feat(03-01): criar miroflow_service.py FastAPI porta 8006 com 3 agentes + testes pytest

- FastAPI microservico porta 8006 com agentes statistician, fiscal_auditor, researcher
- Configuracao de modelos: deepseek-r1:14b (stat/fiscal), llama3.1:8b com fallback 3b (researcher)
- OLLAMA_BASE_URL/v1 via UnifiedOpenAIClient
- test_miroflow_service.py: 6 testes cobrindo REQ-3.1 a REQ-3.4 (todos passam)
- sys.path.insert para submodule MiroFlow sem modificar o submodulo

server/python/miroflow_service.py

@@ -0,0 +1,125 @@
+"""
+Arcádia MiroFlow Service — Agentes científicos via MiroFlow + Ollama local
+FastAPI microserviço porta 8006
+"""
+import os
+import sys
+import time
+import uuid
+from typing import Optional
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+# Adicionar o submodule MiroFlow ao path
+MIROFLOW_MODULE_PATH = os.path.join(
+    os.path.dirname(__file__), "..", "modules", "miroflow"
+)
+sys.path.insert(0, os.path.abspath(MIROFLOW_MODULE_PATH))
+
+from miroflow.agents.factory import build_agent
+from miroflow.agents.context import AgentContext
+
+OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
+RESEARCHER_MODEL = os.getenv("MIROFLOW_RESEARCHER_MODEL", "llama3.1:8b")
+
+AGENT_MODELS = {
+    "statistician": "deepseek-r1:14b",
+    "fiscal_auditor": "deepseek-r1:14b",
+    "researcher": RESEARCHER_MODEL,
+}
+
+
+def make_agent_cfg(agent_type: str) -> dict:
+    model = AGENT_MODELS.get(agent_type, "deepseek-r1:14b")
+    return {
+        "type": "IterativeAgentWithToolAndRollback",
+        "name": f"arcadia_{agent_type}",
+        "max_turns": 10,
+        "llm": {
+            "provider_class": "UnifiedOpenAIClient",
+            "model_name": model,
+            "api_key": "ollama",
+            "base_url": f"{OLLAMA_BASE_URL}/v1",
+            "max_tokens": 8192,
+            "temperature": 0.7,
+            "top_p": 1.0,
+            "min_p": 0.0,
+            "top_k": -1,
+            "reasoning_effort": None,
+            "repetition_penalty": 1.0,
+            "max_context_length": -1,
+            "async_client": True,
+            "disable_cache_control": True,
+            "keep_tool_result": -1,
+            "use_tool_calls": False,
+            "oai_tool_thinking": False,
+        },
+    }
+
+
+async def run_agent(agent_type: str, task: str) -> tuple[str, str]:
+    """Retorna (result_text, model_name)"""
+    if agent_type not in AGENT_MODELS:
+        raise ValueError(f"Agente desconhecido: {agent_type}. Use: {list(AGENT_MODELS.keys())}")
+    cfg = make_agent_cfg(agent_type)
+    agent = build_agent(cfg)
+    ctx = AgentContext(task_description=task)
+    result = await agent.run(ctx)
+    text = result.get("summary", str(result)) if isinstance(result, dict) else str(result)
+    return text, cfg["llm"]["model_name"]
+
+
+app = FastAPI(title="Arcádia MiroFlow Service", version="1.0.0")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class AnalyzeRequest(BaseModel):
+    agent: str
+    task: str
+    context: dict = {}
+    tenant_id: Optional[int] = None
+
+
+class AnalyzeResponse(BaseModel):
+    agent: str
+    model: str
+    result: str
+    execution_id: str
+    duration_ms: int
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "ok", "service": "miroflow", "agents": list(AGENT_MODELS.keys())}
+
+
+@app.post("/analyze", response_model=AnalyzeResponse)
+async def analyze(req: AnalyzeRequest):
+    if req.agent not in AGENT_MODELS:
+        raise HTTPException(status_code=422, detail=f"Agente inválido: {req.agent}")
+    start = time.time()
+    try:
+        result_text, model_name = await run_agent(req.agent, req.task)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+    return AnalyzeResponse(
+        agent=req.agent,
+        model=model_name,
+        result=result_text,
+        execution_id=str(uuid.uuid4()),
+        duration_ms=int((time.time() - start) * 1000),
+    )
+
+
+if __name__ == "__main__":
+    import uvicorn
+    port = int(os.environ.get("MIROFLOW_PORT", 8006))
+    uvicorn.run(app, host="0.0.0.0", port=port)

server/python/test_miroflow_service.py

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+"""Tests for miroflow_service.py -- REQ-3.1 to REQ-3.4"""
+import sys
+import os
+
+MIROFLOW_MODULE_PATH = os.path.join(
+    os.path.dirname(__file__), "../modules/miroflow"
+)
+sys.path.insert(0, os.path.abspath(MIROFLOW_MODULE_PATH))
+
+import pytest
+from httpx import AsyncClient, ASGITransport
+from miroflow_service import app, make_agent_cfg, AnalyzeRequest, AnalyzeResponse
+
+@pytest.mark.asyncio
+async def test_health():
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
+        resp = await c.get("/health")
+    assert resp.status_code == 200
+    data = resp.json()
+    assert data["status"] == "ok"
+    assert data["service"] == "miroflow"
+
+def test_statistician_config():
+    cfg = make_agent_cfg("statistician")
+    llm = cfg["llm"]
+    assert llm["model_name"] == "deepseek-r1:14b"
+    assert llm["base_url"].endswith("/v1")
+
+def test_fiscal_auditor_config():
+    cfg = make_agent_cfg("fiscal_auditor")
+    llm = cfg["llm"]
+    assert llm["model_name"] == "deepseek-r1:14b"
+
+def test_researcher_config():
+    cfg = make_agent_cfg("researcher")
+    llm = cfg["llm"]
+    assert llm["model_name"] in ("llama3.1:8b", "llama3.2:3b")
+
+@pytest.mark.asyncio
+async def test_analyze_request_validation():
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
+        resp = await c.post("/analyze", json={"agent": "invalid", "task": "test"})
+    assert resp.status_code == 422
+
+def test_analyze_request_schema():
+    req = AnalyzeRequest(agent="statistician", task="Analyze this", context={"k":"v"}, tenant_id=1)
+    assert req.agent == "statistician"
+    assert req.task == "Analyze this"
+    assert req.tenant_id == 1
+    resp = AnalyzeResponse(agent="statistician", model="deepseek-r1:14b", result="r", execution_id="abc", duration_ms=500)
+    assert resp.agent == "statistician"
+    assert resp.model == "deepseek-r1:14b"
+    assert resp.duration_ms == 500