diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..2dc1a7d
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,73 @@
+# ─── Arcádia Suite — Variáveis de Ambiente ────────────────────────────────────
+# Copie para .env e preencha os valores.
+# NUNCA commite o arquivo .env real.
+
+# ── Banco de dados ────────────────────────────────────────────────────────────
+DATABASE_URL=postgresql://arcadia:arcadia123@localhost:5432/arcadia
+PGHOST=localhost
+PGPORT=5432
+PGUSER=arcadia
+PGPASSWORD=arcadia123
+PGDATABASE=arcadia
+
+# ── Aplicação ─────────────────────────────────────────────────────────────────
+NODE_ENV=development
+PORT=5000
+SESSION_SECRET=troque-por-string-aleatoria-segura-em-producao
+SSO_SECRET=arcadia-sso-secret-2024-plus-integration-key-secure
+
+# ── Docker Mode (desativa spawn de processos filhos) ─────────────────────────
+# Em produção com Docker, defina como "true"
+DOCKER_MODE=false
+
+# ── URLs dos microserviços Python ─────────────────────────────────────────────
+# Em Docker: use nome do serviço (ex: http://contabil:8003)
+# Em desenvolvimento local: use localhost
+CONTABIL_PYTHON_URL=http://localhost:8003
+BI_PYTHON_URL=http://localhost:8004
+AUTOMATION_PYTHON_URL=http://localhost:8005
+FISCO_PYTHON_URL=http://localhost:8002
+PYTHON_SERVICE_URL=http://localhost:8001
+
+# ── IA — OpenAI ───────────────────────────────────────────────────────────────
+# Deixe vazio se usar apenas Ollama (soberania total)
+OPENAI_API_KEY=
+
+# ── IA — LiteLLM (proxy unificado) ───────────────────────────────────────────
+# Em Docker: http://litellm:4000 | Em dev local: http://localhost:4000
+LITELLM_BASE_URL=http://localhost:4000
+LITELLM_API_KEY=arcadia-internal
+
+# ── IA — Ollama (LLMs locais) ─────────────────────────────────────────────────
+OLLAMA_BASE_URL=http://localhost:11434
+
+# ── Open WebUI ────────────────────────────────────────────────────────────────
+WEBUI_SECRET_KEY=troque-por-string-aleatoria-segura
+
+# ── Arcádia Plus (Laravel/Fiscal) ─────────────────────────────────────────────
+PLUS_URL=http://localhost:8080
+PLUS_PORT=8080
+PLUS_API_TOKEN=
+
+# ── Superset (BI avançado) ────────────────────────────────────────────────────
+SUPERSET_SECRET_KEY=troque-por-string-aleatoria-segura
+SUPERSET_PORT=8088
+
+# ── Redis ─────────────────────────────────────────────────────────────────────
+REDIS_URL=redis://localhost:6379
+
+# ── Domínio (produção) ────────────────────────────────────────────────────────
+DOMAIN=seudominio.com.br
+
+# ── Integrações externas (opcional) ──────────────────────────────────────────
+# ERPNext
+ERPNEXT_URL=
+ERPNEXT_API_KEY=
+ERPNEXT_API_SECRET=
+
+# GitHub (para sync de código)
+GITHUB_TOKEN=
+GITHUB_REPO=
+
+# WhatsApp (Baileys — sessões salvas no banco)
+# Nenhuma variável adicional necessária — configurado via interface
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..ddb0688
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,33 @@
+# ─── Stage 1: Build ───────────────────────────────────────────────────────────
+FROM node:20-alpine AS builder
+
+WORKDIR /app
+
+COPY package*.json ./
+RUN npm ci --ignore-scripts
+
+COPY . .
+RUN npm run build
+
+# ─── Stage 2: Production ──────────────────────────────────────────────────────
+FROM node:20-alpine AS runner
+
+WORKDIR /app
+
+ENV NODE_ENV=production
+
+# Deps de produção apenas
+COPY package*.json ./
+RUN npm ci --omit=dev --ignore-scripts
+
+# Artefatos do build
+COPY --from=builder /app/dist ./dist
+COPY --from=builder /app/shared ./shared
+COPY --from=builder /app/migrations ./migrations
+
+EXPOSE 5000
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
+  CMD wget -qO- http://localhost:5000/api/health || exit 1
+
+CMD ["node", "dist/index.cjs"]
diff --git a/Dockerfile.python b/Dockerfile.python
new file mode 100644
index 0000000..a5127df
--- /dev/null
+++ b/Dockerfile.python
@@ -0,0 +1,47 @@
+# Dockerfile compartilhado para todos os microserviços Python
+# Uso: docker build -f Dockerfile.python --build-arg SERVICE=contabil .
+# Ou via docker-compose com a variável SERVICE_NAME
+
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Dependências de sistema necessárias para psycopg2, signxml e playwright
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    libpq-dev \
+    libxml2-dev \
+    libxslt-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Instalar dependências Python
+COPY pyproject.toml ./
+RUN pip install --no-cache-dir ".[all]" 2>/dev/null || pip install --no-cache-dir \
+    fastapi>=0.128.0 \
+    uvicorn>=0.40.0 \
+    psycopg2-binary>=2.9.11 \
+    pandas>=2.3.3 \
+    numpy>=2.4.1 \
+    pydantic>=2.12.5 \
+    python-dotenv \
+    requests \
+    httpx
+
+# Copiar código dos serviços
+COPY server/python/ ./server/python/
+COPY shared/ ./shared/
+
+# SERVICE_NAME deve ser: contabil | bi | automation | fisco | people
+ENV SERVICE_NAME=contabil
+ENV SERVICE_PORT=8000
+
+# Script de entrada que seleciona o serviço correto
+COPY docker/python-entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+EXPOSE ${SERVICE_PORT}
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=20s --retries=3 \
+  CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:${SERVICE_PORT}/health')" || exit 1
+
+ENTRYPOINT ["/entrypoint.sh"]
diff --git a/docker-compose.prod.yml b/docker-compose.prod.yml
new file mode 100644
index 0000000..732542d
--- /dev/null
+++ b/docker-compose.prod.yml
@@ -0,0 +1,212 @@
+# ─── Arcádia Suite — Produção (Coolify) ───────────────────────────────────────
+# Este arquivo é usado pelo Coolify para deploy automático.
+# NÃO inclui volumes de código-fonte — só artefatos de build.
+# Configurar no Coolify: Environment Variables para todas as vars ${...}
+
+name: arcadia-prod
+
+services:
+
+  # ── Banco de dados com pgvector ─────────────────────────────────────────────
+  db:
+    image: pgvector/pgvector:pg16
+    restart: always
+    environment:
+      POSTGRES_DB: ${PGDATABASE:-arcadia}
+      POSTGRES_USER: ${PGUSER:-arcadia}
+      POSTGRES_PASSWORD: ${PGPASSWORD}
+    volumes:
+      - pgdata:/var/lib/postgresql/data
+      - ./docker/init-pgvector.sql:/docker-entrypoint-initdb.d/01-pgvector.sql
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${PGUSER:-arcadia}"]
+      interval: 10s
+      timeout: 5s
+      retries: 10
+    networks:
+      - arcadia-internal
+
+  # ── Redis ────────────────────────────────────────────────────────────────────
+  redis:
+    image: redis:7-alpine
+    restart: always
+    command: redis-server --maxmemory 256mb --maxmemory-policy allkeys-lru
+    volumes:
+      - redis_data:/data
+    networks:
+      - arcadia-internal
+
+  # ── App principal ─────────────────────────────────────────────────────────
+  app:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    restart: always
+    environment:
+      NODE_ENV: production
+      PORT: 5000
+      DATABASE_URL: postgresql://${PGUSER:-arcadia}:${PGPASSWORD}@db:5432/${PGDATABASE:-arcadia}
+      REDIS_URL: redis://redis:6379
+      DOCKER_MODE: "true"
+      CONTABIL_PYTHON_URL: http://contabil:8003
+      BI_PYTHON_URL: http://bi:8004
+      AUTOMATION_PYTHON_URL: http://automation:8005
+      FISCO_PYTHON_URL: http://fisco:8002
+      PYTHON_SERVICE_URL: http://embeddings:8001
+      SESSION_SECRET: ${SESSION_SECRET}
+      SSO_SECRET: ${SSO_SECRET}
+      OPENAI_API_KEY: ${OPENAI_API_KEY:-}
+      LITELLM_BASE_URL: http://litellm:4000
+      LITELLM_API_KEY: ${LITELLM_API_KEY}
+    ports:
+      - "5000:5000"
+    depends_on:
+      db:
+        condition: service_healthy
+      redis:
+        condition: service_started
+    networks:
+      - arcadia-internal
+      - arcadia-public
+    labels:
+      - "traefik.enable=true"
+      - "traefik.http.routers.arcadia.rule=Host(`${DOMAIN}`)"
+      - "traefik.http.routers.arcadia.tls=true"
+      - "traefik.http.routers.arcadia.tls.certresolver=letsencrypt"
+
+  # ── Microserviços Python ─────────────────────────────────────────────────────
+  contabil:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: always
+    environment:
+      SERVICE_NAME: contabil
+      SERVICE_PORT: 8003
+      CONTABIL_PORT: 8003
+      DATABASE_URL: postgresql://${PGUSER:-arcadia}:${PGPASSWORD}@db:5432/${PGDATABASE:-arcadia}
+    depends_on:
+      db:
+        condition: service_healthy
+    networks:
+      - arcadia-internal
+
+  bi:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: always
+    environment:
+      SERVICE_NAME: bi
+      SERVICE_PORT: 8004
+      BI_PORT: 8004
+      DATABASE_URL: postgresql://${PGUSER:-arcadia}:${PGPASSWORD}@db:5432/${PGDATABASE:-arcadia}
+    depends_on:
+      db:
+        condition: service_healthy
+    networks:
+      - arcadia-internal
+
+  automation:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: always
+    environment:
+      SERVICE_NAME: automation
+      SERVICE_PORT: 8005
+      AUTOMATION_PORT: 8005
+      DATABASE_URL: postgresql://${PGUSER:-arcadia}:${PGPASSWORD}@db:5432/${PGDATABASE:-arcadia}
+    depends_on:
+      db:
+        condition: service_healthy
+    networks:
+      - arcadia-internal
+
+  fisco:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: always
+    environment:
+      SERVICE_NAME: fisco
+      SERVICE_PORT: 8002
+      FISCO_PORT: 8002
+      DATABASE_URL: postgresql://${PGUSER:-arcadia}:${PGPASSWORD}@db:5432/${PGDATABASE:-arcadia}
+    depends_on:
+      db:
+        condition: service_healthy
+    networks:
+      - arcadia-internal
+
+  embeddings:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: always
+    environment:
+      SERVICE_NAME: embeddings
+      SERVICE_PORT: 8001
+      DATABASE_URL: postgresql://${PGUSER:-arcadia}:${PGPASSWORD}@db:5432/${PGDATABASE:-arcadia}
+    depends_on:
+      db:
+        condition: service_healthy
+    networks:
+      - arcadia-internal
+
+  # ── LiteLLM (proxy de LLM) ───────────────────────────────────────────────────
+  litellm:
+    image: ghcr.io/berriai/litellm:main-latest
+    restart: always
+    volumes:
+      - ./docker/litellm-config.yaml:/app/config.yaml
+    command: ["--config", "/app/config.yaml", "--port", "4000"]
+    environment:
+      OPENAI_API_KEY: ${OPENAI_API_KEY:-}
+      LITELLM_MASTER_KEY: ${LITELLM_API_KEY}
+    networks:
+      - arcadia-internal
+
+  # ── Ollama (LLMs locais — ativar se o servidor tiver RAM suficiente) ──────────
+  ollama:
+    image: ollama/ollama:latest
+    restart: always
+    volumes:
+      - ollama_models:/root/.ollama
+    networks:
+      - arcadia-internal
+    profiles: [ai]
+
+  # ── Open WebUI ───────────────────────────────────────────────────────────────
+  open-webui:
+    image: ghcr.io/open-webui/open-webui:main
+    restart: always
+    environment:
+      OLLAMA_BASE_URL: http://ollama:11434
+      WEBUI_SECRET_KEY: ${WEBUI_SECRET_KEY}
+    volumes:
+      - open_webui_data:/app/backend/data
+    depends_on:
+      - ollama
+    networks:
+      - arcadia-internal
+      - arcadia-public
+    labels:
+      - "traefik.enable=true"
+      - "traefik.http.routers.webui.rule=Host(`ai.${DOMAIN}`)"
+      - "traefik.http.routers.webui.tls=true"
+      - "traefik.http.routers.webui.tls.certresolver=letsencrypt"
+      - "traefik.http.services.webui.loadbalancer.server.port=8080"
+    profiles: [ai]
+
+networks:
+  arcadia-internal:
+    driver: bridge
+  arcadia-public:
+    driver: bridge
+
+volumes:
+  pgdata:
+  redis_data:
+  ollama_models:
+  open_webui_data:
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..b2d8866
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,233 @@
+# ─── Arcádia Suite — Desenvolvimento Local ────────────────────────────────────
+# Uso: docker compose up
+# Para subir com IA local: docker compose --profile ai up
+
+name: arcadia-dev
+
+services:
+
+  # ── Banco de dados com pgvector ─────────────────────────────────────────────
+  db:
+    image: pgvector/pgvector:pg16
+    restart: unless-stopped
+    environment:
+      POSTGRES_DB: arcadia
+      POSTGRES_USER: arcadia
+      POSTGRES_PASSWORD: arcadia123
+    volumes:
+      - pgdata:/var/lib/postgresql/data
+      - ./docker/init-pgvector.sql:/docker-entrypoint-initdb.d/01-pgvector.sql
+    ports:
+      - "5432:5432"
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U arcadia -d arcadia"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  # ── Redis (filas de jobs) ────────────────────────────────────────────────────
+  redis:
+    image: redis:7-alpine
+    restart: unless-stopped
+    ports:
+      - "6379:6379"
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  # ── App principal (Node.js + React) ─────────────────────────────────────────
+  app:
+    build:
+      context: .
+      dockerfile: Dockerfile
+      target: builder          # usa stage builder em dev (hot reload via volume)
+    restart: unless-stopped
+    command: npx tsx server/index.ts
+    environment:
+      NODE_ENV: development
+      PORT: 5000
+      DATABASE_URL: postgresql://arcadia:arcadia123@db:5432/arcadia
+      REDIS_URL: redis://redis:6379
+      DOCKER_MODE: "true"      # desativa spawn de processos filhos
+      CONTABIL_PYTHON_URL: http://contabil:8003
+      BI_PYTHON_URL: http://bi:8004
+      AUTOMATION_PYTHON_URL: http://automation:8005
+      FISCO_PYTHON_URL: http://fisco:8002
+      PYTHON_SERVICE_URL: http://embeddings:8001
+      SESSION_SECRET: ${SESSION_SECRET:-arcadia-dev-secret-change-in-prod}
+      SSO_SECRET: ${SSO_SECRET:-arcadia-sso-secret-2024-plus-integration-key-secure}
+      OPENAI_API_KEY: ${OPENAI_API_KEY:-}
+      LITELLM_BASE_URL: http://litellm:4000
+      LITELLM_API_KEY: ${LITELLM_API_KEY:-arcadia-internal}
+    ports:
+      - "5000:5000"
+    volumes:
+      - .:/app
+      - /app/node_modules
+      - /app/dist
+    depends_on:
+      db:
+        condition: service_healthy
+      redis:
+        condition: service_healthy
+      contabil:
+        condition: service_started
+      bi:
+        condition: service_started
+
+  # ── Microserviço Contábil (Python) ──────────────────────────────────────────
+  contabil:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: unless-stopped
+    environment:
+      SERVICE_NAME: contabil
+      SERVICE_PORT: 8003
+      CONTABIL_PORT: 8003
+      DATABASE_URL: postgresql://arcadia:arcadia123@db:5432/arcadia
+    ports:
+      - "8003:8003"
+    depends_on:
+      db:
+        condition: service_healthy
+
+  # ── Microserviço BI Engine (Python) ─────────────────────────────────────────
+  bi:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: unless-stopped
+    environment:
+      SERVICE_NAME: bi
+      SERVICE_PORT: 8004
+      BI_PORT: 8004
+      DATABASE_URL: postgresql://arcadia:arcadia123@db:5432/arcadia
+    ports:
+      - "8004:8004"
+    depends_on:
+      db:
+        condition: service_healthy
+
+  # ── Microserviço Automações (Python) ────────────────────────────────────────
+  automation:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: unless-stopped
+    environment:
+      SERVICE_NAME: automation
+      SERVICE_PORT: 8005
+      AUTOMATION_PORT: 8005
+      DATABASE_URL: postgresql://arcadia:arcadia123@db:5432/arcadia
+    ports:
+      - "8005:8005"
+    depends_on:
+      db:
+        condition: service_healthy
+
+  # ── Microserviço Fiscal (Python) ────────────────────────────────────────────
+  fisco:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: unless-stopped
+    environment:
+      SERVICE_NAME: fisco
+      SERVICE_PORT: 8002
+      FISCO_PORT: 8002
+      DATABASE_URL: postgresql://arcadia:arcadia123@db:5432/arcadia
+    ports:
+      - "8002:8002"
+    depends_on:
+      db:
+        condition: service_healthy
+
+  # ── Serviço de Embeddings (pgvector via Python) ──────────────────────────────
+  embeddings:
+    build:
+      context: .
+      dockerfile: Dockerfile.python
+    restart: unless-stopped
+    environment:
+      SERVICE_NAME: embeddings
+      SERVICE_PORT: 8001
+      DATABASE_URL: postgresql://arcadia:arcadia123@db:5432/arcadia
+    ports:
+      - "8001:8001"
+    depends_on:
+      db:
+        condition: service_healthy
+
+  # ── Apache Superset (BI avançado) ────────────────────────────────────────────
+  superset:
+    image: apache/superset:4.1.0
+    restart: unless-stopped
+    environment:
+      SUPERSET_SECRET_KEY: ${SUPERSET_SECRET_KEY:-superset-secret-change-in-prod}
+      DATABASE_URL: postgresql://arcadia:arcadia123@db:5432/arcadia_superset
+    ports:
+      - "8088:8088"
+    depends_on:
+      db:
+        condition: service_healthy
+    profiles: [bi]
+
+  # ─────────────── PERFIL: ai (Soberania de IA) ────────────────────────────────
+
+  # ── Ollama (LLMs locais) ─────────────────────────────────────────────────────
+  ollama:
+    image: ollama/ollama:latest
+    restart: unless-stopped
+    volumes:
+      - ollama_models:/root/.ollama
+    ports:
+      - "11434:11434"
+    profiles: [ai]
+    # Para GPU NVIDIA: adicione `deploy.resources.reservations.devices`
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
+
+  # ── Open WebUI (interface para devs/consultores) ─────────────────────────────
+  open-webui:
+    image: ghcr.io/open-webui/open-webui:main
+    restart: unless-stopped
+    environment:
+      OLLAMA_BASE_URL: http://ollama:11434
+      WEBUI_SECRET_KEY: ${WEBUI_SECRET_KEY:-webui-secret-change-in-prod}
+      DEFAULT_MODELS: llama3.3,qwen2.5-coder
+      ENABLE_RAG_WEB_SEARCH: "true"
+    ports:
+      - "3001:8080"
+    volumes:
+      - open_webui_data:/app/backend/data
+    depends_on:
+      - ollama
+    profiles: [ai]
+
+  # ── LiteLLM (proxy unificado de LLMs) ───────────────────────────────────────
+  litellm:
+    image: ghcr.io/berriai/litellm:main-latest
+    restart: unless-stopped
+    volumes:
+      - ./docker/litellm-config.yaml:/app/config.yaml
+    command: ["--config", "/app/config.yaml", "--port", "4000", "--detailed_debug"]
+    environment:
+      OPENAI_API_KEY: ${OPENAI_API_KEY:-}
+      OLLAMA_BASE_URL: http://ollama:11434
+      LITELLM_MASTER_KEY: ${LITELLM_API_KEY:-arcadia-internal}
+    ports:
+      - "4000:4000"
+    profiles: [ai]
+
+volumes:
+  pgdata:
+  ollama_models:
+  open_webui_data:
diff --git a/docker/init-pgvector.sql b/docker/init-pgvector.sql
new file mode 100644
index 0000000..d47d650
--- /dev/null
+++ b/docker/init-pgvector.sql
@@ -0,0 +1,11 @@
+-- Inicialização do PostgreSQL com extensão pgvector
+-- Executado automaticamente pelo container na primeira inicialização
+
+-- Habilitar extensão pgvector para busca por similaridade de embeddings
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- Habilitar extensão pg_trgm para busca textual rápida
+CREATE EXTENSION IF NOT EXISTS pg_trgm;
+
+-- Habilitar uuid-ossp para geração de UUIDs
+CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
diff --git a/docker/litellm-config.yaml b/docker/litellm-config.yaml
new file mode 100644
index 0000000..30ddfb1
--- /dev/null
+++ b/docker/litellm-config.yaml
@@ -0,0 +1,55 @@
+# LiteLLM — Proxy unificado de LLMs para o Arcádia Suite
+# Documentação: https://docs.litellm.ai/docs/proxy/configs
+
+model_list:
+
+  # ── OpenAI (quando disponível) ───────────────────────────────────────────────
+  - model_name: gpt-4o
+    litellm_params:
+      model: openai/gpt-4o
+      api_key: os.environ/OPENAI_API_KEY
+
+  - model_name: gpt-4o-mini
+    litellm_params:
+      model: openai/gpt-4o-mini
+      api_key: os.environ/OPENAI_API_KEY
+
+  # ── Ollama (LLMs locais — soberania) ─────────────────────────────────────────
+  - model_name: llama3.3
+    litellm_params:
+      model: ollama/llama3.3
+      api_base: os.environ/OLLAMA_BASE_URL
+
+  - model_name: qwen2.5-coder
+    litellm_params:
+      model: ollama/qwen2.5-coder:7b
+      api_base: os.environ/OLLAMA_BASE_URL
+
+  - model_name: deepseek-r1
+    litellm_params:
+      model: ollama/deepseek-r1:7b
+      api_base: os.environ/OLLAMA_BASE_URL
+
+  # ── Modelo padrão: tenta OpenAI, fallback para Ollama ───────────────────────
+  - model_name: arcadia-default
+    litellm_params:
+      model: openai/gpt-4o-mini
+      api_key: os.environ/OPENAI_API_KEY
+    model_info:
+      fallbacks: ["llama3.3"]
+
+router_settings:
+  routing_strategy: least-busy
+  fallbacks:
+    - {"gpt-4o": ["llama3.3"]}
+    - {"gpt-4o-mini": ["qwen2.5-coder"]}
+    - {"arcadia-default": ["llama3.3"]}
+
+litellm_settings:
+  drop_params: true
+  request_timeout: 120
+  set_verbose: false
+
+general_settings:
+  master_key: os.environ/LITELLM_MASTER_KEY
+  database_url: os.environ/DATABASE_URL
diff --git a/docker/python-entrypoint.sh b/docker/python-entrypoint.sh
new file mode 100644
index 0000000..43f2acc
--- /dev/null
+++ b/docker/python-entrypoint.sh
@@ -0,0 +1,51 @@
+#!/bin/sh
+# Entrypoint dos microserviços Python
+# Seleciona o script correto baseado em SERVICE_NAME
+
+set -e
+
+SERVICE_NAME=${SERVICE_NAME:-contabil}
+SERVICE_PORT=${SERVICE_PORT:-8000}
+
+echo "[entrypoint] Iniciando serviço: $SERVICE_NAME na porta $SERVICE_PORT"
+
+case "$SERVICE_NAME" in
+  contabil)
+    exec python -m uvicorn server.python.contabil_service:app \
+      --host 0.0.0.0 \
+      --port "$SERVICE_PORT" \
+      --workers 2
+    ;;
+  bi)
+    exec python -m uvicorn server.python.bi_engine:app \
+      --host 0.0.0.0 \
+      --port "$SERVICE_PORT" \
+      --workers 2
+    ;;
+  automation)
+    exec python -m uvicorn server.python.automation_engine:app \
+      --host 0.0.0.0 \
+      --port "$SERVICE_PORT" \
+      --workers 1
+    ;;
+  fisco)
+    exec python -m uvicorn server.python.fisco_service:app \
+      --host 0.0.0.0 \
+      --port "$SERVICE_PORT" \
+      --workers 2
+    ;;
+  people)
+    exec python -m uvicorn server.python.people_service:app \
+      --host 0.0.0.0 \
+      --port "$SERVICE_PORT" \
+      --workers 2
+    ;;
+  embeddings)
+    exec python server/python/embeddings_service.py
+    ;;
+  *)
+    echo "[entrypoint] ERRO: SERVICE_NAME desconhecido: $SERVICE_NAME"
+    echo "Valores válidos: contabil | bi | automation | fisco | people | embeddings"
+    exit 1
+    ;;
+esac
diff --git a/server/graph/routes.ts b/server/graph/routes.ts
new file mode 100644
index 0000000..68af4f3
--- /dev/null
+++ b/server/graph/routes.ts
@@ -0,0 +1,213 @@
+import { Router, Request, Response } from "express";
+import { insertGraphNodeSchema, insertGraphEdgeSchema, insertKnowledgeBaseSchema } from "@shared/schema";
+import * as graphService from "./service";
+
+const router = Router();
+
+// Middleware de autenticação
+router.use((req: Request, res: Response, next) => {
+  if (!req.isAuthenticated()) {
+    return res.status(401).json({ error: "Não autenticado" });
+  }
+  next();
+});
+
+const tenantId = (req: Request): number | undefined =>
+  (req.user as any)?.tenantId ?? undefined;
+
+// ─── Nodes ────────────────────────────────────────────────────────────────────
+
+router.get("/nodes", async (req: Request, res: Response) => {
+  try {
+    const type = req.query.type as string | undefined;
+    const limit = req.query.limit ? parseInt(req.query.limit as string) : 100;
+    const nodes = await graphService.getNodes(tenantId(req), type, limit);
+    res.json(nodes);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.get("/nodes/:id", async (req: Request, res: Response) => {
+  try {
+    const node = await graphService.getNodeById(parseInt(req.params.id));
+    if (!node) return res.status(404).json({ error: "Nó não encontrado" });
+    res.json(node);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.post("/nodes", async (req: Request, res: Response) => {
+  try {
+    const parsed = insertGraphNodeSchema.safeParse({
+      ...req.body,
+      tenantId: tenantId(req),
+    });
+    if (!parsed.success) {
+      return res.status(400).json({ error: parsed.error.flatten() });
+    }
+    const node = await graphService.createNode(parsed.data);
+    res.status(201).json(node);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.put("/nodes/:id", async (req: Request, res: Response) => {
+  try {
+    const node = await graphService.updateNode(parseInt(req.params.id), req.body);
+    if (!node) return res.status(404).json({ error: "Nó não encontrado" });
+    res.json(node);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.delete("/nodes/:id", async (req: Request, res: Response) => {
+  try {
+    const deleted = await graphService.deleteNode(parseInt(req.params.id));
+    if (!deleted) return res.status(404).json({ error: "Nó não encontrado" });
+    res.json({ success: true });
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+// ─── Batch de Nodes ───────────────────────────────────────────────────────────
+
+router.post("/nodes/batch", async (req: Request, res: Response) => {
+  try {
+    const { nodes } = req.body as { nodes: any[] };
+    if (!Array.isArray(nodes)) {
+      return res.status(400).json({ error: "Campo 'nodes' deve ser um array" });
+    }
+    const created = await Promise.all(
+      nodes.map((n) =>
+        graphService.createNode({ ...n, tenantId: tenantId(req) })
+      )
+    );
+    res.status(201).json(created);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+// ─── Edges ────────────────────────────────────────────────────────────────────
+
+router.get("/edges", async (req: Request, res: Response) => {
+  try {
+    const sourceId = req.query.sourceId ? parseInt(req.query.sourceId as string) : undefined;
+    const targetId = req.query.targetId ? parseInt(req.query.targetId as string) : undefined;
+    const edges = await graphService.getEdges(sourceId, targetId);
+    res.json(edges);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.post("/edges", async (req: Request, res: Response) => {
+  try {
+    const parsed = insertGraphEdgeSchema.safeParse(req.body);
+    if (!parsed.success) {
+      return res.status(400).json({ error: parsed.error.flatten() });
+    }
+    const edge = await graphService.createEdge(parsed.data);
+    res.status(201).json(edge);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.delete("/edges/:id", async (req: Request, res: Response) => {
+  try {
+    const deleted = await graphService.deleteEdge(parseInt(req.params.id));
+    if (!deleted) return res.status(404).json({ error: "Aresta não encontrada" });
+    res.json({ success: true });
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+// ─── Knowledge Base ───────────────────────────────────────────────────────────
+
+router.get("/knowledge", async (req: Request, res: Response) => {
+  try {
+    const category = req.query.category as string | undefined;
+    const search = req.query.search as string | undefined;
+    const entries = await graphService.getKnowledgeEntries(category, search);
+    res.json(entries);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.get("/knowledge/:id", async (req: Request, res: Response) => {
+  try {
+    const entry = await graphService.getKnowledgeEntry(parseInt(req.params.id));
+    if (!entry) return res.status(404).json({ error: "Entrada não encontrada" });
+    res.json(entry);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.post("/knowledge", async (req: Request, res: Response) => {
+  try {
+    const parsed = insertKnowledgeBaseSchema.safeParse(req.body);
+    if (!parsed.success) {
+      return res.status(400).json({ error: parsed.error.flatten() });
+    }
+    const entry = await graphService.createKnowledgeEntry(parsed.data);
+    res.status(201).json(entry);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.put("/knowledge/:id", async (req: Request, res: Response) => {
+  try {
+    const entry = await graphService.updateKnowledgeEntry(parseInt(req.params.id), req.body);
+    if (!entry) return res.status(404).json({ error: "Entrada não encontrada" });
+    res.json(entry);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+router.delete("/knowledge/:id", async (req: Request, res: Response) => {
+  try {
+    const deleted = await graphService.deleteKnowledgeEntry(parseInt(req.params.id));
+    if (!deleted) return res.status(404).json({ error: "Entrada não encontrada" });
+    res.json({ success: true });
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+// ─── Busca Semântica ──────────────────────────────────────────────────────────
+
+router.post("/search", async (req: Request, res: Response) => {
+  try {
+    const { query, n_results = 5 } = req.body as { query: string; n_results?: number };
+    if (!query) return res.status(400).json({ error: "Campo 'query' é obrigatório" });
+
+    const result = await graphService.semanticSearch(query, n_results);
+    res.json(result);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+// ─── Grafo completo para visualização ────────────────────────────────────────
+
+router.get("/visualization", async (req: Request, res: Response) => {
+  try {
+    const data = await graphService.getGraphData(tenantId(req));
+    res.json(data);
+  } catch (error: any) {
+    res.status(500).json({ error: error.message });
+  }
+});
+
+export default router;
diff --git a/server/graph/service.ts b/server/graph/service.ts
new file mode 100644
index 0000000..f94c176
--- /dev/null
+++ b/server/graph/service.ts
@@ -0,0 +1,242 @@
+import { db } from "../../db/index";
+import { eq, desc, and, sql, ilike, or } from "drizzle-orm";
+import {
+  graphNodes,
+  graphEdges,
+  knowledgeBase,
+  learnedInteractions,
+  insertGraphNodeSchema,
+  insertGraphEdgeSchema,
+  insertKnowledgeBaseSchema,
+  type GraphNode,
+  type GraphEdge,
+  type KnowledgeBaseEntry,
+  type InsertGraphNode,
+  type InsertGraphEdge,
+  type InsertKnowledgeBaseEntry,
+} from "@shared/schema";
+
+const EMBEDDINGS_URL = process.env.PYTHON_SERVICE_URL || "http://localhost:8001";
+
+// ─── Nodes ────────────────────────────────────────────────────────────────────
+
+export async function getNodes(tenantId?: number, type?: string, limit = 100): Promise<GraphNode[]> {
+  const conditions = [];
+  if (tenantId) conditions.push(eq(graphNodes.tenantId, tenantId));
+  if (type) conditions.push(eq(graphNodes.type, type));
+
+  return db
+    .select()
+    .from(graphNodes)
+    .where(conditions.length ? and(...conditions) : undefined)
+    .orderBy(desc(graphNodes.createdAt))
+    .limit(limit);
+}
+
+export async function getNodeById(id: number): Promise<GraphNode | undefined> {
+  const [node] = await db.select().from(graphNodes).where(eq(graphNodes.id, id));
+  return node;
+}
+
+export async function createNode(data: InsertGraphNode): Promise<GraphNode> {
+  const [node] = await db.insert(graphNodes).values(data).returning();
+
+  // Indexar embedding em background (não bloqueia a resposta)
+  const content = typeof data.data === "object" ? JSON.stringify(data.data) : String(data.data);
+  indexNodeEmbedding(node.id, content, data.type).catch(() => {});
+
+  return node;
+}
+
+export async function updateNode(id: number, data: Partial<InsertGraphNode>): Promise<GraphNode | undefined> {
+  const [node] = await db
+    .update(graphNodes)
+    .set({ ...data, updatedAt: new Date() })
+    .where(eq(graphNodes.id, id))
+    .returning();
+  return node;
+}
+
+export async function deleteNode(id: number): Promise<boolean> {
+  const result = await db.delete(graphNodes).where(eq(graphNodes.id, id));
+  return (result.rowCount ?? 0) > 0;
+}
+
+// ─── Edges ────────────────────────────────────────────────────────────────────
+
+export async function getEdges(sourceId?: number, targetId?: number): Promise<GraphEdge[]> {
+  const conditions = [];
+  if (sourceId) conditions.push(eq(graphEdges.sourceId, sourceId));
+  if (targetId) conditions.push(eq(graphEdges.targetId, targetId));
+
+  return db
+    .select()
+    .from(graphEdges)
+    .where(conditions.length ? and(...conditions) : undefined)
+    .orderBy(desc(graphEdges.createdAt));
+}
+
+export async function createEdge(data: InsertGraphEdge): Promise<GraphEdge> {
+  const [edge] = await db.insert(graphEdges).values(data).returning();
+  return edge;
+}
+
+export async function deleteEdge(id: number): Promise<boolean> {
+  const result = await db.delete(graphEdges).where(eq(graphEdges.id, id));
+  return (result.rowCount ?? 0) > 0;
+}
+
+// ─── Knowledge Base ───────────────────────────────────────────────────────────
+
+export async function getKnowledgeEntries(category?: string, search?: string): Promise<KnowledgeBaseEntry[]> {
+  const conditions = [];
+  if (category) conditions.push(eq(knowledgeBase.category, category));
+  if (search) {
+    conditions.push(
+      or(
+        ilike(knowledgeBase.title, `%${search}%`),
+        ilike(knowledgeBase.content, `%${search}%`)
+      )!
+    );
+  }
+
+  return db
+    .select()
+    .from(knowledgeBase)
+    .where(conditions.length ? and(...conditions) : undefined)
+    .orderBy(desc(knowledgeBase.createdAt))
+    .limit(50);
+}
+
+export async function getKnowledgeEntry(id: number): Promise<KnowledgeBaseEntry | undefined> {
+  const [entry] = await db.select().from(knowledgeBase).where(eq(knowledgeBase.id, id));
+  return entry;
+}
+
+export async function createKnowledgeEntry(data: InsertKnowledgeBaseEntry): Promise<KnowledgeBaseEntry> {
+  const [entry] = await db.insert(knowledgeBase).values(data).returning();
+
+  // Indexar no serviço de embeddings
+  indexKnowledgeEmbedding(entry.id, entry.title + "\n" + entry.content, entry.category).catch(() => {});
+
+  return entry;
+}
+
+export async function updateKnowledgeEntry(
+  id: number,
+  data: Partial<InsertKnowledgeBaseEntry>
+): Promise<KnowledgeBaseEntry | undefined> {
+  const [entry] = await db.update(knowledgeBase).set(data).where(eq(knowledgeBase.id, id)).returning();
+  return entry;
+}
+
+export async function deleteKnowledgeEntry(id: number): Promise<boolean> {
+  const result = await db.delete(knowledgeBase).where(eq(knowledgeBase.id, id));
+  return (result.rowCount ?? 0) > 0;
+}
+
+// ─── Busca Semântica ──────────────────────────────────────────────────────────
+
+export async function semanticSearch(
+  query: string,
+  nResults = 5
+): Promise<{ results: any[]; source: "embeddings" | "text_fallback" }> {
+  // Tenta busca vetorial no serviço de embeddings Python
+  try {
+    const response = await fetch(`${EMBEDDINGS_URL}/embeddings/search`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ query, n_results: nResults }),
+      signal: AbortSignal.timeout(5000),
+    });
+
+    if (response.ok) {
+      const data = await response.json();
+      return { results: data.results || [], source: "embeddings" };
+    }
+  } catch {
+    // Serviço de embeddings indisponível — fallback para busca textual
+  }
+
+  // Fallback: busca textual simples no banco
+  const textResults = await db
+    .select()
+    .from(knowledgeBase)
+    .where(
+      or(
+        ilike(knowledgeBase.title, `%${query}%`),
+        ilike(knowledgeBase.content, `%${query}%`)
+      )!
+    )
+    .limit(nResults);
+
+  // Complementa com interações aprendidas
+  const interactionResults = await db
+    .select()
+    .from(learnedInteractions)
+    .where(
+      or(
+        ilike(learnedInteractions.question, `%${query}%`),
+        ilike(learnedInteractions.answer, `%${query}%`)
+      )!
+    )
+    .orderBy(desc(learnedInteractions.createdAt))
+    .limit(nResults);
+
+  return {
+    results: [
+      ...textResults.map((r) => ({ type: "knowledge", score: 0.7, data: r })),
+      ...interactionResults.map((r) => ({ type: "interaction", score: 0.6, data: r })),
+    ],
+    source: "text_fallback",
+  };
+}
+
+// ─── Grafo Completo para Visualização ────────────────────────────────────────
+
+export async function getGraphData(tenantId?: number): Promise<{ nodes: GraphNode[]; edges: GraphEdge[] }> {
+  const nodes = await getNodes(tenantId, undefined, 200);
+  const nodeIds = nodes.map((n) => n.id);
+
+  if (nodeIds.length === 0) return { nodes: [], edges: [] };
+
+  const edges = await db
+    .select()
+    .from(graphEdges)
+    .where(
+      or(
+        sql`${graphEdges.sourceId} = ANY(${sql.raw(`ARRAY[${nodeIds.join(",")}]`)})`,
+        sql`${graphEdges.targetId} = ANY(${sql.raw(`ARRAY[${nodeIds.join(",")}]`)})`
+      )!
+    );
+
+  return { nodes, edges };
+}
+
+// ─── Helpers Privados ────────────────────────────────────────────────────────
+
+async function indexNodeEmbedding(nodeId: number, content: string, type: string) {
+  await fetch(`${EMBEDDINGS_URL}/embeddings/add`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({
+      doc_id: `node_${nodeId}`,
+      document: content,
+      metadata: { type: "graph_node", node_type: type, node_id: nodeId },
+    }),
+    signal: AbortSignal.timeout(10000),
+  });
+}
+
+async function indexKnowledgeEmbedding(entryId: number, content: string, category: string) {
+  await fetch(`${EMBEDDINGS_URL}/embeddings/add`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({
+      doc_id: `kb_${entryId}`,
+      document: content,
+      metadata: { type: "knowledge_base", category, entry_id: entryId },
+    }),
+    signal: AbortSignal.timeout(10000),
+  });
+}
diff --git a/server/index.ts b/server/index.ts
index 865024d..eeaa0e2 100644
--- a/server/index.ts
+++ b/server/index.ts
@@ -201,10 +201,15 @@ function startNodeService(name: string, scriptPath: string, port: number) {
 
 export { managedServices, restartManagedService, stopManagedService, getManagedServiceInfo, getManagedServiceLogs, startNodeService };
 
-startPythonService("contabil", path.join(process.cwd(), "server/python/contabil_service.py"), 8003);
-startPythonService("bi", path.join(process.cwd(), "server/python/bi_engine.py"), 8004);
-startPythonService("automation", path.join(process.cwd(), "server/python/automation_engine.py"), 8005);
-startNodeService("communication", path.join(process.cwd(), "server/communication/engine.ts"), 8006);
+// Em modo Docker cada serviço roda como container independente — não spawnar processos filhos
+if (!process.env.DOCKER_MODE || process.env.DOCKER_MODE === "false") {
+  startPythonService("contabil", path.join(process.cwd(), "server/python/contabil_service.py"), 8003);
+  startPythonService("bi", path.join(process.cwd(), "server/python/bi_engine.py"), 8004);
+  startPythonService("automation", path.join(process.cwd(), "server/python/automation_engine.py"), 8005);
+  startNodeService("communication", path.join(process.cwd(), "server/communication/engine.ts"), 8006);
+} else {
+  console.log("[services] DOCKER_MODE=true — microserviços rodando como containers independentes");
+}
 
 function startShellService(name: string, scriptPath: string, port: number) {
   const existing = managedServices.get(name);
diff --git a/server/python/embeddings_service.py b/server/python/embeddings_service.py
new file mode 100644
index 0000000..e14943e
--- /dev/null
+++ b/server/python/embeddings_service.py
@@ -0,0 +1,250 @@
+"""
+Serviço de Embeddings com pgvector
+
+Expõe endpoints compatíveis com o que o learning/service.ts espera:
+  POST /embeddings/add    — adiciona documento com embedding
+  POST /embeddings/search — busca por similaridade semântica
+  GET  /health            — healthcheck
+
+Usa pgvector (PostgreSQL) em vez de ChromaDB — sem dependência extra.
+"""
+
+import os
+import json
+import logging
+from contextlib import asynccontextmanager
+from typing import Optional
+
+import psycopg2
+import psycopg2.extras
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://arcadia:arcadia123@localhost:5432/arcadia")
+PORT = int(os.getenv("SERVICE_PORT", "8001"))
+EMBEDDING_DIM = 1536  # OpenAI text-embedding-3-small
+
+
+# ─── Modelos Pydantic ──────────────────────────────────────────────────────────
+
+class AddDocumentRequest(BaseModel):
+    doc_id: str
+    document: str
+    metadata: Optional[dict] = {}
+
+class SearchRequest(BaseModel):
+    query: str
+    n_results: int = 5
+    filter_type: Optional[str] = None
+
+
+# ─── Setup do banco ────────────────────────────────────────────────────────────
+
+def get_conn():
+    return psycopg2.connect(DATABASE_URL)
+
+
+def setup_database():
+    """Cria a tabela de embeddings e instala pgvector se não existir."""
+    try:
+        conn = get_conn()
+        cur = conn.cursor()
+
+        cur.execute("CREATE EXTENSION IF NOT EXISTS vector;")
+        cur.execute("""
+            CREATE TABLE IF NOT EXISTS document_embeddings (
+                id          SERIAL PRIMARY KEY,
+                doc_id      TEXT NOT NULL UNIQUE,
+                document    TEXT NOT NULL,
+                metadata    JSONB DEFAULT '{}',
+                embedding   vector(1536),
+                created_at  TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                updated_at  TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            );
+        """)
+        cur.execute("""
+            CREATE INDEX IF NOT EXISTS idx_embeddings_vector
+            ON document_embeddings
+            USING ivfflat (embedding vector_cosine_ops)
+            WITH (lists = 100);
+        """)
+        conn.commit()
+        cur.close()
+        conn.close()
+        logger.info("✅ Tabela document_embeddings pronta com pgvector")
+    except Exception as e:
+        logger.error(f"Erro no setup do banco: {e}")
+
+
+# ─── Embedding via LiteLLM/OpenAI ─────────────────────────────────────────────
+
+def generate_embedding(text: str) -> Optional[list]:
+    """
+    Gera embedding via LiteLLM proxy (que pode usar OpenAI ou Ollama).
+    Retorna None se falhar — documentos sem embedding ainda são indexados (busca textual).
+    """
+    litellm_url = os.getenv("LITELLM_BASE_URL", "http://litellm:4000")
+    api_key = os.getenv("LITELLM_API_KEY", "arcadia-internal")
+
+    try:
+        import urllib.request
+        import urllib.error
+
+        payload = json.dumps({
+            "model": "text-embedding-3-small",
+            "input": text[:8000]  # limite de tokens
+        }).encode()
+
+        req = urllib.request.Request(
+            f"{litellm_url}/v1/embeddings",
+            data=payload,
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {api_key}"
+            }
+        )
+
+        with urllib.request.urlopen(req, timeout=15) as response:
+            result = json.loads(response.read())
+            return result["data"][0]["embedding"]
+
+    except Exception as e:
+        logger.warning(f"Embedding não gerado (usando busca textual como fallback): {e}")
+        return None
+
+
+# ─── App FastAPI ───────────────────────────────────────────────────────────────
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    setup_database()
+    yield
+
+app = FastAPI(
+    title="Arcádia Embeddings Service",
+    description="Serviço de embeddings com pgvector para busca semântica",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "service": "embeddings"}
+
+
+@app.post("/embeddings/add")
+def add_document(req: AddDocumentRequest):
+    """Adiciona ou atualiza um documento com embedding."""
+    embedding = generate_embedding(req.document)
+
+    try:
+        conn = get_conn()
+        cur = conn.cursor()
+
+        if embedding:
+            cur.execute("""
+                INSERT INTO document_embeddings (doc_id, document, metadata, embedding, updated_at)
+                VALUES (%s, %s, %s, %s, CURRENT_TIMESTAMP)
+                ON CONFLICT (doc_id) DO UPDATE SET
+                    document   = EXCLUDED.document,
+                    metadata   = EXCLUDED.metadata,
+                    embedding  = EXCLUDED.embedding,
+                    updated_at = CURRENT_TIMESTAMP
+            """, (req.doc_id, req.document, json.dumps(req.metadata), str(embedding)))
+        else:
+            cur.execute("""
+                INSERT INTO document_embeddings (doc_id, document, metadata, updated_at)
+                VALUES (%s, %s, %s, CURRENT_TIMESTAMP)
+                ON CONFLICT (doc_id) DO UPDATE SET
+                    document   = EXCLUDED.document,
+                    metadata   = EXCLUDED.metadata,
+                    updated_at = CURRENT_TIMESTAMP
+            """, (req.doc_id, req.document, json.dumps(req.metadata)))
+
+        conn.commit()
+        cur.close()
+        conn.close()
+
+        return {
+            "success": True,
+            "doc_id": req.doc_id,
+            "has_embedding": embedding is not None
+        }
+
+    except Exception as e:
+        logger.error(f"Erro ao adicionar documento: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/embeddings/search")
+def search_documents(req: SearchRequest):
+    """Busca documentos similares por embedding ou texto."""
+    query_embedding = generate_embedding(req.query)
+
+    try:
+        conn = get_conn()
+        cur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
+
+        if query_embedding:
+            # Busca vetorial por similaridade coseno
+            cur.execute("""
+                SELECT
+                    doc_id,
+                    document,
+                    metadata,
+                    1 - (embedding <=> %s::vector) AS similarity
+                FROM document_embeddings
+                WHERE embedding IS NOT NULL
+                ORDER BY embedding <=> %s::vector
+                LIMIT %s
+            """, (str(query_embedding), str(query_embedding), req.n_results))
+        else:
+            # Fallback: full-text search com LIKE
+            cur.execute("""
+                SELECT
+                    doc_id,
+                    document,
+                    metadata,
+                    0.5 AS similarity
+                FROM document_embeddings
+                WHERE document ILIKE %s
+                ORDER BY updated_at DESC
+                LIMIT %s
+            """, (f"%{req.query}%", req.n_results))
+
+        rows = cur.fetchall()
+        cur.close()
+        conn.close()
+
+        return {
+            "results": [dict(r) for r in rows],
+            "query": req.query,
+            "method": "vector" if query_embedding else "text_fallback"
+        }
+
+    except Exception as e:
+        logger.error(f"Erro na busca: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/documents/add")
+def add_document_compat(req: AddDocumentRequest):
+    """Alias compatível com o formato do python-service/."""
+    return add_document(req)
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=PORT)
diff --git a/server/routes.ts b/server/routes.ts
index 63569fa..fb18b06 100644
--- a/server/routes.ts
+++ b/server/routes.ts
@@ -58,6 +58,7 @@ import blackboardRoutes from "./blackboard/routes";
 import pipelineRoutes from "./blackboard/pipelineRoutes";
 import { startAllAgents } from "./blackboard/agents";
 import { loadModuleRoutes } from "./modules/loader";
+import graphRoutes from "./graph/routes";
 
 export async function registerRoutes(
   httpServer: Server,
@@ -83,6 +84,7 @@ export async function registerRoutes(
   registerAutomationRoutes(app);
   registerAutomationEngineRoutes(app);
   registerBiRoutes(app);
+  app.use("/api/graph", graphRoutes);
   registerBiEngineRoutes(app);
   registerMetaSetRoutes(app);
   registerCommEngineRoutes(app);