AI plan v1

AI_REFACTOR_PLAN.md

@@ -512,112 +512,14 @@ Cloud Agent:
 
 ---
 
-## Phase 5 — Shared Memory (Agent KV + Chat WS Fix)
+## ~~Phase 5 — Shared Memory (Agent KV + Chat WS Fix)~~ SUPERSEDED
 
-> **Objective:** Give chat agents persistent memory via a KV store on the Electron client. Agents can `store_memory()` to remember user preferences, patterns, and corrections, and `recall_memories()` to retrieve them. All data lives in Electron's SQLite `agent_memory` table (local-first, never stored server-side). This also requires fixing the chat WS handler to support bidirectional tool calls — currently a critical gap that blocks all agent tools from working over the `/chat/stream` endpoint.
+> **Phase 5 has been replaced by Architecture v2's cloud-side memory middleware.**
 >
-> **Electron Phase 5 plan:** `../adiuva/AI_REFACTOR_PLAN.md` Phase 5 section.
+> - Step 5.1 (chat WS bidirectional fix) → moved to `BACKEND_PLAN.md` Step 14 (V2.0.1)
+> - Steps 5.2–5.4 (on-device KV memory) → replaced by Steps 16–20 (MemGPT-style memory on PostgreSQL + pgvector)
 >
-> **Why agent KV matters:** Chat agents are currently stateless — they can't remember "User prefers to-do in lowercase" or "Client X billing cycle is the 15th". With KV memory, agents become learning assistants that improve over time. Users feel the AI "knows them" without any data leaving their device.
->
-> **Why the chat WS fix is critical:** The existing `/chat/stream` WS handler (`app/api/routes/chat.py`) never calls `set_client_executor()`. This means `execute_on_client()` raises `RuntimeError` whenever any agent tool tries to call it during a chat session. All 23 tools are broken over chat WS. This must be fixed before memory tools (or any tools) can work.
->
-> **New Electron tables** (managed by Electron, accessed by backend via `execute_on_client`):
-> - `chat_messages`: `id`, `scope`, `role`, `content`, `error`, `created_at`
-> - `agent_memory`: `id`, `agent_name`, `key`, `value`, `scope`, `created_at`, `updated_at` (unique on `agent_name, key, scope`)
-
-### Step 5.1 — Fix chat WS for bidirectional tool calls (PREREQUISITE)
-
-> **This is the highest-priority backend fix.** Without it, zero agent tools work over the chat WS connection.
-
-- [ ] Rewrite `app/api/routes/chat.py` — `chat_stream()` WS handler:
-  - After auth + accept, receive first frame as `{"type": "chat_request", ...}` (not raw `ChatRequest`)
-  - Parse frame, extract `message` and `context`
-  - Set up a local `pending_calls: dict[str, asyncio.Future]` for tool-call round-trips
-  - Define executor callback:
-    ```python
-    async def execute_callback(payload: dict) -> dict:
-        call_id = payload["id"]
-        fut = asyncio.get_event_loop().create_future()
-        pending_calls[call_id] = fut
-        await websocket.send_text(json.dumps({"type": "tool_call", **payload}))
-        return await asyncio.wait_for(fut, timeout=30.0)
-    ```
-  - Call `set_client_executor(execute_callback)` before orchestrating
-  - Run two concurrent tasks:
-    1. **Receive loop**: dispatches incoming frames — `tool_result` resolves pending Futures, `pong` ignored
-    2. **Orchestration task**: calls `orchestrate_stream()`, wraps chunks in `{"type": "text_chunk", "text": "..."}` frames, sends `{"type": "final", "response": "..."}` on completion
-  - Call `clear_client_executor()` in finally block
-  - Keep heartbeat ping every 30s
-  - 30s timeout on each `tool_result` — tool returns error string to LLM on timeout
-- [ ] Update `orchestrate_stream()` in `app/core/orchestrator.py` if needed:
-  - Ensure it properly yields text chunks (currently chunks by fixed 50-char slices — consider switching to yielding full response as single chunk for now)
-- **Files:** `app/api/routes/chat.py`, `app/core/orchestrator.py`
-- **Outcome:** Full bidirectional WS. Tool calls, text streaming, and heartbeats happen concurrently. All 23 existing agent tools now work over chat WS.
-
-### Step 5.2 — Agent memory tools
-
-- [ ] Create `app/agents/tools/memory_tools.py`:
-  - `create_memory_tools(agent_name: str) -> list[Tool]` — factory function that returns two LangChain `@tool` functions with `agent_name` bound via closure:
-    - **`store_memory(key: str, value: str, scope: str = "global")`**:
-      - Calls `execute_on_client(action="select", table="agentMemory", filters={"agentName": agent_name, "key": key, "scope": scope})`
-      - If row exists: `execute_on_client(action="update", table="agentMemory", data={"id": row["id"], "updates": {"value": value, "updatedAt": <now_ms>}})`
-      - If not: `execute_on_client(action="insert", table="agentMemory", data={"agentName": agent_name, "key": key, "value": value, "scope": scope})`
-      - Returns `"Stored memory: [key] = [value]"`
-    - **`recall_memories(key_pattern: str = None, scope: str = "global", limit: int = 10)`**:
-      - Calls `execute_on_client(action="select", table="agentMemory", filters={"agentName": agent_name, "scope": scope, "search": key_pattern})`
-      - Returns formatted list: `"key1: value1\nkey2: value2\n..."` or `"No memories found."`
-  - Timestamps are Unix milliseconds (consistent with Electron's `Date.now()`)
-  - Agent name scoping: each agent only sees its own memories (filtered by `agentName`)
-- **Files:** `app/agents/tools/memory_tools.py`
-- **Outcome:** Two reusable tools any agent can include. Upsert semantics via select-then-insert/update.
-
-### Step 5.3 — Register memory tools on all agents
-
-- [ ] Update `app/agents/task_agent.py`:
-  - Import `create_memory_tools` from `app/agents/tools/memory_tools`
-  - Add memory tools to `get_tools()`: `return [list_tasks, create_task, ..., *create_memory_tools("task_agent")]`
-  - Append to `_SYSTEM_PROMPT`: `"\n\nYou can store important facts about user preferences using store_memory and recall past facts using recall_memories. Store corrections, preferences, and patterns the user shares (e.g. 'User prefers short task titles', 'Default priority is medium'). Always check memories before giving advice."`
-- [ ] Update `app/agents/project_agent.py` — same pattern with `create_memory_tools("project_agent")`
-- [ ] Update `app/agents/note_agent.py` — same pattern with `create_memory_tools("note_agent")`
-- [ ] Update `app/agents/checkpoint_agent.py` — same pattern with `create_memory_tools("checkpoint_agent")`
-- **Files:** `app/agents/task_agent.py`, `app/agents/project_agent.py`, `app/agents/note_agent.py`, `app/agents/checkpoint_agent.py`
-- **Outcome:** All 4 chat agents can store and recall persistent memories. Each agent's memories are scoped by `agentName`.
-
-### Step 5.4 — Extend ChatContext with agent memories
-
-- [ ] Update `app/schemas.py`:
-  - Add `agent_memories: list[dict[str, Any]] = Field(default_factory=list)` to `ChatContext`
-  - These are pre-loaded by Electron (from `agent_memory` table) and included in every request
-- [ ] Agent `handle()` methods already receive full `context` dict — memories are visible in `context["agent_memories"]`
-- [ ] Agent system prompts reference memories from context: agents see pre-loaded memories AND can call `recall_memories` for targeted lookup
-- **Files:** `app/schemas.py`
-- **Outcome:** Backend receives pre-loaded memories from Electron. Agents have dual-path access: context injection (passive) + tool call (active).
-
-### Phase 5 — Verification
-
-| # | Scenario | Expected |
-|---|---|---|
-| 1 | **Chat WS bidirectional** | Connect → send `chat_request` → receive `tool_call` → respond `tool_result` → receive `text_chunk` → `final` |
-| 2 | **All existing tools work** | "List my tasks" over chat WS → `tool_call(select, tasks)` → Electron returns rows → LLM responds with real task data |
-| 3 | **Store memory** | "Remember that I prefer short task titles" → `store_memory("task_title_preference", "short")` → `tool_call(insert, agentMemory)` → Electron persists |
-| 4 | **Recall memory** | New chat session → "How should I name tasks?" → agent sees pre-loaded memory in context or calls `recall_memories` → references stored preference |
-| 5 | **Upsert semantics** | Store same key twice → only one row exists with updated value |
-| 6 | **Agent scope isolation** | `task_agent` stores memory → `note_agent` cannot see it (filtered by `agentName`) |
-| 7 | **Project scope** | Store memory with `scope="project:<uuid>"` → only visible in that project's chat context |
-| 8 | **Tool timeout** | Disconnect Electron mid-tool-call → 30s timeout → tool returns error → LLM handles gracefully |
-| 9 | **Concurrent tool calls** | Agent calls `list_tasks` then `recall_memories` in sequence → both WS round-trips succeed |
-| 10 | **Existing tests pass** | `pytest` — no regressions in agent tools or orchestrator |
-
-### Phase 5 — Step Dependencies
-
-```
-Step 5.1 (chat WS fix) ──────────────► Step 5.2 (memory tools) ──► Step 5.3 (register on agents)
-                                                                  ──► Step 5.4 (extend ChatContext)
-
-Step 5.1 is the BLOCKER — nothing else works until bidirectional tool calls are wired.
-Steps 5.3 and 5.4 can run in parallel after 5.2.
-```
+> See `BACKEND_PLAN.md` Steps 14–28 for the full Architecture v2 implementation plan.
 
 ---
 

BACKEND_PLAN.md

@@ -516,6 +516,12 @@ adiuva-api/
 | GET | `/api/v1/oauth/{provider}/authorize` | JWT | — | `{authorization_url}` |
 | GET | `/api/v1/oauth/{provider}/callback` | — | OAuth code | `{encrypted_token}` |
 | WS | `/api/v1/ws/device` | JWT | `device_hello` (first frame) | Agent trigger + tool_call frames |
+| GET | `/api/v1/memory/core` | JWT | — | Core memory entries |
+| GET | `/api/v1/memory/associative` | JWT | — | Associative memories |
+| GET | `/api/v1/memory/episodic` | JWT | — | Episodic summaries |
+| GET | `/api/v1/memory/proactive` | JWT | — | Proactive patterns |
+| DELETE | `/api/v1/memory/{type}/{id}` | JWT | — | `{ok: true}` |
+| POST | `/api/v1/oauth/{provider}/refresh` | JWT | — | `{encrypted_token}` |
 
 ---
 
@@ -559,6 +565,171 @@ adiuva-api/
 
 ---
 
+---
+
+## Architecture v2 — Integration Phases
+
+> **Reference:** `architecture-v2.md` — Local-first topology, BYOK LLM keys, MemGPT-style memory middleware, Popup scoping with navigation directives, Batch Agent.
+>
+> These phases build on top of the completed Steps 1–13 and Phase 3 (3.1–3.6). Phase 5 from `AI_REFACTOR_PLAN.md` (on-device KV memory) is superseded by cloud-side memory middleware below.
+
+### Step 14 — Fix chat WS for bidirectional tool calls (V2.0.1)
+> Blocker: `chat_stream()` never calls `set_client_executor()` — all 23 agent tools fail during chat WS sessions.
+
+- [ ] Rewrite `app/api/routes/chat.py` `chat_stream()`:
+  - `pending_calls: dict[str, asyncio.Future]` for tool-call round-trips
+  - Concurrent receive loop (dispatches `tool_result` → resolves futures) + orchestration task
+  - `set_client_executor()` before orchestrating, `clear_client_executor()` in finally
+  - Parse first frame as `{"type": "chat_request", ...}`
+  - Send `{"type": "text_chunk", "text": "..."}` + `{"type": "final", "response": "..."}`
+  - Heartbeat ping every 30s, 30s timeout on tool_result
+- [ ] Tests: verify all 23 tools work over chat WS
+- **Files:** `app/api/routes/chat.py`, `app/core/orchestrator.py`
+- **Outcome:** Full bidirectional chat WS. All agent tools now work over `/chat/stream`.
+
+### Step 15 — Agent scheduler + OAuth endpoints (V2.0.2)
+- [ ] `app/core/agent_scheduler.py`: APScheduler, 60s check loop, PostgreSQL advisory locks for multi-instance
+- [ ] `app/api/routes/oauth.py`: `GET /oauth/{provider}/authorize`, `GET /oauth/{provider}/callback`, `POST /oauth/{provider}/refresh`
+  - Gmail: `gmail.readonly` scope
+  - Outlook/Teams: `Mail.Read`, `ChannelMessage.Read.All` scopes
+  - Encrypts tokens with Fernet, returns encrypted blob for `CloudAgentConfig.oauth_token_encrypted`
+- [ ] Integrate scheduler with FastAPI lifespan (start on startup, shutdown gracefully)
+- **Dependencies:** `apscheduler>=4.0`
+- **Files:** `app/core/agent_scheduler.py` (new), `app/api/routes/oauth.py` (new), `app/main.py`
+- **Outcome:** Agents run on cron schedules. OAuth flow for Gmail/Teams/Outlook.
+
+### Step 16 — BYOK: API key passthrough in LLM factory (V2.1.1)
+- [ ] Add `api_key: str | None = None` param to `get_llm()`, `get_router_llm()`, `embed()`
+- [ ] When provided, use BYOK key instead of `_api_key_for_model()` server fallback
+- [ ] Add Cerebras support: `_api_key_for_model()` handles `cerebras/` prefix
+- [ ] Key is never persisted, never logged
+- **Files:** `app/core/llm.py`, `app/config/settings.py`
+- **Outcome:** LLM factory accepts per-request API keys with server-side fallback.
+
+### Step 17 — BYOK: Thread key through request lifecycle (V2.1.2)
+- [ ] `ContextVar`: `_request_api_key: ContextVar[str | None]` in `app/core/llm.py`
+- [ ] `get_llm()` reads from ContextVar when no explicit `api_key` param
+- [ ] `ChatRequest` schema: add `api_key: str | None = None`
+- [ ] WS handlers set ContextVar from incoming frame's `api_key` field
+- [ ] Fallback: if no BYOK key → server-side key (backward compat + Batch Agent)
+- **Files:** `app/schemas.py`, `app/core/llm.py`, `app/api/routes/chat.py`, `app/core/orchestrator.py`
+- **Outcome:** BYOK key flows from request → orchestrator → agent → LLM. Never stored.
+
+### Step 18 — pgvector + memory DB tables (V2.2.1)
+- [ ] Add `pgvector` to `requirements.txt`
+- [ ] New SQLAlchemy models in `app/models.py`:
+  - `CoreMemory`: id, user_id, key, value, created_at, updated_at
+  - `AssociativeEmbedding`: id, user_id, entity_type, entity_id, label, embedding (pgvector Vector), metadata_json, created_at
+  - `EpisodicSummary`: id, user_id, session_id, summary, key_entities, created_at
+  - `ProactivePattern`: id, user_id, pattern_type, description, confidence, last_detected_at, created_at
+- [ ] Alembic migration with `CREATE EXTENSION IF NOT EXISTS vector`
+- **Files:** `app/models.py`, `requirements.txt`, `alembic/versions/`
+- **Outcome:** Memory tables in PostgreSQL with pgvector support.
+
+### Step 19 — Memory service layer (V2.2.2)
+- [ ] Create `app/core/memory.py` — `MemoryService` class:
+  - `load_core_memory(user_id)`, `write_core_memory(user_id, key, value)` (upsert)
+  - `search_associative(user_id, query_embedding, top_k=5)` (pgvector similarity)
+  - `write_associative(user_id, entity_type, entity_id, label, embedding, metadata)`
+  - `get_recent_episodic(user_id, limit=3)`, `write_episodic(user_id, session_id, summary, key_entities)`
+  - `get_proactive_patterns(user_id)`, `write_proactive_pattern(user_id, ...)`
+  - `delete_memory(user_id, memory_type, memory_id)` — user review/delete
+- Uses async SQLAlchemy sessions from `app/db.py`
+- **Files:** `app/core/memory.py` (new)
+- **Outcome:** Complete CRUD + similarity search for all 4 memory types.
+
+### Step 20 — Memory middleware wrapper (V2.2.3)
+- [ ] Create `app/core/memory_middleware.py`:
+  - `enrich_with_memory(user_id, message, context)`:
+    1. Load core memory (always injected)
+    2. Embed user message → pgvector similarity search on associative memory
+    3. Load recent episodic summaries
+    4. Load proactive patterns
+    5. Return enriched context
+  - `post_process_memory(user_id, message, response, context)`:
+    1. LLM decides what to remember (semi-autonomous)
+    2. Write core memory for preferences
+    3. Write associative for entity relationships
+    4. Compress session into episodic summary when conversation ends
+- **Files:** `app/core/memory_middleware.py` (new)
+- **Outcome:** Memory wraps every orchestrator call — enrich before, learn after.
+
+### Step 21 — Integrate memory into orchestrator (V2.2.4)
+- [ ] Modify `orchestrate()` / `orchestrate_stream()`:
+  - Before `classify_intent`: call `enrich_with_memory()`
+  - After agent response: call `post_process_memory()`
+- [ ] Add `memory_write` tool to Router's system prompt
+- **Files:** `app/core/orchestrator.py`
+- **Outcome:** All chat interactions are memory-enriched. Router can explicitly write memories.
+
+### Step 22 — Memory management API (V2.2.5)
+- [ ] Create `app/api/routes/memory.py`:
+  - `GET /api/v1/memory/core` — list core memories
+  - `GET /api/v1/memory/associative` — list associative memories
+  - `GET /api/v1/memory/episodic` — list episodic summaries
+  - `GET /api/v1/memory/proactive` — list proactive patterns
+  - `DELETE /api/v1/memory/{type}/{id}` — user deletes a memory
+- [ ] Register router in `app/main.py`
+- **Files:** `app/api/routes/memory.py` (new), `app/main.py`
+- **Outcome:** Users can review and delete their memories (semi-autonomous model).
+
+### Step 23 — Scope context + structured response (V2.3.1)
+- [ ] Update `ChatRequest`: add `source: Literal["home", "popup"] = "home"`, `scope: dict | None = None`
+- [ ] New response schemas in `app/schemas.py`:
+  - `AiResponse`: response (text + ui_directive + data), navigation, mutations, context
+  - `NavigationDirective`: action, target, filter
+  - `MutationCommand`: action, data
+  - `ResponseContext`: scope_changed, new_scope
+- [ ] Used when `source == "popup"` or navigation needed; `ChatResponse` kept for backward compat
+- **Files:** `app/schemas.py`
+- **Outcome:** Popup can receive navigation directives and scoped responses.
+
+### Step 24 — Enhanced Router capabilities (V2.3.2)
+- [ ] Update orchestrator system prompt + tool set:
+  - `ask_user_clarification` — return clarification question, WS handler waits for next user message
+  - `render_ui_directive` — specify UI rendering (task_card, chart, diagram)
+  - `cross_entity_resolve` — include navigation directive when scope crosses entities
+- **Files:** `app/core/orchestrator.py`
+- **Outcome:** Router can clarify, render rich UI, and navigate across entities.
+
+### Step 25 — WS protocol evolution (V2.3.3)
+- [ ] Add to `WsFrameType`: `user_request`, `data_request`, `data_response`, `ai_response`, `mutation`
+- [ ] `user_request` = enhanced `chat_request` with source, scope, api_key
+- [ ] `ai_response` = structured response with navigation + mutations + context
+- [ ] Server auto-detects client frame format for backward compat
+- **Files:** `app/schemas.py`, `app/api/routes/chat.py`
+- **Outcome:** v2 WS protocol with full backward compatibility.
+
+### Step 26 — Batch agent implementation (V2.4.1)
+- [ ] Create `app/agents/batch_agent.py` — background agent (not `ChatAgent`):
+  - `pattern_detection`: analyze episodic summaries for recurring patterns
+  - `memory_consolidation`: merge redundant episodic summaries
+  - `suggestion_generation`: create proactive pattern entries
+  - `overdue_detection`: request task data from Electron via device WS
+- [ ] Uses server-side LLM key (not BYOK — runs without user request)
+- [ ] Requires device online for entity data access
+- **Files:** `app/agents/batch_agent.py` (new)
+- **Outcome:** Background agent that learns patterns and generates proactive suggestions.
+
+### Step 27 — Batch agent scheduling + proactive surfacing (V2.4.2)
+- [ ] Integrate with agent scheduler from Step 15
+- [ ] Default: every 6h per user, only when device online
+- [ ] Proactive patterns surfaced via memory middleware in Router context
+- **Files:** `app/core/agent_scheduler.py`, `app/core/memory_middleware.py`
+- **Outcome:** Batch runs automatically. Suggestions appear in chat responses.
+
+### Step 28 — E2E memory encryption + tests (V2.5)
+- [ ] Application-level Fernet encryption for all memory table writes
+- [ ] Encryption key derived from user passphrase, sent with requests
+- [ ] `tests/test_byok.py`: key threading, Cerebras model string, fallback
+- [ ] `tests/test_memory.py`: all 4 memory types, pgvector search, middleware
+- [ ] `tests/test_popup.py`: scope, navigation directives, cross-entity
+- [ ] `tests/test_batch_agent.py`: pattern detection, consolidation
+- **Files:** `app/core/memory.py`, `app/storage/encryption.py`, `tests/`
+- **Outcome:** Fully tested, encrypted memory system.
+
+---
+
 ## Development Rules
 
 1. **NEVER persist user data in plaintext.** The DB stores only auth, billing, storage metadata, and marketplace data. User context arrives in requests and is discarded. Cloud blobs are E2E encrypted client-side — backend only stores opaque bytes.