chore(compose): add shared json-file logging config and component labels
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:
@@ -47,6 +47,20 @@
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# and is the typical re-run target after embedding-model changes).
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# =============================================================================
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# -----------------------------------------------------------------------------
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# Shared logging config — JSON to stdout, picked up by Alloy via the Docker
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# socket on the host and shipped to Loki. Pinning json-file (Docker's default)
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# so Alloy's discovery.docker + loki.source.docker on puck sees a consistent
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# driver across every service, and bounding log retention per container so a
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# misbehaving service can't fill the disk between Alloy tails.
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# -----------------------------------------------------------------------------
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x-logging: &default-logging
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driver: json-file
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options:
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tag: "{{.Name}}"
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max-size: "10m"
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max-file: "5"
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services:
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# ── Static-file seeder: copies /app/staticfiles into the shared volume on
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@@ -60,6 +74,7 @@ services:
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volumes:
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- mnemosyne-static:/shared-static
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restart: "no"
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logging: *default-logging
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# ── Init sidecar: one-shot Postgres migrate + library-type seed. Runs on
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# every `up` and exits. Long-running services below depend on
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@@ -92,10 +107,16 @@ services:
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- DB_PORT=${DB_PORT}
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# Neo4j (load_library_types writes Library defaults into the graph)
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- NEOMODEL_NEO4J_BOLT_URL=${NEOMODEL_NEO4J_BOLT_URL}
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# Logging
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# Logging (MNEMOSYNE_COMPONENT is injected by settings.py into every
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# log line as a static JSON field; Alloy on puck reads the compose
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# service name directly off the Docker label and uses that as the
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# Loki `component` label, but we still set it here so operators
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# tail-ing ``docker logs`` see the same attribution)
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- MNEMOSYNE_COMPONENT=init
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- LOGGING_LEVEL=${LOGGING_LEVEL}
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- DJANGO_LOGGING_LEVEL=${DJANGO_LOGGING_LEVEL}
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restart: "no"
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logging: *default-logging
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# ── App: Django REST API + admin ──────────────────────────────────────────
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@@ -154,9 +175,11 @@ services:
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- RERANKER_MAX_CANDIDATES=${RERANKER_MAX_CANDIDATES}
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- RERANKER_TIMEOUT=${RERANKER_TIMEOUT}
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# Logging
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- MNEMOSYNE_COMPONENT=app
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- LOGGING_LEVEL=${LOGGING_LEVEL}
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- DJANGO_LOGGING_LEVEL=${DJANGO_LOGGING_LEVEL}
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restart: unless-stopped
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logging: *default-logging
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depends_on:
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static-init:
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condition: service_completed_successfully
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@@ -228,9 +251,11 @@ services:
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- RERANKER_MAX_CANDIDATES=${RERANKER_MAX_CANDIDATES}
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- RERANKER_TIMEOUT=${RERANKER_TIMEOUT}
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# Logging
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- MNEMOSYNE_COMPONENT=mcp
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- LOGGING_LEVEL=${LOGGING_LEVEL}
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- DJANGO_LOGGING_LEVEL=${DJANGO_LOGGING_LEVEL}
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restart: unless-stopped
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logging: *default-logging
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depends_on:
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init:
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condition: service_completed_successfully
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@@ -303,9 +328,11 @@ services:
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- EMBEDDING_BATCH_SIZE=${EMBEDDING_BATCH_SIZE}
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- EMBEDDING_TIMEOUT=${EMBEDDING_TIMEOUT}
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# Logging
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- MNEMOSYNE_COMPONENT=worker
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- LOGGING_LEVEL=${LOGGING_LEVEL}
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- CELERY_LOGGING_LEVEL=${CELERY_LOGGING_LEVEL}
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restart: unless-stopped
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logging: *default-logging
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depends_on:
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app:
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condition: service_healthy
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@@ -324,6 +351,7 @@ services:
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web:
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image: nginx:alpine
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restart: unless-stopped
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logging: *default-logging
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depends_on:
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app:
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condition: service_healthy
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@@ -9,7 +9,7 @@ This document describes Mnemosyne's role in the Daedalus + Pallas architecture a
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Mnemosyne exposes two interfaces for the wider Ouranos ecosystem:
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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**.
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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)).
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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)).
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### Phase status
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@@ -17,7 +17,7 @@ Mnemosyne exposes two interfaces for the wider Ouranos ecosystem:
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|-------|------|--------|
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| 1. REST workspace + ingest API for Daedalus | `POST /workspaces/`, `DELETE /workspaces/{id}/`, `POST /ingest/`, `GET /jobs/{id}/` | **Implemented** |
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| 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) |
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| 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** |
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| 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** |
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---
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@@ -367,40 +367,65 @@ mnemosyne_s3_operations_total{operation,status} counter
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- [x] ASGI mount + uvicorn deployment on port 22091; nginx proxies via `/mcp/` on 23090
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- [x] Prometheus metrics (`mnemosyne_mcp_*`)
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### Phase 3 — Per-turn token access control for Daedalus integration ✅ Implemented
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### Phase 3 — Long-lived team JWT access control for Pallas instances ✅ Implemented
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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.
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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.
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**Mnemosyne-side components:**
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- [x] `MCPSigningKey` model — stores active HS256 secrets keyed by `kid`. Managed via `manage.py seed_signing_key --kid <kid>`.
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- [x] `resolve_mcp_jwt(token_string)` in `mcp_server/auth.py` — validates signature, `exp`, `iss`, `jti` replay; returns claims dict.
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- [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`.
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- [x] `_scope_from_claims(claims, arg_workspace_id)` — claims trump tool args; returns `(ws, allowed_libraries)`.
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- [x] `allowed_libraries` on `SearchRequest` — extends `_WORKSPACE_SCOPE_CLAUSE` to include user-managed libraries in addition to the workspace's own.
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- [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.
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- [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.
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- [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.
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- [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).
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- [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.
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- [x] `MCPAuthMiddleware.on_call_tool` — routes team JWTs through `_libraries_for_team`; routes legacy per-turn JWTs through `_scope_from_claims` (backward-compatible).
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- [x] REST control plane at `/mcp_server/api/teams/`:
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- `POST /` — create team by UUID; mints JWT, returns plaintext once.
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- `GET /{id}/` — team state (workspace_ids, active status).
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- `DELETE /{id}/` — soft-delete (`active=False`); all JWTs immediately invalid.
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- `PUT /{id}/workspaces/` — replace workspace assignment list (idempotent).
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- `POST /{id}/rotate/` — mint new JWT with new `active_jti`; returns plaintext once.
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**Token format (HS256):**
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**Team JWT format (HS256):**
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```json
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{
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"iss": "daedalus",
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"sub": "chat",
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"ws": "<workspace_uuid>",
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"libs": [],
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"iss": "mnemosyne",
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"aud": "mnemosyne",
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"typ": "team",
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"sub": "team:<pallas-instance-uuid>",
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"iat": 1746000000,
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"exp": 1746000600,
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"jti": "<uuid4>"
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"exp": 2061360000,
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"jti": "<active_jti uuid>"
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}
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```
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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.
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**Provisioning:**
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**Provisioning (once per Pallas instance):**
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```bash
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# On Mnemosyne host, once:
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docker compose exec app python manage.py seed_signing_key --kid daedalus-1
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# Copy the printed hex → DAEDALUS_MNEMOSYNE_SIGNING_SECRET in Daedalus .env
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# 1. Seed the MCPSigningKey on Mnemosyne (once per deployment, not per instance):
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docker compose exec app python manage.py seed_signing_key --kid daedalus-1 --retire-other
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# The hex stays in Mnemosyne's DB — no operator action required.
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# 2. Register the Pallas instance in Daedalus admin UI (/admin/pallas/).
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# Daedalus calls POST /mcp_server/api/teams/ automatically.
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# The team JWT is minted and stored encrypted in Daedalus.
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# 3. Reveal the JWT via Daedalus admin UI (one-shot):
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# GET /api/v1/pallas/{id}/team-jwt
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# Copy the returned JWT string.
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# 4. Paste into fastagent.secrets.yaml on the Pallas host:
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# mcp:
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# servers:
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# mnemosyne:
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# headers:
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# Authorization: "Bearer <JWT>"
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# 5. Restart the Pallas agent processes.
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# 6. Attach workspaces in Daedalus workspace settings UI.
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# Daedalus calls PUT /mcp_server/api/teams/{id}/workspaces/ automatically.
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```
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See the Daedalus-side spec [§9](../../daedalus/docs/mnemosyne_integration.md#9-phase-2--workspace-scoped-mcp-search-implemented) for the full integration architecture.
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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.
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67
mnemosyne/mnemosyne/log_filters.py
Normal file
67
mnemosyne/mnemosyne/log_filters.py
Normal file
@@ -0,0 +1,67 @@
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"""Logging filters shared across Mnemosyne processes.
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These are project-level (not tied to a Django app) so Celery workers and
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the FastMCP ASGI app can reuse them without importing app modules.
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"""
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from __future__ import annotations
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import logging
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import re
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# Paths that should not show up in INFO when the response is a success.
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# Anything >= 400 still flows through — a failing probe is a real signal.
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_SUPPRESS_PATHS = frozenset(
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{
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"/live/",
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"/live",
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"/ready/",
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"/ready",
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"/healthz",
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"/metrics",
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}
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)
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class SuppressHealthAccessFilter(logging.Filter):
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"""Demote successful access-log records for health endpoints to DEBUG.
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Applied to ``django.server`` (runserver) and ``gunicorn.access`` via
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the ``access`` handler in :data:`mnemosyne.settings.LOGGING`. The filter
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returns ``False`` (drop the record) only when the request path is a
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health endpoint AND the HTTP status is 1xx/2xx/3xx. Any failure on
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``/ready/`` or ``/live/`` still propagates so an operator sees
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readiness flaps.
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The two access loggers format their messages differently:
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* ``django.server`` emits ``'"GET /live/ HTTP/1.1" 200 0'`` as the
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message with no args.
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* ``gunicorn.access`` typically has the path in ``record.args`` when
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the access log format is configured, but many deployments fall
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back to a pre-formatted message. We parse the final rendered
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message in both cases to keep the filter portable across Mnemosyne
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containers (which run gunicorn) and local dev (``runserver``).
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"""
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# Matches the path portion of the quoted request line inside either
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# format. Tolerant of missing trailing slashes and query strings.
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_REQUEST_RE = re.compile(r'"\s*(?:GET|POST|HEAD|OPTIONS|PUT|PATCH|DELETE)\s+(\S+)')
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_STATUS_RE = re.compile(r'"\s+(\d{3})\b')
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def filter(self, record: logging.LogRecord) -> bool:
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msg = record.getMessage()
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path_match = self._REQUEST_RE.search(msg)
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status_match = self._STATUS_RE.search(msg)
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if not path_match or not status_match:
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return True
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path = path_match.group(1).split("?", 1)[0]
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status = int(status_match.group(1))
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# Only suppress successful probes; surface any 4xx/5xx on a
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# health endpoint so operators see readiness flaps.
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if path in _SUPPRESS_PATHS and status < 400:
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return False
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return True
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@@ -278,35 +278,75 @@ THEMIS_NOTIFICATION_POLL_INTERVAL = 60
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THEMIS_NOTIFICATION_MAX_AGE_DAYS = 90
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# --- Structured Logging ---
|
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# All log output is line-delimited JSON on stdout, one record per line.
|
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# Alloy (running on the host / container sidecar) tails the container's
|
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# 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,
|
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# environment, hostname. "component" is injected by the formatter as
|
||||
# a static field based on the MNEMOSYNE_COMPONENT env var set per
|
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# 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",
|
||||
},
|
||||
"static_fields": {
|
||||
"service": "mnemosyne",
|
||||
"component": MNEMOSYNE_COMPONENT,
|
||||
},
|
||||
},
|
||||
"simple": {
|
||||
"format": "[%(levelname)s] %(name)s: %(message)s",
|
||||
},
|
||||
"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"],
|
||||
|
||||
@@ -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",
|
||||
|
||||
Reference in New Issue
Block a user