BUILDING AUTONOMOUS SWARMS & MOBILE APPS

{
  "agent_swarm": {
    "orchestrator": "SwarmManagerV2",
    "nodes": [
      { "id": "agent-analyst", "engine": "gemini-3-pro", "role": "Data analysis" },
      { "id": "agent-developer", "engine": "gemini-3.5-flash", "role": "Code creation" }
    ],
    "channels": { "protocol": "WebSocket", "heartbeat": "10s" }
  }
}

@app.post("/api/v1/dispatch")
async def dispatch_lead(lead: LeadSchema):
    # Запуск CrewAI цепочки
    crew = Crew(
        agents=[LeadEnricher, OfferGenerator],
        tasks=[enrich_task, offer_task],
        verbose=True
    )
    result = crew.kickoff(inputs={"lead": lead.dict()})
    return {"status": "success", "offer": result}

async function analyzeUIBug(page, screenshotBuffer) {
  const model = genAI.getGenerativeModel({ model: "gemini-3.5-flash" });
  const prompt = "Проанализируй скриншот на предмет багов верстки или сломанных элементов UI.";
  const response = await model.generateContent([prompt, {
    inlineData: { data: screenshotBuffer.toString("base64"), mimeType: "image/png" }
  }]);
  return response.response.text();
}

export function useAiTutor(studentId: string) {
  const [session, setSession] = useState(null);

  const generateQuiz = async (topic: string) => {
    const prompt = `Сгенерируй сложный технический вопрос по теме ${topic}`;
    const res = await callDeepSeek({ prompt });
    return JSON.parse(res.text);
  };

  return { generateQuiz, session };
}

async function matchTechnician(job) {
  // Поиск мастеров в радиусе 10км с помощью Redis Geo
  const activeTechs = await redis.georadius('techs', job.lat, job.lng, 10, 'km');

  // Скоринг ИИ-агентом на основе отзывов и квалификации
  const payload = { job: job.description, experts: activeTechs };
  const response = await geminiMatcher.evaluate(payload);

  return response.bestMatchTechId;
}

def query_local_knowledge(user_query: str):
    # Гибридный поиск: Vector + Keyword (BM25)
    documents = chroma_client.hybrid_search(user_query, limit=3)
    context = "\n".join([doc.page_content for doc in documents])

    # Генерация на локальном Llama-3-8B-Instruct
    prompt = f"Контекст: {context}\nВопрос: {user_query}"
    response = llama_cpp_client.complete(prompt, temperature=0.2)
    return response