feat: Phase 0 bootstrap — Qdrant deploy e schema PostgreSQL

- README.md: contesto ALPHA_PROJECT, architettura multi-agent, stack infrastrutturale
- CHANGELOG.md: documenta deploy Qdrant v1.17.0 e creazione database pompeo
- db/postgres.sql: schema DDL database pompeo (user_profile, memory_facts,
  finance_documents, behavioral_context, agent_messages) con multi-tenancy user_id
- db/qdrant.sh: script per creazione/ripristino collections Qdrant
  (episodes, knowledge, preferences) con payload indexes

Design decisions:
- Multi-tenancy via user_id su Qdrant e PostgreSQL (estendibile a nuovi utenti
  senza modifiche infrastrutturali)
- agent_messages come blackboard persistente per il Proactive Arbiter

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

README.md

@@ -42,7 +42,7 @@ Production-grade self-hosted stack. Key components relevant to ALPHA_PROJECT:
 | **n8n** | Primary orchestrator and workflow engine for all agents |
 | **Node-RED** | Event-driven automation, Home Assistant bridge |
 | **Patroni / PostgreSQL** | Persistent structured memory store |
-| **Qdrant** | Vector store for semantic/episodic memory *(to be deployed)* |
+| **Qdrant** | Vector store for semantic/episodic memory — `qdrant.persistence.svc.cluster.local:6333` |
 | **NATS / Redis Streams** | Message broker between agents *(to be chosen and deployed)* |
 | **Authentik** | SSO / IAM (OIDC) |
 | **Home Assistant** | IoT hub — device tracking, automations, sensors |
@@ -160,17 +160,17 @@ CREATE TABLE behavioral_context (
 );
 ```
 
-**2. Semantic memory — Qdrant**
+**2. Semantic memory — Qdrant** — `qdrant.persistence.svc.cluster.local:6333`
 
-Vector embeddings for similarity search. Three collections:
+Vector embeddings for similarity search. Three collections with **multi-tenant design**: isolation via `user_id` payload field (`"martin"`, `"shared"`, future users).
 
 | Collection | Content |
 |---|---|
-| `martin_episodes` | Conversations, episodic facts with timestamp |
-| `martin_knowledge` | Documents, Outline notes, newsletters, knowledge base |
-| `martin_preferences` | Preferences, habits, behavioral patterns |
+| `episodes` | Conversations, episodic facts with timestamp |
+| `knowledge` | Documents, Outline notes, newsletters, knowledge base |
+| `preferences` | Preferences, habits, behavioral patterns |
 
-Each Qdrant point includes a metadata payload for pre-filtering (source, date, category, action_required) to avoid full-scan similarity searches.
+Each Qdrant point includes a metadata payload for pre-filtering (`user_id`, `source`, `date`, `category`, `action_required`) to avoid full-scan similarity searches.
 
 **3. Profile memory — PostgreSQL (static table)**
 
@@ -266,12 +266,15 @@ Notification is sent via **Amazon Echo / Pompeo** (TTS) for voice, and **Telegra
 
 ### Phase 0 — Infrastructure Bootstrap *(prerequisite for everything)*
 
-- [ ] Deploy **Qdrant** on the Kubernetes cluster
-  - Create collections: `martin_episodes`, `martin_knowledge`, `martin_preferences`
-  - Configure payload indexes on: `source`, `category`, `date`, `action_required`
-- [ ] Run **PostgreSQL migrations** on Patroni
-  - Create tables: `memory_facts`, `finance_documents`, `behavioral_context`
-  - Add index on `memory_facts(source, category, expires_at)`
+- [x] ~~Deploy **Qdrant** on the Kubernetes cluster~~ ✅ 2026-03-21
+  - Collections: `episodes`, `knowledge`, `preferences` (multi-tenant via `user_id` payload field)
+  - Payload indexes: `user_id`, `source`, `category`, `date`, `action_required`
+  - Endpoint: `qdrant.persistence.svc.cluster.local:6333`
+- [x] ~~Run **PostgreSQL migrations** on Patroni~~ ✅ 2026-03-21
+  - Database `pompeo` creato (Zalando Operator)
+  - Tabelle: `user_profile`, `memory_facts`, `finance_documents`, `behavioral_context`, `agent_messages`
+  - Multi-tenancy: campo `user_id` su tutte le tabelle, seed `martin` + `shared`
+  - Script DDL: `alpha/db/postgres.sql`
 - [ ] Verify embedding endpoint via Copilot (`text-embedding-3-small`) as bootstrap fallback
 - [ ] Plan migration to local Ollama embedding model once LLM server is online
 
@@ -281,13 +284,13 @@ Notification is sent via **Amazon Echo / Pompeo** (TTS) for voice, and **Telegra
 
 - [ ] **Daily Digest**: after `Parse risposta GPT-4.1`, add:
   - Postgres INSERT into `memory_facts` (source=email, category, subject, detail JSONB, action_required, expires_at)
-  - Embedding generation (Copilot endpoint) → Qdrant upsert into `martin_episodes`
+  - Embedding generation (Copilot endpoint) → Qdrant upsert into `episodes` (user_id=martin)
   - Thread dedup: use `thread_id` as logical key, update existing Qdrant point if thread already exists
 
 - [ ] **Upload Bolletta** + **Upload Documento (Telegram)**: after `Paperless - Patch Metadati`, add:
   - Postgres INSERT into `finance_documents` (correspondent, amount, doc_date, doc_type, tags, paperless_doc_id)
   - Postgres INSERT into `memory_facts` (source=paperless, category=finance, cross-reference)
-  - Embedding of OCR text chunks → Qdrant upsert into `martin_knowledge`
+  - Embedding of OCR text chunks → Qdrant upsert into `knowledge` (user_id=martin)
 
 ---
 
@@ -316,7 +319,7 @@ Notification is sent via **Amazon Echo / Pompeo** (TTS) for voice, and **Telegra
 
 - [ ] **Newsletter Agent**
   - Separate Gmail label for newsletters (excluded from Daily Digest main flow)
-  - Morning cron: summarize + extract relevant articles → `martin_knowledge`
+  - Morning cron: summarize + extract relevant articles → `knowledge`
 
 ---
 
@@ -351,9 +354,9 @@ Notification is sent via **Amazon Echo / Pompeo** (TTS) for voice, and **Telegra
   - PDF → FileWizard OCR → GPT-4.1 metadata extraction (month, gross, net, deductions)
   - Paperless upload with tag `Cedolino`
   - Persist structured data to `finance_documents` (custom fields for payslip)
-  - Trend embedding in `martin_knowledge` for finance agent queries
-- [ ] Behavioral habit modeling: aggregate `behavioral_context` records over time, generate periodic "habit summary" embeddings in `martin_preferences`
-- [ ] Outline → Qdrant pipeline: sync selected Outline documents into `martin_knowledge` on edit/publish event
+  - Trend embedding in `knowledge` for finance agent queries
+- [ ] Behavioral habit modeling: aggregate `behavioral_context` records over time, generate periodic "habit summary" embeddings in `preferences`
+- [ ] Outline → Qdrant pipeline: sync selected Outline documents into `knowledge` on edit/publish event
 - [ ] Chrome browsing history ingestion (privacy-filtered): evaluate browser extension or local export → embedding pipeline for interest/preference modeling
 - [ ] "Posti e persone" graph: structured contact/location model in Postgres, populated from email senders, calendar attendees, Home Assistant presence data
 - [ ] Local embedding model: migrate from Copilot `text-embedding-3-small` to Ollama-served model (e.g. `nomic-embed-text`) once LLM server is stable