# jarvisChat — OpenCode Prompt Sequence # Generated: 2026-07-01 # Execute sequentially. Run full test suite after each task before proceeding. # Test command: ./venv/bin/python -m pytest tests/ -v --- ## ~~TASK 1 — README Cleanup [DONE]~~ Review README.md in the current repo. Remove any node references other than `ultron` (192.168.50.108) and `jarvis` (192.168.50.210). Ensure all references to the project use the exact casing `jarvisChat` — not `Jarvischat`, `JarvisChat`, or `jarvischat`. Do not change any functional content, endpoint documentation, or architecture descriptions — this is a text cleanup only. After editing, verify the file renders cleanly as markdown. Commit with message: `docs: clean up node references and branding consistency`. No new tests required for this task. --- ## ~~TASK 2 — Qwen2.5-Coder llama-server Service on Ultron (Infrastructure) [DONE]~~ **Status: Systemd unit created, verified, and restored.** This task originally defined creation of `/etc/systemd/system/llama-server-coder.service` (port 8082, Qwen2.5-Coder-14B Q5_K_M) as a prerequisite for dynamic model swapping. That sysadmin work is done. **The real Task 2 deliverable — the ability to dynamically swap models based on query classification — is delivered by Roadmap N (Tasks 9–15).** The flow: 1. **Task 13** — Phi-4-mini triage (`triage.py`) classifies the query as `general`, `code`, `search`, or `rag` 2. **Task 13** — `select_node()` picks the best worker node; if the ideal model isn't active, it triggers a swap 3. **Task 14** — `request_model_swap()` publishes `cmd.swap_model` via AMQP `jc.admin` exchange 4. **Task 12** — The node agent on jarvis receives the command, stops the current llama-server, starts the correct one, waits for health, and publishes `model_ready` 5. **Task 14** — ultron receives `model_ready`, updates the cluster registry, and routes the query to the node The swap is async and transparent — the user sees only latency. The UI (Task 15) shows a yellow "swapping" status dot during the transition. The service unit at `/etc/systemd/system/llama-server-coder.service` is the **target** the node agent starts when swapping to code inference. It is not enabled at boot — the AMQP cluster manages activation. See Tasks 9–15 for the actual model swap implementation. No pytest tests required for this infrastructure task. --- ## ~~TASK 3 — Update OpenCode Config to Use Qwen on :8082 [DONE]~~ Update `/home/gramps/.config/opencode/opencode.jsonc` (on this machine, ultron) to point the configured provider at `http://127.0.0.1:8082/v1` instead of `http://127.0.0.1:8081/v1`. The model name in the config should be updated to reflect `qwen2.5-coder-14b` or whatever model ID the llama-server instance at :8082 reports via `/v1/models`. Verify the endpoint is reachable before writing the config change. Do not restart OpenCode — the config change takes effect on next session start. No pytest tests required for this task. --- ## ~~TASK 4 — File/Document Attachment: Backend Ingest Endpoint [DONE]~~ **Status: `POST /api/upload` with mode=(context|ingest|both), PDF/text extraction, Qdrant upsert, SQLite context (1hr expiry). Committed `4a891c8` (v1.9.0).** This task implements the backend half of file/document attachment (TODO #21). The goal is dual-aspect upload: a file can be used as immediate chat context, ingested into the RAG corpus (Qdrant), or both. **Add to `config.py`:** - `UPLOAD_DIR` — path for temporary upload storage, default `/tmp/jarvischat_uploads` - `MAX_UPLOAD_BYTES` — max file size, default 20MB - `SUPPORTED_UPLOAD_TYPES` — set of MIME types: `text/plain`, `text/markdown`, `application/pdf`, `application/json`, `text/x-python`, `text/html` **Create `routers/upload.py`:** Implement `POST /api/upload` (admin required). Accept `multipart/form-data` with: - `file` — the uploaded file (required) - `mode` — string enum: `context` (inject into next chat only), `ingest` (add to RAG corpus), `both` (default: `both`) - `conversation_id` — optional, associates context-mode content with a specific conversation Behavior: - Validate file size against `MAX_UPLOAD_BYTES` — return 413 if exceeded - Validate MIME type against `SUPPORTED_UPLOAD_TYPES` — return 415 if unsupported - For PDF files, extract text using `pypdf` (add to requirements.txt) - For all other types, read as UTF-8 text - If mode includes `ingest`: chunk the extracted text into 512-token overlapping chunks (128-token overlap), generate embeddings via `EMBED_URL` (http://192.168.50.108:11434/api/embeddings, model mxbai-embed-large), upsert into Qdrant collection `jarvischat` with metadata `{source: filename, upload_date: iso_timestamp, type: "upload"}` - If mode includes `context`: store the full extracted text in a new SQLite table `upload_context` with columns `(id INTEGER PRIMARY KEY, conversation_id TEXT, filename TEXT, content TEXT, created_at TEXT, expires_at TEXT)`. Context entries expire after 1 hour. - Return JSON: `{filename, size_bytes, mode, chunks_ingested (if ingest), context_id (if context), message}` **Add `upload_context` table to `db.py`** `init_db()`. **Wire `upload.router` into `app.py`** in the router registration block. **Write `tests/test_upload.py`** covering: - Valid text file upload, mode=ingest — assert chunks_ingested > 0, Qdrant upsert called - Valid text file upload, mode=context — assert context_id returned, row exists in upload_context - Valid text file upload, mode=both — assert both behaviors - File exceeds MAX_UPLOAD_BYTES — assert 413 - Unsupported MIME type — assert 415 - Guest session attempt — assert 403 - PDF extraction path — mock pypdf, assert text extracted and processed Mock Qdrant and EMBED_URL calls via monkeypatch. Do not require live external services in tests. Run full test suite after implementation. All 26 existing tests must continue to pass. --- ## ~~TASK 5 — File/Document Attachment: UI Integration [DONE]~~ **Status: Paperclip icon, file preview pill, gallery overlay, attachment indicators, DELETE/PATCH link/by-conversation endpoints, chat context injection. Committed `81238c0` (v1.10.0).** This task implements the frontend half of TODO #21. The UI is a single file at `templates/index.html`. Add a file attachment button to the chat input area. Requirements: - Paperclip icon button adjacent to the send button - Clicking opens a file picker filtered to supported types (`.txt`, `.md`, `.pdf`, `.json`, `.py`, `.html`) - On file selection, show a pill/badge above the input showing the filename with an X to remove it - On send, if a file is attached: POST to `/api/upload` with `mode=both` and the current `conversation_id`, then include the returned `context_id` in the subsequent `/api/chat` POST body as `upload_context_id` - If the upload fails, show an inline error and do not send the chat message - File attachment state clears after send **Update `/api/chat` in `routers/chat.py`:** - Accept optional `upload_context_id` in the request body - If present, look up the content in `upload_context` table and prepend it to the system prompt as: `\n\n[ATTACHED DOCUMENT: {filename}]\n{content}\n[END DOCUMENT]` - If the context_id is expired or missing, log a warning and continue without it (do not error) **Add to `tests/test_chat_streaming_and_memory_paths.py`:** - Test that a valid `upload_context_id` results in document content being prepended to the system prompt - Test that an expired/missing `upload_context_id` is silently ignored Run full test suite. All existing tests must continue to pass. --- ## ~~TASK 6 — Roadmap I: Terminal Command RAG Hook [DONE]~~ **Status: `POST /api/ingest` with Bearer token auth, `chunk_text()` shared helper, `jc-ingest.sh` script. Committed `1ac21ad` (v0.11.0).** This task implements autonomous RAG ingestion of significant terminal activity (TODO #23). **Create `routers/ingest.py`:** Implement `POST /api/ingest` (requires Bearer token auth — use same `COMPLETIONS_API_KEY` mechanism as `routers/completions.py`). Accept JSON body: - `content` — string, the text to ingest (required) - `source` — string, origin label e.g. `terminal`, `file`, `external` (default: `external`) - `metadata` — optional dict of additional key/value pairs Behavior: - Chunk `content` into 512-token overlapping chunks (128-token overlap) — extract this logic into a shared helper `chunk_text(text, chunk_size=512, overlap=128)` in `rag.py` if not already present - Generate embeddings via `EMBED_URL` - Upsert into Qdrant collection `jarvischat` with metadata `{source, ingest_date: iso_timestamp, ...metadata}` - Return JSON: `{chunks_ingested, source, message}` **Wire `ingest.router` into `app.py`.** **Create `/home/gramps/bin/jc-ingest.sh` on jarvis (192.168.50.210)** — this is a shell script, not a Python file, and lives outside the repo. Write it to stdout/document it clearly so gramps can deploy it manually: ```bash #!/bin/bash # jc-ingest.sh — pipe terminal commands into jarvisChat RAG # Add to ~/.bashrc: export PROMPT_COMMAND="jc_capture" # Function to call after significant commands JC_URL="http://192.168.50.210:8080/api/ingest" JC_TOKEN="${JARVISCHAT_COMPLETIONS_API_KEY}" jc_capture() { local cmd cmd=$(history 1 | sed 's/^[ ]*[0-9]*[ ]*//') # Only ingest significant commands if echo "$cmd" | grep -qE '^(git|pip|systemctl|sudo|vi|vim|curl|wget|apt|python|pytest)'; then curl -s -X POST "$JC_URL" \ -H "Authorization: Bearer $JC_TOKEN" \ -H "Content-Type: application/json" \ -d "{\"content\": $(echo "$cmd" | python3 -c 'import json,sys; print(json.dumps(sys.stdin.read().strip()))'), \"source\": \"terminal\"}" \ > /dev/null 2>&1 & fi } ``` **Write `tests/test_ingest.py`** covering: - Valid ingest with content — assert chunks_ingested > 0 - Missing Bearer token — assert 401 - Wrong Bearer token — assert 403 - Empty content — assert 422 - Qdrant and embed calls mocked via monkeypatch Run full test suite. All existing tests must continue to pass. --- ## ~~TASK 7 — Roadmap J: Startup Hardware Self-Assessment [DONE]~~ **Status: `hardware.py` + `routers/hardware.py` + 4 tests. Committed `7291b8f` (v0.12.0).** On jC startup, probe available hardware and write a living config snapshot. This replaces hardcoded assumptions about VRAM and RAM. **Create `hardware.py`** in the project root: ``` async def assess_hardware() -> dict ``` Probes: - System RAM: `psutil.virtual_memory().total` and `.available` - CPU count: `psutil.cpu_count()` - GPU VRAM total and free: call `rocm-smi --showmeminfo vram --json` via subprocess, parse output. If rocm-smi absent or fails, set VRAM values to 0 and log a warning. - llama-server reachable: GET `LLAMA_SERVER_BASE/v1/models`, timeout 3s. Record True/False and list of available model IDs. - Qdrant reachable: GET `http://192.168.50.108:6333/collections`, timeout 3s. Record True/False and collection list. - SearXNG reachable: GET `http://localhost:8888`, timeout 3s. Record True/False. Returns a dict with all of the above. Writes result as JSON to `hardware_state.json` in the working directory. **Call `assess_hardware()` from the FastAPI `lifespan` context** in `app.py` on startup, after `init_db()`. Log a summary line: `HW: {ram_gb}GB RAM, {vram_mb}MB VRAM, llama={reachable}, qdrant={reachable}, searxng={reachable}`. **Expose `GET /api/hardware`** in a new `routers/hardware.py` — returns the current `hardware_state.json` content as JSON. No auth required (read-only, non-sensitive aggregate stats). **Wire `hardware.router` into `app.py`.** **Write `tests/test_hardware.py`** covering: - `assess_hardware()` with all services reachable (mock subprocess and httpx calls) — assert all fields present - `assess_hardware()` with rocm-smi absent — assert VRAM=0, no exception raised - `assess_hardware()` with llama-server unreachable — assert `llama_reachable=False`, no exception - `GET /api/hardware` — assert returns JSON with expected keys Run full test suite. All existing tests must continue to pass. --- ## ~~TASK 8 — Roadmap K: RAG Corpus Management [DONE]~~ Qdrant collection `jarvischat` currently grows without bound. Implement score-based eviction with hysteresis, pinned sources, operational stats, and a flush command. ### Config — add to `config.py`: ```python RAG_MAX_VECTORS = 50000 # absolute ceiling; eviction targets thresholds below it RAG_EVICTION_HIGH_WATER = 0.80 # fraction of RAG_MAX_VECTORS that triggers eviction RAG_EVICTION_LOW_WATER = 0.20 # fraction where eviction stops RAG_EVICTION_BATCH = 1000 # max points to delete per Qdrant scroll/delete cycle RAG_PINNED_SOURCES = ["upload", "profile"] # never evicted RAG_GRACE_HOURS = 1 # new vectors ineligible for eviction until this old RAG_ACCESS_WEIGHT = 1.0 # score factor: retrieval_count * ACCESS_WEIGHT RAG_AGE_WEIGHT = 0.1 # score factor: ingest_age_hours * AGE_WEIGHT ``` Validations on boot: `high_water > low_water`, `batch > 0`, `max_vectors > 0`. ### Eviction algorithm — add to `rag.py`: ``` score = (retrieval_count * ACCESS_WEIGHT) + (age_hours * AGE_WEIGHT) ``` Lower score = evicted first. Tiebreak: `last_accessed` ASC (older wins). ```python async def get_collection_count() -> int # GET /collections/jarvischat → return vectors_count async def get_collection_stats() -> dict # Return {vector_count, max_vectors, high_water, low_water, percent_full, pinned_sources} async def evict_batch(batch_size: int) -> int # Scroll Qdrant for vectors NOT in RAG_PINNED_SOURCES, WHERE ingest_age > RAG_GRACE_HOURS, # ordered by score ASC, last_accessed ASC. # Delete up to batch_size. Return count deleted. # If 0 evictable vectors found: log warning, return 0 (break loop). async def maybe_evict() -> int # Acquire eviction_lock (asyncio.Lock). # count = get_collection_count() # threshold_high = RAG_MAX_VECTORS * RAG_EVICTION_HIGH_WATER # threshold_low = RAG_MAX_VECTORS * RAG_EVICTION_LOW_WATER # total_evicted = 0 # while count >= threshold_low: # if total_evicted > 0 and count < threshold_low: break # deleted = evict_batch(RAG_EVICTION_BATCH) # if deleted == 0: break # no more unpinned targets # total_evicted += deleted # count -= deleted # if count < threshold_high and total_evicted > 0: break # # only one pass if batch spans the full gap # if count < threshold_low: break # Record total_evicted + timestamp in EVICTION_LOG (list of dicts, kept in memory, max 1000 entries) # Release lock. Return total_evicted. async def get_rag_operational_stats() -> dict # Returns: vector_count, max_vectors, high_water_pct, low_water_pct, # percent_full, pinned_sources, grace_hours, # eviction_counts_last_1m, eviction_counts_last_5m, eviction_counts_last_30m, # at_risk_count (vectors in bottom 10% by score), # pinned_count, avg_retrieval_count ``` ### Edge cases & guards: 1. **Newborn grace** — vectors < `RAG_GRACE_HOURS` old are excluded from eviction scroll (score=0 otherwise → immediate deletion) 2. **All-pinned freeze** — if scroll returns 0 evictable vectors, log warning and break loop 3. **Race** — `asyncio.Lock()` guards `maybe_evict()`; concurrent callers wait their turn 4. **Zero config** — `RAG_MAX_VECTORS <= 0` → eviction disabled; `RAG_EVICTION_BATCH <= 0` → clamped to 1 5. **Legacy payloads** — vectors without `retrieval_count` or `last_accessed` get defaults (0, `ingest_date`) ### Wire eviction: Call `maybe_evict()` after each upsert batch completes in: - `routers/upload.py` — after Qdrant upsert - `routers/ingest.py` — after Qdrant upsert ### Admin endpoints — new `routers/rag_admin.py`: | Method | Endpoint | Description | |--------|----------|-------------| | GET | `/api/rag/stats` | Operational stats (see `get_rag_operational_stats()`) — admin required | | POST | `/api/rag/flush` | Delete ALL points from the Qdrant `jarvischat` collection. Returns `{deleted_count, collection: "jarvischat", status: "flushed"}`. Admin required. | ### In-memory eviction log: ```python EVICTION_LOG: list[dict] = [] # managed by rag.py, max 1000 entries # Each entry: {timestamp: iso, count: N, remaining: N} # Tied to RATE_EVENTS pattern from security.py for rolling window calculations ``` ### Tests — `tests/test_rag_management.py`: - `get_collection_count()` — mock Qdrant GET, assert correct count - `get_collection_stats()` — assert shape matches config - `evict_batch()` — mock Qdrant scroll + delete, assert pinned sources excluded, grace period enforced, batch size respected - `maybe_evict()` — below high water: 0 evicted; at high water: eviction fires; stops at low water; all-pinned scroll returns 0 → breaks - `GET /api/rag/stats` — assert full shape - `POST /api/rag/flush` — assert points deleted, admin required, guest 403 - `POST /api/rag/flush` by guest — assert 403 - Race lock — concurrent calls to `maybe_evict()` queue up, only one evicts Mock all Qdrant calls via monkeypatch. Do not require live services. Run full test suite. All existing tests must continue to pass. --- ## ~~TASK 9 — Roadmap N1: RabbitMQ Install and Service on Ultron (Infrastructure) [DONE]~~ This task runs on ultron (this machine). Install RabbitMQ and verify it is operational. Run the following steps: 1. `apt-get update && apt-get install -y rabbitmq-server` 2. `systemctl enable rabbitmq-server && systemctl start rabbitmq-server` 3. `systemctl status rabbitmq-server` — verify active/running 4. Enable the management plugin: `rabbitmq-plugins enable rabbitmq_management` 5. Create a dedicated jC vhost: `rabbitmqctl add_vhost jarvischat` 6. Create a dedicated user: `rabbitmqctl add_user jarvischat CHANGEME_PASSWORD` — generate a random 24-char alphanumeric password and record it 7. Grant permissions: `rabbitmqctl set_permissions -p jarvischat jarvischat ".*" ".*" ".*"` 8. Verify management UI is reachable: `curl -s -u guest:guest http://localhost:15672/api/overview | python3 -m json.tool` 9. Delete default guest user: `rabbitmqctl delete_user guest` Declare the two topic exchanges needed by jC: - Exchange name: `jc.admin`, type: `topic`, durable: true - Exchange name: `jc.system`, type: `topic`, durable: true Use `rabbitmqadmin` or `curl` against the management API to declare exchanges. Verify both exchanges appear in: `curl -s -u jarvischat:{password} http://localhost:15672/api/exchanges/jarvischat` Write the generated RabbitMQ password to `/home/gramps/.jc_amqp_secret` with mode 600. This will be read by jC as an env var source in subsequent tasks. No pytest tests required for this infrastructure task. --- ## ~~TASK 10 — Roadmap N2: AMQP Connection Layer in jC [DONE]~~ This task adds the core AMQP connection manager to jC. It must connect to RabbitMQ on ultron (localhost from jC's perspective since jC runs on ultron), handle reconnection, and provide a shared channel for all AMQP operations. **Add to `requirements.txt`:** `aio-pika>=9.0.0` **Add to `config.py`:** - `AMQP_URL` — read from env `JARVISCHAT_AMQP_URL`, default `amqp://jarvischat:password@localhost:5672/jarvischat`. The actual password comes from `/home/gramps/.jc_amqp_secret` — read it at startup if the env var is not set. - `AMQP_RECONNECT_DELAY` — seconds between reconnect attempts, default 5 - `AMQP_EXCHANGE_ADMIN` — `jc.admin` - `AMQP_EXCHANGE_SYSTEM` — `jc.system` **Create `amqp.py`** in the project root: ```python # Manages a single persistent aio-pika connection and channel. # Provides: # connect() -> None # establish connection, declare exchanges # disconnect() -> None # graceful close # get_channel() # returns current channel, reconnects if needed # publish(exchange, routing_key, payload: dict) -> None # # publishes JSON-serialized payload as persistent message ``` Connection must: - Reconnect automatically on disconnect with `AMQP_RECONNECT_DELAY` backoff - Log connection events at INFO level - Not raise on publish if disconnected — log error and return (fire-and-forget, jC must not crash if RabbitMQ is down) **Start AMQP connection in `app.py` lifespan** after `assess_hardware()`. Disconnect in lifespan cleanup. **Write `tests/test_amqp.py`** covering: - `publish()` with mocked aio-pika connection — assert message published with correct exchange and routing key - `publish()` when disconnected — assert no exception raised, error logged - `get_channel()` when connection is None — assert reconnect attempted Mock all aio-pika calls via monkeypatch. Do not require a live RabbitMQ instance in tests. Run full test suite. All existing tests must continue to pass. --- ## TASK 11 — Roadmap N3: Cluster Protocol & Registration Handler (Coordinator Side) jC on the coordinator must listen for seven message types across `jc.admin` and `jc.system`, maintain the cluster registry, and expose an application-level event log. ### 11.1 AMQP Protocol — Message Catalog All payloads are JSON, published as persistent messages. | Direction | Exchange | Routing Key | Message Type | Description | |-----------|----------|-------------|-------------|-------------| | Worker → Coordinator | `jc.admin` | `node.{name}.register` | register | Worker requests admission | | Worker → Coordinator | `jc.admin` | `node.{name}.deregister` | deregister | Worker signals graceful departure | | Coordinator → Worker | `jc.admin` | `node.{name}.admitted` | admitted | Coordinator grants admission | | Coordinator → Worker | `jc.admin` | `node.{name}.rejected` | rejected | Coordinator denies admission (with reason) | | Coordinator → Worker | `jc.admin` | `node.{name}.hb_query` | hb_query | Coordinator requests on-demand heartbeat | | Any → Coordinator | `jc.system` | `node.{name}.heartbeat` | heartbeat | Periodic or on-demand health report | | Any → Coordinator | `jc.system` | `node.{name}.event` | event | Application-level syslog event | | Any → All | `jc.system` | `cluster.coordinator.query` | coord_query | Anyone asks "who is coordinator?" | | Coordinator → All | `jc.system` | `cluster.coordinator.response` | coord_response | Coordinator announces itself | ### 11.2 Payload Schemas **register** (worker → coordinator): ```json { "node_name": "jarvis", "node_type": "worker", "ip": "192.168.50.210", "capabilities": { "gpu": true, "gpu_type": "amd", "vram_mb": 8192, "cpu_cores": 8, "ram_gb": 16 }, "active_model": { "name": "llama3.1:latest", "quant": "Q4_K_M", "port": 8081 }, "inventory": [ {"name": "llama3.1:latest", "quant": "Q4_K_M", "port": 8081} ], "status": "active" } ``` **deregister** (worker → coordinator): ```json { "node_name": "jarvis", "reason": "shutdown", "timestamp": "2026-07-06T12:00:00Z" } ``` **hb_query** (coordinator → worker): ```json { "from": "ultron", "type": "hb_query", "timestamp": "2026-07-06T12:00:00Z" } ``` Worker responds by publishing a heartbeat on `jc.system` with routing key `node.{name}.heartbeat`. **heartbeat** (worker → coordinator): ```json { "node_name": "jarvis", "status": "active", "active_model": {"name": "llama3.1:latest", "port": 8081}, "load": {"cpu_pct": 45, "ram_pct": 62, "vram_pct": 38}, "timestamp": "2026-07-06T12:00:00Z" } ``` **coord_query** (any → `cluster.coordinator.query`): ```json {"type": "coord_query", "timestamp": "2026-07-06T12:00:00Z"} ``` Coordinator responds on `cluster.coordinator.response`: ```json { "coordinator_node": "ultron", "cluster_nodes": ["jarvis"], "timestamp": "2026-07-06T12:00:00Z" } ``` **event** (worker → coordinator): ```json { "node_name": "jarvis", "event_type": "model_loaded", "severity": "info", "message": "llama-server started with model llama3.1:latest", "details": {"model": "llama3.1:latest", "port": 8081, "pid": 1234}, "timestamp": "2026-07-06T12:00:00Z" } ``` Severity levels: `info`, `warn`, `error`, `critical`. Coordinator stores in in-memory event log. ### 11.3 Design — Status Transitions Drive the Event Log All admin-level events are *derived* from `register()` and `deregister()` as side effects. There are no separate message types for coordinator election, node staleness, quarantine, or release — those are status transitions that `register()`/`deregister()` emit into `CLUSTER_EVENTS` locally. **Node status lifecycle:** ``` UNKNOWN ──register()──▶ active ──deregister()──▶ (removed) │ hb timeout│(coordinator publishes │ deregister on its behalf) ▼ (removed) ``` **Coordinator status lifecycle:** ``` NONE ──register(node_type=coordinator)──▶ CLUSTER_COORDINATOR set │ deregister()│or timeout ▼ CLUSTER_COORDINATOR cleared ``` **Event categories — two buckets, no granular types:** | Category | When | severity | |----------|------|----------| | `cluster` | Node lifecycle, coordinator changes, model swaps, node offline — everything on `jc.admin` | `info` / `warn` / `error` | | `application` | Worker syslog events (incoming on `jc.system` `node.*.event`) | `info` / `warn` / `error` / `critical` | Every `_push_event()` call uses one of these two categories. The `message` field carries the human-readable detail — no need for event type strings. The reporting tool filters by category + severity. **Channel split:** | Exchange | Event scope | |----------|-------------| | `jc.admin` | Cluster nervous system — `register`, `deregister`, `admitted`, `rejected`, `hb_query` | | `jc.system` | Application events — `heartbeat`, `event` (syslog), `coord_query`/`coord_response` | ### 11.4 Implementation **Add to `amqp.py`:** ```python async def subscribe(exchange, routing_key, callback) -> None # Declare a queue, bind to exchange/routing_key, consume with callback ``` If `subscribe` is called before AMQP is connected, queue the subscription and apply it after `connect()` succeeds. The auto-reconnect path must re-bind all active subscriptions. **Create `cluster.py`** in the project root: ```python # In-memory cluster registry + event log # Survives only while jC is running (not persisted) CLUSTER_NODES: dict[str, NodeRecord] CLUSTER_EVENTS: deque[EventRecord] # bounded at 1000 entries CLUSTER_COORDINATOR: str | None # node_name of active coordinator # NodeRecord fields: # node_name, node_type, ip, status, active_model, inventory, # capabilities: {gpu, gpu_type, vram_mb, cpu_cores, ram_gb} # registered_at, last_seen # EventRecord: # category: str ("cluster" | "application") # severity: str ("info" | "warn" | "error" | "critical") # node_name: str # message: str # details: dict | None # timestamp: str def _push_event(category, severity, node_name, message, details=None) -> None # Append EventRecord to CLUSTER_EVENTS, pop left if > 1000 async def handle_registration(message) -> None # Parse payload, validate required fields (node_name, node_type, ip, active_model, inventory) # Reject if node_name duplicate and CLUSTER_NODES[node_name].status == "active" # If CLUSTER_COORDINATOR is None AND node_type == "coordinator": # set CLUSTER_COORDINATOR = node_name # _push_event("cluster", "info", node_name, "elected coordinator") # publish cluster.coordinator.response on jc.system {coordinator_node, cluster_nodes, timestamp} # Add node to CLUSTER_NODES with status="active" # _push_event("cluster", "info", node_name, f"admitted as {node_type}") # publish admitted on jc.admin node.{name}.admitted {node_name, timestamp, amqp_url} async def handle_deregistration(message) -> None # Parse payload (node_name, reason, timestamp) # If node_name == CLUSTER_COORDINATOR: # clear CLUSTER_COORDINATOR # _push_event("cluster", "warn", node_name, f"coordinator lost — {reason}") # _push_event("cluster", "info", node_name, f"departed — {reason}") # Remove node from CLUSTER_NODES, log it async def handle_heartbeat(message) -> None # Parse: node_name, status, active_model, load, timestamp # If node in CLUSTER_NODES: update last_seen, status, active_model # If node unknown: log warning, do NOT auto-admit async def handle_event(message) -> None # Parse: node_name, event_type, severity, message, details, timestamp # Append to CLUSTER_EVENTS (pop left if > 1000) async def handle_coordinator_query(message) -> None # Respond on jc.system cluster.coordinator.response # Payload: {coordinator_node, cluster_nodes: list(CLUSTER_NODES.keys()), timestamp} def get_cluster_state() -> dict # Return: {nodes: CLUSTER_NODES, coordinator: CLUSTER_COORDINATOR, # events: last 50 CLUSTER_EVENTS} ``` **Subscribe in `app.py` lifespan** after AMQP connects: | Exchange | Routing Key | Handler | |----------|-------------|---------| | `jc.admin` | `node.*.register` | `handle_registration` | | `jc.admin` | `node.*.deregister` | `handle_deregistration` | | `jc.system` | `node.*.heartbeat` | `handle_heartbeat` | | `jc.system` | `node.*.event` | `handle_event` | | `jc.system` | `cluster.coordinator.query` | `handle_coordinator_query` | ### 11.5 API — `GET /api/cluster` New router `routers/cluster.py`: - `GET /api/cluster` — returns full cluster state: `{nodes, coordinator, events}` (last 50 events). No auth required. Wire `cluster.router` into `app.py`. ### 11.6 Tests — `tests/test_cluster.py` Mock all aio-pika calls. Do not require live RabbitMQ. | # | Test | What it asserts | |---|------|-----------------| | 1 | Valid worker registration | Node admitted, CLUSTER_NODES updated, `cluster` event logged, `admitted` message published | | 2 | First coordinator auto-promotion | CLUSTER_COORDINATOR set, `cluster` event with "elected" message, `coord_response` published | | 3 | Duplicate node name rejected | `rejected` message with reason=`duplicate_node_name`, `cluster` event logged | | 4 | Malformed payload rejected | `rejected` message with reason=`malformed_payload` | | 5 | Graceful deregistration | Node removed, `cluster` event logged. If coordinator: CLUSTER_COORDINATOR cleared | | 6 | Heartbeat updates last_seen | Known node's last_seen advances | | 7 | Heartbeat from unknown node | Warning logged, node NOT added | | 8 | Event stored in log | Event appended to CLUSTER_EVENTS; at 1001 entries the oldest is popped | | 9 | Coordinator query produces response | Response published with coordinator name and node list | | 10 | GET /api/cluster shape | Response contains `nodes`, `coordinator`, `events` keys | Run full test suite. All existing tests must continue to pass. --- ## TASK 12 — Roadmap N4: Worker Node Registration Publisher (Jarvis Side) This task creates the worker node AMQP client that runs on jarvis (192.168.50.210). It is a standalone Python script — not part of the jC FastAPI app — that runs as a systemd service on jarvis. **Create `node_agent/agent.py`** in the repo (new directory): The agent: 1. On start: reads local config from `/etc/jc-node-agent.conf` (INI format): - `node_name` — hostname, default from `socket.gethostname()` - `node_ip` — LAN IP, default from socket - `amqp_url` — RabbitMQ URL on ultron, e.g. `amqp://jarvischat:password@192.168.50.108:5672/jarvischat` - `llama_port` — port llama-server/llama-rpc is listening on, default 8081 - `models_dir` — path to GGUF model files, default `/home/gramps/models` - `active_model` — filename of currently active model (without path) 2. Discovers inventory by globbing `models_dir` for `*.gguf` files and parsing name/version/quant from filename using regex pattern: `{name}-{version}-{quant}.gguf` where quant matches `Q[0-9]+_K_[A-Z]+` or similar standard suffixes. 3. Publishes registration request to `jc.admin` exchange, routing key `node.{node_name}.register`: ```json { "node_name": "jarvis", "ip": "192.168.50.210", "active_model": {"name": "...", "version": "...", "quant": "...", "path": "...", "port": 8081}, "inventory": [...] } ``` 4. Listens for response on `jc.admin`, routing key `node.{node_name}.admitted` or `node.{node_name}.rejected`. Logs result. If rejected, exits with error. 5. After admission: publishes heartbeat every 30 seconds to `jc.system`, routing key `node.{node_name}.heartbeat`: ```json {"node_name": "...", "ip": "...", "active_model": "...", "timestamp": "..."} ``` 6. Listens on `jc.admin`, routing key `node.{node_name}.cmd.swap_model`: - Payload: `{model_filename: str}` - Stops current llama-server: `systemctl stop llama-server` - Updates `/etc/jc-node-agent.conf` active_model field - Starts llama-server: `systemctl start llama-server` (assumes service reads active_model from conf or ExecStart is updated) - Waits for llama-server to be healthy: poll `http://localhost:{llama_port}/v1/models` every 2s, timeout 120s - Publishes to `jc.system`, routing key `node.{node_name}.model_ready`: ```json {"node_name": "...", "active_model": "...", "port": ..., "timestamp": "..."} ``` - If startup fails within timeout: publishes `node.{node_name}.model_failed` with error detail **Create `node_agent/requirements.txt`:** `aio-pika>=9.0.0` **Document `/etc/jc-node-agent.conf` format** in a comment block at the top of `agent.py`. **Write `tests/test_node_agent.py`** covering: - Registration payload construction from config + model discovery — assert correct JSON shape - Model swap command handler: success path — assert systemctl calls made, model_ready published - Model swap command handler: timeout path — assert model_failed published - Heartbeat: assert published every interval (mock asyncio.sleep) Mock all aio-pika, subprocess, and httpx calls. **Do not create a systemd service file in this task** — that is a manual deployment step. Document the required service configuration in a comment at the bottom of `agent.py`. Run full test suite. All existing tests must continue to pass. --- ## TASK 13 — Roadmap N5: Query Routing via AMQP + Phi-4-mini Triage This task wires the cluster into jC's chat flow. When a query arrives at `/api/chat`, instead of always routing to the hardcoded `LLAMA_SERVER_BASE`, jC now routes to the best available cluster node based on query context. **Prerequisites:** Tasks 9–12 complete. At least one worker node admitted to cluster. **Install Phi-4-mini on ultron (infrastructure step):** - Download `Phi-4-mini-Instruct-Q4_K_M.gguf` from HuggingFace using `hf download microsoft/Phi-4-mini-instruct --include "*.Q4_K_M.gguf" --local-dir /home/gramps/models` - Create `/etc/systemd/system/llama-server-triage.service` — same pattern as existing llama-server service but: port 8083, model path points to Phi-4-mini GGUF, no `--rpc` flag (runs entirely on ultron CPU/iGPU), description `Llama.cpp Server (Phi-4-mini — triage/routing)` - `systemctl daemon-reload && systemctl enable llama-server-triage && systemctl start llama-server-triage` - Verify: `curl -s http://localhost:8083/v1/models` **Add to `config.py`:** - `TRIAGE_BASE` — `http://127.0.0.1:8083/v1` (Phi-4-mini) - `TRIAGE_TIMEOUT` — 10 seconds - `FALLBACK_TO_DEFAULT` — True (if triage fails or no nodes available, fall back to `LLAMA_SERVER_BASE`) **Create `triage.py`** in the project root: ```python async def classify_query(query: str) -> str # Sends query to Phi-4-mini at TRIAGE_BASE with a classification system prompt. # System prompt instructs model to respond with ONLY one of: # "general", "code", "search", "rag" # Returns the classification string. # Timeout: TRIAGE_TIMEOUT seconds. # On any error: returns "general" (fail-safe). async def select_node(classification: str) -> dict | None # Consults CLUSTER_NODES from cluster.py # For "code": prefer nodes where active_model name contains "coder" or "qwen" # For "general": prefer nodes where active_model name contains "mistral" or "llama" # For "search" or "rag": return None (handled locally by jC) # If no matching node found: return None (triggers FALLBACK_TO_DEFAULT) # Returns NodeRecord dict for selected node, or None async def get_inference_url(query: str) -> str # Combines classify_query + select_node # Returns full base URL: f"http://{node.ip}:{node.active_model.port}/v1" # Falls back to LLAMA_SERVER_BASE if classification=search/rag, no nodes, or triage error ``` **Update `routers/chat.py`:** - Replace the hardcoded `LLAMA_SERVER_BASE` reference with a call to `get_inference_url(user_message)` - The rest of the chat flow (RAG, memory, streaming) is unchanged — only the inference target URL changes **Write `tests/test_triage.py`** covering: - `classify_query()` returns valid classification — mock Phi-4-mini response - `classify_query()` on timeout — assert returns "general", no exception - `select_node("code")` with coder node in cluster — assert correct node returned - `select_node("general")` with no matching node — assert None returned - `get_inference_url()` with code query and coder node available — assert returns node URL - `get_inference_url()` with no nodes in cluster — assert returns LLAMA_SERVER_BASE fallback **Update `tests/test_chat_streaming_and_memory_paths.py`:** - Mock `triage.get_inference_url` to return a fixed URL in all existing tests so they continue to pass without a live cluster Run full test suite. All existing tests must continue to pass. --- ## TASK 14 — Roadmap N6: Model Swap Command Flow This task implements the ultron-side logic for requesting a model swap on a worker node when the ideal model is not currently active. **Add to `cluster.py`:** ```python async def request_model_swap(node_name: str, model_filename: str) -> bool # Publishes to jc.admin exchange, routing key node.{node_name}.cmd.swap_model # Payload: {model_filename, requested_at: iso_timestamp} # Sets node status to "swapping" in CLUSTER_NODES # Returns True if message published successfully async def handle_model_ready(message) -> None # Handles node.{node_name}.model_ready from jc.system # Updates CLUSTER_NODES[node_name].active_model to the new model # Sets node status back to "active" # Logs swap completion with timing async def handle_model_failed(message) -> None # Handles node.{node_name}.model_failed from jc.system # Sets node status to "error" in CLUSTER_NODES # Logs failure with detail from message payload ``` **Subscribe in `app.py` lifespan:** - `jc.system` exchange, routing key `node.*.model_ready` → `handle_model_ready` - `jc.system` exchange, routing key `node.*.model_failed` → `handle_model_failed` **Update `triage.py` `select_node()`:** - If the best-matching node exists but its active_model does not match the ideal model for the classification, AND the node status is "active" (not already swapping): - Call `request_model_swap(node_name, ideal_model_filename)` - Return None (triggers fallback) — the swap happens async, next query will find the right model active - If node status is "swapping": return None (fallback, swap in progress) **Update `GET /api/cluster`** to include node status in response. **Write `tests/test_model_swap.py`** covering: - `request_model_swap()` — assert swap command published, node status set to "swapping" - `handle_model_ready()` — assert active_model updated, status set to "active" - `handle_model_failed()` — assert status set to "error" - `select_node()` with mismatched active model — assert swap requested, None returned - `select_node()` with node status "swapping" — assert None returned without publishing another swap Run full test suite. All existing tests must continue to pass. --- ## TASK 15 — Roadmap N7: Cluster Status UI Surface cluster awareness in the jC frontend (`templates/index.html`). **Add a cluster status panel** to the UI. Requirements: - Small status bar or collapsible panel, visible but unobtrusive - Polls `GET /api/cluster` every 15 seconds - For each admitted node: show node name, active model name, and a colored status dot: - Green: active - Yellow: swapping - Red: error or offline (not seen in last 60 seconds based on last_seen timestamp) - If no nodes in cluster (empty): show "No worker nodes connected" - Panel must not interfere with chat input or conversation list **Update `GET /api/cluster` response** to include `last_seen` per node and a `status` field (`active`, `swapping`, `error`). **Update heartbeat handling in `cluster.py`:** add a handler for `node.*.heartbeat` on `jc.system` that updates `last_seen` timestamp for the node. **Subscribe in `app.py` lifespan:** - `jc.system` exchange, routing key `node.*.heartbeat` → `handle_heartbeat` **Add `handle_heartbeat()` to `cluster.py`:** - Updates `CLUSTER_NODES[node_name].last_seen` to current timestamp - If node was previously marked offline (not in CLUSTER_NODES), log re-registration warning but do not auto-admit — full registration required **Write `tests/test_cluster_heartbeat.py`** covering: - `handle_heartbeat()` for known node — assert last_seen updated - `handle_heartbeat()` for unknown node — assert no crash, warning logged, node not added Run full test suite. All 26+ existing tests must continue to pass. Commit all changes introduced across Tasks 9–15 with message: `feat: Roadmap N — AMQP cluster nervous system complete` --- ## Backlog (Post-Roadmap N) ⏳ ### B1 — Context loss in follow-up questions **Symptom:** After asking "in {context}, explain {b}", a follow-up "what is {b}'s {x}?" gets a non-sequitur response that ignores the original context. **Diagnosis:** `build_system_prompt()` is called fresh per-request with new RAG/memory results keyed to the current message text. These can change between turns and may dilute or override the conversation history. The original system prompt used for turn 1 (including its RAG context) is not stored in the DB — only user/assistant messages are. The inference server receives a different system prompt each turn. **Possible fixes:** - Store the assembled system prompt with each assistant message in the DB - When replaying history, re-send the original system prompts from DB rather than rebuilding - Or: cap RAG/memory injection to only fire on the first message of a conversation, then rely solely on conversation history for follow-ups - Check that llama-server isn't truncating history due to context window overflow (Mistral-Nemo 12B = 128K context, unlikely) ### B2 — Bang-prefixed search routing **Spec:** If a query begins with `!`, route to SearXNG search instead of local inference. **Where:** In `routers/chat.py` `chat()` handler, after `user_message` is extracted. Strip the `!`, set a flag to always trigger auto-search regardless of perplexity/refusal. **Change:** Add a `force_search` flag when `user_message.startswith("!")`, strip the prefix from the message saved to DB, and route directly to the search+summarize path. ### B3 — Docker distribution (v1.0 gate) **Goal:** Ship jarvisChat as a `docker compose` stack so a single command stands up everything. **Services to containerize:** - jarvisChat (FastAPI app + SQLite) - SearXNG - Qdrant - RabbitMQ - llama-server (with optional RPC sidecar for GPU offload) - Ollama (embeddings) **Also needed:** - `Dockerfile` for the jarvisChat app itself - `docker-compose.yml` with all services, volumes, networks, env vars - Setup wizard script (run on first boot) that: - Probes CPU vs GPU (reuses `hardware.py`) - Queries user for admin PIN, node name, IP - Generates `.env` file with correct `LLAMA_SERVER_BASE`, `EMBED_URL`, etc. - Auto-calculates `RAG_MAX_VECTORS` from available RAM: `max(1000, int(available_ram_gb * 100_000))` - Optionally detects and configures RPC GPU offload - Manual install docs remain alongside for bare-metal deployment **This task is only actionable after Tasks 8–15 (RAG eviction + AMQP cluster) are complete.**