RAG & Search Applications

from openai import OpenAI

client = OpenAI(
    base_url="https://kymaapi.com/v1",
    api_key="ky-your-api-key"
)

def retrieve_context(query: str) -> list[str]:
    # Your retrieval logic: vector DB, keyword search, etc.
    return [
        "Kyma API exposes active models through /v1/models and one OpenAI-compatible endpoint.",
        "All models use OpenAI-compatible /v1/chat/completions endpoint.",
        "Gemini 2.5 Flash provides 1M context for large-document RAG.",
    ]

def rag_answer(question: str) -> str:
    chunks = retrieve_context(question)
    context = "\n\n".join(f"[{i+1}] {c}" for i, c in enumerate(chunks))

    response = client.chat.completions.create(
        model="gemini-2.5-flash",
        messages=[
            {
                "role": "system",
                "content": "Answer using only the provided context. "
                           "Cite sources with [1], [2] etc. "
                           "If the answer isn't in the context, say so."
            },
            {
                "role": "user",
                "content": f"Context:\n{context}\n\nQuestion: {question}"
            }
        ]
    )
    return response.choices[0].message.content

print(rag_answer("What models does Kyma API support?"))

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://kymaapi.com/v1",
  apiKey: "ky-your-api-key",
});

function retrieveContext(query) {
  // Your retrieval logic: vector DB, keyword search, etc.
  return [
    "Kyma API exposes active models through /v1/models and one OpenAI-compatible endpoint.",
    "All models use OpenAI-compatible /v1/chat/completions endpoint.",
    "Gemini 2.5 Flash provides 1M context for large-document RAG.",
  ];
}

async function ragAnswer(question) {
  const chunks = retrieveContext(question);
  const context = chunks.map((c, i) => `[${i + 1}] ${c}`).join("\n\n");

  const response = await client.chat.completions.create({
    model: "gemini-2.5-flash",
    messages: [
      {
        role: "system",
        content: "Answer using only the provided context. " +
                 "Cite sources with [1], [2] etc. " +
                 "If the answer isn't in the context, say so.",
      },
      {
        role: "user",
        content: `Context:\n${context}\n\nQuestion: ${question}`,
      },
    ],
  });
  return response.choices[0].message.content;
}

console.log(await ragAnswer("What models does Kyma API support?"));