mypalace 0.10.0

MyPalace — standalone memory service for AI assistants. Full HTTP + gRPC surface, multi-tenancy, graph, cache, workers, rate limits, websockets, observability, analytics, bulk import/export, TTL, embedding migration, audit log, memory versioning.

MyPalace Memory Service

A standalone, lightweight memory service for AI assistants. Stores facts, preferences, and conversation history; serves them back via semantic search and LLM-ready context blocks.

Extracted from mypalclara's Palace memory system as an independent microservice with no mypalclara dependency.

---

What it does

• Memory CRUD + semantic search — embed text, store in Qdrant, retrieve by similarity
• Session/message persistence — conversation threads with PostgreSQL
• Context assembly — combine relevant memories + recent messages into a single payload for LLM prompts
• Pluggable embeddings — HuggingFace (local) or OpenAI (API)
• Pluggable LLM backend — any OpenAI-compatible chat completion endpoint (OpenRouter, OpenAI, etc.)

Project status — v0.10.0

Released to PyPI as mypalace (server) and mypalace-client. Production-ready in scope; see the phase notes below for what's in and what's deliberately left out.

Capabilities (built across phases 1–9):

• Phase 1 — memory CRUD + semantic search, sessions, context assembly, pluggable embeddings (HuggingFace / OpenAI) and LLM backend.

• Phase 2 — episodes + LLM reflection, narrative arcs, FSRS-6 dynamics, intentions with 4 trigger matchers, layered context, smart ingestion (LLM extract + dedup + auto-supersede), manual supersede + audit history.

• Phase 3 — API-key auth with read/write/admin scopes, full multi-tenancy (per-tenant Qdrant collections, key-bound + cross-tenant admin keys), optional FalkorDB graph layer with async writes + neighbors endpoint, optional Redis read-through cache, gRPC MemoryService transport, PyPI/Docker release pipeline.

• Phase 4 — Alembic migrations with auto-stamp on first boot, Prometheus /metrics + OpenTelemetry traces + structlog, Postgres-backed worker queue with SELECT … FOR UPDATE SKIP LOCKED, per-(tenant, key, user) sliding- window rate limits, WebSocket event subscriptions, graph-walked l3_graph_context in layered retrieval.

• Phase 5 — Worker-queue routing for async reflection/synthesis, episode/ intention/arc event publishers, full gRPC mirror of remaining surfaces (22 RPCs / 8 services), cross-tenant admin analytics endpoint.

• Phase 6 — Bulk /v1/admin/export + /v1/admin/import (NDJSON) for DR and tenant migration, memory TTL with worker-driven cleanup, embedding-model migration via /v1/admin/reembed, release-pipeline fixes.

• Phase 7 — Admin operation audit log, append-only memory change history with /v1/memories/{id}/history, cross-tenant search (POST /v1/memories/search?tenant_id=ALL), mypalclara migration guide.

• Phase 8 — production hardening: deep /health/deep that pings each backend, boot-time config validation that refuses to start on bad env, DB-query observability via SQLAlchemy event hooks, production docker-compose + deployment guide.

• Phase 9 — operator UX: mypalace-admin CLI for day-to-day ops, proper k8s /live vs /ready split (so backend blips no longer trigger pod restarts), tunable SQLAlchemy connection pool with pool_pre_ping on by default, scheduled per-tenant backup worker (gzipped NDJSON, atomic publish, mtime-based pruning).

• Phase 10 — mypalclara parity: entity resolver (platform-id → human name registry), personality evolution (LLM-driven self-evolving traits, run via the worker queue), token-based context budget env vars, optional Redis embedding cache with toggle, and VCH (verbatim chat history search via Postgres FTS). Driven by docs/gap-analysis-mypalclara.md.

Deliberately out of scope (operators who need them should fork or deploy separately): per-tenant Postgres schemas, admin web UI, memory clustering / topic discovery, fine-grained per-key tenant-resource scoping.

---

Auth (phase 3 slice 1)

Every /v1/* endpoint requires a valid API key in the X-Palace-Key header. /health, /docs, /redoc, /openapi.json remain public.

Three scopes:

• read — GET /v1/*, POST /v1/memories/search, /list, /episodes/search, /intentions/check, /intentions/format, /context/*
• write — everything else under /v1/*
• admin — /v1/admin/* and /v1/maintenance/*

Scopes are explicit: admin does not auto-grant write or read. When you mint a key, list every scope it should have.

Bootstrap an admin key

Set PALACE_BOOTSTRAP_ADMIN_KEY to a value of the form pk_live_<32 alphanumeric chars>. On lifespan startup, if no admin key exists, Palace inserts a row with read+write+admin scopes. Idempotent.

export PALACE_BOOTSTRAP_ADMIN_KEY=pk_live_$(LC_ALL=C tr -dc 'A-Za-z0-9' </dev/urandom | head -c 32)
echo "$PALACE_BOOTSTRAP_ADMIN_KEY"   # save this — Palace doesn't log it

Mint additional keys

curl -X POST http://localhost:8000/v1/admin/keys \
  -H "X-Palace-Key: $PALACE_BOOTSTRAP_ADMIN_KEY" \
  -H "Content-Type: application/json" \
  -d '{"label": "mypalclara-prod", "scopes": ["read", "write"]}'
# response: {"data": {"key_id": "...", "plaintext_key": "pk_live_...", ...}}

The plaintext is returned once. Palace stores only a bcrypt hash plus an 8-char prefix index for lookup.

Disable auth (development / tests only)

export PALACE_AUTH_DISABLED=true

The mock test suite sets this automatically; live integration tests opt back in per-test.

---

Multi-tenancy (phase 3 slice 2)

Every row in every user-data table carries a tenant_id. API keys are bound to a tenant on creation; the middleware sets request.state.auth.tenant_id from the key, and every service query filters by it. Qdrant collections are per-tenant: palace_memories_<tenant_id>, palace_episodes_<tenant_id>.

A default tenant is created on first boot (PALACE_DEFAULT_TENANT_ID to override). Single-tenant deployments work zero-config.

Tenant ID format

^[a-z0-9_]{1,32}$ — lowercase alphanumeric + underscore, max 32 chars. Anything else → 400.

Mint a tenant-bound key

# Create a tenant
curl -X POST http://localhost:8000/v1/admin/tenants \
  -H "X-Palace-Key: $ADMIN_KEY" \
  -d '{"id": "acme", "label": "Acme Corp"}'

# Issue a key bound to that tenant
curl -X POST http://localhost:8000/v1/admin/keys \
  -H "X-Palace-Key: $ADMIN_KEY" \
  -d '{"label": "acme-prod", "scopes": ["read","write"], "tenant_id": "acme"}'

Cross-tenant admin keys

For migrations or support, mint a key with cross_tenant: true:

curl -X POST http://localhost:8000/v1/admin/keys \
  -H "X-Palace-Key: $ADMIN_KEY" \
  -d '{"label": "support", "scopes": ["read","write","admin"], "cross_tenant": true}'

The bootstrap admin key (from PALACE_BOOTSTRAP_ADMIN_KEY) is a cross-tenant key by default.

Migrations (phase 4 slice 1)

Alembic now manages schema. init_db() still creates tables on first boot for zero-config dev, AND stamps the latest revision so future alembic upgrade head calls know where to start.

# Fresh install — nothing to do; lifespan startup handles it.
.venv/bin/uvicorn mypalace.main:app

# Pre-phase-4 install with existing data — stamp once, then upgrade as usual:
.venv/bin/alembic stamp 2026_05_04_0001_baseline
.venv/bin/alembic upgrade head

# Day-to-day after a new migration lands:
.venv/bin/alembic upgrade head

# Generate a new migration from model changes:
.venv/bin/alembic revision --autogenerate -m "add foo column"

DB URL is read from PALACE_DATABASE_URL (no need to set sqlalchemy.url in alembic.ini).

---

Observability (phase 4 slice 2)

Prometheus metrics

/metrics exposes Prometheus exposition format. Always on, always public (k8s scrapers need that — lock down via your ingress if necessary).

Counters:

• palace_http_requests_total{method, route, status_class}
• palace_http_request_duration_seconds{method, route} (histogram)
• palace_cache_hits_total{namespace}, palace_cache_misses_total{namespace}
• palace_graph_writes_total{kind}, palace_graph_failures_total
• palace_jobs_total{kind, outcome} (populated by phase-4 slice 3)

Routes are normalized — UUIDs and long IDs become {id} so Prometheus label cardinality stays bounded.

OpenTelemetry traces

Optional. Set PALACE_OTLP_ENDPOINT=http://otel-collector:4317 and install the optional extra:

pip install "mypalace[otel]"

Auto-instruments FastAPI + httpx. Service name defaults to mypalace (override via PALACE_OTLP_SERVICE_NAME). No-op if either the env var is unset or the SDK isn't installed.

Structured logs

structlog configured at lifespan startup. Two modes via PALACE_LOG_FORMAT:

• pretty (default) — colored console output, dev-friendly
• json — newline-delimited JSON, production-ready

Every request gets a request_id (read from X-Request-ID header if present, else a fresh uuid4). It's bound to structlog's contextvars so every log line in the request scope carries it. The same X-Request-ID is echoed back on the response.

---

Background workers (phase 4 slice 3)

Postgres-backed job queue using SELECT ... FOR UPDATE SKIP LOCKED. Built-in handlers cover reflection (episode reflection from a session) and synthesis (narrative arc rollup). The web process can opt to enqueue jobs and let a separate worker process pick them up.

# In one terminal: the web server
.venv/bin/uvicorn mypalace.main:app --port 8000

# In another: one or more workers
.venv/bin/python -m mypalace.workers.runner
# (Run multiple — SKIP LOCKED gives them safe concurrency)

Knobs

• PALACE_WORKER_POLL_INTERVAL=1.0 — seconds between polls when idle
• PALACE_WORKER_LEASE_SECONDS=60 — max time a worker holds a claim before another can re-take it
• PALACE_WORKER_MAX_ATTEMPTS=3 — failures past this mark status=failed
• PALACE_WORKER_QUEUE_ENABLED=true (phase 5) — route async-mode /v1/reflection/session and /v1/synthesis/narratives through the worker queue instead of the in-process asyncio.create_task path. Requires at least one worker process; defaults False so single-process deployments without a worker keep working.

Custom handlers

from mypalace.workers import register_handler

async def my_handler(payload: dict, tenant_id: str) -> dict:
    return {"processed": payload}

register_handler("my_kind", my_handler)
# Then enqueue:
from mypalace.workers import enqueue
await enqueue(kind="my_kind", user_id="u1", payload={"x": 1}, tenant_id="default")

The runner picks up my_kind automatically once the registry is populated.

---

Rate limits (phase 4 slice 4)

Optional sliding-window rate limiter, scoped to (tenant, key, user). Requires Redis.

export PALACE_RATE_LIMIT_ENABLED=true
export PALACE_REDIS_URL=redis://localhost:6379
export PALACE_RATE_LIMIT_DEFAULT=120     # req/min for most endpoints
export PALACE_RATE_LIMIT_SEARCH=60       # tighter bucket for /search + /context

Disabled by default. When disabled, the middleware is a fast no-op. When enabled but Redis is unreachable, it fails open (logs a warning, lets the request through) — Palace stays available even when the limiter can't.

Bypass with unlimited scope

Trusted server-to-server keys can opt out by adding the unlimited scope at issuance time:

curl -X POST http://localhost:8000/v1/admin/keys \
  -H "X-Palace-Key: $ADMIN_KEY" \
  -d '{"label":"trusted-svc","scopes":["read","write","unlimited"]}'

Response shape on 429

HTTP/1.1 429 Too Many Requests
Retry-After: 60
Content-Type: application/json

{"error": {"code": "rate_limited", "message": "Too many requests in 60s window: 121/120", "retry_after_seconds": 60}}

---

Graph layer (phase 3 slice 3)

Optional FalkorDB integration. When PALACE_FALKORDB_URL is set, every memory / episode / arc create writes a node into the per-tenant graph (palace_<tenant_id>), and every supersession writes a SUPERSEDES edge. Writes are fire-and-forget — graph failures never break the primary write.

# FalkorDB ships as a Redis module:
podman run -d --name palace-falkor -p 6379:6379 docker.io/falkordb/falkordb:latest
export PALACE_FALKORDB_URL=redis://localhost:6379

Without the env var, the graph layer is a no-op and /v1/graph/* returns 503.

Schema

(:Memory {id, user_id, agent_id, content, memory_type, importance})
(:Episode {id, user_id, summary, significance, timestamp})
(:Arc {id, user_id, title, status})

(Memory)-[:SUPERSEDES]->(Memory)
(Episode)-[:PARTICIPATES_IN]->(Arc)

Querying neighbors

curl "http://localhost:8000/v1/graph/neighbors?node_id=<memory_id>&depth=2&edge_type=SUPERSEDES" \
  -H "X-Palace-Key: $KEY"
# → {"data": {"nodes": [...], "edges": [...]}, "meta": {...}}

Depth is capped at 3 hops; edge_type filter is optional. No raw Cypher passthrough — the only graph API surface is /v1/graph/neighbors.

---

Cache (phase 3 slice 4)

Optional Redis read-through cache for /v1/context/layered and /v1/memories/search. Keys are hashed (tenant_id, namespace, params); TTL defaults to 60s. On memory create/update/delete, all matching tenant cache entries are invalidated.

# FalkorDB and the cache can share the same Redis instance:
export PALACE_REDIS_URL=redis://localhost:6379
# Optional knobs:
export PALACE_CACHE_TTL_SEARCH=60   # seconds
export PALACE_CACHE_TTL_GET=300
# Disable without unsetting URL (e.g. tests):
export PALACE_CACHE_DISABLED=true

Without PALACE_REDIS_URL, every read goes straight to Postgres + Qdrant.

Cache failures degrade to misses — Palace stays correct, just slower.

---

gRPC transport (phase 3 + phase 5)

Optional second transport alongside REST. Phase 3 added MemoryService; phase 5 expanded to a full mirror — SessionService, EpisodeService, ArcService, IntentionService, DynamicsService, RetrievalService, IngestionService, JobService. Same auth (X-Palace-Key in metadata), same scope rules, same singleton services as the HTTP path.

export PALACE_GRPC_PORT=50051
.venv/bin/uvicorn mymypalace.main:app --port 8000
# → starts FastAPI on :8000 AND gRPC on :50051

Auth uses the same X-Palace-Key, sent as gRPC metadata x-palace-key. Scope rules are identical to HTTP.

from mypalace_client.grpc import PalaceGrpcClient

async with PalaceGrpcClient("localhost:50051", api_key="pk_live_...") as client:
    mem = await client.create(user_id="u1", content="hello via gRPC")
    results = await client.search(query="hello", limit=5)

Regenerating stubs

python -m grpc_tools.protoc -I=proto \
    --python_out=mypalace/grpc/_generated \
    --grpc_python_out=mypalace/grpc/_generated \
    proto/palace.proto
# Then re-apply the local import fix in palace_pb2_grpc.py:
#   sed -i '' 's/^import mypalace_pb2/from mypalace.grpc._generated import palace_pb2/' \
#     mypalace/grpc/_generated/palace_pb2_grpc.py

---

Install

From PyPI (server)

pip install mypalace

From PyPI (client only — for AI apps that talk to a remote Palace)

pip install mypalace-client
# Optional gRPC transport:
pip install "mypalace-client[grpc]"

Docker

docker pull bangrocket/mypalace:latest
docker run -p 8000:8000 \
  -e PALACE_DATABASE_URL=postgresql+asyncpg://palace:palace@host/palace \
  -e QDRANT_URL=http://host:6333 \
  -e PALACE_BOOTSTRAP_ADMIN_KEY=pk_live_$(LC_ALL=C tr -dc 'A-Za-z0-9' </dev/urandom | head -c 32) \
  bangrocket/mypalace:latest

Production (single-host docker-compose)

For a real deployment with all five backends + a worker, use the production compose file:

cp .env.example .env
$EDITOR .env       # set PALACE_BOOTSTRAP_ADMIN_KEY + POSTGRES_PASSWORD
docker compose -f docker-compose.prod.yml up -d
curl http://localhost:8000/health/deep | jq

Full setup, scaling, observability, backup, and upgrade instructions are in docs/deployment.md.

Quick start (development)

# 1. Start postgres + qdrant (docker or podman)
docker-compose up -d postgres qdrant
# or:  podman run -d --name palace-postgres -p 5442:5432 \
#         -e POSTGRES_USER=palace -e POSTGRES_PASSWORD=palace -e POSTGRES_DB=palace \
#         docker.io/library/postgres:16-alpine
#      podman run -d --name palace-qdrant -p 6333:6333 docker.io/qdrant/qdrant:latest

# 2. Install (Python 3.12)
python3.12 -m venv .venv && .venv/bin/pip install -e ".[dev]"

# 3. Configure
cp .env.example .env

# 4. Run
.venv/bin/uvicorn mypalace.main:app --reload --port 8000

Or fully containerized: docker-compose up --build.

API docs at http://localhost:8000/docs.

Smoke test

# Store a memory
curl -X POST http://localhost:8000/v1/memories \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"u1","content":"User loves dark mode and uses Vim daily","memory_type":"preference"}'

# Semantic search
curl -X POST http://localhost:8000/v1/memories/search \
  -H 'Content-Type: application/json' \
  -d '{"query":"editor preferences","user_id":"u1","limit":5}'

# Assemble context for an LLM prompt
curl -X POST http://localhost:8000/v1/context \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"u1","query":"what does the user prefer?","max_memories":5}'

---

API

All responses use the envelope { "data": ..., "meta": { "count": N, "took_ms": N } }. Errors return HTTP 4xx/5xx with FastAPI's default body.

Memories

Method	Path	Body
POST	/v1/memories	{ user_id, content, memory_type?, agent_id?, source?, importance?, metadata? }
POST	/v1/memories/search	{ query, user_id?, agent_id?, memory_type?, limit?, min_score? }
GET	/v1/memories/{id}	—
PATCH	/v1/memories/{id}	{ content?, memory_type?, importance?, metadata? }
DELETE	/v1/memories/{id}	—
GET	/v1/users/{user_id}/memories?limit=50	—

memory_type is a free-form string; conventional values: semantic, episodic, preference, fact.

Sessions

Method	Path	Body
POST	/v1/sessions	{ user_id, title? }
GET	/v1/sessions/{id}	— (returns session + ordered messages)
POST	/v1/sessions/{id}/messages	{ user_id, role, content }
PATCH	/v1/sessions/{id}	{ title?, summary? }
DELETE	/v1/sessions/{id}	— (cascades to messages)

Context

Method	Path	Body
POST	/v1/context	{ user_id, query, session_id?, max_memories?, max_messages? }

Health

Method	Path
GET	/health

---

Configuration

All settings come from environment variables (or .env).

Variable	Default	Notes
PALACE_DATABASE_URL	postgresql+asyncpg://palace:palace@localhost/palace	asyncpg driver required
QDRANT_URL	http://localhost:6333
QDRANT_COLLECTION	palace_memories	created on startup if missing
EMBEDDING_PROVIDER	huggingface	huggingface or openai
EMBEDDING_MODEL	BAAI/bge-large-en-v1.5	for HF; pass an OpenAI model name when provider is openai
HF_TOKEN	—	optional, for gated models
OPENAI_API_KEY	—	required if EMBEDDING_PROVIDER=openai
LLM_PROVIDER	openrouter	openrouter or openai
LLM_API_KEY	—	required for any LLM call
LLM_MODEL	openai/gpt-4o-mini	OpenRouter-style or OpenAI model id
LOG_LEVEL	INFO

---

Architecture

palace/
├── main.py              FastAPI app + lifespan (creates tables, ensures Qdrant collection)
├── config.py            Pydantic Settings (.env aware)
├── models.py            SQLModel tables: Memory, Session, Message
├── database.py          Async SQLAlchemy engine + session factory
├── embeddings.py        EmbeddingProvider protocol + HF and OpenAI impls
├── vector.py            Async Qdrant wrapper (ensure/upsert/query/delete)
├── llm.py               Async chat-completion client (OpenAI-compatible)
├── memory_service.py    CRUD + semantic search; lazy embedder
├── session_service.py   Session + message lifecycle
├── context_service.py   Memory search + recent messages → prompt context
└── api/
    ├── common.py        Pydantic request/response models, ApiResponse envelope
    ├── memories.py      memory routes + users-router for /v1/users/{id}/memories
    ├── sessions.py      session routes
    └── context.py       context route

Behavior notes

• Create memory → INSERT into postgres, then embed + UPSERT into Qdrant. Embedding happens outside the DB transaction so a slow embedder doesn't hold a row lock.
• Search → embed query, Qdrant query_points with user_id/agent_id/memory_type filters, then fetch full rows from postgres in one IN-clause. Search bumps access_count and accessed_at on the returned rows.
• Update content → re-embeds and UPSERTs (Qdrant point id == memory id).
• Delete → removes the postgres row, then deletes the Qdrant point.

---

Running tests

.venv/bin/python -m pytest

The test suite uses mocks (no postgres or qdrant required) and covers all 13 routes.

For end-to-end verification, the smoke commands above exercise the whole stack against a live DB + Qdrant.

---

Platform notes

macOS x86_64

torch is capped at 2.2.2 on macOS x86_64 (Apple deprecated x86 wheels for newer torch). pyproject.toml therefore pins numpy<2 and transformers<5 to stay ABI-compatible with that torch. If you are on Apple Silicon (arm64) or Linux, those pins are still safe — just not strictly required.

If you'd rather avoid the torch dependency entirely, set:

EMBEDDING_PROVIDER=openai
EMBEDDING_MODEL=text-embedding-3-small
OPENAI_API_KEY=sk-...

The embedder is loaded lazily, so importing the app no longer triggers a model download.

Container engines

docker-compose.yml works under both Docker Desktop and podman compose. With plain podman run, see the Quick start above for the equivalent commands.

---

Drop-in mode for mypalclara

MyPalace is a strict superset of mypalclara's embedded Palace surface since end of phase 3. To swap mypalclara from embedded to remote MyPalace, follow the step-by-step guide at docs/migrating-mypalclara.md.

The short version:

# 1. install the client
pip install mypalace-client==0.10.0   # or "mypalace-client[grpc]"

# 2. copy examples/mypalclara_router.py into mypalclara as
#    mypalclara/core/memory/routed.py and swap the imports

# 3. set the env and you're done
export USE_PALACE_SERVICE=true
export PALACE_SERVICE_URL=http://mypalace:8000
export PALACE_API_KEY=pk_live_...   # mint via /v1/admin/keys

The router uses explicit pass-throughs — every public method of ClaraMemory + MemoryManager has its own entry, no __getattr__ fallthrough. mypalclara's existing Discord-transcript replay script writes through the router to remote MyPalace with no Palace-specific port — see the migration guide for replay + validation + rollback steps.

Legacy slice notes (kept for historical reference)

Six more endpoints that mypalclara's episode_store.* and MM.reflect_on_session / MM.run_narrative_synthesis callers can route to remote Palace:

• POST /v1/reflection/session?mode={sync,async} — extract episodes from a message list. Default async returns 202 + job id; sync returns the extracted episodes inline.
• POST /v1/synthesis/narratives?mode={sync,async} — roll recent episodes into narrative arcs.
• POST /v1/episodes/search — semantic search over episodes with min_significance filter.
• GET /v1/users/{user_id}/episodes/recent?limit=5 — recent episodes, newest first.
• GET /v1/users/{user_id}/arcs/active?limit=10 — active narrative arcs.
• GET /v1/jobs/{job_id} — poll async job status (pending / completed / failed) and retrieve the result.

Storage: episodes in a separate palace_episodes Qdrant collection; arcs in a narrative_arcs Postgres table with a JSONB key_episode_ids array. Async mode uses pure asyncio (no Celery/arq); jobs in flight don't survive process restarts (caller can re-POST).

LLM extraction uses palace/llm.py's OpenAI-compatible chat-completion client (works against OpenRouter, OpenAI, Anthropic-via-OpenRouter, or any compatible endpoint). Set LLM_API_KEY in .env.

The examples/mypalclara_router.py reference now routes episode_store, reflect_on_session, and run_narrative_synthesis when USE_PALACE_SERVICE=true, falling back to embedded otherwise.

Slice 3 additions: FSRS dynamics

Five more endpoints port mypalclara's FSRS-6 spaced-repetition memory dynamics (per-memory stability/difficulty/retrievability state, access logging, and composite ranking) to remote Palace:

• POST /v1/memories/{id}/promote — apply an FSRS review (default grade=3 / GOOD, signal_type="used_in_response"). Auto-creates the dynamics row on first call.
• POST /v1/memories/{id}/demote — failure signal (equivalent to promote(grade=1)); default reason="user_correction".
• GET /v1/memories/{id}/dynamics?user_id=u1 — read the current FSRS state. 404 if no dynamics row.
• POST /v1/memories/{id}/score — composite ranking breakdown given the caller's semantic score. Returns {composite_score, fsrs_score, retrievability, storage_strength}. Composite formula: composite = 0.6 * semantic + 0.4 * fsrs_score, where fsrs_score = (0.7 * retrievability + 0.3 * storage_strength) * importance_weight.
• POST /v1/maintenance/prune-access-logs?retention_days=90 — admin op; deletes old access log rows.

The FSRS-6 math (palace/dynamics/fsrs.py) is ported character-for-character from mypalclara, with a deterministic regression net (tests/test_dynamics.py) pinning known input -> output values to catch porting drift.

MM.get_last_retrieved_memory_ids stays embedded (slice-3 design D4): the HTTP service is stateless between requests, so an in-process cache wouldn't survive across worker processes. The mypalclara router caches retrieved IDs client-side instead.

Slice 4 additions: intentions

Six endpoints port mypalclara's intentions subsystem — deterministic-trigger reminders that fire when matching keywords, topics, times, or context conditions are detected. No LLM in this slice; all matching is purely structural (keyword regex, time comparison, context dict matching, word-overlap fallback for topic).

• POST /v1/intentions — set a new intention ({user_id, content, trigger_conditions, ...}).
• POST /v1/intentions/check — evaluate all unfired intentions for a user against a message + context. Returns the fired list (sorted by priority); marks fired and deletes any with fire_once=true.
• POST /v1/intentions/format — render fired intentions as a markdown bullet list for system-prompt injection.
• DELETE /v1/intentions/{id} — delete a single intention. 404 if not found.
• GET /v1/users/{user_id}/intentions?fired={true|false|all}&limit=50 — list intentions for a user.
• POST /v1/maintenance/cleanup-intentions — admin op; deletes intentions whose expires_at has passed.

Four trigger types (set via trigger_conditions["type"]):

• keyword — substring match against keywords; optional regex and case_sensitive.
• topic — word-overlap fraction vs. topic >= threshold; optional quick_keywords pre-filter.
• time — fires when current UTC time has reached at (specific) or after (open-ended).
• context — matches a dict of {channel_name, is_dm, time_of_day, day_of_week}; all configured keys must match.

The mypalclara_router.py reference now routes MM.set_intention, MM.check_intentions, and MM.format_intentions_for_prompt when USE_PALACE_SERVICE=true.

Slice 5 additions: layered retrieval + smart ingestion

Three endpoints + an activated flag close out phase 2: multi-tier context assembly, LLM-driven memory extraction with vector dedup + heuristic supersede, and a manual supersede record:

• POST /v1/context/layered — multi-tier context assembly. Parallel-fetches L1 (top semantic memories + recent episodes + active arcs) and L2 (query-filtered memories optionally FSRS-reranked + query-filtered episodes), char-budgets each layer, and optionally pulls recent_messages from a session. Returns a structured dict (caller composes into prompts; Palace stays generic).
• POST /v1/memories/{id}/supersede — manually replace a memory. Creates a new memory and an audit row; demotes the old memory's FSRS state.
• GET /v1/memories/{id}/supersedes — supersession history involving this memory id (either side).
• Activated: infer=true on POST /v1/memories/batch now runs the smart-ingestion pipeline: LLM extracts factual memories from the conversation block; for each candidate, embed + Qdrant-search the nearest existing memory; score > 0.95 skip as duplicate, score > 0.75 heuristic contradiction check (auto-supersede when confidence > 0.7, else skip as similar), else write fresh. Response meta carries supersessions and skipped arrays.

The contradiction heuristic is intentionally simple (no LLM in the hot ingestion path): negation-asymmetry plus stemmed token overlap. The LLM extraction is the only LLM call.

MM.build_prompt_layered, MM.smart_ingest, and MM.supersede_memory graduate to remote in examples/mypalclara_router.py when USE_PALACE_SERVICE=true. Note: the routed build_prompt_layered returns a structured LayeredContext dict instead of typed Messages — mypalclara's caller must adapt (the routed path drops Discord-specific persona/channel layers per phase-2 design D1).

Integration tests

Default pytest runs only the fast mock-based suite (~2s). Live end-to-end tests against real postgres + qdrant are opt-in:

pytest                       # mocks only — fast iteration
pytest -m integration        # live containers via testcontainers-python

The integration suite spins up postgres + qdrant containers per session (via testcontainers-python), truncates tables between tests, and exercises:

• Memory CRUD, semantic search ranking, batch create + filtered list, delete-all with filters (tests/integration/test_memories_live.py).
• Session/message lifecycle and context assembly (tests/integration/test_sessions_live.py).
• palace_client against a live Palace via in-process ASGI transport, proving wire-contract agreement (tests/integration/test_client_e2e.py).

Requires Docker or podman with a running machine. First run pulls postgres + qdrant images and downloads the small embedding model (sentence-transformers/all-MiniLM-L6-v2); subsequent runs are 30-60s.

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Bulk import / export (phase 6 slice 2)

Disaster recovery + tenant migration. Streaming NDJSON, one record per line.

# Stream a tenant dump to a file
curl "http://localhost:8000/v1/admin/export?tenant_id=acme" \
  -H "X-Palace-Key: $ADMIN_KEY" \
  -o palace-acme-export.ndjson

# Re-import into a target tenant (creates the tenant if missing)
curl -X POST "http://localhost:8000/v1/admin/import?tenant_id=acme-restored" \
  -H "X-Palace-Key: $ADMIN_KEY" \
  --data-binary @palace-acme-export.ndjson

Each line is one row prefixed by _type (one of tenant, memory, session, narrative_arc, intention, memory_dynamics, memory_supersession). Vector data is not included — re-embed on import keeps dumps portable across embedding models. The tenant_id query param always wins over any tenant_id field in the dump. Idempotent on primary keys via db.merge(). api_keys are excluded — set up auth on the new deployment separately.

Pair with /v1/admin/reembed (slice 4) by passing ?reembed=false for very large imports, then triggering re-embed afterward.

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Memory TTL (phase 6 slice 3)

Optional time-to-live on memories. Pass ttl_seconds on create:

curl -X POST http://localhost:8000/v1/memories \
  -H "X-Palace-Key: $KEY" \
  -d '{"user_id":"u1","content":"login code: 482719","ttl_seconds":300,"memory_type":"session"}'

The memory's expires_at is set to now + ttl. Search/list/get already exclude expired rows even before cleanup runs (WHERE expires_at IS NULL OR expires_at > now()).

Garbage collection runs as a worker handler. Enqueue per-tenant:

from mypalace.workers import enqueue
await enqueue(kind="cleanup", user_id="system",
              payload={"batch_size": 500}, tenant_id="acme")

Or schedule it via cron / your orchestrator. Operators without a worker process can still delete expired memories via the regular DELETE endpoint — the index excludes them from reads regardless.

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Releasing (operator notes)

Tagging vX.Y.Z triggers .github/workflows/release.yml which runs tests, builds both packages, and (when configured) publishes to PyPI + Docker Hub. Tags ending in -rc* or -beta* route to TestPyPI for rehearsal.

One-time setup

1. PyPI trusted publishing. For each project at https://pypi.org/manage/account/publishing/, add a "GitHub" publisher pointing at this repo + workflow release.yml. Leave the environment field empty. Do this for both:

• mypalace
• mypalace-client

Once configured, the pypa/gh-action-pypi-publish@release/v1 step OIDC-authenticates and uploads with no API token to manage. Repeat the same for https://test.pypi.org if you want rc/beta rehearsals to publish.

2. Docker Hub (optional). If you want Docker images built on every tag, configure three repo settings (Settings → Secrets and variables → Actions):

• Variable PUBLISH_DOCKER = true
• Secret DOCKERHUB_USERNAME = your Docker Hub username
• Secret DOCKERHUB_TOKEN = a Docker Hub access token

Without vars.PUBLISH_DOCKER=true, the docker job is skipped and the GitHub release still cuts.

Cutting a release

# Rehearse on TestPyPI first
git tag -a v0.5.0-rc1 -m "rehearsal"
git push origin v0.5.0-rc1
# Watch https://github.com/BangRocket/mypalace/actions

# Once green, cut the real tag
git tag -a v0.5.0 -m "0.5.0 — see CHANGELOG.md"
git push origin v0.5.0

If a tag's workflow fails, fix the issue, delete the tag locally and remote (git tag -d v0.5.0 && git push --delete origin v0.5.0), then re-tag.

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License

PolyForm Noncommercial 1.0.0