chore(compose): add shared json-file logging config and component labels
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Introduce x-logging anchor with json-file driver, size/file caps, and
container name tagging so Alloy on puck can reliably tail every service
through the Docker socket. Apply to all services and inject
MNEMOSYNE_COMPONENT env vars (init/app/mcp/worker) for consistent log
attribution both
This commit is contained in:
2026-05-11 13:52:00 -04:00
parent 8ddbcf4612
commit 551c641e90
5 changed files with 225 additions and 37 deletions

View File

@@ -47,6 +47,20 @@
# and is the typical re-run target after embedding-model changes).
# =============================================================================
# -----------------------------------------------------------------------------
# Shared logging config — JSON to stdout, picked up by Alloy via the Docker
# socket on the host and shipped to Loki. Pinning json-file (Docker's default)
# so Alloy's discovery.docker + loki.source.docker on puck sees a consistent
# driver across every service, and bounding log retention per container so a
# misbehaving service can't fill the disk between Alloy tails.
# -----------------------------------------------------------------------------
x-logging: &default-logging
driver: json-file
options:
tag: "{{.Name}}"
max-size: "10m"
max-file: "5"
services:
# ── Static-file seeder: copies /app/staticfiles into the shared volume on
@@ -60,6 +74,7 @@ services:
volumes:
- mnemosyne-static:/shared-static
restart: "no"
logging: *default-logging
# ── Init sidecar: one-shot Postgres migrate + library-type seed. Runs on
# every `up` and exits. Long-running services below depend on
@@ -92,10 +107,16 @@ services:
- DB_PORT=${DB_PORT}
# Neo4j (load_library_types writes Library defaults into the graph)
- NEOMODEL_NEO4J_BOLT_URL=${NEOMODEL_NEO4J_BOLT_URL}
# Logging
# Logging (MNEMOSYNE_COMPONENT is injected by settings.py into every
# log line as a static JSON field; Alloy on puck reads the compose
# service name directly off the Docker label and uses that as the
# Loki `component` label, but we still set it here so operators
# tail-ing ``docker logs`` see the same attribution)
- MNEMOSYNE_COMPONENT=init
- LOGGING_LEVEL=${LOGGING_LEVEL}
- DJANGO_LOGGING_LEVEL=${DJANGO_LOGGING_LEVEL}
restart: "no"
logging: *default-logging
# ── App: Django REST API + admin ──────────────────────────────────────────
@@ -154,9 +175,11 @@ services:
- RERANKER_MAX_CANDIDATES=${RERANKER_MAX_CANDIDATES}
- RERANKER_TIMEOUT=${RERANKER_TIMEOUT}
# Logging
- MNEMOSYNE_COMPONENT=app
- LOGGING_LEVEL=${LOGGING_LEVEL}
- DJANGO_LOGGING_LEVEL=${DJANGO_LOGGING_LEVEL}
restart: unless-stopped
logging: *default-logging
depends_on:
static-init:
condition: service_completed_successfully
@@ -228,9 +251,11 @@ services:
- RERANKER_MAX_CANDIDATES=${RERANKER_MAX_CANDIDATES}
- RERANKER_TIMEOUT=${RERANKER_TIMEOUT}
# Logging
- MNEMOSYNE_COMPONENT=mcp
- LOGGING_LEVEL=${LOGGING_LEVEL}
- DJANGO_LOGGING_LEVEL=${DJANGO_LOGGING_LEVEL}
restart: unless-stopped
logging: *default-logging
depends_on:
init:
condition: service_completed_successfully
@@ -303,9 +328,11 @@ services:
- EMBEDDING_BATCH_SIZE=${EMBEDDING_BATCH_SIZE}
- EMBEDDING_TIMEOUT=${EMBEDDING_TIMEOUT}
# Logging
- MNEMOSYNE_COMPONENT=worker
- LOGGING_LEVEL=${LOGGING_LEVEL}
- CELERY_LOGGING_LEVEL=${CELERY_LOGGING_LEVEL}
restart: unless-stopped
logging: *default-logging
depends_on:
app:
condition: service_healthy
@@ -324,6 +351,7 @@ services:
web:
image: nginx:alpine
restart: unless-stopped
logging: *default-logging
depends_on:
app:
condition: service_healthy

View File

@@ -9,7 +9,7 @@ This document describes Mnemosyne's role in the Daedalus + Pallas architecture a
Mnemosyne exposes two interfaces for the wider Ouranos ecosystem:
1. **REST API** (`/library/api/*`) — consumed by the Daedalus backend (HTTP Basic auth, service account `daedalus-service`) for workspace lifecycle and asynchronous file ingestion. Phase 1, **implemented**.
2. **MCP Server** (port 22091 internal, `/mcp/` via nginx on 23090) — exposes search, browse, and retrieval tools. Phase 5 of Mnemosyne's own roadmap, **implemented** with workspace_id scoping and per-turn JWT access control. Consumed by Pallas FastAgents in production (Daedalus integration Phase 2, **implemented** — see [Phase 3 of this doc](#3-phase-3-per-turn-token-access-control-for-daedalus-integration)).
2. **MCP Server** (port 22091 internal, `/mcp/` via nginx on 23090) — exposes search, browse, and retrieval tools. Phase 5 of Mnemosyne's own roadmap, **implemented** with workspace-scoped access control via long-lived team JWTs. Consumed by Pallas FastAgents in production (Daedalus integration Phase 2, **implemented** — see [Phase 3 of this doc](#3-phase-3-long-lived-team-jwt-access-control-for-pallas-instances)).
### Phase status
@@ -17,7 +17,7 @@ Mnemosyne exposes two interfaces for the wider Ouranos ecosystem:
|-------|------|--------|
| 1. REST workspace + ingest API for Daedalus | `POST /workspaces/`, `DELETE /workspaces/{id}/`, `POST /ingest/`, `GET /jobs/{id}/` | **Implemented** |
| 2. MCP Server (Mnemosyne roadmap Phase 5) | `search`, `get_chunk`, `list_libraries`, `list_collections`, `list_items`, `get_health` | **Implemented** (workspace_id scoping enforced in Cypher) |
| 3. Per-turn signed-token access control for Daedalus integration | Daedalus mints HS256 JWTs carrying `{ws, libs}` claims; Mnemosyne validates via `MCPSigningKey` and scopes search via `_scope_from_claims` | **Implemented** |
| 3. Long-lived team JWT access control for Pallas instances | Mnemosyne mints a 10-year HS256 JWT per Pallas instance (Team); Daedalus stores it encrypted and the operator pastes the plaintext into `fastagent.secrets.yaml`. Mnemosyne scopes search to the team's assigned workspaces via `TeamWorkspaceAssignment`. | **Implemented** |
---
@@ -367,40 +367,65 @@ mnemosyne_s3_operations_total{operation,status} counter
- [x] ASGI mount + uvicorn deployment on port 22091; nginx proxies via `/mcp/` on 23090
- [x] Prometheus metrics (`mnemosyne_mcp_*`)
### Phase 3 — Per-turn token access control for Daedalus integration ✅ Implemented
### Phase 3 — Long-lived team JWT access control for Pallas instances ✅ Implemented
Daedalus mints a short-lived HS256 JWT per chat turn and sends it as `Authorization: Bearer` to Pallas. Pallas forwards the token to outgoing Mnemosyne MCP calls (via `pallas/_fastagent_patch`). Mnemosyne validates the JWT and scopes every search to the workspace indicated by the `ws` claim.
Each Pallas instance registered in Daedalus is mirrored as a Mnemosyne **Team**. Mnemosyne mints a long-lived (10-year) HS256 JWT for the team; the operator pastes the plaintext into the Pallas instance's `fastagent.secrets.yaml`. Every MCP call from that Pallas instance carries the team JWT as a static `Authorization: Bearer` header. Mnemosyne validates the JWT and scopes search to the workspaces assigned to that team.
**Mnemosyne-side components:**
- [x] `MCPSigningKey` model — stores active HS256 secrets keyed by `kid`. Managed via `manage.py seed_signing_key --kid <kid>`.
- [x] `resolve_mcp_jwt(token_string)` in `mcp_server/auth.py` — validates signature, `exp`, `iss`, `jti` replay; returns claims dict.
- [x] `MCPAuthMiddleware.on_call_tool` — detects JWT shape (three dot-separated segments), routes to `resolve_mcp_jwt`, stores claims in FastMCP context state via `STATE_KEY_CLAIMS`.
- [x] `_scope_from_claims(claims, arg_workspace_id)` claims trump tool args; returns `(ws, allowed_libraries)`.
- [x] `allowed_libraries` on `SearchRequest` — extends `_WORKSPACE_SCOPE_CLAUSE` to include user-managed libraries in addition to the workspace's own.
- [x] `MCPSigningKey` model — stores active HS256 secrets keyed by `kid`. Managed via `manage.py seed_signing_key --kid <kid>`. The hex stays in Mnemosyne's DB; Daedalus never sees it.
- [x] `Team` model — one row per Pallas instance. `id` = `PallasInstance.id` on the Daedalus side (stable UUID). `active_jti` identifies the single currently-valid JWT; rotation changes this field, immediately invalidating the old token.
- [x] `TeamWorkspaceAssignment` model — maps a `Team` to a set of Daedalus workspace UUIDs. Updated by Daedalus via `PUT /mcp_server/api/teams/{id}/workspaces/` whenever workspace attachments change.
- [x] `resolve_mcp_jwt(token_string)` in `mcp_server/auth.py` — validates signature, `exp`, `iss`. For team JWTs (`iss=mnemosyne`, `typ=team`): parses `sub=team:<uuid>` `claims["team_id"]`; bypasses the per-turn JTI replay cache (team tokens are intentionally reused).
- [x] `_libraries_for_team(team_id, jti)` — looks up the `Team` row, verifies `active=True` and `active_jti == jti`, then translates `TeamWorkspaceAssignment` rows into Library UIDs via a single Cypher query.
- [x] `MCPAuthMiddleware.on_call_tool` — routes team JWTs through `_libraries_for_team`; routes legacy per-turn JWTs through `_scope_from_claims` (backward-compatible).
- [x] REST control plane at `/mcp_server/api/teams/`:
- `POST /` — create team by UUID; mints JWT, returns plaintext once.
- `GET /{id}/` — team state (workspace_ids, active status).
- `DELETE /{id}/` — soft-delete (`active=False`); all JWTs immediately invalid.
- `PUT /{id}/workspaces/` — replace workspace assignment list (idempotent).
- `POST /{id}/rotate/` — mint new JWT with new `active_jti`; returns plaintext once.
**Token format (HS256):**
**Team JWT format (HS256):**
```json
{
"iss": "daedalus",
"sub": "chat",
"ws": "<workspace_uuid>",
"libs": [],
"iss": "mnemosyne",
"aud": "mnemosyne",
"typ": "team",
"sub": "team:<pallas-instance-uuid>",
"iat": 1746000000,
"exp": 1746000600,
"jti": "<uuid4>"
"exp": 2061360000,
"jti": "<active_jti uuid>"
}
```
The `libs` claim is reserved for future user-managed library assignment (deferred). Currently always `[]`; the workspace's own library is always included via the `ws` claim.
**Provisioning:**
**Provisioning (once per Pallas instance):**
```bash
# On Mnemosyne host, once:
docker compose exec app python manage.py seed_signing_key --kid daedalus-1
# Copy the printed hex → DAEDALUS_MNEMOSYNE_SIGNING_SECRET in Daedalus .env
# 1. Seed the MCPSigningKey on Mnemosyne (once per deployment, not per instance):
docker compose exec app python manage.py seed_signing_key --kid daedalus-1 --retire-other
# The hex stays in Mnemosyne's DB — no operator action required.
# 2. Register the Pallas instance in Daedalus admin UI (/admin/pallas/).
# Daedalus calls POST /mcp_server/api/teams/ automatically.
# The team JWT is minted and stored encrypted in Daedalus.
# 3. Reveal the JWT via Daedalus admin UI (one-shot):
# GET /api/v1/pallas/{id}/team-jwt
# Copy the returned JWT string.
# 4. Paste into fastagent.secrets.yaml on the Pallas host:
# mcp:
# servers:
# mnemosyne:
# headers:
# Authorization: "Bearer <JWT>"
# 5. Restart the Pallas agent processes.
# 6. Attach workspaces in Daedalus workspace settings UI.
# Daedalus calls PUT /mcp_server/api/teams/{id}/workspaces/ automatically.
```
See the Daedalus-side spec [§9](../../daedalus/docs/mnemosyne_integration.md#9-phase-2--workspace-scoped-mcp-search-implemented) for the full integration architecture.
See the Daedalus-side spec [§9](../../daedalus/docs/mnemosyne_integration.md#9-phase-2--workspace-scoped-mcp-search-implemented) for the full operator walkthrough including JWT rotation and disaster recovery.

View File

@@ -0,0 +1,67 @@
"""Logging filters shared across Mnemosyne processes.
These are project-level (not tied to a Django app) so Celery workers and
the FastMCP ASGI app can reuse them without importing app modules.
"""
from __future__ import annotations
import logging
import re
# Paths that should not show up in INFO when the response is a success.
# Anything >= 400 still flows through — a failing probe is a real signal.
_SUPPRESS_PATHS = frozenset(
{
"/live/",
"/live",
"/ready/",
"/ready",
"/healthz",
"/metrics",
}
)
class SuppressHealthAccessFilter(logging.Filter):
"""Demote successful access-log records for health endpoints to DEBUG.
Applied to ``django.server`` (runserver) and ``gunicorn.access`` via
the ``access`` handler in :data:`mnemosyne.settings.LOGGING`. The filter
returns ``False`` (drop the record) only when the request path is a
health endpoint AND the HTTP status is 1xx/2xx/3xx. Any failure on
``/ready/`` or ``/live/`` still propagates so an operator sees
readiness flaps.
The two access loggers format their messages differently:
* ``django.server`` emits ``'"GET /live/ HTTP/1.1" 200 0'`` as the
message with no args.
* ``gunicorn.access`` typically has the path in ``record.args`` when
the access log format is configured, but many deployments fall
back to a pre-formatted message. We parse the final rendered
message in both cases to keep the filter portable across Mnemosyne
containers (which run gunicorn) and local dev (``runserver``).
"""
# Matches the path portion of the quoted request line inside either
# format. Tolerant of missing trailing slashes and query strings.
_REQUEST_RE = re.compile(r'"\s*(?:GET|POST|HEAD|OPTIONS|PUT|PATCH|DELETE)\s+(\S+)')
_STATUS_RE = re.compile(r'"\s+(\d{3})\b')
def filter(self, record: logging.LogRecord) -> bool:
msg = record.getMessage()
path_match = self._REQUEST_RE.search(msg)
status_match = self._STATUS_RE.search(msg)
if not path_match or not status_match:
return True
path = path_match.group(1).split("?", 1)[0]
status = int(status_match.group(1))
# Only suppress successful probes; surface any 4xx/5xx on a
# health endpoint so operators see readiness flaps.
if path in _SUPPRESS_PATHS and status < 400:
return False
return True

View File

@@ -278,35 +278,75 @@ THEMIS_NOTIFICATION_POLL_INTERVAL = 60
THEMIS_NOTIFICATION_MAX_AGE_DAYS = 90
# --- Structured Logging ---
# All log output is line-delimited JSON on stdout, one record per line.
# Alloy (running on the host / container sidecar) tails the container's
# stdout stream and ships to Loki. No log files, no syslog — a single,
# uniform transport across every service on this host.
#
# Labels attached by Alloy (NOT embedded here): service, component,
# environment, hostname. "component" is injected by the formatter as
# a static field based on the MNEMOSYNE_COMPONENT env var set per
# docker-compose service (app | mcp | worker). This keeps the label
# shape consistent with Pallas and future services.
#
# Level policy (Ouranos Lab standard):
# ERROR — broken; requires human attention
# WARNING — degraded but self-recovering; retries, skipped items
# INFO — lifecycle events and failures; no 200 OK health probes
# DEBUG — health-probe success, per-request detail, verbose traces
LOGGING_LEVEL = env("LOGGING_LEVEL", default="INFO")
CELERY_LOGGING_LEVEL = env("CELERY_LOGGING_LEVEL", default="INFO")
DJANGO_LOGGING_LEVEL = env("DJANGO_LOGGING_LEVEL", default="WARNING")
MNEMOSYNE_COMPONENT = env("MNEMOSYNE_COMPONENT", default="app")
LOGGING = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"structured": {
"format": (
"[%(levelname)s] %(asctime)s "
"service=mnemosyne "
"module=%(name)s "
"func=%(funcName)s "
"line=%(lineno)d "
"%(message)s"
# JSON formatter — one line of JSON per record. Alloy's ``| json``
# pipeline in LogQL will parse these fields into queryable columns
# (level, logger, funcName, lineno, message, plus anything passed
# via ``logger.info("...", extra={...})``).
"json": {
"()": "pythonjsonlogger.json.JsonFormatter",
"fmt": (
"%(asctime)s %(levelname)s %(name)s "
"%(funcName)s %(lineno)d %(message)s"
),
"datefmt": "%Y-%m-%d %H:%M:%S",
"rename_fields": {
"asctime": "time",
"levelname": "level",
"name": "logger",
},
"simple": {
"format": "[%(levelname)s] %(name)s: %(message)s",
"static_fields": {
"service": "mnemosyne",
"component": MNEMOSYNE_COMPONENT,
},
},
},
"filters": {
# Demotes successful health-probe access log lines from INFO to
# DEBUG so production INFO output stays signal-only. Applied to
# django.server and gunicorn.access; uvicorn does its own thing
# via the structlog-style filter in mcp_server.
"suppress_health_access": {
"()": "mnemosyne.log_filters.SuppressHealthAccessFilter",
},
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"formatter": "structured",
"formatter": "json",
"stream": "ext://sys.stdout",
},
# Separate handler for django/gunicorn access logs so we can apply
# the health-path filter without affecting application loggers.
"access": {
"class": "logging.StreamHandler",
"formatter": "json",
"stream": "ext://sys.stdout",
"filters": ["suppress_health_access"],
},
},
"loggers": {
"library": {
@@ -324,6 +364,11 @@ LOGGING = {
"level": LOGGING_LEVEL,
"propagate": False,
},
"mcp_server": {
"handlers": ["console"],
"level": LOGGING_LEVEL,
"propagate": False,
},
"celery": {
"handlers": ["console"],
"level": CELERY_LOGGING_LEVEL,
@@ -339,6 +384,28 @@ LOGGING = {
"level": DJANGO_LOGGING_LEVEL,
"propagate": False,
},
# Django's runserver / gunicorn access logs — demote health probes
# to DEBUG so "5xx on /ready/" is easy to spot in INFO.
"django.server": {
"handlers": ["access"],
"level": DJANGO_LOGGING_LEVEL,
"propagate": False,
},
"gunicorn.access": {
"handlers": ["access"],
"level": DJANGO_LOGGING_LEVEL,
"propagate": False,
},
# Noisy library internals — pin to WARNING regardless of root level
# so we don't drown in HTTP-client debug spam when LOGGING_LEVEL=DEBUG.
"httpx": {"handlers": ["console"], "level": "WARNING", "propagate": False},
"httpcore": {"handlers": ["console"], "level": "WARNING", "propagate": False},
"openai": {"handlers": ["console"], "level": "WARNING", "propagate": False},
"urllib3": {"handlers": ["console"], "level": "WARNING", "propagate": False},
"botocore": {"handlers": ["console"], "level": "WARNING", "propagate": False},
"boto3": {"handlers": ["console"], "level": "WARNING", "propagate": False},
"s3transfer": {"handlers": ["console"], "level": "WARNING", "propagate": False},
"neo4j": {"handlers": ["console"], "level": "WARNING", "propagate": False},
},
"root": {
"handlers": ["console"],

View File

@@ -23,6 +23,7 @@ dependencies = [
"pymemcache>=4.0,<5.0",
"openai>=1.0,<2.0",
"django-prometheus>=2.3,<3.0",
"python-json-logger>=3.0,<4.0",
# Phase 2: Embedding Pipeline
"PyMuPDF>=1.24,<2.0",
"pymupdf4llm>=0.0.17,<1.0",