Rename: jarvisChat → cAIc (product name)
- jarvisChat/JarvisChat/jarvischat → cAIc/cAIc/caic (branded/lower) - JARVISCHAT_ env vars → CAIC_ - jc- script/config prefix → caic- - jarvis_rag → caic_rag - jarvischat.db / volumes → caic.db / caic_* - AMQP vhost/user jarvischat → caic - Syslog, loggers, docstrings all updated - 47 files, zero stale references, 148 tests pass
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gramps
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# Developer Architecture Guide
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This document explains how JarvisChat is structured, the external services it integrates with, and the key architectural changes made during development.
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This document explains how cAIc is structured, the external services it integrates with, and the key architectural changes made during development.
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## 1. System Overview
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JarvisChat is a single-process FastAPI service with a Jinja2 frontend and SQLite persistence. It connects to an external llama-server for inference and optionally to SearXNG (web search), Qdrant (vector search), and RabbitMQ (AMQP cluster messaging).
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cAIc is a single-process FastAPI service with a Jinja2 frontend and SQLite persistence. It connects to an external llama-server for inference and optionally to SearXNG (web search), Qdrant (vector search), and RabbitMQ (AMQP cluster messaging).
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### 1.1 Module Layout
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| Service | Required | Port | Purpose |
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|---------|----------|------|---------|
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| llama-server (ultron) | Yes | 8081 | LLM inference (OpenAI-compat), RPC offload to jarvis:50052 |
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| llama-server (coordinator) | Yes | 8081 | LLM inference (OpenAI-compat), RPC offload to worker:50052 |
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| SearXNG | No | 8888 | Privacy-respecting web search |
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| Qdrant (ultron) | No | 6333 | Vector database for RAG |
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| Ollama (jarvis) | No | 11434 | Embeddings for RAG chunk vectors |
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| RabbitMQ (ultron) | No | 5672 | AMQP broker for cluster messaging |
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| Qdrant (coordinator) | No | 6333 | Vector database for RAG |
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| Ollama (worker) | No | 11434 | Embeddings for RAG chunk vectors |
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| RabbitMQ (coordinator) | No | 5672 | AMQP broker for cluster messaging |
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| rocm-smi | No | — | AMD GPU stats (host-level) |
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### 1.3 Config Discovery
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| Variable | Default | Service |
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|----------|---------|---------|
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| `LLAMA_SERVER_BASE` | `http://192.168.50.108:8081` | llama-server on ultron |
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| `LLAMA_SERVER_BASE` | `http://192.168.50.108:8081` | llama-server on coordinator |
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| `OLLAMA_BASE` | `http://localhost:11434` | Legacy — all inference goes through LLAMA_SERVER_BASE |
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| `SEARXNG_BASE` | `http://localhost:8888` | SearXNG |
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| `QDRANT_URL` | `http://192.168.50.108:6333` | Qdrant on ultron |
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| `JARVISCHAT_AMQP_URL` | `amqp://jarvischat:password@localhost:5672/jarvischat` | RabbitMQ |
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| `QDRANT_URL` | `http://192.168.50.108:6333` | Qdrant on coordinator |
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| `CAIC_AMQP_URL` | `amqp://caic:password@localhost:5672/caic` | RabbitMQ |
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## 2. Request/Response Architecture
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1. Bearer token auth (same key as completions API)
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2. Chunk text via shared `chunk_text()` helper (512-token chunks, 128-token overlap)
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3. Embed via Ollama `/api/embeddings`
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4. Upsert to Qdrant collection `jarvis_rag`
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4. Upsert to Qdrant collection `caic_rag`
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5. Trigger `maybe_evict()` if collection exceeds high-water mark
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### 2.4 Upload Pipeline (`/api/upload`)
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- Admin PIN hashed with PBKDF2-HMAC-SHA256 + salt
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- Failed PIN attempts tracked per client IP (max 5, 300s lockout)
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- Default PIN allowed only if JARVISCHAT_ALLOW_DEFAULT_PIN=true
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- Default PIN allowed only if CAIC_ALLOW_DEFAULT_PIN=true
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### 4.3 Browser and API Abuse Controls
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### 5.1 Vector Search
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- Qdrant collection `jarvis_rag` on ultron:6333
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- Embeddings via Ollama on jarvis:11434 (`/api/embeddings`)
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- Qdrant collection `caic_rag` on coordinator:6333
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- Embeddings via Ollama on worker:11434 (`/api/embeddings`)
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- Shared `chunk_text(text, chunk_size=512, overlap=128)` helper in rag.py
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- Upload and ingest endpoints share the same chunk+embed+upsert pipeline
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### 6.1 Design Model: Broker-Mediated
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JarvisChat uses a **broker-mediated** cluster design. This is the preferred architecture and is reflected in all implementation decisions below.
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cAIc uses a **broker-mediated** cluster design. This is the preferred architecture and is reflected in all implementation decisions below.
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**How it works:**
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- A single RabbitMQ broker (or clustered set of brokers) acts as the central nervous system
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| **RabbitMQ server** | Required — hosts the broker | Not required — connects as AMQP client only |
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| **RabbitMQ client (aio-pika)** | Required — publishes commands, consumes events | Required — consumes commands, publishes events |
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| **FastAPI / uvicorn** | Required | Not needed |
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| **SQLite** | Required — owns jarvischat.db | Not needed |
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| **SQLite** | Required — owns caic.db | Not needed |
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| **Qdrant** | Optional (recommended) — vector DB for RAG | Not needed |
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| **SearXNG** | Optional — web search | Not needed |
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| **llama-server** | Optional — can share its own GPU for inference | Required — this is why the worker exists |
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```
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Coordinator Worker(s)
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┌────────────────────┐ ┌─────────────────────────┐
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│ jarvisChat │ │ llama-server │
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│ cAIc │ │ llama-server │
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│ (FastAPI + SQLite)│ │ (inference) │
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│ RabbitMQ server │◄──AMQP───────│ aio-pika (agent) │
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│ SearXNG (opt) │ persistent │ ROCm / CUDA (if GPU) │
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Pending implementation (Tasks 10–15):
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- `amqp.py` — aio-pika connection manager with reconnect
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- Node agent on jarvis — registration, heartbeat, command consumer
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- Node agent on worker — registration, heartbeat, command consumer
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- `triage.py` — Phi-4-mini query classification (general/code/search/rag)
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- Dynamic model swap via llama-server RPC
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