) -> None:
+    @property
+    def calls(self) -> list[ToolCallRecord]:
+        return [r for r in self.records if isinstance(r, ToolCallRecord)]
+
+    @property
+    def events(self) -> list[EventRecord]:
+        return [r for r in self.records if isinstance(r, EventRecord)]
+
+    async def write(self, records: Sequence[Record]) -> None:
         self.records.extend(records)

src/fastmcp_feedback/instrumentation/storage.py

@@ -1,4 +1,5 @@
-"""Async SQLAlchemy storage for tool-call records and feedback links.
+"""Async SQLAlchemy storage for tool-call records, events, feedback links and
+embeddings.
 
 Hosts that manage their own schema include ``build_metadata(prefix)`` in their
 Alembic ``env.py`` (``target_metadata``) and pass their existing AsyncEngine to
@@ -6,8 +7,11 @@ Alembic ``env.py`` (``target_metadata``) and pass their existing AsyncEngine to
 """
 
 import asyncio
+import logging
+import random as _random
+import time
 from collections.abc import Sequence
-from datetime import datetime
+from datetime import UTC, datetime, timedelta
 from typing import Any
 
 from sqlalchemy import (
@@ -16,37 +20,93 @@ from sqlalchemy import (
     Column,
     DateTime,
     Float,
+    Index,
     Integer,
     MetaData,
     String,
     Table,
     Text,
+    UniqueConstraint,
     and_,
     delete,
+    exists,
+    inspect,
     or_,
     select,
+    text,
 )
 from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.exc import DBAPIError
 from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
+from sqlalchemy.types import TypeDecorator
 
+from .events import MAX_KEY_CHARS, MAX_KIND_CHARS, EventRecord
 from .record import ToolCallRecord
+from .sinks import Record
+
+logger = logging.getLogger(__name__)
 
 DEFAULT_TABLE_PREFIX = "ffb_"
 
 # none_as_null: a missing payload is SQL NULL, so "WHERE args IS NULL" works.
 _JSON = JSON(none_as_null=True).with_variant(JSONB(none_as_null=True), "postgresql")
 
+DEFAULT_EMBEDDING_DIM = 1024
+# source_type values in {prefix}embeddings. Feedback rows survive pruning.
+EMBEDDING_SOURCES = ("feedback", "call_error", "event", "llm")
+FEEDBACK_SOURCE = "feedback"
+
+
+class EmbeddingVector(TypeDecorator):
+    """pgvector ``VECTOR(dim)`` on PostgreSQL, a JSON list of floats elsewhere.
+
+    pgvector is imported only when a PostgreSQL dialect needs the type, so
+    SQLite hosts never need it installed.
+    """
+
+    impl = JSON
+    cache_ok = True
+
+    def __init__(self, dim: int = DEFAULT_EMBEDDING_DIM):
+        super().__init__()
+        self.dim = dim
+
+    def load_dialect_impl(self, dialect: Any) -> Any:
+        if dialect.name == "postgresql":
+            try:
+                from pgvector.sqlalchemy import VECTOR
+            except ImportError as exc:
+                raise ImportError(
+                    "Embeddings on PostgreSQL need pgvector: install "
+                    '"fastmcp-feedback[embeddings]"'
+                ) from exc
+            return dialect.type_descriptor(VECTOR(self.dim))
+        return dialect.type_descriptor(JSON())
+
+    def process_bind_param(self, value: Any, dialect: Any) -> Any:
+        return None if value is None else [float(v) for v in value]
+
+    def process_result_value(self, value: Any, dialect: Any) -> Any:
+        return None if value is None else [float(v) for v in value]
+
 
 def build_metadata(
-    prefix: str = DEFAULT_TABLE_PREFIX, metadata: MetaData | None = None
+    prefix: str = DEFAULT_TABLE_PREFIX,
+    metadata: MetaData | None = None,
+    embedding_dim: int | None = DEFAULT_EMBEDDING_DIM,
 ) -> MetaData:
-    """Return MetaData holding ``{prefix}tool_calls`` and ``{prefix}feedback_call_links``.
+    """Return MetaData holding ``{prefix}tool_calls``, ``{prefix}events``,
+    ``{prefix}feedback_call_links`` and ``{prefix}embeddings``.
 
     Pass an existing MetaData to add the tables to it instead.
+    ``embedding_dim`` sizes the vector column; None leaves the embeddings
+    table out, for hosts that do not embed and have no pgvector.
     """
     metadata = metadata if metadata is not None else MetaData()
     calls = f"{prefix}tool_calls"
+    events = f"{prefix}events"
     links = f"{prefix}feedback_call_links"
+    embeddings = f"{prefix}embeddings"
     if calls not in metadata.tables:
         Table(
             calls,
@@ -73,6 +133,27 @@ def build_metadata(
             Column("args", _JSON),
             Column("result", _JSON),
             Column("extra", _JSON),
+            # Set on sampled-in ok rows; each stands for 1/sample_rate calls.
+            # NULL for rows recorded unconditionally and before 2026.09.28.
+            Column("sample_rate", Float),
+        )
+    if events not in metadata.tables:
+        # No foreign key to tool_calls for the same reasons as links: an event
+        # can be written before its call leaves the queue, and calls are pruned.
+        Table(
+            events,
+            metadata,
+            Column("id", String(36), primary_key=True),
+            Column("occurred_at", DateTime(timezone=True), nullable=False, index=True),
+            Column("kind", String(MAX_KIND_CHARS), nullable=False, index=True),
+            Column("key", String(MAX_KEY_CHARS), index=True),
+            Column("call_id", String(36), index=True),
+            Column("session_id", String(255)),
+            Column("user_sub", String(255), index=True),
+            Column("caller_kind", String(64)),
+            Column("client_id", String(255)),
+            Column("server_version", String(64)),
+            Column("attrs", _JSON),
         )
     if links not in metadata.tables:
         # No foreign key to tool_calls: a link can be written before its call
@@ -86,11 +167,39 @@ def build_metadata(
             Column("rule", String(32), nullable=False),
             Column("linked_at", DateTime(timezone=True), nullable=False),
         )
+    if embedding_dim is not None and embeddings not in metadata.tables:
+        table = Table(
+            embeddings,
+            metadata,
+            Column("id", String(36), primary_key=True),
+            # feedback, call_error, event or llm
+            Column("source_type", String(16), nullable=False, index=True),
+            # The feedback ref, call id or event id the text came from.
+            Column("source_id", String(255), nullable=False, index=True),
+            Column("model", String(128), nullable=False),
+            Column("dim", Integer, nullable=False),
+            # sha256 hex of ``text``; an unchanged text is not embedded again.
+            Column("text_hash", String(64), nullable=False),
+            # What was embedded: redacted and truncated.
+            Column("text", Text, nullable=False),
+            Column("created_at", DateTime(timezone=True), nullable=False, index=True),
+            Column("embedding", EmbeddingVector(embedding_dim), nullable=False),
+            UniqueConstraint(
+                "source_type", "source_id", "model", name=f"{embeddings}_source_uq"
+            ),
+        )
+        # Cosine HNSW index; other databases search by brute force.
+        Index(
+            f"{embeddings}_hnsw",
+            table.c.embedding,
+            postgresql_using="hnsw",
+            postgresql_ops={"embedding": "vector_cosine_ops"},
+        ).ddl_if(dialect="postgresql")
     return metadata
 
 
 class DatabaseSink:
-    """Write records to ``{prefix}tool_calls`` through an async engine.
+    """Write records to ``{prefix}tool_calls`` and ``{prefix}events``.
 
     Args:
         engine: an AsyncEngine the host already owns, or a URL such as
@@ -100,6 +209,15 @@ class DatabaseSink:
         prefix: table name prefix, so the tables cannot collide with the host's.
         create_tables: create the tables on first use. Off by default; hosts
             with Alembic should generate a migration from ``build_metadata``.
+        retention: delete tool-call and event rows older than this. When set, ``write``
+            runs ``prune()`` at most once per ``prune_interval``, after its
+            insert, in the background writer. None (the default) keeps rows
+            forever. Calls linked to feedback are never pruned.
+        prune_interval: minimum time between automatic prunes.
+        prune_batch: rows deleted per statement, so no single delete holds
+            locks for long.
+        embedding_dim: vector size of ``{prefix}embeddings``; must match the
+            embedder of any EmbeddingSink writing through this sink.
     """
 
     def __init__(
@@ -108,36 +226,248 @@ class DatabaseSink:
         *,
         prefix: str = DEFAULT_TABLE_PREFIX,
         create_tables: bool = False,
+        retention: timedelta | None = None,
+        prune_interval: timedelta = timedelta(hours=1),
+        prune_batch: int = 5000,
+        embedding_dim: int = DEFAULT_EMBEDDING_DIM,
     ):
+        if retention is not None and retention <= timedelta(0):
+            raise ValueError(f"retention must be positive, got {retention!r}")
+        if prune_batch < 1:
+            raise ValueError(f"prune_batch must be at least 1, got {prune_batch!r}")
         self._owns_engine = isinstance(engine, str)
         self.engine = create_async_engine(engine) if isinstance(engine, str) else engine
-        self.metadata = build_metadata(prefix)
+        self.metadata = build_metadata(prefix, embedding_dim=embedding_dim)
         self.table = self.metadata.tables[f"{prefix}tool_calls"]
+        self.events = self.metadata.tables[f"{prefix}events"]
         self.links = self.metadata.tables[f"{prefix}feedback_call_links"]
+        self.embeddings = self.metadata.tables[f"{prefix}embeddings"]
+        self.embedding_dim = embedding_dim
         self.create_tables = create_tables
         self._tables_ready = False
+        # The embeddings table (and on Postgres the vector extension) is only
+        # created once an EmbeddingSink uses this sink; see ensure_embeddings.
+        self._embeddings_ready = False
+        self._embeddings_failed_at: float | None = None
+        self._embeddings_lock = asyncio.Lock()
         # The background writer and link_feedback can both reach _ready() at
         # once; without the lock both run create_all and one fails.
         self._tables_lock = asyncio.Lock()
+        self.retention = retention
+        self.prune_interval = prune_interval
+        self.prune_batch = prune_batch
+        # time.monotonic() of the last automatic prune; None until the first.
+        self._last_prune: float | None = None
 
     async def ensure_tables(self) -> None:
-        """Create the tables if missing. Only called when create_tables=True."""
-        async with self.engine.begin() as conn:
-            await conn.run_sync(self.metadata.create_all)
+        """Create the tables if missing. Only called when create_tables=True.
+
+        Another process starting against the same empty database can create a
+        table between our existence check and our CREATE, which fails ours
+        (SQLite: "already exists"; Postgres: DuplicateTable or a unique
+        violation in pg_type). So on a database error, wait a moment and run
+        create_all once more; it skips whatever the other process made. A
+        second failure is raised.
+        """
+        try:
+            await self._create_all()
+        except DBAPIError as first:
+            await asyncio.sleep(_random.uniform(0.05, 0.25))
+            await self._create_all()
+            logger.info(
+                "table creation failed once (%s), retry succeeded; another "
+                "process was probably creating the same tables",
+                type(first).__name__,
+            )
         self._tables_ready = True
 
+    async def _create_all(self) -> None:
+        # The embeddings table is left to ensure_embeddings, so hosts that do
+        # not embed never need pgvector or the vector extension.
+        tables = [t for t in self.metadata.sorted_tables if t is not self.embeddings]
+        async with self.engine.begin() as conn:
+            await conn.run_sync(self.metadata.create_all, tables=tables)
+
+    async def ensure_embeddings(self) -> bool:
+        """Create ``{prefix}embeddings`` if missing. Only when create_tables=True.
+
+        On PostgreSQL this first runs ``CREATE EXTENSION IF NOT EXISTS vector``.
+        Never raises: a missing privilege, a missing pgvector package or any
+        other failure is logged, and False is returned. A failed attempt is
+        retried at most once a minute. Called by EmbeddingSink before it writes.
+        """
+        if not self.create_tables or self._embeddings_ready:
+            return True
+        async with self._embeddings_lock:
+            if self._embeddings_ready:
+                return True
+            now = time.monotonic()
+            if (
+                self._embeddings_failed_at is not None
+                and now - self._embeddings_failed_at < 60
+            ):
+                return False
+            try:
+                await self._ready()
+                if self.engine.dialect.name == "postgresql":
+                    await self._create_vector_extension()
+                try:
+                    await self._create_embeddings_table()
+                except DBAPIError:
+                    # Another process creating it at the same moment.
+                    await asyncio.sleep(_random.uniform(0.05, 0.25))
+                    await self._create_embeddings_table()
+            except Exception as exc:
+                self._embeddings_failed_at = now
+                logger.warning(
+                    "Could not create %s (%s: %s); embeddings are not stored until "
+                    "it exists. Create it with a migration (see the README) or "
+                    "fix the cause; retrying in a minute.",
+                    self.embeddings.name,
+                    type(exc).__name__,
+                    str(exc).splitlines()[0] if str(exc) else "",
+                )
+                return False
+            self._embeddings_ready = True
+            return True
+
+    async def _create_vector_extension(self) -> None:
+        try:
+            async with self.engine.begin() as conn:
+                await conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
+        except DBAPIError as exc:
+            # Usually a missing privilege. The table step then says whether
+            # the extension exists anyway (a DBA may have created it).
+            logger.warning(
+                "CREATE EXTENSION vector failed (%s); a superuser or database "
+                "owner must run it once in this database",
+                str(exc.orig).splitlines()[0] if exc.orig else type(exc).__name__,
+            )
+
+    async def _create_embeddings_table(self) -> None:
+        async with self.engine.begin() as conn:
+            await conn.run_sync(
+                lambda sync: self.embeddings.create(sync, checkfirst=True)
+            )
+
     async def _ready(self) -> None:
         if self.create_tables and not self._tables_ready:
             async with self._tables_lock:
                 if not self._tables_ready:
                     await self.ensure_tables()
 
-    async def write(self, records: Sequence[ToolCallRecord]) -> None:
-        if not records:
+    async def write(self, records: Sequence[Record]) -> None:
+        calls = [r.to_row() for r in records if isinstance(r, ToolCallRecord)]
+        events = [r.to_row() for r in records if isinstance(r, EventRecord)]
+        if not calls and not events:
             return
         await self._ready()
-        async with self.engine.begin() as conn:
-            await conn.execute(self.table.insert(), [r.to_row() for r in records])
+        # Separate transactions, calls first: a host that has not created the
+        # events table yet still gets its calls stored.
+        if calls:
+            async with self.engine.begin() as conn:
+                await conn.execute(self.table.insert(), calls)
+        if events:
+            async with self.engine.begin() as conn:
+                await conn.execute(self.events.insert(), events)
+        await self._auto_prune()
+
+    async def _auto_prune(self) -> None:
+        if self.retention is None:
+            return
+        now = time.monotonic()
+        if (
+            self._last_prune is not None
+            and now - self._last_prune < self.prune_interval.total_seconds()
+        ):
+            return
+        # Set before running, so a prune that fails is not retried every write.
+        self._last_prune = now
+        try:
+            await self.prune()
+        except Exception:
+            logger.warning("Pruning %s failed", self.table.name, exc_info=True)
+
+    async def prune(self, older_than: datetime | None = None) -> int:
+        """Delete tool-call, event and embedding rows from before ``older_than``.
+
+        Defaults to now minus ``retention``; with neither, deletes nothing.
+        Calls linked to feedback in ``{prefix}feedback_call_links`` are kept,
+        and links are never deleted. Events go by ``occurred_at``, whatever
+        call they belong to. Embeddings go by ``created_at``, except those of
+        feedback (``source_type="feedback"``), which are kept. Deletes
+        ``prune_batch`` rows per transaction until none are left. Returns the
+        total rows deleted from all three tables and logs each table's count.
+        A table that does not exist yet (not migrated) is skipped.
+        """
+        if older_than is None:
+            if self.retention is None:
+                return 0
+            older_than = datetime.now(UTC) - self.retention
+        await self._ready()
+        t, link = self.table, self.links
+        calls = await self._delete_batches(
+            t,
+            select(t.c.id).where(
+                t.c.started_at < older_than,
+                ~exists().where(link.c.call_id == t.c.id),
+            ),
+        )
+        events = 0
+        if await self._has_table(self.events.name):
+            e = self.events
+            events = await self._delete_batches(
+                e, select(e.c.id).where(e.c.occurred_at < older_than)
+            )
+        vectors = 0
+        if await self._has_table(self.embeddings.name):
+            v = self.embeddings
+            vectors = await self._delete_batches(
+                v,
+                select(v.c.id).where(
+                    v.c.created_at < older_than, v.c.source_type != FEEDBACK_SOURCE
+                ),
+            )
+        if calls or events or vectors:
+            logger.info(
+                "Pruned %d rows from %s, %d from %s and %d from %s",
+                calls,
+                t.name,
+                events,
+                self.events.name,
+                vectors,
+                self.embeddings.name,
+            )
+        return calls + events + vectors
+
+    async def _delete_batches(self, table: Any, ids_query: Any) -> int:
+        batch = ids_query.limit(self.prune_batch)
+        total = 0
+        while True:
+            async with self.engine.begin() as conn:
+                ids = (await conn.execute(batch)).scalars().all()
+                if ids:
+                    await conn.execute(delete(table).where(table.c.id.in_(ids)))
+            total += len(ids)
+            if len(ids) < self.prune_batch:
+                return total
+
+    async def _has_table(self, name: str) -> bool:
+        async with self.engine.connect() as conn:
+            return await conn.run_sync(lambda sync: inspect(sync).has_table(name))
+
+    async def events_for(
+        self, key: str, *, kinds: Sequence[str] | None = None, limit: int = 1000
+    ) -> list[dict[str, Any]]:
+        """The newest ``limit`` events for ``key``, newest first."""
+        await self._ready()
+        e = self.events
+        query = select(e).where(e.c.key == key)
+        if kinds is not None:
+            query = query.where(e.c.kind.in_(list(kinds)))
+        query = query.order_by(e.c.occurred_at.desc()).limit(limit)
+        async with self.engine.connect() as conn:
+            return [dict(row) for row in (await conn.execute(query)).mappings()]
 
     async def recent_calls(
         self,

src/fastmcp_feedback/mixins/submission.py

@@ -21,7 +21,7 @@ class SubmissionMixin(BaseFeedbackMixin):
     """Mixin for feedback submission functionality.
 
     Pass an InstrumentationMiddleware to link each submission to the tool
-    calls that led up to it.
+    calls that led up to it and record a ``feedback.submitted`` event for it.
     """
 
     def __init__(
@@ -86,6 +86,17 @@ class SubmissionMixin(BaseFeedbackMixin):
 
             linked_calls = 0
             if self.instrumentation is not None:
+                # Feeds an EmbeddingSink, so similar reports can be found.
+                self.instrumentation.record_event(
+                    "feedback.submitted",
+                    key=feedback_id,
+                    attrs={
+                        "type": request.type,
+                        "title": request.title,
+                        "description": request.description,
+                        "submitter": request.submitter,
+                    },
+                )
                 links = await self.instrumentation.capture_recent_calls()
                 if await self.instrumentation.link_feedback(feedback_id, links):
                     linked_calls = len(links)

tests/_databases.py

@@ -0,0 +1,114 @@
+"""Fresh databases for DatabaseSink tests: SQLite always, Postgres when asked.
+
+Set FFB_TEST_DATABASE_URL to a ``postgresql+asyncpg://`` URL (``make test-pg``
+does this from .env) and every test using the ``db_url`` fixture also runs
+against that server. Each test gets its own uniquely named databases, created
+on first use and dropped at teardown, so no two tests share tables.
+"""
+
+import asyncio
+import os
+import re
+import threading
+import uuid
+from collections.abc import Coroutine
+from typing import Any
+
+from sqlalchemy import text
+from sqlalchemy.engine import URL, make_url
+from sqlalchemy.ext.asyncio import create_async_engine
+from sqlalchemy.pool import NullPool
+
+ENV_VAR = "FFB_TEST_DATABASE_URL"
+SKIP_REASON = f"{ENV_VAR} is not set; run `make pg-up` then `make test-pg`"
+
+
+def postgres_url() -> URL | None:
+    raw = os.environ.get(ENV_VAR, "").strip()
+    if not raw:
+        return None
+    url = make_url(raw)
+    if not url.drivername.startswith("postgresql"):
+        raise ValueError(f"{ENV_VAR} must be a postgresql URL, got {url.drivername}")
+    return url
+
+
+def run_blocking(coro: Coroutine[Any, Any, Any]) -> Any:
+    """Run ``coro`` to completion on its own loop in a worker thread.
+
+    The sink factories in the tests are plain functions called from inside a
+    running event loop, so the admin work cannot await on that loop.
+    """
+    box: dict[str, Any] = {}
+
+    def target() -> None:
+        try:
+            box["value"] = asyncio.run(coro)
+        except BaseException as exc:  # re-raised in the caller's thread
+            box["error"] = exc
+
+    thread = threading.Thread(target=target)
+    thread.start()
+    thread.join()
+    if "error" in box:
+        raise box["error"]
+    return box.get("value")
+
+
+class PostgresDatabases:
+    """Create databases on one server for a single test, then drop them all."""
+
+    def __init__(self, url: URL):
+        self.url = url
+        self.run_id = uuid.uuid4().hex[:10]
+        self.by_label: dict[str, str] = {}
+
+    def _admin(self, database: str | None = None):
+        url = self.url if database is None else self.url.set(database=database)
+        return create_async_engine(
+            url, isolation_level="AUTOCOMMIT", poolclass=NullPool
+        )
+
+    def url_for(self, label: str) -> str:
+        """URL of this test's database for ``label``, created on first request."""
+        if label not in self.by_label:
+            safe = re.sub(r"[^a-z0-9_]", "_", label.lower())
+            name = f"ffb_t_{self.run_id}_{safe}"[:63]
+            run_blocking(self._create(name))
+            self.by_label[label] = name
+        return self.url.set(database=self.by_label[label]).render_as_string(
+            hide_password=False
+        )
+
+    async def _create(self, name: str) -> None:
+        engine = self._admin()
+        try:
+            async with engine.connect() as conn:
+                await conn.execute(text(f'CREATE DATABASE "{name}"'))
+        finally:
+            await engine.dispose()
+        # The compose init script puts vector in template1 already; CI's
+        # service container has no init script, so make sure here.
+        engine = self._admin(name)
+        try:
+            async with engine.connect() as conn:
+                await conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
+        finally:
+            await engine.dispose()
+
+    def drop_all(self) -> None:
+        if self.by_label:
+            run_blocking(self._drop(list(self.by_label.values())))
+            self.by_label.clear()
+
+    async def _drop(self, names: list[str]) -> None:
+        engine = self._admin()
+        try:
+            async with engine.connect() as conn:
+                for name in names:
+                    # FORCE: a sink a test forgot to close must not keep it alive.
+                    await conn.execute(
+                        text(f'DROP DATABASE IF EXISTS "{name}" WITH (FORCE)')
+                    )
+        finally:
+            await engine.dispose()

tests/conftest.py

@@ -16,6 +16,8 @@ from fastmcp_feedback.database import FeedbackDatabase
 from fastmcp_feedback.insights import FeedbackInsights  
 from fastmcp_feedback.models import Base, Feedback, FeedbackType, FeedbackStatus
 
+from ._databases import SKIP_REASON, PostgresDatabases, postgres_url
+
 
 @pytest_asyncio.fixture
 async def event_loop():
@@ -177,6 +179,37 @@ def mock_database(mock_session):
     return db
 
 
+# Database backends for DatabaseSink tests
+
+@pytest.fixture(
+    params=["sqlite", pytest.param("postgres", marks=pytest.mark.postgres)]
+)
+def db_backend(request) -> str:
+    """Which database a ``db_url`` test runs on. Postgres needs FFB_TEST_DATABASE_URL."""
+    if request.param == "postgres" and postgres_url() is None:
+        pytest.skip(SKIP_REASON)
+    return request.param
+
+
+@pytest.fixture
+def db_url(db_backend, tmp_path):
+    """Factory: ``db_url(name)`` returns an async URL for an empty database.
+
+    The same name within one test returns the same database, so a test can
+    reopen it; different names and different tests never share one. SQLite
+    databases are files under tmp_path; Postgres ones are created on the
+    server named by FFB_TEST_DATABASE_URL and dropped after the test.
+    """
+    if db_backend == "sqlite":
+        yield lambda name="calls": f"sqlite+aiosqlite:///{tmp_path}/{name}.db"
+        return
+    databases = PostgresDatabases(postgres_url())
+    try:
+        yield lambda name="calls": databases.url_for(name)
+    finally:
+        databases.drop_all()
+
+
 # Environment helpers
 
 @pytest.fixture(autouse=True)

tests/fixtures/create_tables_race_worker.py

@@ -0,0 +1,39 @@
+"""One writer process for the cross-process create_tables race test.
+
+Usage: python create_tables_race_worker.py <db url> <trigger file> <events>
+
+Builds a DatabaseSink with create_tables=True, waits until the trigger file
+exists so every worker hits the empty database at the same moment, records
+events, flushes, and prints {"sink_errors": n} as JSON. Prints "ready" once
+imports and setup are done, so the parent can release all workers together.
+"""
+
+import asyncio
+import json
+import os
+import sys
+import time
+
+from fastmcp_feedback.instrumentation import DatabaseSink, InstrumentationMiddleware
+
+
+async def main(url: str, trigger: str, count: int) -> dict:
+    sink = DatabaseSink(url, create_tables=True)
+    mw = InstrumentationMiddleware([sink])
+    print("ready", flush=True)
+    deadline = time.monotonic() + 30
+    while not os.path.exists(trigger):
+        if time.monotonic() > deadline:
+            raise SystemExit("trigger file never appeared")
+        await asyncio.sleep(0.001)
+    for i in range(count):
+        mw.record_event("race.write", key=f"{os.getpid()}-{i}")
+    await mw.flush()
+    errors = mw.dispatcher.sink_errors
+    await mw.aclose()  # also disposes the sink's engine
+    return {"sink_errors": errors}
+
+
+if __name__ == "__main__":
+    url, trigger, count = sys.argv[1], sys.argv[2], int(sys.argv[3])
+    print(json.dumps(asyncio.run(main(url, trigger, count))), flush=True)

tests/unit/test_backfill.py

@@ -0,0 +1,565 @@
+"""Tests for backfill_embeddings, backfill_feedback, its CLI, and list/dict
+text flattening.
+
+DB tests take the ``db_url`` fixture, so they run on SQLite and, with
+FFB_TEST_DATABASE_URL set, on Postgres with pgvector.
+"""
+
+import json
+import os
+import subprocess
+import sys
+from datetime import UTC, datetime, timedelta
+
+import pytest
+from sqlalchemy import func, select, update
+
+from fastmcp_feedback.instrumentation import (
+    REDACTED,
+    DatabaseSink,
+    EmbeddingSink,
+    EventRecord,
+    Redactor,
+    backfill_embeddings,
+    backfill_feedback,
+    extract_texts,
+)
+from fastmcp_feedback.instrumentation.backfill.cli import main as cli_main
+
+from .test_embeddings import DIM, SECRET, FakeEmbedder, call_record, event, rows
+
+T0 = datetime(2026, 9, 1, 12, 0, tzinfo=UTC)
+
+
+@pytest.fixture
+async def db(db_url):
+    made = []
+
+    def factory(name="calls", **kwargs):
+        kwargs.setdefault("create_tables", True)
+        kwargs.setdefault("embedding_dim", DIM)
+        sink = DatabaseSink(db_url(name), **kwargs)
+        made.append(sink)
+        return sink
+
+    yield factory
+    for sink in made:
+        await sink.engine.dispose()
+
+
+def at(minutes: float) -> datetime:
+    return T0 + timedelta(minutes=minutes)
+
+
+def error_call(minute, tool="render", message="render timed out", **kw):
+    record = call_record(tool=tool, message=message, **kw)
+    record.started_at = at(minute)
+    return record
+
+
+def ev(minute, kind, key=None, **attrs):
+    record = event(kind, key, **attrs)
+    record.occurred_at = at(minute)
+    return record
+
+
+def mixed_records():
+    """One of everything backfill should pick up, and some it should not."""
+    return [
+        error_call(1, message="render timed out after 30s"),
+        error_call(2, tool="find", outcome="soft_error", message="no object Cube"),
+        call_record(outcome="ok"),
+        ev(
+            3, "feedback.submitted", "bug-1", title="Render hangs", description="at 99%"
+        ),
+        ev(
+            4, "llm.call", model="m", prompt="summarize the log", completion="it failed"
+        ),
+        ev(5, "llm.call", model="m", ok=False, error="rate limited"),
+        ev(6, "llm.call", model="m", ok=True),
+        ev(7, "job.failed", "job-1", error_excerpt=["line one", "line two"]),
+        ev(8, "job.progress", "job-1", percent=50),
+    ]
+
+
+def keyed(stored):
+    return {(r["source_type"], r["source_id"]): r for r in stored}
+
+
+async def count_rows(sink):
+    t = sink.embeddings
+    async with sink.engine.connect() as conn:
+        return (await conn.execute(select(func.count()).select_from(t))).scalar_one()
+
+
+class TestFlattening:
+    def test_list_and_tuple_join_lines(self):
+        records = [
+            event("job.failed", error_excerpt=["  a  ", "", None, "b"]),
+            event("job.failed", error=("x", 1, 2.5)),
+        ]
+        texts = [t.text for t in extract_texts(records)]
+        assert texts == ["a\nb", "x\n1\n2.5"]
+
+    def test_dict_becomes_sorted_key_lines(self):
+        records = [event("job.failed", error={"zeta": "last", "alpha": 1, "mid": None})]
+        [item] = extract_texts(records)
+        assert item.text == "alpha: 1\nzeta: last"
+
+    def test_nested(self):
+        records = [
+            event(
+                "job.failed",
+                error_excerpt=[["a", "b"], {"k": ["c", "d"]}, ("e",)],
+            )
+        ]
+        [item] = extract_texts(records)
+        assert item.text == "a\nb\nk: c\nd\ne"
+        assert "[" not in item.text and "{" not in item.text
+
+    def test_flattening_applies_to_feedback_and_llm(self):
+        records = [
+            event("feedback.submitted", "f1", title=["Two", "parts"], description=None),
+            event("llm.call", prompt=["p1", "p2"], completion={"text": "done"}),
+        ]
+        by_type = {t.source_type: t.text for t in extract_texts(records)}
+        assert by_type["feedback"] == "Two\nparts"
+        assert by_type["llm"] == "prompt: p1\np2\ncompletion: text: done"
+
+    def test_empty_containers_are_no_text(self):
+        records = [event("job.failed", error_excerpt=[], error={}, message=[[""]])]
+        assert extract_texts(records) == []
+
+    def test_bounded(self):
+        deep: list = ["bottom"]
+        for _ in range(100):
+            deep = [deep]
+        huge = ["x" * 1000] * 10_000
+        records = [event("job.failed", error=deep), event("job.failed", message=huge)]
+        texts = extract_texts(records)
+        assert len(texts) == 1  # the deep one is cut off before any text
+        assert len(texts[0].text) < 70_000
+
+    def test_other_values_use_str(self):
+        [item] = extract_texts([event("job.failed", error=42)])
+        assert item.text == "42"
+
+
+class TestBackfill:
+    async def test_everything_once(self, db):
+        store = db()
+        await store.write(mixed_records())
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        counts = await backfill_embeddings(emb)
+        assert counts == {
+            "feedback": 1,
+            "call_error": 2,
+            "event": 2,
+            "llm": 1,
+            "skipped": 0,
+            "errors": 0,
+        }
+        stored = keyed(await rows(store))
+        texts = {k[0]: [] for k in stored}
+        for (source_type, _), row in stored.items():
+            texts[source_type].append(row["text"])
+        assert sorted(texts["call_error"]) == [
+            "find: TimeoutError: no object Cube",
+            "render: TimeoutError: render timed out after 30s",
+        ]
+        assert texts["feedback"] == ["Render hangs\nat 99%"]
+        assert texts["llm"] == ["prompt: summarize the log\ncompletion: it failed"]
+        assert sorted(texts["event"]) == ["line one\nline two", "rate limited"]
+        assert ("feedback", "bug-1") in stored
+
+    async def test_pre_outcome_error_rows(self, db):
+        store = db()
+        old = error_call(1, message="boom from before outcome existed")
+        old_ok = call_record(outcome="ok")
+        await store.write([old, old_ok])
+        t = store.table
+        async with store.engine.begin() as conn:
+            await conn.execute(update(t).values(outcome=None))
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        counts = await backfill_embeddings(emb)
+        assert counts["call_error"] == 1
+        [row] = await rows(store)
+        assert row["source_id"] == old.id
+        assert row["text"] == "render: TimeoutError: boom from before outcome existed"
+
+    async def test_same_text_hash_and_id_as_live_sink(self, db):
+        records = mixed_records() + [
+            ev(9, "job.failed", error={"zeta": 1, "alpha": {"b": 2, "a": [1, 2]}}),
+        ]
+        backfilled, live = db("backfilled"), db("live")
+        await backfilled.write(records)
+        await backfill_embeddings(EmbeddingSink(FakeEmbedder(), backfilled))
+        live_emb = EmbeddingSink(FakeEmbedder(), live)
+        await live_emb.write(records)
+        await live_emb.flush()
+        assert live_emb.errors == 0
+
+        def shape(stored):
+            return sorted(
+                (r["source_type"], r["source_id"], r["text"], r["text_hash"])
+                for r in stored
+            )
+
+        assert shape(await rows(backfilled)) == shape(await rows(live))
+        assert len(await rows(live)) == 7
+
+    async def test_live_records_dedupe_against_backfill(self, db):
+        records = mixed_records() + [
+            ev(9, "job.failed", error={"zeta": 1, "alpha": {"b": 2, "a": [1, 2]}}),
+        ]
+        store = db()
+        await store.write(records)
+        await backfill_embeddings(EmbeddingSink(FakeEmbedder(), store))
+        before = await count_rows(store)
+        embedder = FakeEmbedder()
+        live = EmbeddingSink(embedder, store)
+        # The records as they come back from the database, as a live sink
+        # in another process would have seen them before storing.
+        await live.write(records)
+        await live.flush()
+        assert embedder.calls == []
+        assert live.embedded == 0 and live.skipped == 7
+        assert await count_rows(store) == before
+
+    async def test_second_run_embeds_nothing(self, db):
+        store = db()
+        await store.write(mixed_records())
+        embedder = FakeEmbedder()
+        emb = EmbeddingSink(embedder, store)
+        await backfill_embeddings(emb)
+        calls = len(embedder.calls)
+        again = await backfill_embeddings(emb)
+        assert again == {
+            "feedback": 0,
+            "call_error": 0,
+            "event": 0,
+            "llm": 0,
+            "skipped": 6,
+            "errors": 0,
+        }
+        assert len(embedder.calls) == calls
+
+    async def test_changed_text_replaces(self, db):
+        store = db()
+        record = error_call(1, message="first")
+        await store.write([record])
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        await backfill_embeddings(emb)
+        async with store.engine.begin() as conn:
+            await conn.execute(
+                update(store.table).values(error_message="second"),
+            )
+        counts = await backfill_embeddings(emb)
+        assert counts["call_error"] == 1
+        [row] = await rows(store)
+        assert row["text"] == "render: TimeoutError: second"
+
+    async def test_sources(self, db):
+        store = db()
+        await store.write(mixed_records())
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        counts = await backfill_embeddings(emb, sources=["feedback", "llm"])
+        assert counts == {"feedback": 1, "llm": 1, "skipped": 0, "errors": 0}
+        assert {r["source_type"] for r in await rows(store)} == {"feedback", "llm"}
+
+    async def test_defaults_to_sink_sources(self, db):
+        store = db()
+        await store.write(mixed_records())
+        emb = EmbeddingSink(FakeEmbedder(), store, sources=["call_error"])
+        counts = await backfill_embeddings(emb)
+        assert counts == {"call_error": 2, "skipped": 0, "errors": 0}
+
+    async def test_since_until(self, db):
+        store = db()
+        await store.write([error_call(m, message=f"error {m}") for m in range(10)])
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        counts = await backfill_embeddings(emb, since=at(3), until=at(6))
+        assert counts["call_error"] == 3
+        texts = sorted(r["text"] for r in await rows(store))
+        assert texts == [f"render: TimeoutError: error {m}" for m in (3, 4, 5)]
+        # A naive datetime is UTC.
+        naive = (T0 + timedelta(minutes=8)).replace(tzinfo=None)
+        counts = await backfill_embeddings(emb, since=naive)
+        assert counts["call_error"] == 2
+
+    async def test_limit_takes_oldest_and_resumes(self, db):
+        store = db()
+        await store.write([error_call(m, message=f"error {m}") for m in range(10)])
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        counts = await backfill_embeddings(emb, limit=4, batch_size=3)
+        assert counts["call_error"] == 4
+        texts = sorted(r["text"] for r in await rows(store))
+        assert texts == [f"render: TimeoutError: error {m}" for m in range(4)]
+        counts = await backfill_embeddings(emb, limit=4, batch_size=3)
+        assert counts["call_error"] == 4 and counts["skipped"] == 4
+        counts = await backfill_embeddings(emb, limit=0)
+        assert counts["call_error"] == 0
+
+    async def test_pages_through_many_rows(self, db):
+        store = db()
+        # Identical timestamps too, so paging must break ties on id.
+        records = [error_call(m // 5, message=f"error {m}") for m in range(53)]
+        records += [ev(m // 5, "job.failed", error=f"job error {m}") for m in range(41)]
+        await store.write(records)
+        embedder = FakeEmbedder()
+        emb = EmbeddingSink(embedder, store, batch_size=8)
+        counts = await backfill_embeddings(emb, batch_size=7)
+        assert counts["call_error"] == 53 and counts["event"] == 41
+        assert await count_rows(store) == 94
+        assert len(embedder.seen) == 94  # nothing read twice
+        assert max(len(c) for c in embedder.calls) <= 8
+
+    async def test_embedder_errors_are_counted(self, db):
+        store = db()
+        await store.write([error_call(m, message=f"error {m}") for m in range(5)])
+        emb = EmbeddingSink(FakeEmbedder(fail=True), store, batch_size=2)
+        counts = await backfill_embeddings(emb)
+        assert counts["errors"] == 5 and counts["call_error"] == 0
+        assert await count_rows(store) == 0
+        emb.embedder.fail = False
+        counts = await backfill_embeddings(emb)
+        assert counts["call_error"] == 5 and counts["errors"] == 0
+
+    async def test_wrong_vector_count_is_counted(self, db):
+        class Short(FakeEmbedder):
+            async def embed(self, texts):
+                return (await super().embed(texts))[:-1]
+
+        store = db()
+        await store.write([error_call(1), error_call(2, message="other")])
+        counts = await backfill_embeddings(EmbeddingSink(Short(), store))
+        assert counts["errors"] == 2
+
+    async def test_redaction(self, db):
+        store = db()
+        leaked = error_call(1, message=f"auth failed with {SECRET}")
+        custom = ev(2, "job.failed", error="ticket ACME-1234 failed")
+        await store.write([leaked, custom])
+        embedder = FakeEmbedder()
+        emb = EmbeddingSink(
+            embedder, store, redactor=Redactor(extra_patterns=[r"ACME-\d+"])
+        )
+        await backfill_embeddings(emb)
+        texts = sorted(r["text"] for r in await rows(store))
+        assert SECRET not in "".join(embedder.seen + texts)
+        assert "ACME-1234" not in "".join(embedder.seen + texts)
+        assert f"render: TimeoutError: auth failed with {REDACTED}" in texts
+        assert f"ticket {REDACTED} failed" in texts
+
+    async def test_truncation(self, db):
+        store = db()
+        await store.write([error_call(1, message="x" * 500)])
+        emb = EmbeddingSink(FakeEmbedder(), store, max_chars=50)
+        await backfill_embeddings(emb)
+        [row] = await rows(store)
+        assert len(row["text"]) == 50
+
+    async def test_dry_run_writes_nothing(self, db):
+        store = db()
+        await store.write(mixed_records())
+        embedder = FakeEmbedder()
+        emb = EmbeddingSink(embedder, store)
+        counts = await backfill_embeddings(emb, dry_run=True)
+        assert counts["call_error"] == 2 and counts["feedback"] == 1
+        assert embedder.calls == []
+        assert await rows(store) == []
+
+    async def test_missing_tables_raise(self, db):
+        store = db(create_tables=False)
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        with pytest.raises(RuntimeError, match="does not exist"):
+            await backfill_embeddings(emb)
+        with pytest.raises(RuntimeError, match="does not exist"):
+            await backfill_embeddings(emb, dry_run=True)
+
+    async def test_bad_arguments(self, db):
+        emb = EmbeddingSink(FakeEmbedder(), db())
+        with pytest.raises(ValueError, match="unknown sources"):
+            await backfill_embeddings(emb, sources=["nope"])
+        with pytest.raises(ValueError):
+            await backfill_embeddings(emb, batch_size=0)
+        with pytest.raises(ValueError):
+            await backfill_embeddings(emb, limit=-1)
+
+
+class TestBackfillFeedback:
+    async def test_matches_feedback_event(self, db):
+        own, events = db("own"), db("events")
+        items = [
+            ("bug-1", " Render hangs ", "Stuck at 99%"),
+            ("bug-2", "No description", None),
+            ("bug-3", "", ""),
+        ]
+        counts = await backfill_feedback(EmbeddingSink(FakeEmbedder(), own), items)
+        assert counts == {"feedback": 2, "skipped": 0, "errors": 0}
+        live = EmbeddingSink(FakeEmbedder(), events)
+        await live.write(
+            [
+                EventRecord(
+                    kind="feedback.submitted",
+                    occurred_at=datetime.now(UTC),
+                    key=ref,
+                    attrs={"title": title, "description": desc},
+                )
+                for ref, title, desc in items
+            ]
+        )
+        await live.flush()
+
+        def shape(stored):
+            return sorted((r["source_id"], r["text"], r["text_hash"]) for r in stored)
+
+        assert shape(await rows(own)) == shape(await rows(events))
+        assert shape(await rows(own))[0][1] == "Render hangs\nStuck at 99%"
+
+    async def test_idempotent_and_dedupes_with_events(self, db):
+        store = db()
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        await backfill_feedback(emb, [("bug-1", "Render hangs", "at 99%")])
+        await store.write(
+            [
+                ev(
+                    1,
+                    "feedback.submitted",
+                    "bug-1",
+                    title="Render hangs",
+                    description="at 99%",
+                )
+            ]
+        )
+        counts = await backfill_embeddings(emb)
+        assert counts["feedback"] == 0 and counts["skipped"] == 1
+        counts = await backfill_feedback(
+            emb, iter([("bug-1", "Render hangs", "at 99%")]), batch_size=1
+        )
+        assert counts == {"feedback": 0, "skipped": 1, "errors": 0}
+
+    async def test_redacted(self, db):
+        store = db()
+        emb = EmbeddingSink(FakeEmbedder(), store)
+        await backfill_feedback(emb, [("bug-1", "Leak", f"my key is {SECRET}")])
+        [row] = await rows(store)
+        assert SECRET not in row["text"] and REDACTED in row["text"]
+
+
+class TestCli:
+    async def seeded(self, db):
+        store = db()
+        await store.write(mixed_records())
+        await store.ensure_embeddings()
+        return store
+
+    async def test_dry_run_main(self, db, capsys):
+        store = await self.seeded(db)
+        url = store.engine.url.render_as_string(hide_password=False)
+        code = await _in_thread(
+            cli_main,
+            [
+                "--database-url",
+                url,
+                "--dim",
+                str(DIM),
+                "--dry-run",
+                "--sources",
+                "call_error,feedback",
+            ],
+        )
+        assert code == 0
+        out = json.loads(capsys.readouterr().out.strip().splitlines()[-1])
+        assert out == {
+            "dry_run": True,
+            "feedback": 1,
+            "call_error": 2,
+            "skipped": 0,
+            "errors": 0,
+        }
+        assert await rows(store) == []
+
+    async def test_dry_run_subprocess(self, db):
+        store = await self.seeded(db)
+        url = store.engine.url.render_as_string(hide_password=False)
+        env = {k: v for k, v in os.environ.items() if k != "FFB_EMBED_API_KEY"}
+        proc = await _in_thread(
+            subprocess.run,
+            [
+                sys.executable,
+                "-m",
+                "fastmcp_feedback.instrumentation.backfill",
+                "--database-url",
+                url,
+                "--dim",
+                str(DIM),
+                "--dry-run",
+            ],
+            capture_output=True,
+            text=True,
+            env=env,
+            timeout=60,
+        )
+        assert proc.returncode == 0, proc.stderr
+        out = json.loads(proc.stdout.strip().splitlines()[-1])
+        assert out["call_error"] == 2 and out["llm"] == 1 and out["event"] == 2
+        assert "RuntimeWarning" not in proc.stderr
+
+    async def test_needs_embed_url_without_dry_run(self, capsys):
+        with pytest.raises(SystemExit) as exc:
+            cli_main(["--database-url", "sqlite+aiosqlite:///x.db"])
+        assert exc.value.code == 2
+        assert "--embed-base-url" in capsys.readouterr().err
+
+    async def test_no_api_key_flag(self, capsys):
+        with pytest.raises(SystemExit):
+            cli_main(["--database-url", "sqlite+aiosqlite:///x.db", "--api-key", "k"])
+
+    async def test_setup_error_exit_code(self, tmp_path, capsys):
+        url = f"sqlite+aiosqlite:///{tmp_path / 'empty.db'}"
+        code = await _in_thread(cli_main, ["--database-url", url, "--dry-run"])
+        assert code == 2
+        assert "does not exist" in capsys.readouterr().err
+
+    async def test_api_key_from_env(self, db, monkeypatch):
+        seen = {}
+
+        class Recorder(FakeEmbedder):
+            def __init__(self, base_url, api_key=None, model="m", dim=DIM, **kw):
+                super().__init__(dim=dim, model=model)
+                seen["api_key"] = api_key
+                seen["base_url"] = base_url
+
+        store = await self.seeded(db)
+        url = store.engine.url.render_as_string(hide_password=False)
+        monkeypatch.setattr(
+            "fastmcp_feedback.instrumentation.backfill.cli.OpenAIEmbedder", Recorder
+        )
+        monkeypatch.setenv("MY_KEY", "secret-value")
+        code = await _in_thread(
+            cli_main,
+            [
+                "--database-url",
+                url,
+                "--dim",
+                str(DIM),
+                "--embed-base-url",
+                "https://embed.example/v1",
+                "--api-key-env",
+                "MY_KEY",
+            ],
+        )
+        assert code == 0
+        assert seen == {
+            "api_key": "secret-value",
+            "base_url": "https://embed.example/v1",
+        }
+        assert await count_rows(store) == 6
+
+
+async def _in_thread(fn, *args, **kwargs):
+    # The CLI runs its own event loop, which cannot start on the test's.
+    import asyncio
+
+    return await asyncio.to_thread(fn, *args, **kwargs)

tests/unit/test_correlation.py

@@ -37,7 +37,7 @@ SAME_CLIENT_RULE = RULE_USER_WINDOW if FASTMCP_4 else RULE_SESSION
 
 
 @pytest.fixture
-async def db(tmp_path):
+async def db(db_url):
     """Make DatabaseSinks whose engines are always disposed, even on failure.
 
     An undisposed aiosqlite engine keeps a worker thread alive and the test
@@ -46,7 +46,7 @@ async def db(tmp_path):
     made = []
 
     def factory(name="calls", cls=DatabaseSink):
-        sink = cls(f"sqlite+aiosqlite:///{tmp_path}/{name}.db", create_tables=True)
+        sink = cls(db_url(name), create_tables=True)
         made.append(sink)
         return sink
 

tests/unit/test_create_tables_race.py

@@ -0,0 +1,162 @@
+"""create_tables=True when several processes start against the same empty database.
+
+Both processes see the tables missing and both CREATE them; one fails. The
+sink retries create_all once, which skips what the other process made.
+"""
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+from sqlalchemy import create_engine, func, select
+from sqlalchemy.exc import OperationalError
+
+from fastmcp_feedback.instrumentation import (
+    DatabaseSink,
+    InstrumentationMiddleware,
+    build_metadata,
+)
+
+EVENTS = build_metadata().tables["ffb_events"]
+WORKER = Path(__file__).parent.parent / "fixtures" / "create_tables_race_worker.py"
+
+
+def _already_exists():
+    return OperationalError(
+        "CREATE TABLE ffb_tool_calls",
+        {},
+        Exception("table ffb_tool_calls already exists"),
+    )
+
+
+def race(url: str, trigger: Path, workers: int = 2, events: int = 20) -> list[dict]:
+    """Start the workers, release them together, return their JSON results."""
+    procs = [
+        subprocess.Popen(
+            [sys.executable, str(WORKER), url, str(trigger), str(events)],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+        )
+        for _ in range(workers)
+    ]
+    try:
+        for p in procs:
+            assert p.stdout.readline().strip() == "ready"
+        trigger.touch()
+        results = []
+        for p in procs:
+            out, err = p.communicate(timeout=30)
+            assert p.returncode == 0, err
+            results.append(json.loads(out.strip().splitlines()[-1]))
+        return results
+    finally:
+        for p in procs:
+            if p.poll() is None:
+                p.kill()
+                p.wait()
+
+
+@pytest.mark.slow
+def test_two_processes_cold_start(tmp_path):
+    workers, events = 2, 20
+    for run in range(5):
+        db = tmp_path / f"shared{run}.db"
+        results = race(
+            f"sqlite+aiosqlite:///{db}", tmp_path / f"go{run}", workers, events
+        )
+        engine = create_engine(f"sqlite:///{db}")
+        try:
+            with engine.connect() as conn:
+                stored = conn.execute(
+                    select(func.count()).select_from(EVENTS)
+                ).scalar_one()
+        finally:
+            engine.dispose()
+        assert [r["sink_errors"] for r in results] == [0] * workers, f"run {run}"
+        assert stored == workers * events, f"run {run}"
+
+
+@pytest.fixture
+async def sink(tmp_path):
+    s = DatabaseSink(f"sqlite+aiosqlite:///{tmp_path}/calls.db", create_tables=True)
+    yield s
+    await s.engine.dispose()
+
+
+class TestEnsureTablesRetry:
+    async def test_first_create_fails_retry_succeeds(self, sink, monkeypatch, caplog):
+        real = sink.metadata.create_all
+        calls = []
+
+        def create_all(bind, **kwargs):
+            calls.append(1)
+            if len(calls) == 1:
+                raise _already_exists()
+            return real(bind, **kwargs)
+
+        monkeypatch.setattr(sink.metadata, "create_all", create_all)
+        mw = InstrumentationMiddleware([sink])
+        caplog.set_level("DEBUG", logger="fastmcp_feedback.instrumentation.storage")
+        assert mw.record_event("a.b", key="k") is True
+        await mw.flush()
+        assert mw.dispatcher.sink_errors == 0
+        assert len(calls) == 2
+        assert sink._tables_ready
+        async with sink.engine.connect() as conn:
+            count = (
+                await conn.execute(select(func.count()).select_from(sink.events))
+            ).scalar_one()
+        assert count == 1
+        retry_logs = [r for r in caplog.records if "retry succeeded" in r.getMessage()]
+        assert retry_logs and all(r.levelname == "INFO" for r in retry_logs)
+        assert not [
+            r
+            for r in caplog.records
+            if r.levelname == "WARNING" and r.name.endswith("storage")
+        ]
+
+    async def test_persistent_failure_surfaces(self, sink, monkeypatch):
+        calls = []
+
+        def create_all(bind, **kwargs):
+            calls.append(1)
+            raise _already_exists()
+
+        monkeypatch.setattr(sink.metadata, "create_all", create_all)
+        mw = InstrumentationMiddleware([sink])
+        assert mw.record_event("a.b") is True
+        await mw.flush()
+        assert mw.dispatcher.sink_errors == 1
+        assert len(calls) == 2
+        assert not sink._tables_ready
+        # The next batch tries again rather than assuming the tables exist.
+        assert mw.record_event("a.c") is True
+        await mw.flush()
+        assert mw.dispatcher.sink_errors == 2
+        assert len(calls) == 4
+        await mw.aclose()
+
+    async def test_non_database_error_not_retried(self, sink, monkeypatch):
+        calls = []
+
+        def create_all(bind, **kwargs):
+            calls.append(1)
+            raise RuntimeError("bug")
+
+        monkeypatch.setattr(sink.metadata, "create_all", create_all)
+        with pytest.raises(RuntimeError):
+            await sink.ensure_tables()
+        assert len(calls) == 1
+
+    async def test_create_tables_false_never_creates(self, tmp_path, monkeypatch):
+        s = DatabaseSink(f"sqlite+aiosqlite:///{tmp_path}/none.db")
+        calls = []
+        monkeypatch.setattr(s.metadata, "create_all", lambda *a, **k: calls.append(1))
+        try:
+            await s._ready()
+        finally:
+            await s.engine.dispose()
+        assert calls == []

tests/unit/test_embeddings.py

@@ -0,0 +1,848 @@
+"""Tests for EmbeddingSink, OpenAIEmbedder and similarity search.
+
+DB tests take the ``db_url`` fixture, so they run on SQLite and, with
+FFB_TEST_DATABASE_URL set, on Postgres with pgvector.
+"""
+
+import asyncio
+import contextlib
+import hashlib
+import json
+import logging
+import math
+import os
+import re
+import time
+import uuid
+from datetime import UTC, datetime, timedelta
+
+import pytest
+from fastmcp import Client, FastMCP
+from fastmcp.exceptions import ToolError
+from sqlalchemy import func, select, text, update
+
+from fastmcp_feedback import add_feedback_tools
+from fastmcp_feedback.instrumentation import (
+    REDACTED,
+    DatabaseSink,
+    EmbeddingSink,
+    EventRecord,
+    OpenAIEmbedder,
+    Redactor,
+    ToolCallRecord,
+    build_metadata,
+    extract_texts,
+    instrument,
+)
+from fastmcp_feedback.instrumentation.embeddings import cosine_score
+
+DIM = 256
+SECRET = "sk-abcdefghijklmnopqrstuvwx"
+
+
+def token_vector(text_: str, dim: int = DIM) -> list[float]:
+    """Bag of hashed words, normalized: texts sharing words are close."""
+    v = [0.0] * dim
+    for tok in re.findall(r"[a-z0-9]+", text_.lower()):
+        h = int.from_bytes(hashlib.sha256(tok.encode()).digest()[:4], "big")
+        v[h % dim] += 1.0
+    n = math.sqrt(sum(x * x for x in v)) or 1.0
+    return [x / n for x in v]
+
+
+class FakeEmbedder:
+    def __init__(
+        self,
+        dim=DIM,
+        model="fake-embed",
+        *,
+        fail=False,
+        delay=0.0,
+        gate=None,
+        out_dim=None,
+    ):
+        self.dim = dim
+        self.model = model
+        self.fail = fail
+        self.delay = delay
+        self.gate = gate
+        self.out_dim = out_dim or dim
+        self.calls: list[list[str]] = []
+
+    @property
+    def seen(self) -> list[str]:
+        return [t for batch in self.calls for t in batch]
+
+    async def embed(self, texts):
+        self.calls.append(list(texts))
+        if self.gate is not None:
+            await self.gate.wait()
+        if self.delay:
+            await asyncio.sleep(self.delay)
+        if self.fail:
+            raise RuntimeError("embedding backend down")
+        return [token_vector(t, self.out_dim) for t in texts]
+
+
+@pytest.fixture
+async def db(db_url):
+    made = []
+
+    def factory(name="calls", **kwargs):
+        kwargs.setdefault("create_tables", True)
+        kwargs.setdefault("embedding_dim", DIM)
+        sink = DatabaseSink(db_url(name), **kwargs)
+        made.append(sink)
+        return sink
+
+    yield factory
+    for sink in made:
+        await sink.engine.dispose()
+
+
+def call_record(tool="render", outcome="error", message="render timed out", **kw):
+    return ToolCallRecord(
+        tool=tool,
+        started_at=datetime.now(UTC),
+        duration_ms=1.0,
+        ok=outcome == "ok",
+        outcome=outcome,
+        mode="meta",
+        error_type=None if outcome == "ok" else kw.pop("error_type", "TimeoutError"),
+        error_message=None if outcome == "ok" else message,
+        **kw,
+    )
+
+
+def event(kind, key=None, **attrs):
+    return EventRecord(kind=kind, occurred_at=datetime.now(UTC), key=key, attrs=attrs)
+
+
+async def rows(sink, *where):
+    t = sink.embeddings
+    if not await sink._has_table(t.name):  # created on the first embedding
+        return []
+    async with sink.engine.connect() as conn:
+        result = await conn.execute(select(t).where(*where).order_by(t.c.source_id))
+        return [dict(r) for r in result.mappings()]
+
+
+async def settle(mw, emb):
+    await mw.flush()
+    await emb.flush()
+
+
+class TestExtractTexts:
+    def test_call_errors(self):
+        ok = call_record(outcome="ok")
+        err = call_record(message="render timed out", error_type="TimeoutError")
+        soft = call_record(
+            tool="find",
+            outcome="soft_error",
+            message="status=unknown_target",
+            error_type="SoftError",
+        )
+        items = extract_texts([ok, err, soft])
+        assert [(i.source_type, i.source_id, i.text) for i in items] == [
+            ("call_error", err.id, "render: TimeoutError: render timed out"),
+            ("call_error", soft.id, "find: SoftError: status=unknown_target"),
+        ]
+
+    def test_feedback(self):
+        ev = event(
+            "feedback.submitted",
+            key="bug-7Q2X",
+            title="Render hangs",
+            description="the render tool hung on scene a",
+            type="bug",
+        )
+        [item] = extract_texts([ev])
+        assert (item.source_type, item.source_id, item.text) == (
+            "feedback",
+            "bug-7Q2X",
+            "Render hangs\nthe render tool hung on scene a",
+        )
+
+    def test_event_text_keys(self):
+        ev = event(
+            "job.failed",
+            key="job-1",
+            error_excerpt="solver diverged",
+            reason="residual NaN",
+            nproc=8,
+        )
+        quiet = event("job.finished", key="job-2", duration_s=3)
+        [item] = extract_texts([ev, quiet])
+        assert (item.source_type, item.source_id, item.text) == (
+            "event",
+            ev.id,
+            "solver diverged\nresidual NaN",
+        )
+        [custom] = extract_texts([quiet], event_text_keys=("duration_s",))
+        assert custom.text == "3"
+
+    def test_llm(self):
+        ev = event("llm.call", model="m", prompt="why red?", completion="the light")
+        [item] = extract_texts([ev])
+        assert (item.source_type, item.source_id, item.text) == (
+            "llm",
+            ev.id,
+            "prompt: why red?\ncompletion: the light",
+        )
+
+    def test_llm_without_text_is_a_plain_event(self):
+        ev = event("llm.call", model="m", ok=False, error="upstream 529 overloaded")
+        [item] = extract_texts([ev])
+        assert (item.source_type, item.text) == ("event", "upstream 529 overloaded")
+
+    def test_sources_filter(self):
+        records = [
+            call_record(),
+            event("feedback.submitted", key="1", title="t", description="d"),
+            event("job.failed", error="boom"),
+            event("llm.call", prompt="p"),
+        ]
+        assert {i.source_type for i in extract_texts(records)} == {
+            "call_error",
+            "feedback",
+            "event",
+            "llm",
+        }
+        assert [
+            i.source_type for i in extract_texts(records, sources=["feedback"])
+        ] == ["feedback"]
+
+
+class TestEmbeddingSink:
+    async def test_every_source_lands(self, db):
+        sink = db()
+        fake = FakeEmbedder()
+        emb = EmbeddingSink(fake, sink)
+        err = call_record()
+        records = [
+            err,
+            event(
+                "feedback.submitted",
+                key="42",
+                title="Render hangs",
+                description="scene a",
+            ),
+            event("job.failed", key="job-1", error_excerpt="solver diverged"),
+            event("llm.call", prompt="hello", completion="hi"),
+        ]
+        await emb.write(records)
+        await emb.flush()
+        stored = await rows(sink)
+        assert {r["source_type"] for r in stored} == {
+            "call_error",
+            "feedback",
+            "event",
+            "llm",
+        }
+        for r in stored:
+            assert r["dim"] == DIM and r["model"] == "fake-embed"
+            assert len(r["embedding"]) == DIM
+            assert r["text_hash"] == hashlib.sha256(r["text"].encode()).hexdigest()
+            assert r["created_at"] is not None
+        assert emb.embedded == 4 and emb.errors == 0
+        await emb.aclose()
+
+    async def test_submit_feedback_is_embedded(self, db, tmp_path):
+        app = FastMCP("app")
+        sink = db("fb")
+        fake = FakeEmbedder()
+        emb = EmbeddingSink(fake, sink)
+        mw = instrument(app, [sink, emb])
+        add_feedback_tools(
+            app, database_url=f"sqlite:///{tmp_path}/fb.db", instrumentation=mw
+        )
+        async with Client(app) as client:
+            result = await client.call_tool(
+                "submit_feedback",
+                {
+                    "request": {
+                        "type": "bug",
+                        "title": "Render hangs",
+                        "description": "the render tool hung on scene a",
+                        "submitter": "model",
+                    }
+                },
+            )
+        ref = result.data["feedback_id"]
+        await settle(mw, emb)
+        [row] = await rows(sink, sink.embeddings.c.source_type == "feedback")
+        assert row["source_id"] == ref
+        assert row["text"] == "Render hangs\nthe render tool hung on scene a"
+        [ev] = await mw.events_for(ref, kinds=["feedback.submitted"])
+        assert ev["attrs"]["type"] == "bug" and ev["attrs"]["submitter"] == "model"
+        await mw.aclose()
+
+    async def test_redaction(self, db):
+        sink = db()
+        fake = FakeEmbedder()
+        emb = EmbeddingSink(
+            fake, sink, redactor=Redactor(extra_patterns=[r"acme_\w{8}"])
+        )
+        # Straight to the sink, so upstream redaction cannot hide a gap here.
+        await emb.write(
+            [
+                call_record(message=f"401 with api_key={SECRET} and acme_12345678"),
+                event("job.failed", error=f"Bearer {SECRET}"),
+            ]
+        )
+        await emb.flush()
+        assert fake.seen and all(
+            SECRET not in t and "acme_12345678" not in t for t in fake.seen
+        )
+        stored = await rows(sink)
+        assert len(stored) == 2
+        for r in stored:
+            assert SECRET not in r["text"] and REDACTED in r["text"]
+        await emb.aclose()
+
+    async def test_instrument_hands_over_its_redactor(self, db):
+        app = FastMCP("app")
+        sink = db()
+        emb = EmbeddingSink(FakeEmbedder(), sink)
+        redactor = Redactor(extra_patterns=[r"acme_\w{8}"])
+        mw = instrument(app, [sink, emb], redactor=redactor)
+        assert emb.redactor is redactor
+        await mw.aclose()
+
+    async def test_max_chars(self, db):
+        sink = db()
+        fake = FakeEmbedder()
+        emb = EmbeddingSink(fake, sink, max_chars=50)
+        await emb.write([event("job.failed", error="word " * 100)])
+        await emb.flush()
+        [row] = await rows(sink)
+        assert len(row["text"]) == 50 and fake.seen == [row["text"]]
+        await emb.aclose()
+
+    async def test_dedupe_and_replace(self, db):
+        sink = db()
+        fake = FakeEmbedder()
+        emb = EmbeddingSink(fake, sink)
+        ev = event("feedback.submitted", key="7", title="Render hangs", description="a")
+        await emb.write([ev])
+        await emb.flush()
+        await emb.write([ev, ev])
+        await emb.flush()
+        assert fake.seen == ["Render hangs\na"]
+        assert emb.skipped == 2
+        changed = event(
+            "feedback.submitted", key="7", title="Render hangs", description="b"
+        )
+        await emb.write([changed])
+        await emb.flush()
+        [row] = await rows(sink)
+        assert row["text"] == "Render hangs\nb"
+        assert fake.seen == ["Render hangs\na", "Render hangs\nb"]
+        # Another model's vectors are kept apart.
+        other = EmbeddingSink(FakeEmbedder(model="other"), sink)
+        await other.write([changed])
+        await other.flush()
+        assert len(await rows(sink)) == 2
+        await emb.aclose()
+        await other.aclose()
+
+    async def test_queue_full_drops(self, db):
+        sink = db()
+        gate = asyncio.Event()
+        fake = FakeEmbedder(gate=gate)
+        emb = EmbeddingSink(fake, sink, max_queue=2, batch_size=1)
+        start = time.perf_counter()
+        await emb.write([event("job.failed", error=f"boom {i}") for i in range(6)])
+        assert time.perf_counter() - start < 0.5
+        await asyncio.sleep(0.05)  # the worker takes one and blocks on the gate
+        await emb.write([event("job.failed", error=f"late {i}") for i in range(3)])
+        assert emb.dropped >= 4
+        gate.set()
+        await emb.flush()
+        assert emb.errors == 0
+        assert len(await rows(sink)) == 9 - emb.dropped
+        await emb.aclose()
+
+    async def test_embedder_errors_never_reach_tool_calls(self, db):
+        app = FastMCP("app")
+
+        @app.tool
+        def render(scene: str) -> str:
+            raise RuntimeError(f"render of {scene} timed out")
+
+        @app.tool
+        def ping() -> str:
+            return "pong"
+
+        sink = db()
+        emb = EmbeddingSink(FakeEmbedder(fail=True), sink)
+        mw = instrument(app, [sink, emb])
+        async with Client(app) as client:
+            with contextlib.suppress(ToolError):
+                await client.call_tool("render", {"scene": "a"})
+            assert (await client.call_tool("ping", {})).data == "pong"
+        await settle(mw, emb)
+        assert emb.errors == 1
+        assert mw.dispatcher.sink_errors == 0
+        assert await rows(sink) == []
+        async with sink.engine.connect() as conn:
+            calls = (
+                await conn.execute(select(func.count()).select_from(sink.table))
+            ).scalar_one()
+        assert calls == 2
+        assert await mw.similar("render timed out") == []
+        await mw.aclose()
+
+    async def test_slow_embedder_stays_off_the_call_path(self, db):
+        app = FastMCP("app")
+
+        @app.tool
+        def render() -> str:
+            raise RuntimeError("render timed out")
+
+        sink = db()
+        emb = EmbeddingSink(FakeEmbedder(delay=2.0), sink, timeout=5.0)
+        mw = instrument(app, [sink, emb], sink_timeout=0.5)
+        async with Client(app) as client:
+            start = time.perf_counter()
+            for _ in range(3):
+                with contextlib.suppress(ToolError):
+                    await client.call_tool("render", {})
+            elapsed = time.perf_counter() - start
+        await mw.flush()
+        assert elapsed < 1.5
+        assert mw.dispatcher.sink_errors == 0
+        await emb.flush()
+        assert emb.embedded == 3
+        await mw.aclose()
+
+    async def test_wrong_vector_length_is_an_error(self, db):
+        sink = db()
+        emb = EmbeddingSink(FakeEmbedder(out_dim=DIM + 1), sink)
+        await emb.write([event("job.failed", error="boom")])
+        await emb.flush()
+        assert emb.errors == 1 and await rows(sink) == []
+        await emb.aclose()
+
+    async def test_dim_mismatch_refused_at_construction(self, db):
+        with pytest.raises(ValueError, match="embedding_dim=512"):
+            EmbeddingSink(FakeEmbedder(dim=512), db())
+
+    async def test_unknown_source_refused(self, db):
+        with pytest.raises(ValueError, match="unknown sources"):
+            EmbeddingSink(FakeEmbedder(), db(), sources=["feedback", "chat"])
+
+    async def test_aclose_drains(self, db):
+        sink = db()
+        emb = EmbeddingSink(FakeEmbedder(delay=0.1), sink, batch_size=1)
+        await emb.write([event("job.failed", error=f"e{i}") for i in range(3)])
+        await emb.aclose()
+        assert emb.embedded == 3 and emb.dropped == 0
+
+    async def test_aclose_timeout_drops(self, db):
+        sink = db()
+        emb = EmbeddingSink(
+            FakeEmbedder(gate=asyncio.Event()), sink, batch_size=1, timeout=0.2
+        )
+        await emb.write([event("job.failed", error=f"e{i}") for i in range(3)])
+        await emb.aclose()
+        assert emb.dropped == 2 and emb.embedded == 0
+
+
+class TestLlmText:
+    def app(self, sinks, **kwargs):
+        app = FastMCP("llm")
+        holder = {}
+
+        @app.tool
+        async def ask(q: str) -> str:
+            holder["mw"].record_llm_call(
+                model="m",
+                prompt=f"{q} token={SECRET}",
+                completion="because",
+                attrs={"prompt": "smuggled", "temperature": 0.2},
+            )
+            return "because"
+
+        holder["mw"] = mw = instrument(app, sinks, **kwargs)
+        return app, mw
+
+    async def test_dropped_by_default(self, db):
+        sink = db()
+        fake = FakeEmbedder()
+        emb = EmbeddingSink(fake, sink)
+        app, mw = self.app([sink, emb])
+        async with Client(app) as client:
+            await client.call_tool("ask", {"q": "why is the sky blue"})
+        await settle(mw, emb)
+        [ev] = [e for e in mw.recent_events if e.kind == "llm.call"]
+        assert "prompt" not in ev.attrs and "completion" not in ev.attrs
+        assert ev.attrs["temperature"] == 0.2
+        assert fake.seen == []
+        assert await rows(sink) == []
+        await mw.aclose()
+
+    async def test_embedded_when_captured(self, db):
+        sink = db()
+        fake = FakeEmbedder()
+        emb = EmbeddingSink(fake, sink)
+        app, mw = self.app([sink, emb], capture_llm_text=True, max_error_chars=10)
+        async with Client(app) as client:
+            await client.call_tool("ask", {"q": "why is the sky blue " * 10})
+        await settle(mw, emb)
+        [ev] = [e for e in mw.recent_events if e.kind == "llm.call"]
+        assert len(ev.attrs["prompt"]) == 80  # 8 * max_error_chars
+        assert ev.attrs["completion"] == "because"
+        [row] = await rows(sink)
+        assert row["source_type"] == "llm" and row["source_id"] == ev.id
+        assert row["text"].startswith("prompt: why is the sky blue")
+        assert row["text"].endswith("completion: because")
+        await mw.aclose()
+
+    async def test_captured_text_is_redacted(self, db):
+        sink = db()
+        emb = EmbeddingSink(FakeEmbedder(), sink)
+        app, mw = self.app([sink, emb], capture_llm_text=True)
+        async with Client(app) as client:
+            await client.call_tool("ask", {"q": "q"})
+        await settle(mw, emb)
+        [ev] = [e for e in mw.recent_events if e.kind == "llm.call"]
+        assert SECRET not in ev.attrs["prompt"] and REDACTED in ev.attrs["prompt"]
+        [row] = await rows(sink)
+        assert SECRET not in row["text"]
+        await mw.aclose()
+
+
+FIXTURE = [
+    ("call_error", "c1", "render: TimeoutError: render timed out after 30 s"),
+    ("call_error", "c2", "upload: OSError: disk full on volume data"),
+    ("event", "e1", "render job timed out waiting for the gpu"),
+    ("feedback", "bug-1", "Render hangs\nthe render tool timed out on scene a"),
+    ("feedback", "bug-2", "Dark mode please\nthe ui is too bright at night"),
+    ("llm", "l1", "prompt: summarise the log\ncompletion: nothing notable"),
+]
+
+
+async def seed(sink, emb):
+    from fastmcp_feedback.instrumentation import EmbeddingText
+
+    for source_type, source_id, text_ in FIXTURE:
+        emb._enqueue(EmbeddingText(source_type, source_id, text_))
+    await emb.flush()
+    assert len(await rows(sink)) == len(FIXTURE)
+
+
+class TestSimilar:
+    async def test_ranking_matches_brute_force(self, db, db_backend):
+        sink = db()
+        app = FastMCP("s")
+        emb = EmbeddingSink(FakeEmbedder(), sink)
+        mw = instrument(app, [sink, emb])
+        await seed(sink, emb)
+
+        query = "render timed out"
+        q = token_vector(query)
+        expected = sorted(
+            ((cosine_score(q, token_vector(t)), sid) for _, sid, t in FIXTURE),
+            reverse=True,
+        )
+        found = await mw.similar(query, k=len(FIXTURE))
+        # Rows sharing no word with the query all score 0; their order is free.
+        positive = [sid for score, sid in expected if score > 1e-6]
+        assert len(positive) >= 3
+        assert [r["source_id"] for r in found][: len(positive)] == positive
+        assert {r["source_id"] for r in found} == {sid for _, sid in expected}
+        want = {sid: score for score, sid in expected}
+        for r in found:
+            assert r["score"] == pytest.approx(want[r["source_id"]], abs=1e-4)
+            assert 0.0 <= r["score"] <= 1.0
+            assert r["created_at"].tzinfo is not None
+            assert set(r) == {"source_type", "source_id", "text", "score", "created_at"}
+        assert found[0]["source_id"] == "c1"
+
+        top = await mw.similar(query, k=2)
+        assert [r["source_id"] for r in top] == positive[:2]
+        only = await mw.similar(query, source_types=["feedback"])
+        assert [r["source_id"] for r in only] == ["bug-1", "bug-2"]
+        strong = await mw.similar(query, min_score=0.4)
+        assert strong and all(r["score"] >= 0.4 for r in strong)
+        assert len(strong) < len(FIXTURE)
+        await mw.aclose()
+
+    async def test_similar_feedback_excludes_itself(self, db):
+        sink = db()
+        app = FastMCP("s")
+        emb = EmbeddingSink(FakeEmbedder(), sink)
+        mw = instrument(app, [sink, emb])
+        await seed(sink, emb)
+        found = await mw.similar_feedback("bug-1", k=3)
+        assert "bug-1" not in [r["source_id"] for r in found]
+        assert found[0]["source_id"] in ("c1", "e1")
+        feedback_only = await mw.similar_feedback("bug-1", source_types=["feedback"])
+        assert [r["source_id"] for r in feedback_only] == ["bug-2"]
+        assert await mw.similar_feedback("bug-404") == []
+        await mw.aclose()
+
+    async def test_scan_limit_without_pgvector(self, db, db_backend, caplog):
+        if db_backend == "postgres":
+            pytest.skip("brute force only runs without pgvector")
+        sink = db()
+        app = FastMCP("s")
+        emb = EmbeddingSink(FakeEmbedder(), sink, scan_limit=2)
+        mw = instrument(app, [sink, emb])
+        await seed(sink, emb)
+        caplog.set_level(logging.INFO, "fastmcp_feedback.instrumentation.embeddings")
+        assert len(await mw.similar("render", k=10)) <= 2
+        await mw.similar("render", k=10)
+        assert len([r for r in caplog.records if "scan_limit" in r.getMessage()]) == 1
+        await mw.aclose()
+
+    async def test_never_raises(self, db):
+        app = FastMCP("s")
+        mw = instrument(app, [])
+        assert await mw.similar("x") == []
+        assert await mw.similar_feedback("1") == []
+        await mw.aclose()
+        sink = db()
+        emb = EmbeddingSink(FakeEmbedder(fail=True), sink)
+        mw = instrument(FastMCP("t"), [sink, emb])
+        assert await mw.similar("x") == []
+        # No table yet: the lookup itself fails.
+        assert await mw.similar_feedback("1") == []
+        await mw.aclose()
+
+
+class TestPrune:
+    async def test_keeps_feedback_embeddings(self, db):
+        sink = db(prune_batch=2)
+        emb = EmbeddingSink(FakeEmbedder(), sink)
+        await seed(sink, emb)
+        t = sink.embeddings
+        old = datetime.now(UTC) - timedelta(days=40)
+        async with sink.engine.begin() as conn:
+            await conn.execute(
+                update(t).where(t.c.source_id != "c2").values(created_at=old)
+            )
+        deleted = await sink.prune(older_than=datetime.now(UTC) - timedelta(days=30))
+        assert deleted == 3
+        left = sorted(r["source_id"] for r in await rows(sink))
+        assert left == ["bug-1", "bug-2", "c2"]
+        await emb.aclose()
+
+    async def test_prune_without_embeddings_table(self, db):
+        sink = db()
+        await sink.write([call_record()])
+        assert not await sink._has_table(sink.embeddings.name)
+        assert await sink.prune(older_than=datetime.now(UTC) + timedelta(days=1)) == 1
+
+
+class TestCreateTables:
+    async def test_embeddings_table_only_with_an_embedding_sink(self, db):
+        sink = db()
+        await sink.write([call_record()])
+        assert not await sink._has_table(sink.embeddings.name)
+        emb = EmbeddingSink(FakeEmbedder(), sink)
+        await emb.write([call_record()])
+        await emb.flush()
+        assert await sink._has_table(sink.embeddings.name)
+        await emb.aclose()
+
+    async def test_create_tables_false_creates_nothing(self, db):
+        sink = db(create_tables=False)
+        assert await sink.ensure_embeddings() is True
+        assert not await sink._has_table(sink.embeddings.name)
+
+    async def test_failure_is_logged_not_raised(self, db, monkeypatch, caplog):
+        sink = db()
+
+        async def boom():
+            raise RuntimeError("no room")
+
+        monkeypatch.setattr(sink, "_create_embeddings_table", boom)
+        caplog.set_level(logging.WARNING)
+        emb = EmbeddingSink(FakeEmbedder(), sink)
+        assert await sink.ensure_embeddings() is False
+        assert any("Could not create" in r.getMessage() for r in caplog.records)
+        await emb.write([call_record()])
+        await emb.flush()
+        assert emb.errors == 1  # the insert fails; counted, not raised
+        await emb.aclose()
+
+    @pytest.mark.postgres
+    async def test_postgres_extension_table_and_index(self, db, db_backend):
+        if db_backend != "postgres":
+            pytest.skip("Postgres only")
+        sink = db()
+        await sink._ready()
+        async with sink.engine.begin() as conn:
+            await conn.execute(text("DROP EXTENSION IF EXISTS vector"))
+        assert await sink.ensure_embeddings() is True
+        async with sink.engine.connect() as conn:
+            ext = (
+                await conn.execute(
+                    text("SELECT count(*) FROM pg_extension WHERE extname = 'vector'")
+                )
+            ).scalar_one()
+            index = (
+                await conn.execute(
+                    text(
+                        "SELECT indexdef FROM pg_indexes "
+                        "WHERE indexname = 'ffb_embeddings_hnsw'"
+                    )
+                )
+            ).scalar_one()
+            coltype = (
+                await conn.execute(
+                    text(
+                        "SELECT format_type(atttypid, atttypmod) FROM pg_attribute "
+                        "WHERE attrelid = 'ffb_embeddings'::regclass "
+                        "AND attname = 'embedding'"
+                    )
+                )
+            ).scalar_one()
+        assert ext == 1
+        assert "hnsw" in index and "vector_cosine_ops" in index
+        assert coltype == f"vector({DIM})"
+
+    @pytest.mark.postgres
+    async def test_postgres_without_privilege(self, db, db_backend, db_url, caplog):
+        if db_backend != "postgres":
+            pytest.skip("Postgres only")
+        from sqlalchemy.engine import make_url
+
+        admin = db("priv", embedding_dim=DIM)
+        role = f"ffb_limited_{uuid.uuid4().hex[:8]}"
+        password = uuid.uuid4().hex
+        url = make_url(db_url("priv"))
+        async with admin.engine.begin() as conn:
+            await conn.execute(text("DROP EXTENSION IF EXISTS vector"))
+            await conn.execute(text(f"CREATE ROLE {role} LOGIN PASSWORD '{password}'"))
+            await conn.execute(text(f"GRANT ALL ON SCHEMA public TO {role}"))
+        limited = DatabaseSink(
+            url.set(username=role, password=password).render_as_string(
+                hide_password=False
+            ),
+            create_tables=True,
+            embedding_dim=DIM,
+        )
+        try:
+            caplog.set_level(logging.WARNING)
+            emb = EmbeddingSink(FakeEmbedder(), limited)
+            assert await limited.ensure_embeddings() is False
+            messages = [r.getMessage() for r in caplog.records]
+            assert any("CREATE EXTENSION vector failed" in m for m in messages)
+            assert any("Could not create" in m for m in messages)
+            await limited.write([call_record()])  # the rest still works
+            await emb.write([call_record()])
+            await emb.flush()
+            assert emb.errors == 1
+            await emb.aclose()
+        finally:
+            await limited.engine.dispose()
+            async with admin.engine.begin() as conn:
+                await conn.execute(text(f"DROP OWNED BY {role}"))
+                await conn.execute(text(f"DROP ROLE {role}"))
+
+
+class TestMetadata:
+    def test_build_metadata_tables(self):
+        md = build_metadata("x_", embedding_dim=768)
+        t = md.tables["x_embeddings"]
+        assert t.c.embedding.type.dim == 768
+        assert {c.name for c in t.c} == {
+            "id",
+            "source_type",
+            "source_id",
+            "model",
+            "dim",
+            "text_hash",
+            "text",
+            "created_at",
+            "embedding",
+        }
+        without = build_metadata("y_", embedding_dim=None)
+        assert "y_embeddings" not in without.tables
+        assert "y_tool_calls" in without.tables
+
+
+class TestOpenAIEmbedder:
+    def transport(self, handler):
+        from fastmcp_feedback.instrumentation.embeddings import _http_module
+
+        return _http_module().MockTransport(handler)
+
+    async def test_request_batches_and_order(self):
+        seen = []
+
+        def handler(request):
+            body = json.loads(request.content)
+            seen.append((request.url.path, request.headers.get("authorization"), body))
+            data = [
+                {"index": i, "embedding": [float(len(t)), 0.0, 1.0]}
+                for i, t in enumerate(body["input"])
+            ]
+            from fastmcp_feedback.instrumentation.embeddings import _http_module
+
+            return _http_module().Response(200, json={"data": list(reversed(data))})
+
+        e = OpenAIEmbedder(
+            "https://gw.example/v1/",
+            api_key="k",
+            model="m",
+            dim=3,
+            batch_size=2,
+            transport=self.transport(handler),
+        )
+        out = await e.embed(["a", "bb", "ccc"])
+        assert out == [[1.0, 0.0, 1.0], [2.0, 0.0, 1.0], [3.0, 0.0, 1.0]]
+        assert [s[0] for s in seen] == ["/v1/embeddings", "/v1/embeddings"]
+        assert seen[0][1] == "Bearer k"
+        assert [s[2]["input"] for s in seen] == [["a", "bb"], ["ccc"]]
+        assert all(s[2]["model"] == "m" for s in seen)
+        assert await e.embed([]) == []
+
+    async def test_dim_mismatch_raises(self):
+        from fastmcp_feedback.instrumentation.embeddings import _http_module
+
+        def handler(request):
+            return _http_module().Response(
+                200, json={"data": [{"index": 0, "embedding": [0.1, 0.2]}]}
+            )
+
+        e = OpenAIEmbedder(
+            "https://gw.example/v1", dim=3, transport=self.transport(handler)
+        )
+        with pytest.raises(ValueError, match="2-dim vector, expected 3"):
+            await e.embed(["a"])
+
+    async def test_http_error_raises(self):
+        from fastmcp_feedback.instrumentation.embeddings import _http_module
+
+        e = OpenAIEmbedder(
+            "https://gw.example/v1",
+            dim=3,
+            transport=self.transport(
+                lambda r: _http_module().Response(403, json={"error": "nope"})
+            ),
+        )
+        with pytest.raises(Exception, match="403"):
+            await e.embed(["a"])
+
+
+LIVE_URL = os.environ.get("FFB_TEST_EMBED_URL")
+
+
+@pytest.mark.skipif(not LIVE_URL, reason="FFB_TEST_EMBED_URL is not set")
+async def test_live_gateway():
+    """FFB_TEST_EMBED_URL=https://.../v1 FFB_TEST_EMBED_KEY=... [FFB_TEST_EMBED_MODEL]."""
+    e = OpenAIEmbedder(
+        LIVE_URL,
+        api_key=os.environ.get("FFB_TEST_EMBED_KEY"),
+        model=os.environ.get("FFB_TEST_EMBED_MODEL", "mxbai-embed-large"),
+        dim=int(os.environ.get("FFB_TEST_EMBED_DIM", "1024")),
+    )
+    a, b, c = await e.embed(
+        ["render timed out", "the render took too long and timed out", "dark mode"]
+    )
+    assert len(a) == e.dim
+    assert cosine_score(a, b) > cosine_score(a, c)

tests/unit/test_events.py

@@ -0,0 +1,582 @@
+"""Tests for events: record_event, record_llm_call, events_for and their storage.
+
+Real FastMCP servers through fastmcp.Client, as in test_instrumentation.py.
+"""
+
+import asyncio
+import contextlib
+import io
+import json
+import threading
+from datetime import UTC, datetime, timedelta
+
+import pytest
+from fastmcp import Client, FastMCP
+from fastmcp.exceptions import ToolError
+from sqlalchemy import MetaData, func, select
+from sqlalchemy.exc import DBAPIError
+
+from fastmcp_feedback.instrumentation import (
+    REDACTED,
+    CallbackSink,
+    DatabaseSink,
+    EventRecord,
+    JsonLinesSink,
+    MemorySink,
+    ToolCallRecord,
+    build_metadata,
+    instrument,
+)
+
+
+@pytest.fixture
+async def db(db_url):
+    """DatabaseSinks whose engines are always disposed, or the process hangs."""
+    made = []
+
+    def factory(name="calls", **kwargs):
+        kwargs.setdefault("create_tables", True)
+        sink = DatabaseSink(db_url(name), **kwargs)
+        made.append(sink)
+        return sink
+
+    yield factory
+    for sink in made:
+        await sink.engine.dispose()
+
+
+def make_app(sinks=None, **kwargs):
+    """A server whose tools record events through the middleware returned."""
+    sink = MemorySink()
+    app = FastMCP("events")
+    holder = {}
+    spawned = []
+
+    @app.tool
+    async def submit(job_id: str) -> dict:
+        mw = holder["mw"]
+        mw.record_event("job.submitted", key=job_id, attrs={"case": "wing", "nproc": 4})
+        return {"job_id": job_id}
+
+    @app.tool
+    def submit_sync(job_id: str) -> dict:
+        holder["mw"].record_event("job.submitted", key=job_id)
+        return {"job_id": job_id}
+
+    @app.tool
+    async def spawn(job_id: str) -> dict:
+        """Start a watcher task that records after the call has returned."""
+        started = asyncio.Event()
+
+        async def watcher():
+            started.set()
+            await asyncio.sleep(0.05)
+            holder["mw"].record_event("job.started", key=job_id)
+
+        spawned.append(asyncio.get_running_loop().create_task(watcher()))
+        await started.wait()
+        return {"job_id": job_id}
+
+    @app.tool
+    async def chat(prompt: str) -> str:
+        holder["mw"].record_llm_call(
+            model="claude-opus-4",
+            provider="anthropic",
+            duration_ms=812.5,
+            input_tokens=1200,
+            output_tokens=90,
+            attrs={"cache_read_input_tokens": 1000},
+        )
+        return "ok"
+
+    @app.tool
+    async def chat_fails(prompt: str) -> str:
+        err = RuntimeError(
+            "upstream 401: api_key=sk-abcdefghijklmnopqrstuv " + "x" * 800
+        )
+        holder["mw"].record_llm_call(model="gpt-x", provider="openai", error=err)
+        raise err
+
+    @app.tool
+    def poll() -> dict:
+        return {"status": "idle"}
+
+    @app.tool
+    def poll_with_event() -> dict:
+        holder["mw"].record_event("poll.saw_job", key="j9")
+        return {"status": "idle"}
+
+    mw = instrument(app, sinks if sinks is not None else [sink], **kwargs)
+    holder["mw"] = mw
+    return app, sink, mw, spawned
+
+
+async def call(app, mw, *calls):
+    async with Client(app) as client:
+        for tool, args in calls:
+            with contextlib.suppress(ToolError):
+                await client.call_tool(tool, args)
+    await mw.flush()
+
+
+async def table_rows(sink, table):
+    async with sink.engine.connect() as conn:
+        return [dict(r) for r in (await conn.execute(select(table))).mappings()]
+
+
+def event(kind="job.step", key="j1", minutes_ago=0, **kwargs):
+    return EventRecord(
+        kind=kind,
+        occurred_at=datetime.now(UTC) - timedelta(minutes=minutes_ago),
+        key=key,
+        **kwargs,
+    )
+
+
+class TestLinking:
+    async def test_event_in_tool_links_to_stored_call(self, db):
+        store = db()
+        app, _, mw, _ = make_app([store])
+        await call(app, mw, ("submit", {"job_id": "j1"}))
+        [call_row] = await table_rows(store, store.table)
+        [ev] = await table_rows(store, store.events)
+        assert ev["kind"] == "job.submitted"
+        assert ev["call_id"] == call_row["id"]
+        assert ev["session_id"] == call_row["session_id"]
+        assert ev["call_id"] == mw.recent[-1].id
+
+    async def test_sync_tool_links_too(self):
+        app, sink, mw, _ = make_app()
+        await call(app, mw, ("submit_sync", {"job_id": "j2"}))
+        await asyncio.sleep(0.01)
+        await mw.flush()
+        [ev] = sink.events
+        [rec] = sink.calls
+        assert ev.call_id == rec.id
+
+    async def test_event_outside_call_has_no_call_id(self):
+        app, sink, mw, _ = make_app()
+        assert mw.record_event("job.finished", key="j1") is True
+        await mw.flush()
+        [ev] = sink.events
+        assert ev.call_id is None and ev.session_id is None
+
+    async def test_task_outliving_call_is_not_linked(self):
+        app, sink, mw, spawned = make_app()
+        await call(app, mw, ("spawn", {"job_id": "j3"}))
+        await asyncio.gather(*spawned)
+        await mw.flush()
+        [ev] = sink.events
+        assert ev.kind == "job.started" and ev.call_id is None
+
+    async def test_explicit_call_id_wins(self):
+        app, sink, mw, _ = make_app()
+        mw.record_event("x.y", call_id="given")
+        await mw.flush()
+        assert sink.events[0].call_id == "given"
+
+    async def test_sampled_out_call_kept_when_it_recorded_event(self):
+        app, sink, mw, _ = make_app(
+            sample_rates={"poll_with_event": 0.01, "poll": 0.01},
+            random=lambda: 0.99,
+        )
+        await call(app, mw, ("poll", {}), ("poll_with_event", {}))
+        [rec] = sink.calls
+        [ev] = sink.events
+        assert rec.tool == "poll_with_event" and rec.sample_rate is None
+        assert ev.call_id == rec.id
+
+    async def test_excluded_tool_events_unlinked(self):
+        app, sink, mw, _ = make_app(exclude_tools={"poll_with_event"})
+        await call(app, mw, ("poll_with_event", {}))
+        assert sink.calls == []
+        [ev] = sink.events
+        assert ev.call_id is None
+
+
+class TestRecordEvent:
+    async def test_key_attrs_and_server_version_stored(self, db):
+        store = db()
+        app, _, mw, _ = make_app([store], server_version="1.2.3")
+        mw.record_event(
+            "step.finished",
+            key=42,
+            attrs={"step": "snappyHexMesh", "cells": 1200000, "mesh_ok": True},
+            user_sub="u1",
+        )
+        await mw.flush()
+        [ev] = await table_rows(store, store.events)
+        assert ev["key"] == "42"
+        assert ev["attrs"] == {
+            "step": "snappyHexMesh",
+            "cells": 1200000,
+            "mesh_ok": True,
+        }
+        assert ev["user_sub"] == "u1" and ev["server_version"] == "1.2.3"
+
+    async def test_attrs_redacted(self):
+        app, sink, mw, _ = make_app()
+        mw.record_event(
+            "job.failed",
+            attrs={
+                "token": "abc123",
+                "nested": {"db_password": "hunter2"},
+                "error_excerpt": "curl -H 'Authorization: Bearer abcdefghijklmnop'",
+            },
+        )
+        await mw.flush()
+        attrs = sink.events[0].attrs
+        assert attrs["token"] == REDACTED
+        assert attrs["nested"]["db_password"] == REDACTED
+        assert "abcdefghijklmnop" not in attrs["error_excerpt"]
+
+    async def test_attrs_made_json_safe(self, db):
+        store = db()
+        app, _, mw, _ = make_app([store])
+        when = datetime(2026, 9, 29, tzinfo=UTC)
+        mw.record_event(
+            "solver.progress",
+            key="j1",
+            attrs={"residuals": {"p": float("nan"), "U": 1e-5}, "at": when, 3: (1, 2)},
+        )
+        await mw.flush()
+        [ev] = await table_rows(store, store.events)
+        assert ev["attrs"] == {
+            "residuals": {"p": "nan", "U": 1e-5},
+            "at": when.isoformat(),
+            "3": [1, 2],
+        }
+
+    @pytest.mark.parametrize(
+        "kind",
+        ["", "   ", None, 5, "k" * 129],
+        ids=["empty", "blank", "none", "int", "long"],
+    )
+    async def test_invalid_kind_rejected(self, kind):
+        app, sink, mw, _ = make_app()
+        assert mw.record_event(kind) is False
+        await mw.flush()
+        assert sink.events == [] and list(mw.recent_events) == []
+
+    async def test_invalid_key_and_attrs_rejected(self):
+        app, sink, mw, _ = make_app()
+        assert mw.record_event("a.b", key="k" * 256) is False
+        assert mw.record_event("a.b", attrs=["not", "a", "dict"]) is False
+        await mw.flush()
+        assert sink.events == []
+
+    async def test_mode_off_is_noop(self):
+        app, sink, mw, _ = make_app(mode="off")
+        assert mw.record_event("job.submitted", key="j1") is False
+        assert mw.record_llm_call(model="m") is False
+        await call(app, mw, ("submit", {"job_id": "j1"}))
+        assert sink.records == [] and list(mw.recent_events) == []
+        assert mw.dispatcher.dropped == 0
+
+    async def test_queue_full_returns_false_and_counts(self):
+        app, sink, mw, _ = make_app(max_queue=1)
+        assert mw.record_event("a.one") is True
+        assert mw.record_event("a.two") is False
+        assert mw.dispatcher.dropped == 1
+        await mw.flush()
+        assert [e.kind for e in sink.events] == ["a.one"]
+        assert [e.kind for e in mw.recent_events] == ["a.one"]
+
+    async def test_broken_sink_never_raises(self):
+        def boom(records):
+            raise RuntimeError("sink down")
+
+        app, _, mw, _ = make_app([CallbackSink(boom)])
+        assert mw.record_event("a.b", key="j") is True
+        await mw.flush()
+        assert mw.dispatcher.sink_errors == 1
+
+    async def test_broken_redactor_never_raises(self):
+        app, sink, mw, _ = make_app()
+
+        class Broken:
+            def redact(self, value):
+                raise RuntimeError("bad pattern")
+
+        mw.redactor = Broken()
+        assert mw.record_event("a.b", attrs={"x": 1}) is False
+
+
+class TestThreads:
+    async def test_record_from_thread_while_server_runs(self, db):
+        store = db()
+        app, _, mw, _ = make_app([store])
+        results = []
+        async with Client(app) as client:
+            await client.call_tool("submit", {"job_id": "j1"})
+
+            def watcher():
+                for kind in ("step.started", "job.finished"):
+                    results.append(
+                        mw.record_event(kind, key="j1", attrs={"case": "wing"})
+                    )
+
+            t = threading.Thread(target=watcher)
+            t.start()
+            await asyncio.to_thread(t.join)
+            await asyncio.sleep(0)
+        await mw.flush()
+        assert results == [True, True]
+        rows = await table_rows(store, store.events)
+        assert sorted(r["kind"] for r in rows) == [
+            "job.finished",
+            "job.submitted",
+            "step.started",
+        ]
+        threaded = [r for r in rows if r["kind"] != "job.submitted"]
+        assert all(r["call_id"] is None for r in threaded)
+        assert [e.kind for e in mw.recent_events] == [
+            "job.submitted",
+            "step.started",
+            "job.finished",
+        ]
+
+    async def test_thread_with_its_own_loop_hands_over(self):
+        app, sink, mw, _ = make_app()
+        mw.record_event("a.first")  # binds the dispatcher to this loop
+        out = []
+        t = threading.Thread(
+            target=lambda: out.append(asyncio.run(self._record_in_loop(mw)))
+        )
+        t.start()
+        await asyncio.to_thread(t.join)
+        await asyncio.sleep(0)
+        await mw.flush()
+        assert out == [True]
+        assert [e.kind for e in sink.events] == ["a.first", "a.second"]
+        assert mw.dispatcher._loop is asyncio.get_running_loop()
+
+    @staticmethod
+    async def _record_in_loop(mw):
+        return mw.record_event("a.second")
+
+    def test_no_loop_and_nothing_recorded_drops(self):
+        app, sink, mw, _ = make_app()
+        assert mw.record_event("job.started", key="j1") is False
+        assert mw.dispatcher.dropped == 1
+        assert list(mw.recent_events) == []
+
+    async def test_thread_before_anything_recorded_drops(self):
+        app, sink, mw, _ = make_app()
+        out = []
+        t = threading.Thread(target=lambda: out.append(mw.record_event("a.b")))
+        t.start()
+        await asyncio.to_thread(t.join)
+        assert out == [False] and mw.dispatcher.dropped == 1
+
+
+class TestLlmCall:
+    async def test_attrs_and_linking(self, db):
+        store = db()
+        app, _, mw, _ = make_app([store])
+        await call(app, mw, ("chat", {"prompt": "hi"}))
+        [call_row] = await table_rows(store, store.table)
+        [ev] = await table_rows(store, store.events)
+        assert ev["kind"] == "llm.call" and ev["call_id"] == call_row["id"]
+        assert ev["attrs"] == {
+            "cache_read_input_tokens": 1000,
+            "model": "claude-opus-4",
+            "provider": "anthropic",
+            "duration_ms": 812.5,
+            "input_tokens": 1200,
+            "output_tokens": 90,
+            "ok": True,
+        }
+
+    async def test_error_redacted_truncated_and_not_ok(self):
+        app, sink, mw, _ = make_app(max_error_chars=100)
+        await call(app, mw, ("chat_fails", {"prompt": "hi"}))
+        [ev] = sink.events
+        attrs = ev.attrs
+        assert attrs["ok"] is False and attrs["error_type"] == "RuntimeError"
+        assert "sk-abcdefghijklmnopqrstuv" not in attrs["error"]
+        assert len(attrs["error"]) == 100
+        assert ev.call_id == sink.calls[0].id and sink.calls[0].outcome == "error"
+
+    async def test_named_fields_win_and_ok_explicit(self):
+        app, sink, mw, _ = make_app()
+        mw.record_llm_call(
+            model="m", ok=False, key="turn-1", attrs={"model": "spoofed", "round": 2}
+        )
+        await mw.flush()
+        [ev] = sink.events
+        assert ev.key == "turn-1"
+        assert ev.attrs == {"model": "m", "round": 2, "ok": False}
+
+
+class TestSinks:
+    async def test_database_sink_mixed_batch(self, db):
+        store = db()
+        rec = ToolCallRecord(
+            tool="t",
+            started_at=datetime.now(UTC),
+            duration_ms=1.0,
+            ok=True,
+            mode="meta",
+        )
+        ev = event(call_id=rec.id, attrs={"a": 1})
+        await store.write([rec, ev])
+        assert [r["id"] for r in await table_rows(store, store.table)] == [rec.id]
+        [row] = await table_rows(store, store.events)
+        assert row["id"] == ev.id and row["call_id"] == rec.id
+
+    async def test_hand_built_event_attrs_made_json_safe(self, db):
+        store = db()
+        await store.write([event(attrs={"p": float("nan"), "q": float("-inf")})])
+        [row] = await table_rows(store, store.events)
+        assert row["attrs"] == {"p": "nan", "q": "-inf"}
+
+    async def test_database_sink_events_only_and_calls_only(self, db):
+        store = db()
+        await store.write([event()])
+        assert len(await table_rows(store, store.events)) == 1
+        assert await table_rows(store, store.table) == []
+
+    async def test_calls_stored_when_events_table_missing(self, db):
+        store = db("old", create_tables=False)
+        old = MetaData()
+        build_metadata("ffb_", old)
+        old.remove(old.tables["ffb_events"])
+        async with store.engine.begin() as conn:
+            await conn.run_sync(old.create_all)
+        rec = ToolCallRecord(
+            tool="t",
+            started_at=datetime.now(UTC),
+            duration_ms=1.0,
+            ok=True,
+            mode="meta",
+        )
+        # SQLite raises OperationalError, Postgres ProgrammingError.
+        with pytest.raises(DBAPIError, match="no such table|does not exist"):
+            await store.write([rec, event()])
+        assert len(await table_rows(store, store.table)) == 1
+
+    async def test_json_lines_record_field(self):
+        stream = io.StringIO()
+        app, _, mw, _ = make_app([JsonLinesSink(stream)])
+        await call(app, mw, ("submit", {"job_id": "j1"}))
+        lines = [json.loads(line) for line in stream.getvalue().splitlines()]
+        by_type = {line["record"]: line for line in lines}
+        assert set(by_type) == {"tool_call", "event"}
+        assert by_type["event"]["kind"] == "job.submitted"
+        assert by_type["event"]["call_id"] == by_type["tool_call"]["id"]
+        assert by_type["tool_call"]["tool"] == "submit"
+
+    async def test_memory_sink_calls_and_events(self):
+        app, sink, mw, _ = make_app()
+        await call(app, mw, ("submit", {"job_id": "j1"}), ("poll", {}))
+        assert [r.tool for r in sink.calls] == ["submit", "poll"]
+        assert [e.kind for e in sink.events] == ["job.submitted"]
+        assert len(sink.records) == 3
+
+
+class TestEventsFor:
+    async def test_merges_database_and_memory_in_time_order(self, db):
+        store = db()
+        app, _, mw, _ = make_app([store])
+        # Written by another worker: only in the database.
+        await store.write([event("job.submitted", minutes_ago=30)])
+        # Recorded here and flushed: in both, returned once.
+        mw.record_event("step.started", key="j1")
+        await mw.flush()
+        # Recorded here, not yet written: only in memory.
+        pending = event("step.finished", minutes_ago=-1)
+        mw.recent_events.append(pending)
+        # Another job, and a kind filtered out below.
+        mw.record_event("job.submitted", key="j2")
+        await mw.flush()
+
+        rows = await mw.events_for("j1")
+        assert [r["kind"] for r in rows] == [
+            "job.submitted",
+            "step.started",
+            "step.finished",
+        ]
+        assert all(r["occurred_at"].tzinfo is not None for r in rows)
+        assert rows[-1]["id"] == pending.id
+        assert len({r["id"] for r in rows}) == 3
+
+        steps = await mw.events_for("j1", kinds=["step.started", "step.finished"])
+        assert [r["kind"] for r in steps] == ["step.started", "step.finished"]
+        last = await mw.events_for("j1", limit=2)
+        assert [r["kind"] for r in last] == ["step.started", "step.finished"]
+
+    async def test_memory_only_without_database(self):
+        app, sink, mw, _ = make_app()
+        await call(app, mw, ("submit", {"job_id": "j1"}))
+        mw.record_event("job.finished", key="j1")
+        rows = await mw.events_for("j1")
+        assert [r["kind"] for r in rows] == ["job.submitted", "job.finished"]
+        assert rows[0]["call_id"] == sink.calls[0].id
+
+    async def test_database_failure_falls_back_to_memory(self, db):
+        store = db("nodb", create_tables=False)
+        app, _, mw, _ = make_app([store])
+        mw.recent_events.append(event("job.submitted"))
+        rows = await mw.events_for("j1")
+        assert [r["kind"] for r in rows] == ["job.submitted"]
+
+
+class TestPrune:
+    async def test_prune_deletes_old_events_in_batches(self, db):
+        store = db(retention=timedelta(days=30), prune_batch=2)
+        old_call = ToolCallRecord(
+            tool="old",
+            started_at=datetime.now(UTC) - timedelta(days=40),
+            duration_ms=1.0,
+            ok=True,
+            mode="meta",
+        )
+        store._last_prune = float("inf")  # keep write() from pruning first
+        await store.write(
+            [old_call]
+            + [event(f"old.{i}", minutes_ago=60 * 24 * 40) for i in range(5)]
+            + [event("new.one"), event("new.two", minutes_ago=60 * 24 * 29)]
+        )
+        statements = []
+        from sqlalchemy import event as sa_event
+
+        def count_deletes(conn, cursor, statement, *args):
+            if statement.lstrip().upper().startswith("DELETE FROM FFB_EVENTS"):
+                statements.append(statement)
+
+        sa_event.listen(
+            store.engine.sync_engine, "before_cursor_execute", count_deletes
+        )
+        deleted = await store.prune()
+        assert deleted == 6  # 1 call + 5 events
+        assert len(statements) == 3  # 2 + 2 + 1
+        kinds = sorted(r["kind"] for r in await table_rows(store, store.events))
+        assert kinds == ["new.one", "new.two"]
+
+    async def test_auto_prune_covers_events(self, db):
+        store = db(retention=timedelta(days=30))
+        await store.write([event("old", minutes_ago=60 * 24 * 31), event("new")])
+        assert [r["kind"] for r in await table_rows(store, store.events)] == ["new"]
+
+    async def test_prune_without_events_table(self, db):
+        store = db("legacy", create_tables=False)
+        old = MetaData()
+        build_metadata("ffb_", old)
+        old.remove(old.tables["ffb_events"])
+        async with store.engine.begin() as conn:
+            await conn.run_sync(old.create_all)
+        rec = ToolCallRecord(
+            tool="old",
+            started_at=datetime.now(UTC) - timedelta(days=40),
+            duration_ms=1.0,
+            ok=True,
+            mode="meta",
+        )
+        await store.write([rec])
+        assert await store.prune(datetime.now(UTC) - timedelta(days=30)) == 1
+        async with store.engine.connect() as conn:
+            n = (
+                await conn.execute(select(func.count()).select_from(store.table))
+            ).scalar()
+        assert n == 0

tests/unit/test_instrumentation.py

@@ -373,9 +373,8 @@ class TestSinks:
 
 
 class TestDatabaseSink:
-    async def test_end_to_end_with_created_tables(self, tmp_path):
-        url = f"sqlite+aiosqlite:///{tmp_path}/calls.db"
-        db = DatabaseSink(url, create_tables=True)
+    async def test_end_to_end_with_created_tables(self, db_url):
+        db = DatabaseSink(db_url(), create_tables=True)
         app, _, mw = make_app(sinks=[db], mode="full", server_version="9.9")
         await call(app, "create_token", {"label": "ci"})
         with pytest.raises(ToolError):
@@ -397,8 +396,8 @@ class TestDatabaseSink:
         assert rows[0]["server_version"] == "9.9"
         assert "hunter2" not in json.dumps([dict(r) for r in rows], default=str)
 
-    async def test_prefix_and_host_owned_engine(self, tmp_path):
-        engine = create_async_engine(f"sqlite+aiosqlite:///{tmp_path}/host.db")
+    async def test_prefix_and_host_owned_engine(self, db_url):
+        engine = create_async_engine(db_url("host"))
         db = DatabaseSink(engine, prefix="qa_", create_tables=True)
         app, _, mw = make_app(sinks=[db])
         await call(app, "echo", {"text": "hi"})
@@ -408,10 +407,14 @@ class TestDatabaseSink:
         async with engine.connect() as conn:
             tables = await conn.run_sync(lambda c: inspect(c).get_table_names())
         await engine.dispose()
-        assert sorted(tables) == ["qa_feedback_call_links", "qa_tool_calls"]
+        assert sorted(tables) == [
+            "qa_events",
+            "qa_feedback_call_links",
+            "qa_tool_calls",
+        ]
 
-    async def test_no_tables_created_by_default(self, tmp_path):
-        engine = create_async_engine(f"sqlite+aiosqlite:///{tmp_path}/host.db")
+    async def test_no_tables_created_by_default(self, db_url):
+        engine = create_async_engine(db_url("host"))
         app, _, mw = make_app(sinks=[DatabaseSink(engine)])
         result = await call(app, "echo", {"text": "hi"})
         await mw.flush()
@@ -425,9 +428,9 @@ class TestDatabaseSink:
         assert tables == []
         assert mw.dispatcher.sink_errors == 1
 
-    async def test_absent_payloads_are_sql_null(self, tmp_path):
+    async def test_absent_payloads_are_sql_null(self, db_url):
         """Meta mode stores no payloads; they must be SQL NULL, not JSON null."""
-        db = DatabaseSink(f"sqlite+aiosqlite:///{tmp_path}/n.db", create_tables=True)
+        db = DatabaseSink(db_url("n"), create_tables=True)
         app, _, mw = make_app(sinks=[db])
         await call(app, "echo", {"text": "hi"})
         await mw.flush()
@@ -444,7 +447,12 @@ class TestDatabaseSink:
         host = MetaData()
         assert build_metadata("ffb_", host) is host
         build_metadata("ffb_", host)
-        assert sorted(host.tables) == ["ffb_feedback_call_links", "ffb_tool_calls"]
+        assert sorted(host.tables) == [
+            "ffb_embeddings",
+            "ffb_events",
+            "ffb_feedback_call_links",
+            "ffb_tool_calls",
+        ]
 
 
 class TestIntegration:

tests/unit/test_retention_sampling.py

@@ -0,0 +1,400 @@
+"""Tests for per-tool exclusion, sampling and retention pruning.
+
+Real FastMCP servers through fastmcp.Client, as in test_instrumentation.py.
+"""
+
+import logging
+import time
+from datetime import UTC, datetime, timedelta
+
+import pytest
+from fastmcp import Client, FastMCP
+from fastmcp.exceptions import ToolError
+from sqlalchemy import event, func, select
+
+from fastmcp_feedback.instrumentation import (
+    DatabaseSink,
+    MemorySink,
+    ToolCallRecord,
+    instrument,
+)
+
+
+@pytest.fixture
+async def db(db_url):
+    """DatabaseSinks whose engines are always disposed, or the process hangs."""
+    made = []
+
+    def factory(name="calls", **kwargs):
+        sink = DatabaseSink(db_url(name), create_tables=True, **kwargs)
+        made.append(sink)
+        return sink
+
+    yield factory
+    for sink in made:
+        await sink.engine.dispose()
+
+
+class Rolls:
+    """Stand-in for random.random that returns the given values in turn."""
+
+    def __init__(self, *values):
+        self.values = list(values)
+        self.calls = 0
+
+    def __call__(self):
+        self.calls += 1
+        return self.values.pop(0)
+
+
+def make_app(sinks=None, **kwargs):
+    sink = MemorySink()
+    app = FastMCP("sampling")
+
+    @app.tool
+    def poll() -> dict:
+        return {"status": "idle"}
+
+    @app.tool
+    def render(status: str) -> dict:
+        return {"status": status, "error": f"render said {status}"}
+
+    @app.tool
+    def crash() -> dict:
+        raise RuntimeError("exploded")
+
+    @app.tool
+    def other() -> str:
+        return "fine"
+
+    mw = instrument(app, sinks if sinks is not None else [sink], **kwargs)
+    return app, sink, mw
+
+
+async def call(app, mw, *calls):
+    """Run (tool, args) pairs; return the results, None where the tool raised."""
+    results = []
+    async with Client(app) as client:
+        for tool, args in calls:
+            try:
+                results.append(await client.call_tool(tool, args))
+            except ToolError:
+                results.append(None)
+    await mw.flush()
+    return results
+
+
+def old_record(tool="old", days=40, **kwargs):
+    return ToolCallRecord(
+        tool=tool,
+        started_at=datetime.now(UTC) - timedelta(days=days),
+        duration_ms=1.0,
+        ok=True,
+        mode="meta",
+        **kwargs,
+    )
+
+
+async def rows(sink, *columns):
+    t = sink.table
+    cols = [t.c[c] for c in columns] if columns else [t.c.tool]
+    async with sink.engine.connect() as conn:
+        return [
+            tuple(r)
+            for r in (await conn.execute(select(*cols).order_by(t.c.started_at))).all()
+        ]
+
+
+async def count(sink):
+    async with sink.engine.connect() as conn:
+        return (
+            await conn.execute(select(func.count()).select_from(sink.table))
+        ).scalar()
+
+
+class TestExclusion:
+    async def test_excluded_tool_not_recorded_nor_kept(self):
+        app, sink, mw = make_app(exclude_tools={"poll"})
+        results = await call(app, mw, ("poll", {}), ("other", {}), ("poll", {}))
+        assert [r.tool for r in sink.records] == ["other"]
+        assert [r.tool for r in mw.recent] == ["other"]
+        # The call itself is untouched.
+        assert results[0].structured_content == {"status": "idle"}
+
+    async def test_excluded_tool_failures_not_recorded_either(self):
+        app, sink, mw = make_app(exclude_tools=["crash"])
+        [result] = await call(app, mw, ("crash", {}))
+        assert result is None  # the error still reached the client
+        assert sink.records == []
+        assert list(mw.recent) == []
+
+    async def test_excluded_tool_skips_hooks(self):
+        seen = []
+        app, sink, mw = make_app(
+            exclude_tools={"poll"}, identity_resolver=lambda c: seen.append(1) or {}
+        )
+        await call(app, mw, ("poll", {}))
+        assert seen == []
+
+
+class TestSampling:
+    async def test_recorded_when_random_below_rate(self):
+        rolls = Rolls(0.29, 0.3, 0.31, 0.0)
+        app, sink, mw = make_app(sample_rates={"poll": 0.3}, random=rolls)
+        await call(app, mw, *[("poll", {})] * 4)
+        assert rolls.calls == 4
+        assert len(sink.records) == 2
+        assert [r.sample_rate for r in sink.records] == [0.3, 0.3]
+        assert all(r.outcome == "ok" for r in sink.records)
+
+    async def test_sampled_out_calls_not_in_recent(self):
+        app, sink, mw = make_app(sample_rates={"poll": 0.5}, random=Rolls(0.9, 0.1))
+        results = await call(app, mw, ("poll", {}), ("poll", {}))
+        assert len(sink.records) == 1
+        assert [r.id for r in mw.recent] == [sink.records[0].id]
+        # A sampled-out call still returns its result.
+        assert results[0].structured_content == {"status": "idle"}
+
+    async def test_sampled_out_ok_calls_skip_hooks(self):
+        seen = []
+
+        def who(context):
+            seen.append("identity")
+            return {}
+
+        def enrich(tool, args, result, context):
+            seen.append("enrich")
+            return {}
+
+        app, sink, mw = make_app(
+            sample_rates={"poll": 0.1},
+            random=Rolls(0.99),
+            identity_resolver=who,
+            enricher=enrich,
+        )
+        await call(app, mw, ("poll", {}))
+        assert sink.records == []
+        assert seen == []
+
+    async def test_unsampled_tools_recorded_without_rate_or_roll(self):
+        rolls = Rolls()
+        app, sink, mw = make_app(sample_rates={"poll": 0.1}, random=rolls)
+        await call(app, mw, ("other", {}), ("other", {}))
+        assert rolls.calls == 0
+        assert [(r.tool, r.sample_rate) for r in sink.records] == [
+            ("other", None),
+            ("other", None),
+        ]
+
+    async def test_rate_of_one_records_everything_with_rate(self):
+        app, sink, mw = make_app(sample_rates={"poll": 1}, random=Rolls(0.999))
+        await call(app, mw, ("poll", {}))
+        [record] = sink.records
+        assert record.sample_rate == 1.0
+
+    async def test_exception_of_sampled_tool_always_recorded(self):
+        # Rolls would sample every call out; failures must not consult them.
+        rolls = Rolls(0.99, 0.99)
+        app, sink, mw = make_app(sample_rates={"crash": 0.01}, random=rolls)
+        await call(app, mw, ("crash", {}), ("crash", {}))
+        assert [(r.outcome, r.error_type, r.sample_rate) for r in sink.records] == [
+            ("error", "RuntimeError", None),
+            ("error", "RuntimeError", None),
+        ]
+        assert len(mw.recent) == 2
+
+    async def test_soft_error_of_sampled_tool_always_recorded(self):
+        rolls = Rolls(0.99, 0.99)
+        app, sink, mw = make_app(sample_rates={"render": 0.01}, random=rolls)
+        await call(
+            app, mw, ("render", {"status": "failed"}), ("render", {"status": "done"})
+        )
+        assert [(r.outcome, r.sample_rate) for r in sink.records] == [
+            ("soft_error", None)
+        ]
+        assert sink.records[0].error_message == "status=failed: render said failed"
+        assert [r.id for r in mw.recent] == [sink.records[0].id]
+
+    async def test_sampled_in_failure_has_no_rate(self):
+        app, sink, mw = make_app(sample_rates={"crash": 0.5}, random=Rolls(0.0))
+        await call(app, mw, ("crash", {}))
+        [record] = sink.records
+        assert record.sample_rate is None
+
+    @pytest.mark.parametrize("rate", [0, -0.1, 1.01, 2, float("nan")])
+    def test_invalid_rates_rejected(self, rate):
+        with pytest.raises(ValueError, match="poll"):
+            make_app(sample_rates={"poll": rate})
+
+    def test_non_numeric_rate_rejected(self):
+        with pytest.raises((TypeError, ValueError)):
+            make_app(sample_rates={"poll": "often"})
+
+    async def test_estimated_count(self):
+        """sum(1 / coalesce(sample_rate, 1)) estimates the true call count."""
+        rolls = Rolls(*[i / 10 for i in range(10)])
+        app, sink, mw = make_app(sample_rates={"poll": 0.2}, random=rolls)
+        await call(app, mw, *[("poll", {})] * 10, ("crash", {}))
+        estimate = sum(1 / (r.sample_rate or 1) for r in sink.records)
+        assert estimate == pytest.approx(11)
+
+
+class TestDatabaseSinkColumn:
+    async def test_writes_sample_rate(self, db):
+        sink = db()
+        app, _, mw = make_app(
+            sinks=[sink],
+            sample_rates={"poll": 0.25, "crash": 0.25},
+            random=Rolls(0.1, 0.9),
+        )
+        await call(app, mw, ("poll", {}), ("poll", {}), ("crash", {}), ("other", {}))
+        assert await rows(sink, "tool", "outcome", "sample_rate") == [
+            ("poll", "ok", 0.25),
+            ("crash", "error", None),
+            ("other", "ok", None),
+        ]
+
+
+class TestPrune:
+    async def test_deletes_old_rows_keeps_new(self, db):
+        sink = db()
+        await sink.write([old_record("a", days=40), old_record("b", days=31)])
+        await sink.write([old_record("c", days=29), old_record("d", days=0)])
+        deleted = await sink.prune(datetime.now(UTC) - timedelta(days=30))
+        assert deleted == 2
+        assert await rows(sink) == [("c",), ("d",)]
+
+    async def test_default_cutoff_uses_retention(self, db):
+        sink = db(retention=timedelta(days=30))
+        sink._last_prune = time.monotonic()  # keep write() from pruning
+        await sink.write([old_record("a", days=40), old_record("b", days=1)])
+        assert await count(sink) == 2
+        assert await sink.prune() == 1
+        assert await rows(sink) == [("b",)]
+
+    async def test_no_retention_no_cutoff_is_a_no_op(self, db):
+        sink = db()
+        await sink.write([old_record(days=400)])
+        assert await sink.prune() == 0
+        assert await count(sink) == 1
+
+    async def test_keeps_linked_calls_and_links(self, db):
+        sink = db()
+        linked, plain = old_record("linked"), old_record("plain")
+        await sink.write([linked, plain])
+        await sink.replace_links(
+            "bug-1",
+            [
+                {
+                    "feedback_ref": "bug-1",
+                    "call_id": linked.id,
+                    "position": 0,
+                    "rule": "session",
+                    "linked_at": datetime.now(UTC),
+                },
+                {
+                    # A link to a call that is not in this database.
+                    "feedback_ref": "bug-1",
+                    "call_id": "missing",
+                    "position": 1,
+                    "rule": "session",
+                    "linked_at": datetime.now(UTC),
+                },
+            ],
+        )
+        assert await sink.prune(datetime.now(UTC)) == 1
+        assert await rows(sink) == [("linked",)]
+        context = await sink.linked_calls("bug-1")
+        assert [(r["call_id"], r["tool"]) for r in context] == [
+            (linked.id, "linked"),
+            ("missing", None),
+        ]
+
+    async def test_deletes_in_batches(self, db):
+        sink = db(prune_batch=3)
+        await sink.write(
+            [old_record(f"t{i}") for i in range(10)] + [old_record("new", 0)]
+        )
+        deletes = []
+
+        def spy(conn, cursor, statement, params, context, executemany):
+            if statement.lstrip().upper().startswith("DELETE"):
+                deletes.append(statement)
+
+        event.listen(sink.engine.sync_engine, "before_cursor_execute", spy)
+        try:
+            assert await sink.prune(datetime.now(UTC) - timedelta(days=1)) == 10
+        finally:
+            event.remove(sink.engine.sync_engine, "before_cursor_execute", spy)
+        assert len(deletes) == 4  # 3 + 3 + 3 + 1
+        assert await rows(sink) == [("new",)]
+
+    async def test_exact_multiple_of_batch(self, db):
+        sink = db(prune_batch=5)
+        await sink.write([old_record(f"t{i}") for i in range(10)])
+        assert await sink.prune(datetime.now(UTC)) == 10
+        assert await count(sink) == 0
+
+    def test_invalid_settings_rejected(self):
+        with pytest.raises(ValueError):
+            DatabaseSink("sqlite+aiosqlite://", retention=timedelta(0))
+        with pytest.raises(ValueError):
+            DatabaseSink("sqlite+aiosqlite://", prune_batch=0)
+
+
+class TestAutomaticPrune:
+    async def test_first_write_prunes_then_waits_for_interval(self, db):
+        sink = db(retention=timedelta(days=30), prune_interval=timedelta(hours=1))
+        await sink.write([old_record("a"), old_record("new", 0)])
+        assert await rows(sink) == [("new",)]
+
+        await sink.write([old_record("b")])
+        assert await count(sink) == 2  # inside the interval: not pruned
+
+        sink._last_prune = time.monotonic() - 3601
+        await sink.write([old_record("c", 0)])
+        assert sorted(await rows(sink)) == [("c",), ("new",)]
+
+    async def test_no_retention_never_prunes(self, db):
+        sink = db()
+        await sink.write([old_record(days=4000)])
+        assert await count(sink) == 1
+        assert sink._last_prune is None
+
+    async def test_failing_prune_does_not_stop_inserts(self, db, caplog):
+        sink = db(retention=timedelta(days=30))
+        attempts = []
+
+        async def broken(older_than=None):
+            attempts.append(older_than)
+            raise RuntimeError("prune blew up")
+
+        sink.prune = broken
+        with caplog.at_level(logging.WARNING):
+            await sink.write([old_record("a", 0)])
+            await sink.write([old_record("b", 0)])
+        assert await rows(sink) == [("a",), ("b",)]
+        assert len(attempts) == 1  # not retried on every write
+        assert "prune blew up" in caplog.text
+
+    async def test_through_the_middleware(self, db):
+        sink = db(retention=timedelta(days=30))
+        sink._last_prune = time.monotonic()
+        await sink.write([old_record("stale")])
+        sink._last_prune = None
+        app, _, mw = make_app(sinks=[sink])
+        await call(app, mw, ("other", {}))
+        assert mw.dispatcher.sink_errors == 0
+        assert await rows(sink) == [("other",)]
+
+    async def test_prune_failure_through_middleware_keeps_records(self, db):
+        sink = db(retention=timedelta(days=30))
+
+        async def broken(older_than=None):
+            raise RuntimeError("no")
+
+        sink.prune = broken
+        app, _, mw = make_app(sinks=[sink])
+        await call(app, mw, ("other", {}), ("poll", {}))
+        assert mw.dispatcher.sink_errors == 0
+        assert sorted(await rows(sink)) == [("other",), ("poll",)]

tests/unit/test_soft_errors.py

@@ -39,14 +39,12 @@ except TypeError:  # added in FastMCP 3.4
 
 
 @pytest.fixture
-async def db(tmp_path):
+async def db(db_url):
     """DatabaseSinks whose engines are always disposed, or the process hangs."""
     made = []
 
     def factory(name="calls"):
-        sink = DatabaseSink(
-            f"sqlite+aiosqlite:///{tmp_path}/{name}.db", create_tables=True
-        )
+        sink = DatabaseSink(db_url(name), create_tables=True)
         made.append(sink)
         return sink
 

tests/unit/test_test_databases.py

@@ -0,0 +1,70 @@
+"""Checks on the db_url fixture itself: isolation between tests, and pgvector."""
+
+from datetime import UTC, datetime
+
+import pytest
+from sqlalchemy import func, select, text
+
+from fastmcp_feedback.instrumentation import DatabaseSink, ToolCallRecord
+
+SHARED_ID = "00000000-0000-0000-0000-00000000f00d"
+
+
+@pytest.fixture
+async def sink(db_url):
+    made = DatabaseSink(db_url(), create_tables=True)
+    yield made
+    await made.engine.dispose()
+
+
+async def write_fixed_row(sink):
+    """Insert a row with a fixed primary key and return the table's row count.
+
+    Two tests that did this against one shared database would collide on the
+    key, so each passing proves it had a database of its own.
+    """
+    rec = ToolCallRecord(
+        id=SHARED_ID,
+        tool="t",
+        started_at=datetime.now(UTC),
+        duration_ms=1.0,
+        ok=True,
+        mode="meta",
+    )
+    await sink.write([rec])
+    async with sink.engine.connect() as conn:
+        return (
+            await conn.execute(select(func.count()).select_from(sink.table))
+        ).scalar()
+
+
+class TestIsolation:
+    async def test_first_writer(self, sink):
+        assert await write_fixed_row(sink) == 1
+
+    async def test_second_writer(self, sink):
+        assert await write_fixed_row(sink) == 1
+
+    def test_names_map_to_databases(self, db_url):
+        assert db_url("a") == db_url("a")
+        assert db_url("a") != db_url("b")
+
+
+async def test_vector_extension_available(db_url, db_backend):
+    if db_backend != "postgres":
+        pytest.skip("pgvector is Postgres only")
+    sink = DatabaseSink(db_url())
+    try:
+        async with sink.engine.connect() as conn:
+            version = (
+                await conn.execute(
+                    text("SELECT extversion FROM pg_extension WHERE extname = 'vector'")
+                )
+            ).scalar()
+            distance = (
+                await conn.execute(text("SELECT '[1,2,3]'::vector <-> '[1,2,4]'"))
+            ).scalar()
+    finally:
+        await sink.engine.dispose()
+    assert version is not None
+    assert distance == 1.0

uv.lock

@@ -51,6 +51,54 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
+[[package]]
+name = "asyncpg"
+version = "0.31.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fe/cc/d18065ce2380d80b1bcce927c24a2642efd38918e33fd724bc4bca904877/asyncpg-0.31.0.tar.gz", hash = "sha256:c989386c83940bfbd787180f2b1519415e2d3d6277a70d9d0f0145ac73500735", size = 993667, upload-time = "2025-11-24T23:27:00.812Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/08/17/cc02bc49bc350623d050fa139e34ea512cd6e020562f2a7312a7bcae4bc9/asyncpg-0.31.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:eee690960e8ab85063ba93af2ce128c0f52fd655fdff9fdb1a28df01329f031d", size = 643159, upload-time = "2025-11-24T23:25:36.443Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/62/4ded7d400a7b651adf06f49ea8f73100cca07c6df012119594d1e3447aa6/asyncpg-0.31.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2657204552b75f8288de08ca60faf4a99a65deef3a71d1467454123205a88fab", size = 638157, upload-time = "2025-11-24T23:25:37.89Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/5b/4179538a9a72166a0bf60ad783b1ef16efb7960e4d7b9afe9f77a5551680/asyncpg-0.31.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a429e842a3a4b4ea240ea52d7fe3f82d5149853249306f7ff166cb9948faa46c", size = 2918051, upload-time = "2025-11-24T23:25:39.461Z" },
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 version = "25.4.0"
@@ -553,7 +601,7 @@ wheels = [
 
 [[package]]
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-version = "2026.9.27.4"
+version = "2026.9.30.1"
 source = { editable = "." }
 dependencies = [
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@@ -564,7 +612,9 @@ dependencies = [
 [package.optional-dependencies]
 all = [
     { name = "aiosqlite" },
+    { name = "asyncpg" },
     { name = "mypy" },
+    { name = "pgvector" },
     { name = "psycopg2-binary" },
     { name = "pymysql" },
     { name = "pytest" },
@@ -577,7 +627,9 @@ all = [
 ]
 dev = [
     { name = "aiosqlite" },
+    { name = "asyncpg" },
     { name = "mypy" },
+    { name = "pgvector" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-cov" },
@@ -586,6 +638,9 @@ dev = [
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     { name = "sqlalchemy", extra = ["asyncio"] },
 ]
+embeddings = [
+    { name = "pgvector" },
+]
 instrumentation = [
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     { name = "sqlalchemy", extra = ["asyncio"] },
@@ -598,6 +653,8 @@ postgresql = [
 ]
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+    { name = "asyncpg" },
+    { name = "pgvector" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-cov" },
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     { name = "aiosqlite", marker = "extra == 'test'", specifier = ">=0.22.1" },
+    { name = "asyncpg", marker = "extra == 'dev'", specifier = ">=0.31.0" },
+    { name = "asyncpg", marker = "extra == 'test'", specifier = ">=0.31.0" },
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     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.7.0" },
+    { name = "pgvector", marker = "extra == 'dev'", specifier = ">=0.5.0" },
+    { name = "pgvector", marker = "extra == 'embeddings'", specifier = ">=0.5.0" },
+    { name = "pgvector", marker = "extra == 'test'", specifier = ">=0.5.0" },
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+provides-extras = ["postgresql", "instrumentation", "embeddings", "mysql", "dev", "test", "all"]
 
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+version = "0.5.0"
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 version = "4.5.1"